imImpAll {idem} | R Documentation |
Impute missing data
Description
Conduct imputation under benchmark assumptions or for sensitivity analysis for a given set of subjects using the model fitting results
Usage
imImpAll(
fit.rst,
data.all = NULL,
deltas = 0,
normal = TRUE,
n.imp = 5,
endponly = TRUE,
update.progress = NULL,
imputeNone = FALSE,
...,
seed = NULL
)
Arguments
fit.rst |
A class |
data.all |
A dataframe containing subjects with missing data. The
default value is NULL, in which case the function will impute missing
data for subjects in the original dataset in the class |
deltas |
Vector of imputation sensitivity parameters |
normal |
Logical variable indicating whether normality assumption should be made for the residuals |
n.imp |
Number of complete datasets required |
endponly |
Logical variable that indicates whether clinical outcomes not
used in calculating the functional outcome are considered as missing and
should be imputed. The default is |
update.progress |
Parameter reserved for run |
imputeNone |
If |
... |
options to call STAN sampling. These options include
|
seed |
Random seed |
Value
If imputeNone
is TRUE, return a dataset with the original data for the
subset of subjects who died at the end of the study or had no missing outcomes.
Otherwise, return a class IDEMIMP
list with components
- lst.var
List of parameters
- complete
A dataset with the original data for the subset of subjects who died at the end of the study or had no missing outcomes and the
n.imp
imputed missing outcomes for subjects who need missing value imputation.- n.imp
Number of imputed complete datasets
- deltas
Imputation sensitivity parameters
- org.data
Original dataset
- normal
Normal assumption for the imputation
- stan.par
STAN options
Examples
## Not run:
rst.abc <- imData(abc, trt="TRT", surv="SURV", outcome=c("Y1","Y2"),
y0=NULL, endfml="Y2",
trt.label = c("UC+SBT", "SAT+SBT"),
cov=c("AGE"), duration=365, bounds=c(0,100));
rst.fit <- imFitModel(rst.abc);
rst.imp <- imImpAll(rst.fit, deltas=c(-0.25,0,0.25),
normal=TRUE, chains = 2, iter = 2000, warmup = 1000);
## End(Not run)