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(19,613,18,611,19,594,19,595,22,591,23,589,20,584,14,573,14,567,14,569,15,621,11,629,17,628,16,612,20,595,24,597,23,593,20,590,21,580,19,574,23,573,23,573,23,620,23,626,27,620,26,588,17,566,24,557,26,561,24,549,27,532,27,526,26,511,24,499,23,555,23,565,24,542,17,527,21,510,19,514,22,517,22,508,18,493,16,490,14,469,12,478,14,528,16,534,8,518,3,506,0,502,5,516,1,528,1,533,3,536,6,537,7,524,8,536,14,587,14,597,13,581),dim=c(2,61),dimnames=list(c('ICONS','WLH'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('ICONS','WLH'),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
ICONS WLH M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 19 613 1 0 0 0 0 0 0 0 0 0 0
2 18 611 0 1 0 0 0 0 0 0 0 0 0
3 19 594 0 0 1 0 0 0 0 0 0 0 0
4 19 595 0 0 0 1 0 0 0 0 0 0 0
5 22 591 0 0 0 0 1 0 0 0 0 0 0
6 23 589 0 0 0 0 0 1 0 0 0 0 0
7 20 584 0 0 0 0 0 0 1 0 0 0 0
8 14 573 0 0 0 0 0 0 0 1 0 0 0
9 14 567 0 0 0 0 0 0 0 0 1 0 0
10 14 569 0 0 0 0 0 0 0 0 0 1 0
11 15 621 0 0 0 0 0 0 0 0 0 0 1
12 11 629 0 0 0 0 0 0 0 0 0 0 0
13 17 628 1 0 0 0 0 0 0 0 0 0 0
14 16 612 0 1 0 0 0 0 0 0 0 0 0
15 20 595 0 0 1 0 0 0 0 0 0 0 0
16 24 597 0 0 0 1 0 0 0 0 0 0 0
17 23 593 0 0 0 0 1 0 0 0 0 0 0
18 20 590 0 0 0 0 0 1 0 0 0 0 0
19 21 580 0 0 0 0 0 0 1 0 0 0 0
20 19 574 0 0 0 0 0 0 0 1 0 0 0
21 23 573 0 0 0 0 0 0 0 0 1 0 0
22 23 573 0 0 0 0 0 0 0 0 0 1 0
23 23 620 0 0 0 0 0 0 0 0 0 0 1
24 23 626 0 0 0 0 0 0 0 0 0 0 0
25 27 620 1 0 0 0 0 0 0 0 0 0 0
26 26 588 0 1 0 0 0 0 0 0 0 0 0
27 17 566 0 0 1 0 0 0 0 0 0 0 0
28 24 557 0 0 0 1 0 0 0 0 0 0 0
29 26 561 0 0 0 0 1 0 0 0 0 0 0
30 24 549 0 0 0 0 0 1 0 0 0 0 0
31 27 532 0 0 0 0 0 0 1 0 0 0 0
32 27 526 0 0 0 0 0 0 0 1 0 0 0
33 26 511 0 0 0 0 0 0 0 0 1 0 0
34 24 499 0 0 0 0 0 0 0 0 0 1 0
35 23 555 0 0 0 0 0 0 0 0 0 0 1
36 23 565 0 0 0 0 0 0 0 0 0 0 0
37 24 542 1 0 0 0 0 0 0 0 0 0 0
38 17 527 0 1 0 0 0 0 0 0 0 0 0
39 21 510 0 0 1 0 0 0 0 0 0 0 0
40 19 514 0 0 0 1 0 0 0 0 0 0 0
41 22 517 0 0 0 0 1 0 0 0 0 0 0
42 22 508 0 0 0 0 0 1 0 0 0 0 0
43 18 493 0 0 0 0 0 0 1 0 0 0 0
44 16 490 0 0 0 0 0 0 0 1 0 0 0
45 14 469 0 0 0 0 0 0 0 0 1 0 0
46 12 478 0 0 0 0 0 0 0 0 0 1 0
47 14 528 0 0 0 0 0 0 0 0 0 0 1
48 16 534 0 0 0 0 0 0 0 0 0 0 0
49 8 518 1 0 0 0 0 0 0 0 0 0 0
50 3 506 0 1 0 0 0 0 0 0 0 0 0
51 0 502 0 0 1 0 0 0 0 0 0 0 0
52 5 516 0 0 0 1 0 0 0 0 0 0 0
53 1 528 0 0 0 0 1 0 0 0 0 0 0
54 1 533 0 0 0 0 0 1 0 0 0 0 0
55 3 536 0 0 0 0 0 0 1 0 0 0 0
56 6 537 0 0 0 0 0 0 0 1 0 0 0
57 7 524 0 0 0 0 0 0 0 0 1 0 0
58 8 536 0 0 0 0 0 0 0 0 0 1 0
59 14 587 0 0 0 0 0 0 0 0 0 0 1
60 14 597 0 0 0 0 0 0 0 0 0 0 0
61 13 581 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) WLH M1 M2 M3 M4
-18.15390 0.06024 0.99357 -0.11085 0.21685 2.87227
M5 M6 M7 M8 M9 M10
3.33974 2.79275 3.12287 2.02407 3.09876 2.36623
M11
0.88192
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.993 -4.388 1.092 4.270 11.443
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -18.15390 15.83795 -1.146 0.257
WLH 0.06024 0.02622 2.298 0.026 *
M1 0.99357 4.57297 0.217 0.829
