R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(20366,0,22782,0,19169,0,13807,0,29743,0,25591,0,29096,0,26482,0,22405,0,27044,0,17970,0,18730,0,19684,0,19785,0,18479,0,10698,0,31956,0,29506,0,34506,0,27165,0,26736,0,23691,0,18157,0,17328,0,18205,0,20995,0,17382,0,9367,0,31124,0,26551,0,30651,0,25859,0,25100,0,25778,0,20418,0,18688,0,20424,0,24776,0,19814,0,12738,0,31566,0,30111,0,30019,0,31934,1,25826,1,26835,1,20205,1,17789,1,20520,1,22518,1,15572,1,11509,1,25447,1,24090,1,27786,1,26195,1,20516,1,22759,1,19028,1,16971,1,20036,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 20366 0 1 0 0 0 0 0 0 0 0 0 0 1
2 22782 0 0 1 0 0 0 0 0 0 0 0 0 2
3 19169 0 0 0 1 0 0 0 0 0 0 0 0 3
4 13807 0 0 0 0 1 0 0 0 0 0 0 0 4
5 29743 0 0 0 0 0 1 0 0 0 0 0 0 5
6 25591 0 0 0 0 0 0 1 0 0 0 0 0 6
7 29096 0 0 0 0 0 0 0 1 0 0 0 0 7
8 26482 0 0 0 0 0 0 0 0 1 0 0 0 8
9 22405 0 0 0 0 0 0 0 0 0 1 0 0 9
10 27044 0 0 0 0 0 0 0 0 0 0 1 0 10
11 17970 0 0 0 0 0 0 0 0 0 0 0 1 11
12 18730 0 0 0 0 0 0 0 0 0 0 0 0 12
13 19684 0 1 0 0 0 0 0 0 0 0 0 0 13
14 19785 0 0 1 0 0 0 0 0 0 0 0 0 14
15 18479 0 0 0 1 0 0 0 0 0 0 0 0 15
16 10698 0 0 0 0 1 0 0 0 0 0 0 0 16
17 31956 0 0 0 0 0 1 0 0 0 0 0 0 17
18 29506 0 0 0 0 0 0 1 0 0 0 0 0 18
19 34506 0 0 0 0 0 0 0 1 0 0 0 0 19
20 27165 0 0 0 0 0 0 0 0 1 0 0 0 20
21 26736 0 0 0 0 0 0 0 0 0 1 0 0 21
22 23691 0 0 0 0 0 0 0 0 0 0 1 0 22
23 18157 0 0 0 0 0 0 0 0 0 0 0 1 23
24 17328 0 0 0 0 0 0 0 0 0 0 0 0 24
25 18205 0 1 0 0 0 0 0 0 0 0 0 0 25
26 20995 0 0 1 0 0 0 0 0 0 0 0 0 26
27 17382 0 0 0 1 0 0 0 0 0 0 0 0 27
28 9367 0 0 0 0 1 0 0 0 0 0 0 0 28
29 31124 0 0 0 0 0 1 0 0 0 0 0 0 29
30 26551 0 0 0 0 0 0 1 0 0 0 0 0 30
31 30651 0 0 0 0 0 0 0 1 0 0 0 0 31
32 25859 0 0 0 0 0 0 0 0 1 0 0 0 32
33 25100 0 0 0 0 0 0 0 0 0 1 0 0 33
34 25778 0 0 0 0 0 0 0 0 0 0 1 0 34
35 20418 0 0 0 0 0 0 0 0 0 0 0 1 35
36 18688 0 0 0 0 0 0 0 0 0 0 0 0 36
37 20424 0 1 0 0 0 0 0 0 0 0 0 0 37
38 24776 0 0 1 0 0 0 0 0 0 0 0 0 38
39 19814 0 0 0 1 0 0 0 0 0 0 0 0 39
40 12738 0 0 0 0 1 0 0 0 0 0 0 0 40
41 31566 0 0 0 0 0 1 0 0 0 0 0 0 41
42 30111 0 0 0 0 0 0 1 0 0 0 0 0 42
43 30019 0 0 0 0 0 0 0 1 0 0 0 0 43
44 31934 1 0 0 0 0 0 0 0 1 0 0 0 44
45 25826 1 0 0 0 0 0 0 0 0 1 0 0 45
46 26835 1 0 0 0 0 0 0 0 0 0 1 0 46
47 20205 1 0 0 0 0 0 0 0 0 0 0 1 47
48 17789 1 0 0 0 0 0 0 0 0 0 0 0 48
49 20520 1 1 0 0 0 0 0 0 0 0 0 0 49
50 22518 1 0 1 0 0 0 0 0 0 0 0 0 50
51 15572 1 0 0 1 0 0 0 0 0 0 0 0 51
52 11509 1 0 0 0 1 0 0 0 0 0 0 0 52
53 25447 1 0 0 0 0 1 0 0 0 0 0 0 53
54 24090 1 0 0 0 0 0 1 0 0 0 0 0 54
55 27786 1 0 0 0 0 0 0 1 0 0 0 0 55
56 26195 1 0 0 0 0 0 0 0 1 0 0 0 56
57 20516 1 0 0 0 0 0 0 0 0 1 0 0 57
58 22759 1 0 0 0 0 0 0 0 0 0 1 0 58
59 19028 1 0 0 0 0 0 0 0 0 0 0 1 59
60 16971 1 0 0 0 0 0 0 0 0 0 0 0 60
61 20036 1 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) X M1 M2 M3 M4
18131.475 -1137.486 1926.679 4104.925 10.683 -6454.960
M5 M6 M7 M8 M9 M10
11882.198 9078.556 12314.114 9650.769 6234.127 7332.684
M11 t
1260.642 6.242
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3760.0 -1393.3 134.9 1107.0 5014.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18131.475 1075.087 16.865 < 2e-16 ***
