Description Usage Arguments Details Value References Examples
isr
performs imputation of missing values based on an optionally
specified model. Missingness is assumed to be missing at random (MAR).
1  isr(X, M, Xinit, mi = 1, burnIn = 100, thinning = 20, intercept = T)

X 
A matrix of points to be imputed or used for covariates by isr.

M 
A boolean valued optional matrix specifying the factorized pdf of the joint multivariate normal distribution of the variables requiring imputation.
A description of the factorized pdf is provided in the details.
The column names of 
Xinit 
An optional matrix with the same dimensions of 
mi 
A scalar indicating the number of imputations to return 
burnIn 
A scalar indicating the number of iterations to burn in before returning imputations. Note, that burnIn is the total number of iterations, no thinning is performed until multiple imputation generation starts. 
thinning 
A scalar that represents the amount of thinning for the MCMC routine. A value of one implies no thinning. 
intercept 
A logical value identifying if the imputation model should have an intercept. 
The ISR algorithm performs Bayesian multivariate normal imputation. This imputation follows two steps, an imputation step and a prediction step. In the imputation step, the missing values are imputed from a NormalInverseWishart model with noninformative priors. In the prediction step, the parameters are estimated using both the observed and imputed values.
Imputation of parameters are done through the conditional factoring of the joint pdf.
A conditional factoring is an expansion of the joint pdf of all
the dependent variables in X
. e.g. Pr(XZ) = Pr(X1,X2,X3Z) = Pr(X1,Z) Pr(X2X1,Z) Pr(X3X1,X2,Z),
where the right hand side is the fully conditional specification for the dependent variables X1X3 and independent variable Z.
This function returns a list with two elements: param
a three dimensional array
of parameter estimates of the factored pdf. The last dimension is an index for the multiple imputations.
imputed
a three dimensional array of X
with imputed values, the last dimension is an
index for the multiple imputations.
Robbins, M. W., & White, T. K. (2011). Farm commodity payments and imputation in the Agricultural Resource Management Survey. American journal of agricultural economics, DOI: 10.1093/ajae/aaq166.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  # simulation parameters
set.seed(100)
n < 30
p < 5
missing < 10
# generate a covar matrix
covarMatrix < rWishart(1,p+1,diag(p))[,,1]
# simulation of variables under the variable relationships
U < chol(covarMatrix)
X < matrix(rnorm(n*p), nrow=n) %*% U
# make some data missing
X[sample(1:(n*p),size=missing)] < NA
# specify relationships
fitMatrix < matrix( c(
# Covar2 CoVar1 Var1 Var2 Var3
# 1. Var1
TRUE, TRUE, FALSE, FALSE, FALSE,
# 2. Var2
TRUE, TRUE, FALSE, FALSE, FALSE,
# 3. Var3
TRUE, TRUE, TRUE, TRUE, FALSE
),nrow=3,byrow=TRUE)
covarList < c('Covar2', 'CoVar1', 'Var1', 'Var2','Var3')
# setup names
colnames(fitMatrix) < covarList
rownames(fitMatrix) < covarList[1:2]
colnames(X) < covarList
XImputed < isr(X,fitMatrix)

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