如何解决多元自适应回归样条中选择项的数量
我正在基于6个预测因子对土壤容重进行回归分析。我尝试了插入符号包中的多元自适应回归样条。结果表明,最优模型的最终值为nprune = 8且度=1。但是,当我提取模型系数时,仅选择了7个项(包括截距)。谁能再解释一下自适应回归样条的两个调整参数导致结果(nprune和degree的最终值与所选项的数量和R所示的交互复杂度不匹配)?代码和结果如下所示:
model.bulk <- train(BD ~.,data.bulk,trControl = trainControl(method ="repeatedcv",number = 10,repeats = 10),method = "earth",metric = "RMSE")
最优模型的最终值为nprune = 8,度= 1
Multivariate Adaptive Regression Spline
86 samples
6 predictor
No pre-processing
resampling: Cross-Validated (10 fold,repeated 10 times)
Summary of sample sizes: 77,78,76,77,...
resampling results across tuning parameters:
nprune RMSE Rsquared MAE
2 0.1236698 0.3078943 0.10330518
8 0.1080978 0.4794419 0.08786858
14 0.1087380 0.4707099 0.08853226
Tuning parameter 'degree' was held constant at a value of 1
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were nprune = 8 and degree = 1.
最终模型中只有7个选定的术语吗?
Call: earth(x=matrix[86,6],y=c(1.405,1.596,1...),keepxy=TRUE,degree=1,nprune=8)
coefficients
(Intercept) 1.20609922
h(1.7-OC) 0.09059255
h(2.50917-Iw) -0.08033033
h(SAND-43.2) 0.00483245
h(CLAY-5.6) 0.17138133
h(CLAY-6.71) -0.17448152
h(SSQ-2.5) 0.07798563
Selected 7 of 16 terms,and 5 of 6 predictors
Termination condition: Reached nk 21
Importance: Iw,CLAY,SSQ,SAND,OC,D-unused
Number of terms at each degree of interaction: 1 6 (additive model)
GCV 0.01027685 RSS 0.6368061 GRSq 0.4947044 RSq 0.6273052
非常感谢您!
版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。