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 correlation 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 for the chosen effect size 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 correlation matrix `R.res` should be entered as a separate argument as in baboon.parms_R. Another acceptable format is is a named list of matrices and vectors containing different summary statistics as well as the correlation matrix as in baboon.parms_list. By setting the option `univariate` to `TRUE`, multiple `ANOVA`s can be run on each parameter independently.

Examples

Run this code

# x is a data frame with separate correlation matrix
multivariate(baboon.parms_df, R.res = baboon.parms_R)
# x is a list with the correlation matrix included
multivariate(baboon.parms_list, univariate = TRUE)
# 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)