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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
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(589,130,595,139,584,127,591,135,573,122,589,130,567,117,584,127,569,112,573,122,621,113,567,117,629,149,569,112,628,157,621,113,612,157,629,149,595,147,628,157,597,137,612,157,593,132,595,147,590,125,597,137,580,123,593,132,574,117,590,125,573,114,580,123,573,111,574,117,620,112,573,114,626,144,573,111,620,150,620,112,588,149,626,144,566,134,620,150,557,123,588,149,561,116,566,134,549,117,557,123,532,111,561,116,526,105,549,117,511,102,532,111,499,95,526,105,555,93,511,102,565,124,499,95,542,130,555,93,527,124,565,124,510,115,542,130,514,106,527,124,517,105,510,115,508,105,514,106,493,101,517,105,490,95,508,105,469,93,493,101,478,84,490,95,528,87,469,93,534,116,478,84,518,120,528,87,506,117,534,116,502,109,518,120,516,105,506,117,528,107,502,109,533,109,516,105,536,109,528,107,537,108,533,109,524,107,536,109,536,99,537,108,587,103,524,107,597,131,536,99,581,137,587,103,564,135,597,131),dim=c(4,57),dimnames=list(c('Y','X','Y1','Y2'),1:57))
> y <- array(NA,dim=c(4,57),dimnames=list(c('Y','X','Y1','Y2'),1:57))
> 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 Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 589 130 595 139 1 0 0 0 0 0 0 0 0 0 0 1
2 584 127 591 135 0 1 0 0 0 0 0 0 0 0 0 2
3 573 122 589 130 0 0 1 0 0 0 0 0 0 0 0 3
4 567 117 584 127 0 0 0 1 0 0 0 0 0 0 0 4
5 569 112 573 122 0 0 0 0 1 0 0 0 0 0 0 5
6 621 113 567 117 0 0 0 0 0 1 0 0 0 0 0 6
7 629 149 569 112 0 0 0 0 0 0 1 0 0 0 0 7
8 628 157 621 113 0 0 0 0 0 0 0 1 0 0 0 8
9 612 157 629 149 0 0 0 0 0 0 0 0 1 0 0 9
10 595 147 628 157 0 0 0 0 0 0 0 0 0 1 0 10
11 597 137 612 157 0 0 0 0 0 0 0 0 0 0 1 11
12 593 132 595 147 0 0 0 0 0 0 0 0 0 0 0 12
13 590 125 597 137 1 0 0 0 0 0 0 0 0 0 0 13
14 580 123 593 132 0 1 0 0 0 0 0 0 0 0 0 14
15 574 117 590 125 0 0 1 0 0 0 0 0 0 0 0 15
16 573 114 580 123 0 0 0 1 0 0 0 0 0 0 0 16
17 573 111 574 117 0 0 0 0 1 0 0 0 0 0 0 17
18 620 112 573 114 0 0 0 0 0 1 0 0 0 0 0 18
19 626 144 573 111 0 0 0 0 0 0 1 0 0 0 0 19
20 620 150 620 112 0 0 0 0 0 0 0 1 0 0 0 20
21 588 149 626 144 0 0 0 0 0 0 0 0 1 0 0 21
22 566 134 620 150 0 0 0 0 0 0 0 0 0 1 0 22
23 557 123 588 149 0 0 0 0 0 0 0 0 0 0 1 23
24 561 116 566 134 0 0 0 0 0 0 0 0 0 0 0 24
25 549 117 557 123 1 0 0 0 0 0 0 0 0 0 0 25
26 532 111 561 116 0 1 0 0 0 0 0 0 0 0 0 26
27 526 105 549 117 0 0 1 0 0 0 0 0 0 0 0 27
28 511 102 532 111 0 0 0 1 0 0 0 0 0 0 0 28
29 499 95 526 105 0 0 0 0 1 0 0 0 0 0 0 29
30 555 93 511 102 0 0 0 0 0 1 0 0 0 0 0 30
31 565 124 499 95 0 0 0 0 0 0 1 0 0 0 0 31
32 542 130 555 93 0 0 0 0 0 0 0 1 0 0 0 32
33 527 124 565 124 0 0 0 0 0 0 0 0 1 0 0 33
34 510 115 542 130 0 0 0 0 0 0 0 0 0 1 0 34
35 514 106 527 124 0 0 0 0 0 0 0 0 0 0 1 35
36 517 105 510 115 0 0 0 0 0 0 0 0 0 0 0 36
37 508 105 514 106 1 0 0 0 0 0 0 0 0 0 0 37
38 493 101 517 105 0 1 0 0 0 0 0 0 0 0 0 38
39 490 95 508 105 0 0 1 0 0 0 0 0 0 0 0 39
40 469 93 493 101 0 0 0 1 0 0 0 0 0 0 0 40
41 478 84 490 95 0 0 0 0 1 0 0 0 0 0 0 41
42 528 87 469 93 0 0 0 0 0 1 0 0 0 0 0 42
43 534 116 478 84 0 0 0 0 0 0 1 0 0 0 0 43
44 518 120 528 87 0 0 0 0 0 0 0 1 0 0 0 44
