| Title: | "Risk Model Regression and Analysis with Complex Non-Linear Models" |
|---|---|
| Description: | Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Grey competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grant 80NSSC19M0161 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109. |
| Authors: | Eric Giunta [aut, cre]
 , Amir Bahadori [ctb]
, Dan Andresen [ctb],
Linda Walsh [ctb] ,
Benjamin French [ctb]
, Lawrence Dauer
[ctb], John Boice Jr [ctb]
, Kansas State
University [cph], NASA [fnd], NCRP [fnd], NRC [fnd] |
| Maintainer: | Eric Giunta <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.4.2 |
| Built: | 2024-10-22 07:22:33 UTC |
| Source: | CRAN |
Check_Dupe_Columns checks for duplicated columns, columns with the same values, and columns with single value. Currently not updated for multi-terms
Check_Dupe_Columns(df, cols, term_n, verbose = 0, factor_check = FALSE)Check_Dupe_Columns(df, cols, term_n, verbose = 0, factor_check = FALSE)
df |
a data.table containing the columns of interest |
cols |
columns to check |
term_n |
term numbers for each element of the model |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
factor_check |
a boolean used to skip comparing columns of the form ?_? with the same initial string, which is used for factored columns |
returns the usable columns
Other Data Cleaning Functions:
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 2, 1, 0, 1, 0) df <- data.table::data.table("a" = a, "b" = b, "c" = c) cols <- c("a", "b", "c") term_n <- c(0, 0, 1) unique_cols <- Check_Dupe_Columns(df, cols, term_n)library(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 2, 1, 0, 1, 0) df <- data.table::data.table("a" = a, "b" = b, "c" = c) cols <- c("a", "b", "c") term_n <- c(0, 0, 1) unique_cols <- Check_Dupe_Columns(df, cols, term_n)
Check_Trunc creates columns to use for truncation
Check_Trunc(df, ce, verbose = 0)Check_Trunc(df, ce, verbose = 0)
df |
a data.table containing the columns of interest |
ce |
columns to check for truncation, (t0, t1, event) |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
returns the updated data and time period columns
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" ce <- c("%trunc%", "Ending_Age") val <- Check_Trunc(df, ce) df <- val$df ce <- val$celibrary(data.table) df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" ce <- c("%trunc%", "Ending_Age") val <- Check_Trunc(df, ce) df <- val$df ce <- val$ce
Check_Verbose checks and assigns verbosity values
Check_Verbose(verbose)Check_Verbose(verbose)
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
returns correct verbose value
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
Correct_Formula_Order checks the order of formulas given and corrects any ordering issues, orders alphabetically, by term number, etc.
Correct_Formula_Order( term_n, tform, keep_constant, a_n, names, der_iden = 0, cons_mat = matrix(c(0)), cons_vec = c(0), verbose = FALSE, model_control = list() )Correct_Formula_Order( term_n, tform, keep_constant, a_n, names, der_iden = 0, cons_mat = matrix(c(0)), cons_vec = c(0), verbose = FALSE, model_control = list() )
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
names |
columns for elements of the model, used to identify data columns |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
cons_mat |
Matrix containing coefficients for system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for system of linear constraints, formatted as vector |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
returns the corrected lists
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) ## basic example code reproduced from the starting-description vignette term_n <- c(0, 1, 1, 0, 0) tform <- c("loglin", "quad_slope", "lin", "lin_int", "lin_slope") keep_constant <- c(0, 0, 0, 1, 0) a_n <- c(1, 2, 3, 4, 5) names <- c("a", "a", "a", "a", "a") val <- Correct_Formula_Order(term_n, tform, keep_constant, a_n, names, cons_mat = matrix(c(0)), cons_vec = c(0) ) term_n <- val$term_n tform <- val$tform keep_constant <- val$keep_constant a_n <- val$a_n der_iden <- val$der_iden names <- val$nameslibrary(data.table) ## basic example code reproduced from the starting-description vignette term_n <- c(0, 1, 1, 0, 0) tform <- c("loglin", "quad_slope", "lin", "lin_int", "lin_slope") keep_constant <- c(0, 0, 0, 1, 0) a_n <- c(1, 2, 3, 4, 5) names <- c("a", "a", "a", "a", "a") val <- Correct_Formula_Order(term_n, tform, keep_constant, a_n, names, cons_mat = matrix(c(0)), cons_vec = c(0) ) term_n <- val$term_n tform <- val$tform keep_constant <- val$keep_constant a_n <- val$a_n der_iden <- val$der_iden names <- val$names
RunCoxRegression uses user provided data, vectors specifying the model,
and options to calculate relative risk for every row in the provided data
Cox_Relative_Risk( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, control = list(), model_control = list() )Cox_Relative_Risk( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, control = list(), model_control = list() )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
returns a list of the final results
Other Plotting Wrapper Functions:
RunCoxPlots()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) fir <- 0 tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" a_n <- c(1.1, 0.1, 0.2, 0.5) # used to test at a specific point keep_constant <- c(0, 0, 0, 0) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- Cox_Relative_Risk( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) fir <- 0 tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" a_n <- c(1.1, 0.1, 0.2, 0.5) # used to test at a specific point keep_constant <- c(0, 0, 0, 0) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- Cox_Relative_Risk( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control )
Date_Shift generates a new dataframe with a column containing time difference in a given unit
Date_Shift(df, dcol0, dcol1, col_name, units = "days")Date_Shift(df, dcol0, dcol1, col_name, units = "days")
df |
a data.table containing the columns of interest |
dcol0 |
list of starting month, day, and year |
dcol1 |
list of ending month, day, and year |
col_name |
vector of new column names |
units |
time unit to use |
returns the updated dataframe
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) m0 <- c(1, 1, 2, 2) m1 <- c(2, 2, 3, 3) d0 <- c(1, 2, 3, 4) d1 <- c(6, 7, 8, 9) y0 <- c(1990, 1991, 1997, 1998) y1 <- c(2001, 2003, 2005, 2006) df <- data.table::data.table("m0" = m0, "m1" = m1, "d0" = d0, "d1" = d1, "y0" = y0, "y1" = y1) df <- Date_Shift(df, c("m0", "d0", "y0"), c("m1", "d1", "y1"), "date_since")library(data.table) m0 <- c(1, 1, 2, 2) m1 <- c(2, 2, 3, 3) d0 <- c(1, 2, 3, 4) d1 <- c(6, 7, 8, 9) y0 <- c(1990, 1991, 1997, 1998) y1 <- c(2001, 2003, 2005, 2006) df <- data.table::data.table("m0" = m0, "m1" = m1, "d0" = d0, "d1" = d1, "y0" = y0, "y1" = y1) df <- Date_Shift(df, c("m0", "d0", "y0"), c("m1", "d1", "y1"), "date_since")
Def_Control checks and assigns default values
Def_Control(control)Def_Control(control)
