| Type: | Package |
| Title: | Diagnostic Tools and Unit Tests for Statistical Estimators |
| Version: | 0.0.3 |
| Author: | Dmitry Otryakhin 
[aut, cre] |
| Maintainer: | Dmitry Otryakhin <d.otryakhin.acad@protonmail.ch> |
| Description: | Extension of 'testthat' package to make unit tests on empirical distributions of estimators and functions for diagnostics of their finite-sample performance. |
| License: | GPL-3 |
| URL: | https://gitlab.com/Dmitry_Otryakhin/diagnostics-and-tests-for-statistical-estimators |
| Encoding: | UTF-8 |
| Imports: | foreach (≥ 1.5.1), reshape2 (≥ 1.4.4), ggplot2 (≥ 3.3.2), goftest (≥ 1.2-2), testthat (≥ 3.0.0), rlang |
| RoxygenNote: | 7.1.1 |
| Suggests: | knitr, rmarkdown, doParallel, gridExtra |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2021-04-16 21:06:21 UTC; d |
| Repository: | CRAN |
| Date/Publication: | 2021-04-16 21:40:03 UTC |
Sample estimators' values for different sample sizes
Description
For every sample size value the function creates a sample and evaluates the estimators Nmc times.
Usage
Estim_diagnost(Nmc, s, Inference, packages = NULL)
Arguments
Nmc |
number of repetitions |
s |
numeric vector of sample sizes |
Inference |
function of s creating a sample and evaluating estimators (see details) |
packages |
list of packages to pass to foreach loop |
Value
data frame with estimators' values
Examples
Nmc=400
s<-c(1e2,1e3)
Inference<-function(s){
rrr<-rnorm(n=s)
list(Mn=mean(rrr), Sd=sd(rrr))
}
data <- Estim_diagnost(Nmc, s, Inference)
estims_qqplot(data)
estims_boxplot(data)
#
Inference<-function(s){
rrr<-2/0
list(Mn=mean(rrr), Sd=sd(rrr))
}
head(Estim_diagnost(Nmc, s, Inference))
#
Inference<-function(s){
rrr<-rnorm(n=s)
rrr[2]<-"dwq"
list(Mn=mean(rrr), Sd=sd(rrr))
}
head(Estim_diagnost(Nmc, s, Inference))
Boxplot of estimates
Description
Plot boxplots of estimators for different sample sizes.
Usage
estims_boxplot(data, sep = FALSE)
Arguments
data |
data frame returned by |
sep |
indicates whether all plots will be stacked together or returned as elements of a list |
Value
ggplot2 object
Examples
Nmc=400
s<-seq(from = 1, to = 10, by = 2)*1e3
Inference<-function(s){
rrr<-rnorm(n=s)
list(Mn=mean(rrr), Sd=sd(rrr))
}
data <- Estim_diagnost(Nmc, s, Inference)
estims_boxplot(data)
estims_boxplot(data, sep=TRUE)
QQ-plot of estimator empirical distributions
Description
Plot QQ-plots of estimators' empirical distributions for different sample sizes.
Usage
estims_qqplot(data, sep = FALSE, ...)
Arguments
data |
data frame returned by |
sep |
indicates whether all plots will be stacked together or returned as elements of a list |
... |
parameters to pass to stat_qq function |
Value
ggplot2 object
Examples
library(ggplot2)
Nmc=500
s<-c(1e3,4e3)
Inference<-function(s){
rrr<-rnorm(n=s)
list(Mn=mean(rrr), Sd=sd(rrr))
}
data <- Estim_diagnost(Nmc, s, Inference)
lisst <- estims_qqplot(data, sep=TRUE)
lisst[2][[1]] + geom_abline(intercept = 1)
pl_joint<-estims_qqplot(data)
pl_joint + geom_abline(slope=1)
pl_joint<-estims_qqplot(data, distribution = stats::qt, dparams = list(df=3, ncp=0.1))
pl_joint + geom_abline(slope=1)
Test a parametric distribution
Description
Expectation checking whether a given sample comes from a certain parametric distribution. The underlying procedure is Anderson-Darling test of goodness-of-fit ad.test.
The expectation throws an error when the test's p-value is smaller than the threshold p-value.
Usage
expect_distfit(sample, p_value = 0.001, nulldist, ...)
Arguments
sample |
to test |
p_value |
threshold p-value of the test |
nulldist |
null distribution |
... |
parameters to pass to the null distribution |
Value
Invisibly returns a p-value of the test.
Examples
# Gaussianity test
## Not run:
x<-rnorm(n=1e4,5,6)
expect_distfit(sample=x, nulldist="pnorm", mean=5, sd=6.3)
expect_distfit(sample=x, nulldist="pnorm", mean=5, sd=6)
## End(Not run)
# Uniformity test
x<-runif(n=1e4,-1,6)
expect_distfit(sample=x, nulldist="punif", min=-1, max=6)
Test a Gaussian distribution
Description
Expectation checking whether a given sample comes from Gaussian distribution with arbitrary parameters. The underlying procedure is Shapiro- Wilk's test of normality shapiro.test.
The expectation throws an error when the test's p-value is smaller than the threshold p-value.
Usage
expect_gaussian(sample, p_value = 0.001)
Arguments
sample |
to test |
p_value |
threshold p-value of the test |
Details
shapiro.test allows the number of non-missing values to be between 3 and 5000.
Value
Invisibly returns a p-value of the test.
Examples
x<-rnorm(n=1e3,5,6)
expect_gaussian(sample=x)
#The following test doesn't pass
## Not run:
x<-runif(n=1e2,-1,6)
expect_gaussian(sample=x)
## End(Not run)
Test a mean-value using t-test
Description
Expectation checking whether values from a given sample have a certain mean or that two samples have the same mean. The underlying procedure is Student's t-test t.test.
The expectation throws an error when the test's p-value is smaller than the threshold p-value.
Usage
expect_mean_equal(p_value = 0.001, ...)
Arguments
p_value |
threshold p-value of the test |
... |
parameters to pass to t.test function including data sample(s) |
Value
Invisibly returns a p-value of the test
Examples
# This test doesn't pass
## Not run:
x<-1:1e3
expect_mean_equal(x=x)
## End(Not run)
# This one passes, but shouldn't
x<-rnorm(1e3) + 0.01
expect_mean_equal(x=x)
x<-rnorm(1e3)
expect_mean_equal(x=x)
# check if 2 samples have the same mean
x<-rnorm(1e3, mean=10)
y<-rnorm(1e3, mean=10)
expect_mean_equal(x=x, y=y)