mlmm: Fit Multiple Linear Mixed Model
In LMMstar: Repeated Measurement Models for Discrete Times

View source: R/mlmm.R

mlmm	R Documentation

Fit Multiple Linear Mixed Model

Description

Fit several linear mixed models, extract relevant coefficients, and combine them into a single table.

Usage

mlmm(
  ...,
  data,
  by,
  contrast.rbind = NULL,
  effects = NULL,
  robust = FALSE,
  df = TRUE,
  ci = TRUE,
  name.short = c(TRUE, TRUE),
  transform.sigma = NULL,
  transform.k = NULL,
  transform.rho = NULL,
  transform.names = TRUE,
  trace = TRUE
)

Arguments

`...`	arguments passed to `lmm`.
`data`	[data.frame] dataset (in the long format) containing the observations.
`by`	[character] variable used to split the dataset. On each split a seperate linear mixed model is fit.
`contrast.rbind`	[character or numeric matrix] Contrast to be be applied to compare the groups. Argument passed to the argument `effects` of `rbind.Wald_lmm`. Right hand side can be specified via an attribute `"rhs"`.
`effects`	[character or numeric matrix] Linear combinations of coefficients relative to which Wald test should be computed. Argument passed to `anova.lmm`. Right hand side can be specified via an attribute `"rhs"`.
`robust`	[logical] Should robust standard errors (aka sandwich estimator) be output instead of the model-based standard errors. Argument passed to `anova.lmm`.
`df`	[logical] Should the degree of freedom be computed using a Satterthwaite approximation? Argument passed to `anova.lmm`.
`ci`	[logical] Should a confidence interval be output for each hypothesis? Argument passed to `anova.lmm`.
`name.short`	[logical vector of length 2] use short names for the output coefficients: omit the name of the by variable, omit the regression variable name when the same regression variable is used in all models.
`transform.sigma, transform.k, transform.rho, transform.names`	[character] transformation used on certain type of parameters.
`trace`	[interger, >0] Show the progress of the execution of the function.

Details

Grouping variable in argument repetition: when numeric, it will be converted into a factor variable, possibly adding a leading 0 to preserve the ordering. This transformation may cause inconsistency when combining results between different lmm object. This is why the grouping variable should preferably be of type character or factor.

Examples

#### univariate regression ####
if(require(lava) && require(multcomp)){

set.seed(10)
d1 <- cbind(sim(lvm(Y~0.5*X1), 25), group = "A")
d2 <- cbind(sim(lvm(Y~0.1*X1), 100), group = "B")
d3 <- cbind(sim(lvm(Y~0.01*X1), 1000), group = "C")
d1$id <- 1:NROW(d1)
d2$id <- 1:NROW(d2)
d3$id <- 1:NROW(d3)

d <- rbind(d1,d2,d3)

e.mlmm <- mlmm(Y~X1, data = d, by = "group", effects = "X1=0")
summary(e.mlmm)
summary(e.mlmm, method = "single-step")
summary(e.mlmm, method = "single-step2")

## re-work contrast
summary(anova(e.mlmm, effects = mcp(X1 = "Dunnett")), method = "none")
## summary(mlmm(Y~X1, data = d, by = "group", effects = mcp(X1="Dunnett")))
}

#### multivariate regression ####
set.seed(10)
dL <- sampleRem(250, n.times = 3, format = "long")

e.mlmm <- mlmm(Y~X1+X2+X6, repetition = ~visit|id, data = dL,
               by = "X4", structure = "CS")
summary(e.mlmm)

e.mlmmX1 <- mlmm(Y~X1+X2+X6, repetition = ~visit|id, data = dL,
               by = "X4", effects = "X1=0", structure = "CS")
summary(e.mlmmX1)
summary(e.mlmmX1, method = "single-step")

LMMstar documentation built on Nov. 9, 2023, 1:06 a.m.