| Title: | Fast Imputations Using 'Rcpp' and 'Armadillo' |
|---|---|
| Description: | Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search. |
| Authors: | Maciej Nasinski [aut, cre] |
| Maintainer: | Maciej Nasinski <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.8.2 |
| Built: | 2024-11-23 06:51:10 UTC |
| Source: | CRAN |
Fast imputations under the object-oriented programming paradigm. There was used quantitative models with a closed-form solution. Thus package is based on linear algebra operations. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. Moreover there are offered a few functions built to work with popular R packages such as 'data.table'.
Please read the vignette for additional information
Maciej Nasinski
https://github.com/Polkas/miceFast
airquality dataset with additional variables
air_missair_miss
A data frame and data table with 154 observations on 11 variables.
numeric Ozone (ppb) - Mean ozone in parts per billion from 1300 to 1500 hours at Roosevelt Island
numeric Solar R (lang) - Solar radiation in Langleys in the frequency band 4000–7700 Angstroms from 0800 to 1200 hours at Central Park
numeric Wind (mph) - Average wind speed in miles per hour at 0700 and 1000 hours at LaGuardia Airport
numeric Temperature (degrees F) - Maximum daily temperature in degrees Fahrenheit at La Guardia Airport.
numeric Day of month (1–31)
numeric a constant
numeric id
numeric positive values weights
factor Month (1–12)
character discrete version of Solar.R (5-levels)
character discrete version of Ozone (7-levels)
factor discrete version of Ozone (7-levels)
logical Ozone higher than its mean
Daily readings of the following air quality values for May 1, 1973 (a Tuesday) to September 30, 1973.
The data were obtained from the New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).
Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983) Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.
## Not run: library(data.table) data(airquality) data <- cbind(as.matrix(airquality[, -5]), Intercept = 1, index = 1:nrow(airquality), # a numeric vector - positive values weights = rnorm(nrow(airquality), 1, 0.01), # months as groups groups = airquality[, 5] ) # data.table air_miss <- data.table(data) air_miss$groups <- factor(air_miss$groups) # Distribution of Ozone - close to log-normal # hist(air_miss$Ozone) # Additional vars # Make a character variable to show package capabilities air_miss$x_character <- as.character(cut(air_miss$Solar.R, seq(0, 350, 70))) # Discrete version of dependent variable air_miss$Ozone_chac <- as.character(cut(air_miss$Ozone, seq(0, 160, 20))) air_miss$Ozone_f <- cut(air_miss$Ozone, seq(0, 160, 20)) air_miss$Ozone_high <- air_miss$Ozone > mean(air_miss$Ozone, na.rm = T) ## End(Not run)## Not run: library(data.table) data(airquality) data <- cbind(as.matrix(airquality[, -5]), Intercept = 1, index = 1:nrow(airquality), # a numeric vector - positive values weights = rnorm(nrow(airquality), 1, 0.01), # months as groups groups = airquality[, 5] ) # data.table air_miss <- data.table(data) air_miss$groups <- factor(air_miss$groups) # Distribution of Ozone - close to log-normal # hist(air_miss$Ozone) # Additional vars # Make a character variable to show package capabilities air_miss$x_character <- as.character(cut(air_miss$Solar.R, seq(0, 350, 70))) # Discrete version of dependent variable air_miss$Ozone_chac <- as.character(cut(air_miss$Ozone, seq(0, 160, 20))) air_miss$Ozone_f <- cut(air_miss$Ozone, seq(0, 160, 20)) air_miss$Ozone_high <- air_miss$Ozone > mean(air_miss$Ozone, na.rm = T) ## End(Not run)
ggplot2 visualization to support which imputation method to choose
compare_imp(df, origin, target)compare_imp(df, origin, target)
df |
data.frame with origin variable and the new one with imputations |
origin |
character value - the name of origin variable with values before any imputations |
target |
character vector - names of variables with applied imputations |
ggplot2 object
library(miceFast) library(ggplot2) data(air_miss) air_miss$Ozone_imp <- fill_NA( x = air_miss, model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE ) air_miss$Ozone_imp2 <- fill_NA_N( x = air_miss, model = "pmm", posit_y = 1, posit_x = c(4, 6), logreg = TRUE ) compare_imp(air_miss, origin = "Ozone", "Ozone_imp") compare_imp(air_miss, origin = "Ozone", c("Ozone_imp", "Ozone_imp2"))library(miceFast) library(ggplot2) data(air_miss) air_miss$Ozone_imp <- fill_NA( x = air_miss, model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE ) air_miss$Ozone_imp2 <- fill_NA_N( x = air_miss, model = "pmm", posit_y = 1, posit_x = c(4, 6), logreg = TRUE ) compare_imp(air_miss, origin = "Ozone", "Ozone_imp") compare_imp(air_miss, origin = "Ozone", c("Ozone_imp", "Ozone_imp2"))
fill_NA function for the imputations purpose.Regular imputations to fill the missing data. Non missing independent variables are used to approximate a missing observations for a dependent variable. Quantitative models were built under Rcpp packages and the C++ library Armadillo.
fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06) ## S3 method for class 'data.frame' fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06) ## S3 method for class 'data.table' fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06) ## S3 method for class 'matrix' fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06)fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06) ## S3 method for class 'data.frame' fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06) ## S3 method for class 'data.table' fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06) ## S3 method for class 'matrix' fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06)
x |
a numeric matrix or data.frame/data.table (factor/character/numeric/logical) - variables |
model |
a character - posibble options ("lda","lm_pred","lm_bayes","lm_noise") |
posit_y |
an integer/character - a position/name of dependent variable |
posit_x |
an integer/character vector - positions/names of independent variables |
w |
a numeric vector - a weighting variable - only positive values, Default:NULL |
logreg |
a boolean - if dependent variable has log-normal distribution (numeric). If TRUE log-regression is evaluated and then returned exponential of results., Default: FALSE |
ridge |
a numeric - a value added to diagonal elements of the x'x matrix, Default:1e-5 |
load imputations in a numeric/logical/character/factor (similar to the input type) vector format
fill_NA(data.frame): S3 method for data.frame
fill_NA(data.table): s3 method for data.table
fill_NA(matrix): S3 method for matrix
There is assumed that users add the intercept by their own. The miceFast module provides the most efficient environment, the second recommended option is to use data.table and the numeric matrix data type. The lda model is assessed only if there are more than 15 complete observations and for the lms models if number of independent variables is smaller than number of observations.
library(miceFast) library(dplyr) library(data.table) ### Data # airquality dataset with additional variables data(air_miss) ### Intro: dplyr # IMPUTATIONS air_miss <- air_miss %>% # Imputations with a grouping option (models are separately assessed for each group) # taking into account provided weights group_by(groups) %>% do(mutate(., Solar_R_imp = fill_NA( x = ., model = "lm_pred", posit_y = "Solar.R", posit_x = c("Wind", "Temp", "Intercept"), w = .[["weights"]] ))) %>% ungroup() %>% # Imputations - discrete variable mutate(x_character_imp = fill_NA( x = ., model = "lda", posit_y = "x_character", posit_x = c("Wind", "Temp") )) %>% # logreg was used because almost log-normal distribution of Ozone # imputations around mean mutate(Ozone_imp1 = fill_NA( x = ., model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept"), logreg = TRUE )) %>% # imputations using positions - Intercept, Temp mutate(Ozone_imp2 = fill_NA( x = ., model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE )) %>% # multiple imputations (average of x30 imputations) # with a factor independent variable, weights and logreg options mutate(Ozone_imp3 = fill_NA_N( x = ., model = "lm_noise", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 30 )) %>% mutate(Ozone_imp4 = fill_NA_N( x = ., model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 30 )) %>% group_by(groups) %>% do(mutate(., Ozone_imp5 = fill_NA( x = ., model = "lm_pred", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE ))) %>% do(mutate(., Ozone_imp6 = fill_NA_N( x = ., model = "pmm", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 20 ))) %>% ungroup() %>% # Average of a few methods mutate(Ozone_imp_mix = rowMeans(select(., starts_with("Ozone_imp")))) %>% # Protecting against collinearity or low number of observations - across small groups # Be carful when using a grouping option # because of lack of protection against collinearity or low number of observations. # There could be used a tryCatch(fill_NA(...),error=function(e) return(...)) group_by(groups) %>% do(mutate(., Ozone_chac_imp = tryCatch( fill_NA( x = ., model = "lda", posit_y = "Ozone_chac", posit_x = c( "Intercept", "Month", "Day", "Temp", "x_character_imp" ), w = .[["weights"]] ), error = function(e) .[["Ozone_chac"]] ))) %>% ungroup() # Sample of results air_miss[which(is.na(air_miss[, 1]))[1:5], ] ### Intro: data.table # IMPUTATIONS # Imputations with a grouping option (models are separately assessed for each group) # taking into account provided weights data(air_miss) setDT(air_miss) air_miss[, Solar_R_imp := fill_NA_N( x = .SD, model = "lm_bayes", posit_y = "Solar.R", posit_x = c("Wind", "Temp", "Intercept"), w = .SD[["weights"]], k = 100 ), by = .