Additional options available in the jointVIP package

# Additional options available in the `jointVIP` package

## Setting up to demonstrate `jointVIP`

See the Get started with jointVIP vignette to get started on how to use `jointVIP` package. Using the same data sets, this vignette’s main purpose is to demonstrate other options that are available.

    library(jointVIP)

    # gentle reminder of how to create a new jointVIP object
    new_jointVIP = create_jointVIP(treatment = treatment,
                                   outcome = outcome,
                                   covariates = covariates,
                                   pilot_df = pilot_df,
                                   analysis_df = analysis_df)

    # gentle reminder of how to create a new post_jointVIP object
    post_optmatch_jointVIP = create_post_jointVIP(new_jointVIP,
                                                 post_analysis_df = optmatch_df)

## Demonstration for additional options in `summary()` and `print()`

    # # simplest usage
    # summary(new_jointVIP)

    summary(new_jointVIP,
            smd = 'pooled',
            use_abs = FALSE,
            bias_tol = 0.005)
    #> Max bias is 0.093
    #> Min bias is -0.003
    #> 4 variables are above the desired 0.005 absolute bias tolerance
    #> 8 variables can be plotted

    print(new_jointVIP,
          smd = 'pooled',
          use_abs = FALSE,
          bias_tol = 0.005)
    #>           bias
    #> log_re75 0.093
    #> log_re74 0.038
    #> marr     0.007
    #> nodegree 0.005

    # not run
    # get_measures(new_jointVIP, smd = 'cross-sample')

The `summary()` and `print()` functions have the same additional parameters and uses rounded numbers to the third decimal place.

- The `smd` parameter allows only `pooled` or `cross-sample` options. The cross-sample is based on the analysis sample numerator and pilot sample denominator (equivalent to standardized version of the one-sample omitted variable bias). The cross-sample version is the default. The pooled version of the standardized mean difference (standard) can be specified. The `pooled` option is the standard option used in balance tables and Love plots that uses both treated and control variances from the analysis data set to construct the SMD. Note this `pooled` option applies the same formula to both binary and continuous variables.

- The `use_abs` parameter takes in either TRUE or FALSE, stating if set TRUE (default), the absolute measures are used. Otherwise, signed measured are used if set to be FALSE.

- The `bias_tol` parameter is to set the absolute bias tolerance that one wishes to examine at a glance. The default is 0.01.

Under the hood, `get_measures()` function is used to calculate. If the researcher wishes to save the measures calculated, perhaps `get_measures()` would be used; example is shown above. Only signed measures are presented as outputs for that function.

## Demonstration for additional options in `plot()`

    # # simplest usage
    # plot(new_jointVIP)

    plot(new_jointVIP,
         smd = 'pooled',
         use_abs = FALSE,
         plot_title = 'Signed version of the jointVIP with pooled SMD')

    plot(new_jointVIP,
         bias_curve_cutoffs = c(0.005, 0.05, 0.10),
         text_size = 5,
         label_cut_std_md = 0.1,
         max.overlaps = 15,
         plot_title = 'Increased text size and bias curve specifications',
         expanded_y_curvelab = 0.002
         #label_cut_outcome_cor = 0.2,
         #label_cut_bias = 0.1
         )

    plot(new_jointVIP,
         bias_curves = FALSE,
         add_var_labs = FALSE,
         plot_title = 'No bias curves or variable labels'
         )

There are many parameters for the `plot()` option. The `smd` and `use_abs` options functions the same as above. The other main parameter input is `plot_title`, which allows users to specify the title of the plot. Additional parameters not listed as a main parameter is explained and example usage is shown above.

- `bias_curve_cutoffs`: draws bias curves by the specifications. This is only used when `smd` is specified as `cross-sample`.
- `text_size`: text size of the variable labels can be increased.
- `max.overlaps`: maximum overlap of the variable labels.
- `label_cut_std_md`: standardized mean difference label cutoff, an example would be, if you wish to label all variables with standardized mean difference above 0.1.
- `label_cut_outcome_cor`: outcome correlation label cutoff, an example would be, if you wish to label all variables with outcome correlation above 0.2.
- `label_cut_bias`: bias label cutoff, an example would be, if you wish to label all variables with bias difference above 0.1.
- `bias_curves`: TRUE (default) draws the omitted variable bias curves, FALSE suppresses the bias curves. If `bias_curve_cutoffs` also specified, `bias_curves` takes priority. This is only used when `smd` is specified as `cross-sample`.
- `add_var_labs`: TRUE (default) adds variable labels. This suppresses all `label_cut` inputs if specified FALSE.
- `expanded_y_curvelab`: if one wishes to expand the y-axis, the bias curve labels don’t automatically get updated. So, this allows the fine-tuning of the bias-curve labels. Typically this is used under the hood for bootstrap version of the plot. However, user can specify this if they wish. This is only used when `smd` is specified as `cross-sample`.

# Post-analysis parameters examples

The same variables are specified in the Get started with jointVIP vignette; here we choose a matching example to demonstrate the additional parameters.

## Demonstration for additional options in `summary()` and `print()`

    # get_post_measures(post_optmatch_jointVIP, smd = 'cross-sample')

    summary(post_optmatch_jointVIP,
            use_abs = FALSE,
            bias_tol = 0.01,
            post_bias_tol = 0.001)
    #> Max bias is 0.113
    #> Min bias is -0.003
    #> 2 variables are above the desired 0.01 absolute bias tolerance
    #> 8 variables can be plotted
    #>
    #> Max absolute post-bias is 0.005
    #> Post-measure has 6 variable(s) above the desired 0.001 absolute bias tolerance

    print(post_optmatch_jointVIP,
          bias_tol = 0.001)
    #>           bias post_bias
    #> log_re75 0.113     0.005
    #> log_re74 0.045     0.003
    #> marr     0.005     0.003
    #> nodegree 0.005     0.004
    #> black    0.003     0.002
    #> age      0.002     0.003
    #> educ     0.001     0.001
    #> hisp     0.001     0.001

    plot(post_optmatch_jointVIP,
         plot_title = "Post-match jointVIP using rcbalance matching",
         smd = 'cross-sample',
         use_abs = FALSE,
         add_post_labs = TRUE,
         post_label_cut_bias = 0.001)

All of the options from above can be used; below will only address additional parameters or function outputs.

- `get_post_measures()` takes in a post_jointVIP object and `smd` specifications for measures from the post-matched results.
- `summary()` function adds text output of post-match results in addition to pre-match results. The `post_bias_tol` specifically is a `summary()` parameter that outputs post_bias tolerance for text comparison rounded to the fourth decimal place.
- `print()` function adds another column to post-match bias.
- `plot()` function includes two new parameters:
  - `add_post_labs` TRUE (default) shows the variable labels post-matching/weighting; FALSE suppresses it.
  - `post_label_cut_bias` numeric number for variable labels; only used when `add_post_labs` is TRUE.