R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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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.
> x <- array(list(2756.76
+ ,10872
+ ,2645.64
+ ,2497.84
+ ,2448.05
+ ,2454.62
+ ,2407.6
+ ,2472.81
+ ,2408.64
+ ,2440.25
+ ,2350.44
+ ,2849.27
+ ,10625
+ ,2756.76
+ ,2645.64
+ ,2497.84
+ ,2448.05
+ ,2454.62
+ ,2407.6
+ ,2472.81
+ ,2408.64
+ ,2440.25
+ ,2921.44
+ ,10407
+ ,2849.27
+ ,2756.76
+ ,2645.64
+ ,2497.84
+ ,2448.05
+ ,2454.62
+ ,2407.6
+ ,2472.81
+ ,2408.64
+ ,2981.85
+ ,10463
+ ,2921.44
+ ,2849.27
+ ,2756.76
+ ,2645.64
+ ,2497.84
+ ,2448.05
+ ,2454.62
+ ,2407.6
+ ,2472.81
+ ,3080.58
+ ,10556
+ ,2981.85
+ ,2921.44
+ ,2849.27
+ ,2756.76
+ ,2645.64
+ ,2497.84
+ ,2448.05
+ ,2454.62
+ ,2407.6
+ ,3106.22
+ ,10646
+ ,3080.58
+ ,2981.85
+ ,2921.44
+ ,2849.27
+ ,2756.76
+ ,2645.64
+ ,2497.84
+ ,2448.05
+ ,2454.62
+ ,3119.31
+ ,10702
+ ,3106.22
+ ,3080.58
+ ,2981.85
+ ,2921.44
+ ,2849.27
+ ,2756.76
+ ,2645.64
+ ,2497.84
+ ,2448.05
+ ,3061.26
+ ,11353
+ ,3119.31
+ ,3106.22
+ ,3080.58
+ ,2981.85
+ ,2921.44
+ ,2849.27
+ ,2756.76
+ ,2645.64
+ ,2497.84
+ ,3097.31
+ ,11346
+ ,3061.26
+ ,3119.31
+ ,3106.22
+ ,3080.58
+ ,2981.85
+ ,2921.44
+ ,2849.27
+ ,2756.76
+ ,2645.64
+ ,3161.69
+ ,11451
+ ,3097.31
+ ,3061.26
+ ,3119.31
+ ,3106.22
+ ,3080.58
+ ,2981.85
+ ,2921.44
+ ,2849.27
+ ,2756.76
+ ,3257.16
+ ,11964
+ ,3161.69
+ ,3097.31
+ ,3061.26
+ ,3119.31
+ ,3106.22
+ ,3080.58
+ ,2981.85
+ ,2921.44
+ ,2849.27
+ ,3277.01
+ ,12574
+ ,3257.16
+ ,3161.69
+ ,3097.31
+ ,3061.26
+ ,3119.31
+ ,3106.22
+ ,3080.58
+ ,2981.85
+ ,2921.44
+ ,3295.32
+ ,13031
+ ,3277.01
+ ,3257.16
+ ,3161.69
+ ,3097.31
+ ,3061.26
+ ,3119.31
+ ,3106.22
+ ,3080.58
+ ,2981.85
+ ,3363.99
+ ,13812
+ ,3295.32
+ ,3277.01
+ ,3257.16
+ ,3161.69
+ ,3097.31
+ ,3061.26
+ ,3119.31
+ ,3106.22
+ ,3080.58
+ ,3494.17
+ ,14544
+ ,3363.99
+ ,3295.32
+ ,3277.01
+ ,3257.16
+ ,3161.69
+ ,3097.31
+ ,3061.26
+ ,3119.31
+ ,3106.22
+ ,3667.03
+ ,14931
+ ,3494.17
+ ,3363.99
+ ,3295.32
+ ,3277.01
+ ,3257.16
+ ,3161.69
+ ,3097.31
+ ,3061.26
+ ,3119.31
+ ,3813.06
+ ,14886
+ ,3667.03
+ ,3494.17
+ ,3363.99
+ ,3295.32
+ ,3277.01
+ ,3257.16
+ ,3161.69
+ ,3097.31
+ ,3061.26
+ ,3917.96
+ ,16005
+ ,3813.06
+ ,3667.03
+ ,3494.17
+ ,3363.99
+ ,3295.32
+ ,3277.01
+ ,3257.16
+ ,3161.69
+ ,3097.31
+ ,3895.51
+ ,17064
+ ,3917.96
+ ,3813.06
+ ,3667.03
+ ,3494.17
+ ,3363.99
+ ,3295.32
+ ,3277.01
+ ,3257.16
+ ,3161.69
+ ,3801.06
+ ,15168
+ ,3895.51
+ ,3917.96
+ ,3813.06
+ ,3667.03
+ ,3494.17
+ ,3363.99
+ ,3295.32
+ ,3277.01
+ ,3257.16
+ ,3570.12
+ ,16050
+ ,3801.06
+ ,3895.51
+ ,3917.96
+ ,3813.06
+ ,3667.03
+ ,3494.17
+ ,3363.99
+ ,3295.32
+ ,3277.01
+ ,3701.61
+ ,15839
+ ,3570.12
+ ,3801.06
+ ,3895.51
+ ,3917.96
+ ,3813.06
+ ,3667.03
+ ,3494.17
+ ,3363.99
+ ,3295.32
+ ,3862.27
+ ,15137
+ ,3701.61
+ ,3570.12
+ ,3801.06
+ ,3895.51
+ ,3917.96
+ ,3813.06
+ ,3667.03
+ ,3494.17
+ ,3363.99
+ ,3970.1
+ ,14954
+ ,3862.27
+ ,3701.61
+ ,3570.12
+ ,3801.06
+ ,3895.51
+ ,3917.96
+ ,3813.06
+ ,3667.03
+ ,3494.17
+ ,4138.52
+ ,15648
+ ,3970.1
+ ,3862.27
+ ,3701.61
+ ,3570.12
+ ,3801.06
+ ,3895.51
+ ,3917.96
+ ,3813.06
+ ,3667.03
+ ,4199.75
+ ,15305
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,3701.61
+ ,3570.12
+ ,3801.06
+ ,3895.51
+ ,3917.96
+ ,3813.06