(groups)] %>% # Imputations - discrete variable .[, x_character_imp := fill_NA( x = .SD, model = "lda", posit_y = "x_character", posit_x = c("Wind", "Temp", "groups") )] %>% # logreg was used because almost log-normal distribution of Ozone # imputations around mean .[, Ozone_imp1 := fill_NA( x = .SD, model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept"), logreg = TRUE )] %>% # imputations using positions - Intercept, Temp .[, Ozone_imp2 := fill_NA( x = .SD, model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE )] %>% # model with a factor independent variable # multiple imputations (average of x30 imputations) # with a factor independent variable, weights and logreg options .[, Ozone_imp3 := fill_NA_N( x = .SD, model = "lm_noise", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 30 )] %>% .[, Ozone_imp4 := fill_NA_N( x = .SD, model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 30 )] %>% .[, Ozone_imp5 := fill_NA( x = .SD, model = "lm_pred", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE ), .(groups)] %>% .[, Ozone_imp6 := fill_NA_N( x = .SD, model = "pmm", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 10 ), .(groups)] %>% # Average of a few methods .[, Ozone_imp_mix := apply(.SD, 1, mean), .SDcols = Ozone_imp1:Ozone_imp6] %>% # Protecting against collinearity or low number of observations - across small groups # Be carful when using a data.table grouping option # because of lack of protection against collinearity or low number of observations. # There could be used a tryCatch(fill_NA(...),error=function(e) return(...)) .[, Ozone_chac_imp := tryCatch( fill_NA( x = .SD, model = "lda", posit_y = "Ozone_chac", posit_x = c( "Intercept", "Month", "Day", "Temp", "x_character_imp" ), w = .SD[["weights"]] ), error = function(e) .SD[["Ozone_chac"]] ), .(groups)] # Sample of results air_miss[which(is.na(air_miss[, 1]))[1:5], ]library(miceFast) library(dplyr) library(data.table) ### Data # airquality dataset with additional variables data(air_miss) ### Intro: dplyr # IMPUTATIONS air_miss <- air_miss %>% # Imputations with a grouping option (models are separately assessed for each group) # taking into account provided weights group_by(groups) %>% do(mutate(., Solar_R_imp = fill_NA( x = ., model = "lm_pred", posit_y = "Solar.R", posit_x = c("Wind", "Temp", "Intercept"), w = .[["weights"]] ))) %>% ungroup() %>% # Imputations - discrete variable mutate(x_character_imp = fill_NA( x = ., model = "lda", posit_y = "x_character", posit_x = c("Wind", "Temp") )) %>% # logreg was used because almost log-normal distribution of Ozone # imputations around mean mutate(Ozone_imp1 = fill_NA( x = ., model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept"), logreg = TRUE )) %>% # imputations using positions - Intercept, Temp mutate(Ozone_imp2 = fill_NA( x = ., model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE )) %>% # multiple imputations (average of x30 imputations) # with a factor independent variable, weights and logreg options mutate(Ozone_imp3 = fill_NA_N( x = ., model = "lm_noise", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 30 )) %>% mutate(Ozone_imp4 = fill_NA_N( x = ., model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 30 )) %>% group_by(groups) %>% do(mutate(., Ozone_imp5 = fill_NA( x = ., model = "lm_pred", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE ))) %>% do(mutate(., Ozone_imp6 = fill_NA_N( x = ., model = "pmm", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 20 ))) %>% ungroup() %>% # Average of a few methods mutate(Ozone_imp_mix = rowMeans(select(., starts_with("Ozone_imp")))) %>% # Protecting against collinearity or low number of observations - across small groups # Be carful when using a grouping option # because of lack of protection against collinearity or low number of observations. # There could be used a tryCatch(fill_NA(...),error=function(e) return(...)) group_by(groups) %>% do(mutate(., Ozone_chac_imp = tryCatch( fill_NA( x = ., model = "lda", posit_y = "Ozone_chac", posit_x = c( "Intercept", "Month", "Day", "Temp", "x_character_imp" ), w = .[["weights"]] ), error = function(e) .[["Ozone_chac"]] ))) %>% ungroup() # Sample of results air_miss[which(is.na(air_miss[, 1]))[1:5], ] ### Intro: data.table # IMPUTATIONS # Imputations with a grouping option (models are separately assessed for each group) # taking into account provided weights data(air_miss) setDT(air_miss) air_miss[, Solar_R_imp := fill_NA_N( x = .SD, model = "lm_bayes", posit_y = "Solar.R", posit_x = c("Wind", "Temp", "Intercept"), w = .SD[["weights"]], k = 100 ), by = .