Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationThu, 10 Dec 2009 13:51:36 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/10/t1260478468j8qbb0fzyqgoqkv.htm/, Retrieved Fri, 29 Mar 2024 06:15:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65799, Retrieved Fri, 29 Mar 2024 06:15:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Standard Deviation-Mean Plot] [Identifying Integ...] [2009-11-22 12:50:05] [b98453cac15ba1066b407e146608df68]
-    D        [Standard Deviation-Mean Plot] [Shwws8_v4] [2009-11-27 21:44:00] [5f89c040fdf1f8599c99d7f78a662321]
- RMPD            [ARIMA Backward Selection] [Paper] [2009-12-10 20:51:36] [93b66894f6318f3da4fcda772f2ffa6f] [Current]
Feedback Forum

Post a new message
Dataseries X:
17
18
23.8
25.5
25.6
23.7
22
21.3
20.7
20.4
20.3
20.4
19.8
19.5
23.1
23.5
23.5
22.9
21.9
21.5
20.5
20.2
19.4
19.2
18.8
18.8
22.6
23.3
23
21.4
19.9
18.8
18.6
18.4
18.6
19.9
19.2
18.4
21.1
20.5
19.1
18.1
17
17.1
17.4
16.8
15.3
14.3
13.4
15.3
22.1
23.7
22.2
19.5
16.6
17.3
19.8
21.2
21.5
20.6
19.1
19.6
23.5
24




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65799&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65799&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65799&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sma1
Estimates ( 1 )0.9079-0.5119-0.1853-0.1761-0.5163
(p-val)(0.0056 )(0.1188 )(0.4326 )(0.5584 )(0.0265 )
Estimates ( 2 )0.751-0.3671-0.28110-0.5169
(p-val)(0 )(0.0336 )(0.0452 )(NA )(0.0251 )
Estimates ( 3 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sma1 \tabularnewline
Estimates ( 1 ) & 0.9079 & -0.5119 & -0.1853 & -0.1761 & -0.5163 \tabularnewline
(p-val) & (0.0056 ) & (0.1188 ) & (0.4326 ) & (0.5584 ) & (0.0265 ) \tabularnewline
Estimates ( 2 ) & 0.751 & -0.3671 & -0.2811 & 0 & -0.5169 \tabularnewline
(p-val) & (0 ) & (0.0336 ) & (0.0452 ) & (NA ) & (0.0251 ) \tabularnewline
Estimates ( 3 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65799&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.9079[/C][C]-0.5119[/C][C]-0.1853[/C][C]-0.1761[/C][C]-0.5163[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0056 )[/C][C](0.1188 )[/C][C](0.4326 )[/C][C](0.5584 )[/C][C](0.0265 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.751[/C][C]-0.3671[/C][C]-0.2811[/C][C]0[/C][C]-0.5169[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0336 )[/C][C](0.0452 )[/C][C](NA )[/C][C](0.0251 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65799&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65799&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sma1
Estimates ( 1 )0.9079-0.5119-0.1853-0.1761-0.5163
(p-val)(0.0056 )(0.1188 )(0.4326 )(0.5584 )(0.0265 )
Estimates ( 2 )0.751-0.3671-0.28110-0.5169
(p-val)(0 )(0.0336 )(0.0452 )(NA )(0.0251 )
Estimates ( 3 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
-0.0148545423719779
-0.0793689209084312
-0.0994480714991075
0.00787444884259935
-0.0375328718087056
0.0115940108429038
-0.0740966427633277
0.00598695991477802
-0.00865156370251994
0.0731136410304725
-0.0545763687807771
0.0244852722814243
0.00510070027482356
-0.039387558910729
-0.0325164161485102
0.0310299167256611
-0.0504034704863571
-0.0577842407733842
-0.0175954345805030
-0.0815280121942025
0.089063986218658
-0.0679732559404774
0.0881562269030572
0.118550130867758
-0.105120535895025
0.00645597678483207
-0.0276281561580155
-0.0741136378320001
-0.115874428420748
0.0306947770637613
-0.103361642355840
0.0523083905464605
0.0230571546554772
-0.0638004867207937
-0.0701268984623983
-0.0615867544634418
0.0194493538255848
0.214578677517609
0.124849561560074
-0.0533288325814563
0.0391671660688973
0.0618247704732088
-0.0479418878908941
0.199366195249693
0.0891717334470872
-0.0246193472619558
0.122073120069364
-0.00796859797470414
0.0923386353362873
0.0307282935535182
-0.160772726008473
0.0439579951116772

