rbmi_ancova {junco} | R Documentation |
Analysis of Covariance
Description
Performs an analysis of covariance between two groups returning the estimated "treatment effect" (i.e. the contrast between the two treatment groups) and the least square means estimates in each group.
Usage
rbmi_ancova(
data,
vars,
visits = NULL,
weights = c("counterfactual", "equal", "proportional_em", "proportional")
)
Arguments
data |
A |
vars |
A |
visits |
An optional character vector specifying which visits to
fit the ancova model at. If |
weights |
Character, either |
Details
The function works as follows:
Select the first value from
visits
.Subset the data to only the observations that occurred on this visit.
Fit a linear model as
vars$outcome ~ vars$group + vars$covariates
.Extract the "treatment effect" & least square means for each treatment group.
Repeat points 2-3 for all other values in
visits
.
If no value for visits
is provided then it will be set to
unique(data[[vars$visit]])
.
In order to meet the formatting standards set by rbmi_analyse()
the results will be collapsed
into a single list suffixed by the visit name, e.g.:
list( var_visit_1 = list(est = ...), trt_B_visit_1 = list(est = ...), lsm_A_visit_1 = list(est = ...), lsm_B_visit_1 = list(est = ...), var_visit_2 = list(est = ...), trt_B_visit_2 = list(est = ...), lsm_A_visit_2 = list(est = ...), lsm_B_visit_2 = list(est = ...), ... )
Please note that "trt" refers to the treatment effects, and "lsm" refers to the least
square mean results. In the above example vars$group
has two factor levels A and B.
The new "var" refers to the model estimated variance of the residuals.
If you want to include interaction terms in your model this can be done
by providing them to the covariates
argument of rbmi::set_vars()
e.g. set_vars(covariates = c("sex*age"))
.
Value
a list of variance (var_*
), treatment effect (trt_*
), and
least square mean (lsm_*
) estimates for each visit, organized as
described in Details above.
Note
These functions have the rbmi_
prefix to distinguish them from the corresponding
rbmi
package functions, from which they were copied from. Additional features here
include:
Support for more than two treatment groups.
Variance estimates are returned.