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(98.8,6.3,100.5,6.1,110.4,6.1,96.4,6.3,101.9,6.3,106.2,6,81,6.2,94.7,6.4,101,6.8,109.4,7.5,102.3,7.5,90.7,7.6,96.2,7.6,96.1,7.4,106,7.3,103.1,7.1,102,6.9,104.7,6.8,86,7.5,92.1,7.6,106.9,7.8,112.6,8,101.7,8.1,92,8.2,97.4,8.3,97,8.2,105.4,8,102.7,7.9,98.1,7.6,104.5,7.6,87.4,8.3,89.9,8.4,109.8,8.4,111.7,8.4,98.6,8.4,96.9,8.6,95.1,8.9,97,8.8,112.7,8.3,102.9,7.5,97.4,7.2,111.4,7.4,87.4,8.8,96.8,9.3,114.1,9.3,110.3,8.7,103.9,8.2,101.6,8.3,94.6,8.5,95.9,8.6,104.7,8.5,102.8,8.2,98.1,8.1,113.9,7.9,80.9,8.6,95.7,8.7,113.2,8.7,105.9,8.5,108.8,8.4,102.3,8.5,99,8.7,100.7,8.7,115.5,8.6,100.7,8.5,109.9,8.3,114.6,8,85.4,8.2,100.5,8.1,114.8,8.1,116.5,8,112.9,7.9,102,7.9,106,8,105.3,8,118.8,7.9,106.1,8,109.3,7.7,117.2,7.2,92.5,7.5,104.2,7.3,112.5,7,122.4,7,113.3,7,100,7.2,110.7,7.3,112.8,7.1,109.8,6.8,117.3,6.4,109.1,6.1,115.9,6.5,96,7.7,99.8,7.9,116.8,7.5,115.7,6.9,99.4,6.6,94.3,6.9,91,7.7),dim=c(2,97),dimnames=list(c('Y','X'),1:97))
> y <- array(NA,dim=c(2,97),dimnames=list(c('Y','X'),1:97))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 98.8 6.3 1 0 0 0 0 0 0 0 0 0 0 1
2 100.5 6.1 0 1 0 0 0 0 0 0 0 0 0 2
3 110.4 6.1 0 0 1 0 0 0 0 0 0 0 0 3
4 96.4 6.3 0 0 0 1 0 0 0 0 0 0 0 4
5 101.9 6.3 0 0 0 0 1 0 0 0 0 0 0 5
6 106.2 6.0 0 0 0 0 0 1 0 0 0 0 0 6
7 81.0 6.2 0 0 0 0 0 0 1 0 0 0 0 7
8 94.7 6.4 0 0 0 0 0 0 0 1 0 0 0 8
9 101.0 6.8 0 0 0 0 0 0 0 0 1 0 0 9
10 109.4 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 102.3 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 90.7 7.6 0 0 0 0 0 0 0 0 0 0 0 12
13 96.2 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 96.1 7.4 0 1 0 0 0 0 0 0 0 0 0 14
15 106.0 7.3 0 0 1 0 0 0 0 0 0 0 0 15
16 103.1 7.1 0 0 0 1 0 0 0 0 0 0 0 16
17 102.0 6.9 0 0 0 0 1 0 0 0 0 0 0 17
18 104.7 6.8 0 0 0 0 0 1 0 0 0 0 0 18
19 86.0 7.5 0 0 0 0 0 0 1 0 0 0 0 19
20 92.1 7.6 0 0 0 0 0 0 0 1 0 0 0 20
21 106.9 7.8 0 0 0 0 0 0 0 0 1 0 0 21
22 112.6 8.0 0 0 0 0 0 0 0 0 0 1 0 22
23 101.7 8.1 0 0 0 0 0 0 0 0 0 0 1 23
24 92.0 8.2 0 0 0 0 0 0 0 0 0 0 0 24
25 97.4 8.3 1 0 0 0 0 0 0 0 0 0 0 25
26 97.0 8.2 0 1 0 0 0 0 0 0 0 0 0 26
27 105.4 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 102.7 7.9 0 0 0 1 0 0 0 0 0 0 0 28
29 98.1 7.6 0 0 0 0 1 0 0 0 0 0 0 29
30 104.5 7.6 0 0 0 0 0 1 0 0 0 0 0 30
31 87.4 8.3 0 0 0 0 0 0 1 0 0 0 0 31
32 89.9 8.4 0 0 0 0 0 0 0 1 0 0 0 32
33 109.8 8.4 0 0 0 0 0 0 0 0 1 0 0 33
34 111.7 8.4 0 0 0 0 0 0 0 0 0 1 0 34
35 98.6 8.4 0 0 0 0 0 0 0 0 0 0 1 35
36 96.9 8.6 0 0 0 0 0 0 0 0 0 0 0 36
37 95.1 8.9 1 0 0 0 0 0 0 0 0 0 0 37
38 97.0 8.8 0 1 0 0 0 0 0 0 0 0 0 38
39 112.7 8.3 0 0 1 0 0 0 0 0 0 0 0 39
40 102.9 7.5 0 0 0 1 0 0 0 0 0 0 0 40
41 97.4 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 111.4 7.4 0 0 0 0 0 1 0 0 0 0 0 42