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0148545423719779 \tabularnewline
-0.0793689209084312 \tabularnewline
-0.0994480714991075 \tabularnewline
0.00787444884259935 \tabularnewline
-0.0375328718087056 \tabularnewline
0.0115940108429038 \tabularnewline
-0.0740966427633277 \tabularnewline
0.00598695991477802 \tabularnewline
-0.00865156370251994 \tabularnewline
0.0731136410304725 \tabularnewline
-0.0545763687807771 \tabularnewline
0.0244852722814243 \tabularnewline
0.00510070027482356 \tabularnewline
-0.039387558910729 \tabularnewline
-0.0325164161485102 \tabularnewline
0.0310299167256611 \tabularnewline
-0.0504034704863571 \tabularnewline
-0.0577842407733842 \tabularnewline
-0.0175954345805030 \tabularnewline
-0.0815280121942025 \tabularnewline
0.089063986218658 \tabularnewline
-0.0679732559404774 \tabularnewline
0.0881562269030572 \tabularnewline
0.118550130867758 \tabularnewline
-0.105120535895025 \tabularnewline
0.00645597678483207 \tabularnewline
-0.0276281561580155 \tabularnewline
-0.0741136378320001 \tabularnewline
-0.115874428420748 \tabularnewline
0.0306947770637613 \tabularnewline
-0.103361642355840 \tabularnewline
0.0523083905464605 \tabularnewline
0.0230571546554772 \tabularnewline
-0.0638004867207937 \tabularnewline
-0.0701268984623983 \tabularnewline
-0.0615867544634418 \tabularnewline
0.0194493538255848 \tabularnewline
0.214578677517609 \tabularnewline
0.124849561560074 \tabularnewline
-0.0533288325814563 \tabularnewline
0.0391671660688973 \tabularnewline
0.0618247704732088 \tabularnewline
-0.0479418878908941 \tabularnewline
0.199366195249693 \tabularnewline
0.0891717334470872 \tabularnewline
-0.0246193472619558 \tabularnewline
0.122073120069364 \tabularnewline
-0.00796859797470414 \tabularnewline
0.0923386353362873 \tabularnewline
0.0307282935535182 \tabularnewline
-0.160772726008473 \tabularnewline
0.0439579951116772 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65799&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0148545423719779[/C][/ROW]
[ROW][C]-0.0793689209084312[/C][/ROW]
[ROW][C]-0.0994480714991075[/C][/ROW]
[ROW][C]0.00787444884259935[/C][/ROW]
[ROW][C]-0.0375328718087056[/C][/ROW]
[ROW][C]0.0115940108429038[/C][/ROW]
[ROW][C]-0.0740966427633277[/C][/ROW]
[ROW][C]0.00598695991477802[/C][/ROW]
[ROW][C]-0.00865156370251994[/C][/ROW]
[ROW][C]0.0731136410304725[/C][/ROW]
[ROW][C]-0.0545763687807771[/C][/ROW]
[ROW][C]0.0244852722814243[/C][/ROW]
[ROW][C]0.00510070027482356[/C][/ROW]
[ROW][C]-0.039387558910729[/C][/ROW]
[ROW][C]-0.0325164161485102[/C][/ROW]
[ROW][C]0.0310299167256611[/C][/ROW]
[ROW][C]-0.0504034704863571[/C][/ROW]
[ROW][C]-0.0577842407733842[/C][/ROW]
[ROW][C]-0.0175954345805030[/C][/ROW]
[ROW][C]-0.0815280121942025[/C][/ROW]
[ROW][C]0.089063986218658[/C][/ROW]
[ROW][C]-0.0679732559404774[/C][/ROW]
[ROW][C]0.0881562269030572[/C][/ROW]
[ROW][C]0.118550130867758[/C][/ROW]
[ROW][C]-0.105120535895025[/C][/ROW]
[ROW][C]0.00645597678483207[/C][/ROW]
[ROW][C]-0.0276281561580155[/C][/ROW]
[ROW][C]-0.0741136378320001[/C][/ROW]
[ROW][C]-0.115874428420748[/C][/ROW]
[ROW][C]0.0306947770637613[/C][/ROW]
[ROW][C]-0.103361642355840[/C][/ROW]
[ROW][C]0.0523083905464605[/C][/ROW]
[ROW][C]0.0230571546554772[/C][/ROW]
[ROW][C]-0.0638004867207937[/C][/ROW]
[ROW][C]-0.0701268984623983[/C][/ROW]
[ROW][C]-0.0615867544634418[/C][/ROW]
[ROW][C]0.0194493538255848[/C][/ROW]
[ROW][C]0.214578677517609[/C][/ROW]
[ROW][C]0.124849561560074[/C][/ROW]
[ROW][C]-0.0533288325814563[/C][/ROW]
[ROW][C]0.0391671660688973[/C][/ROW]
[ROW][C]0.0618247704732088[/C][/ROW]
[ROW][C]-0.0479418878908941[/C][/ROW]
[ROW][C]0.199366195249693[/C][/ROW]
[ROW][C]0.0891717334470872[/C][/ROW]
[ROW][C]-0.0246193472619558[/C][/ROW]
[ROW][C]0.122073120069364[/C][/ROW]
[ROW][C]-0.00796859797470414[/C][/ROW]
[ROW][C]0.0923386353362873[/C][/ROW]
[ROW][C]0.0307282935535182[/C][/ROW]
[ROW][C]-0.160772726008473[/C][/ROW]
[ROW][C]0.0439579951116772[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65799&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65799&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
-0.0148545423719779
-0.0793689209084312
-0.0994480714991075
0.00787444884259935
-0.0375328718087056
0.0115940108429038
-0.0740966427633277
0.00598695991477802
-0.00865156370251994
0.0731136410304725
-0.0545763687807771
0.0244852722814243
0.00510070027482356
-0.039387558910729
-0.0325164161485102
0.0310299167256611
-0.0504034704863571
-0.0577842407733842
-0.0175954345805030
-0.0815280121942025
0.089063986218658
-0.0679732559404774
0.0881562269030572
0.118550130867758
-0.105120535895025
0.00645597678483207
-0.0276281561580155
-0.0741136378320001
-0.115874428420748
0.0306947770637613
-0.103361642355840
0.0523083905464605
0.0230571546554772
-0.0638004867207937
-0.0701268984623983
-0.0615867544634418
0.0194493538255848
0.214578677517609
0.124849561560074
-0.0533288325814563
0.0391671660688973
0.0618247704732088
-0.0479418878908941
0.199366195249693
0.0891717334470872
-0.0246193472619558
0.122073120069364
-0.00796859797470414
0.0923386353362873
0.0307282935535182
-0.160772726008473
0.0439579951116772



Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 1 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')