var.summary {gnonadd}

Variance summary statistics

Description

Estimates the variance effect of several continuous variables jointly

Usage

var.summary(qt, x, iter_num = 50, eps_param = 1e-10)

Arguments

qt

A numeric vector.

x

A data frame, each column represents a covariate that should be numeric.

iter_num

An integer. Represents the number of iterations performed in the Gauss-Newton algorithm

eps_param

A number. The Gauss-Newton algorithm terminates if the incriment change of all variance estimates is smaller than this number.

Value

A list with the following objects: * summary, a dataframe with a variance effect estimate for each variable and summary statistics * chi2, the chi2 statistic obtained by considering all parameteres jointly * df, degrees of freedom for the chi2 statistic * pval, p-value of the model * adjusted_values, a vector with qt values that have been adjusted for both mean and variance effects

Examples

n_val <- 50000
x <- as.data.frame(matrix(0,nrow = n_val, ncol = 4))
colnames(x) <- c('A','B','C','D')
for(i in 1:4) {
x[, i] <- rnorm(n_val)
}
var_vec <- exp(0.2 * x[, 1] - 0.3 * x[, 4])
qt_vec <- rnorm(n_val, 0, sqrt(var_vec))
res <- var.summary(qt_vec, x)