univariate_pairwise: post hoc univariate analysis to MANOVA

In bassam-abulnoor/TestDimorph: Analysis Of The Interpopulation Difference In Degree of Sexual Dimorphism Using Summary Statistics

Description

post hoc univariate analysis to MANOVA

Usage

univariate_pairwise(x, out, padjust, digits, lower.tail, ...)

Arguments

x	A data frame containing summary statistics.
out	output of multivariate function
padjust	Method of p.value adjustment for multiple comparisons following p.adjust.methods Default: "none".
digits	Number of significant digits, Default: 4
lower.tail	Logical; if TRUE probabilities are 'P[X <= x]', otherwise, 'P[X > x]'., Default: FALSE
...	Arguments passed on to multivariate 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 CI confidence interval coverage takes value from 0 to 1, Default: 0.95.

bassam-abulnoor/TestDimorph documentation built on Jan. 29, 2021, 8:21 a.m.