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(95.1,136,97,133,112.7,126,102.9,120,97.4,114,111.4,116,87.4,153,96.8,162,114.1,161,110.3,149,103.9,139,101.6,135,94.6,130,95.9,127,104.7,122,102.8,117,98.1,112,113.9,113,80.9,149,95.7,157,113.2,157,105.9,147,108.8,137,102.3,132,99,125,100.7,123,115.5,117,100.7,114,109.9,111,114.6,112,85.4,144,100.5,150,114.8,149,116.5,134,112.9,123,102,116,106,117,105.3,111,118.8,105,106.1,102,109.3,95,117.2,93,92.5,124,104.2,130,112.5,124,122.4,115,113.3,106,100,105,110.7,105,112.8,101,109.8,95,117.3,93,109.1,84,115.9,87,96,116,99.8,120,116.8,117,115.7,109,99.4,105,94.3,107,91,109),dim=c(2,61),dimnames=list(c('tip','wrk'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('tip','wrk'),1:61))
> 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
tip wrk M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 95.1 136 1 0 0 0 0 0 0 0 0 0 0 1
2 97.0 133 0 1 0 0 0 0 0 0 0 0 0 2
3 112.7 126 0 0 1 0 0 0 0 0 0 0 0 3
4 102.9 120 0 0 0 1 0 0 0 0 0 0 0 4
5 97.4 114 0 0 0 0 1 0 0 0 0 0 0 5
6 111.4 116 0 0 0 0 0 1 0 0 0 0 0 6
7 87.4 153 0 0 0 0 0 0 1 0 0 0 0 7
8 96.8 162 0 0 0 0 0 0 0 1 0 0 0 8
9 114.1 161 0 0 0 0 0 0 0 0 1 0 0 9
10 110.3 149 0 0 0 0 0 0 0 0 0 1 0 10
11 103.9 139 0 0 0 0 0 0 0 0 0 0 1 11
12 101.6 135 0 0 0 0 0 0 0 0 0 0 0 12
13 94.6 130 1 0 0 0 0 0 0 0 0 0 0 13
14 95.9 127 0 1 0 0 0 0 0 0 0 0 0 14
15 104.7 122 0 0 1 0 0 0 0 0 0 0 0 15
16 102.8 117 0 0 0 1 0 0 0 0 0 0 0 16
17 98.1 112 0 0 0 0 1 0 0 0 0 0 0 17
18 113.9 113 0 0 0 0 0 1 0 0 0 0 0 18
19 80.9 149 0 0 0 0 0 0 1 0 0 0 0 19
20 95.7 157 0 0 0 0 0 0 0 1 0 0 0 20
21 113.2 157 0 0 0 0 0 0 0 0 1 0 0 21
22 105.9 147 0 0 0 0 0 0 0 0 0 1 0 22
23 108.8 137 0 0 0 0 0 0 0 0 0 0 1 23
24 102.3 132 0 0 0 0 0 0 0 0 0 0 0 24
25 99.0 125 1 0 0 0 0 0 0 0 0 0 0 25
26 100.7 123 0 1 0 0 0 0 0 0 0 0 0 26
27 115.5 117 0 0 1 0 0 0 0 0 0 0 0 27
28 100.7 114 0 0 0 1 0 0 0 0 0 0 0 28
29 109.9 111 0 0 0 0 1 0 0 0 0 0 0 29
30 114.6 112 0 0 0 0 0 1 0 0 0 0 0 30
31 85.4 144 0 0 0 0 0 0 1 0 0 0 0 31
32 100.5 150 0 0 0 0 0 0 0 1 0 0 0 32
33 114.8 149 0 0 0 0 0 0 0 0 1 0 0 33
34 116.5 134 0 0 0 0 0 0 0 0 0 1 0 34
35 112.9 123 0 0 0 0 0 0 0 0 0 0 1 35
36 102.0 116 0 0 0 0 0 0 0 0 0 0 0 36
37 106.0 117 1 0 0 0 0 0 0 0 0 0 0 37
38 105.3 111 0 1 0 0 0 0 0 0 0 0 0 38
39 118.8 105 0 0 1 0 0 0 0 0 0 0 0 39
40 106.1 102 0 0 0 1 0 0 0 0 0 0 0 40
41 109.3 95 0 0 0 0 1 0 0 0 0 0 0 41
42 117.2 93 0 0 0 0 0 1 0 0 0 0 0 42
43 92.5 124 0 0 0 0 0 0 1 0 0 0 0 43
44 104.2 130 0 0 0 0 0 0 0 1 0 0 0 44
45 112.5 124 0 0 0 0 0 0 0 0 1 0 0 45
46 122.4 115 0 0 0 0 0 0 0 0 0 1 0 46
47 113.3 106 0 0 0 0 0 0 0 0 0 0 1 47
48 100.0 105 0 0 0 0 0 0 0 0 0 0 0 48
49 110.7 105 1 0 0 0 0 0 0 0 0 0 0 49
50 112.8 101 0 1 0 0 0 0 0 0 0 0 0 50
51 109.8 95 0 0 1 0 0 0 0 0 0 0 0 51
52 117.3 93 0 0 0 1 0 0 0 0 0 0 0 52
53 109.1 84 0 0 0 0 1 0 0 0 0 0 0 53
54 115.9 87 0 0 0 0 0 1 0 0 0 0 0 54
55 96.0 116 0 0 0 0 0 0 1 0 0 0 0 55
56 99.8 120 0 0 0 0 0 0 0 1 0 0 0 56
57 116.8 117 0 0 0 0 0 0 0 0 1 0 0 57
58 115.7 109 0 0 0 0 0 0 0 0 0 1 0 58
59 99.4 105 0 0 0 0 0 0 0 0 0 0 1 59
60 94.3 107 0 0 0 0 0 0 0 0 0 0 0 60
61 91.0 109 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) wrk M1 M2 M3 M4
122.259778 -0.185269 -0.416978 2.251994 11.105183 4.065963
