pencal (version 1.3.2)

getlmm: Extract model fits from step 1 of PRC-LMM

Description

Utility function to extract the estimated fixed effect parameters and variances of the random effects from an object fitted using `fit_lmms`

Usage

getlmm(object, yname, what = "betas")

Value

A vector containing the estimated fixed-effect parameters if `what = 'betas'`, the usual T table produced by `nlme` if `what = 'tTable'`, or the estimated variance-covariance matrix of the random effects and the estimated variance of the error if `what = 'variances'`

Arguments

object

the output of `fit_lmms`

yname

a character giving the name of the longitudinal variable for which you want to extract information

what

one of the following: `'betas'` for the estimates of the regression coefficients; `'tTable'` for the usual T table produced by `nlme`; `'variances'` for the estimates of the variances (and covariances) of the random effects and of the variance of the error term

Author

Mirko Signorelli

References

Signorelli, M., Spitali, P., Al-Khalili Szigyarto, C, The MARK-MD Consortium, Tsonaka, R. (2021). Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data. Statistics in Medicine, 40 (27), 6178-6196. DOI: 10.1002/sim.9178

See Also

fit_lmms

Examples

# generate example data
set.seed(1234)
p = 4 # number of longitudinal predictors
simdata = simulate_prclmm_data(n = 100, p = p, p.relev = 2, 
             seed = 123, t.values = c(0, 0.2, 0.5, 1, 1.5, 2))
 
# specify options for cluster bootstrap optimism correction
# procedure and for parallel computing 
do.bootstrap = FALSE
# IMPORTANT: set do.bootstrap = TRUE to compute the optimism correction!
n.boots = ifelse(do.bootstrap, 100, 0)
more.cores = FALSE
# IMPORTANT: set more.cores = TRUE to speed computations up!
if (!more.cores) n.cores = 2
if (more.cores) {
   # identify number of available cores on your machine
   n.cores = parallel::detectCores()
   if (is.na(n.cores)) n.cores = 2
}

# step 1 of PRC-LMM: estimate the LMMs
y.names = paste('marker', 1:p, sep = '')
step1 = fit_lmms(y.names = y.names, 
                 fixefs = ~ age, ranefs = ~ age | id, 
                 long.data = simdata$long.data, 
                 surv.data = simdata$surv.data,
                 t.from.base = t.from.base,
                 n.boots = n.boots, n.cores = n.cores)
                 
# estimated betas and variances for the 5th marker:
getlmm(step1, 'marker2', 'betas')
getlmm(step1, 'marker2', 'tTable')
getlmm(step1, 'marker2', 'variances')