sjstats (version 0.16.0)

se: Standard Error for variables or coefficients

Description

Compute standard error for a variable, for all variables of a data frame, for joint random and fixed effects coefficients of (non-/linear) mixed models, the adjusted standard errors for generalized linear (mixed) models, or for intraclass correlation coefficients (ICC).

Usage

se(x, ...)
# S3 method for icc.lme4
se(x, nsim = 100, ...)

Arguments

x

(Numeric) vector, a data frame, an lm, glm, merMod (lme4), or stanreg model object, an ICC object (as obtained by the icc-function) or a list with estimate and p-value. For the latter case, the list must contain elements named estimate and p.value (see 'Examples' and 'Details').

...

Currently not used.

nsim

Numeric, the number of simulations for calculating the standard error for intraclass correlation coefficients, as obtained by the icc-function.

Value

The standard error of x.

Details

For linear mixed models, and generalized linear mixed models, this function computes the standard errors for joint (sums of) random and fixed effects coefficients (unlike se.coef, which returns the standard error for fixed and random effects separately). Hence, se() returns the appropriate standard errors for coef.merMod.

For generalized linear models, approximated standard errors, using the delta method for transformed regression parameters are returned (Oehlert 1992).

The standard error for the icc is based on bootstrapping, thus, the nsim-argument is required. See 'Examples'.

se() also returns the standard error of an estimate (regression coefficient) and p-value, assuming a normal distribution to compute the z-score from the p-value (formula in short: b / qnorm(p / 2)). See 'Examples'.

References

Oehlert GW. 1992. A note on the delta method. American Statistician 46(1).

Gelman A 2017. How to interpret confidence intervals? http://andrewgelman.com/2017/03/04/interpret-confidence-intervals/

Examples

# NOT RUN {
# compute standard error for vector
se(rnorm(n = 100, mean = 3))

# compute standard error for each variable in a data frame
data(efc)
se(efc[, 1:3])

# compute standard error for merMod-coefficients
library(lme4)
fit <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
se(fit)

# compute odds-ratio adjusted standard errors, based on delta method
# with first-order Taylor approximation.
data(efc)
efc$services <- sjmisc::dicho(efc$tot_sc_e, dich.by = 0)
fit <- glm(services ~ neg_c_7 + c161sex + e42dep,
           data = efc, family = binomial(link = "logit"))
se(fit)

# compute odds-ratio adjusted standard errors for generalized
# linear mixed model, also based on delta method
library(lme4)
library(sjmisc)
# create binary response
sleepstudy$Reaction.dicho <- dicho(sleepstudy$Reaction, dich.by = "median")
fit <- glmer(Reaction.dicho ~ Days + (Days | Subject),
             data = sleepstudy, family = binomial("logit"))
se(fit)

# compute standard error from regression coefficient and p-value
se(list(estimate = .3, p.value = .002))

# }
# NOT RUN {
# compute standard error of ICC for the linear mixed model
icc(fit)
se(icc(fit))

# the standard error for the ICC can be computed manually in this way,
# taking the fitted model example from above
library(dplyr)
library(purrr)
dummy <- sleepstudy %>%
  # generate 100 bootstrap replicates of dataset
  bootstrap(100) %>%
  # run mixed effects regression on each bootstrap replicate
  # and compute ICC for each "bootstrapped" regression
  mutate(
    models = map(strap, ~lmer(Reaction ~ Days + (Days | Subject), data = .x)),
    icc = map_dbl(models, ~icc(.x))
  )

# now compute SE and p-values for the bootstrapped ICC, values
# may differ from above example due to random seed
boot_se(dummy, icc)
boot_p(dummy, icc)
# }
# NOT RUN {

# }