Description Usage Arguments References See Also Examples
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.
1 2 3 4 5 6 7 | 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. 1-20. <URL:http://dx.doi.org/10.18637/jss.v081.i05>.
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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | ## 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: Multi-class 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: Multi-class 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: Multi-class 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)
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