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
returns a filled list
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "ties" = "breslow", "double_step" = 1 ) control <- Def_Control(control)library(data.table) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "ties" = "breslow", "double_step" = 1 ) control <- Def_Control(control)
Def_Control_Guess checks and assigns default values
Def_Control_Guess(guesses_control, a_n)Def_Control_Guess(guesses_control, a_n)
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
returns a filled list
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "loglin_min" = -1, "loglin_max" = 1, "loglin_method" = "uniform" ) a_n <- c(0.1, 2, 1.3) guesses_control <- Def_Control_Guess(guesses_control, a_n)library(data.table) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "loglin_min" = -1, "loglin_max" = 1, "loglin_method" = "uniform" ) a_n <- c(0.1, 2, 1.3) guesses_control <- Def_Control_Guess(guesses_control, a_n)
Def_model_control checks and assigns default values
Def_model_control(control)Def_model_control(control)
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
returns a filled list
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) control <- list("single" = TRUE) control <- Def_model_control(control)library(data.table) control <- list("single" = TRUE) control <- Def_model_control(control)
Def_model_control checks and assigns default values for modelform options
Def_modelform_fix(control, model_control, modelform, term_n)Def_modelform_fix(control, model_control, modelform, term_n)
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
term_n |
term numbers for each element of the model |
returns a filled list
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "ties" = "breslow", "double_step" = 1 ) control <- Def_Control(control) model_control <- list("single" = TRUE) model_control <- Def_model_control(model_control) term_n <- c(0, 1, 1) modelform <- "a" val <- Def_modelform_fix(control, model_control, modelform, term_n) model_control <- val$model_control modelform <- val$modelformlibrary(data.table) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "ties" = "breslow", "double_step" = 1 ) control <- Def_Control(control) model_control <- list("single" = TRUE) model_control <- Def_model_control(model_control) term_n <- c(0, 1, 1) modelform <- "a" val <- Def_modelform_fix(control, model_control, modelform, term_n) model_control <- val$model_control modelform <- val$modelform
factorize uses user provided list of columns to define new parameter for each unique value and update the data.table.
Not for interaction terms
factorize(df, col_list, verbose = 0)factorize(df, col_list, verbose = 0)
df |
a data.table containing the columns of interest |
col_list |
an array of column names that should have factor terms defined |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
returns a list with two named fields. df for the updated dataframe, and cols for the new column names
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 2, 1, 0, 1, 0) df <- data.table::data.table("a" = a, "b" = b, "c" = c) col_list <- c("c") val <- factorize(df, col_list) df <- val$df new_col <- val$colslibrary(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 2, 1, 0, 1, 0) df <- data.table::data.table("a" = a, "b" = b, "c" = c) col_list <- c("c") val <- factorize(df, col_list) df <- val$df new_col <- val$cols
factorize_par uses user provided list of columns to define new parameter for each unique value and update the data.table.
Not for interaction terms
factorize_par(df, col_list, verbose = 0, nthreads = as.numeric(detectCores()))factorize_par(df, col_list, verbose = 0, nthreads = as.numeric(detectCores()))
df |
a data.table containing the columns of interest |
col_list |
an array of column names that should have factor terms defined |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
nthreads |
number of threads to use, do not use more threads than available on your machine |
returns a list with two named fields. df for the updated dataframe, and cols for the new column names
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
gen_time_dep(),
interact_them()
library(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 2, 1, 0, 1, 0) df <- data.table::data.table("a" = a, "b" = b, "c" = c) col_list <- c("c") val <- factorize_par(df, col_list, FALSE, 2) df <- val$df new_col <- val$colslibrary(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 2, 1, 0, 1, 0) df <- data.table::data.table("a" = a, "b" = b, "c" = c) col_list <- c("c") val <- factorize_par(df, col_list, FALSE, 2) df <- val$df new_col <- val$cols
Gather_Guesses_CPP called from within R, uses a list of options and the model definition to generate a list of parameters and iterations that do not produce errors
Gather_Guesses_CPP( df, dfc, names, term_n, tform, keep_constant, a_n, x_all, a_n_default, modelform, fir, control, guesses_control, model_control = list() )Gather_Guesses_CPP( df, dfc, names, term_n, tform, keep_constant, a_n, x_all, a_n_default, modelform, fir, control, guesses_control, model_control = list() )
df |
a data.table containing the columns of interest |
dfc |
vector matching subterm number to matrix column |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
x_all |
covariate matrix |
a_n_default |
center of parameter distribution guessing scope |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
returns a list of the final results
library(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 0, 0, 0, 1, 0) d <- c(3, 4, 5, 6, 7, 8, 9) df <- data.table::data.table("a" = a, "b" = b, "c" = c, "d" = d) time1 <- "a" time2 <- "b" event <- "c" names <- c("d") term_n <- c(0) tform <- c("loglin") keep_constant <- c(0) a_n <- c(-0.1) a_n_default <- a_n modelform <- "M" fir <- 0 der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-9, "deriv_epsilon" = 1e-9, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list() model_control <- list() all_names <- unique(names(df)) dfc <- match(names, all_names) term_tot <- max(term_n) + 1 x_all <- as.matrix(df[, all_names, with = FALSE]) control <- Def_Control(control) guesses_control <- Def_Control_Guess(guesses_control, a_n) model_control <- Def_model_control(model_control) Gather_Guesses_CPP( df, dfc, names, term_n, tform, keep_constant, a_n, x_all, a_n_default, modelform, fir, control, guesses_control )library(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 0, 0, 0, 1, 0) d <- c(3, 4, 5, 6, 7, 8, 9) df <- data.table::data.table("a" = a, "b" = b, "c" = c, "d" = d) time1 <- "a" time2 <- "b" event <- "c" names <- c("d") term_n <- c(0) tform <- c("loglin") keep_constant <- c(0) a_n <- c(-0.1) a_n_default <- a_n modelform <- "M" fir <- 0 der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-9, "deriv_epsilon" = 1e-9, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list() model_control <- list() all_names <- unique(names(df)) dfc <- match(names, all_names) term_tot <- max(term_n) + 1 x_all <- as.matrix(df[, all_names, with = FALSE]) control <- Def_Control(control) guesses_control <- Def_Control_Guess(guesses_control, a_n) model_control <- Def_model_control(model_control) Gather_Guesses_CPP( df, dfc, names, term_n, tform, keep_constant, a_n, x_all, a_n_default, modelform, fir, control, guesses_control )
