lme_model: Fits a Linear Mixed Effects Model

In cccrm: Concordance Correlation Coefficient for Repeated (and Non-Repeated) Measures

Fits a Linear Mixed Effects Model

Description

Fits a Linear Mixed Effects Model

Usage

lme_model(
  dataset,
  ry,
  rind,
  rtime = NULL,
  rmet = NULL,
  vecD = NULL,
  covar = NULL,
  rho = 0,
  int = FALSE,
  cl = 0.95,
  control.lme = list(),
  apVar = TRUE,
  ...
)

Arguments

dataset	an object of class data.frame.
ry	Character string. Name of the outcome in the data set.
rind	Character string. Name of the subject variable in the data set.
rtime	Character string. Name of the time variable in the data set.
rmet	Character string. Name of the method variable in the data set.
vecD	Vector of weights. The length of the vector must be the same as the number of repeated measures.
covar	Character vector. Name of covariates to include in the linear mixed model as fixed effects.
rho	Within subject correlation structure. A value of 0 (default option) stands for compound symmetry and 1 is used for autoregressive of order 1 structure.
int	Boolean indicating if the subject-method interaction has to be included in the model.
cl	Confidence level.
control.lme	A list of control values for the estimation algorithm used in lme function. For further details see lme help.
apVar	Logical. Should the asymptotic variance-covariance matrix of the variance components be estimated in the linear mixed model? (Defaults to TRUE).
...	To pass further arguments.

Value

an object of class lme.

Examples

# Reliability ICC
set.seed(2024)
df <- ccc_sim_data(b = NULL, g = NULL, mu = -0.25, sa = 1.5, se = 1)
mod1 <- lme_model(df,"y","id")
mod1

#Non-longitudinal Methods comparison data
set.seed(2024)
df2 <- ccc_sim_data(n=50,b = c(0,1), mu = -0.25, sa = 1.5, se = 1, nrep=2)
mod2 <- lme_model(df2,"y","id",rmet="met")
mod2

# Longitudinal Methods comparison data
set.seed(2024)
df3 <- ccc_sim_data(n=50, b = c(0,1), g=c(0,0.25,0.5), mu = -0.25, sa = 1.5, 
                    sab=0.25,sag=0.5,bg=c(0,0.5,0.75,0,1,1),se = 1, nrep=2)

mod3 <- lme_model(df3,"y","id","times","met",control.lme=nlme::lmeControl(opt = 'optim'))
mod3

cccrm documentation built on Oct. 19, 2024, 9:06 a.m.