R中的自举：预测

我正在运行一个程序，在这个程序中，我进行OLS回归，然后从实际观测值中减去系数，以保留残差

model1 = lm(data = final, obs ~ day + poly(temp,2) + prpn + school + lag1) # linear model  
predfit = predict(model1, final) # predicted values

residuals = data.frame(final$obs - predfit) # obtain residuals

我想引导我的模型，然后对引导系数做同样的事情。我试着用以下方法来做：

lboot <- lm.boot(model1, R = 1000)
predfit = predict(lboot, final)

residuals = data.frame(final$obs - predfit) # obtain residuals

这也不起作用

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我如何引导我的模型，然后在此基础上进行预测？

如果您试图使用引导拟合最佳OLS，我会使用

插入符号

包

library(caret)

#separate indep and dep variables
indepVars = final[,-final$obs]
depVar = final$obs

#train model
ols.train = train(indepVars, depVar, method='lm',
                  trControl = trainControl(method='boot', number=1000))

#make prediction and get residuals
ols.pred = predict(ols.train, indepVars)
residuals = ols.pred - final$obs

---

残差只是

model1$residuals

，定义“不工作”非常感谢！

library(caret)

#separate indep and dep variables
indepVars = final[,-final$obs]
depVar = final$obs

#train model
ols.train = train(indepVars, depVar, method='lm',
                  trControl = trainControl(method='boot', number=1000))

#make prediction and get residuals
ols.pred = predict(ols.train, indepVars)
residuals = ols.pred - final$obs