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(7.3,7.9,7.6,9.1,7.5,9.4,7.6,9.4,7.9,9.1,7.9,9,8.1,9.3,8.2,9.9,8,9.8,7.5,9.3,6.8,8.3,6.5,8,6.6,8.5,7.6,10.4,8,11.1,8.1,10.9,7.7,10,7.5,9.2,7.6,9.2,7.8,9.5,7.8,9.6,7.8,9.5,7.5,9.1,7.5,8.9,7.1,9,7.5,10.1,7.5,10.3,7.6,10.2,7.7,9.6,7.7,9.2,7.9,9.3,8.1,9.4,8.2,9.4,8.2,9.2,8.2,9,7.9,9,7.3,9,6.9,9.8,6.6,10,6.7,9.8,6.9,9.3,7,9,7.1,9,7.2,9.1,7.1,9.1,6.9,9.1,7,9.2,6.8,8.8,6.4,8.3,6.7,8.4,6.6,8.1,6.4,7.7,6.3,7.9,6.2,7.9,6.5,8,6.8,7.9,6.8,7.6,6.4,7.1,6.1,6.8,5.8,6.5,6.1,6.9,7.2,8.2,7.3,8.7,6.9,8.3,6.1,7.9,5.8,7.5,6.2,7.8,7.1,8.3,7.7,8.4,7.9,8.2,7.7,7.7,7.4,7.2,7.5,7.3),dim=c(2,73),dimnames=list(c('WGM','WGV'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('WGM','WGV'),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 = 'Do not include Seasonal 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
WGM WGV
1 7.3 7.9
2 7.6 9.1
3 7.5 9.4
4 7.6 9.4
5 7.9 9.1
6 7.9 9.0
7 8.1 9.3
8 8.2 9.9
9 8.0 9.8
10 7.5 9.3
11 6.8 8.3
12 6.5 8.0
13 6.6 8.5
14 7.6 10.4
15 8.0 11.1
16 8.1 10.9
17 7.7 10.0
18 7.5 9.2
19 7.6 9.2
20 7.8 9.5
21 7.8 9.6
22 7.8 9.5
23 7.5 9.1
24 7.5 8.9
25 7.1 9.0
26 7.5 10.1
27 7.5 10.3
28 7.6 10.2
29 7.7 9.6
30 7.7 9.2
31 7.9 9.3
32 8.1 9.4
33 8.2 9.4
34 8.2 9.2
35 8.2 9.0
36 7.9 9.0
37 7.3 9.0
38 6.9 9.8
39 6.6 10.0
40 6.7 9.8
41 6.9 9.3
42 7.0 9.0
43 7.1 9.0
44 7.2 9.1
45 7.1 9.1
46 6.9 9.1
47 7.0 9.2
48 6.8 8.8
49 6.4 8.3
50 6.7 8.4
51 6.6 8.1
52 6.4 7.7
53 6.3 7.9
54 6.2 7.9
55 6.5 8.0
56 6.8 7.9
57 6.8 7.6
58 6.4 7.1
59 6.1 6.8
60 5.8 6.5
61 6.1 6.9
62 7.2 8.2
63 7.3 8.7
64 6.9 8.3
65 6.1 7.9
66 5.8 7.5
67 6.2 7.8
68 7.1 8.3
69 7.7 8.4
70 7.9 8.2
71 7.7 7.7
72 7.4 7.2
73 7.5 7.3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) WGV
3.3697 0.4370
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.14013 -0.34086 -0.03347 0.27839 0.96507
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.3697 0.5358 6.289 2.30e-08 ***
WGV 0.4370 0.0604 7.236 4.32e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4931 on 71 degrees of freedom
Multiple R-squared: 0.4245, Adjusted R-squared: 0.4164
F-statistic: 52.37 on 1 and 71 DF, p-value: 4.319e-10
> 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,] 0.0787070370 0.1574140740 0.92129296
[2,] 0.0658207654 0.1316415309 0.93417923
[3,] 0.0739120516 0.1478241031 0.92608795
[4,] 0.0455554816 0.0911109631 0.95444452
[5,] 0.0205581783 0.0411163566 0.97944182
[6,] 0.0173843932 0.0347687864 0.98261561
[7,] 0.0390512711 0.0781025423 0.96094873
[8,] 0.0546145793 0.1092291586 0.94538542
[9,] 0.0897142756 0.1794285512 0.91028572
[10,] 0.1674387134 0.3348774268 0.83256129
[11,] 0.1582959437 0.3165918874 0.84170406
[12,] 0.1121573159 0.2243146318 0.88784268
[13,] 0.0774244905 0.1548489811 0.92257551
[14,] 0.0503187797 0.1006375594 0.94968122
[15,] 0.0326718122 0.0653436245 0.96732819
[16,] 0.0219962366 0.0439924732 0.97800376
