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 Normal-Inverse-Wishart model with non-informative 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(X|Z) = Pr(X1,X2,X3|Z) = Pr(X1,Z) Pr(X2|X1,Z) Pr(X3|X1,X2,Z),
where the right hand side is the fully conditional specification for the dependent variables X1-X3 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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