| Title: | 1d Goodness of Fit Tests |
|---|---|
| Description: | Routines that allow the user to run a large number of goodness-of-fit tests. It allows for data to be continuous or discrete. It includes routines to estimate the power of the tests and display them as a power graph. |
| Authors: | Wolfgang Rolke [aut, cre]
|
| Maintainer: | Wolfgang Rolke <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 2.1.1 |
| Built: | 2024-10-22 09:22:07 UTC |
| Source: | CRAN |
This function checks whether the inputs have the correct format
check.functions(pnull, rnull, phat = function(x) -99, vals, x)check.functions(pnull, rnull, phat = function(x) -99, vals, x)
pnull |
cdf under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 |
vals |
vector of discrete values |
x |
data |
This function finds the power of various chi-square tests for continuous data
chi_power_cont( pnull, ralt, param_alt, qnull = NA, phat = function(x) -99, w = function(x) -99, alpha = 0.05, Range = c(-99999, 99999), B = 1000, nbins = c(50, 10), rate = 0, minexpcount = 5, ChiUsePhat = TRUE )chi_power_cont( pnull, ralt, param_alt, qnull = NA, phat = function(x) -99, w = function(x) -99, alpha = 0.05, Range = c(-99999, 99999), B = 1000, nbins = c(50, 10), rate = 0, minexpcount = 5, ChiUsePhat = TRUE )
pnull |
function to find cdf under null hypothesis |
ralt |
function to generate data under alternative hypothesis |
param_alt |
vector of parameter values for distribution under alternative hypothesis |
qnull |
=NA function to find quantiles under null hypothesis, if available |
phat |
=function(x) -99, function to estimate parameters |
w |
=function(x) -99, optional weight function |
alpha |
=0.05, the level of the hypothesis test |
Range |
=c(-99999, 99999) limits of possible observations, if any |
B |
=1000 number of simulation runs to find power |
nbins |
=c(50,10), number of bins for chi square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
=TRUE, if TRUE param is estimated parameters and no minimization is used |
A numeric matrix of power values.
This function finds the power of various chi-square tests for continuous data
chi_power_disc( pnull, ralt, param_alt, phat = function(x) -99, alpha = 0.05, B = 1000, nbins = c(50, 10), rate = 0, minexpcount = 5, ChiUsePhat = TRUE )chi_power_disc( pnull, ralt, param_alt, phat = function(x) -99, alpha = 0.05, B = 1000, nbins = c(50, 10), rate = 0, minexpcount = 5, ChiUsePhat = TRUE )
pnull |
function to find cdf under null hypothesis |
ralt |
function to generate data under alternative hypothesis |
param_alt |
vector of parameter values for distribution under alternative hypothesis |
phat |
=function(x) -99, routine to estimate parameters |
alpha |
=0.05, the level of the hypothesis test |
B |
=1000 number of simulation runs to find power |
nbins |
=c(50,10), number of bins for chi square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, should chi square use minimum chi square method? |
A numeric matrix of power values.
This function performs a number of chi-square gof tests for continuous data
chi_test_cont( x, pnull, w = function(x) -99, phat = function(x) -99, qnull = NA, nbins = c(50, 10), rate = 0, Range = c(-99999, 99999), minexpcount = 5, ChiUsePhat = TRUE, allbins )chi_test_cont( x, pnull, w = function(x) -99, phat = function(x) -99, qnull = NA, nbins = c(50, 10), rate = 0, Range = c(-99999, 99999), minexpcount = 5, ChiUsePhat = TRUE, allbins )
x |
data set |
pnull |
cdf under the null hypothesis |
w |
function to find weights of observations, returns -99 if data is unweighted |
phat |
=function(x) -99, estimated parameters, or starting values of multi-D minimum chi square minimization, or -99 if no estimation is done |
qnull |
=NA quantile function, if available |
nbins |
=c(50, 10) number of bins for chi-square tests |
rate |
=0, rate of Poisson if sample size is random |
Range |
=c(-99999, 99999) limits of possible observations, if any |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
=TRUE, if TRUE param is estimated parameters and no minimization is used |
allbins |
set of bins to use |
A numeric matrix of test statistics, degrees of freedom and p.values
This function performs a number of chi-square gof tests for continuous data
chi_test_disc( x, pnull, phat = function(x) -99, nbins = c(50, 10), rate = 0, minexpcount = 5, ChiUsePhat = TRUE, allbins )chi_test_disc( x, pnull, phat = function(x) -99, nbins = c(50, 10), rate = 0, minexpcount = 5, ChiUsePhat = TRUE, allbins )
x |
data set |
pnull |
cdf under the null hypothesis |
phat |
=function(x) -99, function to estimate parameters, or starting values of multi-D minimum chi square minimization, or -99 if no parameters are estimated |
nbins |
=c(50, 10) number of bins for chi-square tests |
rate |
=0, rate of Poisson if sample size is random |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
allbins |
set of bins to use |
A numeric matrix of test statistics, degrees of freedom and p.values
Find the power of various gof tests for continuous data.
