[英]Extract data from Partial least square regression on R
我想使用偏最小二乘回歸來找到最有代表性的變量來預測我的數據。 這是我的代碼:
library(pls)
potion<-read.table("potion-insomnie.txt",header=T)
potionTrain <- potion[1:182,]
potionTest <- potion[183:192,]
potion1 <- plsr(Sommeil ~ Aubepine + Bave + Poudre + Pavot, data = potionTrain, validation = "LOO")
summary(lm(potion1))
給出了這個答案:
Call:
lm(formula = potion1)
Residuals:
Min 1Q Median 3Q Max
-14.9475 -5.3961 0.0056 5.2321 20.5847
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.63931 1.67955 22.410 < 2e-16 ***
Aubepine -0.28226 0.05195 -5.434 1.81e-07 ***
Bave -1.79894 0.26849 -6.700 2.68e-10 ***
Poudre 0.35420 0.72849 0.486 0.627
Pavot -0.47678 0.52027 -0.916 0.361
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.845 on 177 degrees of freedom
Multiple R-squared: 0.293, Adjusted R-squared: 0.277
F-statistic: 18.34 on 4 and 177 DF, p-value: 1.271e-12
我推斷只有變量Aubepine
et Bave
具有代表性。 所以我用這兩個變量重新模擬了模型:
potion1 <- plsr(Sommeil ~ Aubepine + Bave, data = potionTrain, validation = "LOO")
我的情節是:
plot(potion1, ncomp = 2, asp = 1, line = TRUE)
以下是預測值與測量值的關系圖:
問題是我在圖上看到線性回歸,但我不知道它的等式和R²
。 可能嗎 ?
第一部分是多元回歸線性(ANOVA)嗎?
pacman::p_load(pls)
data(mtcars)
potion <- mtcars
potionTrain <- potion[1:28,]
potionTest <- potion[29:32,]
potion1 <- plsr(mpg ~ cyl + disp + hp + drat, data = potionTrain, validation = "LOO")
coef(potion1) # coefficeints
scores(potion1) # scores
## R^2:
R2(potion1, estimate = "train")
## cross-validated R^2:
R2(potion1)
## Both:
R2(potion1, estimate = "all")
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