43 87.4 8.8 0 0 0 0 0 0 1 0 0 0 0 43
44 96.8 9.3 0 0 0 0 0 0 0 1 0 0 0 44
45 114.1 9.3 0 0 0 0 0 0 0 0 1 0 0 45
46 110.3 8.7 0 0 0 0 0 0 0 0 0 1 0 46
47 103.9 8.2 0 0 0 0 0 0 0 0 0 0 1 47
48 101.6 8.3 0 0 0 0 0 0 0 0 0 0 0 48
49 94.6 8.5 1 0 0 0 0 0 0 0 0 0 0 49
50 95.9 8.6 0 1 0 0 0 0 0 0 0 0 0 50
51 104.7 8.5 0 0 1 0 0 0 0 0 0 0 0 51
52 102.8 8.2 0 0 0 1 0 0 0 0 0 0 0 52
53 98.1 8.1 0 0 0 0 1 0 0 0 0 0 0 53
54 113.9 7.9 0 0 0 0 0 1 0 0 0 0 0 54
55 80.9 8.6 0 0 0 0 0 0 1 0 0 0 0 55
56 95.7 8.7 0 0 0 0 0 0 0 1 0 0 0 56
57 113.2 8.7 0 0 0 0 0 0 0 0 1 0 0 57
58 105.9 8.5 0 0 0 0 0 0 0 0 0 1 0 58
59 108.8 8.4 0 0 0 0 0 0 0 0 0 0 1 59
60 102.3 8.5 0 0 0 0 0 0 0 0 0 0 0 60
61 99.0 8.7 1 0 0 0 0 0 0 0 0 0 0 61
62 100.7 8.7 0 1 0 0 0 0 0 0 0 0 0 62
63 115.5 8.6 0 0 1 0 0 0 0 0 0 0 0 63
64 100.7 8.5 0 0 0 1 0 0 0 0 0 0 0 64
65 109.9 8.3 0 0 0 0 1 0 0 0 0 0 0 65
66 114.6 8.0 0 0 0 0 0 1 0 0 0 0 0 66
67 85.4 8.2 0 0 0 0 0 0 1 0 0 0 0 67
68 100.5 8.1 0 0 0 0 0 0 0 1 0 0 0 68
69 114.8 8.1 0 0 0 0 0 0 0 0 1 0 0 69
70 116.5 8.0 0 0 0 0 0 0 0 0 0 1 0 70
71 112.9 7.9 0 0 0 0 0 0 0 0 0 0 1 71
72 102.0 7.9 0 0 0 0 0 0 0 0 0 0 0 72
73 106.0 8.0 1 0 0 0 0 0 0 0 0 0 0 73
74 105.3 8.0 0 1 0 0 0 0 0 0 0 0 0 74
75 118.8 7.9 0 0 1 0 0 0 0 0 0 0 0 75
76 106.1 8.0 0 0 0 1 0 0 0 0 0 0 0 76
77 109.3 7.7 0 0 0 0 1 0 0 0 0 0 0 77
78 117.2 7.2 0 0 0 0 0 1 0 0 0 0 0 78
79 92.5 7.5 0 0 0 0 0 0 1 0 0 0 0 79
80 104.2 7.3 0 0 0 0 0 0 0 1 0 0 0 80
81 112.5 7.0 0 0 0 0 0 0 0 0 1 0 0 81
82 122.4 7.0 0 0 0 0 0 0 0 0 0 1 0 82
83 113.3 7.0 0 0 0 0 0 0 0 0 0 0 1 83
84 100.0 7.2 0 0 0 0 0 0 0 0 0 0 0 84
85 110.7 7.3 1 0 0 0 0 0 0 0 0 0 0 85
86 112.8 7.1 0 1 0 0 0 0 0 0 0 0 0 86
87 109.8 6.8 0 0 1 0 0 0 0 0 0 0 0 87
88 117.3 6.4 0 0 0 1 0 0 0 0 0 0 0 88
89 109.1 6.1 0 0 0 0 1 0 0 0 0 0 0 89
90 115.9 6.5 0 0 0 0 0 1 0 0 0 0 0 90
91 96.0 7.7 0 0 0 0 0 0 1 0 0 0 0 91
92 99.8 7.9 0 0 0 0 0 0 0 1 0 0 0 92
93 116.8 7.5 0 0 0 0 0 0 0 0 1 0 0 93
94 115.7 6.9 0 0 0 0 0 0 0 0 0 1 0 94
95 99.4 6.6 0 0 0 0 0 0 0 0 0 0 1 95
96 94.3 6.9 0 0 0 0 0 0 0 0 0 0 0 96
97 91.0 7.7 1 0 0 0 0 0 0 0 0 0 0 97
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
97.7415 -0.8150 1.8702 4.3000 13.7930 7.1032
M5 M6 M7 M8 M9 M10
6.0407 13.6699 -9.8692 -0.2543 14.0462 15.7957
M11 t
7.6397 0.1143
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.4232 -2.2188 0.8326 2.7413 7.6129
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.74154 4.79057 20.403 < 2e-16 ***
X -0.81505 0.57639 -1.414 0.16108
M1 1.87018 2.04927 0.913 0.36409
M2 4.29996 2.11271 2.035 0.04501 *
M3 13.79303 2.11449 6.523 5.10e-09 ***
M4 7.10321 2.12279 3.346 0.00123 **
M5 6.04071 2.13873 2.824 0.00593 **
M6 13.66991 2.14848 6.363 1.03e-08 ***
M7 -9.86924 2.10861 -4.680 1.10e-05 ***
