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(19,75.8,18,72.6,19,71.9,19,74.8,22,72.9,23,72.9,20,79.9,14,74,14,76,14,69.6,15,77.3,11,75.2,17,75.8,16,77.6,20,76.7,24,77,23,77.9,20,76.7,21,71.9,19,73.4,23,72.5,23,73.7,23,69.5,23,74.7,27,72.5,26,72.1,17,70.7,24,71.4,26,69.5,24,73.5,27,72.4,27,74.5,26,72.2,24,73,23,73.3,23,71.3,24,73.6,17,71.3,21,71.2,19,81.4,22,76.1,22,71.1,18,75.7,16,70,14,68.5,12,56.7,14,57.9,16,58.8,8,59.3,3,61.3,0,62.9,5,61.4,1,64.5,1,63.8,3,61.6,6,64.7),dim=c(2,56),dimnames=list(c('indcvtr','dzcg
'),1:56))
> y <- array(NA,dim=c(2,56),dimnames=list(c('indcvtr','dzcg
'),1:56))
> 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 = '2'
> #'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
dzcg\r indcvtr M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 75.8 19 1 0 0 0 0 0 0 0 0 0 0 1
2 72.6 18 0 1 0 0 0 0 0 0 0 0 0 2
3 71.9 19 0 0 1 0 0 0 0 0 0 0 0 3
4 74.8 19 0 0 0 1 0 0 0 0 0 0 0 4
5 72.9 22 0 0 0 0 1 0 0 0 0 0 0 5
6 72.9 23 0 0 0 0 0 1 0 0 0 0 0 6
7 79.9 20 0 0 0 0 0 0 1 0 0 0 0 7
8 74.0 14 0 0 0 0 0 0 0 1 0 0 0 8
9 76.0 14 0 0 0 0 0 0 0 0 1 0 0 9
10 69.6 14 0 0 0 0 0 0 0 0 0 1 0 10
11 77.3 15 0 0 0 0 0 0 0 0 0 0 1 11
12 75.2 11 0 0 0 0 0 0 0 0 0 0 0 12
13 75.8 17 1 0 0 0 0 0 0 0 0 0 0 13
14 77.6 16 0 1 0 0 0 0 0 0 0 0 0 14
15 76.7 20 0 0 1 0 0 0 0 0 0 0 0 15
16 77.0 24 0 0 0 1 0 0 0 0 0 0 0 16
17 77.9 23 0 0 0 0 1 0 0 0 0 0 0 17
18 76.7 20 0 0 0 0 0 1 0 0 0 0 0 18
19 71.9 21 0 0 0 0 0 0 1 0 0 0 0 19
20 73.4 19 0 0 0 0 0 0 0 1 0 0 0 20
21 72.5 23 0 0 0 0 0 0 0 0 1 0 0 21
22 73.7 23 0 0 0 0 0 0 0 0 0 1 0 22
23 69.5 23 0 0 0 0 0 0 0 0 0 0 1 23
24 74.7 23 0 0 0 0 0 0 0 0 0 0 0 24
25 72.5 27 1 0 0 0 0 0 0 0 0 0 0 25
26 72.1 26 0 1 0 0 0 0 0 0 0 0 0 26
27 70.7 17 0 0 1 0 0 0 0 0 0 0 0 27
28 71.4 24 0 0 0 1 0 0 0 0 0 0 0 28
29 69.5 26 0 0 0 0 1 0 0 0 0 0 0 29
30 73.5 24 0 0 0 0 0 1 0 0 0 0 0 30
31 72.4 27 0 0 0 0 0 0 1 0 0 0 0 31
32 74.5 27 0 0 0 0 0 0 0 1 0 0 0 32
33 72.2 26 0 0 0 0 0 0 0 0 1 0 0 33
34 73.0 24 0 0 0 0 0 0 0 0 0 1 0 34
35 73.3 23 0 0 0 0 0 0 0 0 0 0 1 35
36 71.3 23 0 0 0 0 0 0 0 0 0 0 0 36
37 73.6 24 1 0 0 0 0 0 0 0 0 0 0 37
38 71.3 17 0 1 0 0 0 0 0 0 0 0 0 38
39 71.2 21 0 0 1 0 0 0 0 0 0 0 0 39
40 81.4 19 0 0 0 1 0 0 0 0 0 0 0 40
41 76.1 22 0 0 0 0 1 0 0 0 0 0 0 41
42 71.1 22 0 0 0 0 0 1 0 0 0 0 0 42
43 75.7 18 0 0 0 0 0 0 1 0 0 0 0 43
44 70.0 16 0 0 0 0 0 0 0 1 0 0 0 44
45 68.5 14 0 0 0 0 0 0 0 0 1 0 0 45
46 56.7 12 0 0 0 0 0 0 0 0 0 1 0 46
47 57.9 14 0 0 0 0 0 0 0 0 0 0 1 47
48 58.8 16 0 0 0 0 0 0 0 0 0 0 0 48
49 59.3 8 1 0 0 0 0 0 0 0 0 0 0 49
50 61.3 3 0 1 0 0 0 0 0 0 0 0 0 50
51 62.9 0 0 0 1 0 0 0 0 0 0 0 0 51
52 61.4 5 0 0 0 1 0 0 0 0 0 0 0 52
53 64.5 1 0 0 0 0 1 0 0 0 0 0 0 53
54 63.8 1 0 0 0 0 0 1 0 0 0 0 0 54
55 61.6 3 0 0 0 0 0 0 1 0 0 0 0 55
56 64.7 6 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) indcvtr M1 M2 M3 M4
69.7953 0.3061 0.2734 0.9512 1.0142 2.8565
M5 M6 M7 M8 M9 M10
1.8322 1.6765 2.6171 2.2451 1.4557 -2.1088
M11 t
-0.8324 -0.1794
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.28216 -2.46205 0.07895 2.29497 10.10784