45 506 117 534 116 0 0 0 0 0 0 0 0 1 0 0 45
46 502 109 518 120 0 0 0 0 0 0 0 0 0 1 0 46
47 516 105 506 117 0 0 0 0 0 0 0 0 0 0 1 47
48 528 107 502 109 0 0 0 0 0 0 0 0 0 0 0 48
49 533 109 516 105 1 0 0 0 0 0 0 0 0 0 0 49
50 536 109 528 107 0 1 0 0 0 0 0 0 0 0 0 50
51 537 108 533 109 0 0 1 0 0 0 0 0 0 0 0 51
52 524 107 536 109 0 0 0 1 0 0 0 0 0 0 0 52
53 536 99 537 108 0 0 0 0 1 0 0 0 0 0 0 53
54 587 103 524 107 0 0 0 0 0 1 0 0 0 0 0 54
55 597 131 536 99 0 0 0 0 0 0 1 0 0 0 0 55
56 581 137 587 103 0 0 0 0 0 0 0 1 0 0 0 56
57 564 135 597 131 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
74.8462 2.1938 0.5838 -0.7846 -10.6853 -16.6926
M3 M4 M5 M6 M7 M8
-10.2731 -12.6972 2.5456 54.8658 -12.0358 -66.5419
M9 M10 M11 t
-60.0193 -40.3834 -10.1517 0.1505
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.8711 -4.0143 0.2656 4.1184 16.2423
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 74.8462 30.3515 2.466 0.017937 *
X 2.1938 0.3322 6.604 5.99e-08 ***
Y1 0.5838 0.1457 4.007 0.000253 ***
Y2 -0.7846 0.4230 -1.855 0.070814 .
M1 -10.6853 6.2605 -1.707 0.095425 .
M2 -16.6926 7.2326 -2.308 0.026123 *
M3 -10.2731 7.5977 -1.352 0.183740
M4 -12.6972 7.7273 -1.643 0.107996
M5 2.5456 9.1672 0.278 0.782647
M6 54.8658 8.5830 6.392 1.20e-07 ***
M7 -12.0358 13.8133 -0.871 0.388653
M8 -66.5419 17.0990 -3.892 0.000359 ***
M9 -60.0193 6.9789 -8.600 1.01e-10 ***
M10 -40.3834 5.8120 -6.948 1.95e-08 ***
M11 -10.1517 6.1607 -1.648 0.107037
t 0.1505 0.1101 1.366 0.179311
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.746 on 41 degrees of freedom
Multiple R-squared: 0.9743, Adjusted R-squared: 0.9649
F-statistic: 103.7 on 15 and 41 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,] 0.02837689 0.056753786 0.971623107
[2,] 0.04479219 0.089584376 0.955207812
[3,] 0.30917010 0.618340200 0.690829900
[4,] 0.22562822 0.451256443 0.774371778
[5,] 0.26509404 0.530188080 0.734905960
[6,] 0.32480487 0.649609733 0.675195133
[7,] 0.36911457 0.738229140 0.630885430
[8,] 0.36363182 0.727263635 0.636368183
[9,] 0.28357887 0.567157749 0.716421125
[10,] 0.40908882 0.818177632 0.590911184
[11,] 0.64770234 0.704595326 0.352297663
[12,] 0.82904663 0.341906748 0.170953374
[13,] 0.91948345 0.161033106 0.080516553
[14,] 0.87464227 0.250715466 0.125357733
[15,] 0.96723017 0.065539661 0.032769831
[16,] 0.96307591 0.073848188 0.036924094
[17,] 0.97008005 0.059839903 0.029919952
[18,] 0.96780978 0.064380441 0.032190221
[19,] 0.99588871 0.008222579 0.004111289
[20,] 0.99544504 0.009109921 0.004554960
> postscript(file="/var/www/html/rcomp/tmp/15c8u1258745565.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20r521258745565.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mpg81258745565.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4r1j81258745565.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vl7f1258745565.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 = 57
Frequency = 1
1 2 3 4 5 6
1.1829346 7.8180313 -1.5382365 6.2695457 6.3442610 3.2598648
7 8 9 10 11 12
-6.0550559 0.1765284 1.0774225 -6.9109038 -4.0143259 -5.2684307
13 14 15 16 17 18
8.6094461 7.2662060 4.1183828 16.2422866 6.2258120 -3.2085304
19 20 21 22 23 24
