如何为R中的每个变量提取GAM {mgcv}的拟合值? [英] How to extract fitted values of GAM {mgcv} for each variable in R?
问题描述
我正在寻找一种添加模型中每个变量的预测值(实际的，不是标准化的)的方法
I'm searching for a method to add the predicted (real, not standardized) values of every single variable in my model
> model<-gam(LN_Brutto~s(agecont,by=Sex)+factor(Sex)+te(Month,Age)+s(Month,by=Sex),
data=bears)
> model<-gam(LN_Brutto~s(agecont,by=Sex)+factor(Sex)+te(Month,Age)+s(Month,by=Sex),
data=bears)
这是我的模型的摘要:
> summary(m13)
Family: gaussian
Link function: identity
Formula:
LN_Brutto ~ s(agecont, by = Sex) + factor(Sex) + te(Month, Age) +
s(Month, by = Sex)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.32057 0.01071 403.34 <2e-16 ***
factor(Sex)m 0.27708 0.01376 20.14 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(agecont):Sexf 8.1611 8.7526 20.170 < 2e-16 ***
s(agecont):Sexm 6.6695 7.5523 32.689 < 2e-16 ***
te(Month,Age) 10.3651 12.7201 6.784 2.19e-12 ***
s(Month):Sexf 0.9701 0.9701 0.641 0.430
s(Month):Sexm 1.3750 1.6855 0.193 0.787
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Rank: 60/62
R-sq.(adj) = 0.781 Deviance explained = 78.7%
GCV = 0.048221 Scale est. = 0.046918 n = 1093
此代码提供了预测值:
> predict<-predict(m13, type = "terms")
结果如下:
factor(Sex) s(agecont):Sexf s(agecont):Sexm te(Month,Age) s(Month):Sexf s(Month):Sexm
1 0.2770806 0.000000000 0.111763696 -0.077845764 0.000000000 0.0007840912
2 0.2770806 0.000000000 0.240016156 -0.049143798 0.000000000 0.0007840912
3 0.2770806 0.000000000 0.034328752 0.046524454 0.000000000 -0.0058871897
4 0.0000000 -0.786533918 0.000000000 -0.067942427 0.021990192 0.0000000000
5 0.0000000 0.074434715 0.000000000 0.046524454 0.021990192 0.0000000000
6 0.0000000 0.161121563 0.000000000 0.089599601 0.021990192 0.0000000000
7 0.0000000 0.074434715 0.000000000 0.046524454 0.021990192 0.0000000000
8 0.2770806 0.000000000 -0.298597370 -0.007877328 0.000000000 -0.0058871897
...
但是我想这些只是标准化的预测值，而不是真实值(真实值应该没有负值！?).
But I guess these are just standardized predicted values and not the real values (the real ones should have no negative values!?).
那么有人知道我必须在代码中进行哪些修改才能获得真实的价值吗? 任何的想法?谢谢！
So does anyone know what I have to modify in the code, to get the real values? Any idea? Thank you!
推荐答案
不太确定我是否正确跟随了您，
但是predict(model, type = "terms")可能是您正在寻找的解决方案.
Not quite sure if I follow you correctly,
but predict(model, type = "terms") might be the solution you're looking for.
更新
我不认为这些是标准化的.可能某些系数只是负数.
I don't think these are standardised. Possibly some of the coefficients are just negative.
考虑帮助文件?mgcv:::predict.gam中的示例:
Consider the example from the help file ?mgcv:::predict.gam:
library(mgcv)
n<-200
sig <- 2
dat <- gamSim(1,n=n,scale=sig)
b<-gam(y~s(x0)+s(I(x1^2))+s(x2)+offset(x3),data=dat)
以下结果表明，这些实际上是用于每个预测变量以计算拟合值的贡献(通过计算每个贡献的总和，然后加上截距和偏移量).
The results below illustrate that these are in fact the contributions that are being used for each predictor to calculate the fitted values (by calculating the sum of each of these contributions and then adding the intercept and the offset).
> head(predict(b))
1 2 3 4 5 6
9.263322 2.822200 7.137201 4.902631 14.558401 11.889092
> head(rowSums(predict(b, type = "terms")) + attr(predict(b, type = "terms"), "constant") + dat$x3)
1 2 3 4 5 6
9.263322 2.822200 7.137201 4.902631 14.558401 11.889092
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