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inst/doc/matrix-t-estimation.R
In MixMatrix: Classification with Matrix Variate Normal and t Distributions
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(MixMatrix)
## ----mleone-------------------------------------------------------------------
set.seed(20190622)
sigma = (1/7) * rWishart(1, 7, 1*diag(3:1))[,,1]
A = rmatrixt(n=100,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
V = sigma, df = 7)
results=MLmatrixt(A, df = 7)
print(results)
## ----dontrun, eval=FALSE, echo = FALSE----------------------------------------
# ### Here is the long simulation
# library(ggplot2)
#
# set.seed(20181102)
#
# df = c(5, 10, 20)
# df5 <- rep(0,200)
# df10 <- rep(0,200)
# df100 <- rep(0,200)
# df550 <- rep(0,200)
# df1050 <- rep(0,200)
# df2050 <- rep(0,200)
# df5100 <- rep(0,200)
# df10100 <- rep(0,200)
# df20100 <- rep(0,200)
#
# meanmat = matrix(0,5,3)
# U = diag(5)
# V = diag(3)
#
# for(i in 1:200){
# df5[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 35, U =U, V =V), fixed = FALSE)$nu
# df10[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 35, U =U, V =V), fixed = FALSE)$nu
# df100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 35, U =U, V =V), fixed = FALSE)$nu
# df550[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 50, U =U, V =V), fixed = FALSE)$nu
# df1050[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 50, U =U, V =V), fixed = FALSE)$nu
# df2050[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 50, U =U, V =V), fixed = FALSE)$nu
# df5100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 100, U =U, V =V), fixed = FALSE)$nu
# df10100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 100, U =U, V =V), fixed = FALSE)$nu
# df20100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 100, U =U, V =V), fixed = FALSE)$nu
# }
#
# truedataframe = data.frame(truedf = factor(c(5,10,20),
# label = c('5 df', '10 df', '20 df')),
# estdf = c(5,10,20))
#
# dfdataframe = data.frame(truedf = factor(rep(rep(c(5,10,20), each = 200),3),
# label = c('5 df', '10 df', '20 df')),
# estdf = c(df5, df10, df100, df550, df1050, df2050, df5100, df10100, df20100),
# samplesize = factor(rep(c(35,50,100), each = 600)))
# library(tidyverse)
#
# denseplot <- ggplot(data = subset(dfdataframe, estdf < 200),
# aes(x=estdf, fill=samplesize)) +
# geom_density(alpha = .5) +
# geom_vline(data = truedataframe,
# mapping = aes(xintercept = estdf),
# size = .5) +
# theme_bw() +
# theme(axis.ticks.y=element_blank(), axis.text.y=element_blank(),
# strip.text = element_text(size = 8),
# legend.justification=c(1,0), legend.position=c(.95,.4),
# legend.background = element_blank(),
# legend.text =element_text(size = 8), legend.title = element_text(size = 8)) +
# ggtitle("Density plot of estimated degrees of freedom compared to actual") +
# xlab(NULL) +
# ylab(NULL) +
# scale_fill_manual(values = c("#050505", "#E69F00", "#56B4E9"),
# name = "Sample Size") +
# facet_wrap(factor(truedf)~., scales="free") +
# NULL
# denseplot
#
#
# knitr::kable(dfdataframe %>% group_by(truedf, samplesize) %>%
# summarize(min = min(estdf), max = max(estdf),
# median = median(estdf),
# mean=mean(estdf),
# sd = sd(estdf)))
# ### Here ends the long simulation
#
## ----dftenexample, echo = FALSE, message = FALSE, warning = FALSE-------------
##### Here is what is really run
set.seed(20190621)
df10 <- rep(0,50)
df1050 <- rep(0,50)
df10100 <- rep(0,50)
for(i in 1:50){
