Loess plot with density distributions for propensity scores and outcomes on top and right, respectively.

1 2 3 4 | ```
loess.plot(x, response, treatment, responseTitle = "",
treatmentTitle = "Treatment", percentPoints.treat = 0.1,
percentPoints.control = 0.01, points.treat.alpha = 0.1,
points.control.alpha = 0.1, plot.strata, plot.strata.alpha = 0.2, ...)
``` |

`x` |
vector of propensity scores. |

`response` |
the response variable. |

`treatment` |
the treatment varaible as a logical type. |

`responseTitle` |
the label to use for the y-axis (i.e. the name of the response variable) |

`treatmentTitle` |
the label to use for the treatment legend. |

`percentPoints.treat` |
the percentage of treatment points to randomly plot. |

`percentPoints.control` |
the percentage of control points to randomly plot. |

`points.treat.alpha` |
the transparency level for treatment points. |

`points.control.alpha` |
the transparency level for control points. |

`plot.strata` |
an integer value greater than 2 indicating the number of vertical lines to plot corresponding to quantiles. |

`plot.strata.alpha` |
the alpha level for the vertical lines. |

`...` |
other parameters passed to |

a ggplot2 figure

plot.mlpsa

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## Not run:
require(multilevelPSA)
require(party)
data(pisana)
data(pisa.psa.cols)
cnt = 'USA' #Can change this to USA, MEX, or CAN
pisana2 = pisana[pisana$CNT == cnt,]
pisana2$treat <- as.integer(pisana2$PUBPRIV) %% 2
lr.results <- glm(treat ~ ., data=pisana2[,c('treat',pisa.psa.cols)], family='binomial')
st = data.frame(ps=fitted(lr.results),
math=apply(pisana2[,paste('PV', 1:5, 'MATH', sep='')], 1, mean),
pubpriv=pisana2$treat)
st$treat = as.logical(st$pubpriv)
loess.plot(st$ps, response=st$math, treatment=st$treat, percentPoints.control = 0.4,
percentPoints.treat=0.4)
## End(Not run)
``` |

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