+ ,4290.89
+ ,15579
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,3701.61
+ ,3570.12
+ ,3801.06
+ ,3895.51
+ ,3917.96
+ ,4443.91
+ ,16348
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,3701.61
+ ,3570.12
+ ,3801.06
+ ,3895.51
+ ,4502.64
+ ,15928
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,3701.61
+ ,3570.12
+ ,3801.06
+ ,4356.98
+ ,16171
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,3701.61
+ ,3570.12
+ ,4591.27
+ ,15937
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,3701.61
+ ,4696.96
+ ,15713
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,4621.4
+ ,15594
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,4562.84
+ ,15683
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,4202.52
+ ,16438
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4296.49
+ ,17032
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4435.23
+ ,17696
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4105.18
+ ,17745
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4116.68
+ ,19394
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,3844.49
+ ,20148
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,3720.98
+ ,20108
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,3674.4
+ ,18584
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,3857.62
+ ,18441
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,3801.06
+ ,18391
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,3504.37
+ ,19178
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,3032.6
+ ,18079
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,3047.03
+ ,18483
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,2962.34
+ ,19644
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,2197.82
+ ,19195
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,2014.45
+ ,19650
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,1862.83
+ ,20830
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,1905.41
+ ,23595
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,1810.99
+ ,22937
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,1670.07
+ ,21814
+ ,1810.99
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,1864.44
+ ,21928
+ ,1670.07
+ ,1810.99
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,2052.02
+ ,21777
+ ,1864.44
+ ,1670.07
+ ,1810.99
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,2029.6
+ ,21383
+ ,2052.02
+ ,1864.44
+ ,1670.07
+ ,1810.99
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,2070.83
+ ,21467
+ ,2029.6
+ ,2052.02
+ ,1864.44
+ ,1670.07
+ ,1810.99
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,2293.41
+ ,22052
+ ,2070.83
+ ,2029.6
+ ,2052.02
+ ,1864.44
+ ,1670.07
+ ,1810.99
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,2443.27
+ ,22680
+ ,2293.41
+ ,2070.83
+ ,2029.6
+ ,2052.02
+ ,1864.44
+ ,1670.07
+ ,1810.99
+ ,1905.41
+ ,1862.83)
+ ,dim=c(11
+ ,60)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4'
+ ,'Y5'
+ ,'Y6'
+ ,'Y7'
+ ,'Y8'
+ ,'Y9')
+ ,1:60))
> y <- array(NA,dim=c(11,60),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4','Y5','Y6','Y7','Y8','Y9'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 Y5 Y6 Y7
1 2756.76 10872 2645.64 2497.84 2448.05 2454.62 2407.60 2472.81 2408.64
2 2849.27 10625 2756.76 2645.64 2497.84 2448.05 2454.62 2407.60 2472.81