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 69.79533 3.06501 22.772 < 2e-16 ***
indcvtr 0.30611 0.08601 3.559 0.00094 ***
M1 0.27345 2.67611 0.102 0.91910
M2 0.95117 2.68726 0.354 0.72514
M3 1.01423 2.68936 0.377 0.70798
M4 2.85652 2.67197 1.069 0.29114
M5 1.83225 2.67121 0.686 0.49653
M6 1.67653 2.67093 0.628 0.53360
M7 2.61714 2.67113 0.980 0.33280
M8 2.24509 2.67451 0.839 0.40598
M9 1.45571 2.81726 0.517 0.60807
M10 -2.10879 2.81632 -0.749 0.45817
M11 -0.83245 2.81562 -0.296 0.76895
t -0.17939 0.03771 -4.757 2.33e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.981 on 42 degrees of freedom
Multiple R-squared: 0.6319, Adjusted R-squared: 0.5179
F-statistic: 5.545 on 13 and 42 DF, p-value: 1.017e-05
> 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,] 0.09876638 0.19753275 0.9012336
[2,] 0.03452854 0.06905708 0.9654715
[3,] 0.37509555 0.75019110 0.6249044
[4,] 0.24833998 0.49667997 0.7516600
[5,] 0.15901637 0.31803274 0.8409836
[6,] 0.15259732 0.30519463 0.8474027
[7,] 0.17319139 0.34638278 0.8268086
[8,] 0.14493674 0.28987349 0.8550633
[9,] 0.09727372 0.19454743 0.9027263
[10,] 0.06171025 0.12342051 0.9382897
[11,] 0.05755888 0.11511777 0.9424411
[12,] 0.05553101 0.11106202 0.9444690
[13,] 0.10118253 0.20236506 0.8988175
[14,] 0.07425135 0.14850270 0.9257486
[15,] 0.12668572 0.25337144 0.8733143
[16,] 0.26271648 0.52543297 0.7372835
[17,] 0.38897238 0.77794476 0.6110276
[18,] 0.35209484 0.70418969 0.6479052
[19,] 0.27455164 0.54910327 0.7254484
[20,] 0.18558911 0.37117822 0.8144109
[21,] 0.13019164 0.26038328 0.8698084
[22,] 0.07439077 0.14878154 0.9256092
[23,] 0.03969392 0.07938785 0.9603061
> postscript(file="/var/www/html/rcomp/tmp/1nzgh1260733210.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/248oa1260733210.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35wwo1260733210.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43ctx1260733210.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/506hj1260733210.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 = 56
Frequency = 1
1 2 3 4 5 6
0.09454415 -3.29767351 -4.18744764 -2.95034292 -4.56500411 -4.53600000
7 8 9 10 11 12
2.62110472 -0.89079466 2.07797947 -0.57812936 5.71881622 4.19019713
13 14 15 16 17 18
2.85948973 4.46727207 2.45917146 -0.12815914 2.28161499 2.33505441
19 20 21 22 23 24
-3.53227618 -0.86861088 -2.02427207 2.91961910 -2.37732649 2.16961910
25 26 27 28 29 30
-1.34887064 -1.94108830 -0.46977413 -3.57543121 -4.88398357 0.06334702
31 32 33 34 35 36
-2.71620123 -0.06475359 -1.08987064 4.06623819 3.57540144 0.92234702
37 38 39 40 41 42
2.82218378 2.16661910 0.95851848 10.10784086 5.09317967 0.42829261
43 44 45 46 47 48
5.49150616 0.95517146 1.03616324 -6.40772793 -6.91689117 -7.28216324
49 50 51 52 53 54
-4.42734702 -1.39512936 1.23953183 -3.45390760 2.07419302 1.70930595
55 56
-1.86413347 0.86898768
> postscript(file="/var/www/html/rcomp/tmp/6shz41260733211.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 0.09454415 NA
1 -3.29767351 0.09454415
2 -4.18744764 -3.29767351
3 -2.95034292 -4.18744764
4 -4.56500411 -2.95034292
