我正在运行一个程序,在其中进行OLS回归,然后从实际观测值中减去系数以保持残差。
model1 = lm(data = final, obs ~ day + poly(temp,2) + prpn + school + lag1) # linear model
predfit = predict(model1, final) # predicted values
residuals = data.frame(final$obs - predfit) # obtain residuals
我想引导我的模型,然后对自举系数进行同样的处理。我尝试通过以下方式进行此操作:
lboot <- lm.boot(model1, R = 1000)
predfit = predict(lboot, final)
residuals = data.frame(final$obs - predfit) # obtain residuals
但是,这不起作用。我也尝试:
boot_predict(model1, final, R = 1000, condense = T, comparison = "difference")
而且这也不起作用。
我如何引导我的模型,然后基于该模型进行预测?
[如果您尝试使用引导程序来适应最佳的OLS,我将使用caret
软件包。
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