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.
You can install the development version of Keng
from
GitHub with:
install.packages("devtools")
devtools::install_github("qyaozh/Keng", dependencies = TRUE, build_vignettes = TRUE)
Before using the Keng
package, load it using the library()
function.
library(Keng)
Here is a list of the data and functions gathered in the Keng
package.
Their usages are detailed in the documentation.
depress
is a subset of data from a research about depression and
coping.
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).
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.
power_r()
conducts a priori power analysis and plan the sample size
for r; post-hoc power analysis would also be conducted with known sample
size n.
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()
conducts a priori power analysis and plans the sample size
for one or a set of predictors in regression analysis; post-hoc power
analysis would also be conducted with known sample size n.
Keng_power
classpower_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.
pick_sl()
and pick_dcb()
have been added to randomly pick numbers
for Chinese Super Lotto and Double Color Balls.
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