com_item_missingness {dataquieR}R Documentation

Summarize missingness columnwise (in variable)

Description

Item-Missingness (also referred to as item nonresponse (De Leeuw et al. 2003)) describes the missingness of single values, e.g. blanks or empty data cells in a data set. Item-Missingness occurs for example in case a respondent does not provide information for a certain question, a question is overlooked by accident, a programming failure occurs or a provided answer were missed while entering the data.

Usage

com_item_missingness(
  study_data,
  meta_data,
  resp_vars = NULL,
  label_col,
  show_causes = TRUE,
  cause_label_df,
  include_sysmiss = NULL,
  threshold_value,
  suppressWarnings = FALSE
)

Arguments

study_data

data.frame the data frame that contains the measurements

meta_data

data.frame the data frame that contains metadata attributes of study data

resp_vars

variable list the name of the measurement variables

label_col

variable attribute the name of the column in the metadata with labels of variables

show_causes

logical if TRUE, then the distribution of missing codes is shown

cause_label_df

data.frame missing code table. If missing codes have labels the respective data frame must be specified here

include_sysmiss

logical Optional, if TRUE system missingness (NAs) is evaluated in the summary plot

threshold_value

numeric from=0 to=100. a numerical value ranging from 0-100

suppressWarnings

logical warn about mixed missing and jump code lists

Value

a list with:

ALGORITHM OF THIS IMPLEMENTATION:

See Also

Online Documentation


[Package dataquieR version 1.0.5 Index]