lmer: multimembership linear mixed effects models

In jvparidon/lmerMultiMember: Multiple membership random effects

multimembership linear mixed effects models

Description

lme4::lmer but with multimembership random effects

Usage

lmer(
  formula,
  data = NULL,
  REML = TRUE,
  control = lme4::lmerControl(),
  start = NULL,
  verbose = 0L,
  weights = NULL,
  na.action = na.omit,
  offset = NULL,
  contrasts = NULL,
  devFunOnly = FALSE,
  memberships = NULL
)

Arguments

formula	a two-sided linear formula object describing both the fixed-effects and random-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. Random-effects terms are distinguished by vertical bars (|) separating expressions for design matrices from grouping factors. Two vertical bars (||) can be used to specify multiple uncorrelated random effects for the same grouping variable. (Because of the way it is implemented, the ||-syntax works only for design matrices containing numeric (continuous) predictors; to fit models with independent categorical effects, see dummy or the lmer_alt function from the afex package.)
data	an optional data frame containing the variables named in formula. By default the variables are taken from the environment from which lmer is called. While data is optional, the package authors strongly recommend its use, especially when later applying methods such as update and drop1 to the fitted model (such methods are not guaranteed to work properly if data is omitted). If data is omitted, variables will be taken from the environment of formula (if specified as a formula) or from the parent frame (if specified as a character vector).
REML	logical scalar - Should the estimates be chosen to optimize the REML criterion (as opposed to the log-likelihood)?
control	a list (of correct class, resulting from lmerControl() or glmerControl() respectively) containing control parameters, including the nonlinear optimizer to be used and parameters to be passed through to the nonlinear optimizer, see the *lmerControl documentation for details.
start	a named list of starting values for the parameters in the model. For lmer this can be a numeric vector or a list with one component named "theta".
verbose	integer scalar. If > 0 verbose output is generated during the optimization of the parameter estimates. If > 1 verbose output is generated during the individual penalized iteratively reweighted least squares (PIRLS) steps.
weights	an optional vector of ‘prior weights’ to be used in the fitting process. Should be NULL or a numeric vector. Prior weights are not normalized or standardized in any way. In particular, the diagonal of the residual covariance matrix is the squared residual standard deviation parameter sigma times the vector of inverse weights. Therefore, if the weights have relatively large magnitudes, then in order to compensate, the sigma parameter will also need to have a relatively large magnitude.
na.action	a function that indicates what should happen when the data contain NAs. The default action (na.omit, inherited from the 'factory fresh' value of getOption("na.action")) strips any observations with any missing values in any variables.
offset	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See model.offset.
contrasts	an optional list. See the contrasts.arg of model.matrix.default.
devFunOnly	logical - return only the deviance evaluation function. Note that because the deviance function operates on variables stored in its environment, it may not return exactly the same values on subsequent calls (but the results should always be within machine tolerance).
memberships	named list of weight matrices that will replace any (dummy) random effects with matching names

Value

lme4 model object

Examples

df <- data.frame(
  x = seq(60) + runif(60, 0, 10),
  y = seq(60) + rep(runif(6, 0, 10), 10),
  memberships = rep(c("a,b,c", "a,c", "a", "b", "b,a", "b,c,a"), 10)
)
weights <- weights_from_vector(df$memberships)

# note that the grouping variable name is arbitrary -- it just has
# to match the name in the list and doesn't need to correspond to a column
# name in the data
lmer(y ~ x + (1 | members),
  data = df,
  memberships = list(members = weights)
)

jvparidon/lmerMultiMember documentation built on Oct. 22, 2023, 3:42 p.m.