R 多元自适应回归样条中的选择项数

我正在根据6个预测因子对土壤容重进行回归。我尝试了多元自适应回归样条插值软件包。结果表明，优化模型的最终值为nprune=8和degree=1。然而，当我提取模型系数时，只选择了7项（包括截距）。有人能解释更多关于自适应回归样条曲线的两个调整参数导致结果的原因吗（nprune和DEGRE的最终值与所选术语的数量和R所示的交互复杂性不匹配）？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")

最终模型中只有7个选定术语

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

非常感谢你

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