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(8.4,420,8.4,418,8.4,410,8.6,418,8.9,426,8.8,428,8.3,430,7.5,424,7.2,423,7.4,427,8.8,441,9.3,449,9.3,452,8.7,462,8.2,455,8.3,461,8.5,461,8.6,463,8.5,462,8.2,456,8.1,455,7.9,456,8.6,472,8.7,472,8.7,471,8.5,465,8.4,459,8.5,465,8.7,468,8.7,467,8.6,463,8.5,460,8.3,462,8.00,461,8.2,476,8.1,476,8.1,471,8.00,453,7.9,443,7.9,442,8.00,444,8.00,438,7.9,427,8.00,424,7.7,416,7.2,406,7.5,431,7.3,434,7.00,418,7.00,412,7.00,404,7.2,409,7.3,412,7.1,406,6.8,398,6.4,397,6.1,385,6.5,390,7.7,413,7.9,413,7.5,401,6.9,397,6.6,397,6.9,409,7.7,419,8.00,424,8.00,428,7.7,430,7.3,424,7.4,433,8.1,456,8.3,459,8.2,446),dim=c(2,73),dimnames=list(c('wgb','nwwz'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('wgb','nwwz'),1:73))
> 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
wgb nwwz M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.4 420 1 0 0 0 0 0 0 0 0 0 0
2 8.4 418 0 1 0 0 0 0 0 0 0 0 0
3 8.4 410 0 0 1 0 0 0 0 0 0 0 0
4 8.6 418 0 0 0 1 0 0 0 0 0 0 0
5 8.9 426 0 0 0 0 1 0 0 0 0 0 0
6 8.8 428 0 0 0 0 0 1 0 0 0 0 0
7 8.3 430 0 0 0 0 0 0 1 0 0 0 0
8 7.5 424 0 0 0 0 0 0 0 1 0 0 0
9 7.2 423 0 0 0 0 0 0 0 0 1 0 0
10 7.4 427 0 0 0 0 0 0 0 0 0 1 0
11 8.8 441 0 0 0 0 0 0 0 0 0 0 1
12 9.3 449 0 0 0 0 0 0 0 0 0 0 0
13 9.3 452 1 0 0 0 0 0 0 0 0 0 0
14 8.7 462 0 1 0 0 0 0 0 0 0 0 0
15 8.2 455 0 0 1 0 0 0 0 0 0 0 0
16 8.3 461 0 0 0 1 0 0 0 0 0 0 0
17 8.5 461 0 0 0 0 1 0 0 0 0 0 0
18 8.6 463 0 0 0 0 0 1 0 0 0 0 0
19 8.5 462 0 0 0 0 0 0 1 0 0 0 0
20 8.2 456 0 0 0 0 0 0 0 1 0 0 0
21 8.1 455 0 0 0 0 0 0 0 0 1 0 0
22 7.9 456 0 0 0 0 0 0 0 0 0 1 0
23 8.6 472 0 0 0 0 0 0 0 0 0 0 1
24 8.7 472 0 0 0 0 0 0 0 0 0 0 0
25 8.7 471 1 0 0 0 0 0 0 0 0 0 0
26 8.5 465 0 1 0 0 0 0 0 0 0 0 0
27 8.4 459 0 0 1 0 0 0 0 0 0 0 0
28 8.5 465 0 0 0 1 0 0 0 0 0 0 0
29 8.7 468 0 0 0 0 1 0 0 0 0 0 0
30 8.7 467 0 0 0 0 0 1 0 0 0 0 0
31 8.6 463 0 0 0 0 0 0 1 0 0 0 0
32 8.5 460 0 0 0 0 0 0 0 1 0 0 0
33 8.3 462 0 0 0 0 0 0 0 0 1 0 0
34 8.0 461 0 0 0 0 0 0 0 0 0 1 0
35 8.2 476 0 0 0 0 0 0 0 0 0 0 1
36 8.1 476 0 0 0 0 0 0 0 0 0 0 0
37 8.1 471 1 0 0 0 0 0 0 0 0 0 0
38 8.0 453 0 1 0 0 0 0 0 0 0 0 0
39 7.9 443 0 0 1 0 0 0 0 0 0 0 0
40 7.9 442 0 0 0 1 0 0 0 0 0 0 0
41 8.0 444 0 0 0 0 1 0 0 0 0 0 0
42 8.0 438 0 0 0 0 0 1 0 0 0 0 0
43 7.9 427 0 0 0 0 0 0 1 0 0 0 0
44 8.0 424 0 0 0 0 0 0 0 1 0 0 0
45 7.7 416 0 0 0 0 0 0 0 0 1 0 0
46 7.2 406 0 0 0 0 0 0 0 0 0 1 0
47 7.5 431 0 0 0 0 0 0 0 0 0 0 1
48 7.3 434 0 0 0 0 0 0 0 0 0 0 0
49 7.0 418 1 0 0 0 0 0 0 0 0 0 0
50 7.0 412 0 1 0 0 0 0 0 0 0 0 0
51 7.0 404 0 0 1 0 0 0 0 0 0 0 0
52 7.2 409 0 0 0 1 0 0 0 0 0 0 0
53 7.3 412 0 0 0 0 1 0 0 0 0 0 0
54 7.1 406 0 0 0 0 0 1 0 0 0 0 0
55 6.8 398 0 0 0 0 0 0 1 0 0 0 0
56 6.4 397 0 0 0 0 0 0 0 1 0 0 0
57 6.1 385 0 0 0 0 0 0 0 0 1 0 0
58 6.5 390 0 0 0 0 0 0 0 0 0 1 0
59 7.7 413 0 0 0 0 0 0 0 0 0 0 1
60 7.9 413 0 0 0 0 0 0 0 0 0 0 0
61 7.5 401 1 0 0 0 0 0 0 0 0 0 0
62 6.9 397 0 1 0 0 0 0 0 0 0 0 0
63 6.6 397 0 0 1 0 0 0 0 0 0 0 0
64 6.9 409 0 0 0 1 0 0 0 0 0 0 0
65 7.7 419 0 0 0 0 1 0 0 0 0 0 0
66 8.0 424 0 0 0 0 0 1 0 0 0 0 0
67 8.0 428 0 0 0 0 0 0 1 0 0 0 0