3 2921.44 10407 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62 2407.60
4 2981.85 10463 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05 2454.62
5 3080.58 10556 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84 2448.05
6 3106.22 10646 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64 2497.84
7 3119.31 10702 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76 2645.64
8 3061.26 11353 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27 2756.76
9 3097.31 11346 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44 2849.27
10 3161.69 11451 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85 2921.44
11 3257.16 11964 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58 2981.85
12 3277.01 12574 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22 3080.58
13 3295.32 13031 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31 3106.22
14 3363.99 13812 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26 3119.31
15 3494.17 14544 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31 3061.26
16 3667.03 14931 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69 3097.31
17 3813.06 14886 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16 3161.69
18 3917.96 16005 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01 3257.16
19 3895.51 17064 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32 3277.01
20 3801.06 15168 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99 3295.32
21 3570.12 16050 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17 3363.99
22 3701.61 15839 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03 3494.17
23 3862.27 15137 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06 3667.03
24 3970.10 14954 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96 3813.06
25 4138.52 15648 3970.10 3862.27 3701.61 3570.12 3801.06 3895.51 3917.96
26 4199.75 15305 4138.52 3970.10 3862.27 3701.61 3570.12 3801.06 3895.51
27 4290.89 15579 4199.75 4138.52 3970.10 3862.27 3701.61 3570.12 3801.06
28 4443.91 16348 4290.89 4199.75 4138.52 3970.10 3862.27 3701.61 3570.12
29 4502.64 15928 4443.91 4290.89 4199.75 4138.52 3970.10 3862.27 3701.61
30 4356.98 16171 4502.64 4443.91 4290.89 4199.75 4138.52 3970.10 3862.27
31 4591.27 15937 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52 3970.10
32 4696.96 15713 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75 4138.52
33 4621.40 15594 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89 4199.75
34 4562.84 15683 4621.40 4696.96 4591.27 4356.98 4502.64 4443.91 4290.89
35 4202.52 16438 4562.84 4621.40 4696.96 4591.27 4356.98 4502.64 4443.91
36 4296.49 17032 4202.52 4562.84 4621.40 4696.96 4591.27 4356.98 4502.64
37 4435.23 17696 4296.49 4202.52 4562.84 4621.40 4696.96 4591.27 4356.98
38 4105.18 17745 4435.23 4296.49 4202.52 4562.84 4621.40 4696.96 4591.27
39 4116.68 19394 4105.18 4435.23 4296.49 4202.52 4562.84 4621.40 4696.96
40 3844.49 20148 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84 4621.40
41 3720.98 20108 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52 4562.84
42 3674.40 18584 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49 4202.52
43 3857.62 18441 3674.40 3720.98 3844.49 4116.68 4105.18 4435.23 4296.49
44 3801.06 18391 3857.62 3674.40 3720.98 3844.49 4116.68 4105.18 4435.23