M8 -0.25435 2.10841 -0.121 0.90427
M9 14.04616 2.10787 6.664 2.74e-09 ***
M10 15.79573 2.10736 7.496 6.51e-11 ***
M11 7.63973 2.10859 3.623 0.00050 ***
t 0.11430 0.01544 7.405 9.84e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.214 on 83 degrees of freedom
Multiple R-squared: 0.8058, Adjusted R-squared: 0.7754
F-statistic: 26.5 on 13 and 83 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,] 0.2231052379 0.4462104759 0.7768948
[2,] 0.1331139557 0.2662279114 0.8668860
[3,] 0.2408011316 0.4816022632 0.7591989
[4,] 0.1490622275 0.2981244550 0.8509378
[5,] 0.1544531977 0.3089063953 0.8455468
[6,] 0.0944037259 0.1888074518 0.9055963
[7,] 0.0642078344 0.1284156689 0.9357922
[8,] 0.0359339606 0.0718679211 0.9640660
[9,] 0.0202194504 0.0404389009 0.9797805
[10,] 0.0111505555 0.0223011109 0.9888494
[11,] 0.0083015705 0.0166031410 0.9916984
[12,] 0.0049866715 0.0099733430 0.9950133
[13,] 0.0061768231 0.0123536462 0.9938232
[14,] 0.0035818647 0.0071637294 0.9964181
[15,] 0.0035914393 0.0071828786 0.9964086
[16,] 0.0029133861 0.0058267722 0.9970866
[17,] 0.0036010672 0.0072021344 0.9963989
[18,] 0.0020103221 0.0040206443 0.9979897
[19,] 0.0021287670 0.0042575339 0.9978712
[20,] 0.0021028781 0.0042057563 0.9978971
[21,] 0.0014245100 0.0028490200 0.9985755
[22,] 0.0007935997 0.0015871993 0.9992064
[23,] 0.0007974861 0.0015949721 0.9992025
[24,] 0.0004183135 0.0008366269 0.9995817
[25,] 0.0006646288 0.0013292577 0.9993354
[26,] 0.0007170333 0.0014340666 0.9992830
[27,] 0.0004227949 0.0008455897 0.9995772
[28,] 0.0003476238 0.0006952476 0.9996524
[29,] 0.0007316769 0.0014633538 0.9992683
[30,] 0.0004620910 0.0009241820 0.9995379
[31,] 0.0002523053 0.0005046106 0.9997477
[32,] 0.0003589960 0.0007179921 0.9996410
[33,] 0.0004001372 0.0008002744 0.9995999
[34,] 0.0004672041 0.0009344081 0.9995328
[35,] 0.0007829457 0.0015658914 0.9992171
[36,] 0.0004834319 0.0009668639 0.9995166
[37,] 0.0007541610 0.0015083219 0.9992458
[38,] 0.0008116564 0.0016233127 0.9991883
[39,] 0.0021020005 0.0042040010 0.9978980
[40,] 0.0016327329 0.0032654657 0.9983673
[41,] 0.0011644059 0.0023288119 0.9988356
[42,] 0.0048405484 0.0096810967 0.9951595
[43,] 0.0045789559 0.0091579119 0.9954210
[44,] 0.0043029195 0.0086058389 0.9956971
[45,] 0.0030030809 0.0060061617 0.9969969
[46,] 0.0030003567 0.0060007134 0.9969996
[47,] 0.0026056078 0.0052112156 0.9973944
[48,] 0.0048844579 0.0097689158 0.9951155
[49,] 0.0059653585 0.0119307169 0.9940346
[50,] 0.0041254401 0.0082508801 0.9958746
[51,] 0.0116386887 0.0232773774 0.9883613
[52,] 0.0108488757 0.0216977514 0.9891511
[53,] 0.0072889044 0.0145778088 0.9927111
[54,] 0.0063643954 0.0127287907 0.9936356
[55,] 0.0051672420 0.0103344840 0.9948328
