smoothed.heavy: A function for smoothing under multivariate t response...

In lmenssp: Linear Mixed Effects Models with Non-Stationary Stochastic Processes

Description

Smooths random components of the mixed model with a stationary or non-stationary stochastic process component, under multivariate t response distribution

Usage

smoothed.heavy(formula, data, id, process, timeVar, estimate, subj.id = NULL)

Arguments

formula	a typical R formula for the fixed effects component of the model
data	a data frame from which the variables are to be extracted
id	a vector for subject identification
process	a character string, "bm" for Brownian motion, "ibm" for integrated Brownian motion, "iou" for integrated Ornstein-Uhlenbeck process, "sgp-powered-power-method" for stationary process with powered correlation function, and "sgp-matern-kappa" for stationary process with Matern correlation function
timeVar	a vector for the time variable
estimate	a vector for the maximum likelihood estimates
subj.id	a vector of IDs of the subject for whom smoothing is to be carried out

Details

For details of "process" see lmenssp

Value

Returns the results as lists for the random intercept and stochastic process

Author(s)

Ozgur Asar, Peter J. Diggle

References

Asar O, Ritchie J, Kalra P, Diggle PJ (2015) Acute kidney injury amongst chronic kidney disease patients: a case-study in statistical modelling. To be submitted.

Pinheiro JC, Liu C, Wu YN. (2001) Efficient algorithms for robust estimation in linear mixed-effects models using the multivariate t distribution. Journal of Computational and Graphical Statistics 10, 249-276.

Examples

# loading the data set and subsetting it for the first 20 patients 
# for the sake illustration of the usage of the functions
data(data.sim.ibm.heavy)
data.sim.ibm.heavy.short <- data.sim.ibm.heavy[data.sim.ibm.heavy$id <= 20, ]

# a formula to be used 
formula <- log.egfr ~ sex + bage + fu + pwl

# estimating the parameters
# tol.em is set to 10^-1 and tol.lmenssp to 10^-2 only for illustration, 
# decrease these values in your applications
fit.heavy <- lmenssp.heavy(formula = formula, data = data.sim.ibm.heavy.short, 
       id = data.sim.ibm.heavy.short$id, timeVar = data.sim.ibm.heavy.short$fu, init.em = 5, 
       maxiter.em = 1000, tol.em = 10^-1, 
       process = "ibm", silent = FALSE, dof.est = c(0.1, 10, 0.0001), tol.cd = 0.001,
       tol.lmenssp = 10^-2, silent.lmenssp = FALSE)
fit.heavy

# smoothing for the patients with ID = 1, 2, 3, 4
smo.heavy <- smoothed.heavy(formula = formula, data = data.sim.ibm.heavy.short, 
      id = data.sim.ibm.heavy.short$id, process = "ibm", timeVar = data.sim.ibm.heavy.short$fu, 
      estimate = fit.heavy$est, subj.id = c(1, 2, 3, 4))
smo.heavy 

lmenssp documentation built on May 2, 2019, 8:32 a.m.