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Functions that return the PRESS statistic (predictive residual sum of squares) and predictive r-squared for a linear model (class lm) in R
# Example usage for model_fit_stats.R
# Set up some data
x <- seq(from=0, to=10, by=0.5)
y1 <- 2*x + rnorm(length(x))
# We want to compare two different linear models:
my.lm1 <- lm(y1 ~ sin(x))
my.lm2 <- lm(y1 ~ x)
# We will use plyr for this.
# Now call model_fit_stats() for each lm model that
# we have, using ldply. This returns the results in
# a data frame that is easily used for further
# calculations.
ldply(list(my.lm1, my.lm2), model_fit_stats)
# adply() also works, though it should be less robust
# than ldply().
#adply(list(my.lm1, my.lm2), 1, model_fit_stats)
#' @title Model Fit Statistics
#' @description Returns lm model fit statistics R-squared, adjucted R-squared,
#' predicted R-squared and PRESS.
#' Thanks to John Mount for his 6-June-2014 blog post, R style tip: prefer functions that return data frames" for
#' the idea \link{}
#' @return Returns a data frame with one row and a column for each statistic
#' @param linear.model A \code{lm()} model.
model_fit_stats <- function(linear.model) {
r.sqr <- summary(linear.model)$r.squared
adj.r.sqr <- summary(linear.model)$adj.r.squared
pre.r.sqr <- pred_r_squared(linear.model)
PRESS <- PRESS(linear.model)
return.df <- data.frame(r.squared = r.sqr, adj.r.squared = adj.r.sqr, pred.r.squared = pre.r.sqr, press = PRESS)
#' @title Predictive R-squared
#' @author Thomas Hopper
#' @description returns the predictive r-squared. Requires the function PRESS(), which returns
#' the PRESS statistic.
#' @param linear.model A linear regression model (class 'lm'). Required.
pred_r_squared <- function(linear.model) {
#' Use anova() to get the sum of squares for the linear model
lm.anova <- anova(linear.model)
#' Calculate the total sum of squares
tss <- sum(lm.anova$'Sum Sq')
# Calculate the predictive R^2
pred.r.squared <- 1-PRESS(linear.model)/(tss)
#' @title PRESS
#' @author Thomas Hopper
#' @description Returns the PRESS statistic (predictive residual sum of squares).
#' Useful for evaluating predictive power of regression models.
#' @param linear.model A linear regression model (class 'lm'). Required.
PRESS <- function(linear.model) {
#' calculate the predictive residuals
pr <- residuals(linear.model)/(1-lm.influence(linear.model)$hat)
#' calculate the PRESS
PRESS <- sum(pr^2)
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