df10[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 25), fixed = FALSE, df = 5, max.iter = 20)$nu)
df1050[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 50), fixed = FALSE, df = 5, max.iter = 20)$nu)
df10100[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 100), fixed = FALSE, df = 5, max.iter = 20)$nu)
}
dfdataframe = data.frame(label = c('10 df'),
estdf = c(df10, df1050, df10100),
samplesize = factor(rep(c(25,50,100), each = 50)))
library(ggplot2)
library(dplyr)
library(magrittr)
denseplot <- ggplot(data = subset(dfdataframe, estdf < 200),aes(x=estdf, fill=samplesize)) +
geom_density(alpha = .5) +
geom_vline(mapping = aes(xintercept = 10),
size = .5) +
theme_bw() +
theme(axis.ticks.y=element_blank(), axis.text.y=element_blank(), strip.text = element_text(size = 8),
legend.justification=c(1,0), legend.position=c(.95,.4),
legend.background = element_blank(),
legend.text =element_text(size = 8), legend.title = element_text(size = 8)) +
ggtitle("Density plot of estimated degrees of freedom compared to actual") +
xlab(NULL) +
ylab(NULL) +
scale_fill_manual(values = c("#050505", "#E69F00", "#56B4E9"), name = "Sample Size") +
# facet_wrap(factor(truedf)~., scales="free") +
NULL
denseplot
knitr::kable(dfdataframe %>% group_by(samplesize) %>%
summarize(min = min(estdf), max = max(estdf),
median = median(estdf),
mean=mean(estdf),
sd = sd(estdf)))
#### Here ends what is really run
## ----genlda-------------------------------------------------------------------
A <- rmatrixt(30, mean = matrix(0, nrow=2, ncol=3), df = 10)
B <- rmatrixt(30, mean = matrix(c(1,0), nrow=2, ncol=3), df = 10)
C <- rmatrixt(30, mean = matrix(c(0,1), nrow=2, ncol=3), df = 10)
ABC <- array(c(A,B,C), dim = c(2,3,90))
groups <- factor(c(rep("A",30),rep("B",30),rep("C",30)))
prior = c(30,30,30)/90
matlda <- matrixlda(x = ABC,grouping = groups, prior = prior,
method = 't', nu = 10, fixed = TRUE)
predict(matlda, newdata = ABC[,,c(1,31,61)])
## ----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(20190622)
# sigma = (1/7) * rWishart(1, 7, 1*diag(3:1))[,,1]
# A = rmatrixt(n=100,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
# V = sigma, df = 7)
# results=MLmatrixt(A, df = 7)
# print(results)
# ### Here is the long simulation
# library(ggplot2)
#
# set.seed(20181102)
#
# df = c(5, 10, 20)
# df5 <- rep(0,200)
# df10 <- rep(0,200)
# df100 <- rep(0,200)
# df550 <- rep(0,200)
# df1050 <- rep(0,200)
# df2050 <- rep(0,200)
# df5100 <- rep(0,200)
# df10100 <- rep(0,200)
# df20100 <- rep(0,200)
#
# meanmat = matrix(0,5,3)
# U = diag(5)
# V = diag(3)
#
# for(i in 1:200){
# df5[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 35, U =U, V =V), fixed = FALSE)$nu
# df10[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 35, U =U, V =V), fixed = FALSE)$nu
# df100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 35, U =U, V =V), fixed = FALSE)$nu
# df550[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 50, U =U, V =V), fixed = FALSE)$nu
# df1050[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 50, U =U, V =V), fixed = FALSE)$nu
# df2050[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 50, U =U, V =V), fixed = FALSE)$nu
# df5100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 100, U =U, V =V), fixed = FALSE)$nu
# df10100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 100, U =U, V =V), fixed = FALSE)$nu
# df20100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 100, U =U, V =V), fixed = FALSE)$nu
# }
#
# truedataframe = data.frame(truedf = factor(c(5,10,20),
# label = c('5 df', '10 df', '20 df')),
# estdf = c(5,10,20))
#
# dfdataframe = data.frame(truedf = factor(rep(rep(c(5,10,20), each = 200),3),
# label = c('5 df', '10 df', '20 df')),
# estdf = c(df5, df10, df100, df550, df1050, df2050, df5100, df10100, df20100),
# samplesize = factor(rep(c(35,50,100), each = 600)))
# library(tidyverse)
#
# denseplot <- ggplot(data = subset(dfdataframe, estdf < 200),
# aes(x=estdf, fill=samplesize)) +
# geom_density(alpha = .5) +
# geom_vline(data = truedataframe,
# mapping = aes(xintercept = estdf),
# size = .5) +
# theme_bw() +
# theme(axis.ticks.y=element_blank(), axis.text.y=element_blank(),
# strip.text = element_text(size = 8),
# legend.justification=c(1,0), legend.position=c(.95,.4),
# legend.background = element_blank(),
# legend.text =element_text(size = 8), legend.title = element_text(size = 8)) +
# ggtitle("Density plot of estimated degrees of freedom compared to actual") +
# xlab(NULL) +
# ylab(NULL) +
# scale_fill_manual(values = c("#050505", "#E69F00", "#56B4E9"),
# name = "Sample Size") +
# facet_wrap(factor(truedf)~., scales="free") +
# NULL
# denseplot
#
#
# knitr::kable(dfdataframe %>% group_by(truedf, samplesize) %>%
# summarize(min = min(estdf), max = max(estdf),
# median = median(estdf),
# mean=mean(estdf),
# sd = sd(estdf)))
# ### Here ends the long simulation
#
# ##### Here is what is really run
#
# set.seed(20190621)
# df10 <- rep(0,50)
# df1050 <- rep(0,50)
# df10100 <- rep(0,50)
#
# for(i in 1:50){
# df10[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 25), fixed = FALSE, df = 5, max.iter = 20)$nu)
# df1050[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 50), fixed = FALSE, df = 5, max.iter = 20)$nu)
# df10100[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 100), fixed = FALSE, df = 5, max.iter = 20)$nu)
# }
#
#
# dfdataframe = data.frame(label = c('10 df'),
# estdf = c(df10, df1050, df10100),
# samplesize = factor(rep(c(25,50,100), each = 50)))
# library(ggplot2)
# library(dplyr)
# library(magrittr)
# denseplot <- ggplot(data = subset(dfdataframe, estdf < 200),aes(x=estdf, fill=samplesize)) +
# geom_density(alpha = .5) +
# geom_vline(mapping = aes(xintercept = 10),
# size = .5) +
# theme_bw() +
# theme(axis.ticks.y=element_blank(), axis.text.y=element_blank(), strip.text = element_text(size = 8),
# legend.justification=c(1,0), legend.position=c(.95,.4),
# legend.background = element_blank(),
# legend.text =element_text(size = 8), legend.title = element_text(size = 8)) +
# ggtitle("Density plot of estimated degrees of freedom compared to actual") +
# xlab(NULL) +
# ylab(NULL) +
# scale_fill_manual(values = c("#050505", "#E69F00", "#56B4E9"), name = "Sample Size") +
# # facet_wrap(factor(truedf)~., scales="free") +
# NULL
# denseplot
#
#
# knitr::kable(dfdataframe %>% group_by(samplesize) %>%
# summarize(min = min(estdf), max = max(estdf),
# median = median(estdf),
# mean=mean(estdf),
# sd = sd(estdf)))
#
# #### Here ends what is really run
#
# A <- rmatrixt(30, mean = matrix(0, nrow=2, ncol=3), df = 10)
# B <- rmatrixt(30, mean = matrix(c(1,0), nrow=2, ncol=3), df = 10)
# C <- rmatrixt(30, mean = matrix(c(0,1), nrow=2, ncol=3), df = 10)
# ABC <- array(c(A,B,C), dim = c(2,3,90))
# groups <- factor(c(rep("A",30),rep("B",30),rep("C",30)))
# prior = c(30,30,30)/90
# matlda <- matrixlda(x = ABC,grouping = groups, prior = prior,
# method = 't', nu = 10, fixed = TRUE)
# predict(matlda, newdata = ABC[,,c(1,31,61)])
#
# sessionInfo()
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## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(MixMatrix)
