R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(37,30,47,35,30,43,82,40,47,19,52,136,80,42,54,66,81,63,137,72,107,58,36,52,79,77,54,84,48,96,83,66,61,53,30,74,69,59,42,65,70,100,63,105,82,81,75,102,121,98,76,77,63,37,35,23,40,29,37,51,20,28,13,22,25,13,16,13,16,17,25,14,8,7,10,7,10,3),dim=c(1,78),dimnames=list(c('Months'),1:78))
> y <- array(NA,dim=c(1,78),dimnames=list(c('Months'),1:78))
> 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
Months M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 37 1 0 0 0 0 0 0 0 0 0 0 1
2 30 0 1 0 0 0 0 0 0 0 0 0 2
3 47 0 0 1 0 0 0 0 0 0 0 0 3
4 35 0 0 0 1 0 0 0 0 0 0 0 4
5 30 0 0 0 0 1 0 0 0 0 0 0 5
6 43 0 0 0 0 0 1 0 0 0 0 0 6
7 82 0 0 0 0 0 0 1 0 0 0 0 7
8 40 0 0 0 0 0 0 0 1 0 0 0 8
9 47 0 0 0 0 0 0 0 0 1 0 0 9
10 19 0 0 0 0 0 0 0 0 0 1 0 10
11 52 0 0 0 0 0 0 0 0 0 0 1 11
12 136 0 0 0 0 0 0 0 0 0 0 0 12
13 80 1 0 0 0 0 0 0 0 0 0 0 13
14 42 0 1 0 0 0 0 0 0 0 0 0 14
15 54 0 0 1 0 0 0 0 0 0 0 0 15
16 66 0 0 0 1 0 0 0 0 0 0 0 16
17 81 0 0 0 0 1 0 0 0 0 0 0 17
18 63 0 0 0 0 0 1 0 0 0 0 0 18
19 137 0 0 0 0 0 0 1 0 0 0 0 19
20 72 0 0 0 0 0 0 0 1 0 0 0 20
21 107 0 0 0 0 0 0 0 0 1 0 0 21
22 58 0 0 0 0 0 0 0 0 0 1 0 22
23 36 0 0 0 0 0 0 0 0 0 0 1 23
24 52 0 0 0 0 0 0 0 0 0 0 0 24
25 79 1 0 0 0 0 0 0 0 0 0 0 25
26 77 0 1 0 0 0 0 0 0 0 0 0 26
27 54 0 0 1 0 0 0 0 0 0 0 0 27
28 84 0 0 0 1 0 0 0 0 0 0 0 28
29 48 0 0 0 0 1 0 0 0 0 0 0 29
30 96 0 0 0 0 0 1 0 0 0 0 0 30
31 83 0 0 0 0 0 0 1 0 0 0 0 31
32 66 0 0 0 0 0 0 0 1 0 0 0 32
33 61 0 0 0 0 0 0 0 0 1 0 0 33
34 53 0 0 0 0 0 0 0 0 0 1 0 34
35 30 0 0 0 0 0 0 0 0 0 0 1 35
36 74 0 0 0 0 0 0 0 0 0 0 0 36
37 69 1 0 0 0 0 0 0 0 0 0 0 37
38 59 0 1 0 0 0 0 0 0 0 0 0 38
39 42 0 0 1 0 0 0 0 0 0 0 0 39
40 65 0 0 0 1 0 0 0 0 0 0 0 40
41 70 0 0 0 0 1 0 0 0 0 0 0 41
42 100 0 0 0 0 0 1 0 0 0 0 0 42
43 63 0 0 0 0 0 0 1 0 0 0 0 43
44 105 0 0 0 0 0 0 0 1 0 0 0 44
45 82 0 0 0 0 0 0 0 0 1 0 0 45
46 81 0 0 0 0 0 0 0 0 0 1 0 46
47 75 0 0 0 0 0 0 0 0 0 0 1 47
48 102 0 0 0 0 0 0 0 0 0 0 0 48
49 121 1 0 0 0 0 0 0 0 0 0 0 49
50 98 0 1 0 0 0 0 0 0 0 0 0 50
51 76 0 0 1 0 0 0 0 0 0 0 0 51
52 77 0 0 0 1 0 0 0 0 0 0 0 52
53 63 0 0 0 0 1 0 0 0 0 0 0 53
54 37 0 0 0 0 0 1 0 0 0 0 0 54
55 35 0 0 0 0 0 0 1 0 0 0 0 55
56 23 0 0 0 0 0 0 0 1 0 0 0 56
57 40 0 0 0 0 0 0 0 0 1 0 0 57
58 29 0 0 0 0 0 0 0 0 0 1 0 58
59 37 0 0 0 0 0 0 0 0 0 0 1 59
60 51 0 0 0 0 0 0 0 0 0 0 0 60
61 20 1 0 0 0 0 0 0 0 0 0 0 61
62 28 0 1 0 0 0 0 0 0 0 0 0 62
63 13 0 0 1 0 0 0 0 0 0 0 0 63
64 22 0 0 0 1 0 0 0 0 0 0 0 64
65 25 0 0 0 0 1 0 0 0 0 0 0 65
66 13 0 0 0 0 0 1 0 0 0 0 0 66
67 16 0 0 0 0 0 0 1 0 0 0 0 67
68 13 0 0 0 0 0 0 0 1 0 0 0 68
69 16 0 0 0 0 0 0 0 0 1 0 0 69
70 17 0 0 0 0 0 0 0 0 0 1 0 70
71 25 0 0 0 0 0 0 0 0 0 0 1 71
72 14 0 0 0 0 0 0 0 0 0 0 0 72
73 8 1 0 0 0 0 0 0 0 0 0 0 73
74 7 0 1 0 0 0 0 0 0 0 0 0 74
75 10 0 0 1 0 0 0 0 0 0 0 0 75
76 7 0 0 0 1 0 0 0 0 0 0 0 76
77 10 0 0 0 0 1 0 0 0 0 0 0 77
78 3 0 0 0 0 0 1 0 0 0 0 0 78
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
97.3269 -15.4318 -25.2454 -31.0591 -21.8727 -25.4006
M6 M7 M8 M9 M10 M11
-20.7857 -5.2413 -20.7930 -14.5114 -29.8965 -29.6149
