summary.tmleMSM | R Documentation |
These functions are all methods for class tmleMSM
, summary.tmleMSM
objects
## S3 method for class 'tmleMSM'
summary(object, ...)
## S3 method for class 'tmleMSM'
print(x, ...)
## S3 method for class 'summary.tmleMSM'
print(x, ...)
object |
an object of class |
x |
an object of class |
... |
currently ignored. |
print.tmleMSM
prints the estimate, standard error, p-value, and 95% confidence interval only. print.summary.tmleMSM
, called indirectly by entering the command summary(result) (where result
has class tmleMSM
), outputs additional information.
estimates |
matrix of MSM parameter estimates, standard errors, pvalues, upper and lower bounds on 95% confidence intervals |
sigma |
variance-covariance matrix |
Qmodel |
working model used to obtain initial estimate of |
Qterms |
terms in the model for |
Qcoef |
coefficient of each term in model for |
gmodel |
model used to estimate treatment mechanism |
gterms |
terms in the treatment mechanism model |
gcoef |
coefficient of each term in model for treatment mechanism |
gtype |
description of estimation procedure for treatment mechanism, e.g. "SuperLearner" |
g.AVmodel |
model used to estimate h(A,V) (or h(A,T)) |
g.AVterms |
terms in the model for h(A,V) |
g.AVcoef |
coefficient of each term in model for h(A,V) |
g.AVtype |
description of estimation procedure for h(A,V) |
g.Deltamodel |
model used to estimate missingness mechanism |
g.Deltaterms |
terms in the missingness mechanism model |
g.Deltacoef |
coefficient of each term in model for missingness mechanism |
g.Deltatype |
description of estimation procedure for missingness |
psi.Qinit |
MSM parameter estimates based on initial (untargeted) estimated |
Susan Gruber
tmleMSM
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