TestDimorph (version 0.4.1)

multivariate: Multivariate Analysis Of Sexual Dimorphism

Description

Multivariate extension of Greene t test t_greene

Usage

multivariate(
  x,
  R.res = NULL,
  Trait = 1,
  Pop = 2,
  type_manova = "II",
  manova_test_statistic = "W",
  interact_manova = TRUE,
  es_manova = "none",
  univariate = FALSE,
  padjust = "none",
  ...,
  lower.tail = FALSE,
  CI = 0.95,
  digits = 4
)

Value

MANOVA table. When the term is followed by `(E)` an exact f-value is calculated.

Arguments

x

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

Trait

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

Pop

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

type_manova

type of MANOVA test "I","II" or "III", Default:"II".

manova_test_statistic

type of test statistic used either "W" for "Wilks","P" for "Pillai", "HL" for "Hotelling-Lawley" or "R" for "Roy's largest root", Default: "W".

interact_manova

Logical; if TRUE calculates MANOVA for the interaction effects,Default: TRUE.

es_manova

effect size either ,"eta" for eta squared, or "none"for not reporting an effect size, Default:"none".

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 Default: "none".

...

Additional arguments that could be passed to univariate

lower.tail

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

CI

confidence interval coverage takes value from 0 to 1, Default: 0.95.

digits

Number of significant digits, Default: 4

Details

Data can be entered either as a 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 baboon.parms_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 `ANOVA`s can be run on each parameter independently with the required p.value correction using p.adjust.methods.

Examples

# x is a data frame with separate correlational matrix
library(TestDimorph)
multivariate(baboon.parms_df, R.res = baboon.parms_R)
# x is a list with the correlational matrix included
library(TestDimorph)
multivariate(baboon.parms_list, univariate = TRUE, padjust = "bonferroni")
# reproduces results from Konigsberg (1991)
multivariate(baboon.parms_df, R.res = baboon.parms_R)[3, ]
multivariate(baboon.parms_df, R.res = baboon.parms_R, interact_manova = FALSE)