t
-0.6149
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-44.280 -20.999 -2.904 13.727 69.236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.3269 13.4786 7.221 7.06e-10 ***
M1 -15.4318 16.3208 -0.946 0.3479
M2 -25.2454 16.3148 -1.547 0.1266
M3 -31.0591 16.3101 -1.904 0.0613 .
M4 -21.8727 16.3068 -1.341 0.1845
M5 -25.4006 16.3048 -1.558 0.1241
M6 -20.7857 16.3041 -1.275 0.2069
M7 -5.2413 16.9357 -0.309 0.7579
M8 -20.7930 16.9299 -1.228 0.2238
M9 -14.5114 16.9254 -0.857 0.3944
M10 -29.8965 16.9222 -1.767 0.0820 .
M11 -29.6149 16.9202 -1.750 0.0848 .
t -0.6149 0.1478 -4.160 9.51e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 29.31 on 65 degrees of freedom
Multiple R-squared: 0.2785, Adjusted R-squared: 0.1453
F-statistic: 2.09 on 12 and 65 DF, p-value: 0.02970
> 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,] 0.12408882 2.481776e-01 8.759112e-01
[2,] 0.11814355 2.362871e-01 8.818565e-01
[3,] 0.06065998 1.213200e-01 9.393400e-01
[4,] 0.07037434 1.407487e-01 9.296257e-01
[5,] 0.03422120 6.844240e-02 9.657788e-01
[6,] 0.03827911 7.655822e-02 9.617209e-01
[7,] 0.02015077 4.030154e-02 9.798492e-01
[8,] 0.09235386 1.847077e-01 9.076461e-01
[9,] 0.81358712 3.728258e-01 1.864129e-01
[10,] 0.76021654 4.795669e-01 2.397835e-01
[11,] 0.69933623 6.013275e-01 3.006638e-01
[12,] 0.68473221 6.305356e-01 3.152678e-01
[13,] 0.60695948 7.860810e-01 3.930405e-01
[14,] 0.66578565 6.684287e-01 3.342143e-01
[15,] 0.60592728 7.881454e-01 3.940727e-01
[16,] 0.66444037 6.711193e-01 3.355596e-01
[17,] 0.61270765 7.745847e-01 3.872923e-01
[18,] 0.62801904 7.439619e-01 3.719810e-01
[19,] 0.60069434 7.986113e-01 3.993057e-01
[20,] 0.75035459 4.992908e-01 2.496454e-01
[21,] 0.78143812 4.371238e-01 2.185619e-01
[22,] 0.78414078 4.317184e-01 2.158592e-01
[23,] 0.81503609 3.699278e-01 1.849639e-01
[24,] 0.90404736 1.919053e-01 9.595264e-02
[25,] 0.92046301 1.590740e-01 7.953699e-02
[26,] 0.92803361 1.439328e-01 7.196639e-02
[27,] 0.91027160 1.794568e-01 8.972840e-02
[28,] 0.92909751 1.418050e-01 7.090249e-02
[29,] 0.95373368 9.253265e-02 4.626632e-02
[30,] 0.92990049 1.401990e-01 7.009951e-02
[31,] 0.90907375 1.818525e-01 9.092625e-02
[32,] 0.87909646 2.418071e-01 1.209035e-01
[33,] 0.85493608 2.901278e-01 1.450639e-01
[34,] 0.98637639 2.724723e-02 1.362361e-02
[35,] 0.99789550 4.208994e-03 2.104497e-03
[36,] 0.99932901 1.341983e-03 6.709914e-04
[37,] 0.99996434 7.131647e-05 3.565823e-05
[38,] 0.99998994 2.011084e-05 1.005542e-05
[39,] 0.99998287 3.425934e-05 1.712967e-05
[40,] 0.99997070 5.860797e-05 2.930399e-05
[41,] 0.99993426 1.314705e-04 6.573523e-05
[42,] 0.99983998 3.200498e-04 1.600249e-04
[43,] 0.99941505 1.169903e-03 5.849513e-04
[44,] 0.99785351 4.292972e-03 2.146486e-03
[45,] 0.99970951 5.809717e-04 2.904859e-04
[46,] 0.99835276 3.294482e-03 1.647241e-03
[47,] 0.99599892 8.002164e-03 4.001082e-03
> postscript(file="/var/www/html/rcomp/tmp/1p4c61291146830.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20vt91291146830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30vt91291146830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40vt91291146830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50vt91291146830.ps",horizontal=F,onefile=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 = 78
