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 the splitting 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.
For classification splitting, 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
an rpart object.
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 by RColorBrewer::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     <- weather
target <- "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)

R语言实现决策回归树的包rpart相关推荐

R语言构建logistic回归模型：WVPlots包PRTPlot函数可视化获取logistic回归模型的最优阈值、优化（precision、enrichment）和recall之间的折衷
R语言构建logistic回归模型:WVPlots包PRTPlot函数可视化获取logistic回归模型的最佳阈值(改变阈值以优化精确度(precision.enrichment)和查全率(recal ...
R语言构建logistic回归模型并评估模型：模型预测结果抽样、可视化模型分类预测的概率分布情况、使用WVPlots包绘制ROC曲线并计算AUC值
R语言构建logistic回归模型并评估模型:模型预测结果抽样.可视化模型分类预测的概率分布情况.使用WVPlots包绘制ROC曲线并计算AUC值目录
R语言系统自带及附属包开元可用数据集汇总
R语言系统自带及附属包开元可用数据集汇总目录 R语言系统自带及附属包开元可用数据集汇总 #R自带数据集 #R的各种包自带数据集 #R自带数据集向量 euro #欧元汇率,长度为11,每个元素都有命 ...
R语言可视化学习笔记之ggridges包绘制山峦图
作者:严涛浙江大学作物遗传育种在读研究生(生物信息学方向)伪码农,R语言爱好者,爱开源. 严涛老师的绘图教程还有: gganimate |诺奖文章里面的动图绘制教程来了!! ggplot2学习笔记之 ...
语言nomogram校准曲线图_医学统计与R语言：Meta 回归作图（Meta regression Plot）
微信公众号:医学统计与R语言如果你觉得对你有帮助,欢迎转发输入1: install.packages("metafor") library(metafor) dat.bcg 结果 ...
R语言主成分回归（PCR）、多元线性回归特征降维分析光谱数据和汽车油耗、性能数据...
原文链接:http://tecdat.cn/?p=24152 什么是PCR?(PCR = PCA + MLR)(点击文末"阅读原文"获取完整代码数据). • PCR是处理许多 x ...
手把手教你使用R语言做LASSO 回归
LASSO 回归也叫套索回归,是通过生成一个惩罚函数是回归模型中的变量系数进行压缩,达到防止过度拟合,解决严重共线性的问题,LASSO 回归最先由英国人Robert Tibshirani提出,目前在预 ...
决策回归树回归算法30
1.决策回归树原理概述与分类树一样裂分指标,使用的是MSE.MAE MSE(y,y^)=1nsamples∑i=0nsamples−1(yi−y^i)2\text{MSE}(y, \hat{y}) ...
R语言中的管道操作——magrittr包
R语言中的管道操作--magrittr包一.项目环境开发工具:RStudio R:3.5.2 相关包:magritter 二.数据准备以及问题阐述这次要解决的问题是如何使用提取数据框中所有包含缺 ...

R语言实现决策回归树的包rpart

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rattle

fancyRpartPlot()的用法

参数介绍

实例

R语言实现决策回归树的包rpart相关推荐

最新文章

热门文章