twoarmglmcount.dr {precmed} | R Documentation |
Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence information
Description
Newton-Raphson algorithm is used to solve the estimating equation bar S_n (delta) = 0
Usage
twoarmglmcount.dr(
y,
x.cate,
time,
trt,
ps,
f1.predictor,
f0.predictor,
error.maxNR = 0.001,
max.iterNR = 150,
tune = c(0.5, 2)
)
Arguments
y |
Observed outcome; vector of size |
x.cate |
Matrix of |
time |
Log-transformed person-years of follow-up; vector of size |
trt |
Treatment received; vector of size |
ps |
Estimated propensity scores for all observations; vector of size |
f1.predictor |
Initial predictions of the outcome (expected number of relapses for one unit of exposure time)
conditioned on the covariates |
f0.predictor |
Initial predictions of the outcome (expected number of relapses for one unit of exposure time)
conditioned on the covariates |
error.maxNR |
A numerical value > 0 indicating the minimum value of the mean absolute
error in Newton Raphson algorithm. Used only if |
max.iterNR |
A positive integer indicating the maximum number of iterations in the
Newton Raphson algorithm. Used only if |
tune |
A vector of 2 numerical values > 0 specifying tuning parameters for the
Newton Raphson algorithm. |
Value
coef: Doubly robust estimators of the regression coefficients delta_0
; vector of size p
+ 1 (intercept included)
vcov: Variance-covariance matrix of the estimated coefficient delta_0
; matrix of size p
+ 1 by p
+ 1
converge: Indicator that the Newton Raphson algorithm converged for delta_0
; boolean