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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 computationTue, 04 Dec 2012 16:27:38 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/04/t1354656477axk998yzoijb30b.htm/, Retrieved Thu, 28 Mar 2024 15:55:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196643, Retrieved Thu, 28 Mar 2024 15:55:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-08 19:22:39] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP   [Variance Reduction Matrix] [] [2012-12-03 22:49:17] [147786ccb76fa00e429d4b9f5f28b291]
- RMP     [ARIMA Backward Selection] [] [2012-12-04 20:57:46] [147786ccb76fa00e429d4b9f5f28b291]
- R P         [ARIMA Backward Selection] [] [2012-12-04 21:27:38] [26ce3afa84a4087bb435ca409d5552c3] [Current]
- RMPD          [Skewness and Kurtosis Test] [] [2012-12-04 21:44:03] [147786ccb76fa00e429d4b9f5f28b291]
- RMPD            [ARIMA Forecasting] [] [2012-12-19 17:22:32] [147786ccb76fa00e429d4b9f5f28b291]
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Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196643&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196643&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196643&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.15350.19230.1727-0.1524
(p-val)(0.7714 )(0.3051 )(0.219 )(0.7747 )
Estimates ( 2 )-0.29940.15130.18610
(p-val)(0.0195 )(0.2446 )(0.1366 )(NA )
Estimates ( 3 )-0.339100.14550
(p-val)(0.007 )(NA )(0.2292 )(NA )
Estimates ( 4 )-0.3115000
(p-val)(0.0123 )(NA )(NA )(NA )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 \tabularnewline
Estimates ( 1 ) & -0.1535 & 0.1923 & 0.1727 & -0.1524 \tabularnewline
(p-val) & (0.7714 ) & (0.3051 ) & (0.219 ) & (0.7747 ) \tabularnewline
Estimates ( 2 ) & -0.2994 & 0.1513 & 0.1861 & 0 \tabularnewline
(p-val) & (0.0195 ) & (0.2446 ) & (0.1366 ) & (NA ) \tabularnewline
Estimates ( 3 ) & -0.3391 & 0 & 0.1455 & 0 \tabularnewline
(p-val) & (0.007 ) & (NA ) & (0.2292 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.3115 & 0 & 0 & 0 \tabularnewline
(p-val) & (0.0123 ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196643&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][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.1535[/C][C]0.1923[/C][C]0.1727[/C][C]-0.1524[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7714 )[/C][C](0.3051 )[/C][C](0.219 )[/C][C](0.7747 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.2994[/C][C]0.1513[/C][C]0.1861[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0195 )[/C][C](0.2446 )[/C][C](0.1366 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.3391[/C][C]0[/C][C]0.1455[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.007 )[/C][C](NA )[/C][C](0.2292 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.3115[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0123 )[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196643&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196643&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
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.15350.19230.1727-0.1524
(p-val)(0.7714 )(0.3051 )(0.219 )(0.7747 )
Estimates ( 2 )-0.29940.15130.18610
(p-val)(0.0195 )(0.2446 )(0.1366 )(NA )
Estimates ( 3 )-0.339100.14550
(p-val)(0.007 )(NA )(0.2292 )(NA )
Estimates ( 4 )-0.3115000
(p-val)(0.0123 )(NA )(NA )(NA )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