(groups)] %>% # Imputations - discrete variable .[, x_character_imp := fill_NA( x = .SD, model = "lda", posit_y = "x_character", posit_x = c("Wind", "Temp", "groups") )] %>% # logreg was used because almost log-normal distribution of Ozone # imputations around mean .[, Ozone_imp1 := fill_NA( x = .SD, model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept"), logreg = TRUE )] %>% # imputations using positions - Intercept, Temp .[, Ozone_imp2 := fill_NA( x = .SD, model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE )] %>% # model with a factor independent variable # multiple imputations (average of x30 imputations) # with a factor independent variable, weights and logreg options .[, Ozone_imp3 := fill_NA_N( x = .SD, model = "lm_noise", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 30 )] %>% .[, Ozone_imp4 := fill_NA_N( x = .SD, model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 30 )] %>% .[, Ozone_imp5 := fill_NA( x = .SD, model = "lm_pred", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE ), .(groups)] %>% .[, Ozone_imp6 := fill_NA_N( x = .SD, model = "pmm", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 10 ), .(groups)] %>% # Average of a few methods .[, Ozone_imp_mix := apply(.SD, 1, mean), .SDcols = Ozone_imp1:Ozone_imp6] %>% # Protecting against collinearity or low number of observations - across small groups # Be carful when using a data.table grouping option # because of lack of protection against collinearity or low number of observations. # There could be used a tryCatch(fill_NA(...),error=function(e) return(...)) .[, Ozone_chac_imp := tryCatch( fill_NA( x = .SD, model = "lda", posit_y = "Ozone_chac", posit_x = c( "Intercept", "Month", "Day", "Temp", "x_character_imp" ), w = .SD[["weights"]] ), error = function(e) .SD[["Ozone_chac"]] ), .(groups)] # Sample of results air_miss[which(is.na(air_miss[, 1]))[1:5], ]
fill_NA_N function for the multiple imputations purposeMultiple imputations to fill the missing data. Non missing independent variables are used to approximate a missing observations for a dependent variable. Quantitative models were built under Rcpp packages and the C++ library Armadillo.
fill_NA_N( x, model, posit_y, posit_x, w = NULL, logreg = FALSE, k = 10, ridge = 1e-06 ) ## S3 method for class 'data.frame' fill_NA_N( x, model, posit_y, posit_x, w = NULL, logreg = FALSE, k = 10, ridge = 1e-06 ) ## S3 method for class 'data.table' fill_NA_N( x, model, posit_y, posit_x, w = NULL, logreg = FALSE, k = 10, ridge = 1e-06 ) ## S3 method for class 'matrix' fill_NA_N( x, model, posit_y, posit_x, w = NULL, logreg = FALSE, k = 10, ridge = 1e-06 )fill_NA_N( x, model, posit_y, posit_x, w = NULL, logreg = FALSE, k = 10, ridge = 1e-06 ) ## S3 method for class 'data.frame' fill_NA_N( x, model, posit_y, posit_x, w = NULL, logreg = FALSE, k = 10, ridge = 1e-06 ) ## S3 method for class 'data.table' fill_NA_N( x, model, posit_y, posit_x, w = NULL, logreg = FALSE, k = 10, ridge = 1e-06 ) ## S3 method for class 'matrix' fill_NA_N( x, model, posit_y, posit_x, w = NULL, logreg = FALSE, k = 10, ridge = 1e-06 )
x |
a numeric matrix or data.frame/data.table (factor/character/numeric/logical) - variables |
model |
a character - posibble options ("lm_bayes","lm_noise","pmm") |
posit_y |
an integer/character - a position/name of dependent variable |
posit_x |
an integer/character vector - positions/names of independent variables |
w |
a numeric vector - a weighting variable - only positive values, Default: NULL |
logreg |
a boolean - if dependent variable has log-normal distribution (numeric). If TRUE log-regression is evaluated and then returned exponential of results., Default: FALSE |
k |
an integer - a number of multiple imputations or for pmm a number of closest points from which a one random value is taken, Default:10 |
ridge |
a numeric - a value added to diagonal elements of the x'x matrix, Default:1e-5 |
load imputations in a numeric/character/factor (similar to the input type) vector format
fill_NA_N(data.frame): s3 method for data.frame
fill_NA_N(data.table): S3 method for data.table
fill_NA_N(matrix): S3 method for matrix
There is assumed that users add the intercept by their own. The miceFast module provides the most efficient environment, the second recommended option is to use data.table and the numeric matrix data type. The lda model is assessed only if there are more than 15 complete observations and for the lms models if number of variables is smaller than number of observations.
