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Obtain Imputed Datasets

Source: R/mvn_imputation.R
impute_data.Rd

Impute missing values, assuming a multivariate normal distribution, with the posterior predictive distribution. For binary, ordinal, and mixed (a combination of discrete and continuous) data, the values are first imputed for the latent data and then converted to the original scale.

Usage

impute_data(
  Y,
  type = "continuous",
  lambda = NULL,
  mixed_type = NULL,
  iter = 1000,
  progress = TRUE
)

Arguments

Y

Matrix (or data frame) of dimensions n (observations) by p (variables).

type

Character string. Which type of data for Y ? The options include continuous, binary, ordinal, or mixed. Note that mixed can be used for data with only ordinal variables. See the note for further details.

lambda

Numeric. A regularization parameter, which defaults to p + 2. A larger value results in more shrinkage.

mixed_type

Numeric vector. An indicator of length p for which variables should be treated as ranks. (1 for rank and 0 to assume the observed marginal distribution). The default is currently to treat all integer variables as ranks when type = "mixed" and NULL otherwise. See note for further details.

iter

Number of iterations (posterior samples; defaults to 1000).

progress

Logical. Should a progress bar be included (defaults to TRUE) ?

Value

An object of class mvn_imputation:

  • imputed_datasets An array including the imputed datasets.

Details

Missing values are imputed with the approach described in Hoff (2009) . The basic idea is to impute the missing values with the respective posterior pedictive distribution, given the observed data, as the model is being estimated. Note that the default is TRUE, but this ignored when there are no missing values. If set to FALSE, and there are missing values, list-wise deletion is performed with na.omit.

References

Hoff PD (2009). A first course in Bayesian statistical methods, volume 580. Springer.

Examples

# \donttest{
# obs
n <- 5000

# n missing
n_missing <- 1000

# variables
p <- 16

# data
Y <- MASS::mvrnorm(n, rep(0, p), ptsd_cor1)

# for checking
Ymain <- Y

# all possible indices
indices <- which(matrix(0, n, p) == 0,
                 arr.ind = TRUE)

# random sample of 1000 missing values
na_indices <- indices[sample(5:nrow(indices),
                             size = n_missing,
                             replace = FALSE),]

# fill with NA
Y[na_indices] <- NA

# missing = 1
Y_miss <- ifelse(is.na(Y), 1, 0)

# true values (to check)
true <- unlist(sapply(1:p, function(x)
        Ymain[which(Y_miss[,x] == 1),x] ))

# impute
fit_missing <- impute_data(Y, progress = FALSE, iter = 250)

# impute
fit_missing <- impute_data(Y,
                           progress = TRUE,
                           iter = 250)
#> BGGM: Imputing
#> BGGM: Finished

# }