M2 -0.11085 4.80583 -0.023 0.982
M3 0.21685 4.86951 0.045 0.965
M4 2.87227 4.85743 0.591 0.557
M5 3.33974 4.84705 0.689 0.494
M6 2.79275 4.86744 0.574 0.569
M7 3.12287 4.91788 0.635 0.528
M8 2.02407 4.95111 0.409 0.684
M9 3.09876 5.03713 0.615 0.541
M10 2.36623 5.01899 0.471 0.639
M11 0.88192 4.77757 0.185 0.854
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.547 on 48 degrees of freedom
Multiple R-squared: 0.1168, Adjusted R-squared: -0.104
F-statistic: 0.5291 on 12 and 48 DF, p-value: 0.885
> 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,] 0.0247686257 0.0495372514 0.9752314
[2,] 0.0056355818 0.0112711636 0.9943644
[3,] 0.0018545340 0.0037090680 0.9981455
[4,] 0.0003681604 0.0007363208 0.9996318
[5,] 0.0003269575 0.0006539151 0.9996730
[6,] 0.0011836399 0.0023672799 0.9988164
[7,] 0.0015704089 0.0031408178 0.9984296
[8,] 0.0016101336 0.0032202673 0.9983899
[9,] 0.0038831683 0.0077663365 0.9961168
[10,] 0.0045904377 0.0091808754 0.9954096
[11,] 0.0034589374 0.0069178747 0.9965411
[12,] 0.0025009781 0.0050019563 0.9974990
[13,] 0.0013049845 0.0026099689 0.9986950
[14,] 0.0008189729 0.0016379458 0.9991810
[15,] 0.0004847230 0.0009694461 0.9995153
[16,] 0.0004253290 0.0008506580 0.9995747
[17,] 0.0005453938 0.0010907876 0.9994546
[18,] 0.0005435299 0.0010870598 0.9994565
[19,] 0.0004671910 0.0009343820 0.9995328
[20,] 0.0002862930 0.0005725859 0.9997137
[21,] 0.0001469653 0.0002939306 0.9998530
[22,] 0.0001664620 0.0003329239 0.9998335
[23,] 0.0005274306 0.0010548611 0.9994726
[24,] 0.0021107452 0.0042214904 0.9978893
[25,] 0.0043916566 0.0087833132 0.9956083
[26,] 0.0281726809 0.0563453617 0.9718273
[27,] 0.2159713340 0.4319426680 0.7840287
[28,] 0.5250254586 0.9499490829 0.4749745
[29,] 0.6856881583 0.6286236834 0.3143118
[30,] 0.7654908680 0.4690182640 0.2345091
> postscript(file="/var/www/html/rcomp/tmp/1rknl1258657884.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2isx71258657884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38nol1258657884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qfmp1258657884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5g7t21258657884.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
-0.7670526 -0.5421462 1.1542385 -1.5614249 1.2120658 2.8795368
7 8 9 10 11 12
-0.1493768 -4.3879342 -5.1011845 -4.4891364 -5.1373287 -8.7373287
13 14 15 16 17 18
-3.6706591 -2.6023866 2.0939981 3.3180943 2.0915849 -0.1807036
19 20 21 22 23 24
1.0915849 0.5518254 3.5373730 4.2699019 2.9229117 3.4433926
25 26 27 28 29 30
6.8112644 8.8433837 0.8409706 5.7277115 7.0192787 6.2891541
31 32 33 34 35 36
9.9831256 11.4433660 10.2722797 9.7276938 6.8385397 7.1180589
37 38 39 40 41 42
8.5100179 3.5180500 8.2144347 3.3180500 5.6698577 6.7590117
43 44 45 46 47 48
3.3325024 2.6120215 0.8023778 -1.0072572 -0.5349687 1.9855122
49 50 51 52 53 54
-6.0442117 -9.2169010 -12.3036419 -10.8024309 -15.9927871 -15.7469990
55 56 57 58 59 60
-14.2578361 -10.2192787 -9.5108459 -8.5012022 -4.0891541 -3.8096349
61
-4.8393589
> postscript(file="/var/www/html/rcomp/tmp/6ll5g1258657884.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 -0.7670526 NA