library(miceFast) library(dplyr) library(data.table) ### Data # airquality dataset with additional variables data(air_miss) ### Intro: dplyr # IMPUTATIONS air_miss <- air_miss %>% # Imputations with a grouping option (models are separately assessed for each group) # taking into account provided weights group_by(groups) %>% do(mutate(., Solar_R_imp = fill_NA( x = ., model = "lm_pred", posit_y = "Solar.R", posit_x = c("Wind", "Temp", "Intercept"), w = .[["weights"]] ))) %>% ungroup() %>% # Imputations - discrete variable mutate(x_character_imp = fill_NA( x = ., model = "lda", posit_y = "x_character", posit_x = c("Wind", "Temp") )) %>% # logreg was used because almost log-normal distribution of Ozone # imputations around mean mutate(Ozone_imp1 = fill_NA( x = ., model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept"), logreg = TRUE )) %>% # imputations using positions - Intercept, Temp mutate(Ozone_imp2 = fill_NA( x = ., model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE )) %>% # multiple imputations (average of x30 imputations) # with a factor independent variable, weights and logreg options mutate(Ozone_imp3 = fill_NA_N( x = ., model = "lm_noise", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 30 )) %>% mutate(Ozone_imp4 = fill_NA_N( x = ., model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 30 )) %>% group_by(groups) %>% do(mutate(., Ozone_imp5 = fill_NA( x = ., model = "lm_pred", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE ))) %>% do(mutate(., Ozone_imp6 = fill_NA_N( x = ., model = "pmm", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 20 ))) %>% ungroup() %>% # Average of a few methods mutate(Ozone_imp_mix = rowMeans(select(., starts_with("Ozone_imp")))) %>% # Protecting against collinearity or low number of observations - across small groups # Be carful when using a grouping option # because of lack of protection against collinearity or low number of observations. # There could be used a tryCatch(fill_NA(...),error=function(e) return(...)) group_by(groups) %>% do(mutate(., Ozone_chac_imp = tryCatch( fill_NA( x = ., model = "lda", posit_y = "Ozone_chac", posit_x = c( "Intercept", "Month", "Day", "Temp", "x_character_imp" ), w = .[["weights"]] ), error = function(e) .[["Ozone_chac"]] ))) %>% ungroup() # Sample of results air_miss[which(is.na(air_miss[, 1]))[1:5], ] ### Intro: data.table # IMPUTATIONS # Imputations with a grouping option (models are separately assessed for each group) # taking into account provided weights data(air_miss) setDT(air_miss) air_miss[, Solar_R_imp := fill_NA_N( x = .SD, model = "lm_bayes", posit_y = "Solar.R", posit_x = c("Wind", "Temp", "Intercept"), w = .SD[["weights"]], k = 100 ), by = .(groups)] %>% # Imputations - discrete variable .[, x_character_imp := fill_NA( x = .SD, model = "lda", posit_y = "x_character", posit_x = c("Wind", "Temp", "groups") )] %>% # logreg was used because almost log-normal distribution of Ozone # imputations around mean .[, Ozone_imp1 := fill_NA( x = .SD, model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept"), logreg = TRUE )] %>% # imputations using positions - Intercept, Temp .[, Ozone_imp2 := fill_NA( x = .SD, model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE )] %>% # model with a factor independent variable # multiple imputations (average of x30 imputations) # with a factor independent variable, weights and logreg options .[, Ozone_imp3 := fill_NA_N( x = .SD, model = "lm_noise", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 30 )] %>% .[, Ozone_imp4 := fill_NA_N( x = .SD, model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 30 )] %>% .[, Ozone_imp5 := fill_NA( x = .SD, model = "lm_pred", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE ), .(groups)] %>% .[, Ozone_imp6 := fill_NA_N( x = .SD, model = "pmm", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 10 ), .(groups)] %>% # Average of a few methods .[, Ozone_imp_mix := apply(.SD, 1, mean), .SDcols = Ozone_imp1:Ozone_imp6] %>% # Protecting against collinearity or low number of observations - across small groups # Be carful when using a data.table grouping option # because of lack of protection against collinearity or low number of observations. # There could be used a tryCatch(fill_NA(...),error=function(e) return(...)) .[, Ozone_chac_imp := tryCatch( fill_NA( x = .SD, model = "lda", posit_y = "Ozone_chac", posit_x = c( "Intercept", "Month", "Day", "Temp", "x_character_imp" ), w = .SD[["weights"]] ), error = function(e) .SD[["Ozone_chac"]] ), .