-3.0116089 5.5265954 -9.3493883 -10.0189330 -7.3720830 2.7575831
25 26 27 28 29 30
-4.2772574 -10.0851064 -1.7020164 -2.6296716 -15.8710780 -1.5506786
31 32 33 34 35 36
8.7075368 -7.3623230 2.6106901 3.7034350 1.1150098 -1.1295935
37 38 39 40 41 42
-8.9911506 -11.8952976 -3.0482290 -11.7681460 -1.3734698 0.2655672
43 44 45 46 47 48
-2.9179836 -0.1745572 6.9832838 13.2264018 10.2713991 3.6404412
49 50 51 52 53 54
3.4760272 6.8961668 2.1700992 -8.1140148 4.6744748 1.2337770
55 56 57
3.2771115 1.8337565 -1.3220081
> postscript(file="/var/www/html/rcomp/tmp/6f0du1258745565.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 1.1829346 NA
1 7.8180313 1.1829346
2 -1.5382365 7.8180313
3 6.2695457 -1.5382365
4 6.3442610 6.2695457
5 3.2598648 6.3442610
6 -6.0550559 3.2598648
7 0.1765284 -6.0550559
8 1.0774225 0.1765284
9 -6.9109038 1.0774225
10 -4.0143259 -6.9109038
11 -5.2684307 -4.0143259
12 8.6094461 -5.2684307
13 7.2662060 8.6094461
14 4.1183828 7.2662060
15 16.2422866 4.1183828
16 6.2258120 16.2422866
17 -3.2085304 6.2258120
18 -3.0116089 -3.2085304
19 5.5265954 -3.0116089
20 -9.3493883 5.5265954
21 -10.0189330 -9.3493883
22 -7.3720830 -10.0189330
23 2.7575831 -7.3720830
24 -4.2772574 2.7575831
25 -10.0851064 -4.2772574
26 -1.7020164 -10.0851064
27 -2.6296716 -1.7020164
28 -15.8710780 -2.6296716
29 -1.5506786 -15.8710780
30 8.7075368 -1.5506786
31 -7.3623230 8.7075368
32 2.6106901 -7.3623230
33 3.7034350 2.6106901
34 1.1150098 3.7034350
35 -1.1295935 1.1150098
36 -8.9911506 -1.1295935
37 -11.8952976 -8.9911506
38 -3.0482290 -11.8952976
39 -11.7681460 -3.0482290
40 -1.3734698 -11.7681460
41 0.2655672 -1.3734698
42 -2.9179836 0.2655672
43 -0.1745572 -2.9179836
44 6.9832838 -0.1745572
45 13.2264018 6.9832838
46 10.2713991 13.2264018
47 3.6404412 10.2713991
48 3.4760272 3.6404412
49 6.8961668 3.4760272
50 2.1700992 6.8961668
51 -8.1140148 2.1700992
52 4.6744748 -8.1140148
53 1.2337770 4.6744748
54 3.2771115 1.2337770
55 1.8337565 3.2771115
56 -1.3220081 1.8337565
57 NA -1.3220081
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.8180313 1.1829346
[2,] -1.5382365 7.8180313
[3,] 6.2695457 -1.5382365
[4,] 6.3442610 6.2695457
[5,] 3.2598648 6.3442610
[6,] -6.0550559 3.2598648
[7,] 0.1765284 -6.0550559
[8,] 1.0774225 0.1765284
[9,] -6.9109038 1.0774225
[10,] -4.0143259 -6.9109038
[11,] -5.2684307 -4.0143259
[12,] 8.6094461 -5.2684307
[13,] 7.2662060 8.6094461
[14,] 4.1183828 7.2662060
[15,] 16.2422866 4.1183828
[16,] 6.2258120 16.2422866
[17,] -3.2085304 6.2258120
[18,] -3.0116089 -3.2085304
[19,] 5.5265954 -3.0116089
[20,] -9.3493883 5.5265954
[21,] -10.0189330 -9.3493883
[22,] -7.3720830 -10.0189330
[23,] 2.7575831 -7.3720830
[24,] -4.2772574 2.7575831
[25,] -10.0851064 -4.2772574
[26,] -1.7020164 -10.0851064
[27,] -2.6296716 -1.7020164
[28,] -15.8710780 -2.6296716
[29,] -1.5506786 -15.8710780
[30,] 8.7075368 -1.5506786
[31,] -7.3623230 8.7075368
[32,] 2.6106901 -7.3623230
[33,] 3.7034350 2.6106901
[34,] 1.1150098 3.7034350
[35,] -1.1295935 1.1150098
[36,] -8.9911506 -1.1295935
[37,] -11.8952976 -8.9911506
[38,] -3.0482290 -11.8952976
[39,] -11.7681460 -3.0482290
[40,] -1.3734698 -11.7681460
[41,] 0.2655672 -1.3734698
[42,] -2.9179836 0.2655672
[43,] -0.1745572 -2.9179836