45 3504.37 19178 3801.06 3857.62 3674.40 3720.98 3844.49 4116.68 4105.18
46 3032.60 18079 3504.37 3801.06 3857.62 3674.40 3720.98 3844.49 4116.68
47 3047.03 18483 3032.60 3504.37 3801.06 3857.62 3674.40 3720.98 3844.49
48 2962.34 19644 3047.03 3032.60 3504.37 3801.06 3857.62 3674.40 3720.98
49 2197.82 19195 2962.34 3047.03 3032.60 3504.37 3801.06 3857.62 3674.40
50 2014.45 19650 2197.82 2962.34 3047.03 3032.60 3504.37 3801.06 3857.62
51 1862.83 20830 2014.45 2197.82 2962.34 3047.03 3032.60 3504.37 3801.06
52 1905.41 23595 1862.83 2014.45 2197.82 2962.34 3047.03 3032.60 3504.37
53 1810.99 22937 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03 3032.60
54 1670.07 21814 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34 3047.03
55 1864.44 21928 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82 2962.34
56 2052.02 21777 1864.44 1670.07 1810.99 1905.41 1862.83 2014.45 2197.82
57 2029.60 21383 2052.02 1864.44 1670.07 1810.99 1905.41 1862.83 2014.45
58 2070.83 21467 2029.60 2052.02 1864.44 1670.07 1810.99 1905.41 1862.83
59 2293.41 22052 2070.83 2029.60 2052.02 1864.44 1670.07 1810.99 1905.41
60 2443.27 22680 2293.41 2070.83 2029.60 2052.02 1864.44 1670.07 1810.99
Y8 Y9 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2440.25 2350.44 1 0 0 0 0 0 0 0 0 0 0 1
2 2408.64 2440.25 0 1 0 0 0 0 0 0 0 0 0 2
3 2472.81 2408.64 0 0 1 0 0 0 0 0 0 0 0 3
4 2407.60 2472.81 0 0 0 1 0 0 0 0 0 0 0 4
5 2454.62 2407.60 0 0 0 0 1 0 0 0 0 0 0 5
6 2448.05 2454.62 0 0 0 0 0 1 0 0 0 0 0 6
7 2497.84 2448.05 0 0 0 0 0 0 1 0 0 0 0 7
8 2645.64 2497.84 0 0 0 0 0 0 0 1 0 0 0 8
9 2756.76 2645.64 0 0 0 0 0 0 0 0 1 0 0 9
10 2849.27 2756.76 0 0 0 0 0 0 0 0 0 1 0 10
11 2921.44 2849.27 0 0 0 0 0 0 0 0 0 0 1 11
12 2981.85 2921.44 0 0 0 0 0 0 0 0 0 0 0 12
13 3080.58 2981.85 1 0 0 0 0 0 0 0 0 0 0 13
14 3106.22 3080.58 0 1 0 0 0 0 0 0 0 0 0 14
15 3119.31 3106.22 0 0 1 0 0 0 0 0 0 0 0 15
16 3061.26 3119.31 0 0 0 1 0 0 0 0 0 0 0 16
17 3097.31 3061.26 0 0 0 0 1 0 0 0 0 0 0 17
18 3161.69 3097.31 0 0 0 0 0 1 0 0 0 0 0 18
19 3257.16 3161.69 0 0 0 0 0 0 1 0 0 0 0 19
20 3277.01 3257.16 0 0 0 0 0 0 0 1 0 0 0 20
21 3295.32 3277.01 0 0 0 0 0 0 0 0 1 0 0 21
22 3363.99 3295.32 0 0 0 0 0 0 0 0 0 1 0 22
23 3494.17 3363.99 0 0 0 0 0 0 0 0 0 0 1 23
24 3667.03 3494.17 0 0 0 0 0 0 0 0 0 0 0 24
25 3813.06 3667.03 1 0 0 0 0 0 0 0 0 0 0 25
26 3917.96 3813.06 0 1 0 0 0 0 0 0 0 0 0 26
27 3895.51 3917.96 0 0 1 0 0 0 0 0 0 0 0 27
28 3801.06 3895.51 0 0 0 1 0 0 0 0 0 0 0 28
29 3570.12 3801.06 0 0 0 0 1 0 0 0 0 0 0 29
30 3701.61 3570.12 0 0 0 0 0 1 0 0 0 0 0 30
31 3862.27 3701.61 0 0 0 0 0 0 1 0 0 0 0 31
32 3970.10 3862.27 0 0 0 0 0 0 0 1 0 0 0 32
33 4138.52 3970.10 0 0 0 0 0 0 0 0 1 0 0 33
34 4199.75 4138.52 0 0 0 0 0 0 0 0 0 1 0 34
35 4290.89 4199.75 0 0 0 0 0 0 0 0 0 0 1 35
36 4443.91 4290.89 0 0 0 0 0 0 0 0 0 0 0 36
37 4502.64 4443.91 1 0 0 0 0 0 0 0 0 0 0 37
38 4356.98 4502.64 0 1 0 0 0 0 0 0 0 0 0 38
39 4591.27 4356.98 0 0 1 0 0 0 0 0 0 0 0 39
40 4696.96 4591.27 0 0 0 1 0 0 0 0 0 0 0 40
41 4621.40 4696.96 0 0 0 0 1 0 0 0 0 0 0 41
42 4562.84 4621.40 0 0 0 0 0 1 0 0 0 0 0 42
43 4202.52 4562.84 0 0 0 0 0 0 1 0 0 0 0 43
44 4296.49 4202.52 0 0 0 0 0 0 0 1 0 0 0 44
45 4435.23 4296.49 0 0 0 0 0 0 0 0 1 0 0 45
46 4105.18 4435.23 0 0 0 0 0 0 0 0 0 1 0 46
