如何通过向量列表循环 function

Question

I have a list called x containing 8 vectors of different lengths, which I randomly generated like so:

set.seed(1852020)

# specify how many vectors I want
N <- c(1:8)

# set up output
x <- vector("list", length(N))

# loop to generate binomial vectors of a random length between 1 and 50
for (i in N) {
  n <- sample(50, 1, replace = TRUE)
  x[[i]] <- rbinom(n, 1, .66)
}

x
[[1]]
[1] 1 1 1 1 1

[[2]]
 [1] 1 1 1 0 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 1 1

[[3]]
 [1] 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 0 1 0 1

[[4]]
[1] 1 1

[[5]]
 [1] 1 1 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1

[[6]]
 [1] 0 1 1 1 1 0 0 1 1 1 1 1 0 1 1

[[7]]
[1] 1 1 1 1

[[8]]
 [1] 1 1 1 0 0 0 1 1 1 0 0 1 0

I now want to feed each vector through a function I have written called learn :

learn <- function(prior, e){
  out <- ifelse(is.na(e), prior,
                ifelse(e == 1, (0.6*prior) / ((0.6*prior) + (1-0.6)*(1-prior)),
                       ((1-0.6)*prior) / (((1-0.6)*prior) + (0.6*(1-prior)))))
  out
}

How can I do this so that the prior argument of my function updates between each value from x is fed through as the e argument? For example, if I start with a prior of 0.5 and feed through x[[4]] , it should do this:

> learn(0.5, 1)
[1] 0.6
> learn(0.6, 1)
[1] 0.6923077

My desired output would be a vector of 8 values corresponding to the 8 vectors contained in list x .

---

If it is helpful, x can be turned into a dataframe like so:

f <- function(data) {
  nCol <- max(vapply(data, length, 0))
  data <- lapply(data, function(row) c(row, rep(NA, nCol-length(row))))
  data <- matrix(unlist(data), nrow=length(data), ncol=nCol, byrow=TRUE)
  data.frame(data)
}

x <- f(x)

> x
  X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26 X27 X28 X29 X30 X31 X32 X33 X34 X35 X36 X37 X38 X39 X40 X41 X42 X43 X44 X45 X46 X47 X48
1  1  1  1  1  1 NA NA NA NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
2  1  1  1  0  0  1  1  0  1   0   1   0   1   1   1   1   1   1   1   1   0   1   1   0   1   0   0   1   0   1   0   0   0   0   0   1   0   0   1   0   1   0   1   0   0   1   1   1
3  0  1  1  0  0  1  0  1  1   1   1   1   1   1   1   0   1   0   1   1   0   1   0   1   0   1  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
4  1  1 NA NA NA NA NA NA NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
5  1  1  1  1  0  0  0  1  1   0   1   1   0   0   1   1   0   1   1   0   1   1   1   1   0   1   1   0   1   1  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
6  0  1  1  1  1  0  0  1  1   1   1   1   0   1   1  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
7  1  1  1  1 NA NA NA NA NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
8  1  1  1  0  0  0  1  1  1   0   0   1   0  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA

Answer 1

Try to do this:

test <- function(vec){
  prior <- 0.5
  for (i in vec){
    res <- learn(prior, i)
    prior <- res
  }
  return(prior)
}

results <- sapply(x, test)

Here I assume that for each vector the prior starts with is 0.5. Output:

[1] 0.5000000 0.9885711 0.5000000 0.9885711 0.9948880 0.9624468 0.9746472 0.9447084

If instead you want that prior is also updated between vectors in the list do this:

test <- function(vec, new_prior){
  for (i in vec){
    res <- learn(new_prior, i)
    new_prior <- res
  }
  return(new_prior)
}

prior <- 0.5
final_array <-  c()
for (l in x){
  result <-test(l, prior)
  final_array <- c(final_array, result)
  prior <- result
}
final_array

Output:

[1] 0.5000000 0.9885711 0.9885711 0.9998664 0.9999993 1.0000000 1.0000000 1.0000000