In R I want to do some regression on multivariate response on all predictors, for univariate response, I know the formula is like
在R中我想对所有预测因子的多变量响应进行一些回归,对于单变量响应,我知道公式就像
y~., this is to use all predictors to regress y, what if now I face 100 response, I can not type 100 yi like y1+y2+y3...+y4~x, so how to use all predictors to regress multivariate response?
y~。,这是使用所有预测变量来回归y,如果现在我面对100响应,我不能像y1 + y2 + y3 ... + y4~x那样输入100 yi,那么如何使用所有预测变量来回归多变量反应?
2 个解决方案
#1
10
In R, the multivariate formula is to use cbind() for your Y variable. Thus, the formula would be:
在R中,多变量公式是使用cbind()作为Y变量。因此,公式将是:
model <- lm(cbind(y1, y2, y3, y4)~x)
#2
1
That's relatively easy if y is a matrix with 100 columns. In that case you do it the same way. For example:
如果y是具有100列的矩阵,则相对容易。在这种情况下,你以同样的方式做到这一点。例如:
lm(y ~ x)
will do a linear regression of y onto the columns of x.
将对y的列进行线性回归。
#1
10
In R, the multivariate formula is to use cbind() for your Y variable. Thus, the formula would be:
在R中,多变量公式是使用cbind()作为Y变量。因此,公式将是:
model <- lm(cbind(y1, y2, y3, y4)~x)
#2
1
That's relatively easy if y is a matrix with 100 columns. In that case you do it the same way. For example:
如果y是具有100列的矩阵,则相对容易。在这种情况下,你以同样的方式做到这一点。例如:
lm(y ~ x)
will do a linear regression of y onto the columns of x.
将对y的列进行线性回归。