47 4116.68 4105.18 0 0 0 0 0 0 0 0 0 0 1 47
48 3844.49 4116.68 0 0 0 0 0 0 0 0 0 0 0 48
49 3720.98 3844.49 1 0 0 0 0 0 0 0 0 0 0 49
50 3674.40 3720.98 0 1 0 0 0 0 0 0 0 0 0 50
51 3857.62 3674.40 0 0 1 0 0 0 0 0 0 0 0 51
52 3801.06 3857.62 0 0 0 1 0 0 0 0 0 0 0 52
53 3504.37 3801.06 0 0 0 0 1 0 0 0 0 0 0 53
54 3032.60 3504.37 0 0 0 0 0 1 0 0 0 0 0 54
55 3047.03 3032.60 0 0 0 0 0 0 1 0 0 0 0 55
56 2962.34 3047.03 0 0 0 0 0 0 0 1 0 0 0 56
57 2197.82 2962.34 0 0 0 0 0 0 0 0 1 0 0 57
58 2014.45 2197.82 0 0 0 0 0 0 0 0 0 1 0 58
59 1862.83 2014.45 0 0 0 0 0 0 0 0 0 0 1 59
60 1905.41 1862.83 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
129.71899 0.01363 1.19581 -0.23859 0.19659 -0.14513
Y5 Y6 Y7 Y8 Y9 M1
0.18514 -0.28714 -0.04744 0.21478 -0.13615 -97.83303
M2 M3 M4 M5 M6 M7
-45.53301 -21.50111 -32.11097 -35.34444 -68.18011 91.20358
M8 M9 M10 M11 t
-75.51801 -152.12179 -78.35959 2.82384 -3.29252
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-534.75 -82.38 27.26 89.99 298.54
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 129.71899 320.68029 0.405 0.688
X 0.01363 0.02770 0.492 0.626
Y1 1.19581 0.16317 7.329 1.04e-08 ***
Y2 -0.23859 0.25417 -0.939 0.354
Y3 0.19659 0.25801 0.762 0.451
Y4 -0.14513 0.25497 -0.569 0.573
Y5 0.18514 0.25083 0.738 0.465
Y6 -0.28714 0.25291 -1.135 0.264
Y7 -0.04744 0.25631 -0.185 0.854
Y8 0.21478 0.25839 0.831 0.411
Y9 -0.13615 0.18712 -0.728 0.471
M1 -97.83303 124.41864 -0.786 0.437
M2 -45.53301 128.71983 -0.354 0.726
M3 -21.50111 127.64207 -0.168 0.867
M4 -32.11097 130.13686 -0.247 0.806
M5 -35.34444 127.30335 -0.278 0.783
M6 -68.18011 127.35261 -0.535 0.596
M7 91.20358 123.38372 0.739 0.464
M8 -75.51801 119.30968 -0.633 0.531
M9 -152.12179 122.89341 -1.238 0.224
M10 -78.35959 126.19600 -0.621 0.538
M11 2.82384 121.09135 0.023 0.982
t -3.29252 6.15901 -0.535 0.596
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 179.2 on 37 degrees of freedom
Multiple R-squared: 0.9721, Adjusted R-squared: 0.9555
F-statistic: 58.53 on 22 and 37 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.072508138 0.145016277 0.9274919
[2,] 0.021150669 0.042301337 0.9788493
[3,] 0.011021527 0.022043053 0.9889785
[4,] 0.010270172 0.020540345 0.9897298
[5,] 0.004219123 0.008438245 0.9957809
[6,] 0.007030126 0.014060252 0.9929699
[7,] 0.009504116 0.019008232 0.9904959
[8,] 0.003476688 0.006953376 0.9965233
[9,] 0.004119769 0.008239539 0.9958802
> postscript(file="/var/www/html/rcomp/tmp/1k8hb1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2k4iz1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/33kem1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zp441261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5crcg1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
61.762759 -5.092693 -63.094770 -40.300135 -25.746472 -37.573918
7 8 9 10 11 12
-179.641019 -103.217222 104.837606 39.256754 18.335042 -71.852308
13 14 15 16 17 18
35.715799 3.365680 31.394710 89.649246 67.474544 42.167853
19 20 21 22 23 24
-274.745281 -119.869535 -167.369050 186.308048 93.714923 102.904516
25 26 27 28 29 30
221.203202 61.853007 21.571419 80.461371 71.756490 -136.119148
31 32 33 34 35 36
142.144079 124.056391 75.941293 41.240471 -302.181655 134.034688
37 38 39 40 41 42
216.072403 -150.070337 99.238210 -219.950312 -16.156342 13.239400
