View source: R/propensity_scores.R

propensity_scores | R Documentation |

'propensity_scores' builds a logistic regression with the target as the treatment variable and the covariates as the independent variables.

propensity_scores( data, f = NA, simple = pkg.env$simple, family = binomial(), ... )

`data` |
a data frame containing the variables in the model.
This should be the same data used in |

`f` |
(optional) an object of class "formula" that overrides the default parameter |

`simple` |
a boolean indicator to build default formula with interactions. If true, interactions will be excluded. If false, interactions will be included. By default, simple is set to false. |

`family` |
the family to be used in the general linear model.
By default, this is set to |

`...` |
additional arguments that may be passed to the underlying |

`propensity_scores`

returns an object of `class "propensity_scores"`

The functions `print`

, `summary`

, and `predict`

can be used to interact with
the underlying `glm`

model.

An object of class `"propensity_scores"`

is a list containing the following:

`call` |
the matched call. |

`formula` |
the formula used in the model. |

`model` |
the underlying glm model. |

`p.scores` |
the estimated propensity scores. |

library(causaldata) data(nhefs) nhefs.nmv <- nhefs[which(!is.na(nhefs$wt82)), ] nhefs.nmv$qsmk <- as.factor(nhefs.nmv$qsmk) confounders <- c( "sex", "race", "age", "education", "smokeintensity", "smokeyrs", "exercise", "active", "wt71" ) init_params(wt82_71, qsmk, covariates = confounders, data = nhefs.nmv ) p.score <- propensity_scores(nhefs.nmv) p.score

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