867.88771306744
-2589.39674546118
39314.9242944509
14453.4622141849
486.29450337017
22155.4389721592
25593.2069423751
316675.679799225
-112240.573550224
-67776.6230099865
42113.3312450601
72466.6444183648
-89635.4481509431
-45304.6035927241
-72955.0453587909
27778.7662845435
109945.838867168
-190281.842652476
58572.7872484705
-312133.435237775
194191.436344934
219610.611333987
144895.404377687
59624.459125522
169042.354388903
106033.631872625
49354.0176460777
-56330.0789116722
-100760.24497969
-10063.7778391061
-149797.518979373
-84340.0243213298
-42472.8163348963
-39425.0358647259
-78703.4126089705
-101042.744840936
-83879.2819582617
-92823.6483936284
53018.4057761265
93214.8226750383
-143259.649019981
104137.172392977
56762.2863927986
64080.8236179731
85682.6861487174
35105.7376076042
21445.2467541424
22067.1450226752
-12856.0238900807
43798.0163925142
-91872.180306289
-120060.363136992
76164.1188452531
-103723.488331498
-28463.522217617
-65438.4042213328
17403.2049560243
-13006.6554056241
-1329.70930915323
-21338.2874517854
4837.48897849824

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
867.88771306744 \tabularnewline
-2589.39674546118 \tabularnewline
39314.9242944509 \tabularnewline
14453.4622141849 \tabularnewline
486.29450337017 \tabularnewline
22155.4389721592 \tabularnewline
25593.2069423751 \tabularnewline
316675.679799225 \tabularnewline
-112240.573550224 \tabularnewline
-67776.6230099865 \tabularnewline
42113.3312450601 \tabularnewline
72466.6444183648 \tabularnewline
-89635.4481509431 \tabularnewline
-45304.6035927241 \tabularnewline
-72955.0453587909 \tabularnewline
27778.7662845435 \tabularnewline
109945.838867168 \tabularnewline
-190281.842652476 \tabularnewline
58572.7872484705 \tabularnewline
-312133.435237775 \tabularnewline
194191.436344934 \tabularnewline
219610.611333987 \tabularnewline
144895.404377687 \tabularnewline
59624.459125522 \tabularnewline
169042.354388903 \tabularnewline
106033.631872625 \tabularnewline
49354.0176460777 \tabularnewline
-56330.0789116722 \tabularnewline
-100760.24497969 \tabularnewline
-10063.7778391061 \tabularnewline
-149797.518979373 \tabularnewline
-84340.0243213298 \tabularnewline
-42472.8163348963 \tabularnewline
-39425.0358647259 \tabularnewline
-78703.4126089705 \tabularnewline
-101042.744840936 \tabularnewline
-83879.2819582617 \tabularnewline
-92823.6483936284 \tabularnewline
53018.4057761265 \tabularnewline
93214.8226750383 \tabularnewline
-143259.649019981 \tabularnewline
104137.172392977 \tabularnewline
56762.2863927986 \tabularnewline
64080.8236179731 \tabularnewline
85682.6861487174 \tabularnewline
35105.7376076042 \tabularnewline
21445.2467541424 \tabularnewline
22067.1450226752 \tabularnewline
-12856.0238900807 \tabularnewline
43798.0163925142 \tabularnewline
-91872.180306289 \tabularnewline
-120060.363136992 \tabularnewline
76164.1188452531 \tabularnewline
-103723.488331498 \tabularnewline
-28463.522217617 \tabularnewline
-65438.4042213328 \tabularnewline
17403.2049560243 \tabularnewline
-13006.6554056241 \tabularnewline
-1329.70930915323 \tabularnewline
-21338.2874517854 \tabularnewline
4837.48897849824 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196643&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]867.88771306744[/C][/ROW]
[ROW][C]-2589.39674546118[/C][/ROW]
[ROW][C]39314.9242944509[/C][/ROW]
[ROW][C]14453.4622141849[/C][/ROW]