43 44 45 46 47 48
298.544012 7.688294 -96.214460 -289.933672 85.008076 -82.217338
49 50 51 52 53 54
-534.754164 89.944344 -89.109569 90.139830 -97.328220 118.285813
55 56 57 58 59 60
13.698209 91.342072 82.804611 23.128400 105.123615 -82.869559
> postscript(file="/var/www/html/rcomp/tmp/6scsl1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 61.762759 NA
1 -5.092693 61.762759
2 -63.094770 -5.092693
3 -40.300135 -63.094770
4 -25.746472 -40.300135
5 -37.573918 -25.746472
6 -179.641019 -37.573918
7 -103.217222 -179.641019
8 104.837606 -103.217222
9 39.256754 104.837606
10 18.335042 39.256754
11 -71.852308 18.335042
12 35.715799 -71.852308
13 3.365680 35.715799
14 31.394710 3.365680
15 89.649246 31.394710
16 67.474544 89.649246
17 42.167853 67.474544
18 -274.745281 42.167853
19 -119.869535 -274.745281
20 -167.369050 -119.869535
21 186.308048 -167.369050
22 93.714923 186.308048
23 102.904516 93.714923
24 221.203202 102.904516
25 61.853007 221.203202
26 21.571419 61.853007
27 80.461371 21.571419
28 71.756490 80.461371
29 -136.119148 71.756490
30 142.144079 -136.119148
31 124.056391 142.144079
32 75.941293 124.056391
33 41.240471 75.941293
34 -302.181655 41.240471
35 134.034688 -302.181655
36 216.072403 134.034688
37 -150.070337 216.072403
38 99.238210 -150.070337
39 -219.950312 99.238210
40 -16.156342 -219.950312
41 13.239400 -16.156342
42 298.544012 13.239400
43 7.688294 298.544012
44 -96.214460 7.688294
45 -289.933672 -96.214460
46 85.008076 -289.933672
47 -82.217338 85.008076
48 -534.754164 -82.217338
49 89.944344 -534.754164
50 -89.109569 89.944344
51 90.139830 -89.109569
52 -97.328220 90.139830
53 118.285813 -97.328220
54 13.698209 118.285813
55 91.342072 13.698209
56 82.804611 91.342072
57 23.128400 82.804611
58 105.123615 23.128400
59 -82.869559 105.123615
60 NA -82.869559
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.092693 61.762759
[2,] -63.094770 -5.092693
[3,] -40.300135 -63.094770
[4,] -25.746472 -40.300135
[5,] -37.573918 -25.746472
[6,] -179.641019 -37.573918
[7,] -103.217222 -179.641019
[8,] 104.837606 -103.217222
[9,] 39.256754 104.837606
[10,] 18.335042 39.256754
[11,] -71.852308 18.335042
[12,] 35.715799 -71.852308
[13,] 3.365680 35.715799
[14,] 31.394710 3.365680
[15,] 89.649246 31.394710
[16,] 67.474544 89.649246
[17,] 42.167853 67.474544
[18,] -274.745281 42.167853
[19,] -119.869535 -274.745281
[20,] -167.369050 -119.869535
[21,] 186.308048 -167.369050
[22,] 93.714923 186.308048
[23,] 102.904516 93.714923
[24,] 221.203202 102.904516
[25,] 61.853007 221.203202
[26,] 21.571419 61.853007
[27,] 80.461371 21.571419
[28,] 71.756490 80.461371
[29,] -136.119148 71.756490
[30,] 142.144079 -136.119148
[31,] 124.056391 142.144079
[32,] 75.941293 124.056391
[33,] 41.240471 75.941293
[34,] -302.181655 41.240471
[35,] 134.034688 -302.181655
[36,] 216.072403 134.034688
[37,] -150.070337 216.072403
[38,] 99.238210 -150.070337
[39,] -219.950312 99.238210
[40,] -16.156342 -219.950312
[41,] 13.239400 -16.156342
[42,] 298.544012 13.239400
[43,] 7.688294 298.544012
[44,] -96.214460 7.688294
[45,] -289.933672 -96.214460
[46,] 85.008076 -289.933672
[47,] -82.217338 85.008076
[48,] -534.754164 -82.217338
[49,] 89.944344 -534.754164
[50,] -89.109569 89.944344
[51,] 90.139830 -89.109569
