[英]How to vectorize likelihood calculation under multiple parameters?
I am trying to implement a bernoulli mixture and was wondering how to vectorize the calculations correctly without looping. 我正在尝试实现bernoulli混合,并且想知道如何正确向量化计算而不会循环。
I have tried various versions of apply but can't get the desired output (dim = c(5,4,2). Should my component parameters be in a list instead of a matrix? 我尝试过各种版本的Apply,但无法获得所需的输出(dim = c(5,4,2)。我的组件参数应该在列表中而不是在矩阵中吗?
set.seed(123)
#Data
X <- matrix(sample(c(0,1), 20, replace = TRUE, prob = c(.6, .4)),
nrow = 5, ncol = 4)
#Params
parameters <- matrix(runif(8), nrow = 2, ncol = 4)
#Would like to vectorize this
dbinom(X, 1, parameters[1,], log = TRUE)
dbinom(X, 1, parameters[2,], log = TRUE)
We loop through the rows of parameters
with apply
and apply the dbinom
我们使用apply
dbinom
parameters
行,然后应用dbinom
out1 <- do.call(`c`, apply(parameters, 1, function(x)
list(dbinom(X, 1, x, log = TRUE))))
identical(out1[[1]], dbinom(X, 1, parameters[1,], log = TRUE))
#[1] TRUE
identical(out1[[2]], dbinom(X, 1, parameters[2,], log = TRUE))
#[1] TRUE
Or using pmap
或使用pmap
library(purrr)
out2 <- pmap(list(x = list(X), size = 1, prob = split(parameters,
row(parameters)), log = TRUE), dbinom)
identical(out1, out2)
#[1] TRUE
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