(groups)] # Sample of results air_miss[which(is.na(air_miss[, 1]))[1:5], ]library(miceFast) library(dplyr) library(data.table) ### Data # airquality dataset with additional variables data(air_miss) ### Intro: dplyr # IMPUTATIONS air_miss <- air_miss %>% # Imputations with a grouping option (models are separately assessed for each group) # taking into account provided weights group_by(groups) %>% do(mutate(., Solar_R_imp = fill_NA( x = ., model = "lm_pred", posit_y = "Solar.R", posit_x = c("Wind", "Temp", "Intercept"), w = .[["weights"]] ))) %>% ungroup() %>% # Imputations - discrete variable mutate(x_character_imp = fill_NA( x = ., model = "lda", posit_y = "x_character", posit_x = c("Wind", "Temp") )) %>% # logreg was used because almost log-normal distribution of Ozone # imputations around mean mutate(Ozone_imp1 = fill_NA( x = ., model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept"), logreg = TRUE )) %>% # imputations using positions - Intercept, Temp mutate(Ozone_imp2 = fill_NA( x = ., model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE )) %>% # multiple imputations (average of x30 imputations) # with a factor independent variable, weights and logreg options mutate(Ozone_imp3 = fill_NA_N( x = ., model = "lm_noise", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 30 )) %>% mutate(Ozone_imp4 = fill_NA_N( x = ., model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 30 )) %>% group_by(groups) %>% do(mutate(., Ozone_imp5 = fill_NA( x = ., model = "lm_pred", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE ))) %>% do(mutate(., Ozone_imp6 = fill_NA_N( x = ., model = "pmm", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .[["weights"]], logreg = TRUE, k = 20 ))) %>% ungroup() %>% # Average of a few methods mutate(Ozone_imp_mix = rowMeans(select(., starts_with("Ozone_imp")))) %>% # Protecting against collinearity or low number of observations - across small groups # Be carful when using a grouping option # because of lack of protection against collinearity or low number of observations. # There could be used a tryCatch(fill_NA(...),error=function(e) return(...)) group_by(groups) %>% do(mutate(., Ozone_chac_imp = tryCatch( fill_NA( x = ., model = "lda", posit_y = "Ozone_chac", posit_x = c( "Intercept", "Month", "Day", "Temp", "x_character_imp" ), w = .[["weights"]] ), error = function(e) .[["Ozone_chac"]] ))) %>% ungroup() # Sample of results air_miss[which(is.na(air_miss[, 1]))[1:5], ] ### Intro: data.table # IMPUTATIONS # Imputations with a grouping option (models are separately assessed for each group) # taking into account provided weights data(air_miss) setDT(air_miss) air_miss[, Solar_R_imp := fill_NA_N( x = .SD, model = "lm_bayes", posit_y = "Solar.R", posit_x = c("Wind", "Temp", "Intercept"), w = .SD[["weights"]], k = 100 ), by = .(groups)] %>% # Imputations - discrete variable .[, x_character_imp := fill_NA( x = .SD, model = "lda", posit_y = "x_character", posit_x = c("Wind", "Temp", "groups") )] %>% # logreg was used because almost log-normal distribution of Ozone # imputations around mean .[, Ozone_imp1 := fill_NA( x = .SD, model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept"), logreg = TRUE )] %>% # imputations using positions - Intercept, Temp .[, Ozone_imp2 := fill_NA( x = .SD, model = "lm_bayes", posit_y = 1, posit_x = c(4, 6), logreg = TRUE )] %>% # model with a factor independent variable # multiple imputations (average of x30 imputations) # with a factor independent variable, weights and logreg options .[, Ozone_imp3 := fill_NA_N( x = .SD, model = "lm_noise", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 30 )] %>% .[, Ozone_imp4 := fill_NA_N( x = .SD, model = "lm_bayes", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 30 )] %>% .[, Ozone_imp5 := fill_NA( x = .SD, model = "lm_pred", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE ), .(groups)] %>% .[, Ozone_imp6 := fill_NA_N( x = .SD, model = "pmm", posit_y = "Ozone", posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"), w = .SD[["weights"]], logreg = TRUE, k = 10 ), .(groups)] %>% # Average of a few methods .[, Ozone_imp_mix := apply(.SD, 1, mean), .SDcols = Ozone_imp1:Ozone_imp6] %>% # Protecting against collinearity or low number of observations - across small groups # Be carful when using a data.table grouping option # because of lack of protection against collinearity or low number of observations. # There could be used a tryCatch(fill_NA(...),error=function(e) return(...)) .[, Ozone_chac_imp := tryCatch( fill_NA( x = .SD, model = "lda", posit_y = "Ozone_chac", posit_x = c( "Intercept", "Month", "Day", "Temp", "x_character_imp" ), w = .SD[["weights"]] ), error = function(e) .SD[["Ozone_chac"]] ), .(groups)] # Sample of results air_miss[which(is.na(air_miss[, 1]))[1:5], ]
naive_fill_NA function for the simple and automatic imputationAutomatically fill the missing data with a simple imputation method, impute with sampling the non missing values. It is recommended to use this function for each categorical variable separately.