gcc_version Checks default c++ compiler, part of configuration script
gcc_version()gcc_version()
returns a string representation of gcc, clang, or c++ output
gen_time_dep generates a new dataframe with time dependent covariates by applying a grid in time
gen_time_dep( df, time1, time2, event0, iscox, dt, new_names, dep_cols, func_form, fname, tform, nthreads = as.numeric(detectCores()) )gen_time_dep( df, time1, time2, event0, iscox, dt, new_names, dep_cols, func_form, fname, tform, nthreads = as.numeric(detectCores()) )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
iscox |
boolean if rows not at event times should not be kept, rows are removed if true. a Cox proportional hazards model does not use rows with intervals not containing event times |
dt |
spacing in time for new rows |
new_names |
list of new names to use instead of default, default used if entry is ” |
dep_cols |
columns that are not needed in the new dataframe |
func_form |
vector of functions to apply to each time-dependent covariate. Of the form func(df, time) returning a vector of the new column value |
fname |
filename used for new dataframe |
tform |
list of string function identifiers, used for linear/step |
nthreads |
number of threads to use, do not use more threads than available on your machine |
returns the updated dataframe
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
interact_them()
library(data.table) # Adapted from the tests a <- c(20, 20, 5, 10, 15) b <- c(1, 2, 1, 1, 2) c <- c(0, 0, 1, 1, 1) df <- data.table::data.table("a" = a, "b" = b, "c" = c) time1 <- "%trunc%" time2 <- "a" event <- "c" control <- list( "lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-9, "deriv_epsilon" = 1e-9, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) grt_f <- function(df, time_col) { return((df[, "b"] * df[, get(time_col)])[[1]]) } func_form <- c("lin") df_new <- gen_time_dep( df, time1, time2, event, TRUE, 0.01, c("grt"), c(), c(grt_f), paste("test", "_new.csv", sep = ""), func_form, 2 ) file.remove("test_new.csv")library(data.table) # Adapted from the tests a <- c(20, 20, 5, 10, 15) b <- c(1, 2, 1, 1, 2) c <- c(0, 0, 1, 1, 1) df <- data.table::data.table("a" = a, "b" = b, "c" = c) time1 <- "%trunc%" time2 <- "a" event <- "c" control <- list( "lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-9, "deriv_epsilon" = 1e-9, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) grt_f <- function(df, time_col) { return((df[, "b"] * df[, get(time_col)])[[1]]) } func_form <- c("lin") df_new <- gen_time_dep( df, time1, time2, event, TRUE, 0.01, c("grt"), c(), c(grt_f), paste("test", "_new.csv", sep = ""), func_form, 2 ) file.remove("test_new.csv")
get_os checks the system OS, part of configuration script
get_os()get_os()
returns a string representation of OS
GetCensWeight uses user provided data, time/event columns, vectors specifying the model, and options generate an estimate of the censoring rate, plots, and returns the data
GetCensWeight( df, time1, time2, event0, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options, model_control = list(), strat_col = "e" )GetCensWeight( df, time1, time2, event0, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options, model_control = list(), strat_col = "e" )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
plot_options |
list of parameters controlling the plot options, see RunCoxPlots() for different options |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
strat_col |
column to stratify by if needed |
saves the plots in the current directory and returns a data.table of time and corresponding hazard, cumulative hazard, and survival
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 df$censor <- (df$Cancer_Status == 0) event <- "censor" control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) plot_options <- list( "name" = paste(tempfile(), "run_06", sep = ""), "verbose" = FALSE, "studyID" = "studyID", "age_unit" = "years" ) dft <- GetCensWeight( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options ) t_ref <- dft$t surv_ref <- dft$surv t_c <- df$t1 cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$ylibrary(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 df$censor <- (df$Cancer_Status == 0) event <- "censor" control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) plot_options <- list( "name" = paste(tempfile(), "run_06", sep = ""), "verbose" = FALSE, "studyID" = "studyID", "age_unit" = "years" ) dft <- GetCensWeight( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options ) t_ref <- dft$t surv_ref <- dft$surv t_c <- df$t1 cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$y
interact_them uses user provided interactions define interaction terms and update the data.table. assumes interaction is "+" or "*" and applies basic anti-aliasing to avoid duplicates
interact_them(df, interactions, new_names, verbose = 0)interact_them(df, interactions, new_names, verbose = 0)
df |
a data.table containing the columns of interest |
interactions |
array of strings, each one is of form term1?*?term2" for term1 interaction of type * or + with term2, "?" dlimits |
new_names |
list of new names to use instead of default, default used if entry is ” |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
returns a list with two named fields. df for the updated dataframe, and cols for the new column names
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep()
library(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 2, 1, 0, 1, 0) df <- data.table::data.table("a" = a, "b" = b, "c" = c) interactions <- c("a?+?b", "a?*?c") new_names <- c("ab", "ac") vals <- interact_them(df, interactions, new_names) df <- vals$df new_col <- vals$colslibrary(data.table) a <- c(0, 1, 2, 3, 4, 5, 6) b <- c(1, 2, 3, 4, 5, 6, 7) c <- c(0, 1, 2, 1, 0, 1, 0) df <- data.table::data.table("a" = a, "b" = b, "c" = c) interactions <- c("a?+?b", "a?*?c") new_names <- c("ab", "ac") vals <- interact_them(df, interactions, new_names) df <- vals$df new_col <- vals$cols
Joint_Multiple_Events generates input for a regression with multiple non-independent events and models
Joint_Multiple_Events( df, events, name_list, term_n_list = list(), tform_list = list(), keep_constant_list = list(), a_n_list = list() )Joint_Multiple_Events( df, events, name_list, term_n_list = list(), tform_list = list(), keep_constant_list = list(), a_n_list = list() )
df |
a data.table containing the columns of interest |
events |
vector of event column names |
name_list |
list of vectors for columns for event specific or shared model elements, required |
term_n_list |
list of vectors for term numbers for event specific or shared model elements, defaults to term 0 |
tform_list |
list of vectors for subterm types for event specific or shared model elements, defaults to loglinear |
