Keng

Keng is the abbreviation of “Knock Errors off Nice Guesses.” Hope the functions and data gathered in the Keng package help to ease your life.

1 Installation

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

# install pak if it is not installed
if (!requireNamespace("pak", quietly = TRUE)) {
  install.packages("pak")
}

# install the developing version of Keng from GitHub
pak::pak("qyaozh/Keng")

2 Load

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

library(Keng)

3 List of contents

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

3.1 Data

Four data sets (i.e., depress, depress1, depress2, depress3) from the D (depression) research.

Four data sets (i.e., well, well1, well2, well3) from the W (well-being) research.

3.2 Variable transformation

Scale() could standardize the mean and standard deviation of x (including transforming it to its z-score). To change the origin of x, just change its mean.

divide() could divide a vector into three groups, using the criterion of 1 SD, or proportions like 0.27.

3.3 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, and 0.001 with known sample size n.

test_r() tests the significance and compute the post-hoc power of r with known sample size n.

powered_r() conducts post-hoc power analysis with known sample size n. 

power_r() conducts a priori power analysis and plan the sample size for r.

3.4 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.

powered_lm() conducts post-hoc power analysis with known sample size n.

power_lm() conducts a priori power analysis and plans the sample size for one or a set of predictors in regression analysis.

3.5 The Keng_power class

power_r() and power_lm() return the Keng_power class, which has print() and plot() methods.

print() prints primary but not all contents of the Keng_power class.

plot() plots the power against sample size for the Keng_power class.

3.6 pick_* tools

pick_sl() and pick_dcb() have been added to randomly pick numbers for Chinese Super Lotto and Double Color Balls.

3.7 Assess OBE-based course objective achievement

assess_coa() calculates course objective achievement based on students’ grades per session, weights of each session, and weights of course objectives within each session.