naive_fill_NA(x) ## S3 method for class 'data.frame' naive_fill_NA(x) ## S3 method for class 'data.table' naive_fill_NA(x) ## S3 method for class 'matrix' naive_fill_NA(x)naive_fill_NA(x) ## S3 method for class 'data.frame' naive_fill_NA(x) ## S3 method for class 'data.table' naive_fill_NA(x) ## S3 method for class 'matrix' naive_fill_NA(x)
x |
a numeric matrix or data.frame/data.table (factor/character/numeric/logical variables) |
object with a similar structure to the input but without missing values.
naive_fill_NA(data.frame): S3 method for data.frame
naive_fill_NA(data.table): S3 method for data.table
naive_fill_NA(matrix): S3 method for matrix
this is a very simple and fast solution but not recommended, for more complex solutions please check the vignette.
## Not run: library(miceFast) data(air_miss) naive_fill_NA(air_miss) # Could be useful to run it separately for each group level do.call(rbind, Map(naive_fill_NA, split(air_miss, air_miss$groups))) ## End(Not run)## Not run: library(miceFast) data(air_miss) naive_fill_NA(air_miss) # Could be useful to run it separately for each group level do.call(rbind, Map(naive_fill_NA, split(air_miss, air_miss$groups))) ## End(Not run)
this function using pre-sorting of a y and the binary search the one of the k closest value for each miss is returned.
neibo(y, miss, k)neibo(y, miss, k)
y |
numeric vector values to be look up |
miss |
numeric vector a values to be look for |
k |
integer a number of values which should be taken into account during sampling one of the k closest point |
a numeric vector
"Rcpp_corrData"
This C++ class could be used to build a corrData object by invoking new(corrData,...) function.
Class "C++Object", directly.
All reference classes extend and inherit methods from "envRefClass".
initialize(...):~~
finalize():~~
fill(...):generating data
This is only frame for building C++ object which could be used to implement certain methods. Check the vignette for more details of implementing methods.
See the documentation for RcppArmadillo and Rcpp for more details of how this class was built.
#showClass("Rcpp_corrData") show(corrData)#showClass("Rcpp_corrData") show(corrData)
"Rcpp_miceFast"
This C++ class could be used to build a miceFast objects by invoking new(miceFast) function.
Class "C++Object", directly.
All reference classes extend and inherit methods from "envRefClass".
set_data(...):providing data by a reference - a numeric matrix
set_g(...):providing a grouping variable by a reference - a numeric vector WITOUT NA values - positive values
set_w(...):providing a weightinh variable by a reference - a numeric vector WITOUT NA values - positive values
set_ridge(...):providing a ridge i.e. the disturbance to diag of XX, default 1e-6
get_data(...):retrieving the data
get_w(...):retrieving the weighting variable
get_g(...):retireiving the grouping variable
get_ridge(...):retireiving the ridge disturbance
get_index(...):getting the index
impute(...):impute data under characterstics from the object like a optional grouping or weighting variable
impute_N(...):multiple imputations - impute data under characterstics from the object like a optional grouping or weighting variable
update_var(...):permanently update the variable at the object and data. Use it only if you are sure about model parameters
get_models(...):get possible quantitative models for a certain type of dependent variable
get_model(...):get a recommended quantitative model for a certain type of dependent variable
which_updated(...):which variables at the object was modified by update_var
sort_byg(...):sort data by the grouping variable
is_sorted_byg(...):check if data is sorted by the grouping variable
vifs(...):Variance inflation factors (VIF) - helps to check when the predictor variables are not linearly related
initialize(...):...
finalize():...