1 -0.5421462 -0.7670526
2 1.1542385 -0.5421462
3 -1.5614249 1.1542385
4 1.2120658 -1.5614249
5 2.8795368 1.2120658
6 -0.1493768 2.8795368
7 -4.3879342 -0.1493768
8 -5.1011845 -4.3879342
9 -4.4891364 -5.1011845
10 -5.1373287 -4.4891364
11 -8.7373287 -5.1373287
12 -3.6706591 -8.7373287
13 -2.6023866 -3.6706591
14 2.0939981 -2.6023866
15 3.3180943 2.0939981
16 2.0915849 3.3180943
17 -0.1807036 2.0915849
18 1.0915849 -0.1807036
19 0.5518254 1.0915849
20 3.5373730 0.5518254
21 4.2699019 3.5373730
22 2.9229117 4.2699019
23 3.4433926 2.9229117
24 6.8112644 3.4433926
25 8.8433837 6.8112644
26 0.8409706 8.8433837
27 5.7277115 0.8409706
28 7.0192787 5.7277115
29 6.2891541 7.0192787
30 9.9831256 6.2891541
31 11.4433660 9.9831256
32 10.2722797 11.4433660
33 9.7276938 10.2722797
34 6.8385397 9.7276938
35 7.1180589 6.8385397
36 8.5100179 7.1180589
37 3.5180500 8.5100179
38 8.2144347 3.5180500
39 3.3180500 8.2144347
40 5.6698577 3.3180500
41 6.7590117 5.6698577
42 3.3325024 6.7590117
43 2.6120215 3.3325024
44 0.8023778 2.6120215
45 -1.0072572 0.8023778
46 -0.5349687 -1.0072572
47 1.9855122 -0.5349687
48 -6.0442117 1.9855122
49 -9.2169010 -6.0442117
50 -12.3036419 -9.2169010
51 -10.8024309 -12.3036419
52 -15.9927871 -10.8024309
53 -15.7469990 -15.9927871
54 -14.2578361 -15.7469990
55 -10.2192787 -14.2578361
56 -9.5108459 -10.2192787
57 -8.5012022 -9.5108459
58 -4.0891541 -8.5012022
59 -3.8096349 -4.0891541
60 -4.8393589 -3.8096349
61 NA -4.8393589
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.5421462 -0.7670526
[2,] 1.1542385 -0.5421462
[3,] -1.5614249 1.1542385
[4,] 1.2120658 -1.5614249
[5,] 2.8795368 1.2120658
[6,] -0.1493768 2.8795368
[7,] -4.3879342 -0.1493768
[8,] -5.1011845 -4.3879342
[9,] -4.4891364 -5.1011845
[10,] -5.1373287 -4.4891364
[11,] -8.7373287 -5.1373287
[12,] -3.6706591 -8.7373287
[13,] -2.6023866 -3.6706591
[14,] 2.0939981 -2.6023866
[15,] 3.3180943 2.0939981
[16,] 2.0915849 3.3180943
[17,] -0.1807036 2.0915849
[18,] 1.0915849 -0.1807036
[19,] 0.5518254 1.0915849
[20,] 3.5373730 0.5518254
[21,] 4.2699019 3.5373730
[22,] 2.9229117 4.2699019
[23,] 3.4433926 2.9229117
[24,] 6.8112644 3.4433926
[25,] 8.8433837 6.8112644
[26,] 0.8409706 8.8433837
[27,] 5.7277115 0.8409706
[28,] 7.0192787 5.7277115
[29,] 6.2891541 7.0192787
[30,] 9.9831256 6.2891541
[31,] 11.4433660 9.9831256
[32,] 10.2722797 11.4433660
[33,] 9.7276938 10.2722797
[34,] 6.8385397 9.7276938
[35,] 7.1180589 6.8385397
[36,] 8.5100179 7.1180589
[37,] 3.5180500 8.5100179
[38,] 8.2144347 3.5180500
[39,] 3.3180500 8.2144347
[40,] 5.6698577 3.3180500
[41,] 6.7590117 5.6698577
[42,] 3.3325024 6.7590117
[43,] 2.6120215 3.3325024
[44,] 0.8023778 2.6120215
[45,] -1.0072572 0.8023778
[46,] -0.5349687 -1.0072572
[47,] 1.9855122 -0.5349687
[48,] -6.0442117 1.9855122
[49,] -9.2169010 -6.0442117
[50,] -12.3036419 -9.2169010
[51,] -10.8024309 -12.3036419
[52,] -15.9927871 -10.8024309
[53,] -15.7469990 -15.9927871
[54,] -14.2578361 -15.7469990
[55,] -10.2192787 -14.2578361