[17,] 0.0139993753 0.0279987505 0.98600062
[18,] 0.0090868444 0.0181736889 0.99091316
[19,] 0.0052618237 0.0105236473 0.99473818
[20,] 0.0031051767 0.0062103535 0.99689482
[21,] 0.0024763820 0.0049527639 0.99752362
[22,] 0.0021095837 0.0042191674 0.99789042
[23,] 0.0020072711 0.0040145422 0.99799273
[24,] 0.0013251304 0.0026502607 0.99867487
[25,] 0.0007297308 0.0014594617 0.99927027
[26,] 0.0004736113 0.0009472225 0.99952639
[27,] 0.0004543748 0.0009087496 0.99954563
[28,] 0.0007760161 0.0015520322 0.99922398
[29,] 0.0019518791 0.0039037582 0.99804812
[30,] 0.0061979705 0.0123959409 0.99380203
[31,] 0.0233427980 0.0466855960 0.97665720
[32,] 0.0346713831 0.0693427661 0.96532862
[33,] 0.0278451660 0.0556903320 0.97215483
[34,] 0.0522469639 0.1044939278 0.94775304
[35,] 0.1668631819 0.3337263639 0.83313682
[36,] 0.2712131472 0.5424262943 0.72878685
[37,] 0.2770484179 0.5540968359 0.72295158
[38,] 0.2470373760 0.4940747520 0.75296262
[39,] 0.2074596552 0.4149193104 0.79254034
[40,] 0.1672484491 0.3344968982 0.83275155
[41,] 0.1365724026 0.2731448052 0.86342760
[42,] 0.1266461838 0.2532923676 0.87335382
[43,] 0.1110966299 0.2221932598 0.88890337
[44,] 0.1043731574 0.2087463147 0.89562684
[45,] 0.1276106048 0.2552212096 0.87238940
[46,] 0.1137489218 0.2274978436 0.88625108
[47,] 0.0970657855 0.1941315709 0.90293421
[48,] 0.0814070613 0.1628141226 0.91859294
[49,] 0.0852419526 0.1704839052 0.91475805
[50,] 0.1052895744 0.2105791488 0.89471043
[51,] 0.0960557768 0.1921115537 0.90394422
[52,] 0.0682568784 0.1365137569 0.93174312
[53,] 0.0462891920 0.0925783840 0.95371081
[54,] 0.0296596366 0.0593192733 0.97034036
[55,] 0.0190165788 0.0380331576 0.98098342
[56,] 0.0142378379 0.0284756757 0.98576216
[57,] 0.0115653101 0.0231306201 0.98843469
[58,] 0.0067138033 0.0134276067 0.99328620
[59,] 0.0034813399 0.0069626798 0.99651866
[60,] 0.0018601956 0.0037203912 0.99813980
[61,] 0.0061520311 0.0123040622 0.99384797
[62,] 0.0900404694 0.1800809389 0.90995953
[63,] 0.7352541985 0.5294916031 0.26474580
[64,] 0.9714032883 0.0571934233 0.02859671
> postscript(file="/var/www/html/rcomp/tmp/1kuiu1258730665.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2tgwv1258730665.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3f2te1258730665.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mt841258730665.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cndd1258730665.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
0.477662696 0.253209539 0.022096249 0.122096249 0.553209539 0.596913968
7 8 9 10 11 12
0.665800679 0.503574100 0.347278530 0.065800679 -0.197155023 -0.366041733
13 14 15 16 17 18
-0.484563883 -0.314948049 -0.220879058 -0.033470198 -0.040130330 0.109505109
19 20 21 22 23 24
0.209505109 0.278391819 0.234687390 0.278391819 0.153209539 0.240618398
25 26 27 28 29 30
-0.203086032 -0.283834760 -0.371243619 -0.227539189 0.134687390 0.309505109