keep_constant_list |
list of vectors for constant elements for event specific or shared model elements, defaults to free (0) |
a_n_list |
list of vectors for parameter values for event specific or shared model elements, defaults to term 0 |
returns the updated dataframe and model inputs
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Replace_Missing(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) a <- c(0, 0, 0, 1, 1, 1) b <- c(1, 1, 1, 2, 2, 2) c <- c(0, 1, 2, 2, 1, 0) d <- c(1, 1, 0, 0, 1, 1) e <- c(0, 1, 1, 1, 0, 0) df <- data.table("t0" = a, "t1" = b, "e0" = c, "e1" = d, "fac" = e) time1 <- "t0" time2 <- "t1" df$pyr <- df$t1 - df$t0 pyr <- "pyr" events <- c("e0", "e1") names_e0 <- c("fac") names_e1 <- c("fac") names_shared <- c("t0", "t0") term_n_e0 <- c(0) term_n_e1 <- c(0) term_n_shared <- c(0, 0) tform_e0 <- c("loglin") tform_e1 <- c("loglin") tform_shared <- c("quad_slope", "loglin_top") keep_constant_e0 <- c(0) keep_constant_e1 <- c(0) keep_constant_shared <- c(0, 0) a_n_e0 <- c(-0.1) a_n_e1 <- c(0.1) a_n_shared <- c(0.001, -0.02) name_list <- list("shared" = names_shared, "e0" = names_e0, "e1" = names_e1) term_n_list <- list("shared" = term_n_shared, "e0" = term_n_e0, "e1" = term_n_e1) tform_list <- list("shared" = tform_shared, "e0" = tform_e0, "e1" = tform_e1) keep_constant_list <- list( "shared" = keep_constant_shared, "e0" = keep_constant_e0, "e1" = keep_constant_e1 ) a_n_list <- list("shared" = a_n_shared, "e0" = a_n_e0, "e1" = a_n_e1) val <- Joint_Multiple_Events( df, events, name_list, term_n_list, tform_list, keep_constant_list, a_n_list )library(data.table) a <- c(0, 0, 0, 1, 1, 1) b <- c(1, 1, 1, 2, 2, 2) c <- c(0, 1, 2, 2, 1, 0) d <- c(1, 1, 0, 0, 1, 1) e <- c(0, 1, 1, 1, 0, 0) df <- data.table("t0" = a, "t1" = b, "e0" = c, "e1" = d, "fac" = e) time1 <- "t0" time2 <- "t1" df$pyr <- df$t1 - df$t0 pyr <- "pyr" events <- c("e0", "e1") names_e0 <- c("fac") names_e1 <- c("fac") names_shared <- c("t0", "t0") term_n_e0 <- c(0) term_n_e1 <- c(0) term_n_shared <- c(0, 0) tform_e0 <- c("loglin") tform_e1 <- c("loglin") tform_shared <- c("quad_slope", "loglin_top") keep_constant_e0 <- c(0) keep_constant_e1 <- c(0) keep_constant_shared <- c(0, 0) a_n_e0 <- c(-0.1) a_n_e1 <- c(0.1) a_n_shared <- c(0.001, -0.02) name_list <- list("shared" = names_shared, "e0" = names_e0, "e1" = names_e1) term_n_list <- list("shared" = term_n_shared, "e0" = term_n_e0, "e1" = term_n_e1) tform_list <- list("shared" = tform_shared, "e0" = tform_e0, "e1" = tform_e1) keep_constant_list <- list( "shared" = keep_constant_shared, "e0" = keep_constant_e0, "e1" = keep_constant_e1 ) a_n_list <- list("shared" = a_n_shared, "e0" = a_n_e0, "e1" = a_n_e1) val <- Joint_Multiple_Events( df, events, name_list, term_n_list, tform_list, keep_constant_list, a_n_list )
Likelihood_Ratio_Test uses two models and calculates the ratio
Likelihood_Ratio_Test(alternative_model, null_model)Likelihood_Ratio_Test(alternative_model, null_model)
alternative_model |
the new model of interest in list form, output from a poisson regression |
null_model |
a model to compare against, in list form |
returns the score statistic
library(data.table) # In an actual example, one would run two seperate RunCoxRegression regressions, # assigning the results to e0 and e1 e0 <- list("name" = "First Model", "LogLik" = -120) e1 <- list("name" = "New Model", "LogLik" = -100) score <- Likelihood_Ratio_Test(e1, e0)library(data.table) # In an actual example, one would run two seperate RunCoxRegression regressions, # assigning the results to e0 and e1 e0 <- list("name" = "First Model", "LogLik" = -120) e1 <- list("name" = "New Model", "LogLik" = -100) score <- Likelihood_Ratio_Test(e1, e0)
Linked_Dose_Formula Calculates all parameters for linear-quadratic and linear-exponential linked formulas
Linked_Dose_Formula(tforms, paras, verbose = 0)Linked_Dose_Formula(tforms, paras, verbose = 0)
tforms |
list of formula types |
paras |
list of formula parameters |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
returns list of full parameters
library(data.table) tforms <- list("cov_0" = "quad", "cov_1" = "exp") paras <- list("cov_0" = c(1, 3.45), "cov_1" = c(1.2, 4.5, 0.1)) full_paras <- Linked_Dose_Formula(tforms, paras)library(data.table) tforms <- list("cov_0" = "quad", "cov_1" = "exp") paras <- list("cov_0" = c(1, 3.45), "cov_1" = c(1.2, 4.5, 0.1)) full_paras <- Linked_Dose_Formula(tforms, paras)
Linked_Lin_Exp_Para Calculates what the additional parameter would be for a desired maximum
Linked_Lin_Exp_Para(y, a0, a1_goal, verbose = 0)Linked_Lin_Exp_Para(y, a0, a1_goal, verbose = 0)
y |
point formula switch |
a0 |
linear slope |
a1_goal |
exponential maximum desired |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
returns parameter used by Colossus
library(data.table) y <- 7.6 a0 <- 1.2 a1_goal <- 15 full_paras <- Linked_Lin_Exp_Para(y, a0, a1_goal)library(data.table) y <- 7.6 a0 <- 1.2 a1_goal <- 15 full_paras <- Linked_Lin_Exp_Para(y, a0, a1_goal)
OMP_Check Called directly from R, checks the omp flag and returns if omp is enabled
OMP_Check()OMP_Check()
boolean: True for OMP allowed
Rcomp_version Checks how R was compiled, part of configuration script
Rcomp_version()Rcomp_version()
returns a string representation of gcc, clang, or R CMD config CC output
Rcpp_version checks ~/.R/Makevars script for default compilers set, part of configuration script
Rcpp_version()Rcpp_version()
returns a string representation of gcc, clang, or head ~/.R/Makevars
Replace_Missing checks each column and fills in NA values
Replace_Missing(df, name_list, msv, verbose = FALSE)Replace_Missing(df, name_list, msv, verbose = FALSE)
df |
a data.table containing the columns of interest |
name_list |
vector of string column names to check |
msv |
value to replace na with, same used for every column used |
verbose |
integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0. |
returns a filled datatable
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Time_Since(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, NA, 47, 36, NA, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0) ) df <- Replace_Missing(df, c("Starting_Age", "Ending_Age"), 70)library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, NA, 47, 36, NA, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0) ) df <- Replace_Missing(df, c("Starting_Age", "Ending_Age"), 70)
RunCoxRegression uses user provided data and time/event columns
to calculate the log-likelihood with constant hazard ratio
RunCoxNull( df, time1 = "start", time2 = "end", event0 = "event", control = list() )RunCoxNull( df, time1 = "start", time2 = "end", event0 = "event", control = list() )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_STRATA(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" control <- list( "ncores" = 2, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxNull(df, time1, time2, event, control)library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" control <- list( "ncores" = 2, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxNull(df, time1, time2, event, control)
