groupcompare (version 1.0.1)

groupcompare-package: Comparing Two Groups with Various Descriptive Statistics

Description

The ` groupcompare` package performs various statistical tests to compare two groups. It calculates descriptive statistics and quantile statistics and then conducts some normality tests and variance homogeneity tests. Based on these assumptions checks, it provides results from t-test or Wilcoxon rank sum test, permutation tests, and bootstrap confidence intervals.

Author

Zeynel Cebeci, A. Firat Ozdemir, Engin Yildiztepe

Details

The main function `groupcompare` of the package is designed to compare two independent or paired groups using various statistical tests. It calculates descriptive statistics and quantile statistics. Then it performs Shapiro-Wilk normality tests, variance homogeneity test (Levene's test), t-test, Wilcoxon signed-rank sum test (or Mann-Whitney U test), permutation tests, and bootstrap confidence intervals.

groupcompare

The main function which compares descriptive statistics of two groups using a variety of statistical tests.

bivarplot

Generates various plots to visualize and compare the distribution and characteristics of two variables.

bootstrap

Calculates bootstrap confidence intervals for the descriptive statistics or any statistic implemented in a custom function.

calchubermeandif

Computes the difference between Huber???s M-estimator of location of two groups in long data format.

calcquantdif

Calculates the differences between specified quantiles for grouped data.

calcquantile

Calculates the quantiles (percentiles) for a given vector of data at specified fractions.

calcstatdif

Calculates the differences in multiple statistics (mean, median, IQR, variance) for grouped data.

ci2df

Converts a list of confidence intervals into a data frame.

descstats

Calculates the common and robust descriptive statistics.

ghdist

Generates a random sample from the g-and-h (gh) distribution with specified parameters.

groupdata

A data set contains seven data frames with two variables from various distributions.

hdqe

Computes the Harrell-Davis quantile estimator for given quantile levels.

intnorm

Performs an inverse normal transformation on non-normally distributed data.

levene.test

Performs the Levene test to check the homogeneity of variances across groups.

long2wide

Converts long-format data to wide-format data by splitting based on groups.

permtest

Performs a permutation test on long-format data to evaluate differences between two groups using a specified test statistic.

quail

A data frame containing daily weight gains (in grams) of two quail breeds during a fattening period.

wide2long

Converts wide-format data to long-format data.

See Also

bootstrap, permtest, ghdist, bivarplot

Examples

# \donttest{
# Sample dataset in long format
set.seed(123)
group1 <- rnorm(30, mean=50, sd=2)
group2 <- rnorm(30, mean=51, sd=3)
df <- data.frame(value=c(group1, group2), group=rep(c("A", "B"), each=30))

# Compare the groups using various descriptive statistics
result <-  groupcompare(df, cl=0.95, alternative="two.sided",
  q=c(0.25, 0.5, 0.75), qt=0, R=500, out=FALSE, verbose=TRUE)
result

# Compare the groups using Huber's M-estimator of location with bootstrap
bshubermean <- bootstrap(df, statistic=calchubermeandif, 
  alternative="two.sided", alpha=0.05, R=500)
bshubermean

permhubermean <- permtest(df, statistic=calchubermeandif, 
  alternative="two.sided", R=500)
permhubermean$pval
# }