predictions following the fit of a merlin model
# S3 method for merlin
predict(
object,
stat = "eta",
type = "fixedonly",
predmodel = 1,
causes = NULL,
at = NULL,
contrast = NULL,
...
)merlin model object
specifies which prediction, which can be one of:
eta the expected value of the complex predictor
mu the expected value of the response variable
hazard the hazard function
chazard the cumulative hazard function
logchazard the log cumulative hazard function
survival the survival function
cif the cumulative incidence function
rmst calculates the restricted mean survival time, which is the integral of the
survival function within the interval (0,t], where t is the time at which predictions are made.
If multiple survival models have been specified in your merlin model, then it will assume all
of them are cause-specific competing risks models, and include them in the calculation. If
this is not the case, you can override which models are included by using the causes
option. rmst = t - totaltimelost.
timelost calculates the time lost due to a particular event occurring, within
the interval (0,t]. In a single event survival model, this is the integral of the cif between
(0,t]. If multiple survival models are specified in the merlin model then by default all are
assumed to be cause-specific event time models contributing to the calculation. This can be
overridden using the causes option.
totaltimelost total time lost due to all competing events, within (0,t]. If multiple
survival models are specified in the merlin model then by default all are assumed to
be cause-specific event time models contributing to the calculation. This can be overridden
using the causes option. totaltimelost is the sum of the timelost due to
all causes.
cifdifference calculates the difference in cif predictions between values
of a covariate specified using the contrast option.
hdifference calculates the difference in hazard predictions between values
of a covariate specified using the contrast option.
rmstdifference calculates the difference in rmst predictions between values
of a covariate specified using the contrast option.
mudifference calculates the difference in mu predictions between values
of a covariate specified using the contrast option.
etadifference calculates the difference in eta predictions between values
of a covariate specified using the contrast option.
the type of prediction, either:
fixedonly prediction calculated based only on the fixed effects; the default.
marginal prediction calculated marginally with respect to the latent variables. the
stat is calculated by integrating the prediction function with respect to all the latent
variables over their entire support.
specifies which model to obtain predictions from; default is predmodel=1
is for use when calculating predictions from a competing risks merlin model.
By default, cif, rmst, timelost and totaltimelost assume that all
survival models included in the merlin model are cause-specific hazard models contributing to
the calculation. If this is not the case, then you can specify which models (indexed using
the order they appear in your merlin model by using the causes option, e.g.
causes=c(1,2).
specify covariate values for prediction. Fixed values of covariates should be specified in a list e.g. at = c("trt" = 1, "age" = 50).
specifies the values of a covariate to be used when comparing statistics,
such as when using the cifdifference option to compare cumulative incidence functions,
e.g. contrast = c("trt" = 0, "trt" = 1).
other options
Crowther MJ. Extended multivariate generalised linear and non-linear mixed effects models. https://arxiv.org/abs/1710.02223
Crowther MJ. merlin - a unified framework for data analysis and methods development in Stata. https://arxiv.org/abs/1806.01615
Martin EC, Gasparini A, Crowther MJ. merlin - an R package for mixed effects regression of linear, non-linear and user-defined models.
# NOT RUN {
library(merlin)
data(pbc.merlin, package = "merlin")
# Linear fixed-effects model
mod <-merlin(model = list(logb ~ year),
family = "gaussian",
data = pbc.merlin)
predict(mod,stat="eta",type="fixedonly")
# }
Run the code above in your browser using DataLab