Predict() - 也许我不理解它 [英] Predict() - Maybe I'm not understanding it

问题描述

我今天早些时候发布关于我在使用 predict 时遇到的错误代码>功能.我能够纠正这一点，并认为我走在正确的道路上.

I posted earlier today about an error I was getting with using the predict function. I was able to get that corrected, and thought I was on the right path.

我有许多观察结果(实际值)，并且我有一些数据点想要推断或预测.我使用 lm 创建了一个模型，然后我尝试使用 predict 和实际值作为预测器输入.

I have a number of observations (actuals) and I have a few data points that I want to extrapolate or predict. I used lm to create a model, then I tried to use predict with the actual value that will serve as the predictor input.

这段代码在我之前的帖子中都是重复的，但这里是:

This code is all repeated from my previous post, but here it is:

df <- read.table(text = '
     Quarter Coupon      Total
1   "Dec 06"  25027.072  132450574
2   "Dec 07"  76386.820  194154767
3   "Dec 08"  79622.147  221571135
4   "Dec 09"  74114.416  205880072
5   "Dec 10"  70993.058  188666980
6   "Jun 06"  12048.162  139137919
7   "Jun 07"  46889.369  165276325
8   "Jun 08"  84732.537  207074374
9   "Jun 09"  83240.084  221945162
10  "Jun 10"  81970.143  236954249
11  "Mar 06"   3451.248  116811392
12  "Mar 07"  34201.197  155190418
13  "Mar 08"  73232.900  212492488
14  "Mar 09"  70644.948  203663201
15  "Mar 10"  72314.945  203427892
16  "Mar 11"  88708.663  214061240
17  "Sep 06"  15027.252  121285335
18  "Sep 07"  60228.793  195428991
19  "Sep 08"  85507.062  257651399
20  "Sep 09"  77763.365  215048147
21  "Sep 10"  62259.691  168862119', header=TRUE)

str(df)
'data.frame':   21 obs. of  3 variables:
 $ Quarter   : Factor w/ 24 levels "Dec 06","Dec 07",..: 1 2 3 4 5 7 8 9 10 11 ...
 $ Coupon: num  25027 76387 79622 74114 70993 ...
 $ Total: num  132450574 194154767 221571135 205880072 188666980 ...

代码:

model <- lm(df$Total ~ df$Coupon, data=df)

> model

Call:
lm(formula = df$Total ~ df$Coupon)

Coefficients:
(Intercept)    df$Coupon  
  107286259         1349 

预测代码(基于之前的帮助):

Predict code (based on previous help):

(这些是我想用来获得预测值的预测值)

(These are the predictor values I want to use to get the predicted value)

Quarter = c("Jun 11", "Sep 11", "Dec 11")
Total = c(79037022, 83100656, 104299800)
Coupon = data.frame(Quarter, Total)

Coupon$estimate <- predict(model, newdate = Coupon$Total)

现在，当我运行它时，我收到此错误消息:

Now, when I run that, I get this error message:

Error in `$<-.data.frame`(`*tmp*`, "estimate", value = c(60980.3823396919,  : 
  replacement has 21 rows, data has 3

我用来构建模型的原始数据框有 21 个观测值.我现在正尝试根据模型预测 3 个值.

My original data frame that I used to build the model had 21 observations in it. I am now trying to predict 3 values based on the model.

我不是真正理解这个函数，就是我的代码有错误.

I either don't truly understand this function, or have an error in my code.

将不胜感激.

谢谢

推荐答案

首先要使用

model <- lm(Total ~ Coupon, data=df)

not model <-lm(df$Total ~ df$Coupon, data=df).

其次，通过说 lm(Total ~ Coupon)，您正在拟合一个使用 Total 作为响应变量的模型，使用 Coupon作为预测器.也就是说，您的模型采用 Total = a + b*Coupon 形式，其中 a 和 b 是要估计的系数.请注意，响应位于 ~ 的左侧，而预测器位于右侧.

Second, by saying lm(Total ~ Coupon), you are fitting a model that uses Total as the response variable, with Coupon as the predictor. That is, your model is of the form Total = a + b*Coupon, with a and b the coefficients to be estimated. Note that the response goes on the left side of the ~, and the predictor(s) on the right.

因此，当您要求 R 为您提供模型的预测值时，您必须提供一组新的预测器值，即 Coupon 的新值，而不是 Total.

Because of this, when you ask R to give you predicted values for the model, you have to provide a set of new predictor values, ie new values of Coupon, not Total.

第三，根据您对 newdata 的规范判断，您实际上是在寻找一个模型，以适应 Coupon 作为 Total 的函数>，而不是相反.为此:

Third, judging by your specification of newdata, it looks like you're actually after a model to fit Coupon as a function of Total, not the other way around. To do this:

model <- lm(Coupon ~ Total, data=df)
new.df <- data.frame(Total=c(79037022, 83100656, 104299800))
predict(model, new.df)

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