ceplot {condvis}  R Documentation 
Creates an interactive conditional expectation plot, which consists of two main parts. One part is a single plot depicting a section through a fitted model surface, or conditional expectation. The other part shows small data summaries which give the current condition, which can be altered by clicking with the mouse.
ceplot(data, model, response = NULL, sectionvars = NULL,
conditionvars = NULL, threshold = NULL, lambda = NULL,
distance = c("euclidean", "maxnorm"), type = c("default", "separate",
"shiny"), view3d = FALSE, Corder = "default", selectortype = "minimal",
conf = FALSE, probs = FALSE, col = "black", pch = NULL,
residuals = FALSE, xsplotpar = NULL, modelpar = NULL,
xcplotpar = NULL)
data 
A dataframe containing the data to plot 
model 
A model object, or list of model objects 
response 
Character name of response in 
sectionvars 
Character name of variable(s) from 
conditionvars 
Character names of conditioning variables from

threshold 
This is a threshold distance. Points further than

lambda 
A constant to multiply by number of factor mismatches in
constructing a general dissimilarity measure. If left 
distance 
A character vector describing the type of distance measure to
use, either 
type 
This specifies the type of interactive plot. 
view3d 
Logical; if 
Corder 
Character name for method of ordering conditioning variables.
See 
selectortype 
Type of condition selector plots to use. Must be

conf 
Logical; if 
probs 
Logical; if 
col 
Colour for observed data. 
pch 
Plot symbols for observed data. 
residuals 
Logical; if 
xsplotpar 
Plotting parameters for section visualisation as a list,
passed to 
modelpar 
Plotting parameters for models as a list, passed to

xcplotpar 
Plotting parameters for condition selector plots as a list,
passed to 
O'Connell M, Hurley CB and Domijan K (2017). “Conditional Visualization for Statistical Models: An Introduction to the condvis Package in R.”Journal of Statistical Software, 81(5), pp. 120. <URL:http://dx.doi.org/10.18637/jss.v081.i05>.
## Not run:
## Example 1: Multivariate regression, xs one continuous predictor
mtcars$cyl < as.factor(mtcars$cyl)
library(mgcv)
model1 < list(
quadratic = lm(mpg ~ cyl + hp + wt + I(wt^2), data = mtcars),
additive = mgcv::gam(mpg ~ cyl + hp + s(wt), data = mtcars))
conditionvars1 < list(c("cyl", "hp"))
ceplot(data = mtcars, model = model1, response = "mpg", sectionvars = "wt",
conditionvars = conditionvars1, threshold = 0.3, conf = T)
## Example 2: Binary classification, xs one categorical predictor
mtcars$cyl < as.factor(mtcars$cyl)
mtcars$am < as.factor(mtcars$am)
library(e1071)
model2 < list(
svm = svm(am ~ mpg + wt + cyl, data = mtcars, family = "binomial"),
glm = glm(am ~ mpg + wt + cyl, data = mtcars, family = "binomial"))
ceplot(data = mtcars, model = model2, sectionvars = "wt", threshold = 1,
type = "shiny")
## Example 3: Multivariate regression, xs both continuous
mtcars$cyl < as.factor(mtcars$cyl)
mtcars$gear < as.factor(mtcars$gear)
library(e1071)
model3 < list(svm(mpg ~ wt + qsec + cyl + hp + gear,
data = mtcars, family = "binomial"))
conditionvars3 < list(c("cyl","gear"), "hp")
ceplot(data = mtcars, model = model3, sectionvars = c("wt", "qsec"),
threshold = 1, conditionvars = conditionvars3)
ceplot(data = mtcars, model = model3, sectionvars = c("wt", "qsec"),
threshold = 1, type = "separate", view3d = T)
## Example 4: Multiclass classification, xs both categorical
mtcars$cyl < as.factor(mtcars$cyl)
mtcars$vs < as.factor(mtcars$vs)
mtcars$am < as.factor(mtcars$am)
mtcars$gear < as.factor(mtcars$gear)
mtcars$carb < as.factor(mtcars$carb)
library(e1071)
model4 < list(svm(carb ~ ., data = mtcars, family = "binomial"))
ceplot(data = mtcars, model = model4, sectionvars = c("cyl", "gear"),
threshold = 3)
## Example 5: Multiclass classification, xs both continuous
data(wine)
wine$Class < as.factor(wine$Class)
library(e1071)
model5 < list(svm(Class ~ ., data = wine, probability = TRUE))
ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
threshold = 3, probs = TRUE)
ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
threshold = 3, type = "separate")
ceplot(data = wine, model = model5, sectionvars = c("Hue", "Flavanoids"),
threshold = 3, type = "separate", selectortype = "pcp")
## Example 6: Multiclass classification, xs with one categorical predictor,
## and one continuous predictor.
mtcars$cyl < as.factor(mtcars$cyl)
mtcars$carb < as.factor(mtcars$carb)
library(e1071)
model6 < list(svm(cyl ~ carb + wt + hp, data = mtcars, family = "binomial"))
ceplot(data = mtcars, model = model6, threshold = 1, sectionvars = c("carb",
"wt"), conditionvars = "hp")
## End(Not run)