`ps.cont` calculates propensity scores using gradient boosted regression and provides diagnostics of the resulting propensity scores.
ps.cont(
formula,
data,
n.trees = 10000,
interaction.depth = 3,
shrinkage = 0.01,
bag.fraction = 1,
sampw = NULL,
print.level = 2,
verbose = FALSE,
stop.method = "wcor",
treat.as.cont = FALSE,
...
)An object of class [formula]: a symbolic description of the propensity score model to be fit with the treatment variable on the left side of the formula and the potential confounding variables on the right side.
A dataset that includes the treatment as well as the potential confounding variables.
Number of gbm iterations passed on to [gbm]. Default: 10000.
A positive integer denoting the tree depth used in gradient boosting. Default: 3.
A numeric value between 0 and 1 denoting the learning rate. See [gbm] for more details. Default: 0.01.
A numeric value between 0 and 1 denoting the fraction of the observations randomly selected in each iteration of the gradient boosting algorithm to propose the next tree. See [gbm] for more details. Default: 1.0.
Optional sampling weights.
The amount of detail to print to the screen. Default: 2.
If `TRUE`, lots of information will be printed to monitor the the progress of the fitting. Default: `FALSE`.
A method or methods of measuring and summarizing balance across pretreatment variables. Current options are `wcor`, the weighted Pearson correlation, summarized by using the mean across the pretreatment variables. Default: `wcor`.
Used as a check on whether the exposure has greater than five levels. If it does not and treat.as.cont=FALSE, an error will be produced. Default: FALSE
Additional arguments that are passed to ps function.
Returns an object of class `ps.cont`, a list containing
* `gbm.obj` The returned [gbm] object.
* `treat` The treatment variable.
* `desc` A list containing balance tables for each method selected in `stop.methods`. Includes a component for the unweighted analysis names “unw”. Each `desc` component includes a list with the following components
- `ess` The effective sample size.
- `n` The number of subjects.
- `max.wcor` The largest weighted correlation across the covariates.
- `mean.wcor` The average weighted correlation across the covariates.
- `rms.wcor` The root mean square of the absolute weighted correlations across the covariates.
- `bal.tab` a (potentially large) table summarizing the quality of the weights for balancing the distribution of the pretreatment covariates. This table is best extracted using the [bal.table] method. See the help for [bal.table] for details.
- `n.trees` The estimated optimal number of [gbm] iterations to optimize the loss function.
* `ps.den` Denominator values for the propensity score weights.
* `ps.num` Numerator values for the propensity score weights.
* `w` The propensity score weights. If sampling weights are given then these are incorporated into these weights.
* `datestamp` Records the date of the analysis.
* `parameters` Saves the `ps.cont` call.
* `alerts` Text containing any warnings accumulated during the estimation.
* `iters` A sequence of iterations used in the GBM fits used by `plot` function.
* `balance` The balance measures for the pretreatment covariates used in plotting.
* `sampw` The sampling weights as specified in the `sampw` argument.
* `preds` Predicted values based on the propensity score model.
* `covariates` Data frame containing the covariates used in the propensity score model.
* `n.trees` Maximum number of trees considered in GBM fit.
* `data` Data as specified in the `data` argument.
Zhu, Y., Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for estimating generalized propensity scores with continuous treatments. *Journal of Causal Inference*, 3(1), 25-40. doi:10.1515/jci-2014-0022
[gbm], [plot.ps.cont], [bal.table], [summary.ps.cont]
# NOT RUN {
# }
# NOT RUN {
test.mod <- ps.cont(tss_0 ~ sfs8p_0 + sati_0 + sp_sm_0
+ recov_0 + subsgrps_n + treat, data=dat
# }
# NOT RUN {
# }
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