CoxphME | R Documentation |
Estimates a mixed-effects additive transformation model with flexible smooth parameterization for the baseline transformation (log-cumulative baseline hazard) and the inverse link set to the CDF of the standard minimum extreme value distribution (see Hothorn et al., 2018).
CoxphME(
formula,
data,
subset,
weights,
offset,
na.action = na.omit,
silent = TRUE,
resid = FALSE,
do_update = FALSE,
estinit = TRUE,
initpar = NULL,
fixed = NULL,
nofit = FALSE,
control = optim_control(),
...
)
formula |
A formula describing the model. Smooth additive terms are
defined the way as in |
data |
an optional data frame, list or environment (or object
coercible by |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of case weights to be used in the fitting
process. Should be |
offset |
this can be used to specify an _a priori_ known component to
be included in the linear predictor during fitting. This
should be |
na.action |
a function which indicates what should happen when the data
contain |
silent |
Logical. Make TMB functionality silent. |
resid |
Logical. If |
do_update |
Logical. If |
estinit |
Logical. Estimate a vector of initial values for the fixed effects parameters from a (fixed effects only) mlt model |
initpar |
Named list of initial parameter values, if |
fixed |
a named vector of fixed regression coefficients; the names need to correspond to column names of the design matrix |
nofit |
logical, if TRUE, creates the model object, but does not run the optimization |
control |
list with controls for optimization |
... |
Optional arguments to |
The model extends tram::Coxph
with random effects and
(optionally penalized) additive terms. For details on mixed-effect
transformation models, see Tamasi and Hothorn (2021).
The elements of the linear predictor are parameterized with positive
parameters (i.e. negative = FALSE
in tram
).
A CoxphME
model object.
Hothorn, Torsten, Lisa Möst, and Peter Bühlmann. "Most Likely Transformations." Scandinavian Journal of Statistics 45, no. 1 (March 2018): 110–34. <doi:10.1111/sjos.12291>
Tamasi, Balint, and Torsten Hothorn. "tramME: Mixed-Effects Transformation Models Using Template Model Builder." The R Journal 13, no. 2 (2021): 398–418. <doi:10.32614/RJ-2021-075>
library("survival")
rats$litter <- factor(rats$litter)
m <- CoxphME(Surv(time, status) ~ rx + (1 | litter), data = rats,
log_first = TRUE)
summary(m)
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