install.packages("forecast")
library(forecast)
# 使用英国每月死于肺病的人数数据
fit<- auto.arima(mdeaths)
# 设置置信区间
forecast(fit, level=c(80, 95, 99), h=3)
# 绘制时间序列趋势图
plot(forecast(fit), shadecols="oldstyle")

install.packages("ellipse")
library(ellipse)
data(mtcars)
fit <- lm(mpg ~ ., mtcars)
plotcorr(summary(fit, correlation = TRUE)$correlation)

install.packages("dygraphs")
library(dygraphs)
data=data.frame( time=c( seq(0,20,0.5), 40), value=runif(42))
str(data)
dygraph(data)

install.packages(corrgram)
library(corrgram)
library(mtcarData)
library(mtcar)
corrgram(mtcars, order=TRUE, lower.panel=panel.ellipse,
         upper.panel=panel.pts, text.panel=panel.txt,
         diag.panel=panel.minmax,
         main="Corrgram of mtcars data using scatter plots
         and ellipses")

cols <- colorRampPalette(c("darkgoldenrod4", "burlywood1",
                           "darkkhaki", "darkgreen"))
corrgram(mtcars, order=TRUE, col.regions=cols,
         lower.panel=panel.shade,
         upper.panel=panel.conf, text.panel=panel.txt,
         main="A Corrgram (or Horse) of a Different Color")

install.packages("iterators")
install.packages("corrgram")
install.packages("kernlab")
library(iterators)
library(kernlab)
library(corrgram)
corrgram(mtcars, 
         order=TRUE,
         lower.panel=panel.shade,
         upper.panel=panel.pie,
         text.panel=panel.txt,
         main="Correlogram of mtcar intercorrelations")

install.packages("CMplot")
library("CMplot")
data(pig60K)                        
CMplot(pig60K[sample(1:nrow(pig60K),10000),c(1:4)],
plot.type="c",
threshold=c(0.01,0.05)/nrow(pig60K),
threshold.col=c('red','orange'),
multracks=FALSE, 
chr.den.col=NULL, 
file.output = F)