Package: OptSig 2.2

OptSig: Optimal Level of Significance for Regression and Other Statistical Tests

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

Authors:Jae H. Kim <[email protected]>

OptSig_2.2.tar.gz
OptSig_2.2.zip(r-4.7)OptSig_2.2.zip(r-4.6)OptSig_2.2.zip(r-4.5)
OptSig_2.2.tgz(r-4.6-any)OptSig_2.2.tgz(r-4.5-any)
OptSig_2.2.tar.gz(r-4.7-any)OptSig_2.2.tar.gz(r-4.6-any)
OptSig_2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
OptSig/json (API)

# Install 'OptSig' in R:
install.packages('OptSig', repos = c('https://jh8080.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • data1 - Data for the U.S. production function estimation

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 20 scripts 175 downloads 16 exports 1 dependencies

Last updated from:2fbf7b2e65. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK111
source / vignettesOK205
linux-release-x86_64OK106
macos-release-arm64OK171
macos-oldrel-arm64OK210
windows-develOK97
windows-releaseOK86
windows-oldrelOK73
wasm-releaseOK86

Exports:Opt.sig.norm.testOpt.sig.t.testOptSig.2pOptSig.2p2nOptSig.anovaOptSig.BootOptSig.BootWeightOptSig.ChisqOptSig.FOptSig.pOptSig.rOptSig.t2nOptSig.WeightPower.ChisqPower.FR.OLS

Dependencies:pwr

Readme and manuals

Help Manual

Help pageTopics
Optimal Level of Significance for Regression and Other Statistical TestsOptSig-package OptSig
Data for the U.S. production function estimationdata1
Optimal significance level calculation for the mean of a normal distribution (known variance)Opt.sig.norm.test
Optimal significance level calculation for t-tests of means (one sample, two samples and paired samples)Opt.sig.t.test
Optimal significance level calculation for the test for two proportions (same sample sizes)OptSig.2p
Optimal significance level calculation for the test for two proportions (different sample sizes)OptSig.2p2n
Optimal significance level calculation for balanced one-way analysis of variance testsOptSig.anova
Optimal Significance Level for the F-test using the bootstrapOptSig.Boot
Weighted Optimal Significance Level for the F-test based on the bootstrapOptSig.BootWeight
Optimal Significance Level for a Chi-square testOptSig.Chisq
Optimal Significance Level for an F-testOptSig.F
Optimal significance level calculation for proportion tests (one sample)OptSig.p
Optimal significance level calculation for correlation testOptSig.r
Optimal significance level calculation for two samples (different sizes) t-tests of meansOptSig.t2n
Weighted Optimal Significance Level for the F-test based on the assumption of normality in the error termOptSig.Weight
Function to calculate the power of a Chi-square testPower.Chisq
Function to calculate the power of an F-testPower.F
Restricted OLS estimation and F-testR.OLS