gof_power( pnull, vals = NA, rnull, ralt, param_alt, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, alpha = 0.05, Range = c(-Inf, Inf), B = c(1000, 1000), nbins = c(50, 10), rate = 0, maxProcessors, minexpcount = 5, ChiUsePhat = TRUE )gof_power( pnull, vals = NA, rnull, ralt, param_alt, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, alpha = 0.05, Range = c(-Inf, Inf), B = c(1000, 1000), nbins = c(50, 10), rate = 0, maxProcessors, minexpcount = 5, ChiUsePhat = TRUE )
pnull |
function to find cdf under null hypothesis |
vals |
=NA, values of rv, if data is discrete, NA if data is continuous |
rnull |
function to generate data under null hypothesis |
ralt |
function to generate data under alternative hypothesis |
param_alt |
vector of parameter values for distribution under alternative hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99 function to estimate parameters from the data, or -99 |
TS |
user supplied function to find test statistics |
TSextra |
=NA, list provided to TS |
alpha |
=0.05, the level of the hypothesis test |
Range |
=c(-Inf, Inf) limits of possible observations, if any |
B |
=c(1000, 1000), number of simulation runs to find power and null distribution |
nbins |
=c(100,10), number of bins for chi square tests. |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
maxProcessors |
maximum of number of processors to use, 1 if no parallel processing is needed or number of cores-1 if missing |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
A numeric matrix of power values.
# Power of tests when null hypothesis specifies the standard normal distribution but # true data comes from a normal distribution with mean different from 0. pnull = function(x) pnorm(x) rnull = function() rnorm(50) ralt = function(mu) rnorm(50, mu) TSextra = list(qnull=function(x) qnorm(x)) gof_power(pnull, NA, rnull, ralt, c(0.25, 0.5), TSextra=TSextra, B=c(500, 500)) # Power of tests when null hypothesis specifies normal distribution and # mean and standard deviation are estimated from the data. # Example is not run because it takes several minutes. # true data comes from a normal distribution with mean different from 0. pnull = function(x, p=c(0, 1)) pnorm(x, p[1], ifelse(p[2]>0.001, p[2], 0.001)) rnull = function(p=c(0, 1)) rnorm(50, p[1], ifelse(p[2]>0.001, p[2], 0.001)) phat = function(x) c(mean(x), sd(x)) TSextra = list(qnull = function(x, p=c(0, 1)) qnorm(x, p[1], ifelse(p[2]>0.001, p[2], 0.001))) gof_power(pnull, NA, rnull, ralt, c(0, 1), phat=phat, TSextra=TSextra, B=c(200, 200), maxProcessor=2) # Power of tests when null hypothesis specifies Poisson rv with rate 100 and # true rate is 100.5 vals = 0:250 pnull = function() ppois(0:250, 100) rnull =function () table(c(0:250, rpois(1000, 100)))-1 ralt =function (p) table(c(0:250, rpois(1000, p)))-1 gof_power(pnull, vals, rnull, ralt, param_alt=100.5, B=c(500,500)) # Power of tests when null hypothesis specifies a Binomial n=10 distribution # with the success probability estimated vals = 0:10 pnull=function(p) pbinom(0:10, 10, ifelse(0<p&p<1, p, 0.001)) rnull=function(p) table(c(0:10, rbinom(1000, 10, ifelse(0<p&p<1, p, 0.001))))-1 ralt=function(p) table(c(0:10, rbinom(1000, 10, p)))-1 phat=function(x) mean(rep(0:10,x))/10 gof_power(pnull, vals, rnull, ralt, c(0.5, 0.6), phat=phat, B=c(200, 200), maxProcessor=2)# Power