[ROW][C]486.29450337017[/C][/ROW]
[ROW][C]22155.4389721592[/C][/ROW]
[ROW][C]25593.2069423751[/C][/ROW]
[ROW][C]316675.679799225[/C][/ROW]
[ROW][C]-112240.573550224[/C][/ROW]
[ROW][C]-67776.6230099865[/C][/ROW]
[ROW][C]42113.3312450601[/C][/ROW]
[ROW][C]72466.6444183648[/C][/ROW]
[ROW][C]-89635.4481509431[/C][/ROW]
[ROW][C]-45304.6035927241[/C][/ROW]
[ROW][C]-72955.0453587909[/C][/ROW]
[ROW][C]27778.7662845435[/C][/ROW]
[ROW][C]109945.838867168[/C][/ROW]
[ROW][C]-190281.842652476[/C][/ROW]
[ROW][C]58572.7872484705[/C][/ROW]
[ROW][C]-312133.435237775[/C][/ROW]
[ROW][C]194191.436344934[/C][/ROW]
[ROW][C]219610.611333987[/C][/ROW]
[ROW][C]144895.404377687[/C][/ROW]
[ROW][C]59624.459125522[/C][/ROW]
[ROW][C]169042.354388903[/C][/ROW]
[ROW][C]106033.631872625[/C][/ROW]
[ROW][C]49354.0176460777[/C][/ROW]
[ROW][C]-56330.0789116722[/C][/ROW]
[ROW][C]-100760.24497969[/C][/ROW]
[ROW][C]-10063.7778391061[/C][/ROW]
[ROW][C]-149797.518979373[/C][/ROW]
[ROW][C]-84340.0243213298[/C][/ROW]
[ROW][C]-42472.8163348963[/C][/ROW]
[ROW][C]-39425.0358647259[/C][/ROW]
[ROW][C]-78703.4126089705[/C][/ROW]
[ROW][C]-101042.744840936[/C][/ROW]
[ROW][C]-83879.2819582617[/C][/ROW]
[ROW][C]-92823.6483936284[/C][/ROW]
[ROW][C]53018.4057761265[/C][/ROW]
[ROW][C]93214.8226750383[/C][/ROW]
[ROW][C]-143259.649019981[/C][/ROW]
[ROW][C]104137.172392977[/C][/ROW]
[ROW][C]56762.2863927986[/C][/ROW]
[ROW][C]64080.8236179731[/C][/ROW]
[ROW][C]85682.6861487174[/C][/ROW]
[ROW][C]35105.7376076042[/C][/ROW]
[ROW][C]21445.2467541424[/C][/ROW]
[ROW][C]22067.1450226752[/C][/ROW]
[ROW][C]-12856.0238900807[/C][/ROW]
[ROW][C]43798.0163925142[/C][/ROW]
[ROW][C]-91872.180306289[/C][/ROW]
[ROW][C]-120060.363136992[/C][/ROW]
[ROW][C]76164.1188452531[/C][/ROW]
[ROW][C]-103723.488331498[/C][/ROW]
[ROW][C]-28463.522217617[/C][/ROW]
[ROW][C]-65438.4042213328[/C][/ROW]
[ROW][C]17403.2049560243[/C][/ROW]
[ROW][C]-13006.6554056241[/C][/ROW]
[ROW][C]-1329.70930915323[/C][/ROW]
[ROW][C]-21338.2874517854[/C][/ROW]
[ROW][C]4837.48897849824[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196643&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196643&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
867.88771306744
-2589.39674546118
39314.9242944509
14453.4622141849
486.29450337017
22155.4389721592
25593.2069423751
316675.679799225
-112240.573550224
-67776.6230099865
42113.3312450601
72466.6444183648
-89635.4481509431
-45304.6035927241
-72955.0453587909
27778.7662845435
109945.838867168
-190281.842652476
58572.7872484705
-312133.435237775
194191.436344934
219610.611333987
144895.404377687
59624.459125522
169042.354388903
106033.631872625
49354.0176460777
-56330.0789116722
-100760.24497969
-10063.7778391061
-149797.518979373
-84340.0243213298
-42472.8163348963
-39425.0358647259
-78703.4126089705
-101042.744840936
-83879.2819582617
-92823.6483936284
53018.4057761265
93214.8226750383
-143259.649019981
104137.172392977
56762.2863927986
64080.8236179731
85682.6861487174
35105.7376076042
21445.2467541424
22067.1450226752
-12856.0238900807
43798.0163925142
-91872.180306289
-120060.363136992
76164.1188452531
-103723.488331498
-28463.522217617
-65438.4042213328
17403.2049560243
-13006.6554056241
-1329.70930915323
-21338.2874517854
4837.48897849824



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 0 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 0 ;
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')