`jointVIP`

package`jointVIP`

See the Get started with jointVIP vignette to get started on how to
use `jointVIP`

package. Using the same data sets, this
vignetteâ€™s main purpose is to demonstrate other options that are
available.

```
library(jointVIP)
# gentle reminder of how to create a new jointVIP object
= create_jointVIP(treatment = treatment,
new_jointVIP outcome = outcome,
covariates = covariates,
pilot_df = pilot_df,
analysis_df = analysis_df)
# gentle reminder of how to create a new post_jointVIP object
= create_post_jointVIP(new_jointVIP,
post_optmatch_jointVIP post_analysis_df = optmatch_df)
```

`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.

`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 donâ€™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`

.

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

`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.