[56,] -9.5108459 -10.2192787
[57,] -8.5012022 -9.5108459
[58,] -4.0891541 -8.5012022
[59,] -3.8096349 -4.0891541
[60,] -4.8393589 -3.8096349
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.5421462 -0.7670526
2 1.1542385 -0.5421462
3 -1.5614249 1.1542385
4 1.2120658 -1.5614249
5 2.8795368 1.2120658
6 -0.1493768 2.8795368
7 -4.3879342 -0.1493768
8 -5.1011845 -4.3879342
9 -4.4891364 -5.1011845
10 -5.1373287 -4.4891364
11 -8.7373287 -5.1373287
12 -3.6706591 -8.7373287
13 -2.6023866 -3.6706591
14 2.0939981 -2.6023866
15 3.3180943 2.0939981
16 2.0915849 3.3180943
17 -0.1807036 2.0915849
18 1.0915849 -0.1807036
19 0.5518254 1.0915849
20 3.5373730 0.5518254
21 4.2699019 3.5373730
22 2.9229117 4.2699019
23 3.4433926 2.9229117
24 6.8112644 3.4433926
25 8.8433837 6.8112644
26 0.8409706 8.8433837
27 5.7277115 0.8409706
28 7.0192787 5.7277115
29 6.2891541 7.0192787
30 9.9831256 6.2891541
31 11.4433660 9.9831256
32 10.2722797 11.4433660
33 9.7276938 10.2722797
34 6.8385397 9.7276938
35 7.1180589 6.8385397
36 8.5100179 7.1180589
37 3.5180500 8.5100179
38 8.2144347 3.5180500
39 3.3180500 8.2144347
40 5.6698577 3.3180500
41 6.7590117 5.6698577
42 3.3325024 6.7590117
43 2.6120215 3.3325024
44 0.8023778 2.6120215
45 -1.0072572 0.8023778
46 -0.5349687 -1.0072572
47 1.9855122 -0.5349687
48 -6.0442117 1.9855122
49 -9.2169010 -6.0442117
50 -12.3036419 -9.2169010
51 -10.8024309 -12.3036419
52 -15.9927871 -10.8024309
53 -15.7469990 -15.9927871
54 -14.2578361 -15.7469990
55 -10.2192787 -14.2578361
56 -9.5108459 -10.2192787
57 -8.5012022 -9.5108459
58 -4.0891541 -8.5012022
59 -3.8096349 -4.0891541
60 -4.8393589 -3.8096349
> 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/7rqdd1258657884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ve4l1258657884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91a6j1258657884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10q8l51258657884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115win1258657885.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/122l1h1258657885.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/13vwry1258657885.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/14rd8j1258657885.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/15m0w21258657885.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/16m1ff1258657885.tab")
+ }
>
> system("convert tmp/1rknl1258657884.ps tmp/1rknl1258657884.png")
> system("convert tmp/2isx71258657884.ps tmp/2isx71258657884.png")
> system("convert tmp/38nol1258657884.ps tmp/38nol1258657884.png")
> system("convert tmp/4qfmp1258657884.ps tmp/4qfmp1258657884.png")
> system("convert tmp/5g7t21258657884.ps tmp/5g7t21258657884.png")
> system("convert tmp/6ll5g1258657884.ps tmp/6ll5g1258657884.png")
> system("convert tmp/7rqdd1258657884.ps tmp/7rqdd1258657884.png")
> system("convert tmp/8ve4l1258657884.ps tmp/8ve4l1258657884.png")
> system("convert tmp/91a6j1258657884.ps tmp/91a6j1258657884.png")
> system("convert tmp/10q8l51258657884.ps tmp/10q8l51258657884.png")
>
>
> proc.time()
user system elapsed
2.415 1.542 4.691