Tidying methods for glmmTMB models

These methods tidy the coefficients of mixed effects models, particularly responses of the merMod class

# S3 method for glmmTMB
tidy(
  x,
  effects = c("ran_pars", "fixed"),
  component = c("cond", "zi"),
  scales = NULL,
  ran_prefix = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = "Wald",
  exponentiate = FALSE,
  ...
)

# S3 method for glmmTMB
augment(x, data = stats::model.frame(x), newdata = NULL, ...)

# S3 method for glmmTMB
glance(x, ...)

Arguments

x: An object of class merMod, such as those from lmer, glmer, or nlmer
effects: A character vector including one or more of "fixed" (fixed-effect parameters), "ran_pars" (variances and covariances or standard deviations and correlations of random effect terms) or "ran_vals" (conditional modes/BLUPs/latent variable estimates)
component: which component to extract (e.g. cond for conditional effects (i.e., traditional fixed effects); zi for zero-inflation model; disp for dispersion model
scales: scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if scales is NULL) or ‘"varcov"’ (variances and covariances). NA means no transformation, appropriate e.g. for fixed effects; inverse-link transformations (exponentiation or logistic) are not yet implemented, but may be in the future.
ran_prefix: a length-2 character vector specifying the strings to use as prefixes for self- (variance/standard deviation) and cross- (covariance/correlation) random effects terms
conf.int: whether to include a confidence interval
conf.level: confidence level for CI
conf.method: method for computing confidence intervals (see confint.merMod)
exponentiate: whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if TRUE, also scales the standard errors by the exponentiated coefficient, transforming them to the new scale
...: extra arguments (not used)
data: original data this was fitted on; if not given this will attempt to be reconstructed
newdata: new data to be used for prediction; optional

Value

All tidying methods return a tibble. The structure depends on the method chosen. tidy returns one row for each estimated effect, either with groups depending on the effects parameter. It contains the columns

group: the group within which the random effect is being estimated: NA for fixed effects
level: level within group (NA except for modes)
term: term being estimated
estimate: estimated coefficient
std.error: standard error
statistic: t- or Z-statistic (NA for modes)
p.value: P-value computed from t-statistic (may be missing/NA)

augment returns one row for each original observation, with columns (each prepended by a .) added. Included are the columns

.fitted: predicted values
.resid: residuals
.fixed: predicted values with no random effects

glance returns one row with the columns

sigma: the square root of the estimated residual variance
logLik: the data's log-likelihood under the model
AIC: the Akaike Information Criterion
BIC: the Bayesian Information Criterion
deviance: deviance

Details

When the modeling was performed with na.action = "na.omit" (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with na.action = "na.exclude", one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to augment and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

Note

zero-inflation parameters (including the intercept) are reported on the logit scale

Examples

if (require("glmmTMB") && require("lme4")
    ## &&
    ## make sure package versions are OK
    ## checkDepPackageVersion(dep_pkg = "TMB",
    ##                       this_pkg = "glmmTMB",
    ##                        warn = FALSE) &&
    ## checkDepPackageVersion(dep_pkg = "Matrix",
    ##                       this_pkg = "TMB",
    ##                      warn = FALSE)
)
{
    data("sleepstudy",package="lme4")
    ## original model:
    if (FALSE) {
        lmm1 <- glmmTMB(Reaction ~ Days + (Days | Subject), sleepstudy)
    }
    ## load stored object
    L <- load(system.file("extdata","glmmTMB_example.rda",package="broom.mixed"))
    for (obj in L) {
       assign(obj, glmmTMB::up2date(get(obj)))
    }
    tidy(lmm1)
    tidy(lmm1, effects = "fixed")
    tidy(lmm1, effects = "fixed", conf.int=TRUE)
    tidy(lmm1, effects = "fixed", conf.int=TRUE, conf.method="uniroot")
    ## FIX: tidy(lmm1, effects = "ran_vals", conf.int=TRUE)
    head(augment(lmm1, sleepstudy))
    glance(lmm1)

    ## original model:
    ##  glmm1 <- glmmTMB(incidence/size ~ period + (1 | herd),
    ##                  data = cbpp, family = binomial, weights=size)
    tidy(glmm1)
    tidy(glmm1, effects = "fixed")
    tidy(glmm1, effects = "fixed", exponentiate=TRUE)
    tidy(glmm1, effects = "fixed", conf.int=TRUE, exponentiate=TRUE)
    head(augment(glmm1, cbpp))
    head(augment(glmm1, cbpp, type.residuals="pearson"))
    glance(glmm1)
if (FALSE) {
    ## profile CIs - a little bit slower but more accurate
    tidy(glmm1, effects = "fixed", conf.int=TRUE, exponentiate=TRUE, conf.method="profile")
}
}
#> Loading required package: glmmTMB
#> Warning: there is no package called ‘glmmTMB’

Arguments

Value

Details

Note

See also

Examples