## ----mleone-------------------------------------------------------------------
set.seed(20190622)
sigma = (1/7) * rWishart(1, 7, 1*diag(3:1))[,,1]
A = rmatrixt(n=100,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
V = sigma, df = 7)
results=MLmatrixt(A, df = 7)
print(results)
## ----dontrun, eval=FALSE, echo = FALSE----------------------------------------
# ### Here is the long simulation
# library(ggplot2)
#
# set.seed(20181102)
#
# df = c(5, 10, 20)
# df5 <- rep(0,200)
# df10 <- rep(0,200)
# df100 <- rep(0,200)
# df550 <- rep(0,200)
# df1050 <- rep(0,200)
# df2050 <- rep(0,200)
# df5100 <- rep(0,200)
# df10100 <- rep(0,200)
# df20100 <- rep(0,200)
#
# meanmat = matrix(0,5,3)
# U = diag(5)
# V = diag(3)
#
# for(i in 1:200){
# df5[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 35, U =U, V =V), fixed = FALSE)$nu
# df10[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 35, U =U, V =V), fixed = FALSE)$nu
# df100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 35, U =U, V =V), fixed = FALSE)$nu
# df550[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 50, U =U, V =V), fixed = FALSE)$nu
# df1050[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 50, U =U, V =V), fixed = FALSE)$nu
# df2050[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 50, U =U, V =V), fixed = FALSE)$nu
# df5100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 100, U =U, V =V), fixed = FALSE)$nu
# df10100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 100, U =U, V =V), fixed = FALSE)$nu
# df20100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 100, U =U, V =V), fixed = FALSE)$nu
# }
#
# truedataframe = data.frame(truedf = factor(c(5,10,20),
# label = c('5 df', '10 df', '20 df')),
# estdf = c(5,10,20))
#
# dfdataframe = data.frame(truedf = factor(rep(rep(c(5,10,20), each = 200),3),
# label = c('5 df', '10 df', '20 df')),
# estdf = c(df5, df10, df100, df550, df1050, df2050, df5100, df10100, df20100),
# samplesize = factor(rep(c(35,50,100), each = 600)))
# library(tidyverse)
#
# denseplot <- ggplot(data = subset(dfdataframe, estdf < 200),
# aes(x=estdf, fill=samplesize)) +
# geom_density(alpha = .5) +
# geom_vline(data = truedataframe,
# mapping = aes(xintercept = estdf),
# size = .5) +
# theme_bw() +
# theme(axis.ticks.y=element_blank(), axis.text.y=element_blank(),
# strip.text = element_text(size = 8),
# legend.justification=c(1,0), legend.position=c(.95,.4),
# legend.background = element_blank(),
# legend.text =element_text(size = 8), legend.title = element_text(size = 8)) +
# ggtitle("Density plot of estimated degrees of freedom compared to actual") +
# xlab(NULL) +
# ylab(NULL) +
# scale_fill_manual(values = c("#050505", "#E69F00", "#56B4E9"),
# name = "Sample Size") +
# facet_wrap(factor(truedf)~., scales="free") +
# NULL
# denseplot
#
#
# knitr::kable(dfdataframe %>% group_by(truedf, samplesize) %>%
# summarize(min = min(estdf), max = max(estdf),
# median = median(estdf),
# mean=mean(estdf),
# sd = sd(estdf)))
# ### Here ends the long simulation
#
## ----dftenexample, echo = FALSE, message = FALSE, warning = FALSE-------------
##### Here is what is really run
set.seed(20190621)
df10 <- rep(0,50)
df1050 <- rep(0,50)
df10100 <- rep(0,50)
for(i in 1:50){
df10[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 25), fixed = FALSE, df = 5, max.iter = 20)$nu)
