R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(105.7,109.5,105.3,102.8,100.6,97.6,110.3,107.2,107.2,108.1,97.1,92.2,112.2,111.6,115.7,111.3,104.2,103.2,112.7,106.4,102.6,110.6,95.2,89,112.5,116.8,107.2,113.6,101.8,102.6,122.7,110.3,110.5,121.6,100.3,100.7,123.4,127.1,124.1,131.2,111.6,114.2,130.1,125.9,119,133.8,107.5,113.5,134.4,126.8,135.6,139.9,129.8,131,153.1,134.1,144.1,155.9,123.3,128.1,144.3,153,149.9,150.9,141,138.9,157.4,142.9,151.7,161,138.5,135.9,151.5,164,159.1,157,142.1,144.8,152.1,154.6,148.7,157.7,146.4,136.5),dim=c(1,84),dimnames=list(c('Totale_industrie'),1:84))
> y <- array(NA,dim=c(1,84),dimnames=list(c('Totale_industrie'),1:84))
> 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
Totale_industrie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 105.7 1 0 0 0 0 0 0 0 0 0 0 1
2 109.5 0 1 0 0 0 0 0 0 0 0 0 2
3 105.3 0 0 1 0 0 0 0 0 0 0 0 3
4 102.8 0 0 0 1 0 0 0 0 0 0 0 4
5 100.6 0 0 0 0 1 0 0 0 0 0 0 5
6 97.6 0 0 0 0 0 1 0 0 0 0 0 6
7 110.3 0 0 0 0 0 0 1 0 0 0 0 7
8 107.2 0 0 0 0 0 0 0 1 0 0 0 8
9 107.2 0 0 0 0 0 0 0 0 1 0 0 9
10 108.1 0 0 0 0 0 0 0 0 0 1 0 10
11 97.1 0 0 0 0 0 0 0 0 0 0 1 11
12 92.2 0 0 0 0 0 0 0 0 0 0 0 12
13 112.2 1 0 0 0 0 0 0 0 0 0 0 13
14 111.6 0 1 0 0 0 0 0 0 0 0 0 14
15 115.7 0 0 1 0 0 0 0 0 0 0 0 15
16 111.3 0 0 0 1 0 0 0 0 0 0 0 16
17 104.2 0 0 0 0 1 0 0 0 0 0 0 17
18 103.2 0 0 0 0 0 1 0 0 0 0 0 18
19 112.7 0 0 0 0 0 0 1 0 0 0 0 19
20 106.4 0 0 0 0 0 0 0 1 0 0 0 20
21 102.6 0 0 0 0 0 0 0 0 1 0 0 21
22 110.6 0 0 0 0 0 0 0 0 0 1 0 22
23 95.2 0 0 0 0 0 0 0 0 0 0 1 23
24 89.0 0 0 0 0 0 0 0 0 0 0 0 24
25 112.5 1 0 0 0 0 0 0 0 0 0 0 25
26 116.8 0 1 0 0 0 0 0 0 0 0 0 26
27 107.2 0 0 1 0 0 0 0 0 0 0 0 27
28 113.6 0 0 0 1 0 0 0 0 0 0 0 28
29 101.8 0 0 0 0 1 0 0 0 0 0 0 29
30 102.6 0 0 0 0 0 1 0 0 0 0 0 30
31 122.7 0 0 0 0 0 0 1 0 0 0 0 31
32 110.3 0 0 0 0 0 0 0 1 0 0 0 32
33 110.5 0 0 0 0 0 0 0 0 1 0 0 33
34 121.6 0 0 0 0 0 0 0 0 0 1 0 34
35 100.3 0 0 0 0 0 0 0 0 0 0 1 35
36 100.7 0 0 0 0 0 0 0 0 0 0 0 36
37 123.4 1 0 0 0 0 0 0 0 0 0 0 37
38 127.1 0 1 0 0 0 0 0 0 0 0 0 38
39 124.1 0 0 1 0 0 0 0 0 0 0 0 39
40 131.2 0 0 0 1 0 0 0 0 0 0 0 40
41 111.6 0 0 0 0 1 0 0 0 0 0 0 41
42 114.2 0 0 0 0 0 1 0 0 0 0 0 42
43 130.1 0 0 0 0 0 0 1 0 0 0 0 43
44 125.9 0 0 0 0 0 0 0 1 0 0 0 44
45 119.0 0 0 0 0 0 0 0 0 1 0 0 45
46 133.8 0 0 0 0 0 0 0 0 0 1 0 46
47 107.5 0 0 0 0 0 0 0 0 0 0 1 47
48 113.5 0 0 0 0 0 0 0 0 0 0 0 48
49 134.4 1 0 0 0 0 0 0 0 0 0 0 49
50 126.8 0 1 0 0 0 0 0 0 0 0 0 50
51 135.6 0 0 1 0 0 0 0 0 0 0 0 51
52 139.9 0 0 0 1 0 0 0 0 0 0 0 52
53 129.8 0 0 0 0 1 0 0 0 0 0 0 53
54 131.0 0 0 0 0 0 1 0 0 0 0 0 54
55 153.1 0 0 0 0 0 0 1 0 0 0 0 55
56 134.1 0 0 0 0 0 0 0 1 0 0 0 56
57 144.1 0 0 0 0 0 0 0 0 1 0 0 57
58 155.9 0 0 0 0 0 0 0 0 0 1 0 58
59 123.3 0 0 0 0 0 0 0 0 0 0 1 59
60 128.1 0 0 0 0 0 0 0 0 0 0 0 60
