Description Usage Arguments Value References See Also Examples

'ps.cont' calculates propensity scores using gradient boosted regression and provides diagnostics of the resulting propensity scores.

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`formula` |
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. |

`data` |
A dataset that includes the treatment as well as the potential confounding variables. |

`n.trees` |
Number of gbm iterations passed on to [gbm]. Default: 10000. |

`interaction.depth` |
A positive integer denoting the tree depth used in gradient boosting. Default: 3. |

`shrinkage` |
A numeric value between 0 and 1 denoting the learning rate. See [gbm] for more details. Default: 0.01. |

`bag.fraction` |
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. |

`sampw` |
Optional sampling weights. |

`print.level` |
The amount of detail to print to the screen. Default: 2. |

`verbose` |
If 'TRUE', lots of information will be printed to monitor the the progress of the fitting. Default: 'FALSE'. |

`stop.method` |
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'. |

`treat.as.cont` |
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]

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