我对如何使用像lm()这样的函数需要一个公式和一个data.frame的想法并不十分清楚. 在网络上,我对不同的方法感到不满意,但有时R会给我们警告和其他内容
I do not have very clear idea of how to use functions like lm() that ask for a formula and a data.frame. On the web I red about different approach but sometimes R give us warnings and other stuff
例如,假设一个线性模型,其中输出矢量y由矩阵X解释.
Suppose for example a linear model where the output vector y is explained by the matrix X.
我认为最好的方法是使用data.frame(特别是如果以后要使用预测函数的话).
I red that the best way is to use a data.frame (expecially if we are going to use the predict function later).
在X是矩阵的情况下,这是使用lm的最佳方法吗?
In situation where the X is a matrix is this the best way to use lm?
n=100 p=20 n_new=50 X=matrix(rnorm(n*p),n,p) Y=rnorm(n) data=list("x"=X,"y"=Y) l=lm(y~x,data) X_new=matrix(rnorm(n_new*p),n_new,p) pred=predict(l,as.data.frame(X_new))推荐答案
怎么样:
l <- lm(y~.,data=data.frame(X,y=Y)) pred <- predict(l,data.frame(X_new))在这种情况下,R自动构造列名(X1 ... X20),但是当您使用y~.语法时,您无需知道它们.
In this case R constructs the column names (X1 ... X20) automatically, but when you use the y~. syntax you don't need to know them.
或者,如果您总是要基于矩阵拟合线性回归,则可以使用lm.fit()并使用矩阵乘法自己计算预测:您必须使用cbind(1,.)添加截距列.
Alternatively, if you are always going to fit linear regressions based on a matrix, you can use lm.fit() and compute the predictions yourself using matrix multiplication: you have to use cbind(1,.) to add an intercept column.
fit <- lm.fit(cbind(1,X),Y) all(coef(l)==fit$coefficients) ## TRUE pred <- cbind(1,X_new) %*% fit$coefficients(您也可以使用cbind(1,X_new) %*% coef(l).)这很有效,但是它跳过了很多错误检查步骤,因此请谨慎使用...
(You could also use cbind(1,X_new) %*% coef(l).) This is efficient, but it skips a lot of the error-checking steps, so use it with caution ...
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在R中使用lm的正确方法
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