61 144.3 1 0 0 0 0 0 0 0 0 0 0 61
62 153.0 0 1 0 0 0 0 0 0 0 0 0 62
63 149.9 0 0 1 0 0 0 0 0 0 0 0 63
64 150.9 0 0 0 1 0 0 0 0 0 0 0 64
65 141.0 0 0 0 0 1 0 0 0 0 0 0 65
66 138.9 0 0 0 0 0 1 0 0 0 0 0 66
67 157.4 0 0 0 0 0 0 1 0 0 0 0 67
68 142.9 0 0 0 0 0 0 0 1 0 0 0 68
69 151.7 0 0 0 0 0 0 0 0 1 0 0 69
70 161.0 0 0 0 0 0 0 0 0 0 1 0 70
71 138.5 0 0 0 0 0 0 0 0 0 0 1 71
72 135.9 0 0 0 0 0 0 0 0 0 0 0 72
73 151.5 1 0 0 0 0 0 0 0 0 0 0 73
74 164.0 0 1 0 0 0 0 0 0 0 0 0 74
75 159.1 0 0 1 0 0 0 0 0 0 0 0 75
76 157.0 0 0 0 1 0 0 0 0 0 0 0 76
77 142.1 0 0 0 0 1 0 0 0 0 0 0 77
78 144.8 0 0 0 0 0 1 0 0 0 0 0 78
79 152.1 0 0 0 0 0 0 1 0 0 0 0 79
80 154.6 0 0 0 0 0 0 0 1 0 0 0 80
81 148.7 0 0 0 0 0 0 0 0 1 0 0 81
82 157.7 0 0 0 0 0 0 0 0 0 1 0 82
83 146.4 0 0 0 0 0 0 0 0 0 0 1 83
84 136.5 0 0 0 0 0 0 0 0 0 0 0 84
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
77.8714 20.7964 23.5929 21.1464 21.8000 10.2536
M6 M7 M8 M9 M10 M11
9.6786 24.0893 15.2000 14.7964 23.3214 2.5179
t
0.7464
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.9857 -4.0714 -0.3143 4.1464 11.4143
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.87143 2.55665 30.458 < 2e-16 ***
M1 20.79643 3.14491 6.613 5.98e-09 ***
M2 23.59286 3.14255 7.508 1.37e-10 ***
M3 21.14643 3.14040 6.734 3.60e-09 ***
M4 21.80000 3.13848 6.946 1.47e-09 ***
M5 10.25357 3.13679 3.269 0.00167 **
M6 9.67857 3.13532 3.087 0.00288 **
M7 24.08929 3.13407 7.686 6.38e-11 ***
M8 15.20000 3.13305 4.851 7.02e-06 ***
M9 14.79643 3.13226 4.724 1.13e-05 ***
M10 23.32143 3.13169 7.447 1.77e-10 ***
M11 2.51786 3.13135 0.804 0.42404
t 0.74643 0.02663 28.028 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.858 on 71 degrees of freedom
Multiple R-squared: 0.9266, Adjusted R-squared: 0.9142
F-statistic: 74.73 on 12 and 71 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,] 0.129381925 0.25876385 0.87061808
[2,] 0.077908291 0.15581658 0.92209171
[3,] 0.034123605 0.06824721 0.96587640
[4,] 0.022529715 0.04505943 0.97747029
[5,] 0.035291875 0.07058375 0.96470813
[6,] 0.093851737 0.18770347 0.90614826
[7,] 0.054868144 0.10973629 0.94513186
[8,] 0.050564480 0.10112896 0.94943552
[9,] 0.050974319 0.10194864 0.94902568
[10,] 0.028839596 0.05767919 0.97116040
[11,] 0.017566141 0.03513228 0.98243386
[12,] 0.032509755 0.06501951 0.96749024
[13,] 0.024186272 0.04837254 0.97581373
[14,] 0.019107356 0.03821471 0.98089264
[15,] 0.011351289 0.02270258 0.98864871
[16,] 0.019624121 0.03924824 0.98037588
[17,] 0.011765100 0.02353020 0.98823490
[18,] 0.007633994 0.01526799 0.99236601
[19,] 0.014313818 0.02862764 0.98568618
[20,] 0.008781780 0.01756356 0.99121822
[21,] 0.008341700 0.01668340 0.99165830
[22,] 0.009542734 0.01908547 0.99045727
[23,] 0.011393726 0.02278745 0.98860627
[24,] 0.012994119 0.02598824 0.98700588
[25,] 0.044617438 0.08923488 0.95538256
