Standard methods for lmm fits

Methods are provided for extracting likelihood quantities, fitted values, residuals, model matrices and formulas, as well as for prediction, type III testing and fixed-effect confidence intervals.

Usage

# S3 method for class 'lmm'
print(x, ...)

# S3 method for class 'lmm'
coef(object, ...)

# S3 method for class 'lmm'
vcov(object, adjusted = TRUE, ...)

# S3 method for class 'lmm'
logLik(object, ...)

# S3 method for class 'lmm'
AIC(object, ..., k = 2)

# S3 method for class 'lmm'
BIC(object, ...)

# S3 method for class 'lmm'
deviance(object, ...)

# S3 method for class 'lmm'
nobs(object, ...)

# S3 method for class 'lmm'
fitted(object, type = c("conditional", "marginal"), ...)

# S3 method for class 'lmm'
residuals(object, type = c("response", "pearson", "marginal"), ...)

# S3 method for class 'lmm'
plot(x, which = c("residuals", "qq", "fitted"), ...)

# S3 method for class 'lmm'
sigma(object, ...)

# S3 method for class 'lmm'
predict(
  object,
  newdata = NULL,
  re.form = NULL,
  allow.new.levels = FALSE,
  se.fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  na.action = stats::na.pass,
  ...
)

# S3 method for class 'lmm'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'lmm'
update(object, formula., ..., evaluate = TRUE)

# S3 method for class 'lmm'
model.matrix(object, data = NULL, ...)

# S3 method for class 'lmm'
model.frame(formula, ...)

# S3 method for class 'lmm'
formula(x, fixed.only = TRUE, ...)

# S3 method for class 'lmm'
terms(x, ...)

# S3 method for class 'lmm'
anova(
  object,
  ...,
  type = 3,
  refit = TRUE,
  test = c("chisq", "parametric.bootstrap"),
  nsim = 199L,
  seed = NULL
)

# S3 method for class 'lmm'
confint(object, parm = names(object$coefficients), level = 0.95, ...)

Arguments

x, object, formula

An lmm object. The formula name follows the argument name of the model.frame() generic.

...

Additional fitted lmm models for likelihood-ratio comparison, or arguments passed to the corresponding method.

adjusted

Whether vcov() returns the Kenward-Roger-adjusted covariance when available.

k

Penalty per parameter used by AIC().

type

For fitted(), either "conditional" or "marginal". For residuals(), one of "response", "pearson", or "marginal". For anova(), only type 3 is implemented.

which

Diagnostic plot to draw: standardized residuals against fitted values, a normal quantile-quantile plot, or observed against fitted values.

newdata

Optional data frame used for prediction.

re.form

NULL includes every empirical random-effect term, NA or ~ 0 excludes all random effects, and a random-effects formula or list of formulas selects a subset of fitted terms.

allow.new.levels

Whether conditional predictions may include new grouping levels. Their random contribution is zero when allowed.

se.fit

Whether predict() returns standard errors for the expected response. These standard errors use the fixed-effect covariance and condition on any empirical random effects included through re.form.

interval

Prediction interval type. "confidence" describes the expected response. "prediction" additionally includes the fitted residual variance but not uncertainty in empirical random effects.

level

Confidence level for fixed-effect and covariance intervals.

na.action

Missing-value action retained for prediction-method compatibility.

nsim

Number of simulations for a parametric-bootstrap comparison.

seed

Optional integer seed for a reproducible parametric bootstrap.

formula.

Optional formula update accepted by update().

evaluate

Whether update() evaluates the updated call.

data

Optional data frame used to construct a fixed-effects model matrix.

fixed.only

Whether formula() returns only the fixed formula. If FALSE, it returns fixed, random, and repeated formulas in a list.

refit

Whether REML models that differ in fixed effects are refitted with ML before comparison.

test

Reference test for likelihood-ratio model comparisons. The default "chisq" uses the usual asymptotic chi-squared reference. The "parametric.bootstrap" option simulates under each smaller model and is appropriate when covariance parameters can lie on a boundary.

parm

Parameters requested from confint(), supplied by name or fixed effect position. Use "beta_" for every fixed effect and "theta_" for every covariance parameter.

Value

The return value follows the corresponding base R generic.

Details

Conditional fitted values and predictions include empirical random effects. Marginal values use fixed effects only. In prediction from new data, known grouping levels use their fitted random effect. New levels are rejected by default and receive a random contribution of zero only when explicitly allowed.

ranef() always returns a named list with one tibble per random-effect term, including for models with a single term. It returns an empty list for marginal models. sigma() is defined as sqrt(mean(diag(R))); it is a descriptive residual scale when residual variances differ across observations.

References

Self, S. G., and Liang, K.-Y. (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. Journal of the American Statistical Association, 82(398), 605-610. doi:10.1080/01621459.1987.10478472

Davison, A. C., and Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press. doi:10.1017/CBO9780511802843