TestDimorph (version 0.3.0)

multivariate: Multivariate Analysis Of Sexual Dimorphism

Description

Multivariate extension of Greene t-test Tg

Usage

multivariate(
  x,
  R.res = NULL,
  Parms = 1,
  Pop = 2,
  es = FALSE,
  univariate = FALSE,
  padjust = "none",
  ...,
  lower.tail = FALSE,
  digits = 4
)

Arguments

x

Tibble/Data frame or list containing summary statistics for multiple parameters measured in both sexes in two or more populations.

R.res

Pooled within correlational matrix, Default: NULL

Parms

Number of the column containing names of measured parameters, Default: 1

Pop

Number of the column containing populations' names, Default: 2

es

Logical; if TRUE effect size is included in the output , Default: FALSE

univariate

Logical; if TRUE conducts multiple univariate analyses on different parameters separately, Default: FALSE

padjust

Method of p.value adjustment for multiple comparisons following p.adjust.methods, Default: 'none'

...

Additional arguments that could be passed to the univariate function

lower.tail

Logical; if TRUE probabilities are P[X <= x], otherwise, P[X > x]., Default: FALSE

digits

Number of significant digits, Default: 4

Value

Tibble of MANOVA results

Details

Data can be entered either as a tibble/data frame of summary statistics as in baboon.parms_df . In that case the pooled within correlational matrix R.res should be entered as a separate argument as in R. Another acceptable format is a named list of matrices containing different summary statistics as well as the correlational matrix as in baboon.parms_list. By setting the option univariate to TRUE, multiple ANOVAs can be run on each parameter independently with the required p value correction using p.adjust.methods.

References

konigsberg1991historicalTestDimorph

Examples

# NOT RUN {
 # x is a data frame with separate correlational matrix
 library(TestDimorph)
 multivariate(baboon.parms_df, R.res = R)
 # x is a list with the correlational matrix included
 library(TestDimorph)
 multivariate(baboon.parms_list, univariate = TRUE, padjust = 'bonferroni')
# }