[44,] 6.9832838 -0.1745572
[45,] 13.2264018 6.9832838
[46,] 10.2713991 13.2264018
[47,] 3.6404412 10.2713991
[48,] 3.4760272 3.6404412
[49,] 6.8961668 3.4760272
[50,] 2.1700992 6.8961668
[51,] -8.1140148 2.1700992
[52,] 4.6744748 -8.1140148
[53,] 1.2337770 4.6744748
[54,] 3.2771115 1.2337770
[55,] 1.8337565 3.2771115
[56,] -1.3220081 1.8337565
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.8180313 1.1829346
2 -1.5382365 7.8180313
3 6.2695457 -1.5382365
4 6.3442610 6.2695457
5 3.2598648 6.3442610
6 -6.0550559 3.2598648
7 0.1765284 -6.0550559
8 1.0774225 0.1765284
9 -6.9109038 1.0774225
10 -4.0143259 -6.9109038
11 -5.2684307 -4.0143259
12 8.6094461 -5.2684307
13 7.2662060 8.6094461
14 4.1183828 7.2662060
15 16.2422866 4.1183828
16 6.2258120 16.2422866
17 -3.2085304 6.2258120
18 -3.0116089 -3.2085304
19 5.5265954 -3.0116089
20 -9.3493883 5.5265954
21 -10.0189330 -9.3493883
22 -7.3720830 -10.0189330
23 2.7575831 -7.3720830
24 -4.2772574 2.7575831
25 -10.0851064 -4.2772574
26 -1.7020164 -10.0851064
27 -2.6296716 -1.7020164
28 -15.8710780 -2.6296716
29 -1.5506786 -15.8710780
30 8.7075368 -1.5506786
31 -7.3623230 8.7075368
32 2.6106901 -7.3623230
33 3.7034350 2.6106901
34 1.1150098 3.7034350
35 -1.1295935 1.1150098
36 -8.9911506 -1.1295935
37 -11.8952976 -8.9911506
38 -3.0482290 -11.8952976
39 -11.7681460 -3.0482290
40 -1.3734698 -11.7681460
41 0.2655672 -1.3734698
42 -2.9179836 0.2655672
43 -0.1745572 -2.9179836
44 6.9832838 -0.1745572
45 13.2264018 6.9832838
46 10.2713991 13.2264018
47 3.6404412 10.2713991
48 3.4760272 3.6404412
49 6.8961668 3.4760272
50 2.1700992 6.8961668
51 -8.1140148 2.1700992
52 4.6744748 -8.1140148
53 1.2337770 4.6744748
54 3.2771115 1.2337770
55 1.8337565 3.2771115
56 -1.3220081 1.8337565
> 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/7zpzk1258745565.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8euxh1258745565.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/922s81258745565.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103js61258745565.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mhwf1258745565.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/12fafo1258745565.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/13shzj1258745565.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/14pl251258745565.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/15qes71258745565.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/16i5p51258745566.tab")
+ }
>
> system("convert tmp/15c8u1258745565.ps tmp/15c8u1258745565.png")
> system("convert tmp/20r521258745565.ps tmp/20r521258745565.png")
> system("convert tmp/3mpg81258745565.ps tmp/3mpg81258745565.png")
> system("convert tmp/4r1j81258745565.ps tmp/4r1j81258745565.png")
> system("convert tmp/5vl7f1258745565.ps tmp/5vl7f1258745565.png")
> system("convert tmp/6f0du1258745565.ps tmp/6f0du1258745565.png")
> system("convert tmp/7zpzk1258745565.ps tmp/7zpzk1258745565.png")
> system("convert tmp/8euxh1258745565.ps tmp/8euxh1258745565.png")
> system("convert tmp/922s81258745565.ps tmp/922s81258745565.png")
> system("convert tmp/103js61258745565.ps tmp/103js61258745565.png")
>
>
> proc.time()
user system elapsed
2.366 1.582 2.751