使用 R 中的交互项进行线性回归预测

发布于 2025-01-17 22:35:51 字数 957 浏览 1 评论 0原文

我正在尝试编码使用交互项并使用模型生成样本外预测的模型。

我的培训样本有3个变量和11行。我的测试样本有3个变量和1行。

我的代码如下。

inter.model <- lm(Y.train ~ Y.lag.train +  X.1.train + X.1.train:X.2.train)

但是，我不太确定R如何处理交互项。我使用模型和测试数据的系数编码了预测。

inter.prediction <- inter.model$coef[1] + inter.model$coef[2]*Y.lag.test + 
        inter.model$coef[3]*X.1.test + (inter.model$coef[4]*X.1.test*X.2.test)

我想确保正确编码这些预测。因此，我试图用R的预测功能生产它们。

inter.pred.function <- predict(inter.model, newdata=test_data)

但是，我收到一条错误消息：

Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : 
  variable lengths differ (found for 'X.2.train')
In addition: Warning message:
'newdata' had 1 row but variables found have 11 rows

names(test_data)
[1] "Y.lag.test" "X.1.test" "X.1.test:X.2.test"

因此，我的问题是，您如何用R中的交互项进行编码并进行线性回归预测？

原文

I am trying to code a model which uses interaction term and generate out-of-sample predictions using the model.

My training sample has 3 variables and 11 rows.
My test sample has 3 variables and 1 row.

My code is the following.

inter.model <- lm(Y.train ~ Y.lag.train +  X.1.train + X.1.train:X.2.train)

However, I am not quite sure how R handles the interaction terms.
I have coded the predictions using the coefficients from the model and the test data.

inter.prediction <- inter.model$coef[1] + inter.model$coef[2]*Y.lag.test + 
        inter.model$coef[3]*X.1.test + (inter.model$coef[4]*X.1.test*X.2.test)

I wanted to make sure that these predictions were correctly coded. Thus, I tried to produce them with the R´s predict-function.

inter.pred.function <- predict(inter.model, newdata=test_data)

However, I am getting a error message:

Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : 
  variable lengths differ (found for 'X.2.train')
In addition: Warning message:
'newdata' had 1 row but variables found have 11 rows

names(test_data)
[1] "Y.lag.test" "X.1.test" "X.1.test:X.2.test"

So, my question is, how do you code and make linear regression predictions with interaction terms in R?

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ぽ尐不点ル 2025-01-24 22:35:51

您不需要“ x.1.test：x.2.test”在新数据中，互动是在stats ::: precadion.lm中自动创建的。通过model.matrix。

fit <- lm(mpg ~ hp*am, mtcars[1:10, ])

test <- mtcars[-(1:10), c('mpg', 'hp', 'am')]

as.numeric(predict(fit, newdata=test))
# [1] 20.220513 17.430053 17.430053 17.430053 16.206167 15.716612 14.982281 25.658824 27.141176 25.764706
# [11] 21.493355 18.898716 18.898716 14.247949 17.674830 25.658824 23.011765 20.682353  4.694118 14.117647
# [21] -2.823529 21.105882

You won't need "X.1.test:X.2.test" in your new data, the interaction is created automatically in stats:::predict.lm via the model.matrix.

fit <- lm(mpg ~ hp*am, mtcars[1:10, ])

test <- mtcars[-(1:10), c('mpg', 'hp', 'am')]

as.numeric(predict(fit, newdata=test))
# [1] 20.220513 17.430053 17.430053 17.430053 16.206167 15.716612 14.982281 25.658824 27.141176 25.764706
# [11] 21.493355 18.898716 18.898716 14.247949 17.674830 25.658824 23.011765 20.682353  4.694118 14.117647
# [21] -2.823529 21.105882

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