[26,] 0.034996329 0.06999266 0.96500367
[27,] 0.027816913 0.05563383 0.97218309
[28,] 0.025008636 0.05001727 0.97499136
[29,] 0.025666489 0.05133298 0.97433351
[30,] 0.030274935 0.06054987 0.96972507
[31,] 0.048051765 0.09610353 0.95194823
[32,] 0.069821816 0.13964363 0.93017818
[33,] 0.079442180 0.15888436 0.92055782
[34,] 0.070552456 0.14110491 0.92944754
[35,] 0.402815069 0.80563014 0.59718493
[36,] 0.538432352 0.92313530 0.46156765
[37,] 0.600349235 0.79930153 0.39965077
[38,] 0.613952780 0.77209444 0.38604722
[39,] 0.624306837 0.75138633 0.37569316
[40,] 0.776391904 0.44721619 0.22360810
[41,] 0.777986921 0.44402616 0.22201308
[42,] 0.809421603 0.38115679 0.19057840
[43,] 0.863169251 0.27366150 0.13683075
[44,] 0.942969097 0.11406181 0.05703090
[45,] 0.920540781 0.15891844 0.07945922
[46,] 0.883601794 0.23279641 0.11639821
[47,] 0.897154178 0.20569164 0.10284582
[48,] 0.889576732 0.22084654 0.11042327
[49,] 0.844762197 0.31047561 0.15523780
[50,] 0.759006990 0.48198602 0.24099301
[51,] 0.672189582 0.65562084 0.32781042
[52,] 0.652776974 0.69444605 0.34722303
[53,] 0.770066964 0.45986607 0.22993304
> postscript(file="/var/www/html/rcomp/tmp/155hh1230126221.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2raqw1230126221.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zgfc1230126221.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xgd41230126221.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rf8a1230126221.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 = 84
Frequency = 1
1 2 3 4 5 6
6.28571429 6.54285714 4.04285714 0.14285714 8.74285714 5.57142857
7 8 9 10 11 12
3.11428571 8.15714286 7.81428571 -0.55714286 8.50000000 5.37142857
13 14 15 16 17 18
3.82857143 -0.31428571 5.48571429 -0.31428571 3.38571429 2.21428571
19 20 21 22 23 24
-3.44285714 -1.60000000 -5.74285714 -7.01428571 -2.35714286 -6.78571429
25 26 27 28 29 30
-4.82857143 -4.07142857 -11.97142857 -6.97142857 -7.97142857 -7.34285714
31 32 33 34 35 36
-2.40000000 -6.65714286 -6.80000000 -4.97142857 -6.21428571 -4.04285714
37 38 39 40 41 42
-2.88571429 -2.72857143 -4.02857143 1.67142857 -7.12857143 -4.70000000
43 44 45 46 47 48
-3.95714286 -0.01428571 -7.25714286 -1.72857143 -7.97142857 -0.20000000
49 50 51 52 53 54
-0.84285714 -11.98571429 -1.48571429 1.41428571 2.11428571 3.14285714
55 56 57 58 59 60
10.08571429 -0.77142857 8.88571429 11.41428571 -1.12857143 5.44285714
61 62 63 64 65 66
0.10000000 5.25714286 3.85714286 3.45714286 4.35714286 2.08571429
67 68 69 70 71 72
5.42857143 -0.92857143 7.52857143 7.55714286 5.11428571 4.28571429
73 74 75 76 77 78
-1.65714286 7.30000000 4.10000000 0.60000000 -3.50000000 -0.97142857
79 80 81 82 83 84
-8.82857143 1.81428571 -4.42857143 -4.70000000 4.05714286 -4.07142857
