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(562,573,561,572,555,566,544,555,537,548,543,554,594,605,611,622,613,624,611,622,594,605,595,606,591,602,589,600,584,595,573,584,567,578,569,580,621,632,629,640,628,639,612,623,595,606,597,608,593,604,590,601,580,591,574,585,573,584,573,584,620,631,626,637,620,631,588,599,566,577,557,568,561,572,549,560,532,543,526,537,511,522,499,510,555,566,565,576,542,553,527,538,510,521,514,525,517,528,508,519,493,504,490,501,469,480,478,489,528,539,534,545,518,529,506,517,502,513,516,527,528,539),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 = 'No 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
1 562 573 1 0 0 0 0 0 0 0 0 0 0
2 561 572 0 1 0 0 0 0 0 0 0 0 0
3 555 566 0 0 1 0 0 0 0 0 0 0 0
4 544 555 0 0 0 1 0 0 0 0 0 0 0
5 537 548 0 0 0 0 1 0 0 0 0 0 0
6 543 554 0 0 0 0 0 1 0 0 0 0 0
7 594 605 0 0 0 0 0 0 1 0 0 0 0
8 611 622 0 0 0 0 0 0 0 1 0 0 0
9 613 624 0 0 0 0 0 0 0 0 1 0 0
10 611 622 0 0 0 0 0 0 0 0 0 1 0
11 594 605 0 0 0 0 0 0 0 0 0 0 1
12 595 606 0 0 0 0 0 0 0 0 0 0 0
13 591 602 1 0 0 0 0 0 0 0 0 0 0
14 589 600 0 1 0 0 0 0 0 0 0 0 0
15 584 595 0 0 1 0 0 0 0 0 0 0 0
16 573 584 0 0 0 1 0 0 0 0 0 0 0
17 567 578 0 0 0 0 1 0 0 0 0 0 0
18 569 580 0 0 0 0 0 1 0 0 0 0 0
19 621 632 0 0 0 0 0 0 1 0 0 0 0
20 629 640 0 0 0 0 0 0 0 1 0 0 0
21 628 639 0 0 0 0 0 0 0 0 1 0 0
22 612 623 0 0 0 0 0 0 0 0 0 1 0
23 595 606 0 0 0 0 0 0 0 0 0 0 1
24 597 608 0 0 0 0 0 0 0 0 0 0 0
25 593 604 1 0 0 0 0 0 0 0 0 0 0
26 590 601 0 1 0 0 0 0 0 0 0 0 0
27 580 591 0 0 1 0 0 0 0 0 0 0 0
28 574 585 0 0 0 1 0 0 0 0 0 0 0
29 573 584 0 0 0 0 1 0 0 0 0 0 0
30 573 584 0 0 0 0 0 1 0 0 0 0 0
31 620 631 0 0 0 0 0 0 1 0 0 0 0
32 626 637 0 0 0 0 0 0 0 1 0 0 0
33 620 631 0 0 0 0 0 0 0 0 1 0 0
34 588 599 0 0 0 0 0 0 0 0 0 1 0
35 566 577 0 0 0 0 0 0 0 0 0 0 1
36 557 568 0 0 0 0 0 0 0 0 0 0 0
37 561 572 1 0 0 0 0 0 0 0 0 0 0
38 549 560 0 1 0 0 0 0 0 0 0 0 0
39 532 543 0 0 1 0 0 0 0 0 0 0 0
40 526 537 0 0 0 1 0 0 0 0 0 0 0
41 511 522 0 0 0 0 1 0 0 0 0 0 0
42 499 510 0 0 0 0 0 1 0 0 0 0 0
43 555 566 0 0 0 0 0 0 1 0 0 0 0
44 565 576 0 0 0 0 0 0 0 1 0 0 0
45 542 553 0 0 0 0 0 0 0 0 1 0 0
46 527 538 0 0 0 0 0 0 0 0 0 1 0
47 510 521 0 0 0 0 0 0 0 0 0 0 1
48 514 525 0 0 0 0 0 0 0 0 0 0 0
49 517 528 1 0 0 0 0 0 0 0 0 0 0
50 508 519 0 1 0 0 0 0 0 0 0 0 0
51 493 504 0 0 1 0 0 0 0 0 0 0 0
52 490 501 0 0 0 1 0 0 0 0 0 0 0
53 469 480 0 0 0 0 1 0 0 0 0 0 0
54 478 489 0 0 0 0 0 1 0 0 0 0 0
55 528 539 0 0 0 0 0 0 1 0 0 0 0
56 534 545 0 0 0 0 0 0 0 1 0 0 0
57 518 529 0 0 0 0 0 0 0 0 1 0 0
58 506 517 0 0 0 0 0 0 0 0 0 1 0
59 502 513 0 0 0 0 0 0 0 0 0 0 1
60 516 527 0 0 0 0 0 0 0 0 0 0 0
61 528 539 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
-1.100e+01 1.000e+00 1.498e-13 3.152e-15 7.174e-17 1.262e-15