of tests when null hypothesis specifies the standard normal distribution but # true data comes from a normal distribution with mean different from 0. pnull = function(x) pnorm(x) rnull = function() rnorm(50) ralt = function(mu) rnorm(50, mu) TSextra = list(qnull=function(x) qnorm(x)) gof_power(pnull, NA, rnull, ralt, c(0.25, 0.5), TSextra=TSextra, B=c(500, 500)) # Power of tests when null hypothesis specifies normal distribution and # mean and standard deviation are estimated from the data. # Example is not run because it takes several minutes. # true data comes from a normal distribution with mean different from 0. pnull = function(x, p=c(0, 1)) pnorm(x, p[1], ifelse(p[2]>0.001, p[2], 0.001)) rnull = function(p=c(0, 1)) rnorm(50, p[1], ifelse(p[2]>0.001, p[2], 0.001)) phat = function(x) c(mean(x), sd(x)) TSextra = list(qnull = function(x, p=c(0, 1)) qnorm(x, p[1], ifelse(p[2]>0.001, p[2], 0.001))) gof_power(pnull, NA, rnull, ralt, c(0, 1), phat=phat, TSextra=TSextra, B=c(200, 200), maxProcessor=2) # Power of tests when null hypothesis specifies Poisson rv with rate 100 and # true rate is 100.5 vals = 0:250 pnull = function() ppois(0:250, 100) rnull =function () table(c(0:250, rpois(1000, 100)))-1 ralt =function (p) table(c(0:250, rpois(1000, p)))-1 gof_power(pnull, vals, rnull, ralt, param_alt=100.5, B=c(500,500)) # Power of tests when null hypothesis specifies a Binomial n=10 distribution # with the success probability estimated vals = 0:10 pnull=function(p) pbinom(0:10, 10, ifelse(0<p&p<1, p, 0.001)) rnull=function(p) table(c(0:10, rbinom(1000, 10, ifelse(0<p&p<1, p, 0.001))))-1 ralt=function(p) table(c(0:10, rbinom(1000, 10, p)))-1 phat=function(x) mean(rep(0:10,x))/10 gof_power(pnull, vals, rnull, ralt, c(0.5, 0.6), phat=phat, B=c(200, 200), maxProcessor=2)
Find the power of various gof tests for continuous data.
gof_power_cont( pnull, rnull, ralt, param_alt, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, alpha = 0.05, Range = c(-Inf, Inf), B = c(1000, 1000), nbins = c(100, 10), rate = 0, maxProcessors, minexpcount = 5, ChiUsePhat = TRUE )gof_power_cont( pnull, rnull, ralt, param_alt, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, alpha = 0.05, Range = c(-Inf, Inf), B = c(1000, 1000), nbins = c(100, 10), rate = 0, maxProcessors, minexpcount = 5, ChiUsePhat = TRUE )
pnull |
function to find cdf under null hypothesis |
rnull |
function to generate data under null hypothesis |
ralt |
function to generate data under alternative hypothesis |
param_alt |
vector of parameter values for distribution under alternative hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters aare estimated |
TS |
user supplied function to find test statistics, if any |
TSextra |
=NA, list provided to TS |
alpha |
=0.05, the level of the hypothesis test |
Range |
=c(-Inf, Inf) limits of possible observations, if any |
B |
=c(1000, 1000), number of simulation runs to find power and null distribution |
nbins |
=c(100,10), number of bins for chi square tests. |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
maxProcessors |
maximum of number of processors to use, 1 if no parallel processing is needed or number of cores-1 if missing |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
=TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
A numeric matrix of power values.