M5 M6 M7 M8 M9 M10
1.759153 11.789222 -8.252116 3.935457 18.412667 16.296567
M11 t
8.171005 -0.004801
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.2944 -2.5762 0.5826 2.9329 8.5456
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 122.259778 20.152348 6.067 2.13e-07 ***
wrk -0.185269 0.137589 -1.347 0.18459
M1 -0.416978 3.000392 -0.139 0.89006
M2 2.251994 3.288569 0.685 0.49684
M3 11.105183 3.573807 3.107 0.00320 **
M4 4.065963 3.793315 1.072 0.28925
M5 1.759153 4.244522 0.414 0.68043
M6 11.789222 4.084826 2.886 0.00587 **
M7 -8.252116 3.703298 -2.228 0.03068 *
M8 3.935457 4.331012 0.909 0.36816
M9 18.412667 4.191855 4.392 6.34e-05 ***
M10 16.296567 3.420990 4.764 1.86e-05 ***
M11 8.171005 3.126150 2.614 0.01199 *
t -0.004801 0.106805 -0.045 0.96434
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.918 on 47 degrees of freedom
Multiple R-squared: 0.7719, Adjusted R-squared: 0.7088
F-statistic: 12.23 on 13 and 47 DF, p-value: 5.178e-11
> 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,] 0.19987690 0.39975380 0.8001231
[2,] 0.13044564 0.26089128 0.8695544
[3,] 0.13723469 0.27446938 0.8627653
[4,] 0.07405376 0.14810752 0.9259462
[5,] 0.03570707 0.07141414 0.9642929
[6,] 0.02857464 0.05714929 0.9714254
[7,] 0.03576152 0.07152304 0.9642385
[8,] 0.01999257 0.03998515 0.9800074
[9,] 0.03786960 0.07573920 0.9621304
[10,] 0.04551883 0.09103767 0.9544812
[11,] 0.05271390 0.10542779 0.9472861
[12,] 0.07963222 0.15926444 0.9203678
[13,] 0.20795746 0.41591492 0.7920425
[14,] 0.14729261 0.29458523 0.8527074
[15,] 0.12234859 0.24469718 0.8776514
[16,] 0.09313612 0.18627223 0.9068639
[17,] 0.08574133 0.17148265 0.9142587
[18,] 0.08562277 0.17124554 0.9143772
[19,] 0.12646568 0.25293137 0.8735343
[20,] 0.09805722 0.19611443 0.9019428
[21,] 0.08632693 0.17265386 0.9136731
[22,] 0.07268286 0.14536573 0.9273171
[23,] 0.13539398 0.27078796 0.8646060
[24,] 0.25945182 0.51890364 0.7405482
[25,] 0.18874256 0.37748513 0.8112574
[26,] 0.12468861 0.24937722 0.8753114
[27,] 0.10095911 0.20191821 0.8990409
[28,] 0.08884825 0.17769650 0.9111517
> postscript(file="/var/www/html/rcomp/tmp/1a40t1260974340.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2q4tj1260974340.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jl9a1260974340.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4y5d81260974340.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55ovf1260974340.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 = 61
Frequency = 1
1 2 3 4 5 6
-1.5414783 -2.8614555 2.6932760 -1.1743148 -5.4743148 -1.1290462
7 8 9 10 11 12
1.7720279 0.6566724 3.2989946 -0.6033276 -0.7256499 4.4090816
13 14 15 16 17 18
-3.0954820 -5.0154592 -5.9901907 -1.7725129 -5.0872444 0.8727556
19 20 21 22 23 24
-5.4114388 -1.3120628 1.7155279 -5.3162572 3.8614205 4.6108835
25 26 27 28 29 30
0.4357828 -0.8989259 3.9410741 -4.3707110 6.5850945 1.4450945
31 32 33 34 35 36
-1.7801740 2.2486649 1.8909871 2.9328593 5.4252685 1.4041944
37 38 39 40 41 42
6.0112420 1.5354592 5.0754592 -1.1363260 3.0784055 0.5825999
43 44 45 46 47 48
1.6720628 2.3009017 -4.9831187 5.3703647 2.7333109 -2.5761520
49 50 51 52 53 54
8.5456270 7.2403813 -5.7196187 8.4538647 0.8980591 -1.7714038