Frequency = 1
1 2 3 4 5 6
-44.2802198 -40.8516484 -17.4230769 -37.9945055 -38.8516484 -29.8516484
7 8 9 10 11 12
-5.7811355 -31.6144689 -30.2811355 -42.2811355 -8.9478022 46.0521978
13 14 15 16 17 18
6.0989011 -21.4725275 -3.0439560 0.3846154 19.5274725 -2.4725275
19 20 21 22 23 24
56.5979853 7.7646520 37.0979853 4.0979853 -17.5686813 -30.5686813
25 26 27 28 29 30
12.4780220 20.9065934 4.3351648 25.7637363 -6.0934066 37.9065934
31 32 33 34 35 36
9.9771062 9.1437729 -1.5228938 6.4771062 -16.1895604 -1.1895604
37 38 39 40 41 42
9.8571429 10.2857143 -0.2857143 14.1428571 23.2857143 49.2857143
43 44 45 46 47 48
-2.6437729 55.5228938 26.8562271 41.8562271 36.1895604 34.1895604
49 50 51 52 53 54
69.2362637 56.6648352 41.0934066 33.5219780 23.6648352 -6.3351648
55 56 57 58 59 60
-23.2646520 -19.0979853 -7.7646520 -2.7646520 5.5686813 -9.4313187
61 62 63 64 65 66
-24.3846154 -5.9560440 -14.5274725 -14.0989011 -6.9560440 -22.9560440
67 68 69 70 71 72
-34.8855311 -21.7188645 -24.3855311 -7.3855311 0.9478022 -39.0521978
73 74 75 76 77 78
-29.0054945 -19.5769231 -10.1483516 -21.7197802 -14.5769231 -25.5769231
> postscript(file="/var/www/html/rcomp/tmp/6a4au1291146830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 78
Frequency = 1
lag(myerror, k = 1) myerror
0 -44.2802198 NA
1 -40.8516484 -44.2802198
2 -17.4230769 -40.8516484
3 -37.9945055 -17.4230769
4 -38.8516484 -37.9945055
5 -29.8516484 -38.8516484
6 -5.7811355 -29.8516484
7 -31.6144689 -5.7811355
8 -30.2811355 -31.6144689
9 -42.2811355 -30.2811355
10 -8.9478022 -42.2811355
11 46.0521978 -8.9478022
12 6.0989011 46.0521978
13 -21.4725275 6.0989011
14 -3.0439560 -21.4725275
15 0.3846154 -3.0439560
16 19.5274725 0.3846154
17 -2.4725275 19.5274725
18 56.5979853 -2.4725275
19 7.7646520 56.5979853
20 37.0979853 7.7646520
21 4.0979853 37.0979853
22 -17.5686813 4.0979853
23 -30.5686813 -17.5686813
24 12.4780220 -30.5686813
25 20.9065934 12.4780220
26 4.3351648 20.9065934
27 25.7637363 4.3351648
28 -6.0934066 25.7637363
29 37.9065934 -6.0934066
30 9.9771062 37.9065934
31 9.1437729 9.9771062
32 -1.5228938 9.1437729
33 6.4771062 -1.5228938
34 -16.1895604 6.4771062
35 -1.1895604 -16.1895604
36 9.8571429 -1.1895604
37 10.2857143 9.8571429
38 -0.2857143 10.2857143
39 14.1428571 -0.2857143
40 23.2857143 14.1428571
41 49.2857143 23.2857143
42 -2.6437729 49.2857143
43 55.5228938 -2.6437729
44 26.8562271 55.5228938
45 41.8562271 26.8562271
46 36.1895604 41.8562271
47 34.1895604 36.1895604
48 69.2362637 34.1895604
49 56.6648352 69.2362637
50 41.0934066 56.6648352
51 33.5219780 41.0934066
52 23.6648352 33.5219780
53 -6.3351648 23.6648352
54 -23.2646520 -6.3351648
55 -19.0979853 -23.2646520
56 -7.7646520 -19.0979853
57 -2.7646520 -7.7646520
58 5.5686813 -2.7646520
59 -9.4313187 5.5686813
60 -24.3846154 -9.4313187
61 -5.9560440 -24.3846154
62 -14.5274725 -5.9560440
63 -14.0989011 -14.5274725
64 -6.9560440 -14.0989011
65 -22.9560440 -6.9560440
