estimate.lmm: Delta Method for Mixed Models

In LMMstar: Repeated Measurement Models for Discrete Times

Delta Method for Mixed Models

Description

Perform a first order delta method

Usage

## S3 method for class 'lmm'
estimate(
  x,
  f,
  df = !is.null(x$df),
  robust = FALSE,
  type.information = NULL,
  level = 0.95,
  method.numDeriv = NULL,
  average = FALSE,
  transform.sigma = NULL,
  transform.k = NULL,
  transform.rho = NULL,
  ...
)

Arguments

x	a lmm object.
f	[function] function of the model coefficient computing the parameter(s) of interest. Can accept extra-arguments.
df	[logical] Should degree of freedom, computed using Satterthwaite approximation, for the parameter of interest be output.
robust	[logical] Should robust standard errors (aka sandwich estimator) be output instead of the model-based standard errors.
type.information	[character] Should the expected information be used (i.e. minus the expected second derivative) or the observed inforamtion (i.e. minus the second derivative).
level	[numeric,0-1] the confidence level of the confidence intervals.
method.numDeriv	[character] method used to approximate the gradient: either "simple" or "Richardson". Passed to numDeriv::jacobian.
average	[logical] is the estimand the average output of argument f? Otherwise consider each individual output of argument f.
transform.sigma	[character] Transformation used on the variance coefficient for the reference level. One of "none", "log", "square", "logsquare" - see details.
transform.k	[character] Transformation used on the variance coefficients relative to the other levels. One of "none", "log", "square", "logsquare", "sd", "logsd", "var", "logvar" - see details.
transform.rho	[character] Transformation used on the correlation coefficients. One of "none", "atanh", "cov" - see details.
...	extra arguments passed to f.

Examples

if(require(lava) && require(nlme)){

#### Random effect ####
set.seed(10)
dL <- sampleRem(1e2, n.times = 3, format = "long")
e.lmm1 <- lmm(Y ~ X1+X2+X3 + (1|id), repetition = ~visit|id, data = dL)
nlme::ranef(e.lmm1)
e.ranef <- estimate(e.lmm1, f  = function(p){nlme::ranef(e.lmm1, p = p)$estimate})
e.ranef

if(require(ggplot2)){
df.gg <- cbind(index = 1:NROW(e.ranef), e.ranef)
gg.ranef <- ggplot(df.gg, aes(x = index, y=estimate, ymin=lower, ymax = upper))
gg.ranef + geom_point() + geom_errorbar() + ylab("estimated random effect") + xlab("id")
}

#### ANCOVA via mixed model ####
set.seed(10)
d <- sampleRem(1e2, n.time = 2)
e.ANCOVA1 <- lm(Y2~Y1+X1, data = d)

if(require(reshape2)){
   dL2 <- melt(d, id.vars = c("id","Y1","X1"),  measure.vars = c("Y1","Y2"))
   e.lmm <- lmm(value ~ variable + variable:X1, data = dL2, repetition = ~variable|id)

   e.delta <- estimate(e.lmm, function(p){
       c(Y1 = p["rho(Y1,Y2)"]*p["k.Y2"],
         X1 = p["variableY2:X1"]-p["k.Y2"]*p["rho(Y1,Y2)"]*p["variableY1:X1"])
})
   ## same estimate and similar standard errors. 
   e.delta
   summary(e.ANCOVA1)$coef
   ## Degrees of freedom are a bit off though
}

}

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