| predict.qgcompfit {qgcomp} | R Documentation |
Default prediction method for a qgcompfit object (non-survival outcomes only)
Description
get predicted values from a qgcompfit object, or make predictions
in a new set of data based on the qgcompfit object. Note that when making predictions
from an object from qgcomp.glm.boot, the predictions are made from the (conditional) g-computation
model rather than the marginal structural model. Predictions from the marginal
structural model can be obtained via msm.predict. Note
that this function accepts non-quantized exposures in "newdata" and automatically
quantizes them according to the quantile cutpoints in the original fit.
Usage
## S3 method for class 'qgcompfit'
predict(object, expnms = NULL, newdata = NULL, type = "response", ...)
Arguments
object |
"qgcompfit" object from |
expnms |
character vector of exposures of interest |
newdata |
(optional) new set of data with all predictors from "qgcompfit" object |
type |
(from predict.glm) the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities. The "terms" option returns a matrix giving the fitted values of each term in the model formula on the linear predictor scale. |
... |
arguments to predict.glm |
Examples
set.seed(50)
dat <- data.frame(y=runif(50), x1=runif(50), x2=runif(50), z=runif(50))
obj1 <- qgcomp.glm.noboot(y ~ z + x1 + x2, expnms = c('x1', 'x2'), data=dat, q=2)
obj2 <- qgcomp.glm.boot(y ~ z + x1 + x2, expnms = c('x1', 'x2'), data=dat, q=2, B=10, seed=125)
set.seed(52)
dat2 <- data.frame(y=runif(50), x1=runif(50), x2=runif(50), z=runif(50))
summary(predict(obj1, expnms = c('x1', 'x2'), newdata=dat2))
summary(predict(obj2, expnms = c('x1', 'x2'), newdata=dat2))