M5 M6 M7 M8 M9 M10
2.125e-15 2.661e-15 2.715e-15 -2.734e-15 4.279e-15 4.758e-16
M11
1.291e-15
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.529e-13 -2.684e-15 -7.011e-16 2.373e-15 7.412e-13
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.100e+01 2.368e-13 -4.646e+13 <2e-16 ***
X 1.000e+00 4.073e-16 2.455e+15 <2e-16 ***
M1 1.498e-13 7.100e-14 2.109e+00 0.0401 *
M2 3.152e-15 7.416e-14 4.300e-02 0.9663
M3 7.174e-17 7.420e-14 1.000e-03 0.9992
M4 1.262e-15 7.438e-14 1.700e-02 0.9865
M5 2.125e-15 7.481e-14 2.800e-02 0.9775
M6 2.661e-15 7.476e-14 3.600e-02 0.9718
M7 2.715e-15 7.501e-14 3.600e-02 0.9713
M8 -2.734e-15 7.568e-14 -3.600e-02 0.9713
M9 4.279e-15 7.504e-14 5.700e-02 0.9548
M10 4.758e-16 7.434e-14 6.000e-03 0.9949
M11 1.291e-15 7.415e-14 1.700e-02 0.9862
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.172e-13 on 48 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 6.361e+29 on 12 and 48 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,] 8.226928e-06 1.645386e-05 9.999918e-01
[2,] 7.148369e-07 1.429674e-06 9.999993e-01
[3,] 8.815844e-14 1.763169e-13 1.000000e+00
[4,] 7.029471e-01 5.941057e-01 2.970529e-01
[5,] 1.000000e+00 2.089740e-59 1.044870e-59
[6,] 9.037656e-01 1.924688e-01 9.623440e-02
[7,] 9.978164e-01 4.367147e-03 2.183574e-03
[8,] 9.956651e-01 8.669805e-03 4.334903e-03
[9,] 9.941335e-01 1.173299e-02 5.866494e-03
[10,] 6.229330e-18 1.245866e-17 1.000000e+00
[11,] 4.402590e-14 8.805180e-14 1.000000e+00
[12,] 1.000000e+00 4.025165e-23 2.012582e-23
[13,] 1.000000e+00 2.848458e-35 1.424229e-35
[14,] 1.000000e+00 2.110572e-10 1.055286e-10
[15,] 3.399092e-05 6.798183e-05 9.999660e-01
[16,] 5.453296e-29 1.090659e-28 1.000000e+00
[17,] 5.614791e-03 1.122958e-02 9.943852e-01
[18,] 1.000000e+00 3.236823e-10 1.618412e-10
[19,] 6.690668e-03 1.338134e-02 9.933093e-01
[20,] 9.866810e-01 2.663810e-02 1.331905e-02
[21,] 6.662558e-34 1.332512e-33 1.000000e+00
[22,] 8.802336e-03 1.760467e-02 9.911977e-01
[23,] 5.989428e-01 8.021143e-01 4.010572e-01
[24,] 1.000000e+00 3.977524e-14 1.988762e-14
[25,] 5.588865e-12 1.117773e-11 1.000000e+00
[26,] 1.000000e+00 3.364306e-10 1.682153e-10
[27,] 1.000000e+00 4.980118e-13 2.490059e-13
[28,] 1.000000e+00 5.341512e-08 2.670756e-08
[29,] 7.050857e-03 1.410171e-02 9.929491e-01
[30,] 9.999763e-01 4.744762e-05 2.372381e-05
> postscript(file="/var/www/html/rcomp/tmp/1p4ew1258733021.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21alv1258733021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/360dw1258733021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nu7r1258733021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50jp21258733021.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
