R中的预测功能出现问题

问题描述

我加载了内置的R数据女性"，该数据具有美国女性平均身高和相应体重的表格数据.该表有15行.我试图使用这些数据来预测身高特定值的体重.我首先建立了线性模型，并给出了新的预测值.但是R仍然从原始数据中得出15个数字.

I loaded the inbuilt R data 'women' which has a tabular data of average American women's height and corresponding weight. this table has 15 rows. Using this data I am trying to predict the weight for specific values of height. I made a linear model first and gave new values to predict. But R still comes up with the 15 figures from the original data.

我是回归的初学者，所以请告诉我我在这里做错了什么.

I am a beginner in regression so please tell me if I am doing anything wrong here.

 data()
> women<-data.frame(women)
> names(women)
[1] "height" "weight"
> plot(women$weight~women$height)
> model<-lm(women$weight~women$height,data=women)
> new<-data.frame(height=c(82,83,84,85))
> wgt.prediction<-predict(model,new)
Warning message:
'newdata' had 4 rows but variables found have 15 rows
 > wgt.prediction
   1        2        3        4        5        6        7        8        9          10       11       12       13
112.5833 116.0333 119.4833 122.9333 126.3833 129.8333 133.2833 136.7333 140.1833 143.6333 147.0833 150.5333 153.9833
  14       15
157.4333 160.8833

推荐答案

请注意，在原始数据范围之外推断预测可能会得出较差的答案；但是，请忽略尝试以下操作.

Note that extrapolating predictions outside the range of the original data can give poor answers; however, ignoring that try the following.

首先，没有必要使用data()或data.frame.无论如何，women都可以使用，并且它已经是一个数据框.

First, it is not necessary to use data() or data.frame. women will be available to you anyways and it is already a data frame.

此外，在问题中将模型的自变量指定为women$height，但预测将其指定为height.不知道women$height和height是相同的.

Also, the model's independent variable was specified in the question as women$height but the prediction specified it as height. It does not know that women$height and height are the same.

将所有代码替换为此:

fo <- weight ~ height
model <- lm(fo, women)
heigths <- c(82, 83, 84, 85)
weights <- predict(model, data.frame(height = heights))

给予:

> weights
       1        2        3        4
195.3833 198.8333 202.2833 205.7333

使用预测值(即使用weights)和由model定义的回归线(在图表后续)绘制数据:

To plot the data with the predictions (i.e. with weights) and the regression line defined by model (continued after graph):

plot(fo, women, xlim = range(c(height, heights)), ylim = range(c(weight, weights)))
points(weights ~ heights, col = "red", pch = 20)
abline(model)

尽管通常使用predict，但是鉴于公式中使用$引入的问题，使用原始公式的另一种方法是计算这样的预测:

Although normally one uses predict, given the problem introduced by using $ in the formula, an alternative using your original formulation would be to calculate the predictions like this:

model0 <- lm(women$weight ~ women$height)
cbind(1, 82:85) %*% coef(model0)

给予:

         [,1]
[1,] 195.3833
[2,] 198.8333
[3,] 202.2833
[4,] 205.7333

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