# Obtain Imputed Datasets 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. ```r 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`) ? ## Returns An object of class `mvn_imputation`: * `imputed_datasets` An array including the imputed datasets. ## Details Missing values are imputed with the approach described in \insertCite hoff2009first;textualBGGM. 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`. ## Examples ```r # 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) ``` ## References \insertAllCited

impute_data function