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Last data update: 2014.03.03

R: Print AIC Values

PrintAIC

R Documentation

Print AIC Values

Description

Calculates and outputs AIC value for some models including ARMA, ARIMA, SARIMA, ARMAX, ARIMAX, SARIMAX, ARCH and GARCH. To classify and extracts the best model by AIC values.

Usage

PrintAIC(DataTimeSeries, order = c(p, d = NULL, q = NULL), 
seas = list(order = c(P = NULL, D = NULL, Q = NULL), frequency = NULL), 
type = NULL,xreg=NULL)

Arguments

`DataTimeSeries`	Observation series.
`order`	If type="ARMA" (or ARMAX) then 'order' is a vector contain two positive integer which are order of ARMA model (or ARMAX model). If type="ARIMA" (or ARIMAX) then 'order' is a vector contain three positive integer which are order of ARIMA model (or ARIMAX model). If type="SARIMA" (or SARIMAX) then 'order' is a vector contain three positive integer which are order of ARIMA model (or ARIMAX model) of the non-seasonal part of the SARIMA model (or SARIMAX model). If type="ARCH" then 'order' is a positive integer which are order of ARCH model. If type="GARCH" then 'order' is a vector contain two positive integer which are order of GARCH model.
`seas`	A list contain two part which are 'order' and 'frequency'. 'order' part is a vector contain three positive integer which are order of ARIMA model of the seasonal part of the SARIMA model (or SARIMAX model). 'frequency' part is frequency of observation series.
`type`	Type of models.
`xreg`	Optionally, a vector or matrix of external regressors, which must have the same number of rows as x.

Details

The first, function identify type of models according to 'type' parameter. The next, test other parameters. All of parameters are reasonable, function will combine orders of models. And then, to calculate AIC value of each model. The last step, performing ranked and extracting the best model.

Value

`mohinh`	Calculation results.
`best`	The best model following AIC value.

Note

You must be careful with 'order' and 'type' parameter of models.

Author(s)

Tran Thi Ngoc Han <tranthingochan01011994@gmail.com>

Mai Thi Hong Diem <maidiemks@gmail.com>

Hong Viet Minh <hongvietminh@gmail.com>

References

Nguyen Thi Diem My va Hong Viet Minh, Phan tich chuoi thoi gian voi su ho tro cua package AnalyzeTS.

Hong Viet Minh, Luan van tot nghiep dai hoc: Phan tich so lieu thong ke voi ngon ngu R.

Examples

sl<-c(180,165,110,126,125,134,163,153,122,171,171,155
,175,248,99,187,173,147,184,108,171,195,192,163)
PrintAIC(sl,order=c(1,4),type="ARMA")
PrintAIC(sl,order=c(1,1,4),type="ARIMA")
PrintAIC(sl,order=c(1,1,4),seas=list(order=c(0,0,1),frequency=4),
type="SARIMA")
PrintAIC(sl,order=c(4),type="ARCH")
PrintAIC(sl,order=c(1,4),type="GARCH")


#The ARIMAX models
#A factor
date<-as.factor(c("Tue","Wed","Thu","Fri","Mon","Tue","Wed",
"Thu","Fri","Mon","Tue","Wed","Thu","Fri","Mon","Tue","Wed",
"Thu","Fri","Mon","Tue","Wed","Thu","Fri","Mon","Tue","Wed",
"Thu","Fri","Mon","Tue","Wed","Thu"))

#Observation series.
coffee<-c(5,6,8,4,3,7,6,0,3,2,3,4,9,1,3,8,7,8,2,3,8,6,4,
4,6,7,6,5,2,3,8,4,4)
coffee<-ts(coffee,start=c(1,2),frequency=5)
Mon<-1*(date=="Mon")
event<-data.frame(Mon)
PrintAIC(coffee,order=c(2,2),xreg=event,type="ARMAX")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(AnalyzeTS)
Loading required package: MASS
Loading required package: TSA
Loading required package: leaps
Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-12. For overview type 'help("mgcv-package")'.
Loading required package: tseries

Attaching package: 'TSA'

The following objects are masked from 'package:stats':

    acf, arima

The following object is masked from 'package:utils':

    tar

Loading required package: TTR
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AnalyzeTS/PrintAIC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: PrintAIC
> ### Title: Print AIC Values
> ### Aliases: PrintAIC
> ### Keywords: PrintAIC
> 
> ### ** Examples
> 
> sl<-c(180,165,110,126,125,134,163,153,122,171,171,155
+ ,175,248,99,187,173,147,184,108,171,195,192,163)
> PrintAIC(sl,order=c(1,4),type="ARMA")
$Models
        ARMA models      AIC values  Sort by AIC
Model 1   ARMA(0,1) ...AIC = 241.72 .......... 2
Model 2   ARMA(0,2) ...AIC = 243.61 .......... 3
Model 3   ARMA(0,3)  ...AIC = 244.9 .......... 5
Model 4   ARMA(0,4) ...AIC = 246.78 .......... 8
Model 5   ARMA(1,0)  ...AIC = 241.7 .......... 1
Model 6   ARMA(1,1)  ...AIC = 243.7 .......... 4
Model 7   ARMA(1,2) ...AIC = 245.41 .......... 6
Model 8   ARMA(1,3) ...AIC = 246.52 .......... 7
Model 9   ARMA(1,4) ...AIC = 248.46 .......... 9