df1050[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 50), fixed = FALSE, df = 5, max.iter = 20)$nu)
df10100[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 100), fixed = FALSE, df = 5, max.iter = 20)$nu)
}
dfdataframe = data.frame(label = c('10 df'),
estdf = c(df10, df1050, df10100),
samplesize = factor(rep(c(25,50,100), each = 50)))
library(ggplot2)
library(dplyr)
library(magrittr)
denseplot <- ggplot(data = subset(dfdataframe, estdf < 200),aes(x=estdf, fill=samplesize)) +
geom_density(alpha = .5) +
geom_vline(mapping = aes(xintercept = 10),
size = .5) +
theme_bw() +
theme(axis.ticks.y=element_blank(), axis.text.y=element_blank(), strip.text = element_text(size = 8),
legend.justification=c(1,0), legend.position=c(.95,.4),
legend.background = element_blank(),
legend.text =element_text(size = 8), legend.title = element_text(size = 8)) +
ggtitle("Density plot of estimated degrees of freedom compared to actual") +
xlab(NULL) +
ylab(NULL) +
scale_fill_manual(values = c("#050505", "#E69F00", "#56B4E9"), name = "Sample Size") +
# facet_wrap(factor(truedf)~., scales="free") +
NULL
denseplot
knitr::kable(dfdataframe %>% group_by(samplesize) %>%
summarize(min = min(estdf), max = max(estdf),
median = median(estdf),
mean=mean(estdf),
sd = sd(estdf)))
#### Here ends what is really run
## ----genlda-------------------------------------------------------------------
A <- rmatrixt(30, mean = matrix(0, nrow=2, ncol=3), df = 10)
B <- rmatrixt(30, mean = matrix(c(1,0), nrow=2, ncol=3), df = 10)
C <- rmatrixt(30, mean = matrix(c(0,1), nrow=2, ncol=3), df = 10)
ABC <- array(c(A,B,C), dim = c(2,3,90))
groups <- factor(c(rep("A",30),rep("B",30),rep("C",30)))
prior = c(30,30,30)/90
matlda <- matrixlda(x = ABC,grouping = groups, prior = prior,
method = 't', nu = 10, fixed = TRUE)
predict(matlda, newdata = ABC[,,c(1,31,61)])
## ----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(20190622)
# sigma = (1/7) * rWishart(1, 7, 1*diag(3:1))[,,1]
# A = rmatrixt(n=100,mean=matrix(c(100,0,-100,0,25,-1000),nrow=2),
# V = sigma, df = 7)
# results=MLmatrixt(A, df = 7)
# print(results)
# ### Here is the long simulation
# library(ggplot2)
#
# set.seed(20181102)
#
# df = c(5, 10, 20)
# df5 <- rep(0,200)
# df10 <- rep(0,200)
# df100 <- rep(0,200)
# df550 <- rep(0,200)
# df1050 <- rep(0,200)
# df2050 <- rep(0,200)
# df5100 <- rep(0,200)
# df10100 <- rep(0,200)
# df20100 <- rep(0,200)
#
# meanmat = matrix(0,5,3)
# U = diag(5)
# V = diag(3)
#
# for(i in 1:200){
# df5[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 35, U =U, V =V), fixed = FALSE)$nu
# df10[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 35, U =U, V =V), fixed = FALSE)$nu
# df100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 35, U =U, V =V), fixed = FALSE)$nu
# df550[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 50, U =U, V =V), fixed = FALSE)$nu
# df1050[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 50, U =U, V =V), fixed = FALSE)$nu
# df2050[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 50, U =U, V =V), fixed = FALSE)$nu
# df5100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 5, n = 100, U =U, V =V), fixed = FALSE)$nu
# df10100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 10, n = 100, U =U, V =V), fixed = FALSE)$nu
# df20100[i] = MLmatrixt(rmatrixt(mean = meanmat,
# df = 20, n = 100, U =U, V =V), fixed = FALSE)$nu
# }
#
# truedataframe = data.frame(truedf = factor(c(5,10,20),
# label = c('5 df', '10 df', '20 df')),
# estdf = c(5,10,20))