X -1137.486 924.549 -1.230 0.22470
M1 1926.679 1202.869 1.602 0.11591
M2 4104.925 1262.093 3.252 0.00212 **
M3 10.683 1260.585 0.008 0.99327
M4 -6454.960 1259.523 -5.125 5.51e-06 ***
M5 11882.198 1258.909 9.438 2.00e-12 ***
M6 9078.556 1258.744 7.212 3.90e-09 ***
M7 12314.114 1259.028 9.781 6.52e-13 ***
M8 9650.769 1257.185 7.676 7.77e-10 ***
M9 6234.127 1255.611 4.965 9.47e-06 ***
M10 7332.684 1254.486 5.845 4.61e-07 ***
M11 1260.642 1253.810 1.005 0.31983
t 6.242 23.768 0.263 0.79398
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1982 on 47 degrees of freedom
Multiple R-squared: 0.9059, Adjusted R-squared: 0.8799
F-statistic: 34.81 on 13 and 47 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,] 0.4176823 0.8353645 0.5823177
[2,] 0.5862682 0.8274636 0.4137318
[3,] 0.7837503 0.4324994 0.2162497
[4,] 0.6786235 0.6427530 0.3213765
[5,] 0.6902515 0.6194971 0.3097485
[6,] 0.7170309 0.5659383 0.2829691
[7,] 0.6376212 0.7247576 0.3623788
[8,] 0.5625367 0.8749265 0.4374633
[9,] 0.5533514 0.8932971 0.4466486
[10,] 0.5259907 0.9480187 0.4740093
[11,] 0.4568831 0.9137662 0.5431169
[12,] 0.5414365 0.9171269 0.4585635
[13,] 0.4481270 0.8962540 0.5518730
[14,] 0.4007256 0.8014513 0.5992744
[15,] 0.3123365 0.6246730 0.6876635
[16,] 0.4877822 0.9755644 0.5122178
[17,] 0.4056419 0.8112837 0.5943581
[18,] 0.3572928 0.7145857 0.6427072
[19,] 0.3882899 0.7765798 0.6117101
[20,] 0.3692891 0.7385781 0.6307109
[21,] 0.5799424 0.8401152 0.4200576
[22,] 0.5921384 0.8157233 0.4078616
[23,] 0.4857757 0.9715513 0.5142243
[24,] 0.5162404 0.9675193 0.4837596
[25,] 0.4319706 0.8639412 0.5680294
[26,] 0.4557297 0.9114594 0.5442703
[27,] 0.3291250 0.6582500 0.6708750
[28,] 0.3600804 0.7201607 0.6399196
> postscript(file="/var/www/html/rcomp/tmp/17jkg1258729748.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2imrr1258729748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nvwz1258729748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/476081258729748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lllz1258729748.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
301.60410 533.11570 1008.11570 2105.51570 -301.88430 -1656.48430
7 8 9 10 11 12
-1393.28430 -1350.18157 -2016.78157 1517.41843 -1490.78157 523.61843
13 14 15 16 17 18
-455.30239 -2538.79078 243.20922 -1078.39078 1836.20922 2183.60922
19 20 21 22 23 24
3941.80922 -742.08805 2239.31195 -1910.48805 -1378.68805 -953.28805
25 26 27 28 29 30
-2009.20887 -1403.69727 -928.69727 -2484.29727 929.30273 -846.29727
31 32 33 34 35 36
11.90273 -2122.99454 528.40546 101.60546 807.40546 331.80546
37 38 39 40 41 42
134.88464 2302.39625 1428.39625 811.79625 1296.39625 2638.79625
43 44 45 46 47 48
-695.00375 5014.58532 2316.98532 2221.18532 1656.98532 495.38532
49 50 51 52 53 54
1293.46451 1106.97611 -1751.02389 645.37611 -3760.02389 -2319.62389
55 56 57 58 59 60
-1865.42389 -799.32116 -3067.92116 -1929.72116 405.07884 -397.52116
61
734.55802
> postscript(file="/var/www/html/rcomp/tmp/6zv1u1258729748.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
0 301.60410 NA
1 533.11570 301.60410