31 32 33 34 35 36
0.465800679 0.622096249 0.722096249 0.809505109 0.896913968 0.596913968
37 38 39 40 41 42
-0.003086032 -0.752721470 -1.140130330 -0.952721470 -0.534199321 -0.303086032
43 44 45 46 47 48
-0.203086032 -0.146790461 -0.246790461 -0.446790461 -0.390494891 -0.415677172
49 50 51 52 53 54
-0.597155023 -0.340859453 -0.309746163 -0.334928444 -0.522337304 -0.622337304
55 56 57 58 59 60
-0.366041733 -0.022337304 0.108775986 -0.072701865 -0.241588576 -0.410475286
61 62 63 64 65 66
-0.285293006 0.246549407 0.128027258 -0.097155023 -0.722337304 -0.847519584
67 68 69 70 71 72
-0.578632874 0.102844977 0.659140547 0.946549407 0.965071556 0.883593705
73
0.939889275
> postscript(file="/var/www/html/rcomp/tmp/6t7ku1258730665.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 0.477662696 NA
1 0.253209539 0.477662696
2 0.022096249 0.253209539
3 0.122096249 0.022096249
4 0.553209539 0.122096249
5 0.596913968 0.553209539
6 0.665800679 0.596913968
7 0.503574100 0.665800679
8 0.347278530 0.503574100
9 0.065800679 0.347278530
10 -0.197155023 0.065800679
11 -0.366041733 -0.197155023
12 -0.484563883 -0.366041733
13 -0.314948049 -0.484563883
14 -0.220879058 -0.314948049
15 -0.033470198 -0.220879058
16 -0.040130330 -0.033470198
17 0.109505109 -0.040130330
18 0.209505109 0.109505109
19 0.278391819 0.209505109
20 0.234687390 0.278391819
21 0.278391819 0.234687390
22 0.153209539 0.278391819
23 0.240618398 0.153209539
24 -0.203086032 0.240618398
25 -0.283834760 -0.203086032
26 -0.371243619 -0.283834760
27 -0.227539189 -0.371243619
28 0.134687390 -0.227539189
29 0.309505109 0.134687390
30 0.465800679 0.309505109
31 0.622096249 0.465800679
32 0.722096249 0.622096249
33 0.809505109 0.722096249
34 0.896913968 0.809505109
35 0.596913968 0.896913968
36 -0.003086032 0.596913968
37 -0.752721470 -0.003086032
38 -1.140130330 -0.752721470
39 -0.952721470 -1.140130330
40 -0.534199321 -0.952721470
41 -0.303086032 -0.534199321
42 -0.203086032 -0.303086032
43 -0.146790461 -0.203086032
44 -0.246790461 -0.146790461
45 -0.446790461 -0.246790461
46 -0.390494891 -0.446790461
47 -0.415677172 -0.390494891
48 -0.597155023 -0.415677172
49 -0.340859453 -0.597155023
50 -0.309746163 -0.340859453
51 -0.334928444 -0.309746163
52 -0.522337304 -0.334928444
53 -0.622337304 -0.522337304
54 -0.366041733 -0.622337304
55 -0.022337304 -0.366041733
56 0.108775986 -0.022337304
57 -0.072701865 0.108775986
58 -0.241588576 -0.072701865
59 -0.410475286 -0.241588576
60 -0.285293006 -0.410475286
61 0.246549407 -0.285293006
62 0.128027258 0.246549407
63 -0.097155023 0.128027258
64 -0.722337304 -0.097155023
65 -0.847519584 -0.722337304
66 -0.578632874 -0.847519584
67 0.102844977 -0.578632874
68 0.659140547 0.102844977
69 0.946549407 0.659140547
70 0.965071556 0.946549407
71 0.883593705 0.965071556
72 0.939889275 0.883593705
73 NA 0.939889275
