crtree | R Documentation |
Classification and regression trees based on the rpart package
crtree(
dataset,
rvar,
evar,
type = "",
lev = "",
wts = "None",
minsplit = 2,
minbucket = round(minsplit/3),
cp = 0.001,
pcp = NA,
nodes = NA,
K = 10,
seed = 1234,
split = "gini",
prior = NA,
adjprob = TRUE,
cost = NA,
margin = NA,
check = "",
data_filter = "",
arr = "",
rows = NULL,
envir = parent.frame()
)
dataset |
Dataset |
rvar |
The response variable in the model |
evar |
Explanatory variables in the model |
type |
Model type (i.e., "classification" or "regression") |
lev |
The level in the response variable defined as _success_ |
wts |
Weights to use in estimation |
minsplit |
The minimum number of observations that must exist in a node in order for a split to be attempted. |
minbucket |
the minimum number of observations in any terminal <leaf> node. If only one of minbucket or minsplit is specified, the code either sets minsplit to minbucket*3 or minbucket to minsplit/3, as appropriate. |
cp |
Minimum proportion of root node deviance required for split (default = 0.001) |
pcp |
Complexity parameter to use for pruning |
nodes |
Maximum size of tree in number of nodes to return |
K |
Number of folds use in cross-validation |
seed |
Random seed used for cross-validation |
split |
Splitting criterion to use (i.e., "gini" or "information") |
prior |
Adjust the initial probability for the selected level (e.g., set to .5 in unbalanced samples) |
adjprob |
Setting a prior will rescale the predicted probabilities. Set adjprob to TRUE to adjust the probabilities back to their original scale after estimation |
cost |
Cost for each treatment (e.g., mailing) |
margin |
Margin associated with a successful treatment (e.g., a purchase) |
check |
Optional estimation parameters (e.g., "standardize") |
data_filter |
Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
arr |
Expression to arrange (sort) the data on (e.g., "color, desc(price)") |
rows |
Rows to select from the specified dataset |
envir |
Environment to extract data from |
See https://radiant-rstats.github.io/docs/model/crtree.html for an example in Radiant
A list with all variables defined in crtree as an object of class tree
summary.crtree
to summarize results
plot.crtree
to plot results
predict.crtree
for prediction
crtree(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
result <- crtree(titanic, "survived", c("pclass", "sex")) %>% summary()
result <- crtree(diamonds, "price", c("carat", "clarity"), type = "regression") %>% str()
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