Keng(庚)

The Keng package is named after Loo-Keng Hua, who made great achievements in mathematics mainly through self-study. Loo-Keng Hua encouraged novices to show their axe skills at the gate of Ban’s house, so the Keng package comes. In addition, Keng is the abbreviation of “Knock Errors off Nice Guesses.”

The Keng package aims to automate the computations Qingyao repeat in his psychological research and teaching. Hope the functions and data gathered in this package help to ease your life.

Installation

You can install the development version of Keng from GitHub with:

install.packages("devtools")
devtools::install_github("qyaozh/Keng", dependencies = TRUE, build_vignettes = TRUE)

Load

Before using the Keng package, load it using the library() function.

library(Keng)

List of contents

Here is a list of the functions and data gathered in the Keng package. Their usages are detailed in the documentation.

Data

depress is a subset of data from a research about depression and coping.

Variable transformation

Scale() could change the origin of a numeric vector x (including mean-centering it), or standardize the mean and standard deviation of x (including transforming it to its z-score).

Pearson’s r

cut_r() gives you the cut-off values of Pearson’s r at the significance levels of p = 0.1, 0.05, 0.01, 0.001 when the sample size n is known.

test_r() tests the significance of r using t-test and Fisher’s z when r and n is known.

The linear model

compare_lm() compares lm()’s fitted outputs using PRE, R², f², and post-hoc power.

calc_pre() calculates PRE from partial correlation, Cohen’s f, or f_squared.

power_lm() computes the post-hoc power and/or plans the sample size for one or a set of predictors in regression analysis.