RunCoxPlots uses user provided data, time/event columns,
vectors specifying the model, and options to choose and save plots
RunCoxPlots( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, control = list(), plot_options = list(), model_control = list() )RunCoxPlots( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, control = list(), plot_options = list(), model_control = list() )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
plot_options |
list of parameters controlling the plot options, see RunCoxPlots() for different options |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
saves the plots in the current directory and returns a string
Other Plotting Wrapper Functions:
Cox_Relative_Risk()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(-0.1, 0.5, 1.1, -0.3) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) # setting maxiter below 0 forces the function to calculate the score # and return plot_options <- list( "type" = c("surv", paste(tempfile(), "run", sep = "" )), "studyid" = "UserID", "verbose" = FALSE ) RunCoxPlots( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(-0.1, 0.5, 1.1, -0.3) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) # setting maxiter below 0 forces the function to calculate the score # and return plot_options <- list( "type" = c("surv", paste(tempfile(), "run", sep = "" )), "studyid" = "UserID", "verbose" = FALSE ) RunCoxPlots( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options )
RunCoxRegression uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence
and starting position
RunCoxRegression( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list() )RunCoxRegression( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list() )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_STRATA(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxRegression( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxRegression( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control )
RunCoxRegression_Basic uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions
RunCoxRegression_Basic( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), keep_constant = c(0), a_n = c(0), der_iden = 0, control = list() )RunCoxRegression_Basic( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), keep_constant = c(0), a_n = c(0), der_iden = 0, control = list() )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_STRATA(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxRegression_Basic( df, time1, time2, event, names, keep_constant, a_n, der_iden, control )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxRegression_Basic( df, time1, time2, event, names, keep_constant, a_n, der_iden, control )
RunCoxRegression_CR uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence, starting positions, and censoring adjustment
RunCoxRegression_CR( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), cens_weight = "null" )RunCoxRegression_CR( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), cens_weight = "null" )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
cens_weight |
column containing the row weights |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_STRATA(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 2, 1, 2, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) # weights the probability that a row would continue to extend without censoring, # for risk group calculation df$cens_weight <- c(0.83, 0.37, 0.26, 0.34, 0.55, 0.23, 0.27) # censoring weight is generated by the survival library finegray function, or by hand. # The ratio of weight at event end point to weight at row endpoint is used. e <- RunCoxRegression_CR( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, "cens_weight" )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 2, 1, 2, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) # weights the probability that a row would continue to extend without censoring, # for risk group calculation df$cens_weight <- c(0.83, 0.37, 0.26, 0.34, 0.55, 0.23, 0.27) # censoring weight is generated by the survival library finegray function, or by hand. # The ratio of weight at event end point to weight at row endpoint is used. e <- RunCoxRegression_CR( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, "cens_weight" )
RunCoxRegression_Guesses_CPP uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions. Has additional options to starting with several initial guesses
RunCoxRegression_Guesses_CPP( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_control = list(), strat_col = "null", model_control = list(), cens_weight = "null" )RunCoxRegression_Guesses_CPP( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_control = list(), strat_col = "null", model_control = list(), cens_weight = "null" )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cens_weight |
column containing the row weights |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_STRATA(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e <- RunCoxRegression_Guesses_CPP( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e <- RunCoxRegression_Guesses_CPP( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col )
RunCoxRegression_Omnibus uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence
and starting positions. Has additional options for starting with several
initial guesses, using stratification, multiplicative loglinear 1-term,
competing risks, and calculation without derivatives
RunCoxRegression_Omnibus( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", cens_weight = "null", model_control = list(), cons_mat = as.matrix(c(0)), cons_vec = c(0) )RunCoxRegression_Omnibus( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", cens_weight = "null", model_control = list(), cons_mat = as.matrix(c(0)), cons_vec = c(0) )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
cens_weight |
column containing the row weights |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for system of linear constraints, formatted as vector |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_STRATA(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4)) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiters" = c(5, 5, 5), "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1, "guesses" = 2 ) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, model_control = list( "single" = FALSE, "basic" = FALSE, "cr" = FALSE, "null" = FALSE ) )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4)) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiters" = c(5, 5, 5), "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1, "guesses" = 2 ) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, model_control = list( "single" = FALSE, "basic" = FALSE, "cr" = FALSE, "null" = FALSE ) )
RunCoxRegression_Omnibus_Multidose uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence
and starting positions. Used for 2DMC column uncertainty methods.
Returns optimized parameters, log-likelihood, and standard deviation for each realization.