Find the power of various gof tests for discrete data.
gof_power_disc( pnull, rnull, vals, ralt, param_alt, phat = function(x) -99, TS, TSextra = NA, alpha = 0.05, B = c(1000, 1000), nbins = c(100, 10), rate = 0, maxProcessors, minexpcount = 5, ChiUsePhat = TRUE )gof_power_disc( pnull, rnull, vals, ralt, param_alt, phat = function(x) -99, TS, TSextra = NA, alpha = 0.05, B = c(1000, 1000), nbins = c(100, 10), rate = 0, maxProcessors, minexpcount = 5, ChiUsePhat = TRUE )
pnull |
cumulative distribution function under the null hypothesis |
rnull |
a function to generate data under null hypothesis |
vals |
values of discrete rv. |
ralt |
function to generate data under alternative hypothesis |
param_alt |
vector of parameter values for distribution under alternative hypothesis |
phat |
=function(x) -99, function to estimate parameters from the data, -99 if no parameters are estimated |
TS |
user supplied function to find test statistics, if any |
TSextra |
=NA, list passed to TS, if desired |
alpha |
=0.05, the level of the hypothesis test |
B |
=c(1000, 1000), number of simulation runs to find power and null distribution |
nbins |
=c(100, 10) number of bins for chi square tests |
rate |
rate of Poisson if sample size is random |
maxProcessors |
maximum of number of processors to use, 1 if no parallel processing is needed or number of cores-1 if missing |
minexpcount |
=5 minimal number of expected counts in each bin for chi square tests |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
A numeric matrix of power values.
This function performs a number of gof tests
gof_test( x, vals = NA, pnull, rnull, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, Range = c(-Inf, Inf), B = 5000, minexpcount = 5, ChiUsePhat = TRUE, maxProcessors = 1, doMethods = "all" )gof_test( x, vals = NA, pnull, rnull, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, Range = c(-Inf, Inf), B = 5000, minexpcount = 5, ChiUsePhat = TRUE, maxProcessors = 1, doMethods = "all" )
x |
data set |
vals |
=NA, values of discrete RV, or NA if data is continuous |
pnull |
cdf under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters are estimated |
TS |
user supplied function to find test statistics, if any |
TSextra |
=NA, list passed to TS, if desired, or NA |
nbins |
=c(100, 10) number of bins for chi-square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
Range |
=c(-Inf, Inf) limits of possible observations, if any, for chi-square tests |
B |
=5000 number of simulation runs |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
maxProcessors |
=1, number of processors to use in parallel processing. |
doMethods |
Methods to include in tests |
A list with vectors of test statistics and p.values
# Tests to see whether data comes from a standard normal distribution. pnull = function(x) pnorm(x) rnull = function() rnorm(100) x = rnorm(100) gof_test(x, NA, pnull, rnull) # Tests to see whether data comes from a normal distribution with standard deviation 1 # and the mean estimated. pnull=function(x, m) pnorm(x, m) rnull=function(m) rnorm(100, m) TSextra = list(qnull=function(x, m=0) qnorm(x, m), pnull=function(x, m=0) pnorm(x, m), phat=function(x) mean(x)) phat=function(x) mean(x) x = rnorm(100, 1, 2) gof_test(x, NA, pnull, rnull, phat=phat, TSextra=TSextra) # Tests to see whether data comes from a binomial (10, 0.5) distribution. vals=0:10 pnull = function() pbinom(0:10, 10, 0.5) rnull = function() table(c(0:10, rbinom(1000, 10, 0.5)))-1 x = rnull() gof_test(x, vals, pnull, rnull, doMethods="all") # Tests to see whether data comes from a binomial distribution with # the success probability estimated from the data. pnull = function(p=0.5) pbinom(0:10, 10, ifelse(p>0&&p<1, p, 0.001)) rnull = function(p=0.5) table(c(0:10, rbinom(1000, 10, ifelse(p>0&&p<1, p, 0.001))))-1 