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Summary

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

R package Path.Analysis provides a comprehensive textual and illustrative analysis on raw data or a correlation matrix to extract correlation coefficients, path direct and indirect effect along with testing direct effects. Later, it draws 3 kinds of correlation plot, diagram and a Heatmap.

'Path.Analysis' is very easy to use and provides a good plotting options in visualization method, graphic layout, color, legend, text labels, etc. It also provides p-values of direct effects to help users determine the statistical significance of the correlations and direct effects.

For examples, see its #online vignette.

This package is licensed under the MIT license, and available on CRAN: https://cran.r-project.org/package=Path.Analysis.

Basic examples

library(Path.Analysis)
data(dtsimp)
Path.Analysis(dtsimp, 1, rplot = FALSE, rdend = FALSE)
library(Path.Analysis)
data(dtraw)
Path.Analysis(dtraw, 1, rplot = TRUE, rdend = FALSE)

Download and Install

To download the release version of the package on CRAN, type the following at the R command line:

install.packages('Path.Analysis')

To download the development version of the package, type the following at the R command line:

devtools::install_github('abeyran/Path.Analysis', build_vignettes = TRUE)

How to cite

To cite corrplot properly, call the R built-in command citation('Path.Analysis') as follows:

citation('Rapth')

Reporting bugs and other issues

If you encounter a clear bug, please file a minimal reproducible example on github.

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Version

Install

install.packages('Path.Analysis')

Monthly Downloads

152

Version

0.1

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Ali Arminian

Last Published

September 25th, 2024

Functions in Path.Analysis (0.1)

corr

Correlation Analysis
dtraw2

Dataset 3: a number of 9 traits measured on 35 Camelina DH lines.
cor_plot

Drawing the correlogram
Path.Analysis

Path Coefficient Analysis
desc

Descriptive statistics
dataprep

Data preparation
matdiag

Direct and Indirect Effects Matrices and Diagram
network.plot

Network plot
heat_map

Creating the Heatmap chart
dtsimp

Dataset 1: a dependent (y) and 3 independent(x1 to x3) variables.
heart

Dataset 6: Heart Disease data set
dtseqr

Dataset5
dtseq

Dataset 4: a dataframe consisting of 7 variables measured on 8 observations.
dtraw

Dataset 2: a number of 9 traits measured on 35 Camelina DH lines.
reg

Multiple Linear Regression