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.253209539 0.477662696
[2,] 0.022096249 0.253209539
[3,] 0.122096249 0.022096249
[4,] 0.553209539 0.122096249
[5,] 0.596913968 0.553209539
[6,] 0.665800679 0.596913968
[7,] 0.503574100 0.665800679
[8,] 0.347278530 0.503574100
[9,] 0.065800679 0.347278530
[10,] -0.197155023 0.065800679
[11,] -0.366041733 -0.197155023
[12,] -0.484563883 -0.366041733
[13,] -0.314948049 -0.484563883
[14,] -0.220879058 -0.314948049
[15,] -0.033470198 -0.220879058
[16,] -0.040130330 -0.033470198
[17,] 0.109505109 -0.040130330
[18,] 0.209505109 0.109505109
[19,] 0.278391819 0.209505109
[20,] 0.234687390 0.278391819
[21,] 0.278391819 0.234687390
[22,] 0.153209539 0.278391819
[23,] 0.240618398 0.153209539
[24,] -0.203086032 0.240618398
[25,] -0.283834760 -0.203086032
[26,] -0.371243619 -0.283834760
[27,] -0.227539189 -0.371243619
[28,] 0.134687390 -0.227539189
[29,] 0.309505109 0.134687390
[30,] 0.465800679 0.309505109
[31,] 0.622096249 0.465800679
[32,] 0.722096249 0.622096249
[33,] 0.809505109 0.722096249
[34,] 0.896913968 0.809505109
[35,] 0.596913968 0.896913968
[36,] -0.003086032 0.596913968
[37,] -0.752721470 -0.003086032
[38,] -1.140130330 -0.752721470
[39,] -0.952721470 -1.140130330
[40,] -0.534199321 -0.952721470
[41,] -0.303086032 -0.534199321
[42,] -0.203086032 -0.303086032
[43,] -0.146790461 -0.203086032
[44,] -0.246790461 -0.146790461
[45,] -0.446790461 -0.246790461
[46,] -0.390494891 -0.446790461
[47,] -0.415677172 -0.390494891
[48,] -0.597155023 -0.415677172
[49,] -0.340859453 -0.597155023
[50,] -0.309746163 -0.340859453
[51,] -0.334928444 -0.309746163
[52,] -0.522337304 -0.334928444
[53,] -0.622337304 -0.522337304
[54,] -0.366041733 -0.622337304
[55,] -0.022337304 -0.366041733
[56,] 0.108775986 -0.022337304
[57,] -0.072701865 0.108775986
[58,] -0.241588576 -0.072701865
[59,] -0.410475286 -0.241588576
[60,] -0.285293006 -0.410475286
[61,] 0.246549407 -0.285293006
[62,] 0.128027258 0.246549407
[63,] -0.097155023 0.128027258
[64,] -0.722337304 -0.097155023
[65,] -0.847519584 -0.722337304
[66,] -0.578632874 -0.847519584
[67,] 0.102844977 -0.578632874
[68,] 0.659140547 0.102844977
[69,] 0.946549407 0.659140547
[70,] 0.965071556 0.946549407
[71,] 0.883593705 0.965071556
[72,] 0.939889275 0.883593705
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.253209539 0.477662696
2 0.022096249 0.253209539
3 0.122096249 0.022096249
4 0.553209539 0.122096249
5 0.596913968 0.553209539
6 0.665800679 0.596913968
7 0.503574100 0.665800679
8 0.347278530 0.503574100
9 0.065800679 0.347278530
10 -0.197155023 0.065800679
11 -0.366041733 -0.197155023
12 -0.484563883 -0.366041733
13 -0.314948049 -0.484563883
14 -0.220879058 -0.314948049
15 -0.033470198 -0.220879058
16 -0.040130330 -0.033470198
17 0.109505109 -0.040130330
18 0.209505109 0.109505109
19 0.278391819 0.209505109
20 0.234687390 0.278391819
21 0.278391819 0.234687390