This is only frame for building C++ object which could be used to implement certain methods. Check the vignette for more details of implementing these methods.
Vigniette: https://CRAN.R-project.org/package=miceFast
See the documentation for RcppArmadillo and Rcpp for more details of how this class was built.
#showClass("Rcpp_miceFast") show(miceFast) new(miceFast)#showClass("Rcpp_miceFast") show(miceFast) new(miceFast)
wrapper around UpSetR::upset for vizualization of NA values
Visualization of set intersections using novel UpSet matrix design.
upset_NA(...)upset_NA(...)
... |
all arguments accepted by UpSetR::upset where the first one is expected to be a data. |
Visualization of set data in the layout described by Lex and Gehlenborg in https://www.nature.com/articles/nmeth.3033. UpSet also allows for visualization of queries on intersections and elements, along with custom queries queries implemented using Hadley Wickham's apply function. To further analyze the data contained in the intersections, the user may select additional attribute plots to be displayed alongside the UpSet plot. The user also has the the ability to pass their own plots into the function to further analyze data belonging to queries of interest. Most aspects of the UpSet plot are customizable, allowing the user to select the plot that best suits their style. Depending on how the features are selected, UpSet can display between 25-65 sets and between 40-100 intersections.
Data set must be formatted as described on the original UpSet github page: https://github.com/VCG/upset/wiki.
Lex et al. (2014). UpSet: Visualization of Intersecting Sets IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2014), vol 20, pp. 1983-1992, (2014).
Lex and Gehlenborg (2014). Points of view: Sets and intersections. Nature Methods 11, 779 (2014). https://www.nature.com/articles/nmeth.3033
library(miceFast) library(UpSetR) upset_NA(airquality) upset_NA(air_miss, 6)library(miceFast) library(UpSetR) upset_NA(airquality) upset_NA(air_miss, 6)
VIF function for assessing VIF.VIF measure how much the variance of the estimated regression coefficients are inflated. It helps to identify when the predictor variables are linearly related. You have to decide which variable should be delete. Usually values higher than 10 (around), mean a collinearity problem.
VIF(x, posit_y, posit_x, correct = FALSE) ## S3 method for class 'data.frame' VIF(x, posit_y, posit_x, correct = FALSE) ## S3 method for class 'data.table' VIF(x, posit_y, posit_x, correct = FALSE) ## S3 method for class 'matrix' VIF(x, posit_y, posit_x, correct = FALSE)VIF(x, posit_y, posit_x, correct = FALSE) ## S3 method for class 'data.frame' VIF(x, posit_y, posit_x, correct = FALSE) ## S3 method for class 'data.table' VIF(x, posit_y, posit_x, correct = FALSE) ## S3 method for class 'matrix' VIF(x, posit_y, posit_x, correct = FALSE)
x |
a numeric matrix or data.frame/data.table (factor/character/numeric) - variables |
posit_y |
an integer/character - a position/name of dependent variable. This variable is taken into account only for getting complete cases. |
posit_x |
an integer/character vector - positions/names of independent variables |
correct |
a boolean - basic or corrected - Default: FALSE |
load a numeric vector with VIF for all variables provided by posit_x
VIF(data.frame):
VIF(data.table):
VIF(matrix):
vif_corrected = vif_basic^(1/(2*df))
## Not run: library(miceFast) library(data.table) airquality2 <- airquality airquality2$Temp2 <- airquality2$Temp**2 airquality2$Month <- factor(airquality2$Month) data_DT <- data.table(airquality2) data_DT[, .(vifs = VIF( x = .SD, posit_y = "Ozone", posit_x = c("Solar.R", "Wind", "Temp", "Month", "Day", "Temp2"), correct = FALSE ))][["vifs.V1"]] data_DT[, .(vifs = VIF( x = .SD, posit_y = 1, posit_x = c(2, 3, 4, 5, 6, 7), correct = TRUE ))][["vifs.V1"]] ## End(Not run)## Not run: library(miceFast) library(data.table) airquality2 <- airquality airquality2$Temp2 <- airquality2$Temp**2 airquality2$Month <- factor(airquality2$Month) data_DT <- data.table(airquality2) data_DT[, .(vifs = VIF( x = .SD, posit_y = "Ozone", posit_x = c("Solar.R", "Wind", "Temp", "Month", "Day", "Temp2"), correct = FALSE ))][["vifs.V1"]] data_DT[, .(vifs = VIF( x = .SD, posit_y = 1, posit_x = c(2, 3, 4, 5, 6, 7), correct = TRUE ))][["vifs.V1"]] ## End(Not run)