multivariate {TestDimorph}

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
)

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.

Value

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

See Also

baboon.parms_df

Examples

# 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)