[56,] 0.0028337570 0.0056675141 0.9971662
[57,] 0.0015866744 0.0031733488 0.9984133
[58,] 0.0019600599 0.0039201198 0.9980399
[59,] 0.0025669702 0.0051339404 0.9974330
[60,] 0.0060717466 0.0121434933 0.9939283
[61,] 0.0039811184 0.0079622368 0.9960189
[62,] 0.0038348963 0.0076697926 0.9961651
[63,] 0.0080902139 0.0161804278 0.9919098
[64,] 0.0031576376 0.0063152752 0.9968424
> postscript(file="/var/www/html/rcomp/tmp/1wlhb1258650109.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ech51258650109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33gyn1258650109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a7be1258650109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vzcd1258650109.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 = 97
Frequency = 1
1 2 3 4 5 6
4.2087853 3.2016909 3.4943246 -3.7671555 2.6810426 -1.0069674
7 8 9 10 11 12
-2.6191160 1.5147008 -6.2740911 0.8325725 1.7742656 -2.2187988
13 14 15 16 17 18
1.2967168 -1.5103776 -1.2992488 2.2132512 1.8984393 -3.2265607
19 20 21 22 23 24
2.0688155 -1.4788726 -0.9306745 3.0684642 0.2916623 -1.8014021
25 26 27 28 29 30
1.6956185 -1.3299709 -2.7003472 1.0936578 -2.8026590 -4.1461541
31 32 33 34 35 36
2.7492222 -4.3984659 1.0867222 1.1228509 -3.9354560 2.0529846
37 38 39 40 41 42
-1.4869848 -2.2125742 3.4725346 -0.4039953 -5.2003121 1.2192028
43 44 45 46 47 48
1.7851139 1.8634457 4.7486338 -1.4042673 -0.1700991 5.1368365
49 50 51 52 53 54
-3.6846379 -4.8472174 -5.7360886 -1.3050936 -5.1384005 2.7550945
55 56 57 58 59 60
-6.2495292 -1.0972174 1.9879708 -7.3389105 3.5212777 4.6282133
61 62 63 64 65 66
-0.4932611 -1.3373456 3.7737832 -4.5322119 5.4529763 2.1649663
67 68 69 70 71 72
-3.4471823 1.8421196 1.7273077 1.4819315 5.8421196 2.4675502
73 74 75 76 77 78
4.5645708 1.3204864 5.1316151 -0.9113699 2.9923132 2.7412933
79 80 81 82 83 84
1.7106496 3.5184466 -2.8408803 5.1952485 4.1369416 -1.4746178
85 86 87 88 89 90
7.3224028 6.7153084 -6.1365728 7.6129172 0.1166004 -0.5008747
91 92 93 94 95 96
4.0020264 -1.7641567 0.4950115 -2.9578897 -11.4607115 -8.7907659
97
-13.4232104
> postscript(file="/var/www/html/rcomp/tmp/6zwr31258650109.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 = 97
Frequency = 1
lag(myerror, k = 1) myerror
0 4.2087853 NA
1 3.2016909 4.2087853
2 3.4943246 3.2016909
3 -3.7671555 3.4943246
4 2.6810426 -3.7671555
5 -1.0069674 2.6810426
6 -2.6191160 -1.0069674
7 1.5147008 -2.6191160
8 -6.2740911 1.5147008
9 0.8325725 -6.2740911
10 1.7742656 0.8325725
11 -2.2187988 1.7742656
12 1.2967168 -2.2187988
13 -1.5103776 1.2967168
14 -1.2992488 -1.5103776
15 2.2132512 -1.2992488
16 1.8984393 2.2132512
17 -3.2265607 1.8984393
18 2.0688155 -3.2265607
19 -1.4788726 2.0688155
20 -0.9306745 -1.4788726
21 3.0684642 -0.9306745