7.411765e-13 7.172843e-17 1.365155e-15 5.037221e-16 -3.219309e-15
6 7 8 9 10
-2.318934e-15 9.490744e-16 3.991790e-16 -2.139649e-16 -2.810286e-15
11 12 13 14 15
2.372773e-15 -3.794600e-15 -1.485989e-13 -1.277020e-15 1.480947e-17
16 17 18 19 20
-8.466233e-16 -4.815947e-16 -8.344268e-16 -1.471964e-15 -5.951798e-15
21 22 23 24 25
1.598982e-15 3.942309e-15 -5.085487e-15 -4.500265e-15 -1.528573e-13
26 27 28 29 30
-1.629852e-15 -2.126576e-15 -1.199455e-15 -1.710409e-15 -2.245755e-15
31 32 33 34 35
-1.119132e-15 -4.893302e-15 -2.683789e-15 1.752137e-15 -1.824275e-16
36 37 38 39 40
2.063501e-15 -1.482280e-13 4.017640e-16 4.151222e-15 5.078342e-15
41 42 43 44 45
2.401610e-15 6.100249e-15 -1.277687e-15 4.194956e-15 -3.584598e-15
46 47 48 49 50
-1.594103e-15 3.588956e-15 3.468514e-15 -1.473583e-13 2.433380e-15
51 52 53 54 55
-3.404610e-15 -3.535986e-15 3.009702e-15 -7.011327e-16 2.919708e-15
56 57 58 59 60
6.250965e-15 4.883371e-15 -1.290057e-15 -6.938148e-16 2.762850e-15
61
-1.441340e-13
> postscript(file="/var/www/html/rcomp/tmp/6jdp21258733021.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 7.411765e-13 NA
1 7.172843e-17 7.411765e-13
2 1.365155e-15 7.172843e-17
3 5.037221e-16 1.365155e-15
4 -3.219309e-15 5.037221e-16
5 -2.318934e-15 -3.219309e-15
6 9.490744e-16 -2.318934e-15
7 3.991790e-16 9.490744e-16
8 -2.139649e-16 3.991790e-16
9 -2.810286e-15 -2.139649e-16
10 2.372773e-15 -2.810286e-15
11 -3.794600e-15 2.372773e-15
12 -1.485989e-13 -3.794600e-15
13 -1.277020e-15 -1.485989e-13
14 1.480947e-17 -1.277020e-15
15 -8.466233e-16 1.480947e-17
16 -4.815947e-16 -8.466233e-16
17 -8.344268e-16 -4.815947e-16
18 -1.471964e-15 -8.344268e-16
19 -5.951798e-15 -1.471964e-15
20 1.598982e-15 -5.951798e-15
21 3.942309e-15 1.598982e-15
22 -5.085487e-15 3.942309e-15
23 -4.500265e-15 -5.085487e-15
24 -1.528573e-13 -4.500265e-15
25 -1.629852e-15 -1.528573e-13
26 -2.126576e-15 -1.629852e-15
27 -1.199455e-15 -2.126576e-15
28 -1.710409e-15 -1.199455e-15
29 -2.245755e-15 -1.710409e-15
30 -1.119132e-15 -2.245755e-15
31 -4.893302e-15 -1.119132e-15
32 -2.683789e-15 -4.893302e-15
33 1.752137e-15 -2.683789e-15
34 -1.824275e-16 1.752137e-15
35 2.063501e-15 -1.824275e-16
36 -1.482280e-13 2.063501e-15
37 4.017640e-16 -1.482280e-13
38 4.151222e-15 4.017640e-16
39 5.078342e-15 4.151222e-15
40 2.401610e-15 5.078342e-15
41 6.100249e-15 2.401610e-15
42 -1.277687e-15 6.100249e-15
43 4.194956e-15 -1.277687e-15
44 -3.584598e-15 4.194956e-15
45 -1.594103e-15 -3.584598e-15
46 3.588956e-15 -1.594103e-15
47 3.468514e-15 3.588956e-15
48 -1.473583e-13 3.468514e-15
49 2.433380e-15 -1.473583e-13
50 -3.404610e-15 2.433380e-15
51 -3.535986e-15 -3.404610e-15
52 3.009702e-15 -3.535986e-15
53 -7.011327e-16 3.009702e-15
54 2.919708e-15 -7.011327e-16
55 6.250965e-15 2.919708e-15