Has additional options for using stratification,
multiplicative loglinear 1-term,
competing risks, and calculation without derivatives
RunCoxRegression_Omnibus_Multidose( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, realization_columns = matrix(c("temp00", "temp01", "temp10", "temp11"), nrow = 2), realization_index = c("temp0", "temp1"), control = list(), strat_col = "null", cens_weight = "null", model_control = list(), cons_mat = as.matrix(c(0)), cons_vec = c(0) )RunCoxRegression_Omnibus_Multidose( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, realization_columns = matrix(c("temp00", "temp01", "temp10", "temp11"), nrow = 2), realization_index = c("temp0", "temp1"), control = list(), strat_col = "null", cens_weight = "null", model_control = list(), cons_mat = as.matrix(c(0)), cons_vec = c(0) )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
realization_columns |
used for multi-realization regressions. Matrix of column names with rows for each column with realizations, columns for each realization |
realization_index |
used for multi-realization regressions. Vector of column names, one for each column with realizations. each name should be used in the "names" variable in the equation definition |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
cens_weight |
column containing the row weights |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for system of linear constraints, formatted as vector |
returns a list of the final results for each realization
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_STRATA(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "t0" = c(18, 20, 18, 19, 21, 20, 18), "t1" = c(30, 45, 57, 47, 36, 60, 55), "lung" = c(0, 0, 1, 0, 1, 0, 0), "dose" = c(0, 1, 1, 0, 1, 0, 1) ) set.seed(3742) df$rand <- floor(runif(nrow(df), min = 0, max = 5)) df$rand0 <- floor(runif(nrow(df), min = 0, max = 5)) df$rand1 <- floor(runif(nrow(df), min = 0, max = 5)) df$rand2 <- floor(runif(nrow(df), min = 0, max = 5)) time1 <- "t0" time2 <- "t1" names <- c("dose", "rand") term_n <- c(0, 0) tform <- c("loglin", "loglin") realization_columns <- matrix(c("rand0", "rand1", "rand2"), nrow = 1) realization_index <- c("rand") keep_constant <- c(1, 0) a_n <- c(0, 0) modelform <- "M" fir <- 0 der_iden <- 0 cens_weight <- c(0) event <- "lung" a_n <- c(-0.1, -0.1) keep_constant <- c(0, 0) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 1, "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxRegression_Omnibus_Multidose(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, realization_columns = realization_columns, realization_index = realization_index, control = control, strat_col = "fac", model_control = list(), cens_weight = "null" )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "t0" = c(18, 20, 18, 19, 21, 20, 18), "t1" = c(30, 45, 57, 47, 36, 60, 55), "lung" = c(0, 0, 1, 0, 1, 0, 0), "dose" = c(0, 1, 1, 0, 1, 0, 1) ) set.seed(3742) df$rand <- floor(runif(nrow(df), min = 0, max = 5)) df$rand0 <- floor(runif(nrow(df), min = 0, max = 5)) df$rand1 <- floor(runif(nrow(df), min = 0, max = 5)) df$rand2 <- floor(runif(nrow(df), min = 0, max = 5)) time1 <- "t0" time2 <- "t1" names <- c("dose", "rand") term_n <- c(0, 0) tform <- c("loglin", "loglin") realization_columns <- matrix(c("rand0", "rand1", "rand2"), nrow = 1) realization_index <- c("rand") keep_constant <- c(1, 0) a_n <- c(0, 0) modelform <- "M" fir <- 0 der_iden <- 0 cens_weight <- c(0) event <- "lung" a_n <- c(-0.1, -0.1) keep_constant <- c(0, 0) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 1, "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxRegression_Omnibus_Multidose(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, realization_columns = realization_columns, realization_index = realization_index, control = control, strat_col = "fac", model_control = list(), cens_weight = "null" )
RunCoxRegression_Single uses user provided data, time/event columns, vectors specifying the model, and options and returns the log-likelihood
RunCoxRegression_Single( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, control = list() )RunCoxRegression_Single( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, control = list() )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_STRATA(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point keep_constant <- c(0, 0, 0, 0) control <- list( "ncores" = 2, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxRegression_Single( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point keep_constant <- c(0, 0, 0, 0) control <- list( "ncores" = 2, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) e <- RunCoxRegression_Single( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control )
RunCoxRegression_STRATA uses user provided data,
time/event columns, vectors specifying the model, and options to control
the convergence and starting positions
RunCoxRegression_STRATA( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null" )RunCoxRegression_STRATA( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null" )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_Single(),
RunCoxRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 0, 0, 1, 0, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) strat_col <- "e" e <- RunCoxRegression_STRATA( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 0, 0, 1, 0, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) strat_col <- "e" e <- RunCoxRegression_STRATA( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col )
RunCoxRegression_Tier_Guesses uses user provided data, time/event
columns, vectors specifying the model, and options to control the
convergence and starting positions, with additional guesses
RunCoxRegression_Tier_Guesses( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_control = list(), strat_col = "null", model_control = list(), cens_weight = "null" )RunCoxRegression_Tier_Guesses( df, time1 = "start", time2 = "end", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_control = list(), strat_col = "null", model_control = list(), cens_weight = "null" )
df |
a data.table containing the columns of interest |
time1 |
column used for time period starts |
time2 |
column used for time period end |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cens_weight |
column containing the row weights |
returns a list of the final results
Other Cox Wrapper Functions:
RunCoxNull(),
RunCoxRegression(),
RunCoxRegression_Basic(),
RunCoxRegression_CR(),
RunCoxRegression_Guesses_CPP(),
RunCoxRegression_Omnibus(),
RunCoxRegression_Omnibus_Multidose(),
RunCoxRegression_STRATA(),
RunCoxRegression_Single()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 0, 0, 1, 0, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "iterations" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = TRUE, term_initial = c(0, 1) ) strat_col <- "e" e <- RunCoxRegression_Tier_Guesses( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 0, 0, 1, 0, 1) ) # For the interval case time1 <- "Starting_Age" time2 <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "iterations" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = TRUE, term_initial = c(0, 1) ) strat_col <- "e" e <- RunCoxRegression_Tier_Guesses( df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col )
RunPoissonEventAssignment uses user provided data, person-year/event columns, vectors specifying the model, and options to calculate background and excess events
RunPoissonEventAssignment( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", model_control = list() )RunPoissonEventAssignment( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", model_control = list() )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) e <- RunPoissonEventAssignment( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) e <- RunPoissonEventAssignment( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control )
RunPoissonEventAssignment_bound uses user provided data, the results of a poisson regression, and options to calculate background and excess events
RunPoissonEventAssignment_bound( df, pyr0 = "pyr", event0 = "event", alternative_model = list(), keep_constant = c(0), modelform = "M", fir = 0, der_iden = 0, check_num = 1, z = 2, control = list(), strat_col = "null", model_control = list() )RunPoissonEventAssignment_bound( df, pyr0 = "pyr", event0 = "event", alternative_model = list(), keep_constant = c(0), modelform = "M", fir = 0, der_iden = 0, check_num = 1, z = 2, control = list(), strat_col = "null", model_control = list() )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
alternative_model |
the new model of interest in list form, output from a poisson regression |
keep_constant |
binary values to denote which parameters to change |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
check_num |
the parameter number to check at the bounds of, indexed from 1 using the order returned by Colossus |
z |
Z score to use for confidence interval |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e0 <- RunPoissonRegression_Omnibus( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col ) e <- RunPoissonEventAssignment_bound( df, pyr, event, e0, keep_constant, modelform, fir, der_iden, 4, 2, control )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e0 <- RunPoissonRegression_Omnibus( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col ) e <- RunPoissonEventAssignment_bound( df, pyr, event, e0, keep_constant, modelform, fir, der_iden, 4, 2, control )
RunPoissonRegression uses user provided data, person-year/event columns, vectors specifying the model, and options to control the convergence and starting positions with no special options
RunPoissonRegression( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list() )RunPoissonRegression( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list() )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) e <- RunPoissonRegression( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) e <- RunPoissonRegression( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control )
RunPoissonRegression_Guesses_CPP uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions. Has additional options to starting with several initial guesses
RunPoissonRegression_Guesses_CPP( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_control = list(), strat_col = "null", model_control = list() )RunPoissonRegression_Guesses_CPP( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_control = list(), strat_col = "null", model_control = list() )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e <- RunPoissonRegression_Guesses_CPP( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e <- RunPoissonRegression_Guesses_CPP( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col )
RunPoissonRegression_Joint_Omnibus uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence and starting positions.
