bigLMStats: Efficiently compute basic statistical inference from...

In stnava/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Efficiently compute basic statistical inference from regressions with multiple outcomes

Description

This function simplifies calculating p-values from linear models in which there are many outcome variables, such as in voxel-wise regressions. To perform such an analysis in R, you can concatenate the outcome variables column-wise into an n by p matrix y, where there are n subjects and p outcomes (see Examples). Calling lm(y~x) calculates the coefficients, but statistical inference is not provided. This function provides basic statistical inference efficiently.

Usage

bigLMStats(mylm, lambda = 0, includeIntercept = FALSE)

Arguments

mylm	Object of class lm.
lambda	Value of ridge penalty for inverting ill-conditioned matrices.
includeIntercept	Whether or not to include p-values for intercept term in result.

Value

A list containing objects:

fstat	F-statistic of whole model (one value per outcome).
pval.model	p-value of model (one value per outcome).
beta	Values of coefficients (one value per predictor per outcome).
beta.std	Standard error of coefficients.
beta.t	T-statistic of coefficients.
beta.pval	p-value of coefficients.

Author(s)

Kandel BM.

Examples

nsub <- 100
set.seed(1500)
x <- 1:nsub
y <- matrix(c(x + rnorm(nsub), sin(x)), nrow = nsub)
x <- cbind(x, x^2)
y1 <- y[, 1]
y2 <- y[, 2]
lm1 <- lm(y1 ~ x)
lm2 <- lm(y2 ~ x)
mylm <- lm(y ~ x)

myest <- bigLMStats(mylm)
print(paste(
  "R beta estimates for first outcome is", summary(lm1)$coefficients[-1, 1],
  "and for second outcome is", summary(lm2)$coefficients[-1, 1]
))
print(paste("and our estimate is", as.numeric(myest$beta[, 1]), as.numeric(myest$beta[, 2])))
print(paste(
  "R std error estimate for first outcome is", summary(lm1)$coefficients[-1, 2],
  "and for second outcome is", summary(lm2)$coefficients[-1, 2],
  "and our estimate is", myest$beta.std[, 1], myest$beta.std[, 2]
))
print(paste(
  "R t value estimate for first outcome is", summary(lm1)$coefficients[-1, 3],
  "and for second outcome is", summary(lm2)$coefficients[-1, 3],
  "and our estimate is", myest$beta.t[, 1], myest$beta.t[, 2]
))
print(paste(
  "R pval for first outcome is", summary(lm1)$coefficients[-1, 4],
  "and for second outcome is", summary(lm2)$coefficients[-1, 4],
  "and our estimate is", myest$beta.pval[, 1], myest$beta.pval[, 2]
))

stnava/ANTsR documentation built on April 16, 2024, 12:17 a.m.