#
# dfdataframe = data.frame(truedf = factor(rep(rep(c(5,10,20), each = 200),3),
# label = c('5 df', '10 df', '20 df')),
# estdf = c(df5, df10, df100, df550, df1050, df2050, df5100, df10100, df20100),
# samplesize = factor(rep(c(35,50,100), each = 600)))
# library(tidyverse)
#
# denseplot <- ggplot(data = subset(dfdataframe, estdf < 200),
# aes(x=estdf, fill=samplesize)) +
# geom_density(alpha = .5) +
# geom_vline(data = truedataframe,
# mapping = aes(xintercept = estdf),
# size = .5) +
# theme_bw() +
# theme(axis.ticks.y=element_blank(), axis.text.y=element_blank(),
# strip.text = element_text(size = 8),
# legend.justification=c(1,0), legend.position=c(.95,.4),
# legend.background = element_blank(),
# legend.text =element_text(size = 8), legend.title = element_text(size = 8)) +
# ggtitle("Density plot of estimated degrees of freedom compared to actual") +
# xlab(NULL) +
# ylab(NULL) +
# scale_fill_manual(values = c("#050505", "#E69F00", "#56B4E9"),
# name = "Sample Size") +
# facet_wrap(factor(truedf)~., scales="free") +
# NULL
# denseplot
#
#
# knitr::kable(dfdataframe %>% group_by(truedf, samplesize) %>%
# summarize(min = min(estdf), max = max(estdf),
# median = median(estdf),
# mean=mean(estdf),
# sd = sd(estdf)))
# ### Here ends the long simulation
#
# ##### Here is what is really run
#
# set.seed(20190621)
# df10 <- rep(0,50)
# df1050 <- rep(0,50)
# df10100 <- rep(0,50)
#
# for(i in 1:50){
# df10[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 25), fixed = FALSE, df = 5, max.iter = 20)$nu)
# df1050[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 50), fixed = FALSE, df = 5, max.iter = 20)$nu)
# df10100[i] = suppressWarnings(MLmatrixt(rmatrixt(mean = matrix(0,5,3),df = 10, n = 100), fixed = FALSE, df = 5, max.iter = 20)$nu)
# }
#
#
# dfdataframe = data.frame(label = c('10 df'),
# estdf = c(df10, df1050, df10100),
# samplesize = factor(rep(c(25,50,100), each = 50)))
# library(ggplot2)
# library(dplyr)
# library(magrittr)
# denseplot <- ggplot(data = subset(dfdataframe, estdf < 200),aes(x=estdf, fill=samplesize)) +
# geom_density(alpha = .5) +
# geom_vline(mapping = aes(xintercept = 10),
# size = .5) +
# theme_bw() +
# theme(axis.ticks.y=element_blank(), axis.text.y=element_blank(), strip.text = element_text(size = 8),
# legend.justification=c(1,0), legend.position=c(.95,.4),
# legend.background = element_blank(),
# legend.text =element_text(size = 8), legend.title = element_text(size = 8)) +
# ggtitle("Density plot of estimated degrees of freedom compared to actual") +
# xlab(NULL) +
# ylab(NULL) +
# scale_fill_manual(values = c("#050505", "#E69F00", "#56B4E9"), name = "Sample Size") +
# # facet_wrap(factor(truedf)~., scales="free") +
# NULL
# denseplot
#
#
# knitr::kable(dfdataframe %>% group_by(samplesize) %>%
# summarize(min = min(estdf), max = max(estdf),
# median = median(estdf),
# mean=mean(estdf),
# sd = sd(estdf)))
#
# #### Here ends what is really run
#
# A <- rmatrixt(30, mean = matrix(0, nrow=2, ncol=3), df = 10)
# B <- rmatrixt(30, mean = matrix(c(1,0), nrow=2, ncol=3), df = 10)
# C <- rmatrixt(30, mean = matrix(c(0,1), nrow=2, ncol=3), df = 10)
# ABC <- array(c(A,B,C), dim = c(2,3,90))
# groups <- factor(c(rep("A",30),rep("B",30),rep("C",30)))
# prior = c(30,30,30)/90
# matlda <- matrixlda(x = ABC,grouping = groups, prior = prior,
# method = 't', nu = 10, fixed = TRUE)
# predict(matlda, newdata = ABC[,,c(1,31,61)])
#
# sessionInfo()
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