Reputation: 223
I have a setup that looks like below
for(V in (seq(1, 250, by = 5))){
for(n in (seq(1, 250, by = 5))){
# 1) Working Algorithm creating a probability
ie. vector in range [0:1]
# 2) Take the natural log of this probability
a <- log(lag(Probability), base = exp(1))
# 3) calculate price differences
b <- abs(diff(Price) -1)
# 4) Then compute correlation between a and b
cor(a, b)
# 5) Here I'd like to save this in the corresponding index of matrix
}
}
So that I get a [V, n] sized matrix as output, that collects from each loop.
I have a few problems with this.
The first problem is that my correlation is not computable, as the Probability
is often 0, creating a ln(0) = -Inf
input in the ln(Probability)
vector. Is there a way to compute the std.dev
or cor
of a Ln
vector with -Inf
inputs?
My second question is how I save this correlation output into a matrix generated for each loop?
Thanks for your help. I hope this is clear enough.
Upvotes: 0
Views: 1977
Reputation: 118799
For your second question (My second question is how I save this correlation output into a matrix generated for each loop?), you could initialise a matrix before the loop and store each computed correlation in the corresponding index like:
sz <- seq(1, 250, by = 5)
out_mat <- matrix(0, nrow=length(sz), ncol=length(sz))
# then continue with your for-loop
for (V in 1:length(sz)) {
for(n in length(sz)) {
# here instead of accessing V and n in computing probability
# use sz[V] and sz[n]
...
...
# after computing the correlation, here use V and n (not sz[V] or sz[n])
out_mat[V, n] <- c # c holds the value of cor(a,b)
}
}
Upvotes: 2
Reputation: 60934
What you can do with -Inf is replace that by NA, for example:
x = runif(10)
x[3] = 1/0
> is.infinite(x)
[1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
x[is.infinite(x)] <- NA
> x
[1] 0.09936348 0.66624531 NA 0.90689357 0.71578917 0.14655174
[7] 0.59561047 0.41944552 0.67203026 0.03263173
And use the na.rm
argument for sd
:
> sd(x, na.rm = TRUE)
[1] 0.3126829
Upvotes: 2