5 -4.53600000 -4.56500411
6 2.62110472 -4.53600000
7 -0.89079466 2.62110472
8 2.07797947 -0.89079466
9 -0.57812936 2.07797947
10 5.71881622 -0.57812936
11 4.19019713 5.71881622
12 2.85948973 4.19019713
13 4.46727207 2.85948973
14 2.45917146 4.46727207
15 -0.12815914 2.45917146
16 2.28161499 -0.12815914
17 2.33505441 2.28161499
18 -3.53227618 2.33505441
19 -0.86861088 -3.53227618
20 -2.02427207 -0.86861088
21 2.91961910 -2.02427207
22 -2.37732649 2.91961910
23 2.16961910 -2.37732649
24 -1.34887064 2.16961910
25 -1.94108830 -1.34887064
26 -0.46977413 -1.94108830
27 -3.57543121 -0.46977413
28 -4.88398357 -3.57543121
29 0.06334702 -4.88398357
30 -2.71620123 0.06334702
31 -0.06475359 -2.71620123
32 -1.08987064 -0.06475359
33 4.06623819 -1.08987064
34 3.57540144 4.06623819
35 0.92234702 3.57540144
36 2.82218378 0.92234702
37 2.16661910 2.82218378
38 0.95851848 2.16661910
39 10.10784086 0.95851848
40 5.09317967 10.10784086
41 0.42829261 5.09317967
42 5.49150616 0.42829261
43 0.95517146 5.49150616
44 1.03616324 0.95517146
45 -6.40772793 1.03616324
46 -6.91689117 -6.40772793
47 -7.28216324 -6.91689117
48 -4.42734702 -7.28216324
49 -1.39512936 -4.42734702
50 1.23953183 -1.39512936
51 -3.45390760 1.23953183
52 2.07419302 -3.45390760
53 1.70930595 2.07419302
54 -1.86413347 1.70930595
55 0.86898768 -1.86413347
56 NA 0.86898768
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.29767351 0.09454415
[2,] -4.18744764 -3.29767351
[3,] -2.95034292 -4.18744764
[4,] -4.56500411 -2.95034292
[5,] -4.53600000 -4.56500411
[6,] 2.62110472 -4.53600000
[7,] -0.89079466 2.62110472
[8,] 2.07797947 -0.89079466
[9,] -0.57812936 2.07797947
[10,] 5.71881622 -0.57812936
[11,] 4.19019713 5.71881622
[12,] 2.85948973 4.19019713
[13,] 4.46727207 2.85948973
[14,] 2.45917146 4.46727207
[15,] -0.12815914 2.45917146
[16,] 2.28161499 -0.12815914
[17,] 2.33505441 2.28161499
[18,] -3.53227618 2.33505441
[19,] -0.86861088 -3.53227618
[20,] -2.02427207 -0.86861088
[21,] 2.91961910 -2.02427207
[22,] -2.37732649 2.91961910
[23,] 2.16961910 -2.37732649
[24,] -1.34887064 2.16961910
[25,] -1.94108830 -1.34887064
[26,] -0.46977413 -1.94108830
[27,] -3.57543121 -0.46977413
[28,] -4.88398357 -3.57543121
[29,] 0.06334702 -4.88398357
[30,] -2.71620123 0.06334702
[31,] -0.06475359 -2.71620123
[32,] -1.08987064 -0.06475359
[33,] 4.06623819 -1.08987064
[34,] 3.57540144 4.06623819
[35,] 0.92234702 3.57540144
[36,] 2.82218378 0.92234702
[37,] 2.16661910 2.82218378
[38,] 0.95851848 2.16661910
[39,] 10.10784086 0.95851848
[40,] 5.09317967 10.10784086
[41,] 0.42829261 5.09317967
[42,] 5.49150616 0.42829261
[43,] 0.95517146 5.49150616
[44,] 1.03616324 0.95517146
[45,] -6.40772793 1.03616324
[46,] -6.91689117 -6.40772793
[47,] -7.28216324 -6.91689117
[48,] -4.42734702 -7.28216324
[49,] -1.39512936 -4.42734702
[50,] 1.23953183 -1.39512936
[51,] -3.45390760 1.23953183
[52,] 2.07419302 -3.45390760
[53,] 1.70930595 2.07419302
[54,] -1.86413347 1.70930595
[55,] 0.86898768 -1.86413347
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.29767351 0.09454415
2 -4.18744764 -3.29767351
