This function simplifies calculating p-values from linear models in which there is a similar formula that is applied many times with a change in only one predictor. The outcome variable is constant. The changing variable should be named vox in the input formula.

bigLMStats2(dataFrame, voxmat, myFormula)

Arguments

dataFrame

This data frame contains all relevant predictors except for the matrix associated with the changing variable, heretofore named vox.

voxmat

The matrix that contains the changing predictor named vox.

myFormula

This is a character string that defines a valid regression formula.

Value

A list of different matrices that contain names derived from the formula and the coefficients of the regression model.

Examples

set.seed(1500) nsub = 100 outcome = rnorm( nsub ) covar = rnorm( nsub ) mat = replicate( nsub, rnorm( nsub ) ) myform = " outcome ~ covar + vox " df = data.frame( outcome = outcome, covar = covar ) result = bigLMStats2( df, mat, myform) print( names( result ) )
#> [1] "estimate" "stdError" "tValue" "pValue"
print( rownames( result$pValue ) )
#> [1] "(Intercept)" "covar" "vox"