extract_sum: Summary Statistics Extraction

In TestDimorph: Analysis of the Interpopulation Difference in Degree of Sexual Dimorphism Using Summary Statistics

Summary Statistics Extraction

Description

Extract summary data needed for other functions from raw data.

Usage

extract_sum(x, Sex = 1, Pop = 2, firstX = 3, test = "tg", run = TRUE, ...)

Arguments

x	Data frame of raw data.
Sex	Number of the column containing sex 'M' for male and 'F' for female, Default: 1
Pop	Number of the column containing populations' names, Default: 2
firstX	Number of column containing measured parameters (First of multiple in case of multivariate analysis), Default: 3
test	'tg' for Greene t test t_greene, 'uni' for univariate, 'aov' for sex specific ANOVA aov_ss, 'multi' for multivariate, and 'van' for van_vark, Default: 1
run	Logical; if TRUE runs the corresponding test after data extraction, Default:TRUE
...	Additional arguments that could be passed to the test of choice

Details

Raw data is entered in a wide format data frame similar to Howells data set. The first two columns contain sex 'Sex' (‘M' for male and 'F' for female) (Default: '1') and populations’ names 'Pop' (Default:'2'). Starting from 'firstX' column (Default: '3'), measured parameters are entered each in a separate column.

Value

Input for other functions.

Examples

# for multivariate test
## Not run: 
extract_sum(Howells, test = "multi")
# for univariate test on a specific parameter
extract_sum(Howells, test = "uni", firstX = 4)

## End(Not run)

TestDimorph documentation built on Nov. 18, 2023, 1:11 a.m.