Description Usage Arguments Value Author(s) References See Also Examples
Calculates predictions from generalized linear models when multiple imputations are used to account for missing values in predictor data.
1 2 
formula 
A formula object providing a symbolic description of the prediction model to be fitted. 
family 
Specification of an appropriate error distribution and link function. 
data 
A data.frame containing calibration data on 
newdata 
A data.frame containing the predictors for observations to be
predicted on 
nimp 
Number of imputations used in the prediction of each observation. 
folds 
Number of foldpartitions defined within 
method 
Imputation combination method. This defaults to

mice.options 
Optional list containing arguments to be supplied to 
A list consisting of 3 components, of which the first is the Call and the last two are matrices of predictions as follows.
pred
Matrix
of predictions on the scale of the response variable of dimension m
by nimp
.
linpred
Matrix of predictions on the scale
of the linear predictor of dimension m
by nimp
.
Bart J A Mertens, b.mertens@lumc.nl
https://arxiv.org/abs/1810.05099
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # Generate a copy of the cll data and construct binary outcome from survival information
cll_bin<cll
cll_bin$srv5y_s[cll_bin$srv5y>12] < 0 # Apply administrative censorship at t=12 months
cll_bin$srv5y[cll_bin$srv5y>12] < 12
cll_bin$Status[cll_bin$srv5y_s==1]< 1 # Define the new binary "Status" outcome variable
cll_bin$Status[cll_bin$srv5y_s==0] < 0 # As numeric > 1:Dead, 0:Alive
cll_bin$Censor < NULL # Remove survival outcomes
cll_bin$srv5y < NULL
cll_bin$srv5y_s < NULL
# Predict observations 501 to 504 using the first 100 records to calibrate predictors
# Remove the identification variable before prediction calibration and imputation.
# Remove outcome for new observations
# Apply predictionaveraging using 5 imputations, set mice option maxit=5.
# Note these settings are only for illustration and should be set to higher values for
# practical use, particularly for nimp.
output<mipred(Status ~ age10+cyto, family=binomial, data=cll_bin[1:100,1],
newdata=cll_bin[501:504,c(1,10)], nimp=5, mice.options=list(maxit=5))

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