66 -34.8855311 -22.9560440
67 -21.7188645 -34.8855311
68 -24.3855311 -21.7188645
69 -7.3855311 -24.3855311
70 0.9478022 -7.3855311
71 -39.0521978 0.9478022
72 -29.0054945 -39.0521978
73 -19.5769231 -29.0054945
74 -10.1483516 -19.5769231
75 -21.7197802 -10.1483516
76 -14.5769231 -21.7197802
77 -25.5769231 -14.5769231
78 NA -25.5769231
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -40.8516484 -44.2802198
[2,] -17.4230769 -40.8516484
[3,] -37.9945055 -17.4230769
[4,] -38.8516484 -37.9945055
[5,] -29.8516484 -38.8516484
[6,] -5.7811355 -29.8516484
[7,] -31.6144689 -5.7811355
[8,] -30.2811355 -31.6144689
[9,] -42.2811355 -30.2811355
[10,] -8.9478022 -42.2811355
[11,] 46.0521978 -8.9478022
[12,] 6.0989011 46.0521978
[13,] -21.4725275 6.0989011
[14,] -3.0439560 -21.4725275
[15,] 0.3846154 -3.0439560
[16,] 19.5274725 0.3846154
[17,] -2.4725275 19.5274725
[18,] 56.5979853 -2.4725275
[19,] 7.7646520 56.5979853
[20,] 37.0979853 7.7646520
[21,] 4.0979853 37.0979853
[22,] -17.5686813 4.0979853
[23,] -30.5686813 -17.5686813
[24,] 12.4780220 -30.5686813
[25,] 20.9065934 12.4780220
[26,] 4.3351648 20.9065934
[27,] 25.7637363 4.3351648
[28,] -6.0934066 25.7637363
[29,] 37.9065934 -6.0934066
[30,] 9.9771062 37.9065934
[31,] 9.1437729 9.9771062
[32,] -1.5228938 9.1437729
[33,] 6.4771062 -1.5228938
[34,] -16.1895604 6.4771062
[35,] -1.1895604 -16.1895604
[36,] 9.8571429 -1.1895604
[37,] 10.2857143 9.8571429
[38,] -0.2857143 10.2857143
[39,] 14.1428571 -0.2857143
[40,] 23.2857143 14.1428571
[41,] 49.2857143 23.2857143
[42,] -2.6437729 49.2857143
[43,] 55.5228938 -2.6437729
[44,] 26.8562271 55.5228938
[45,] 41.8562271 26.8562271
[46,] 36.1895604 41.8562271
[47,] 34.1895604 36.1895604
[48,] 69.2362637 34.1895604
[49,] 56.6648352 69.2362637
[50,] 41.0934066 56.6648352
[51,] 33.5219780 41.0934066
[52,] 23.6648352 33.5219780
[53,] -6.3351648 23.6648352
[54,] -23.2646520 -6.3351648
[55,] -19.0979853 -23.2646520
[56,] -7.7646520 -19.0979853
[57,] -2.7646520 -7.7646520
[58,] 5.5686813 -2.7646520
[59,] -9.4313187 5.5686813
[60,] -24.3846154 -9.4313187
[61,] -5.9560440 -24.3846154
[62,] -14.5274725 -5.9560440
[63,] -14.0989011 -14.5274725
[64,] -6.9560440 -14.0989011
[65,] -22.9560440 -6.9560440
[66,] -34.8855311 -22.9560440
[67,] -21.7188645 -34.8855311
[68,] -24.3855311 -21.7188645
[69,] -7.3855311 -24.3855311
[70,] 0.9478022 -7.3855311
[71,] -39.0521978 0.9478022
[72,] -29.0054945 -39.0521978
[73,] -19.5769231 -29.0054945
[74,] -10.1483516 -19.5769231
[75,] -21.7197802 -10.1483516
[76,] -14.5769231 -21.7197802
[77,] -25.5769231 -14.5769231
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -40.8516484 -44.2802198
2 -17.4230769 -40.8516484
3 -37.9945055 -17.4230769
4 -38.8516484 -37.9945055
5 -29.8516484 -38.8516484
6 -5.7811355 -29.8516484
7 -31.6144689 -5.7811355
8 -30.2811355 -31.6144689
9 -42.2811355 -30.2811355
10 -8.9478022 -42.2811355
11 46.0521978 -8.9478022
12 6.0989011 46.0521978
13 -21.4725275 6.0989011
14 -3.0439560 -21.4725275
15 0.3846154 -3.0439560
16 19.5274725 0.3846154
17 -2.4725275 19.5274725
18 56.5979853 -2.4725275
19 7.7646520 56.5979853