$Best
       The best ARMA model
  ARMA(1,0) ...AIC = 241.7

> PrintAIC(sl,order=c(1,1,4),type="ARIMA")
$Models
        ARIMA models      AIC values  Sort by AIC
Model 1 ARIMA(0,1,1) ...AIC = 234.01 .......... 1
Model 2 ARIMA(0,1,2) ...AIC = 235.42 .......... 2
Model 3 ARIMA(0,1,3) ...AIC = 237.42 .......... 4
Model 4 ARIMA(0,1,4) ...AIC = 238.77 .......... 6
Model 5 ARIMA(1,1,0) ...AIC = 240.62 .......... 8
Model 6 ARIMA(1,1,1) ...AIC = 235.48 .......... 3
Model 7 ARIMA(1,1,2) ...AIC = 237.42 .......... 5
Model 8 ARIMA(1,1,3) ...AIC = 239.32 .......... 7
Model 9 ARIMA(1,1,4) ...AIC = 240.78 .......... 9

$Best
          The best ARIMA model
  ARIMA(0,1,1) ...AIC = 234.01

> PrintAIC(sl,order=c(1,1,4),seas=list(order=c(0,0,1),frequency=4),
+ type="SARIMA")
$Models
                    SARIMA models      AIC values  Sort by AIC
Model 1 SARIMA(0,1,1)*(0,0,1),s=4 ...AIC = 235.74 .......... 2
Model 2 SARIMA(0,1,2)*(0,0,1),s=4 ...AIC = 237.42 .......... 3
Model 3 SARIMA(0,1,3)*(0,0,1),s=4 ...AIC = 238.77 .......... 5
Model 4 SARIMA(0,1,4)*(0,0,1),s=4 ...AIC = 240.72 .......... 7
Model 5 SARIMA(1,1,0)*(0,0,1),s=4 ...AIC = 235.48 .......... 1
Model 6 SARIMA(1,1,1)*(0,0,1),s=4 ...AIC = 237.55 .......... 4
Model 7 SARIMA(1,1,2)*(0,0,1),s=4 ...AIC = 239.36 .......... 6
Model 8 SARIMA(1,1,3)*(0,0,1),s=4 ...AIC = 240.78 .......... 8
Model 9 SARIMA(1,1,4)*(0,0,1),s=4 ...AIC = 242.76 .......... 9

$Best
                      The best SARIMA model
  SARIMA(1,1,0)*(0,0,1),s=4 ...AIC = 235.48

> PrintAIC(sl,order=c(4),type="ARCH")
$Models
        ARCH models      AIC values  Sort by AIC
Model 1     ARCH(1) ...AIC = 305.25 .......... 4
Model 2     ARCH(2)  ...AIC = 292.7 .......... 3
Model 3     ARCH(3) ...AIC = 281.79 .......... 2
Model 4     ARCH(4) ...AIC = 271.12 .......... 1

$Best
      The best ARCH model
  ARCH(4) ...AIC = 271.12

Warning messages:
1: In sqrt(pred$e) : NaNs produced
2: In sqrt(pred$e) : NaNs produced
3: In sqrt(pred$e) : NaNs produced
4: In sqrt(pred$e) : NaNs produced
> PrintAIC(sl,order=c(1,4),type="GARCH")
$mohinh
         GARCH models      AIC values  Sort by AIC
Model  1   GARCH(1,0) ...AIC = 303.16 .......... 4
Model  2   GARCH(1,1) ...AIC = 305.16 .......... 5
Model  3   GARCH(1,2) ...AIC = 295.36 .......... 3
Model  4   GARCH(1,3) ...AIC = 285.56 .......... 2
Model  5   GARCH(1,4) ...AIC = 275.69 .......... 1

$best
        The best GARCH model
  GARCH(1,4) ...AIC = 275.69

Warning messages:
1: In garch(DataTimeSeries, order = c(i1, i2), trace = FALSE) :
  singular information
2: In garch(DataTimeSeries, order = c(i1, i2), trace = FALSE) :
  singular information
3: In sqrt(pred$e) : NaNs produced
4: In sqrt(pred$e) : NaNs produced
5: In sqrt(pred$e) : NaNs produced
> 
> 
> #The ARIMAX models
> #A factor
> date<-as.factor(c("Tue","Wed","Thu","Fri","Mon","Tue","Wed",
+ "Thu","Fri","Mon","Tue","Wed","Thu","Fri","Mon","Tue","Wed",
+ "Thu","Fri","Mon","Tue","Wed","Thu","Fri","Mon","Tue","Wed",
+ "Thu","Fri","Mon","Tue","Wed","Thu"))
> 
> #Observation series.
> coffee<-c(5,6,8,4,3,7,6,0,3,2,3,4,9,1,3,8,7,8,2,3,8,6,4,
+ 4,6,7,6,5,2,3,8,4,4)
> coffee<-ts(coffee,start=c(1,2),frequency=5)
> Mon<-1*(date=="Mon")
> event<-data.frame(Mon)
> PrintAIC(coffee,order=c(2,2),xreg=event,type="ARMAX")
$Models
        ARMAX models      AIC values  Sort by AIC
Model 1   ARMAX(0,1) ...AIC = 153.47 .......... 3
Model 2   ARMAX(0,2) ...AIC = 152.25 .......... 2
Model 3   ARMAX(1,0) ...AIC = 153.48 .......... 4
Model 4   ARMAX(1,1) ...AIC = 154.12 .......... 6
Model 5   ARMAX(1,2) ...AIC = 154.21 .......... 7
Model 6   ARMAX(2,0)    ...AIC = 152 .......... 1
Model 7   ARMAX(2,1) ...AIC = 153.82 .......... 5
Model 8   ARMAX(2,2) ...AIC = 155.82 .......... 8

$Best
     The best ARMAX model
  ARMAX(2,0) ...AIC = 152

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>