phat=function(x) mean(rep(0:10,x))/10 gof_test(x, vals, pnull, rnull, phat=phat)# Tests to see whether data comes from a standard normal distribution. pnull = function(x) pnorm(x) rnull = function() rnorm(100) x = rnorm(100) gof_test(x, NA, pnull, rnull) # Tests to see whether data comes from a normal distribution with standard deviation 1 # and the mean estimated. pnull=function(x, m) pnorm(x, m) rnull=function(m) rnorm(100, m) TSextra = list(qnull=function(x, m=0) qnorm(x, m), pnull=function(x, m=0) pnorm(x, m), phat=function(x) mean(x)) phat=function(x) mean(x) x = rnorm(100, 1, 2) gof_test(x, NA, pnull, rnull, phat=phat, TSextra=TSextra) # Tests to see whether data comes from a binomial (10, 0.5) distribution. vals=0:10 pnull = function() pbinom(0:10, 10, 0.5) rnull = function() table(c(0:10, rbinom(1000, 10, 0.5)))-1 x = rnull() gof_test(x, vals, pnull, rnull, doMethods="all") # Tests to see whether data comes from a binomial distribution with # the success probability estimated from the data. pnull = function(p=0.5) pbinom(0:10, 10, ifelse(p>0&&p<1, p, 0.001)) rnull = function(p=0.5) table(c(0:10, rbinom(1000, 10, ifelse(p>0&&p<1, p, 0.001))))-1 phat=function(x) mean(rep(0:10,x))/10 gof_test(x, vals, pnull, rnull, phat=phat)
This function performs a number of gof tests and finds the adjusted p value for the combined test
gof_test_adjusted_pvalue( x, vals = NA, pnull, rnull, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, Range = c(-Inf, Inf), B = c(5000, 1000), minexpcount = 5, ChiUsePhat = TRUE, doMethods )gof_test_adjusted_pvalue( x, vals = NA, pnull, rnull, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, Range = c(-Inf, Inf), B = c(5000, 1000), minexpcount = 5, ChiUsePhat = TRUE, doMethods )
x |
data set |
vals |
=NA, values of discrete RV, or NA if data is continuous |
pnull |
cdf under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters are estimated |
TS |
user supplied function to find test statistics, if any |
TSextra |
=NA, list passed to TS, if desired, or NA |
nbins |
=c(100, 10) number of bins for chi-square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
Range |
=c(-Inf, Inf) limits of possible observations, if any, for chi-square tests |
B |
=c(5000,1000) number of simulation runs for individual and for adjusted p values |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
doMethods |
Methods to include in tests |
None
# Tests to see whether data comes from a standard normal distribution. pnull = function(x) pnorm(x) rnull = function() rnorm(100) x = rnorm(100) gof_test_adjusted_pvalue(x, NA, pnull, rnull, B=c(1000, 200)) # Tests to see whether data comes from a normal distribution with standard deviation 1 # and the mean estimated. pnull=function(x, m) pnorm(x, m) rnull=function(m) rnorm(100, m) TSextra = list(qnull=function(x, m=0) qnorm(x, m), pnull=function(x, m=0) pnorm(x, m), phat=function(x) mean(x)) phat=function(x) mean(x) x = rnorm(100, 1, 2) gof_test_adjusted_pvalue(x, NA, pnull, rnull, phat=phat, TSextra=TSextra, B=c(1000, 200)) # Tests to see whether data comes from a binomial (10, 0.5) distribution. vals=0:10 pnull = function() pbinom(0:10, 10, 0.5) rnull = function() table(c(0:10, rbinom(1000, 10, 0.5)))-1 x = rnull() gof_test_adjusted_pvalue(x, vals, pnull, rnull, B=c(1000, 200)) # Tests to see whether data comes from a binomial distribution with # the success probability estimated from the data. pnull = function(p=0.5) pbinom(0:10, 10, ifelse(p>0&&p<1, p, 0.001)) rnull = function(p=0.5) table(c(0:10, rbinom(1000, 10, ifelse(p>0&&p<1, p, 0.001))))-1 phat=function(x) mean(rep(0:10,x))/10 gof_test_adjusted_pvalue(x, vals, pnull, rnull, phat=phat, B=c(1000, 200))# Tests to see whether data comes from a standard normal distribution. pnull = function(x) pnorm(x) rnull = function() rnorm(100) x = rnorm(100) gof_test_adjusted_pvalue(x, NA, pnull, rnull, B=c(1000, 200)) # Tests to see whether data comes from a normal distribution with standard deviation 1 # and the mean estimated. pnull=function(x, m) pnorm(x, m) rnull=function(m) rnorm(100, m) TSextra = list(qnull=function(x, m=0) qnorm(x, m), pnull=function(x, m=0) pnorm(x, m), phat=function(x) mean(x)) phat=function(x) mean(x) x = rnorm(100, 1, 2) gof_test_adjusted_pvalue(x, NA, pnull, rnull, phat=phat, TSextra=TSextra, B=c(1000, 200)) # Tests to see whether data comes from a binomial (10, 0.5) distribution. vals=0:10 pnull = function() pbinom(0:10, 10, 0.5) rnull = function() table(c(0:10, rbinom(1000, 10, 0.5)))-1 x = rnull() gof_test_adjusted_pvalue(x, vals, pnull, rnull, B=c(1000, 200)) # Tests to see whether data comes from a binomial distribution with # the success probability estimated from the data. pnull = function(p=0.5) pbinom(0:10, 10, ifelse(p>0&&p<1, p, 0.001)) rnull = function(p=0.5) table(c(0:10, rbinom(1000, 10, ifelse(p>0&&p<1, p, 0.001))))-1 phat=function(x) mean(rep(0:10,x))/10 gof_test_adjusted_pvalue(x, vals, pnull, rnull, phat=phat, B=c(1000, 200))
This function performs a number of gof tests for continuous data
gof_test_cont( x, pnull, rnull, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, Range = c(-Inf, Inf), B = 5000, minexpcount = 5, ChiUsePhat = TRUE, maxProcessors = 1, doMethods = "all" )gof_test_cont( x, pnull, rnull, w = function(x) -99, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, Range = c(-Inf, Inf), B = 5000, minexpcount = 5, ChiUsePhat = TRUE, maxProcessors = 1, doMethods = "all" )
x |
data set |
pnull |
cdf under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters aare estimated |
TS |
user supplied function to find test statistics, if any |
TSextra |
=NA, list passed to TS, if desired |
nbins |
=c(50, 10) number of bins for chi-square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
Range |
=c(-Inf, Inf) limits of possible observations, if any, for chi-square tests |
B |
=5000 number of simulation runs |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
=TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
maxProcessors |
=1, number of processors to use in parallel processing. If missing single processor is used. |
doMethods |
Methods to include in tests |
A list with vectors of test statistics and p.values
This function performs a number of gof tests for continuous data and finds the adjusted p value
gof_test_cont_adj( x, pnull, rnull, w = function(x) -99, phat = function(x) 0, TS, TSextra = NA, nbins = c(50, 10), rate = 0, Range = c(-Inf, Inf), B = c(5000, 1000), minexpcount = 5, ChiUsePhat = TRUE, doMethods = c("W", "ZC", "AD", "ES-s-P") )gof_test_cont_adj( x, pnull, rnull, w = function(x) -99, phat = function(x) 0, TS, TSextra = NA, nbins = c(50, 10), rate = 0, Range = c(-Inf, Inf), B = c(5000, 1000), minexpcount = 5, ChiUsePhat = TRUE, doMethods = c("W", "ZC", "AD", "ES-s-P") )
x |
data set |
pnull |
cdf under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
w |
(Optional) function to calculate weights, returns -99 if no weights |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters aare estimated |
TS |
user supplied function to find test statistics, if any |
TSextra |
=NA, list passed to TS, if desired |
nbins |
=c(50, 10) number of bins for chi-square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
Range |
=c(-Inf, Inf) limits of possible observations, if any, for chi-square tests |
B |
=c(5000,1000) number of simulation runs for p values and for p value distribution |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
=TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
doMethods |
Methods to include in tests |
None
This function performs a number of gof tests for discrete data.