> postscript(file="/var/www/html/rcomp/tmp/6psun1230126221.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 = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 6.28571429 NA
1 6.54285714 6.28571429
2 4.04285714 6.54285714
3 0.14285714 4.04285714
4 8.74285714 0.14285714
5 5.57142857 8.74285714
6 3.11428571 5.57142857
7 8.15714286 3.11428571
8 7.81428571 8.15714286
9 -0.55714286 7.81428571
10 8.50000000 -0.55714286
11 5.37142857 8.50000000
12 3.82857143 5.37142857
13 -0.31428571 3.82857143
14 5.48571429 -0.31428571
15 -0.31428571 5.48571429
16 3.38571429 -0.31428571
17 2.21428571 3.38571429
18 -3.44285714 2.21428571
19 -1.60000000 -3.44285714
20 -5.74285714 -1.60000000
21 -7.01428571 -5.74285714
22 -2.35714286 -7.01428571
23 -6.78571429 -2.35714286
24 -4.82857143 -6.78571429
25 -4.07142857 -4.82857143
26 -11.97142857 -4.07142857
27 -6.97142857 -11.97142857
28 -7.97142857 -6.97142857
29 -7.34285714 -7.97142857
30 -2.40000000 -7.34285714
31 -6.65714286 -2.40000000
32 -6.80000000 -6.65714286
33 -4.97142857 -6.80000000
34 -6.21428571 -4.97142857
35 -4.04285714 -6.21428571
36 -2.88571429 -4.04285714
37 -2.72857143 -2.88571429
38 -4.02857143 -2.72857143
39 1.67142857 -4.02857143
40 -7.12857143 1.67142857
41 -4.70000000 -7.12857143
42 -3.95714286 -4.70000000
43 -0.01428571 -3.95714286
44 -7.25714286 -0.01428571
45 -1.72857143 -7.25714286
46 -7.97142857 -1.72857143
47 -0.20000000 -7.97142857
48 -0.84285714 -0.20000000
49 -11.98571429 -0.84285714
50 -1.48571429 -11.98571429
51 1.41428571 -1.48571429
52 2.11428571 1.41428571
53 3.14285714 2.11428571
54 10.08571429 3.14285714
55 -0.77142857 10.08571429
56 8.88571429 -0.77142857
57 11.41428571 8.88571429
58 -1.12857143 11.41428571
59 5.44285714 -1.12857143
60 0.10000000 5.44285714
61 5.25714286 0.10000000
62 3.85714286 5.25714286
63 3.45714286 3.85714286
64 4.35714286 3.45714286
65 2.08571429 4.35714286
66 5.42857143 2.08571429
67 -0.92857143 5.42857143
68 7.52857143 -0.92857143
69 7.55714286 7.52857143
70 5.11428571 7.55714286
71 4.28571429 5.11428571
72 -1.65714286 4.28571429
73 7.30000000 -1.65714286
74 4.10000000 7.30000000
75 0.60000000 4.10000000
76 -3.50000000 0.60000000
77 -0.97142857 -3.50000000
78 -8.82857143 -0.97142857
79 1.81428571 -8.82857143
80 -4.42857143 1.81428571
81 -4.70000000 -4.42857143
82 4.05714286 -4.70000000
83 -4.07142857 4.05714286
84 NA -4.07142857
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.54285714 6.28571429
[2,] 4.04285714 6.54285714
[3,] 0.14285714 4.04285714
[4,] 8.74285714 0.14285714
[5,] 5.57142857 8.74285714
[6,] 3.11428571 5.57142857
[7,] 8.15714286 3.11428571
[8,] 7.81428571 8.15714286
[9,] -0.55714286 7.81428571
[10,] 8.50000000 -0.55714286
[11,] 5.37142857 8.50000000
[12,] 3.82857143 5.37142857
[13,] -0.31428571 3.82857143
[14,] 5.48571429 -0.31428571
[15,] -0.31428571 5.48571429
[16,] 3.38571429 -0.31428571