55 56 57 58 59 60
3.7475220 -3.8941762 -1.9223910 -2.3836391 -11.2943501 -7.8480075
61
-10.3556914
> postscript(file="/var/www/html/rcomp/tmp/6tjy51260974340.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.5414783 NA
1 -2.8614555 -1.5414783
2 2.6932760 -2.8614555
3 -1.1743148 2.6932760
4 -5.4743148 -1.1743148
5 -1.1290462 -5.4743148
6 1.7720279 -1.1290462
7 0.6566724 1.7720279
8 3.2989946 0.6566724
9 -0.6033276 3.2989946
10 -0.7256499 -0.6033276
11 4.4090816 -0.7256499
12 -3.0954820 4.4090816
13 -5.0154592 -3.0954820
14 -5.9901907 -5.0154592
15 -1.7725129 -5.9901907
16 -5.0872444 -1.7725129
17 0.8727556 -5.0872444
18 -5.4114388 0.8727556
19 -1.3120628 -5.4114388
20 1.7155279 -1.3120628
21 -5.3162572 1.7155279
22 3.8614205 -5.3162572
23 4.6108835 3.8614205
24 0.4357828 4.6108835
25 -0.8989259 0.4357828
26 3.9410741 -0.8989259
27 -4.3707110 3.9410741
28 6.5850945 -4.3707110
29 1.4450945 6.5850945
30 -1.7801740 1.4450945
31 2.2486649 -1.7801740
32 1.8909871 2.2486649
33 2.9328593 1.8909871
34 5.4252685 2.9328593
35 1.4041944 5.4252685
36 6.0112420 1.4041944
37 1.5354592 6.0112420
38 5.0754592 1.5354592
39 -1.1363260 5.0754592
40 3.0784055 -1.1363260
41 0.5825999 3.0784055
42 1.6720628 0.5825999
43 2.3009017 1.6720628
44 -4.9831187 2.3009017
45 5.3703647 -4.9831187
46 2.7333109 5.3703647
47 -2.5761520 2.7333109
48 8.5456270 -2.5761520
49 7.2403813 8.5456270
50 -5.7196187 7.2403813
51 8.4538647 -5.7196187
52 0.8980591 8.4538647
53 -1.7714038 0.8980591
54 3.7475220 -1.7714038
55 -3.8941762 3.7475220
56 -1.9223910 -3.8941762
57 -2.3836391 -1.9223910
58 -11.2943501 -2.3836391
59 -7.8480075 -11.2943501
60 -10.3556914 -7.8480075
61 NA -10.3556914
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.8614555 -1.5414783
[2,] 2.6932760 -2.8614555
[3,] -1.1743148 2.6932760
[4,] -5.4743148 -1.1743148
[5,] -1.1290462 -5.4743148
[6,] 1.7720279 -1.1290462
[7,] 0.6566724 1.7720279
[8,] 3.2989946 0.6566724
[9,] -0.6033276 3.2989946
[10,] -0.7256499 -0.6033276
[11,] 4.4090816 -0.7256499
[12,] -3.0954820 4.4090816
[13,] -5.0154592 -3.0954820
[14,] -5.9901907 -5.0154592
[15,] -1.7725129 -5.9901907
[16,] -5.0872444 -1.7725129
[17,] 0.8727556 -5.0872444
[18,] -5.4114388 0.8727556
[19,] -1.3120628 -5.4114388
[20,] 1.7155279 -1.3120628
[21,] -5.3162572 1.7155279
[22,] 3.8614205 -5.3162572
[23,] 4.6108835 3.8614205
[24,] 0.4357828 4.6108835
[25,] -0.8989259 0.4357828
[26,] 3.9410741 -0.8989259
[27,] -4.3707110 3.9410741
[28,] 6.5850945 -4.3707110
[29,] 1.4450945 6.5850945
[30,] -1.7801740 1.4450945
[31,] 2.2486649 -1.7801740
[32,] 1.8909871 2.2486649
[33,] 2.9328593 1.8909871
[34,] 5.4252685 2.9328593
[35,] 1.4041944 5.4252685
[36,] 6.0112420 1.4041944
[37,] 1.5354592 6.0112420
[38,] 5.0754592 1.5354592
[39,] -1.1363260 5.0754592
[40,] 3.0784055 -1.1363260
[41,] 0.5825999 3.0784055
[42,] 1.6720628 0.5825999
[43,] 2.3009017 1.6720628
[44,] -4.9831187 2.3009017
[45,] 5.3703647 -4.9831187
[46,] 2.7333109 5.3703647
[47,] -2.5761520 2.7333109
[48,] 8.5456270 -2.5761520
[49,] 7.2403813 8.5456270
[50,] -5.7196187 7.2403813
[51,] 8.4538647 -5.7196187
[52,] 0.8980591 8.4538647
[53,] -1.7714038 0.8980591
[54,] 3.7475220 -1.7714038
[55,] -3.8941762 3.7475220
[56,] -1.9223910 -3.8941762
