fitted.lmm: Fitted Outcome Value by a Linear Mixed Model.

In bozenne/repeated: Repeated Measurement Models for Discrete Times

Fitted Outcome Value by a Linear Mixed Model.

Description

Evaluate the expected outcome conditional on covariates or the expected missing outcome value conditional on observed outcomes and covariates based on a linear mixed model.

Usage

## S3 method for class 'lmm'
fitted(
  object,
  newdata = NULL,
  type = "mean",
  se = NULL,
  df = NULL,
  keep.data = NULL,
  format = "long",
  seed = NULL,
  simplify = TRUE,
  ...
)

Arguments

object	a lmm object.
newdata	[data.frame] the covariate values for each cluster.
type	[character] by default fitted values are output (NULL). Can also output the expected outcome (for missing outcomes) based on covariates and other outcome values from the same cluster ("impute"), the change or expected change between baseline and each follow-up ("change"), or the area under the curve of the outcome ("auc", require a numeric repetition variable).
se	[character] passed to predict.lmm to evaluate the standard error of the fitted value, expected outcome, change in expected outcome, or area under the curve.
df	[logical] should a Student's t-distribution be used to model the distribution of the predicted mean. Otherwise a normal distribution is used.
keep.data	[logical] should the dataset relative to which the predictions are evaluated be output along side the predicted values? Only possible in the long format.
format	[character] should the prediction be output in a matrix format with clusters in row and timepoints in columns ("wide"), or in a data.frame/vector with as many rows as observations ("long")
seed	[integer, >0] random number generator (RNG) state used when starting imputation. If NULL no state is set.
simplify	[logical] simplify the data format (vector instead of data.frame) and column names (no mention of the time variable) when possible.
...	additional argument passed the predict.lmm.

Details

Essentially a wrapper function of predict.lmm where the fitted value are stored in the outcome column of the dataset instead of in a separate column.

Value

When format="wide", a data.frame with as many rows as clusters. When format="long", a data.frame with as many rows as observations (keep.data==TRUE) or a vector of length the number of observations (keep.data==TRUE).

Examples

#### single arm trial ####
data(gastricbypassL, package = "LMMstar")
gastricbypassL <- gastricbypassL[order(gastricbypassL$id,gastricbypassL$visit),]
gastricbypassL$weight0 <- unlist(tapply(gastricbypassL$weight,gastricbypassL$id,
function(x){rep(x[1],length(x))}))

eUN.lmm <- lmm(glucagonAUC ~ visit + weight0, repetition = ~visit|id,
               data = gastricbypassL, df = FALSE)

## fitted mean (conditional on covariates only)
fitted(eUN.lmm)
fitted(eUN.lmm, newdata = data.frame(visit = "3", weight0 = 0))
fitted(eUN.lmm, newdata = data.frame(visit = "3", weight0 = 0),
       keep.data = TRUE)

## fitted outcome value (conditional on covariates and covariates)
fitted(eUN.lmm, type = "outcome")
gastricbypassL.O <- fitted(eUN.lmm, type = "outcome", keep.data = TRUE)

if(require(ggplot2)){
gg.outcome <- ggplot(gastricbypassL.O,
                     aes(x=time, y = glucagonAUC, color = impute, group = id))
gg.outcome <- gg.outcome + geom_point() + geom_line()## + facet_wrap(~id)
gg.outcome
}

tapply(gastricbypassL.O$glucagonAUC, gastricbypassL.O$time, mean)
effects(eUN.lmm, variable = NULL)

## fitted change value (conditional on covariates and covariates)
gastricbypassL.C <- fitted(eUN.lmm, type = "change", keep.data = TRUE)

if(require(ggplot2)){
gg.change <- ggplot(gastricbypassL.C,
                    aes(x=time, y = glucagonAUC, color = impute, group = id))
gg.change <- gg.change + geom_point() + geom_line()
gg.change
}

tapply(gastricbypassL.C$glucagonAUC, gastricbypassL.O$time, mean)
effects(eUN.lmm, type = "change", variable = NULL)

## fitted auc (conditional on covariates and covariates)
gastricbypassL.AUC <- fitted(eUN.lmm, type = "auc", keep.data = TRUE)

if(require(ggplot2)){
gg.auc <- ggplot(gastricbypassL.AUC,
                    aes(x = "auc", y = glucagonAUC, color = impute))
gg.auc <- gg.auc + geom_point()
gg.auc
}

mean(gastricbypassL.AUC$glucagonAUC)
effects(eUN.lmm, type = "auc", variable = NULL)

#### two arm trial ####
## Not run: 
if(require(nlmeU) & require(reshape2)){
data(armd.wide, package = "nlmeU")
armd.long <- melt(armd.wide,
                  measure.vars = paste0("visual",c(0,4,12,24,52)),
                  id.var = c("subject","lesion","treat.f","miss.pat"),
                  variable.name = "week",
                  value.name = "visual")

armd.long$week <- factor(armd.long$week, 
                         level = paste0("visual",c(0,4,12,24,52)),
                         labels = c(0,4,12,24,52))

eUN2.lmm <- lmm(visual ~ treat.f*week + lesion,
               repetition = ~week|subject, structure = "UN",
               data = armd.long)

## fitted outcome value (conditional on covariates and covariates)
armd.O <- fitted(eUN2.lmm, type = "outcome", keep.data = TRUE)

gg2.outcome <- ggplot(armd.O,
                     aes(x=week, y = visual, color = impute, group = subject))
gg2.outcome <- gg2.outcome + geom_point() + geom_line() + facet_wrap(~treat.f)
gg2.outcome

aggregate(visual ~ week + treat.f, FUN = mean, data = armd.O)
effects(eUN2.lmm, variable = "treat.f") ## mismatch due to adjustment on lesion

## fitted change value (conditional on covariates and covariates)
armd.C <- fitted(eUN2.lmm, type = "change", keep.data = TRUE)

gg.change <- ggplot(armd.C,
                    aes(x=week, y = visual, color = impute, group = subject))
gg.change <- gg.change + geom_point() + geom_line() + facet_wrap(~treat.f)
gg.change

coef(eUN2.lmm)
effects(eUN2.lmm, type = "change", variable = "treat.f")
effects(eUN2.lmm, type = c("change","difference"), variable = "treat.f")

## fitted auc (conditional on covariates and covariates)
armd.AUC <- fitted(eUN2.lmm, type = "auc", keep.data = TRUE)

gg.auc <- ggplot(armd.AUC, aes(x = treat.f, y = visual, color = impute))
gg.auc <- gg.auc + geom_point()
gg.auc

aggregate(visual ~ treat.f, data = armd.AUC, FUN = "mean")
effects(eUN2.lmm, type = "auc", variable = "treat.f") ## adjusted for lesion
effects(eUN2.lmm, type = c("auc","difference"), variable = "treat.f")
}
## End(Not run)

bozenne/repeated documentation built on July 16, 2025, 11:16 p.m.