The LocationTest

Authors: Neda Mohammadi

Description :

This repository hosts a collection of R code for conducting statistical analysis using t-tests, ANOVA, and nonparametric tests. The code provided checks all necessary assumptions(Normality and equality of variance) and generates informative result tables.

Key Features:

T-test Analysis: The repository includes code for conducting t-tests, allowing you to compare means between two groups. The code supports independent samples t-tests. ANOVA Analysis: In addition to t-tests, the repository covers ANOVA (Analysis of Variance), which is used to compare means across multiple groups. Nonparametric Tests: Recognizing that data may not always meet the assumptions of parametric tests, this repository also includes code for nonparametric tests. The nonparametric tests covered are the "Mann-Whitney U test" and "Wilcoxon rank-sum test". Result Summary: The code generates clear and concise error bars that summarize the results of the statistical tests. These error bars provide essential information such as test statistics, p-values, facilitating the interpretation and presentation of your findings.

Usage:

Clone or download the code from this repository to your local machine. Open the R script using your preferred R environment. Customize the code in "Location.Test" file to fit your specific data and research question.You can specify the type of analysis (t-test, ANOVA, or nonparametric test) or leave them blank. Then specify the appropriate variables." Run the code to perform the statistical tests and obtain the result.

Installation

remotes::install_github("nedamhd/LocationTest")

Example

Data = data.frame(
  edu = as.factor(rbinom(200, 2, .4)),
  sex = as.factor(rbinom(200,1,0.6)),
  height = rnorm(200,170,5)
)

Location.Test(data=Data, var="height",group="edu", Test=NULL, draw_plot=TRUE, save_plot=TRUE, y_adjust=1.8,filename= "plot.123")

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