3 -2.95034292 -4.18744764
4 -4.56500411 -2.95034292
5 -4.53600000 -4.56500411
6 2.62110472 -4.53600000
7 -0.89079466 2.62110472
8 2.07797947 -0.89079466
9 -0.57812936 2.07797947
10 5.71881622 -0.57812936
11 4.19019713 5.71881622
12 2.85948973 4.19019713
13 4.46727207 2.85948973
14 2.45917146 4.46727207
15 -0.12815914 2.45917146
16 2.28161499 -0.12815914
17 2.33505441 2.28161499
18 -3.53227618 2.33505441
19 -0.86861088 -3.53227618
20 -2.02427207 -0.86861088
21 2.91961910 -2.02427207
22 -2.37732649 2.91961910
23 2.16961910 -2.37732649
24 -1.34887064 2.16961910
25 -1.94108830 -1.34887064
26 -0.46977413 -1.94108830
27 -3.57543121 -0.46977413
28 -4.88398357 -3.57543121
29 0.06334702 -4.88398357
30 -2.71620123 0.06334702
31 -0.06475359 -2.71620123
32 -1.08987064 -0.06475359
33 4.06623819 -1.08987064
34 3.57540144 4.06623819
35 0.92234702 3.57540144
36 2.82218378 0.92234702
37 2.16661910 2.82218378
38 0.95851848 2.16661910
39 10.10784086 0.95851848
40 5.09317967 10.10784086
41 0.42829261 5.09317967
42 5.49150616 0.42829261
43 0.95517146 5.49150616
44 1.03616324 0.95517146
45 -6.40772793 1.03616324
46 -6.91689117 -6.40772793
47 -7.28216324 -6.91689117
48 -4.42734702 -7.28216324
49 -1.39512936 -4.42734702
50 1.23953183 -1.39512936
51 -3.45390760 1.23953183
52 2.07419302 -3.45390760
53 1.70930595 2.07419302
54 -1.86413347 1.70930595
55 0.86898768 -1.86413347
> 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/7kvb61260733211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8stor1260733211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xac91260733211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10w3081260733211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114crz1260733211.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/12l1cg1260733211.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/13tg7s1260733211.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/14fsny1260733211.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/15aryl1260733211.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/16m0gf1260733211.tab")
+ }
>
> try(system("convert tmp/1nzgh1260733210.ps tmp/1nzgh1260733210.png",intern=TRUE))
character(0)
> try(system("convert tmp/248oa1260733210.ps tmp/248oa1260733210.png",intern=TRUE))
character(0)
> try(system("convert tmp/35wwo1260733210.ps tmp/35wwo1260733210.png",intern=TRUE))
character(0)
> try(system("convert tmp/43ctx1260733210.ps tmp/43ctx1260733210.png",intern=TRUE))
character(0)
> try(system("convert tmp/506hj1260733210.ps tmp/506hj1260733210.png",intern=TRUE))
character(0)
> try(system("convert tmp/6shz41260733211.ps tmp/6shz41260733211.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kvb61260733211.ps tmp/7kvb61260733211.png",intern=TRUE))
character(0)
> try(system("convert tmp/8stor1260733211.ps tmp/8stor1260733211.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xac91260733211.ps tmp/9xac91260733211.png",intern=TRUE))
character(0)
> try(system("convert tmp/10w3081260733211.ps tmp/10w3081260733211.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.334 1.527 2.764