Has additional options to starting with several initial guesses, uses joint competing risks equation
RunPoissonRegression_Joint_Omnibus( df, pyr0, events, name_list, term_n_list = list(), tform_list = list(), keep_constant_list = list(), a_n_list = list(), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", model_control = list(), cons_mat = as.matrix(c(0)), cons_vec = c(0) )RunPoissonRegression_Joint_Omnibus( df, pyr0, events, name_list, term_n_list = list(), tform_list = list(), keep_constant_list = list(), a_n_list = list(), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", model_control = list(), cons_mat = as.matrix(c(0)), cons_vec = c(0) )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
events |
vector of event column names |
name_list |
list of vectors for columns for event specific or shared model elements, required |
term_n_list |
list of vectors for term numbers for event specific or shared model elements, defaults to term 0 |
tform_list |
list of vectors for subterm types for event specific or shared model elements, defaults to loglinear |
keep_constant_list |
list of vectors for constant elements for event specific or shared model elements, defaults to free (0) |
a_n_list |
list of vectors for parameter values for event specific or shared model elements, defaults to term 0 |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for system of linear constraints, formatted as vector |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette a <- c(0, 0, 0, 1, 1, 1) b <- c(1, 1, 1, 2, 2, 2) c <- c(0, 1, 2, 2, 1, 0) d <- c(1, 1, 0, 0, 1, 1) e <- c(0, 1, 1, 1, 0, 0) f <- c(0, 1, 0, 0, 1, 1) df <- data.table("t0" = a, "t1" = b, "e0" = c, "e1" = d, "fac" = e) time1 <- "t0" time2 <- "t1" df$pyr <- df$t1 - df$t0 pyr <- "pyr" events <- c("e0", "e1") names_e0 <- c("fac") names_e1 <- c("fac") names_shared <- c("t0", "t0") term_n_e0 <- c(0) term_n_e1 <- c(0) term_n_shared <- c(0, 0) tform_e0 <- c("loglin") tform_e1 <- c("loglin") tform_shared <- c("quad_slope", "loglin_top") keep_constant_e0 <- c(0) keep_constant_e1 <- c(0) keep_constant_shared <- c(0, 0) a_n_e0 <- c(-0.1) a_n_e1 <- c(0.1) a_n_shared <- c(0.001, -0.02) name_list <- list("shared" = names_shared, "e0" = names_e0, "e1" = names_e1) term_n_list <- list("shared" = term_n_shared, "e0" = term_n_e0, "e1" = term_n_e1) tform_list <- list("shared" = tform_shared, "e0" = tform_e0, "e1" = tform_e1) keep_constant_list <- list( "shared" = keep_constant_shared, "e0" = keep_constant_e0, "e1" = keep_constant_e1 ) a_n_list <- list("shared" = a_n_shared, "e0" = a_n_e0, "e1" = a_n_e1) der_iden <- 0 modelform <- "M" fir <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "f" e <- RunPoissonRegression_Joint_Omnibus( df, pyr, events, name_list, term_n_list, tform_list, keep_constant_list, a_n_list, modelform, fir, der_iden, control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette a <- c(0, 0, 0, 1, 1, 1) b <- c(1, 1, 1, 2, 2, 2) c <- c(0, 1, 2, 2, 1, 0) d <- c(1, 1, 0, 0, 1, 1) e <- c(0, 1, 1, 1, 0, 0) f <- c(0, 1, 0, 0, 1, 1) df <- data.table("t0" = a, "t1" = b, "e0" = c, "e1" = d, "fac" = e) time1 <- "t0" time2 <- "t1" df$pyr <- df$t1 - df$t0 pyr <- "pyr" events <- c("e0", "e1") names_e0 <- c("fac") names_e1 <- c("fac") names_shared <- c("t0", "t0") term_n_e0 <- c(0) term_n_e1 <- c(0) term_n_shared <- c(0, 0) tform_e0 <- c("loglin") tform_e1 <- c("loglin") tform_shared <- c("quad_slope", "loglin_top") keep_constant_e0 <- c(0) keep_constant_e1 <- c(0) keep_constant_shared <- c(0, 0) a_n_e0 <- c(-0.1) a_n_e1 <- c(0.1) a_n_shared <- c(0.001, -0.02) name_list <- list("shared" = names_shared, "e0" = names_e0, "e1" = names_e1) term_n_list <- list("shared" = term_n_shared, "e0" = term_n_e0, "e1" = term_n_e1) tform_list <- list("shared" = tform_shared, "e0" = tform_e0, "e1" = tform_e1) keep_constant_list <- list( "shared" = keep_constant_shared, "e0" = keep_constant_e0, "e1" = keep_constant_e1 ) a_n_list <- list("shared" = a_n_shared, "e0" = a_n_e0, "e1" = a_n_e1) der_iden <- 0 modelform <- "M" fir <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "f" e <- RunPoissonRegression_Joint_Omnibus( df, pyr, events, name_list, term_n_list, tform_list, keep_constant_list, a_n_list, modelform, fir, der_iden, control, strat_col )
RunPoissonRegression_Omnibus uses user provided data, time/event columns,
vectors specifying the model, and options to control the convergence and starting positions.
