Method to fit a static model corresponding to a ddhazard fit. The method uses weights to ease the memory requirements. See get_survival_case_weights_and_data for details on weights.
coxph like formula with Surv(tstart, tstop, event) on the left hand site of ~.
data
data.frame or environment containing the outcome and co-variates.
by
interval length of the bins in which parameters are fixed.
max_T
end of the last interval interval.
...
arguments passed to glm or speedglm. If only_coef = TRUE then the arguments are passed to glm.control if glm is used.
id
vector of ids for each row of the in the design matrix.
family
"logit" or "exponential" for a static equivalent model of ddhazard.
model
TRUE if you want to save the design matrix used in glm.
weights
weights to use if e.g. a skewed sample is used.
risk_obj
a pre-computed result from a get_risk_obj. Will be used to skip some computations.
speedglm
depreciated.
only_coef
TRUE if only coefficients should be returned. This will only call the speedglm.wfit or glm.fit which will be faster.
mf
model matrix for regression. Needed when only_coef = TRUE
method_use
method to use for estimation. glm uses glm.fit, speedglm uses speedglm.wfit and parallelglm uses a parallel C++ version glm.fit which only gives the coefficients.
n_threads
number of threads to use when method_use is "parallelglm".
Value
The returned list from the glm call or just coefficients depending on the value of only_coef.
# NOT RUN {library(dynamichazard)
fit <- static_glm(
Surv(time, status == 2) ~ log(bili), pbc, id = pbc$id, max_T = 3600,
by = 50)
fit$coefficients
# }