Description Usage Arguments Details Value Author(s) References See Also Examples
‘BBPMM.row’ performs single and multiple imputation (MI) of metric scale variable vectors. For MI, parameter draws from a posterior distribution are replaced by a Bayesian Bootstrap step. Imputations are generated using Predictive Mean Matching (PMM) as described in Little (1988).
1 2 3 
misDataPat 
An object created by 
blockImp 
A scalar or vector containing the number(s) of the block(s) considered for imputation. Per default only the last block is imputed. 
M 
Number of multiple imputations. If M=1, no Bayesian Bootstrap step is carried out. 
outfile 
A character string that specifies the path and file name for the imputed data sets. If outfile=NULL (default), no data set is stored. 
manWeights 
Optional argument containing manual (nonnegative) weights for the PMM step. manWeights can either be a list containing a vector for each missingness pattern, or just a vector, if only one missingness pattern/block exists. In either case, the number of elements in the vector(s) must match the number of variables in the corresponding block. Note that the higher the weight the higher the importance of a good match for the corresponding variable's predictive means. 
stepmod 
Performs variable selection for each imputation model based on the either on Schwarz (Bayes) Information criterion (backward). Default="stepAIC". 
verbose 
The algorithm prints information on weighting matrices and imputation numbers. Default=TRUE. 
tol 
Imported argument from function 
setSeed 
Optional argument to fix the pseudorandom number generator in order to allow for reproducible results. 
... 
Further arguments passed to or from other functions. 
The simultaneous
imputation of several variables is useful for missingbydesign
patterns, such as data fusion or split questionnaire designs. The
predictive means of the imputation variables are weighted by the
inverse of the covariance matrix of the residuals from the regression
of these variables on the complete variables. The intuitive idea
behind is that distances between predictive means should be punished
more severely, if the particular variable can be explained well by the
(completely observed) imputation model variables.
Through partialization and subsequent usage of the residuals the
weight matrix is transformed into a diagonal matrix.
The calculated weights can be adjusted by manual weights. Since the weight matrix
is a Mahalanobis type of distance matrix, the weights are in the denominator and
therefore the lower the weight, the higher the influence. As this is somewhat
counterintuitive, the reciprocal of the manual weights is taken. Therefore, the higher
the manual weight the higher in the influence of the corresponding variable's
predictor on the overall distance.
To identify the missingbydesign patterns it is necessary to transform the raw data set via rowimpPrep
.
The donor/recipient ID pairlist for each imputation and identified pattern
(‘block’) is stored. In general, weightMatrix, model and pairlist
are list objects named ‘M1’ to ‘M<M>’, and each in return is a list object
named ‘block1’ to ‘block<length(blockImp)>’. model contains another list object
with lm
objects for all variables in a particular block.
Unlike BBPMM
this algorithm is not based on sequential
regression. Therefore, imputed variables are conditionally independent
given the completely observed variables (of which at least one must exist).
call 
The call of 
mis.num 
Vector containing the numbers of missing values per column. 
modelselection 
Chosen model selection method for the function call. 
seed 
Chosen seed value for the function call. 
impdata 
A list containing M completed data sets. 
weightMatrix 
A list containing weight matrices for all imputations and blocks. 
model 
A list containing the lmobjects for all imputations and blocks. 
pairlist 
A list containing the donor/recipient pairlist data frames for all imputations and blocks. 
indMatrix 
A matrix with the same dimensions as the incomplete data containing flags for missing values. 
FirstSeed 
First 
LastSeed 
Last 
ignoredvariables 
TRUE / FALSE indicator whether variables were ignored during imputation. 
Florian Meinfelder, Thorsten Schnapp [ctb]
Eddelbuettel, D. and Francois, R. (2011) Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, Vol. 40, No. 8, pp. 1–18. URL http://www.jstatsoft.org/v40/i08/.
Eddelbuettel, D. and Sanderson, C. (2014) RcppArmadillo: Accelerating R with highperformance C++ linear algebra. Computational Statistics and Data Analysis, Vol. 71, March 2014, pp. 1054–1063.
KollerMeinfelder, F. (2009) Analysis of Incomplete Survey Data – Multiple Imputation Via Bayesian Bootstrap Predictive Mean Matching, doctoral thesis.
Little, R.J.A. (1988) MissingData Adjustments in Large Surveys, Journal of Business and Economic Statistics, Vol. 6, No. 3, pp. 287296.
Raghunathan T.E. and Lepkowski, J.M. and Van Hoewyk, J. and Solenberger, P (2001) A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, Vol. 27, pp. 85–95.
Rubin DB (1981) The Bayesian Bootstrap. The Annals of Statistics, Vol. 9, pp. 130–134.
Rubin, D.B. (1987) Multiple Imputation for NonResponse in Surveys. New York: John Wiley & Sons, Inc.
Van Buuren, S. and Brand, J.P.L. and GroothuisOudshoorn, C.G.M. and Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, Vol. 76, No. 12, pp. 1049–1064.
Van Buuren, S. and GroothuisOudshoorn, K. (2011) mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, Vol. 45, No. 3, pp. 1–67. URL http://www.jstatsoft.org/v45/i03/.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. New York: Springer.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ### sample data set with nonnormal variables and a single
### missingness pattern
set.seed(1000)
n < 50
x1 < round(runif(n,0.5,3.5))
x2 < as.factor(c(rep(1,10),rep(2,25),rep(3,15)))
x3 < round(rnorm(n,0,3))
y1 < round(x10.25*(x2==2)+0.5*x3+rnorm(n,0,1))
y1 < ifelse(y1<1,1,y1)
y1 < ifelse(y1>4,5,y1)
y2 < y1+rnorm(n,0,0.5)
y3 < round(x3+rnorm(n,0,2))
data < as.data.frame(cbind(x1,x2,x3,y1,y2,y3))
misrow1 < sample(n,20)
data[misrow1, c(4:6)] < NA
### preparation step
impblock < rowimpPrep(data)
### imputation
imputed.data < BBPMM.row(impblock, M=5)

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