R:多个子样本回归上的约束系数和误差方差 [英] R : constraining coefficients and error variance over multiple subsample regressions

问题描述

我正在与R合作处理145个观测值的样本.我创建了五个子样本，每个子样本有29个观察值，而响应变量q已排序.结果，subset1包含数据帧中输出最低的29行，subset2包含以下29行，依此类推.

I'm working with R on a sample of 145 observations. I have created five subsamples each with 29 observations, while the response variable q has been sorted. As a result, subset1 contains the 29 lines of the data frame with the lowest output, subset2 contains the following 29 lines, etc.

我正在对预测变量x1，x2和x3的变量q进行回归.我现在需要执行两个实验:

I am regressing the variable q on the predictors x1, x2 ans x3. I now need to perform two experiments :

1. 将所有子样本的误差方差约束为相同；
2. 在5个OLS回归中，将x2和x3的系数以及误差方差限制为相同.

1. Constraining the error variance to be the same over all subsamples;
2. Constraining the coefficients on x2 and x3 as well as the error variance to be the same over the 5 OLS regressions.

到目前为止，我的方法是使用软件包plm，该软件包可以执行面板回归.但是，我不知道具体限制误差方差或特定系数.此外，我认为必须有一种方法可以使用R中包含的更基本的工具来做到这一点.

So far my approach has been to use the package plm which allows to perform panel regressions. However, I don't know to specifically constrain the error variance, or specific coefficients. Besides, I think there must be a way to do this with the more basic tools incorporated in R.

请不要犹豫，提供其他方法.预先感谢您的帮助！

Please don't hesitate to provide alternative methods. Thanks in advance for your help !

推荐答案

看起来这就是您所需要的:

Looks like this is all you need:

set.seed(0)
dat <- data.frame(q = sort(rnorm(145)), x1 = rnorm(145), x2 = rnorm(145),
                  x3 = rnorm(145), group = gl(5, 29))

fit <- lm(q ~ x1 * group + x2 + x3, data = dat)

#Coefficients:
#(Intercept)           x1       group2       group3       group4       group5  
#  -1.211435     0.049316     0.610405     1.128571     1.631891     2.502886  
#         x2           x3    x1:group2    x1:group3    x1:group4    x1:group5  
#  -0.027927    -0.015151    -0.004244    -0.074085    -0.044885    -0.074637

在这里，我介绍了分组因子变量group.所有五组的模型估计均同时进行.使用公式:

Here, I have introduced a grouping factor variable group. Model estimation for all five groups are done at the same time. With formula:

q ~ x1 * group + x2 + x3

对于所有组，我们的系数x2和x3是相同的.尽管交互作用x1*group提示我们对于不同组的x1具有不同的截距和斜率.

we have coefficients of x2 and x3 being the same for all groups. While the interaction x1*group suggests that we have different intercept and slope for x1 for different groups.

如果您不想为每个组使用不同的截距，则可以使用公式:

If you don't want different intercept for each group, you can use formula:

q ~ x1 + x1 : group + x2 + x3

这篇关于R:多个子样本回归上的约束系数和误差方差的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！