56 4.883371e-15 6.250965e-15
57 -1.290057e-15 4.883371e-15
58 -6.938148e-16 -1.290057e-15
59 2.762850e-15 -6.938148e-16
60 -1.441340e-13 2.762850e-15
61 NA -1.441340e-13
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.172843e-17 7.411765e-13
[2,] 1.365155e-15 7.172843e-17
[3,] 5.037221e-16 1.365155e-15
[4,] -3.219309e-15 5.037221e-16
[5,] -2.318934e-15 -3.219309e-15
[6,] 9.490744e-16 -2.318934e-15
[7,] 3.991790e-16 9.490744e-16
[8,] -2.139649e-16 3.991790e-16
[9,] -2.810286e-15 -2.139649e-16
[10,] 2.372773e-15 -2.810286e-15
[11,] -3.794600e-15 2.372773e-15
[12,] -1.485989e-13 -3.794600e-15
[13,] -1.277020e-15 -1.485989e-13
[14,] 1.480947e-17 -1.277020e-15
[15,] -8.466233e-16 1.480947e-17
[16,] -4.815947e-16 -8.466233e-16
[17,] -8.344268e-16 -4.815947e-16
[18,] -1.471964e-15 -8.344268e-16
[19,] -5.951798e-15 -1.471964e-15
[20,] 1.598982e-15 -5.951798e-15
[21,] 3.942309e-15 1.598982e-15
[22,] -5.085487e-15 3.942309e-15
[23,] -4.500265e-15 -5.085487e-15
[24,] -1.528573e-13 -4.500265e-15
[25,] -1.629852e-15 -1.528573e-13
[26,] -2.126576e-15 -1.629852e-15
[27,] -1.199455e-15 -2.126576e-15
[28,] -1.710409e-15 -1.199455e-15
[29,] -2.245755e-15 -1.710409e-15
[30,] -1.119132e-15 -2.245755e-15
[31,] -4.893302e-15 -1.119132e-15
[32,] -2.683789e-15 -4.893302e-15
[33,] 1.752137e-15 -2.683789e-15
[34,] -1.824275e-16 1.752137e-15
[35,] 2.063501e-15 -1.824275e-16
[36,] -1.482280e-13 2.063501e-15
[37,] 4.017640e-16 -1.482280e-13
[38,] 4.151222e-15 4.017640e-16
[39,] 5.078342e-15 4.151222e-15
[40,] 2.401610e-15 5.078342e-15
[41,] 6.100249e-15 2.401610e-15
[42,] -1.277687e-15 6.100249e-15
[43,] 4.194956e-15 -1.277687e-15
[44,] -3.584598e-15 4.194956e-15
[45,] -1.594103e-15 -3.584598e-15
[46,] 3.588956e-15 -1.594103e-15
[47,] 3.468514e-15 3.588956e-15
[48,] -1.473583e-13 3.468514e-15
[49,] 2.433380e-15 -1.473583e-13
[50,] -3.404610e-15 2.433380e-15
[51,] -3.535986e-15 -3.404610e-15
[52,] 3.009702e-15 -3.535986e-15
[53,] -7.011327e-16 3.009702e-15
[54,] 2.919708e-15 -7.011327e-16
[55,] 6.250965e-15 2.919708e-15
[56,] 4.883371e-15 6.250965e-15
[57,] -1.290057e-15 4.883371e-15
[58,] -6.938148e-16 -1.290057e-15
[59,] 2.762850e-15 -6.938148e-16
[60,] -1.441340e-13 2.762850e-15
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.172843e-17 7.411765e-13
2 1.365155e-15 7.172843e-17
3 5.037221e-16 1.365155e-15
4 -3.219309e-15 5.037221e-16
5 -2.318934e-15 -3.219309e-15
6 9.490744e-16 -2.318934e-15
7 3.991790e-16 9.490744e-16
8 -2.139649e-16 3.991790e-16
9 -2.810286e-15 -2.139649e-16
10 2.372773e-15 -2.810286e-15
11 -3.794600e-15 2.372773e-15
12 -1.485989e-13 -3.794600e-15
13 -1.277020e-15 -1.485989e-13
14 1.480947e-17 -1.277020e-15
15 -8.466233e-16 1.480947e-17
16 -4.815947e-16 -8.466233e-16
17 -8.344268e-16 -4.815947e-16
