The plots on the next pages depend on the estimated propensity scores. If you want to view the plots without developing a propensity score model, just type a '1' (numeral one, no quotes) in the formula box above, and a model will be fit using just an intercept.
The thin black lines in the stripcharts indicate the mean; in the scatterplots, the thin black lines are loess curves.
After pruning, the pruning limits you specified for continuous variables will be moved inward to the nearest sample value.
The upper subplots for each covariate include all points in the (pruned) dataset, even if those points are missing from the subplots immediately below because the propensity score is missing. This can happen if some variables have missing values and only complete cases are used to estimate the propensity score.
For information about how the absolute standardized mean differences shown in the plot above are calculated, see the documentation for the tableone package.
The dotted vertical line at 0.1 marks a degree of imbalance that many researchers consider to be unacceptable.
Visual Pruner currently displays in the SMD plot only those variables selected for viewing on the 'Prune' page. In general it is important to consider standardized mean differences for squared terms and interactions, as well as for missingness indicators. We hope to add automatic generation of these variables in the future, but in the meantime we recommend adding them to your dataset before importing so that you can select them for viewing.
If the treatment indicator is not a factor, it is converted to one before model fitting, and the name in the formula above will be changed to reflect this.
If imputation is selected on the Specify tab, Visual Pruner first imputes missing covariate values with Hmisc::impute() before fitting the propensity score model. Missingness indicator variables are then created using Hmisc::is.imputed().
See the R tab for more details.