2 1008.11570 533.11570
3 2105.51570 1008.11570
4 -301.88430 2105.51570
5 -1656.48430 -301.88430
6 -1393.28430 -1656.48430
7 -1350.18157 -1393.28430
8 -2016.78157 -1350.18157
9 1517.41843 -2016.78157
10 -1490.78157 1517.41843
11 523.61843 -1490.78157
12 -455.30239 523.61843
13 -2538.79078 -455.30239
14 243.20922 -2538.79078
15 -1078.39078 243.20922
16 1836.20922 -1078.39078
17 2183.60922 1836.20922
18 3941.80922 2183.60922
19 -742.08805 3941.80922
20 2239.31195 -742.08805
21 -1910.48805 2239.31195
22 -1378.68805 -1910.48805
23 -953.28805 -1378.68805
24 -2009.20887 -953.28805
25 -1403.69727 -2009.20887
26 -928.69727 -1403.69727
27 -2484.29727 -928.69727
28 929.30273 -2484.29727
29 -846.29727 929.30273
30 11.90273 -846.29727
31 -2122.99454 11.90273
32 528.40546 -2122.99454
33 101.60546 528.40546
34 807.40546 101.60546
35 331.80546 807.40546
36 134.88464 331.80546
37 2302.39625 134.88464
38 1428.39625 2302.39625
39 811.79625 1428.39625
40 1296.39625 811.79625
41 2638.79625 1296.39625
42 -695.00375 2638.79625
43 5014.58532 -695.00375
44 2316.98532 5014.58532
45 2221.18532 2316.98532
46 1656.98532 2221.18532
47 495.38532 1656.98532
48 1293.46451 495.38532
49 1106.97611 1293.46451
50 -1751.02389 1106.97611
51 645.37611 -1751.02389
52 -3760.02389 645.37611
53 -2319.62389 -3760.02389
54 -1865.42389 -2319.62389
55 -799.32116 -1865.42389
56 -3067.92116 -799.32116
57 -1929.72116 -3067.92116
58 405.07884 -1929.72116
59 -397.52116 405.07884
60 734.55802 -397.52116
61 NA 734.55802
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 533.11570 301.60410
[2,] 1008.11570 533.11570
[3,] 2105.51570 1008.11570
[4,] -301.88430 2105.51570
[5,] -1656.48430 -301.88430
[6,] -1393.28430 -1656.48430
[7,] -1350.18157 -1393.28430
[8,] -2016.78157 -1350.18157
[9,] 1517.41843 -2016.78157
[10,] -1490.78157 1517.41843
[11,] 523.61843 -1490.78157
[12,] -455.30239 523.61843
[13,] -2538.79078 -455.30239
[14,] 243.20922 -2538.79078
[15,] -1078.39078 243.20922
[16,] 1836.20922 -1078.39078
[17,] 2183.60922 1836.20922
[18,] 3941.80922 2183.60922
[19,] -742.08805 3941.80922
[20,] 2239.31195 -742.08805
[21,] -1910.48805 2239.31195
[22,] -1378.68805 -1910.48805
[23,] -953.28805 -1378.68805
[24,] -2009.20887 -953.28805
[25,] -1403.69727 -2009.20887
[26,] -928.69727 -1403.69727
[27,] -2484.29727 -928.69727
[28,] 929.30273 -2484.29727
[29,] -846.29727 929.30273
[30,] 11.90273 -846.29727
[31,] -2122.99454 11.90273
[32,] 528.40546 -2122.99454
[33,] 101.60546 528.40546
[34,] 807.40546 101.60546
[35,] 331.80546 807.40546
[36,] 134.88464 331.80546
[37,] 2302.39625 134.88464
[38,] 1428.39625 2302.39625
[39,] 811.79625 1428.39625
[40,] 1296.39625 811.79625
[41,] 2638.79625 1296.39625
[42,] -695.00375 2638.79625
[43,] 5014.58532 -695.00375
[44,] 2316.98532 5014.58532
[45,] 2221.18532 2316.98532
[46,] 1656.98532 2221.18532
[47,] 495.38532 1656.98532
[48,] 1293.46451 495.38532
[49,] 1106.97611 1293.46451
[50,] -1751.02389 1106.97611
[51,] 645.37611 -1751.02389
[52,] -3760.02389 645.37611
[53,] -2319.62389 -3760.02389
[54,] -1865.42389 -2319.62389
[55,] -799.32116 -1865.42389
[56,] -3067.92116 -799.32116
