mod_plot: Plot out model values

In mosaicModel: An Interface to Statistical Modeling Independent of Model Architecture

Description

Plot out model values

Usage

mod_plot(model = NULL, formula = NULL, data = NULL, bootstrap = 0,
  nlevels = 3, at = list(), class_level = NULL, interval = c("none",
  "confidence", "prediction"), post_transform = NULL, size = 1,
  alpha = 0.8, ...)

Arguments

model	the model to display graphically. Can also be an ensemble produced with mod_ensemble()
formula	setting the y ~ x + color variables
data	optional data set from which to extract levels for explanatory variables
bootstrap	when > 1, this will generate bootstrap replications of the model and plot all of them. Use as an alternative to interval for confidence intervals.
nlevels	how many levels to display for those variables shown at discrete levels
at	named list giving specific values at which to hold the variables. You can accomplish this without forming a list by using .... See examples.
class_level	character string. If a probability for a classifier is being shown, which levels of the response variable to use in the plot. (Default: the first one.)
interval	show confidence or prediction intervals: values "none", "confidence", "prediction"
post_transform	a scalar transformation and new name for the response variable, e.g. post_transform = c(price = exp) to undo a log transformation of price.
size	numerical value for line width (default: 1)
alpha	numerical value in 0 to 1 for transparency (default: 0.8)
...	specific values for explantory variables

Examples

## Not run: 
mod1 <- lm(wage ~ age * sex + sector, data = mosaicData::CPS85)
mod_plot(mod1)
mod_plot(mod1, n = Inf, interval = "confidence") 
mod_plot(mod1, ~ sector + sex + age) # not necessarily a good ordering
mod_plot(mod1, ~ age + sex + sector, nlevels = 8) 
mod2 <- lm(log(wage) ~ age + sex + sector, data = mosaicData::CPS85)
mod_plot(mod2, post_transform = c(wage = exp), 
     interval = "confidence") # undo the log in the display
mod3 <- glm(married == "Married" ~ age + sex * sector,
            data = mosaicData::CPS85, family = "binomial")
mod_plot(mod3)
E3 <- mod_ensemble(mod3, 10)
mod_plot(E3)
mod4 <- rpart::rpart(sector ~ age + sex + married, data = mosaicData::CPS85)
mod_plot(mod4)
mod_plot(mod4, class_level = "manag")
mod5 <- randomForest::randomForest(
         sector ~ age + sex + married, data = mosaicData::CPS85)
mod_plot(mod5)
mod_plot(mod5, class_level = "manag")

## End(Not run)

mosaicModel documentation built on May 2, 2019, 7:59 a.m.