gof_test_disc( x, pnull, rnull, vals, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, B = 5000, minexpcount = 5, ChiUsePhat = TRUE, maxProcessors = 1, doMethods = "Default" )gof_test_disc( x, pnull, rnull, vals, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, B = 5000, minexpcount = 5, ChiUsePhat = TRUE, maxProcessors = 1, doMethods = "Default" )
x |
data set (the counts) |
pnull |
cumulative distribution function under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
vals |
a vector of values of discrete random variables |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters aare estimated |
TS |
=NA, user supplied function to find test statistics |
TSextra |
=NA, list passed to TS, if desired |
nbins |
=c(50, 10) number of bins for chi-square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
B |
=5000 number of simulation runs |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
maxProcessors |
=1, number of processors to use in parallel processing. If missing single processor is used. |
doMethods |
Methods to include in tests |
A numeric matrix of test statistics and p.values
This function performs a number of gof tests for discrete data and finds the adjusted p value
gof_test_disc_adj( x, pnull, rnull, vals, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, B = c(5000, 1000), minexpcount = 5, ChiUsePhat = TRUE, doMethods = c("Wassp1", "W", "AD", "s-P") )gof_test_disc_adj( x, pnull, rnull, vals, phat = function(x) -99, TS, TSextra = NA, nbins = c(50, 10), rate = 0, B = c(5000, 1000), minexpcount = 5, ChiUsePhat = TRUE, doMethods = c("Wassp1", "W", "AD", "s-P") )
x |
data set (the counts) |
pnull |
cumulative distribution function under the null hypothesis |
rnull |
routine to generate data under the null hypothesis |
vals |
a vector of values of discrete random variables |
phat |
=function(x) -99, function to estimate parameters from the data, or -99 if no parameters aare estimated |
TS |
=NA, user supplied function to find test statistics |
TSextra |
=NA, list passed to TS, if desired |
nbins |
=c(50, 10) number of bins for chi-square tests |
rate |
=0 rate of Poisson if sample size is random, 0 if sample size is fixed |
B |
=c(5000, 1000) number of simulation runs for p values and for adjusted p value |
minexpcount |
=5 minimal expected bin count required |
ChiUsePhat |
= TRUE, if TRUE param is estimated parameter, otherwise minimum chi square method is used. |
doMethods |
Methods to include in tests |
A numeric matrix of test statistics and p.values
This function creates several type of bins for continuous data
make_bins_cont( x, pnull, qnull = NA, phat = function(x) -99, DataBased = FALSE, nbins = c(50, 10), minexpcount = 5, Range = c(-99999, 99999) )make_bins_cont( x, pnull, qnull = NA, phat = function(x) -99, DataBased = FALSE, nbins = c(50, 10), minexpcount = 5, Range = c(-99999, 99999) )
x |
data set |
pnull |
cdf under the null hypothesis |
qnull |
=NA quantile function, if available |
phat |
=function(x) -99 parameters for pnull |
DataBased |
=FALSE bins based on data, not expected counts |
nbins |
=c(50, 10) number of bins |
minexpcount |
=5 smallest expected count per bin |
Range |
=c(-99999, 99999) limits of possible observations, if any |
A list of bins and bin probabilities
This function creates several types of bins for discrete data
make_bins_disc( x, pnull, phat = function(x) -99, nbins = c(50, 10), minexpcount = 5 )make_bins_disc( x, pnull, phat = function(x) -99, nbins = c(50, 10), minexpcount = 5 )
x |
counts |
pnull |
cumulative distribution function |
phat |
=function(x) -99, function to estimated parameters, or -99 |
nbins |
=c(50, 10) number of bins |
minexpcount |
=5 smallest expected count per bin |
A list of indices
This function draws the power graph, with curves sorted by the mean power and smoothed for easier reading.
plot_power(pwr, xname = " ", title, Smooth = TRUE, span = 0.25)plot_power(pwr, xname = " ", title, Smooth = TRUE, span = 0.25)
pwr |
a matrix of power values, usually from the twosample_power command |
xname |
Name of variable on x axis |
title |
(Optional) title of graph |
Smooth |
=TRUE lines are smoothed for easier reading |
span |
=0.25bandwidth of smoothing method |
plt, an object of class ggplot.
This function does some rounding to nice numbers
## S3 method for class 'digits' signif(x, d = 4)## S3 method for class 'digits' signif(x, d = 4)
x |
a list of two vectors |
d |
=4 number of digits to round to |
A list with rounded vectors