LMMstar (version 1.1.0)

fitted.lmm: Predicted Mean Value For Linear Mixed Model.

Description

Evaluate the expected mean conditional to covariates or the expected outcome values when missing conditional to observed outcome and covariates. Similar to predict.lmm where the values to condition on are, by default, taking from the dataset used to fit the Linear Mixed Model.

Usage

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

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).

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.

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 ####
if (FALSE) {
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")
}}