[52,] -97.328220 90.139830
[53,] 118.285813 -97.328220
[54,] 13.698209 118.285813
[55,] 91.342072 13.698209
[56,] 82.804611 91.342072
[57,] 23.128400 82.804611
[58,] 105.123615 23.128400
[59,] -82.869559 105.123615
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.092693 61.762759
2 -63.094770 -5.092693
3 -40.300135 -63.094770
4 -25.746472 -40.300135
5 -37.573918 -25.746472
6 -179.641019 -37.573918
7 -103.217222 -179.641019
8 104.837606 -103.217222
9 39.256754 104.837606
10 18.335042 39.256754
11 -71.852308 18.335042
12 35.715799 -71.852308
13 3.365680 35.715799
14 31.394710 3.365680
15 89.649246 31.394710
16 67.474544 89.649246
17 42.167853 67.474544
18 -274.745281 42.167853
19 -119.869535 -274.745281
20 -167.369050 -119.869535
21 186.308048 -167.369050
22 93.714923 186.308048
23 102.904516 93.714923
24 221.203202 102.904516
25 61.853007 221.203202
26 21.571419 61.853007
27 80.461371 21.571419
28 71.756490 80.461371
29 -136.119148 71.756490
30 142.144079 -136.119148
31 124.056391 142.144079
32 75.941293 124.056391
33 41.240471 75.941293
34 -302.181655 41.240471
35 134.034688 -302.181655
36 216.072403 134.034688
37 -150.070337 216.072403
38 99.238210 -150.070337
39 -219.950312 99.238210
40 -16.156342 -219.950312
41 13.239400 -16.156342
42 298.544012 13.239400
43 7.688294 298.544012
44 -96.214460 7.688294
45 -289.933672 -96.214460
46 85.008076 -289.933672
47 -82.217338 85.008076
48 -534.754164 -82.217338
49 89.944344 -534.754164
50 -89.109569 89.944344
51 90.139830 -89.109569
52 -97.328220 90.139830
53 118.285813 -97.328220
54 13.698209 118.285813
55 91.342072 13.698209
56 82.804611 91.342072
57 23.128400 82.804611
58 105.123615 23.128400
59 -82.869559 105.123615
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7z5sq1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ghqf1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rtgn1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104egm1261326223.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11f0dm1261326223.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12oxks1261326223.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13m6ck1261326223.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14zgek1261326223.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15m2ac1261326224.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16lylk1261326224.tab")
+ }
>
> try(system("convert tmp/1k8hb1261326223.ps tmp/1k8hb1261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/2k4iz1261326223.ps tmp/2k4iz1261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/33kem1261326223.ps tmp/33kem1261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zp441261326223.ps tmp/4zp441261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/5crcg1261326223.ps tmp/5crcg1261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/6scsl1261326223.ps tmp/6scsl1261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/7z5sq1261326223.ps tmp/7z5sq1261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ghqf1261326223.ps tmp/8ghqf1261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rtgn1261326223.ps tmp/9rtgn1261326223.png",intern=TRUE))
character(0)
> try(system("convert tmp/104egm1261326223.ps tmp/104egm1261326223.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.276 1.565 2.976