22 0.2916623 3.0684642
23 -1.8014021 0.2916623
24 1.6956185 -1.8014021
25 -1.3299709 1.6956185
26 -2.7003472 -1.3299709
27 1.0936578 -2.7003472
28 -2.8026590 1.0936578
29 -4.1461541 -2.8026590
30 2.7492222 -4.1461541
31 -4.3984659 2.7492222
32 1.0867222 -4.3984659
33 1.1228509 1.0867222
34 -3.9354560 1.1228509
35 2.0529846 -3.9354560
36 -1.4869848 2.0529846
37 -2.2125742 -1.4869848
38 3.4725346 -2.2125742
39 -0.4039953 3.4725346
40 -5.2003121 -0.4039953
41 1.2192028 -5.2003121
42 1.7851139 1.2192028
43 1.8634457 1.7851139
44 4.7486338 1.8634457
45 -1.4042673 4.7486338
46 -0.1700991 -1.4042673
47 5.1368365 -0.1700991
48 -3.6846379 5.1368365
49 -4.8472174 -3.6846379
50 -5.7360886 -4.8472174
51 -1.3050936 -5.7360886
52 -5.1384005 -1.3050936
53 2.7550945 -5.1384005
54 -6.2495292 2.7550945
55 -1.0972174 -6.2495292
56 1.9879708 -1.0972174
57 -7.3389105 1.9879708
58 3.5212777 -7.3389105
59 4.6282133 3.5212777
60 -0.4932611 4.6282133
61 -1.3373456 -0.4932611
62 3.7737832 -1.3373456
63 -4.5322119 3.7737832
64 5.4529763 -4.5322119
65 2.1649663 5.4529763
66 -3.4471823 2.1649663
67 1.8421196 -3.4471823
68 1.7273077 1.8421196
69 1.4819315 1.7273077
70 5.8421196 1.4819315
71 2.4675502 5.8421196
72 4.5645708 2.4675502
73 1.3204864 4.5645708
74 5.1316151 1.3204864
75 -0.9113699 5.1316151
76 2.9923132 -0.9113699
77 2.7412933 2.9923132
78 1.7106496 2.7412933
79 3.5184466 1.7106496
80 -2.8408803 3.5184466
81 5.1952485 -2.8408803
82 4.1369416 5.1952485
83 -1.4746178 4.1369416
84 7.3224028 -1.4746178
85 6.7153084 7.3224028
86 -6.1365728 6.7153084
87 7.6129172 -6.1365728
88 0.1166004 7.6129172
89 -0.5008747 0.1166004
90 4.0020264 -0.5008747
91 -1.7641567 4.0020264
92 0.4950115 -1.7641567
93 -2.9578897 0.4950115
94 -11.4607115 -2.9578897
95 -8.7907659 -11.4607115
96 -13.4232104 -8.7907659
97 NA -13.4232104
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.2016909 4.2087853
[2,] 3.4943246 3.2016909
[3,] -3.7671555 3.4943246
[4,] 2.6810426 -3.7671555
[5,] -1.0069674 2.6810426
[6,] -2.6191160 -1.0069674
[7,] 1.5147008 -2.6191160
[8,] -6.2740911 1.5147008
[9,] 0.8325725 -6.2740911
[10,] 1.7742656 0.8325725
[11,] -2.2187988 1.7742656
[12,] 1.2967168 -2.2187988
[13,] -1.5103776 1.2967168
[14,] -1.2992488 -1.5103776
[15,] 2.2132512 -1.2992488
[16,] 1.8984393 2.2132512
[17,] -3.2265607 1.8984393
[18,] 2.0688155 -3.2265607
[19,] -1.4788726 2.0688155
[20,] -0.9306745 -1.4788726
[21,] 3.0684642 -0.9306745
[22,] 0.2916623 3.0684642
[23,] -1.8014021 0.2916623
[24,] 1.6956185 -1.8014021
[25,] -1.3299709 1.6956185
[26,] -2.7003472 -1.3299709
[27,] 1.0936578 -2.7003472
[28,] -2.8026590 1.0936578
[29,] -4.1461541 -2.8026590
[30,] 2.7492222 -4.1461541
[31,] -4.3984659 2.7492222
[32,] 1.0867222 -4.3984659
[33,] 1.1228509 1.0867222