[17,] 2.21428571 3.38571429
[18,] -3.44285714 2.21428571
[19,] -1.60000000 -3.44285714
[20,] -5.74285714 -1.60000000
[21,] -7.01428571 -5.74285714
[22,] -2.35714286 -7.01428571
[23,] -6.78571429 -2.35714286
[24,] -4.82857143 -6.78571429
[25,] -4.07142857 -4.82857143
[26,] -11.97142857 -4.07142857
[27,] -6.97142857 -11.97142857
[28,] -7.97142857 -6.97142857
[29,] -7.34285714 -7.97142857
[30,] -2.40000000 -7.34285714
[31,] -6.65714286 -2.40000000
[32,] -6.80000000 -6.65714286
[33,] -4.97142857 -6.80000000
[34,] -6.21428571 -4.97142857
[35,] -4.04285714 -6.21428571
[36,] -2.88571429 -4.04285714
[37,] -2.72857143 -2.88571429
[38,] -4.02857143 -2.72857143
[39,] 1.67142857 -4.02857143
[40,] -7.12857143 1.67142857
[41,] -4.70000000 -7.12857143
[42,] -3.95714286 -4.70000000
[43,] -0.01428571 -3.95714286
[44,] -7.25714286 -0.01428571
[45,] -1.72857143 -7.25714286
[46,] -7.97142857 -1.72857143
[47,] -0.20000000 -7.97142857
[48,] -0.84285714 -0.20000000
[49,] -11.98571429 -0.84285714
[50,] -1.48571429 -11.98571429
[51,] 1.41428571 -1.48571429
[52,] 2.11428571 1.41428571
[53,] 3.14285714 2.11428571
[54,] 10.08571429 3.14285714
[55,] -0.77142857 10.08571429
[56,] 8.88571429 -0.77142857
[57,] 11.41428571 8.88571429
[58,] -1.12857143 11.41428571
[59,] 5.44285714 -1.12857143
[60,] 0.10000000 5.44285714
[61,] 5.25714286 0.10000000
[62,] 3.85714286 5.25714286
[63,] 3.45714286 3.85714286
[64,] 4.35714286 3.45714286
[65,] 2.08571429 4.35714286
[66,] 5.42857143 2.08571429
[67,] -0.92857143 5.42857143
[68,] 7.52857143 -0.92857143
[69,] 7.55714286 7.52857143
[70,] 5.11428571 7.55714286
[71,] 4.28571429 5.11428571
[72,] -1.65714286 4.28571429
[73,] 7.30000000 -1.65714286
[74,] 4.10000000 7.30000000
[75,] 0.60000000 4.10000000
[76,] -3.50000000 0.60000000
[77,] -0.97142857 -3.50000000
[78,] -8.82857143 -0.97142857
[79,] 1.81428571 -8.82857143
[80,] -4.42857143 1.81428571
[81,] -4.70000000 -4.42857143
[82,] 4.05714286 -4.70000000
[83,] -4.07142857 4.05714286
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.54285714 6.28571429
2 4.04285714 6.54285714
3 0.14285714 4.04285714
4 8.74285714 0.14285714
5 5.57142857 8.74285714
6 3.11428571 5.57142857
7 8.15714286 3.11428571
8 7.81428571 8.15714286
9 -0.55714286 7.81428571
10 8.50000000 -0.55714286
11 5.37142857 8.50000000
12 3.82857143 5.37142857
13 -0.31428571 3.82857143
14 5.48571429 -0.31428571
15 -0.31428571 5.48571429
16 3.38571429 -0.31428571
17 2.21428571 3.38571429
18 -3.44285714 2.21428571
19 -1.60000000 -3.44285714
20 -5.74285714 -1.60000000
21 -7.01428571 -5.74285714
22 -2.35714286 -7.01428571
23 -6.78571429 -2.35714286
24 -4.82857143 -6.78571429
25 -4.07142857 -4.82857143
26 -11.97142857 -4.07142857
27 -6.97142857 -11.97142857
28 -7.97142857 -6.97142857
29 -7.34285714 -7.97142857
30 -2.40000000 -7.34285714
31 -6.65714286 -2.40000000
