bartDiag | R Documentation |
Displays a selection of diagnostic plots for a BART model.
bartDiag(
model,
data,
response,
burnIn = 0,
threshold = "Youden",
pNorm = FALSE,
showInterval = TRUE,
combineFactors = FALSE
)
model |
a model created from either the BART, modelarts, or bartMachine package. |
data |
A dataframe used to build the model. |
response |
The name of the response for the fit. |
burnIn |
Trace plot will only show iterations above selected burn in value. |
threshold |
A dashed line on some plots to indicate a chosen threshold value (classification only). by default the Youden index is shown. |
pNorm |
apply pnorm to the y-hat data (classification only). |
showInterval |
LOGICAL if TRUE then show 5% and 95% quantile intervals on ROC an PC curves (classification only). |
combineFactors |
Whether or not to combine dummy variables (if present) in display. |
A selection of diagnostic plots.
# For Regression
# Generate Friedman data
fData <- function(n = 200, sigma = 1.0, seed = 1701, nvar = 5) {
set.seed(seed)
x <- matrix(runif(n * nvar), n, nvar)
colnames(x) <- paste0("x", 1:nvar)
Ey <- 10 * sin(pi * x[, 1] * x[, 2]) + 20 * (x[, 3] - 0.5)^2 + 10 * x[, 4] + 5 * x[, 5]
y <- rnorm(n, Ey, sigma)
data <- as.data.frame(cbind(x, y))
return(data)
}
f_data <- fData(nvar = 10)
x <- f_data[, 1:10]
y <- f_data$y
# Create dbarts model
library(dbarts)
set.seed(1701)
dbartModel <- bart(x, y, ntree = 5, keeptrees = TRUE, nskip = 10, ndpost = 10)
bartDiag(model = dbartModel, response = "y", burnIn = 100, data = f_data)
# For Classification
data(iris)
iris2 <- iris[51:150, ]
iris2$Species <- factor(iris2$Species)
# Create dbarts model
dbartModel <- bart(iris2[, 1:4], iris2[, 5], ntree = 5, keeptrees = TRUE, nskip = 10, ndpost = 10)
bartDiag(model = dbartModel, data = iris2, response = iris2$Species)
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