Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(MixMatrix)
## ----mnorm--------------------------------------------------------------------
set.seed(20180203)
x <- matrix(rnorm(6),nrow=3)
x
set.seed(20180203)
y <- rmatrixnorm(n = 1, mean=matrix(rep(0,6),nrow=3))
y
U <- 5 * diag(3) + 1
V <- matrix(c(2,0,0,.1),nrow=2)
mu = matrix(1:6,nrow=3)
set.seed(20180203)
z <- rmatrixnorm(n = 1, mean=mu,U=U,V=V)
z
mu + t(chol(U)) %*% y %*% chol(V)
## ----noptions-----------------------------------------------------------------
set.seed(20180203)
x <- rmatrixnorm(n = 1, mean=matrix(rep(0,6),nrow=3))
x
set.seed(20180203)
y <- rmatrixnorm(n = 100, mean=matrix(rep(0,6),nrow=3),list = TRUE)
y[[1]]
set.seed(20180203)
z <- rmatrixnorm(n = 100, mean=matrix(rep(0,6),nrow=3),array = TRUE)
z[ , , 1]
## ----density------------------------------------------------------------------
set.seed(20180202)
A = rmatrixnorm(n=1,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
L=matrix(c(2,1,0,.1),nrow=2))
dmatrixnorm(A,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
L=matrix(c(2,1,0,.1),nrow=2),log=TRUE )
## ----mleone-------------------------------------------------------------------
set.seed(20180202)
A = rmatrixnorm(n=100,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
L=matrix(c(2,1,0,.1),nrow=2),list=TRUE)
results=MLmatrixnorm(A)
print(results)
## ----mlerow-------------------------------------------------------------------
results.fixrows = MLmatrixnorm(A, row.mean = TRUE, max.iter = 5)
print(results.fixrows)
# this failure is expected with misspecification! The number of iterations is also
# fixed to be low so the vignette compiles quickly.
## ----mlevar,warning=FALSE-----------------------------------------------------
# tolerance and maximum iterations are limited here to make the vignette compile faster
B = rmatrixnorm(n = 50, mean = matrix(1:15,nrow = 5),
U = 3*stats::toeplitz(.6^(1:5)))
MLmatrixnorm(B, row.variance = "AR(1)", tol = 1e-5)
MLmatrixnorm(B, tol = 1e-5)
## ----sessioninfo--------------------------------------------------------------
sessionInfo()
## ----getlabels, echo = FALSE--------------------------------------------------
labs = knitr::all_labels()
labs = labs[!labs %in% c("setup", "toc", "getlabels", "allcode")]
## ----allcode, ref.label = labs, eval = FALSE----------------------------------
# knitr::opts_chunk$set(
# collapse = TRUE,
# comment = "#>"
# )
# set.seed(20180203)
# x <- matrix(rnorm(6),nrow=3)
# x
# set.seed(20180203)
# y <- rmatrixnorm(n = 1, mean=matrix(rep(0,6),nrow=3))
# y
# U <- 5 * diag(3) + 1
# V <- matrix(c(2,0,0,.1),nrow=2)
# mu = matrix(1:6,nrow=3)
# set.seed(20180203)
# z <- rmatrixnorm(n = 1, mean=mu,U=U,V=V)
# z
# mu + t(chol(U)) %*% y %*% chol(V)
#
# set.seed(20180203)
# x <- rmatrixnorm(n = 1, mean=matrix(rep(0,6),nrow=3))
# x
# set.seed(20180203)
# y <- rmatrixnorm(n = 100, mean=matrix(rep(0,6),nrow=3),list = TRUE)
# y[[1]]
# set.seed(20180203)
# z <- rmatrixnorm(n = 100, mean=matrix(rep(0,6),nrow=3),array = TRUE)
# z[ , , 1]
#
# set.seed(20180202)
# A = rmatrixnorm(n=1,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
# L=matrix(c(2,1,0,.1),nrow=2))
# dmatrixnorm(A,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
# L=matrix(c(2,1,0,.1),nrow=2),log=TRUE )
#
# set.seed(20180202)
# A = rmatrixnorm(n=100,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
# L=matrix(c(2,1,0,.1),nrow=2),list=TRUE)
# results=MLmatrixnorm(A)
# print(results)
# results.fixrows = MLmatrixnorm(A, row.mean = TRUE, max.iter = 5)
# print(results.fixrows)
# # this failure is expected with misspecification! The number of iterations is also
# # fixed to be low so the vignette compiles quickly.
# # tolerance and maximum iterations are limited here to make the vignette compile faster
#
# B = rmatrixnorm(n = 50, mean = matrix(1:15,nrow = 5),
# U = 3*stats::toeplitz(.6^(1:5)))
# MLmatrixnorm(B, row.variance = "AR(1)", tol = 1e-5)
# MLmatrixnorm(B, tol = 1e-5)
#
# sessionInfo()
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