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

 sigma.hat R Documentation

## Extract Residual Errors

### Description

This generic function extracts residual errors from a fitted model.

### Usage

```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 gelman@stat.columbia.edu; Yu-Sung Su suyusung@tsinghua.edu.cn

`display`, `summary`, `lm`, `glm`, `lmer`

### Examples

```   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)

```

arm documentation built on Aug. 29, 2022, 1:05 a.m.