Question

I have to apply regression function progressively to a time series data (vector “time” and “tm” and I’m using a For Loop as follow:

top<-length(time)
for(k in 2:top){
    lin.regr<-lm(tm[1:k] ~ log(time[1:k]))
    slope[k]<-coef(lin.regr)[2]
}

But for vectors’ length of about 10k it becomes very slow.
Is there a faster alternative (maybe using apply function)?

In a more easy problem: if I have a vector like x<-c(1:10) how can I build a y vector containing (for example) the progressive sum of x values?
Like:

x
1 2 3 4 5 6 7 8 9 10
y
1  3  6 10 15 21 28 36 45 55

Answer 1

results <- sapply(2:top,function (k) coef(lm(tm[1:k] ~ log(time[1:k])))[2])

~apply family of functions is the fastest way to iterate in R.

can also look at using lm.fit() to speed up your regrssion a bit

cumsum(1:10)

is how to do the second question