20 37.0979853 7.7646520
21 4.0979853 37.0979853
22 -17.5686813 4.0979853
23 -30.5686813 -17.5686813
24 12.4780220 -30.5686813
25 20.9065934 12.4780220
26 4.3351648 20.9065934
27 25.7637363 4.3351648
28 -6.0934066 25.7637363
29 37.9065934 -6.0934066
30 9.9771062 37.9065934
31 9.1437729 9.9771062
32 -1.5228938 9.1437729
33 6.4771062 -1.5228938
34 -16.1895604 6.4771062
35 -1.1895604 -16.1895604
36 9.8571429 -1.1895604
37 10.2857143 9.8571429
38 -0.2857143 10.2857143
39 14.1428571 -0.2857143
40 23.2857143 14.1428571
41 49.2857143 23.2857143
42 -2.6437729 49.2857143
43 55.5228938 -2.6437729
44 26.8562271 55.5228938
45 41.8562271 26.8562271
46 36.1895604 41.8562271
47 34.1895604 36.1895604
48 69.2362637 34.1895604
49 56.6648352 69.2362637
50 41.0934066 56.6648352
51 33.5219780 41.0934066
52 23.6648352 33.5219780
53 -6.3351648 23.6648352
54 -23.2646520 -6.3351648
55 -19.0979853 -23.2646520
56 -7.7646520 -19.0979853
57 -2.7646520 -7.7646520
58 5.5686813 -2.7646520
59 -9.4313187 5.5686813
60 -24.3846154 -9.4313187
61 -5.9560440 -24.3846154
62 -14.5274725 -5.9560440
63 -14.0989011 -14.5274725
64 -6.9560440 -14.0989011
65 -22.9560440 -6.9560440
66 -34.8855311 -22.9560440
67 -21.7188645 -34.8855311
68 -24.3855311 -21.7188645
69 -7.3855311 -24.3855311
70 0.9478022 -7.3855311
71 -39.0521978 0.9478022
72 -29.0054945 -39.0521978
73 -19.5769231 -29.0054945
74 -10.1483516 -19.5769231
75 -21.7197802 -10.1483516
76 -14.5769231 -21.7197802
77 -25.5769231 -14.5769231
> 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/7leax1291146830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8leax1291146830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9leax1291146830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wn901291146830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h5p61291146830.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/1236ou1291146830.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/13hgm21291146830.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/142gk81291146830.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/15grlr1291146831.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/161rkf1291146831.tab")
+ }
>
> try(system("convert tmp/1p4c61291146830.ps tmp/1p4c61291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/20vt91291146830.ps tmp/20vt91291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/30vt91291146830.ps tmp/30vt91291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/40vt91291146830.ps tmp/40vt91291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/50vt91291146830.ps tmp/50vt91291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/6a4au1291146830.ps tmp/6a4au1291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/7leax1291146830.ps tmp/7leax1291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/8leax1291146830.ps tmp/8leax1291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/9leax1291146830.ps tmp/9leax1291146830.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wn901291146830.ps tmp/10wn901291146830.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.637 1.628 6.436