[57,] -1929.72116 -3067.92116
[58,] 405.07884 -1929.72116
[59,] -397.52116 405.07884
[60,] 734.55802 -397.52116
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 533.11570 301.60410
2 1008.11570 533.11570
3 2105.51570 1008.11570
4 -301.88430 2105.51570
5 -1656.48430 -301.88430
6 -1393.28430 -1656.48430
7 -1350.18157 -1393.28430
8 -2016.78157 -1350.18157
9 1517.41843 -2016.78157
10 -1490.78157 1517.41843
11 523.61843 -1490.78157
12 -455.30239 523.61843
13 -2538.79078 -455.30239
14 243.20922 -2538.79078
15 -1078.39078 243.20922
16 1836.20922 -1078.39078
17 2183.60922 1836.20922
18 3941.80922 2183.60922
19 -742.08805 3941.80922
20 2239.31195 -742.08805
21 -1910.48805 2239.31195
22 -1378.68805 -1910.48805
23 -953.28805 -1378.68805
24 -2009.20887 -953.28805
25 -1403.69727 -2009.20887
26 -928.69727 -1403.69727
27 -2484.29727 -928.69727
28 929.30273 -2484.29727
29 -846.29727 929.30273
30 11.90273 -846.29727
31 -2122.99454 11.90273
32 528.40546 -2122.99454
33 101.60546 528.40546
34 807.40546 101.60546
35 331.80546 807.40546
36 134.88464 331.80546
37 2302.39625 134.88464
38 1428.39625 2302.39625
39 811.79625 1428.39625
40 1296.39625 811.79625
41 2638.79625 1296.39625
42 -695.00375 2638.79625
43 5014.58532 -695.00375
44 2316.98532 5014.58532
45 2221.18532 2316.98532
46 1656.98532 2221.18532
47 495.38532 1656.98532
48 1293.46451 495.38532
49 1106.97611 1293.46451
50 -1751.02389 1106.97611
51 645.37611 -1751.02389
52 -3760.02389 645.37611
53 -2319.62389 -3760.02389
54 -1865.42389 -2319.62389
55 -799.32116 -1865.42389
56 -3067.92116 -799.32116
57 -1929.72116 -3067.92116
58 405.07884 -1929.72116
59 -397.52116 405.07884
60 734.55802 -397.52116
> 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/7u6451258729748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87ev31258729748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ruf91258729748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10maxh1258729748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hoqh1258729748.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/12nm5q1258729748.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/13zadg1258729748.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/14ktgf1258729748.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/152gyc1258729748.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/16dcr31258729748.tab")
+ }
>
> system("convert tmp/17jkg1258729748.ps tmp/17jkg1258729748.png")
> system("convert tmp/2imrr1258729748.ps tmp/2imrr1258729748.png")
> system("convert tmp/3nvwz1258729748.ps tmp/3nvwz1258729748.png")
> system("convert tmp/476081258729748.ps tmp/476081258729748.png")
> system("convert tmp/5lllz1258729748.ps tmp/5lllz1258729748.png")
> system("convert tmp/6zv1u1258729748.ps tmp/6zv1u1258729748.png")
> system("convert tmp/7u6451258729748.ps tmp/7u6451258729748.png")
> system("convert tmp/87ev31258729748.ps tmp/87ev31258729748.png")
> system("convert tmp/9ruf91258729748.ps tmp/9ruf91258729748.png")
> system("convert tmp/10maxh1258729748.ps tmp/10maxh1258729748.png")
>
>
> proc.time()
user system elapsed
2.413 1.573 2.801