68 7.7 430 0 0 0 0 0 0 0 1 0 0 0
69 7.3 424 0 0 0 0 0 0 0 0 1 0 0
70 7.4 433 0 0 0 0 0 0 0 0 0 1 0
71 8.1 456 0 0 0 0 0 0 0 0 0 0 1
72 8.3 459 0 0 0 0 0 0 0 0 0 0 0
73 8.2 446 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) nwwz M1 M2 M3 M4
-0.65424 0.01980 0.11551 -0.03316 -0.07112 -0.03993
M5 M6 M7 M8 M9 M10
0.15759 0.18746 0.06354 -0.18036 -0.36122 -0.43762
M11
-0.07046
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.7386 -0.2066 -0.0708 0.1153 1.0630
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.654243 1.062980 -0.615 0.541
nwwz 0.019802 0.002321 8.531 6.12e-12 ***
M1 0.115514 0.261117 0.442 0.660
M2 -0.033164 0.272303 -0.122 0.903
M3 -0.071116 0.274768 -0.259 0.797
M4 -0.039930 0.272464 -0.147 0.884
M5 0.157594 0.271233 0.581 0.563
M6 0.187462 0.271398 0.691 0.492
M7 0.063535 0.272251 0.233 0.816
M8 -0.180358 0.273216 -0.660 0.512
M9 -0.361215 0.274991 -1.314 0.194
M10 -0.437618 0.274407 -1.595 0.116
M11 -0.070461 0.269813 -0.261 0.795
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4672 on 60 degrees of freedom
Multiple R-squared: 0.6233, Adjusted R-squared: 0.548
F-statistic: 8.274 on 12 and 60 DF, p-value: 6.42e-09
> 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,] 0.8634651 0.2730697 0.13653486
[2,] 0.8410576 0.3178848 0.15894240
[3,] 0.7596057 0.4807886 0.24039429
[4,] 0.6653176 0.6693648 0.33468240
[5,] 0.7342508 0.5314985 0.26574924
[6,] 0.8245773 0.3508453 0.17542265
[7,] 0.7815998 0.4368003 0.21840017
[8,] 0.7349756 0.5300488 0.26502438
[9,] 0.7770297 0.4459406 0.22297030
[10,] 0.7264104 0.5471791 0.27358955
[11,] 0.6579109 0.6841782 0.34208912
[12,] 0.5860933 0.8278133 0.41390666
[13,] 0.5060760 0.9878480 0.49392401
[14,] 0.4225449 0.8450899 0.57745505
[15,] 0.3406375 0.6812749 0.65936254
[16,] 0.2741937 0.5483874 0.72580632
[17,] 0.3209854 0.6419707 0.67901464
[18,] 0.3457627 0.6915254 0.65423728
[19,] 0.2893871 0.5787742 0.71061291
[20,] 0.3319861 0.6639722 0.66801389
[21,] 0.5432299 0.9135402 0.45677012
[22,] 0.6687863 0.6624274 0.33121368
[23,] 0.6489981 0.7020037 0.35100187
[24,] 0.6066794 0.7866412 0.39332062
[25,] 0.5817830 0.8364340 0.41821700
[26,] 0.5830078 0.8339843 0.41699217
[27,] 0.5682723 0.8634554 0.43172770
[28,] 0.5235858 0.9528284 0.47641421
[29,] 0.5593329 0.8813342 0.44066708
[30,] 0.6248949 0.7502102 0.37510509
[31,] 0.5979464 0.8041073 0.40205363
[32,] 0.6279062 0.7441877 0.37209384
[33,] 0.8229454 0.3541093 0.17705464
[34,] 0.9407805 0.1184390 0.05921952
[35,] 0.9313404 0.1373192 0.06865962
[36,] 0.9087524 0.1824951 0.09124756
[37,] 0.8765331 0.2469338 0.12346690
[38,] 0.8290003 0.3419994 0.17099971
[39,] 0.8195529 0.3608942 0.18044712
[40,] 0.8319535 0.3360929 0.16804647
[41,] 0.8979652 0.2040697 0.10203484
[42,] 0.9492483 0.1015035 0.05075174
> postscript(file="/var/www/html/rcomp/tmp/10uz11258623759.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28vr41258623759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3i8pc1258623759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4idft1258623759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5u6cw1258623759.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 = 73