Has additional options to starting with several initial guesses
RunPoissonRegression_Omnibus( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", model_control = list(), cons_mat = as.matrix(c(0)), cons_vec = c(0) )RunPoissonRegression_Omnibus( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", model_control = list(), cons_mat = as.matrix(c(0)), cons_vec = c(0) )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
cons_mat |
Matrix containing coefficients for system of linear constraints, formatted as matrix |
cons_vec |
Vector containing constants for system of linear constraints, formatted as vector |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e <- RunPoissonRegression_Omnibus( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e <- RunPoissonRegression_Omnibus( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col )
RunPoissonRegression_Residual uses user provided data, time/event columns,
vectors specifying the model, and options. Calculates residuals or sum of residuals
RunPoissonRegression_Residual( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", model_control = list() )RunPoissonRegression_Residual( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null", model_control = list() )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e <- RunPoissonRegression_Residual( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 1, 0, 0, 0, 1) ) # For the interval case pyr <- "Ending_Age" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1 ) guesses_control <- list( "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = FALSE ) strat_col <- "e" e <- RunPoissonRegression_Residual( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col )
RunPoissonRegression_Single uses user provided data, person-year/event columns, vectors specifying the model, and returns the results
RunPoissonRegression_Single( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", a_n = c(0), modelform = "M", fir = 0, control = list(), keep_constant = rep(0, length(names)) )RunPoissonRegression_Single( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", a_n = c(0), modelform = "M", fir = 0, control = list(), keep_constant = rep(0, length(names)) )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
keep_constant |
binary values to denote which parameters to change |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) e <- RunPoissonRegression_Single( df, pyr, event, names, term_n, tform, a_n, modelform, fir, control )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) e <- RunPoissonRegression_Single( df, pyr, event, names, term_n, tform, a_n, modelform, fir, control )
RunPoissonRegression_STRATA uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions
RunPoissonRegression_STRATA( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null" )RunPoissonRegression_STRATA( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), strat_col = "null" )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_Single(),
RunPoissonRegression_Tier_Guesses()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 0, 0, 1, 0, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) strat_col <- c("e") e <- RunPoissonRegression_STRATA( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 0, 0, 1, 0, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 a_n <- c(0.1, 0.1, 0.1, 0.1) keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) strat_col <- c("e") e <- RunPoissonRegression_STRATA( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, strat_col )
RunPoissonRegression_Tier_Guesses uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions, with additional guesses
RunPoissonRegression_Tier_Guesses( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_control = list(), strat_col = "null", model_control = list() )RunPoissonRegression_Tier_Guesses( df, pyr0 = "pyr", event0 = "event", names = c("CONST"), term_n = c(0), tform = "loglin", keep_constant = c(0), a_n = c(0), modelform = "M", fir = 0, der_iden = 0, control = list(), guesses_control = list(), strat_col = "null", model_control = list() )
df |
a data.table containing the columns of interest |
pyr0 |
column used for person-years per row |
event0 |
column used for event status |
names |
columns for elements of the model, used to identify data columns |
term_n |
term numbers for each element of the model |
tform |
list of string function identifiers, used for linear/step |
keep_constant |
binary values to denote which parameters to change |
a_n |
list of initial parameter values, used to determine number of parameters. May be either a list of vectors or a single vector. |
modelform |
string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E |
fir |
term number for the initial term, used for models of the form T0*f(Ti) in which the order matters |
der_iden |
number for the subterm to test derivative at, only used for testing runs with a single varying parameter, should be smaller than total number of parameters. indexed starting at 0 |
control |
list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options") |
guesses_control |
list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options") |
strat_col |
column to stratify by if needed |
model_control |
controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details |
returns a list of the final results
Other Poisson Wrapper Functions:
RunPoissonEventAssignment(),
RunPoissonEventAssignment_bound(),
RunPoissonRegression(),
RunPoissonRegression_Guesses_CPP(),
RunPoissonRegression_Joint_Omnibus(),
RunPoissonRegression_Omnibus(),
RunPoissonRegression_Residual(),
RunPoissonRegression_STRATA(),
RunPoissonRegression_Single()
library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 0, 0, 1, 0, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) guesses_control <- list( "iterations" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = TRUE, term_initial = c(0, 1) ) strat_col <- c("e") e <- RunPoissonRegression_Tier_Guesses( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col )library(data.table) ## basic example code reproduced from the starting-description vignette df <- data.table::data.table( "UserID" = c(112, 114, 213, 214, 115, 116, 117), "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18), "Ending_Age" = c(30, 45, 57, 47, 36, 60, 55), "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0), "a" = c(0, 1, 1, 0, 1, 0, 1), "b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2), "c" = c(10, 11, 10, 11, 12, 9, 11), "d" = c(0, 0, 0, 1, 1, 1, 1), "e" = c(0, 0, 0, 0, 1, 0, 1) ) # For the interval case df$pyr <- df$Ending_Age - df$Starting_Age pyr <- "pyr" event <- "Cancer_Status" names <- c("a", "b", "c", "d") a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point term_n <- c(0, 1, 1, 2) tform <- c("loglin", "lin", "lin", "plin") modelform <- "M" fir <- 0 keep_constant <- c(0, 0, 0, 0) der_iden <- 0 control <- list( "ncores" = 2, "lr" = 0.75, "maxiter" = 5, "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE, "double_step" = 1 ) guesses_control <- list( "iterations" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1, "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform", strata = TRUE, term_initial = c(0, 1) ) strat_col <- c("e") e <- RunPoissonRegression_Tier_Guesses( df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control, guesses_control, strat_col )
System_Version checks OS, default R c++ compiler, and if OMP is enabled
System_Version()System_Version()
returns a list of results
Time_Since generates a new dataframe with a column containing time since a reference in a given unit
Time_Since(df, dcol0, tref, col_name, units = "days")Time_Since(df, dcol0, tref, col_name, units = "days")
df |
a data.table containing the columns of interest |
dcol0 |
list of ending month, day, and year |
tref |
reference time in date format |
col_name |
vector of new column names |
units |
time unit to use |
returns the updated dataframe
Other Data Cleaning Functions:
Check_Dupe_Columns(),
Check_Trunc(),
Check_Verbose(),
Correct_Formula_Order(),
Date_Shift(),
Def_Control(),
Def_Control_Guess(),
Def_model_control(),
Def_modelform_fix(),
Joint_Multiple_Events(),
Replace_Missing(),
factorize(),
factorize_par(),
gen_time_dep(),
interact_them()
library(data.table) m0 <- c(1, 1, 2, 2) m1 <- c(2, 2, 3, 3) d0 <- c(1, 2, 3, 4) d1 <- c(6, 7, 8, 9) y0 <- c(1990, 1991, 1997, 1998) y1 <- c(2001, 2003, 2005, 2006) df <- data.table::data.table( "m0" = m0, "m1" = m1, "d0" = d0, "d1" = d1, "y0" = y0, "y1" = y1 ) tref <- strptime("3-22-1997", format = "%m-%d-%Y", tz = "UTC") df <- Time_Since(df, c("m1", "d1", "y1"), tref, "date_since")library(data.table) m0 <- c(1, 1, 2, 2) m1 <- c(2, 2, 3, 3) d0 <- c(1, 2, 3, 4) d1 <- c(6, 7, 8, 9) y0 <- c(1990, 1991, 1997, 1998) y1 <- c(2001, 2003, 2005, 2006) df <- data.table::data.table( "m0" = m0, "m1" = m1, "d0" = d0, "d1" = d1, "y0" = y0, "y1" = y1 ) tref <- strptime("3-22-1997", format = "%m-%d-%Y", tz = "UTC") df <- Time_Since(df, c("m1", "d1", "y1"), tref, "date_since")