FI_train: Imputation Training Data

Description Usage Format Author(s) Source

Description

Larger simulated dataset drawn from the same distribution as FI_test and FI_true and used to train the imputation algorithm. 5% of the values are missing. Used with TrainFastImputation.

Usage

1

Format

A data frame with 9 variables and 10000 observations.

user_id_1

Sequential user ids

bounded_below_2

Multivariate normal, transformed using exp(x)

unbounded_3

Multivariate normal

unbounded_4

Multivariate normal

bounded_above_5

Multivariate normal, transformed using -exp(x)

bounded_above_and_below_6

Multivariate normal, transformed using pnorm(x)

unbounded_7

Multivariate normal

unbounded_8

Multivariate normal

categorical_9

"A" if the first of three multivariate normal draws is greatest; "B" if the second is greatest; "C" if the third is greatest

Author(s)

Stephen R. Haptonstahl srh@haptonstahl.org

Source

All columns start as multivariate normal draws. Columns 2, 5, and 6 are transformed. Column 9 is the result of three multivariate normal columns being interpreted as one-hot encoding of a three-valued categorical variable.


FastImputation documentation built on May 1, 2019, 10:53 p.m.