文章目录
介绍rpart()的使用方法参数介绍实例 对rapart对象的美观显示包rattlefancyRpartPlot()的用法参数介绍实例介绍
rpart包中的rpart()函数可以实现决策树和回归树的建模
rpart()的使用方法
rpart(formula, data, weights, subset, na.action = na.rpart, method,model = FALSE, x = FALSE, y = TRUE, parms, control, cost, ...)
参数介绍
formula
a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame (see model.frame).
data
an optional data frame in which to interpret the variables named in the formula.
weights
optional case weights.
subset
optional expression saying that only a subset of the rows of the data should be used in the fit.
na.action
the default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.
method
one of“anova”, “poisson”, “class” or “exp”. If method is missing then the routine tries to make an intelligent guess. If y is a survival object, then method = “exp” is assumed, if y has 2 columns then method = “poisson” is assumed, if y is a factor then method = “class” is assumed, otherwise method = “anova” is assumed. It is wisest to specify the method directly, especially as more criteria may added to the function in future.
Alternatively, method can be a list of functions named init, split and eval. Examples are given in the file ‘tests/usersplits.R’ in the sources, and in the vignettes ‘User Written Split Functions’.
model
if logical: keep a copy of the model frame in the result? If the input value for model is a model frame (likely from an earlier call to the rpart function), then this frame is used rather than constructing new data.
x
keep a copy of the x matrix in the result.
y
keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.
parms
optional parameters for thesplitting function.
Anova splitting has no parameters.
Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates. The default value is 1.
Exponential splitting has the same parameter as Poisson.
Forclassificationsplitting, the list can contain any of: the vector of prior probabilities (component prior), the loss matrix (component loss) or the splitting index (component split). The priors must be positive and sum to 1. The loss matrix must have zeros on the diagonal and positive off-diagonal elements. The splitting index can be gini or information. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to gini. 例如：parms = list(prior = c(0.65,0.35), split = “information”))
control
a list of options that control details of the rpart algorithm. See rpart.control.
cost
a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.
…
arguments to rpart.control may also be specified in the call to rpart. They are checked against the list of valid arguments.
实例
fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)fit2 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,method = 'class',parms = list(prior = c(.65,.35), split = "information"))fit3 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,control = rpart.control(cp = 0.05))plot(fit)text(fit, use.n = TRUE)plot(fit2)text(fit2, use.n = TRUE)plot(fit3)text(fit3, use.n = TRUE)
对rapart对象的美观显示包rattle
rattle包中的fancyRpartPlot()可以使rpart对象得到更好的显示
fancyRpartPlot()的用法
fancyRpartPlot(model, main="", sub, caption, palettes, type=2, ...)
参数介绍
model
anrpartobject.
main
title for the plot.
sub
sub title for the plot. The default is a Rattle string with date, time and username.
caption
caption for bottom right of plot.
palettes
a list of sequential palettes names. As supported byRColorBrewer::brewer.pal the available names are Blues BuGn BuPu GnBu Greens Greys Oranges OrRd PuBu PuBuGn PuRd Purples RdPu Reds YlGn YlGnBu YlOrBr YlOrRd.
type
the type of plot to generate (2).
…
additional arguments passed on to prp.
实例
## Set up the data for modelling.library(rattle)library(rpart)set.seed(42)ds<- weathertarget <- "RainTomorrow"risk <- "RISK_MM"ignore <- c("Date", "Location", risk)vars <- setdiff(names(ds), ignore)nobs <- nrow(ds)form <- formula(paste(target, "~ ."))train <- sample(nobs, 0.7*nobs)test <- setdiff(seq_len(nobs), train)actual <- ds[test, target]risks <- ds[test, risk]# Fit the model.fit <- rpart(form, data=ds[train, vars])## Plot the model.fancyRpartPlot(fit)## Choose different colours.fancyRpartPlot(fit, main='test',sub='test1',caption='Let me think',palettes=c("Greys", "Oranges"),type=1)
