impute_quality_check: Make plots for imputation result

In andyyu6227/MH.datapipe: Preprocessing/filtering mHealth data, imputing NA & check imputation quality

Description

Make plots for imputation result

Usage

impute_quality_check(
  filter_df,
  active_df,
  baseline_df,
  var_name,
  priority_miss_perc,
  n_round,
  seed,
  imp_func
)

Arguments

filter_df	filtered passive data
active_df	active data
baseline_df	baseline data
var_name	passive measurement variable name
priority_miss_perc	Percentage of observations per subject to be labeled as missing;1)value between 0 and 1: same percentage for each subject;2)NA: automatically according to missing percentage of each subject
n_round	number of times to randomly sample observations to be labeled
seed	fix seed so that simulation results are reproduceable
imp_func	the function of imputation method, available options now: #1) missForest :my_missForest; #2) moving average of 7 days: seven_moving_everage

Value

a data frame with imputation quality measurement metrics

andyyu6227/MH.datapipe documentation built on Jan. 1, 2022, 10:18 p.m.