# sigma

0th

Percentile

##### Extract Residual Standard Deviation 'Sigma'

Extract the estimated standard deviation of the errors, the “residual standard deviation” (misnomed also “residual standard error”, e.g., in summary.lm()'s output, from a fitted model).

Many classical statistical models have a scale parameter, typically the standard deviation of a zero-mean normal (or Gaussian) random variable which is denoted as $\sigma$. sigma(.) extracts the estimated parameter from a fitted model, i.e., $\hat\sigma$.

Keywords
models
##### Usage
sigma(object, ...)# S3 method for default
sigma(object, use.fallback = TRUE, ...)
##### Arguments
object

an R object, typically resulting from a model fitting function such as lm.

use.fallback

logical, passed to nobs.

potentially further arguments passed to and from methods. Passed to deviance(*, ...) for the default method.

##### Details

The stats package provides the S3 generic and a default method. The latter is correct typically for (asymptotically / approximately) generalized gaussian (“least squares”) problems, since it is defined as


sigma.default <- function (object, use.fallback = TRUE, ...)
sqrt( deviance(object, ...) / (NN - PP) )


where NN <- nobs(object, use.fallback = use.fallback) and PP <- sum(!is.na(coef(object))) -- where in older R versions this was length(coef(object)) which is too large in case of undetermined coefficients, e.g., for rank deficient model fits.

##### Value

typically a number, the estimated standard deviation of the errors (“residual standard deviation”) for Gaussian models, and---less interpretably---the square root of the residual deviance per degree of freedom in more general models. In some generalized linear modelling (glm) contexts, $sigma^2$ (sigma(.)^2) is called “dispersion (parameter)”. Consequently, for well-fitting binomial or Poisson GLMs, sigma is around 1.

Very strictly speaking, $\hat{\sigma}$ (“$\sigma$ hat”) is actually $\sqrt{\widehat{\sigma^2}}$.

For multivariate linear models (class "mlm"), a vector of sigmas is returned, each corresponding to one column of $Y$.

##### Note

The misnomer “Residual standard error” has been part of too many R (and S) outputs to be easily changed there.

deviance, nobs, vcov.

##### Aliases
• sigma
• sigma.default
• sigma.mlm
##### Examples
library(stats) # NOT RUN { ## -- lm() ------------------------------ lm1 <- lm(Fertility ~ . , data = swiss) sigma(lm1) # ~= 7.165 = "Residual standard error" printed from summary(lm1) stopifnot(all.equal(sigma(lm1), summary(lm1)$sigma, tol=1e-15)) ## -- nls() ----------------------------- DNase1 <- subset(DNase, Run == 1) fm.DN1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) sigma(fm.DN1) # ~= 0.01919 as from summary(..) stopifnot(all.equal(sigma(fm.DN1), summary(fm.DN1)$sigma, tol=1e-15)) # } # NOT RUN { <!-- % example from ./predict.glm.R --> # } # NOT RUN { ## -- glm() ----------------------------- ## -- a) Binomial -- Example from MASS ldose <- rep(0:5, 2) numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) sex <- factor(rep(c("M", "F"), c(6, 6))) SF <- cbind(numdead, numalive = 20-numdead) sigma(budworm.lg <- glm(SF ~ sex*ldose, family = binomial)) ## -- b) Poisson -- from ?glm : ## Dobson (1990) Page 93: Randomized Controlled Trial : counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) sigma(glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())) ## (currently) *differs* from summary(glm.D93)$dispersion # == 1 ## and the *Quasi*poisson's dispersion sigma(glm.qD93 <- update(glm.D93, family = quasipoisson())) sigma (glm.qD93)^2 # 1.282285 is close, but not the same summary(glm.qD93)$dispersion # == 1.2933 ## -- Multivariate lm() "mlm" ----------- utils::example("SSD", echo=FALSE) sigma(mlmfit) # is the same as {but more efficient than} sqrt(diag(estVar(mlmfit))) # } 
Documentation reproduced from package stats, version 3.6.1, License: Part of R 3.6.1

### Community examples

Looks like there are no examples yet.