mcm_lmer {MCM}

Estimate and Test Inter-generational Mobility Effect with Longitudinal Data

Description

This function fits a multilevel mobility contrast model to estimate and test inter-generational mobility effect on an outcome in longitudinal data.

Usage

mcm_lmer(
  formula,
  data = NULL,
  REML = TRUE,
  control = lme4::lmerControl(),
  start = NULL,
  verbose = 0L,
  subset,
  weights,
  na.action,
  offset,
  contrasts = NULL,
  devFunOnly = FALSE,
  origin = NULL,
  destination = NULL,
  time = NULL,
  displayresult = TRUE,
  ...
)

Arguments

formula

Inherit the function form from lme4 package. It is 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.) A typical model used in studying social mobility with longitudinal data takes the form response ~ origin*destination + | id, where respose is the numeric response vector and origin (destination) is a vector indicating the origin (destination). The specification of origin*destination indicates the cross of origin and destination, which is the same as origin + destination + origin:destination where origin:destination indicates the interaction of origin and destination. id is a identifier for the clusters.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called.

REML

logical. Should the estimates be chosen be optimize the restricted log-likelihood (REML) criterial (as opposed to the log-likelihood)?

control

Inherit from lme4 package. It is 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 in lme4 package for details.

start

Inherit from lme4 package. It is a named list of starting values for the parameters in the model.

verbose

Inherit from lme4 package. It is an 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.

subset

optional expression selecting the subset of the rows of data to fit the model.

weights

an optional vector of ‘prior weights’ to be used in the fitting process. Should be NULL or a numeric vector.

na.action

a function which indicates what should happen when the data contain NAs.The default is set by the na.action setting in options and is na.fail if that is unset.

offset

Inherit from lme4 package. 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.

contrasts

an optional list. The default is set as sum-to-zero contrast.

devFunOnly

logical - return only the deviance evaluation function.

origin

a character indicating the column name of origin.

destination

a character indicating the column name of destination.

time

a character indicating the time when individual was observed

displayresult

logical. Should model results be displayed after estimation. The default is TRUE.

...

additional arguments to be passed to the function.

Value

A list containing:

model

Fitted generalized models of outcome on predictors. See more on function glm in package stats.

estimates

Estimated mobility effects.

se

Standard errors of the estimated mobility effects.

significance

Statistical significance of the the estimated mobility effects.

esti_3way

Estimated mobility effects conditional on specific age.

se_3way

Standard errors of the estimated mobility effects conditional specific age.

sig_3way

Statistical significance of the the estimated mobility effects conditional on age.

Examples

library(MCM)
library(lme4)
data("sim_datlmer")
fit_mcm_lmer <- mcm_lmer(yij ~ origin*destination*age +
                           (1|id), data = sim_datlmer,
                         origin = "origin",
                         destination = "destination",
                         time = "age")