# mlmWithCov: Uses covariance/correlation matrix to calculate multivariate... In dominanceanalysis: Dominance Analysis

## Description

Calculate R^2_{XY} and P^2_{YX} for multivariate regression Could be used with `dominanceAnalysis` to perform a multivariate dominance analysis without original data.

## Usage

 `1` ```mlmWithCov(f, x) ```

## Arguments

 `f` formula. Should use `cbind(y1,y2,...,yk)~x1+x2+..+xp` `x` correlation/covariance matrix

## Value

 `r.squared.xy` R^2_{XY} of the regression `p.squared.yx` P^2_{YX} of the regression `formula` formula provided as parameter `cov` covariance/correlation matrix provided as parameter

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```library(car) cor.m<-matrix(c( 1.0000000, 0.7951377, 0.2617168, 0.6720053, 0.3390278, 0.7951377, 1.0000000, 0.3341037, 0.5876337, 0.3404206, 0.2617168, 0.3341037, 1.0000000, 0.3703162, 0.2114153, 0.6720053, 0.5876337, 0.3703162, 1.0000000, 0.3548077, 0.3390278, 0.3404206, 0.2114153, 0.3548077, 1.0000000), 5,5, byrow = TRUE, dimnames = list( c("na","ss","SAT","PPVT","Raven"), c("na","ss","SAT","PPVT","Raven"))) lwith<-mlmWithCov(cbind(na,ss)~SAT+PPVT+Raven,cor.m) da<-dominanceAnalysis(lwith) print(da) summary(da) ```

dominanceanalysis documentation built on Jan. 13, 2021, 3:47 p.m.