Frequency = 1
1 2 3 4 5 6
0.621787354 0.810070308 1.006440336 1.016835854 0.960894304 0.791421662
7 8 9 10 11 12
0.375743791 -0.061549113 -0.160889916 0.036304108 0.791916060 1.063036695
13 14 15 16 17 18
0.888115647 0.238771710 -0.084660503 -0.134660503 -0.132184126 -0.101656769
19 20 21 22 23 24
-0.057927917 0.004779179 0.105438376 -0.037960877 -0.021953407 0.007585155
25 26 27 28 29 30
-0.088126930 -0.020635013 0.036130533 -0.013869467 -0.070799812 -0.080865732
31 32 33 34 35 36
0.022269842 0.225570216 0.166822690 -0.036972081 -0.501162371 -0.671623809
37 38 39 40 41 42
-0.688126930 -0.283008123 -0.147033613 -0.158417927 -0.295546032 -0.206600747
43 44 45 46 47 48
0.035150513 0.438450887 0.477725770 0.252151166 -0.310061532 -0.639929692
49 50 51 52 53 54
-0.738608164 -0.471116247 -0.274746219 -0.204943978 -0.361874324 -0.472929039
55 56 57 58 59 60
-0.490584502 -0.626888610 -0.508404763 -0.131012980 0.246378804 0.375917366
61 62 63 64 65 66
0.098029931 -0.274082634 -0.536130533 -0.504943978 -0.100490010 0.070630625
67 68 69 70 71 72
0.115348272 0.019637442 -0.080692157 -0.082509337 -0.205117553 -0.134985714
73
-0.093070908
> postscript(file="/var/www/html/rcomp/tmp/6aq8p1258623759.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 0.621787354 NA
1 0.810070308 0.621787354
2 1.006440336 0.810070308
3 1.016835854 1.006440336
4 0.960894304 1.016835854
5 0.791421662 0.960894304
6 0.375743791 0.791421662
7 -0.061549113 0.375743791
8 -0.160889916 -0.061549113
9 0.036304108 -0.160889916
10 0.791916060 0.036304108
11 1.063036695 0.791916060
12 0.888115647 1.063036695
13 0.238771710 0.888115647
14 -0.084660503 0.238771710
15 -0.134660503 -0.084660503
16 -0.132184126 -0.134660503
17 -0.101656769 -0.132184126
18 -0.057927917 -0.101656769
19 0.004779179 -0.057927917
20 0.105438376 0.004779179
21 -0.037960877 0.105438376
22 -0.021953407 -0.037960877
23 0.007585155 -0.021953407
24 -0.088126930 0.007585155
25 -0.020635013 -0.088126930
26 0.036130533 -0.020635013
27 -0.013869467 0.036130533
28 -0.070799812 -0.013869467
29 -0.080865732 -0.070799812
30 0.022269842 -0.080865732
31 0.225570216 0.022269842
32 0.166822690 0.225570216
33 -0.036972081 0.166822690
34 -0.501162371 -0.036972081
35 -0.671623809 -0.501162371
36 -0.688126930 -0.671623809
37 -0.283008123 -0.688126930
38 -0.147033613 -0.283008123
39 -0.158417927 -0.147033613
40 -0.295546032 -0.158417927
41 -0.206600747 -0.295546032
42 0.035150513 -0.206600747
43 0.438450887 0.035150513
44 0.477725770 0.438450887
45 0.252151166 0.477725770
46 -0.310061532 0.252151166
47 -0.639929692 -0.310061532
48 -0.738608164 -0.639929692
49 -0.471116247 -0.738608164
50 -0.274746219 -0.471116247
51 -0.204943978 -0.274746219
52 -0.361874324 -0.204943978
53 -0.472929039 -0.361874324
54 -0.490584502 -0.472929039
55 -0.626888610 -0.490584502
56 -0.508404763 -0.626888610
57 -0.131012980 -0.508404763
58 0.246378804 -0.131012980
59 0.375917366 0.246378804
60 0.098029931 0.375917366
61 -0.274082634 0.098029931
