Description Usage Arguments See Also Examples

Plots comparison of results for estimated subgroup treatment effects

1 2 | ```
plotCompare(..., type = c("boxplot", "density", "interaction",
"conditional"), avg.line = TRUE)
``` |

`...` |
the fitted (model or validation) objects to be plotted. Must be either
objects returned from |

`type` |
type of plot. |

`avg.line` |
boolean value of whether or not to plot a line for the average
value in addition to the density (only valid for |

`fit.subgroup`

for function which fits subgroup identification models and
`validate.subgroup`

for function which creates validation results.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ```
library(personalized)
set.seed(123)
n.obs <- 100
n.vars <- 15
x <- matrix(rnorm(n.obs * n.vars, sd = 3), n.obs, n.vars)
# simulate non-randomized treatment
xbetat <- 0.5 + 0.5 * x[,1] - 0.5 * x[,4]
trt.prob <- exp(xbetat) / (1 + exp(xbetat))
trt01 <- rbinom(n.obs, 1, prob = trt.prob)
trt <- 2 * trt01 - 1
# simulate response
delta <- 2 * (0.5 + x[,2] - x[,3] - x[,11] + x[,1] * x[,12])
xbeta <- x[,1] + x[,11] - 2 * x[,12]^2 + x[,13]
xbeta <- xbeta + delta * trt
# continuous outcomes
y <- drop(xbeta) + rnorm(n.obs, sd = 2)
# create function for fitting propensity score model
prop.func <- function(x, trt)
{
# fit propensity score model
propens.model <- cv.glmnet(y = trt,
x = x, family = "binomial")
pi.x <- predict(propens.model, s = "lambda.min",
newx = x, type = "response")[,1]
pi.x
}
subgrp.model <- fit.subgroup(x = x, y = y,
trt = trt01,
propensity.func = prop.func,
loss = "sq_loss_lasso",
nfolds = 5) # option for cv.glmnet
subgrp.model.o <- fit.subgroup(x = x, y = y,
trt = trt01,
propensity.func = prop.func,
loss = "owl_logistic_flip_loss_lasso",
nfolds = 5)
plotCompare(subgrp.model, subgrp.model.o)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.