outcome_regression {CausalModels} | R Documentation |
Outcome Regression
Description
'outcome_regression' builds a linear model using all covariates. The treatment effects are stratified
within the levels of the covariates. The model will automatically provide all discrete covariates in a contrast matrix.
To view estimated change in treatment effect from continuous variables, a list called contrasts
, needs to be given
with specific values to estimate. A vector of values can be given for any particular continuous variable.
Usage
outcome_regression(
data,
f = NA,
simple = pkg.env$simple,
family = gaussian(),
contrasts = list(),
...
)
Arguments
data |
a data frame containing the variables in the model.
This should be the same data used in |
f |
(optional) an object of class "formula" that overrides the default parameter |
simple |
a boolean indicator to build default formula with interactions. If true, interactions will be excluded. If false, interactions will be included. By default, simple is set to false. |
family |
the family to be used in the general linear model.
By default, this is set to |
contrasts |
a list of continuous covariates and values in the model to be included in the contrast matrix
(e.g. |
... |
additional arguments that may be passed to the underlying |
Value
outcome_regression
returns an object of class "outcome_regression"
The functions print
, summary
, and predict
can be used to interact with
the underlying glht
model.
An object of class "outcome_regression"
is a list containing the following:
call |
the matched call. |
formula |
the formula used in the model. |
model |
the underlying glht model. |
ATE |
a data frame containing the ATE, SE, and 95% CI of the ATE. |
ATE.summary |
a more detailed summary of the ATE estimations from glht. |
Examples
library(causaldata)
library(multcomp)
data(nhefs)
nhefs.nmv <- nhefs[which(!is.na(nhefs$wt82)), ]
nhefs.nmv$qsmk <- as.factor(nhefs.nmv$qsmk)
confounders <- c(
"sex", "race", "age", "education", "smokeintensity",
"smokeyrs", "exercise", "active", "wt71"
)
init_params(wt82_71, qsmk,
covariates = confounders,
data = nhefs.nmv
)
out.mod <- outcome_regression(nhefs.nmv, contrasts = list(
age = c(21, 55),
smokeintensity = c(5, 20, 40)
))
print(out.mod)
summary(out.mod)
head(data.frame(preds = predict(out.mod)))