问题描述:

I have 365 columns. In each column I have 60 values. I need to know the rate of change over time for each column (slope or linear coefficient). I created a generic column as a series of numbers from 1:60 to represent the 60 corresponding time intervals. I want to create 356 linear regression models using the generic time stamp column with each of the 365 columns of data.

In other words, I have many columns and I would like to create many linear regression models at once, extract the coefficients and put those coefficients into a new matrix.

First of all, statistically this might not be the best possible approach to analyse temporal data. Although, regarding the approach you propose, it is very simple to build a loop to obtain this:

```
Coefs <- matrix(,ncol(Data),2)#Assuming your generic 1:60 column is not in the same object
for(i in 1:ncol(Data)){
Coefs[i,] <- lm(Data[,i]~GenericColumn)$coefficients
}
```

Here's a way to do it:

```
# Fake data
dat = data.frame(x=1:60, y1=rnorm(60), y2=rnorm(60),
y3=rnorm(60))
t(sapply(names(dat)[-1], function(var){
coef(lm(dat[,var] ~ x, data=dat))
}))
(Intercept) x
y1 0.10858554 -0.004235449
y2 -0.02766542 0.005364577
y3 0.20283168 -0.008160786
```

Now, where's that turpentine soap?