See the Get started with jointVIP vignette to get started on how to
jointVIP package. Using the same data sets, this
vignette’s main purpose is to demonstrate other options that are
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)
# # 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')
print() functions have
the same additional parameters and uses rounded numbers to the third
smd parameter allows only
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
option applies the same formula to both binary and continuous
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.
bias_tol parameter is to set the absolute bias
tolerance that one wishes to examine at a glance. The default is
Under the hood,
get_measures() function is used to
calculate. If the researcher wishes to save the measures calculated,
get_measures() would be used; example is shown
above. Only signed measures are presented as outputs for that
# # 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
use_abs options functions the same as
above. The other main parameter input is
allows users to specify the title of the plot. Additional parameters not
listed as a main parameter is explained and example usage is shown
bias_curve_cutoffs: draws bias curves by the specifications. This is only used when
smdis specified as
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_curvestakes priority. This is only used when
smdis specified as
add_var_labs: TRUE (default) adds variable labels. This suppresses all
label_cutinputs 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
smdis specified as
The same variables are specified in the Get started with jointVIP vignette; here we choose a matching example to demonstrate the additional parameters.
# 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
smdspecifications 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_tolspecifically 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_labsTRUE (default) shows the variable labels post-matching/weighting; FALSE suppresses it.
post_label_cut_biasnumeric number for variable labels; only used when