se.coef {arm}

Extract Standard Errors of Model Coefficients

Description

These functions extract standard errors of model coefficients from objects returned by modeling functions.

Usage

se.coef (object, ...)
se.fixef (object)
se.ranef (object)

## S4 method for signature 'lm'
se.coef(object)
## S4 method for signature 'glm'
se.coef(object)
## S4 method for signature 'merMod'
se.coef(object)

Arguments

object

object of lm, glm and merMod fit

...

other arguments

Details

se.coef extracts standard errors from objects returned by modeling functions. se.fixef extracts standard errors of the fixed effects from objects returned by lmer and glmer functions. se.ranef extracts standard errors of the random effects from objects returned by lmer and glmer functions.

Value

se.coef gives lists of standard errors for coef, se.fixef gives a vector of standard errors for fixef and se.ranef gives a list of standard errors for ranef.

Author(s)

Andrew Gelman [e-mail]; Yu-Sung Su [e-mail]

References

Andrew Gelman and Jennifer Hill. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

See Also

display, coef, sigma.hat,

Examples

# Here's a simple example of a model of the form, y = a + bx + error,
# with 10 observations in each of 10 groups, and with both the
# intercept and the slope varying by group.  First we set up the model and data.

   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))

#  lm fit
   M1 <- lm (y1 ~ x)
   se.coef (M1)

#  glm fit
   M2 <- glm (y2 ~ x)
   se.coef (M2)

#  lmer fit
   M3 <- lmer (y1 ~ x + (1 + x |group))
   se.coef (M3)
   se.fixef (M3)
   se.ranef (M3)

#  glmer fit
   M4 <- glmer (y2 ~ 1 + (0 + x |group), family=binomial(link="logit"))
   se.coef (M4)
   se.fixef (M4)
   se.ranef (M4)