我正在对4x4方差协方差矩阵的后验分布执行特征分解。为此,我使用
eigen
dplyr/tidyverse管道中的函数:
set.seed(1)
# Variance and covariances of 4 variables
A1 <- rnorm(1000,10,1)
A2 <- rnorm(1000,10,1)
A3 <- rnorm(1000,10,1)
A4 <- rnorm(1000,10,1)
C12 <- rnorm(1000,0,1)
C13 <- rnorm(1000,0,1)
C14 <- rnorm(1000,0,1)
C23 <- rnorm(1000,0,1)
C24 <- rnorm(1000,0,1)
C34 <- rnorm(1000,0,1)
# Create posterior tibble
w1_post <- as_tibble(cbind(A1, C12, C13, C14, A2, C23, C24, A3, C34, A4))
# Get 1st-4th eigenvalues of each variance-covariance matrix
w1_post %>%
rowwise %>%
mutate(
eig1 =
eigen(matrix(c(A1, C12, C13, C14, C12, A2, C23, C24, C13, C23,
A3, C34, C14, C24, C34, A4), nrow = 4))[[1]][1],
eig2 =
eigen(matrix(c(A1, C12, C13, C14, C12, A2, C23, C24, C13, C23,
A3, C34, C14, C24, C34, A4), nrow = 4))[[1]][2],
eig3 =
eigen(matrix(c(A1, C12, C13, C14, C12, A2, C23, C24, C13, C23,
A3, C34, C14, C24, C34, A4), nrow = 4))[[1]][3],
eig4 =
eigen(matrix(c(A1, C12, C13, C14, C12, A2, C23, C24, C13, C23,
A3, C34, C14, C24, C34, A4), nrow = 4))[[1]][4]) %>%
select(starts_with('eig')) -> eig_post
生产
> eig_post
Source: local data frame [1,000 x 4]
Groups: <by row>
# A tibble: 1,000 x 4
eig1 eig2 eig3 eig4
<dbl> <dbl> <dbl> <dbl>
1 12.3 11.0 10.4 6.67
2 12.8 10.1 9.19 7.61
3 13.5 12.2 8.20 7.34
4 12.7 12.2 8.91 7.68
5 12.9 9.70 9.41 6.74
6 12.2 10.6 8.62 7.70
7 13.1 12.5 9.21 8.34
8 12.9 9.76 7.87 6.96
9 12.8 11.6 8.21 6.46
10 12.5 11.6 9.85 8.13
# ... with 990 more rows
如您所见,这是每行执行四次特征分解-这比实际需要的多4倍,并减慢了我的脚本!
eigen(*matrix*)[[1]][1:4]
跨越四个变量?
所以我需要得到上面的代码产生了什么,但是每行只做一个特征分解。我原以为这样行得通,但运气不好:
w1_post %>%
rowwise %>%
mutate(c(eig1, eig2, eig3, eig4) =
eigen(matrix(c(A1, C12, C13, C14, C12, A2, C23, C24, C13, C23,
A3, C34, C14, C24, C34, A4), nrow = 4))[[1]][1:4]) %>%
select(starts_with('eig')) -> eig_post