| summary.lmmfit {pencal} | R Documentation |
Extract model fits from step 1 of PRC-LMM
Description
Summary function to extract the estimated fixed effect parameters and variances of the random effects from an object fitted using 'fit_lmms'
Usage
## S3 method for class 'lmmfit'
summary(object, yname, what = "betas", ...)
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 |
... |
additional arguments |
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''
Author(s)
Mirko Signorelli
References
Signorelli, M. (2024). pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors. To appear in: The R Journal. Preprint: arXiv:2309.15600
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