# sigma.hat: Extract Residual Errors In arm: Data Analysis Using Regression and Multilevel/Hierarchical Models

## Description

This generic function extracts residual errors from a fitted model.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```sigma.hat(object,...) ## S3 method for class 'lm' sigma.hat(object,...) ## S3 method for class 'glm' sigma.hat(object,...) ## S3 method for class 'merMod' sigma.hat(object,...) ## S3 method for class 'sim' sigma.hat(object,...) ## S3 method for class 'sim.merMod' sigma.hat(object,...) ```

## Arguments

 `object` any fitted model object of `lm`, `glm` and `merMod` class `...` other arguments

## Author(s)

Andrew Gelman [email protected]; Yu-Sung Su [email protected]

`display`, `summary`, `lm`, `glm`, `lmer`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31``` ``` group <- rep(1:10, rep(10,10)) mu.a <- 0 sigma.a <- 2 mu.b <- 3 sigma.b <- 4 rho <- 0 Sigma.ab <- array (c(sigma.a^2, rho*sigma.a*sigma.b, rho*sigma.a*sigma.b, sigma.b^2), c(2,2)) sigma.y <- 1 ab <- mvrnorm (10, c(mu.a,mu.b), Sigma.ab) a <- ab[,1] b <- ab[,2] x <- rnorm (100) y1 <- rnorm (100, a[group] + b[group]*x, sigma.y) y2 <- rbinom(100, 1, prob=invlogit(a[group] + b*x)) M1 <- lm (y1 ~ x) sigma.hat(M1) M2 <- bayesglm (y1 ~ x, prior.scale=Inf, prior.df=Inf) sigma.hat(M2) # should be same to sigma.hat(M1) M3 <- glm (y2 ~ x, family=binomial(link="logit")) sigma.hat(M3) M4 <- lmer (y1 ~ (1+x|group)) sigma.hat(M4) M5 <- glmer (y2 ~ (1+x|group), family=binomial(link="logit")) sigma.hat(M5) ```