32 -6.80000000 -6.65714286
33 -4.97142857 -6.80000000
34 -6.21428571 -4.97142857
35 -4.04285714 -6.21428571
36 -2.88571429 -4.04285714
37 -2.72857143 -2.88571429
38 -4.02857143 -2.72857143
39 1.67142857 -4.02857143
40 -7.12857143 1.67142857
41 -4.70000000 -7.12857143
42 -3.95714286 -4.70000000
43 -0.01428571 -3.95714286
44 -7.25714286 -0.01428571
45 -1.72857143 -7.25714286
46 -7.97142857 -1.72857143
47 -0.20000000 -7.97142857
48 -0.84285714 -0.20000000
49 -11.98571429 -0.84285714
50 -1.48571429 -11.98571429
51 1.41428571 -1.48571429
52 2.11428571 1.41428571
53 3.14285714 2.11428571
54 10.08571429 3.14285714
55 -0.77142857 10.08571429
56 8.88571429 -0.77142857
57 11.41428571 8.88571429
58 -1.12857143 11.41428571
59 5.44285714 -1.12857143
60 0.10000000 5.44285714
61 5.25714286 0.10000000
62 3.85714286 5.25714286
63 3.45714286 3.85714286
64 4.35714286 3.45714286
65 2.08571429 4.35714286
66 5.42857143 2.08571429
67 -0.92857143 5.42857143
68 7.52857143 -0.92857143
69 7.55714286 7.52857143
70 5.11428571 7.55714286
71 4.28571429 5.11428571
72 -1.65714286 4.28571429
73 7.30000000 -1.65714286
74 4.10000000 7.30000000
75 0.60000000 4.10000000
76 -3.50000000 0.60000000
77 -0.97142857 -3.50000000
78 -8.82857143 -0.97142857
79 1.81428571 -8.82857143
80 -4.42857143 1.81428571
81 -4.70000000 -4.42857143
82 4.05714286 -4.70000000
83 -4.07142857 4.05714286
> 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/7zktq1230126221.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k0lw1230126221.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rm1c1230126221.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10n1ui1230126222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1123os1230126222.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/12qp5c1230126222.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/132ffs1230126222.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/14qyhm1230126222.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/157hrs1230126222.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/16bod81230126222.tab")
+ }
>
> system("convert tmp/155hh1230126221.ps tmp/155hh1230126221.png")
> system("convert tmp/2raqw1230126221.ps tmp/2raqw1230126221.png")
> system("convert tmp/3zgfc1230126221.ps tmp/3zgfc1230126221.png")
> system("convert tmp/4xgd41230126221.ps tmp/4xgd41230126221.png")
> system("convert tmp/5rf8a1230126221.ps tmp/5rf8a1230126221.png")
> system("convert tmp/6psun1230126221.ps tmp/6psun1230126221.png")
> system("convert tmp/7zktq1230126221.ps tmp/7zktq1230126221.png")
> system("convert tmp/8k0lw1230126221.ps tmp/8k0lw1230126221.png")
> system("convert tmp/9rm1c1230126221.ps tmp/9rm1c1230126221.png")
> system("convert tmp/10n1ui1230126222.ps tmp/10n1ui1230126222.png")
>
>
> proc.time()
user system elapsed
2.773 1.633 3.444