summary.tmle | R Documentation |
These functions are all methods for class tmle
, tmle.list
, summary.tmle
, summary.tmle.list
objects
## S3 method for class 'tmle'
summary(object, ...)
## S3 method for class 'tmle.list'
summary(object, ...)
## S3 method for class 'tmle'
print(x, ...)
## S3 method for class 'tmle.list'
print(x, ...)
## S3 method for class 'summary.tmle'
print(x, ...)
## S3 method for class 'summary.tmle.list'
print(x, ...)
object |
an object of class |
x |
an object of class |
... |
currently ignored. |
print.tmle
prints the estimate, variance, p-value, and 95% confidence interval only. print.summary.tmle
, called indirectly by entering the command summary(result) (where result
has class tmle
), outputs additional information. Controlled direct effect estimates have class tmle.list
, a list of two objects of class tmle
. The first item corresponds to Z=0
, the second to Z=1
estimates |
list of parameter estimates, pvalues, and 95% confidence intervals |
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" |
gdiscreteSL |
flag indicating whether discrete SL or ensemble SL was used for treatment mechanism estimation |
g.Zmodel |
model used to estimate intermediate variable assignment mechanism |
g.Zterms |
terms in the intermediate mechanism model |
g.Zcoef |
coefficient of each term in model for intermediate mechanism |
g.Ztype |
description of estimation procedure for intermediate variable |
g.ZdiscreteSL |
flag indicating whether discrete SL or ensemble SL was used for intermediate variable estimation |
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 |
g.DeltadiscreteSL |
flag indicating whether discrete SL or ensemble SL was used for missingness estimation |
Susan Gruber
tmle
# generate data
set.seed(10)
n <- 500
W <- matrix(rnorm(n*3), ncol=3)
A <- rbinom(n,1, 1/(1+exp(-(.1*W[,1] - .1*W[,2] + .5*W[,3]))))
Y <- A + 2*W[,1] + W[,3] + W[,2]^2 + rnorm(n)
colnames(W) <- paste("W",1:3, sep="")
result <- tmle(Y,A,W, Qform="Y~A+W1", g1W=rep(.5, n))
summary(result)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.