R 二元线性回归系数的计算

有人知道如何用两行代码解决附带的问题吗？我相信as.matrix可以创建一个矩阵X，然后使用X%*%X、tX和solveX得到答案。然而，它似乎不起作用。任何答案都会有帮助，谢谢

我建议使用read.csv而不是read.table

您可以在此线程中查看两个函数的差异：

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根据@kon_的答案，这里有一个手工操作的示例：

df <- read.csv("http://pengstats.macssa.com/download/rcc/lmdata.csv")
model1 <- lm(y ~ x1 + x2, data = df)
coefficients(model1) # get the coefficients of your regression model1
summary(model1) # get the summary of model1 

### Based on the formula
X <- cbind(1, df$x1, df$x2) # the column of 1 is to consider the intercept
Y <- df$y
bhat <- solve(t(X) %*% X) %*% t(X) %*% Y # coefficients 
bhat # Note that we got the same coefficients with the lm function

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请告诉我们你到目前为止都做了些什么。请注意，这个公式也允许您计算多元线性回归的系数。

df <- read.csv("http://pengstats.macssa.com/download/rcc/lmdata.csv")
model1 <- lm(y ~ x1 + x2, data = df)
coefficients(model1) # get the coefficients of your regression model1
summary(model1) # get the summary of model1 

### Based on the formula
X <- cbind(1, df$x1, df$x2) # the column of 1 is to consider the intercept
Y <- df$y
bhat <- solve(t(X) %*% X) %*% t(X) %*% Y # coefficients 
bhat # Note that we got the same coefficients with the lm function