62 -0.536130533 -0.274082634
63 -0.504943978 -0.536130533
64 -0.100490010 -0.504943978
65 0.070630625 -0.100490010
66 0.115348272 0.070630625
67 0.019637442 0.115348272
68 -0.080692157 0.019637442
69 -0.082509337 -0.080692157
70 -0.205117553 -0.082509337
71 -0.134985714 -0.205117553
72 -0.093070908 -0.134985714
73 NA -0.093070908
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.810070308 0.621787354
[2,] 1.006440336 0.810070308
[3,] 1.016835854 1.006440336
[4,] 0.960894304 1.016835854
[5,] 0.791421662 0.960894304
[6,] 0.375743791 0.791421662
[7,] -0.061549113 0.375743791
[8,] -0.160889916 -0.061549113
[9,] 0.036304108 -0.160889916
[10,] 0.791916060 0.036304108
[11,] 1.063036695 0.791916060
[12,] 0.888115647 1.063036695
[13,] 0.238771710 0.888115647
[14,] -0.084660503 0.238771710
[15,] -0.134660503 -0.084660503
[16,] -0.132184126 -0.134660503
[17,] -0.101656769 -0.132184126
[18,] -0.057927917 -0.101656769
[19,] 0.004779179 -0.057927917
[20,] 0.105438376 0.004779179
[21,] -0.037960877 0.105438376
[22,] -0.021953407 -0.037960877
[23,] 0.007585155 -0.021953407
[24,] -0.088126930 0.007585155
[25,] -0.020635013 -0.088126930
[26,] 0.036130533 -0.020635013
[27,] -0.013869467 0.036130533
[28,] -0.070799812 -0.013869467
[29,] -0.080865732 -0.070799812
[30,] 0.022269842 -0.080865732
[31,] 0.225570216 0.022269842
[32,] 0.166822690 0.225570216
[33,] -0.036972081 0.166822690
[34,] -0.501162371 -0.036972081
[35,] -0.671623809 -0.501162371
[36,] -0.688126930 -0.671623809
[37,] -0.283008123 -0.688126930
[38,] -0.147033613 -0.283008123
[39,] -0.158417927 -0.147033613
[40,] -0.295546032 -0.158417927
[41,] -0.206600747 -0.295546032
[42,] 0.035150513 -0.206600747
[43,] 0.438450887 0.035150513
[44,] 0.477725770 0.438450887
[45,] 0.252151166 0.477725770
[46,] -0.310061532 0.252151166
[47,] -0.639929692 -0.310061532
[48,] -0.738608164 -0.639929692
[49,] -0.471116247 -0.738608164
[50,] -0.274746219 -0.471116247
[51,] -0.204943978 -0.274746219
[52,] -0.361874324 -0.204943978
[53,] -0.472929039 -0.361874324
[54,] -0.490584502 -0.472929039
[55,] -0.626888610 -0.490584502
[56,] -0.508404763 -0.626888610
[57,] -0.131012980 -0.508404763
[58,] 0.246378804 -0.131012980
[59,] 0.375917366 0.246378804
[60,] 0.098029931 0.375917366
[61,] -0.274082634 0.098029931
[62,] -0.536130533 -0.274082634
[63,] -0.504943978 -0.536130533
[64,] -0.100490010 -0.504943978
[65,] 0.070630625 -0.100490010
[66,] 0.115348272 0.070630625
[67,] 0.019637442 0.115348272
[68,] -0.080692157 0.019637442
[69,] -0.082509337 -0.080692157
[70,] -0.205117553 -0.082509337
[71,] -0.134985714 -0.205117553
[72,] -0.093070908 -0.134985714
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.810070308 0.621787354
2 1.006440336 0.810070308
3 1.016835854 1.006440336
4 0.960894304 1.016835854
5 0.791421662 0.960894304
6 0.375743791 0.791421662
7 -0.061549113 0.375743791
8 -0.160889916 -0.061549113
9 0.036304108 -0.160889916
10 0.791916060 0.036304108
11 1.063036695 0.791916060
12 0.888115647 1.063036695
13 0.238771710 0.888115647
14 -0.084660503 0.238771710
15 -0.134660503 -0.084660503
