View source: R/idem_imputation.R
imImpAll  R Documentation 
Conduct imputation under benchmark assumptions or for sensitivity analysis for a given set of subjects using the model fitting results
imImpAll(
fit.rst,
data.all = NULL,
deltas = 0,
normal = TRUE,
n.imp = 5,
endponly = TRUE,
update.progress = NULL,
imputeNone = FALSE,
...,
seed = NULL
)
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 
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
List of parameters
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.
Number of imputed complete datasets
Imputation sensitivity parameters
Original dataset
Normal assumption for the imputation
STAN options
## 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.