impute_datamatrix {autovarCore} | R Documentation |
This function uses Amelia::amelia
to impute missing (NA
) values in the input data set. This function averages over multiple Amelia imputations to obtain more consistent results. The Amelia imputation model uses all variables of the supplied data_matrix
, the first lag of those variables, time, time squared, and day-part dummies.
impute_datamatrix(data_matrix, measurements_per_day, imputation_iterations)
data_matrix |
The raw, unimputed data matrix. |
measurements_per_day |
The number of measurements per day. This variable is used for adding day part dummy variables to aid the imputation. |
imputation_iterations |
The amount of times the Amelia imputation should be averaged over. |
This function returns the modified matrix.
# create a matrix with some missing values data_matrix <- matrix(nrow = 40, ncol = 3) data_matrix[, ] <- runif(ncol(data_matrix) * nrow(data_matrix), 1, nrow(data_matrix)) while (sum(is.na(data_matrix)) == 0) data_matrix[as.logical(round(runif(ncol(data_matrix) * nrow(data_matrix), -0.3, 0.7)))] <- NA colnames(data_matrix) <- c('rumination', 'happiness', 'activity') data_matrix autovarCore:::impute_datamatrix(data_matrix, 1, 100)