[57,] -2.3836391 -1.9223910
[58,] -11.2943501 -2.3836391
[59,] -7.8480075 -11.2943501
[60,] -10.3556914 -7.8480075
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.8614555 -1.5414783
2 2.6932760 -2.8614555
3 -1.1743148 2.6932760
4 -5.4743148 -1.1743148
5 -1.1290462 -5.4743148
6 1.7720279 -1.1290462
7 0.6566724 1.7720279
8 3.2989946 0.6566724
9 -0.6033276 3.2989946
10 -0.7256499 -0.6033276
11 4.4090816 -0.7256499
12 -3.0954820 4.4090816
13 -5.0154592 -3.0954820
14 -5.9901907 -5.0154592
15 -1.7725129 -5.9901907
16 -5.0872444 -1.7725129
17 0.8727556 -5.0872444
18 -5.4114388 0.8727556
19 -1.3120628 -5.4114388
20 1.7155279 -1.3120628
21 -5.3162572 1.7155279
22 3.8614205 -5.3162572
23 4.6108835 3.8614205
24 0.4357828 4.6108835
25 -0.8989259 0.4357828
26 3.9410741 -0.8989259
27 -4.3707110 3.9410741
28 6.5850945 -4.3707110
29 1.4450945 6.5850945
30 -1.7801740 1.4450945
31 2.2486649 -1.7801740
32 1.8909871 2.2486649
33 2.9328593 1.8909871
34 5.4252685 2.9328593
35 1.4041944 5.4252685
36 6.0112420 1.4041944
37 1.5354592 6.0112420
38 5.0754592 1.5354592
39 -1.1363260 5.0754592
40 3.0784055 -1.1363260
41 0.5825999 3.0784055
42 1.6720628 0.5825999
43 2.3009017 1.6720628
44 -4.9831187 2.3009017
45 5.3703647 -4.9831187
46 2.7333109 5.3703647
47 -2.5761520 2.7333109
48 8.5456270 -2.5761520
49 7.2403813 8.5456270
50 -5.7196187 7.2403813
51 8.4538647 -5.7196187
52 0.8980591 8.4538647
53 -1.7714038 0.8980591
54 3.7475220 -1.7714038
55 -3.8941762 3.7475220
56 -1.9223910 -3.8941762
57 -2.3836391 -1.9223910
58 -11.2943501 -2.3836391
59 -7.8480075 -11.2943501
60 -10.3556914 -7.8480075
> 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/7ekm71260974340.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ze891260974340.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jxjk1260974340.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lloe1260974340.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11no9i1260974340.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/127bsc1260974340.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/133dht1260974340.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/14dolp1260974340.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/151i711260974340.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/16d7uu1260974340.tab")
+ }
> try(system("convert tmp/1a40t1260974340.ps tmp/1a40t1260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/2q4tj1260974340.ps tmp/2q4tj1260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jl9a1260974340.ps tmp/3jl9a1260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y5d81260974340.ps tmp/4y5d81260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/55ovf1260974340.ps tmp/55ovf1260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tjy51260974340.ps tmp/6tjy51260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ekm71260974340.ps tmp/7ekm71260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ze891260974340.ps tmp/8ze891260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jxjk1260974340.ps tmp/9jxjk1260974340.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lloe1260974340.ps tmp/10lloe1260974340.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.432 1.609 5.676