[34,] -3.9354560 1.1228509
[35,] 2.0529846 -3.9354560
[36,] -1.4869848 2.0529846
[37,] -2.2125742 -1.4869848
[38,] 3.4725346 -2.2125742
[39,] -0.4039953 3.4725346
[40,] -5.2003121 -0.4039953
[41,] 1.2192028 -5.2003121
[42,] 1.7851139 1.2192028
[43,] 1.8634457 1.7851139
[44,] 4.7486338 1.8634457
[45,] -1.4042673 4.7486338
[46,] -0.1700991 -1.4042673
[47,] 5.1368365 -0.1700991
[48,] -3.6846379 5.1368365
[49,] -4.8472174 -3.6846379
[50,] -5.7360886 -4.8472174
[51,] -1.3050936 -5.7360886
[52,] -5.1384005 -1.3050936
[53,] 2.7550945 -5.1384005
[54,] -6.2495292 2.7550945
[55,] -1.0972174 -6.2495292
[56,] 1.9879708 -1.0972174
[57,] -7.3389105 1.9879708
[58,] 3.5212777 -7.3389105
[59,] 4.6282133 3.5212777
[60,] -0.4932611 4.6282133
[61,] -1.3373456 -0.4932611
[62,] 3.7737832 -1.3373456
[63,] -4.5322119 3.7737832
[64,] 5.4529763 -4.5322119
[65,] 2.1649663 5.4529763
[66,] -3.4471823 2.1649663
[67,] 1.8421196 -3.4471823
[68,] 1.7273077 1.8421196
[69,] 1.4819315 1.7273077
[70,] 5.8421196 1.4819315
[71,] 2.4675502 5.8421196
[72,] 4.5645708 2.4675502
[73,] 1.3204864 4.5645708
[74,] 5.1316151 1.3204864
[75,] -0.9113699 5.1316151
[76,] 2.9923132 -0.9113699
[77,] 2.7412933 2.9923132
[78,] 1.7106496 2.7412933
[79,] 3.5184466 1.7106496
[80,] -2.8408803 3.5184466
[81,] 5.1952485 -2.8408803
[82,] 4.1369416 5.1952485
[83,] -1.4746178 4.1369416
[84,] 7.3224028 -1.4746178
[85,] 6.7153084 7.3224028
[86,] -6.1365728 6.7153084
[87,] 7.6129172 -6.1365728
[88,] 0.1166004 7.6129172
[89,] -0.5008747 0.1166004
[90,] 4.0020264 -0.5008747
[91,] -1.7641567 4.0020264
[92,] 0.4950115 -1.7641567
[93,] -2.9578897 0.4950115
[94,] -11.4607115 -2.9578897
[95,] -8.7907659 -11.4607115
[96,] -13.4232104 -8.7907659
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.2016909 4.2087853
2 3.4943246 3.2016909
3 -3.7671555 3.4943246
4 2.6810426 -3.7671555
5 -1.0069674 2.6810426
6 -2.6191160 -1.0069674
7 1.5147008 -2.6191160
8 -6.2740911 1.5147008
9 0.8325725 -6.2740911
10 1.7742656 0.8325725
11 -2.2187988 1.7742656
12 1.2967168 -2.2187988
13 -1.5103776 1.2967168
14 -1.2992488 -1.5103776
15 2.2132512 -1.2992488
16 1.8984393 2.2132512
17 -3.2265607 1.8984393
18 2.0688155 -3.2265607
19 -1.4788726 2.0688155
20 -0.9306745 -1.4788726
21 3.0684642 -0.9306745
22 0.2916623 3.0684642
23 -1.8014021 0.2916623
24 1.6956185 -1.8014021
25 -1.3299709 1.6956185
26 -2.7003472 -1.3299709
27 1.0936578 -2.7003472
28 -2.8026590 1.0936578
29 -4.1461541 -2.8026590
30 2.7492222 -4.1461541
31 -4.3984659 2.7492222
32 1.0867222 -4.3984659
33 1.1228509 1.0867222
34 -3.9354560 1.1228509
35 2.0529846 -3.9354560
36 -1.4869848 2.0529846
37 -2.2125742 -1.4869848
38 3.4725346 -2.2125742
39 -0.4039953 3.4725346
40 -5.2003121 -0.4039953
41 1.2192028 -5.2003121
42 1.7851139 1.2192028