16 -0.132184126 -0.134660503
17 -0.101656769 -0.132184126
18 -0.057927917 -0.101656769
19 0.004779179 -0.057927917
20 0.105438376 0.004779179
21 -0.037960877 0.105438376
22 -0.021953407 -0.037960877
23 0.007585155 -0.021953407
24 -0.088126930 0.007585155
25 -0.020635013 -0.088126930
26 0.036130533 -0.020635013
27 -0.013869467 0.036130533
28 -0.070799812 -0.013869467
29 -0.080865732 -0.070799812
30 0.022269842 -0.080865732
31 0.225570216 0.022269842
32 0.166822690 0.225570216
33 -0.036972081 0.166822690
34 -0.501162371 -0.036972081
35 -0.671623809 -0.501162371
36 -0.688126930 -0.671623809
37 -0.283008123 -0.688126930
38 -0.147033613 -0.283008123
39 -0.158417927 -0.147033613
40 -0.295546032 -0.158417927
41 -0.206600747 -0.295546032
42 0.035150513 -0.206600747
43 0.438450887 0.035150513
44 0.477725770 0.438450887
45 0.252151166 0.477725770
46 -0.310061532 0.252151166
47 -0.639929692 -0.310061532
48 -0.738608164 -0.639929692
49 -0.471116247 -0.738608164
50 -0.274746219 -0.471116247
51 -0.204943978 -0.274746219
52 -0.361874324 -0.204943978
53 -0.472929039 -0.361874324
54 -0.490584502 -0.472929039
55 -0.626888610 -0.490584502
56 -0.508404763 -0.626888610
57 -0.131012980 -0.508404763
58 0.246378804 -0.131012980
59 0.375917366 0.246378804
60 0.098029931 0.375917366
61 -0.274082634 0.098029931
62 -0.536130533 -0.274082634
63 -0.504943978 -0.536130533
64 -0.100490010 -0.504943978
65 0.070630625 -0.100490010
66 0.115348272 0.070630625
67 0.019637442 0.115348272
68 -0.080692157 0.019637442
69 -0.082509337 -0.080692157
70 -0.205117553 -0.082509337
71 -0.134985714 -0.205117553
72 -0.093070908 -0.134985714
> 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/7hks61258623759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z6jm1258623759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rqum1258623759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10uspz1258623759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tag61258623759.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/12jhrd1258623759.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/13cme71258623760.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/14xn911258623760.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/15ro9q1258623760.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/16itdz1258623760.tab")
+ }
>
> system("convert tmp/10uz11258623759.ps tmp/10uz11258623759.png")
> system("convert tmp/28vr41258623759.ps tmp/28vr41258623759.png")
> system("convert tmp/3i8pc1258623759.ps tmp/3i8pc1258623759.png")
> system("convert tmp/4idft1258623759.ps tmp/4idft1258623759.png")
> system("convert tmp/5u6cw1258623759.ps tmp/5u6cw1258623759.png")
> system("convert tmp/6aq8p1258623759.ps tmp/6aq8p1258623759.png")
> system("convert tmp/7hks61258623759.ps tmp/7hks61258623759.png")
> system("convert tmp/8z6jm1258623759.ps tmp/8z6jm1258623759.png")
> system("convert tmp/9rqum1258623759.ps tmp/9rqum1258623759.png")
> system("convert tmp/10uspz1258623759.ps tmp/10uspz1258623759.png")
>
>
> proc.time()
user system elapsed
2.526 1.604 3.314