Newton-Raphson algorithm is used to solve the estimating equation bar S_n (delta) = 0
twoarmglmcount.dr(
y,
x.cate,
time,
trt,
ps,
f1.predictor,
f0.predictor,
error.maxNR = 0.001,
max.iterNR = 150,
tune = c(0.5, 2)
)
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
Observed outcome; vector of size n
Matrix of p.cate
baseline covariates; dimension n
by p.cate
Log-transformed person-years of follow-up; vector of size n
Treatment received; vector of size n
units with treatment coded as 0/1
Estimated propensity scores for all observations; vector of size n
Initial predictions of the outcome (expected number of relapses for one unit of exposure time)
conditioned on the covariates x
for treatment group trt = 1; mu_1(x)
, step 1 in the two regression; vector of size n
Initial predictions of the outcome (expected number of relapses for one unit of exposure time)
conditioned on the covariates x
for treatment group trt = 0; mu_0(x)
, step 1 in the two regression; vector of size n
A numerical value > 0 indicating the minimum value of the mean absolute
error in Newton Raphson algorithm. Used only if score.method = 'contrastReg'
.
Default is 0.001
.
A positive integer indicating the maximum number of iterations in the
Newton Raphson algorithm. Used only if score.method = 'contrastReg'
.
Default is 150
.
A vector of 2 numerical values > 0 specifying tuning parameters for the
Newton Raphson algorithm. tune[1]
is the step size, tune[2]
specifies a
quantity to be added to diagonal of the slope matrix to prevent singularity.
Used only if score.method = 'contrastReg'
. Default is c(0.5, 2)
.