43 1.8634457 1.7851139
44 4.7486338 1.8634457
45 -1.4042673 4.7486338
46 -0.1700991 -1.4042673
47 5.1368365 -0.1700991
48 -3.6846379 5.1368365
49 -4.8472174 -3.6846379
50 -5.7360886 -4.8472174
51 -1.3050936 -5.7360886
52 -5.1384005 -1.3050936
53 2.7550945 -5.1384005
54 -6.2495292 2.7550945
55 -1.0972174 -6.2495292
56 1.9879708 -1.0972174
57 -7.3389105 1.9879708
58 3.5212777 -7.3389105
59 4.6282133 3.5212777
60 -0.4932611 4.6282133
61 -1.3373456 -0.4932611
62 3.7737832 -1.3373456
63 -4.5322119 3.7737832
64 5.4529763 -4.5322119
65 2.1649663 5.4529763
66 -3.4471823 2.1649663
67 1.8421196 -3.4471823
68 1.7273077 1.8421196
69 1.4819315 1.7273077
70 5.8421196 1.4819315
71 2.4675502 5.8421196
72 4.5645708 2.4675502
73 1.3204864 4.5645708
74 5.1316151 1.3204864
75 -0.9113699 5.1316151
76 2.9923132 -0.9113699
77 2.7412933 2.9923132
78 1.7106496 2.7412933
79 3.5184466 1.7106496
80 -2.8408803 3.5184466
81 5.1952485 -2.8408803
82 4.1369416 5.1952485
83 -1.4746178 4.1369416
84 7.3224028 -1.4746178
85 6.7153084 7.3224028
86 -6.1365728 6.7153084
87 7.6129172 -6.1365728
88 0.1166004 7.6129172
89 -0.5008747 0.1166004
90 4.0020264 -0.5008747
91 -1.7641567 4.0020264
92 0.4950115 -1.7641567
93 -2.9578897 0.4950115
94 -11.4607115 -2.9578897
95 -8.7907659 -11.4607115
96 -13.4232104 -8.7907659
> 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/74cg01258650109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ka771258650109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93vhk1258650109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xjyr1258650109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119ffb1258650109.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/12cm0g1258650109.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/13kc4w1258650110.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/14qoc11258650110.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/15epwo1258650110.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/1635d01258650110.tab")
+ }
>
> system("convert tmp/1wlhb1258650109.ps tmp/1wlhb1258650109.png")
> system("convert tmp/2ech51258650109.ps tmp/2ech51258650109.png")
> system("convert tmp/33gyn1258650109.ps tmp/33gyn1258650109.png")
> system("convert tmp/4a7be1258650109.ps tmp/4a7be1258650109.png")
> system("convert tmp/5vzcd1258650109.ps tmp/5vzcd1258650109.png")
> system("convert tmp/6zwr31258650109.ps tmp/6zwr31258650109.png")
> system("convert tmp/74cg01258650109.ps tmp/74cg01258650109.png")
> system("convert tmp/8ka771258650109.ps tmp/8ka771258650109.png")
> system("convert tmp/93vhk1258650109.ps tmp/93vhk1258650109.png")
> system("convert tmp/10xjyr1258650109.ps tmp/10xjyr1258650109.png")
>
>
> proc.time()
user system elapsed
2.978 1.639 5.541