22 0.153209539 0.278391819
23 0.240618398 0.153209539
24 -0.203086032 0.240618398
25 -0.283834760 -0.203086032
26 -0.371243619 -0.283834760
27 -0.227539189 -0.371243619
28 0.134687390 -0.227539189
29 0.309505109 0.134687390
30 0.465800679 0.309505109
31 0.622096249 0.465800679
32 0.722096249 0.622096249
33 0.809505109 0.722096249
34 0.896913968 0.809505109
35 0.596913968 0.896913968
36 -0.003086032 0.596913968
37 -0.752721470 -0.003086032
38 -1.140130330 -0.752721470
39 -0.952721470 -1.140130330
40 -0.534199321 -0.952721470
41 -0.303086032 -0.534199321
42 -0.203086032 -0.303086032
43 -0.146790461 -0.203086032
44 -0.246790461 -0.146790461
45 -0.446790461 -0.246790461
46 -0.390494891 -0.446790461
47 -0.415677172 -0.390494891
48 -0.597155023 -0.415677172
49 -0.340859453 -0.597155023
50 -0.309746163 -0.340859453
51 -0.334928444 -0.309746163
52 -0.522337304 -0.334928444
53 -0.622337304 -0.522337304
54 -0.366041733 -0.622337304
55 -0.022337304 -0.366041733
56 0.108775986 -0.022337304
57 -0.072701865 0.108775986
58 -0.241588576 -0.072701865
59 -0.410475286 -0.241588576
60 -0.285293006 -0.410475286
61 0.246549407 -0.285293006
62 0.128027258 0.246549407
63 -0.097155023 0.128027258
64 -0.722337304 -0.097155023
65 -0.847519584 -0.722337304
66 -0.578632874 -0.847519584
67 0.102844977 -0.578632874
68 0.659140547 0.102844977
69 0.946549407 0.659140547
70 0.965071556 0.946549407
71 0.883593705 0.965071556
72 0.939889275 0.883593705
> 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/7plrl1258730665.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80gbl1258730665.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uvxc1258730665.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1067aj1258730665.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tfqt1258730665.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/124spw1258730665.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/136ha91258730665.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/14oep01258730665.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/156b8k1258730665.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/16n1fv1258730665.tab")
+ }
>
> system("convert tmp/1kuiu1258730665.ps tmp/1kuiu1258730665.png")
> system("convert tmp/2tgwv1258730665.ps tmp/2tgwv1258730665.png")
> system("convert tmp/3f2te1258730665.ps tmp/3f2te1258730665.png")
> system("convert tmp/4mt841258730665.ps tmp/4mt841258730665.png")
> system("convert tmp/5cndd1258730665.ps tmp/5cndd1258730665.png")
> system("convert tmp/6t7ku1258730665.ps tmp/6t7ku1258730665.png")
> system("convert tmp/7plrl1258730665.ps tmp/7plrl1258730665.png")
> system("convert tmp/80gbl1258730665.ps tmp/80gbl1258730665.png")
> system("convert tmp/9uvxc1258730665.ps tmp/9uvxc1258730665.png")
> system("convert tmp/1067aj1258730665.ps tmp/1067aj1258730665.png")
>
>
> proc.time()
user system elapsed
2.673 1.598 3.137