missing_table

Returns a Table indicating the number and proportion of NA values for a selected
set of variables.

Easily analyze and visualize differences between samples (e.g., benchmark comparisons, nonresponse comparisons in surveys) on three levels. The comparisons can be univariate, bivariate or multivariate. On univariate level the variables of interest of a survey and a comparison survey (i.e. benchmark) are compared, by calculating one of several difference measures (e.g., relative difference in mean), and an average difference between the surveys. On bivariate level a function can calculate significant differences in correlations for the surveys. And on multivariate levels a function can calculate significant differences in model coefficients between the surveys of comparison. All of those differences can be easily plotted and outputted as a table. For more detailed information on the methods and example use see Rohr, B., Silber, H., & Felderer, B. (2024). Comparing the Accuracy of Univariate, Bivariate, and Multivariate Estimates across Probability and Nonprobability Surveys with Population Benchmarks. Sociological Methodology <doi:10.1177/00811750241280963>.

Bjoern Rohr

sampcompR

Comparing and Visualizing Differences Between Surveys

Barbara Felderer

missing_table function

<dl><dt>dfs</dt>
<dd>A character vector with names of data frames for which the
missings per variable should be displayed.</dd>
<dt>variables</dt>
<dd>A character vector of variable names for which the missings
should be displyed.</dd>
<dt>df_names</dt>
<dd>Either Null or a character vector of names, to relabel the
data frames in the table with.</dd>
<dt>varlabels</dt>
<dd>Either Null, or a character vector of variable names, to
relabel the variables in the table with.</dd></dl>

Arguments

Returns a Table indicating the number and proportion of NA values for a selected
set of variables. — missing_table

<dl>

<dt>dfs</dt>
<dd>A character vector with names of data frames for which the
missings per variable should be displayed.</dd>


<dt>variables</dt>
<dd>A character vector of variable names for which the missings
should be displyed.</dd>


<dt>df_names</dt>
<dd>Either Null or a character vector of names, to relabel the
data frames in the table with.</dd>


<dt>varlabels</dt>
<dd>Either Null, or a character vector of variable names, to
relabel the variables in the table with.</dd>

</dl>

missing_table: Returns a Table indicating the number and proportion of NA values for a selected set of variables.

Description

Usage

Value

Arguments

Examples