18 -1.471964e-15 -8.344268e-16
19 -5.951798e-15 -1.471964e-15
20 1.598982e-15 -5.951798e-15
21 3.942309e-15 1.598982e-15
22 -5.085487e-15 3.942309e-15
23 -4.500265e-15 -5.085487e-15
24 -1.528573e-13 -4.500265e-15
25 -1.629852e-15 -1.528573e-13
26 -2.126576e-15 -1.629852e-15
27 -1.199455e-15 -2.126576e-15
28 -1.710409e-15 -1.199455e-15
29 -2.245755e-15 -1.710409e-15
30 -1.119132e-15 -2.245755e-15
31 -4.893302e-15 -1.119132e-15
32 -2.683789e-15 -4.893302e-15
33 1.752137e-15 -2.683789e-15
34 -1.824275e-16 1.752137e-15
35 2.063501e-15 -1.824275e-16
36 -1.482280e-13 2.063501e-15
37 4.017640e-16 -1.482280e-13
38 4.151222e-15 4.017640e-16
39 5.078342e-15 4.151222e-15
40 2.401610e-15 5.078342e-15
41 6.100249e-15 2.401610e-15
42 -1.277687e-15 6.100249e-15
43 4.194956e-15 -1.277687e-15
44 -3.584598e-15 4.194956e-15
45 -1.594103e-15 -3.584598e-15
46 3.588956e-15 -1.594103e-15
47 3.468514e-15 3.588956e-15
48 -1.473583e-13 3.468514e-15
49 2.433380e-15 -1.473583e-13
50 -3.404610e-15 2.433380e-15
51 -3.535986e-15 -3.404610e-15
52 3.009702e-15 -3.535986e-15
53 -7.011327e-16 3.009702e-15
54 2.919708e-15 -7.011327e-16
55 6.250965e-15 2.919708e-15
56 4.883371e-15 6.250965e-15
57 -1.290057e-15 4.883371e-15
58 -6.938148e-16 -1.290057e-15
59 2.762850e-15 -6.938148e-16
60 -1.441340e-13 2.762850e-15
> 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/7hf9h1258733021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8m97g1258733021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/979sf1258733021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10b3m61258733021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11cpsf1258733021.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/1231cd1258733021.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/13mmdp1258733021.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/14dg0e1258733021.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/15vlut1258733021.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/16ajdj1258733021.tab")
+ }
>
> system("convert tmp/1p4ew1258733021.ps tmp/1p4ew1258733021.png")
> system("convert tmp/21alv1258733021.ps tmp/21alv1258733021.png")
> system("convert tmp/360dw1258733021.ps tmp/360dw1258733021.png")
> system("convert tmp/4nu7r1258733021.ps tmp/4nu7r1258733021.png")
> system("convert tmp/50jp21258733021.ps tmp/50jp21258733021.png")
> system("convert tmp/6jdp21258733021.ps tmp/6jdp21258733021.png")
> system("convert tmp/7hf9h1258733021.ps tmp/7hf9h1258733021.png")
> system("convert tmp/8m97g1258733021.ps tmp/8m97g1258733021.png")
> system("convert tmp/979sf1258733021.ps tmp/979sf1258733021.png")
> system("convert tmp/10b3m61258733021.ps tmp/10b3m61258733021.png")
>
>
> proc.time()
user system elapsed
2.432 1.570 3.476