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(96.8,9.3,114.1,9.3,110.3,8.7,103.9,8.2,101.6,8.3,94.6,8.5,95.9,8.6,104.7,8.5,102.8,8.2,98.1,8.1,113.9,7.9,80.9,8.6,95.7,8.7,113.2,8.7,105.9,8.5,108.8,8.4,102.3,8.5,99,8.7,100.7,8.7,115.5,8.6,100.7,8.5,109.9,8.3,114.6,8,85.4,8.2,100.5,8.1,114.8,8.1,116.5,8,112.9,7.9,102,7.9,106,8,105.3,8,118.8,7.9,106.1,8,109.3,7.7,117.2,7.2,92.5,7.5,104.2,7.3,112.5,7,122.4,7,113.3,7,100,7.2,110.7,7.3,112.8,7.1,109.8,6.8,117.3,6.4,109.1,6.1,115.9,6.5,96,7.7,99.8,7.9,116.8,7.5,115.7,6.9,99.4,6.6,94.3,6.9,91,7.7,93.2,8,103.1,8,94.1,7.7,91.8,7.3,102.7,7.4,82.6,8.1,89.1,8.3),dim=c(2,61),dimnames=list(c('tip','wrk'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('tip','wrk'),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
tip wrk M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 96.8 9.3 1 0 0 0 0 0 0 0 0 0 0
2 114.1 9.3 0 1 0 0 0 0 0 0 0 0 0
3 110.3 8.7 0 0 1 0 0 0 0 0 0 0 0
4 103.9 8.2 0 0 0 1 0 0 0 0 0 0 0
5 101.6 8.3 0 0 0 0 1 0 0 0 0 0 0
6 94.6 8.5 0 0 0 0 0 1 0 0 0 0 0
7 95.9 8.6 0 0 0 0 0 0 1 0 0 0 0
8 104.7 8.5 0 0 0 0 0 0 0 1 0 0 0
9 102.8 8.2 0 0 0 0 0 0 0 0 1 0 0
10 98.1 8.1 0 0 0 0 0 0 0 0 0 1 0
11 113.9 7.9 0 0 0 0 0 0 0 0 0 0 1
12 80.9 8.6 0 0 0 0 0 0 0 0 0 0 0
13 95.7 8.7 1 0 0 0 0 0 0 0 0 0 0
14 113.2 8.7 0 1 0 0 0 0 0 0 0 0 0
15 105.9 8.5 0 0 1 0 0 0 0 0 0 0 0
16 108.8 8.4 0 0 0 1 0 0 0 0 0 0 0
17 102.3 8.5 0 0 0 0 1 0 0 0 0 0 0
18 99.0 8.7 0 0 0 0 0 1 0 0 0 0 0
19 100.7 8.7 0 0 0 0 0 0 1 0 0 0 0
20 115.5 8.6 0 0 0 0 0 0 0 1 0 0 0
21 100.7 8.5 0 0 0 0 0 0 0 0 1 0 0
22 109.9 8.3 0 0 0 0 0 0 0 0 0 1 0
23 114.6 8.0 0 0 0 0 0 0 0 0 0 0 1
24 85.4 8.2 0 0 0 0 0 0 0 0 0 0 0
25 100.5 8.1 1 0 0 0 0 0 0 0 0 0 0
26 114.8 8.1 0 1 0 0 0 0 0 0 0 0 0
27 116.5 8.0 0 0 1 0 0 0 0 0 0 0 0
28 112.9 7.9 0 0 0 1 0 0 0 0 0 0 0
29 102.0 7.9 0 0 0 0 1 0 0 0 0 0 0
30 106.0 8.0 0 0 0 0 0 1 0 0 0 0 0
31 105.3 8.0 0 0 0 0 0 0 1 0 0 0 0
32 118.8 7.9 0 0 0 0 0 0 0 1 0 0 0
33 106.1 8.0 0 0 0 0 0 0 0 0 1 0 0
34 109.3 7.7 0 0 0 0 0 0 0 0 0 1 0
35 117.2 7.2 0 0 0 0 0 0 0 0 0 0 1
36 92.5 7.5 0 0 0 0 0 0 0 0 0 0 0
37 104.2 7.3 1 0 0 0 0 0 0 0 0 0 0
38 112.5 7.0 0 1 0 0 0 0 0 0 0 0 0
39 122.4 7.0 0 0 1 0 0 0 0 0 0 0 0
40 113.3 7.0 0 0 0 1 0 0 0 0 0 0 0
41 100.0 7.2 0 0 0 0 1 0 0 0 0 0 0
42 110.7 7.3 0 0 0 0 0 1 0 0 0 0 0
43 112.8 7.1 0 0 0 0 0 0 1 0 0 0 0
44 109.8 6.8 0 0 0 0 0 0 0 1 0 0 0
45 117.3 6.4 0 0 0 0 0 0 0 0 1 0 0
46 109.1 6.1 0 0 0 0 0 0 0 0 0 1 0
47 115.9 6.5 0 0 0 0 0 0 0 0 0 0 1
48 96.0 7.7 0 0 0 0 0 0 0 0 0 0 0
49 99.8 7.9 1 0 0 0 0 0 0 0 0 0 0
50 116.8 7.5 0 1 0 0 0 0 0 0 0 0 0
51 115.7 6.9 0 0 1 0 0 0 0 0 0 0 0
52 99.4 6.6 0 0 0 1 0 0 0 0 0 0 0
53 94.3 6.9 0 0 0 0 1 0 0 0 0 0 0
54 91.0 7.7 0 0 0 0 0 1 0 0 0 0 0
55 93.2 8.0 0 0 0 0 0 0 1 0 0 0 0
56 103.1 8.0 0 0 0 0 0 0 0 1 0 0 0
57 94.1 7.7 0 0 0 0 0 0 0 0 1 0 0
58 91.8 7.3 0 0 0 0 0 0 0 0 0 1 0
59 102.7 7.4 0 0 0 0 0 0 0 0 0 0 1
60 82.6 8.1 0 0 0 0 0 0 0 0 0 0 0
61 89.1 8.3 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) wrk M1 M2 M3 M4
109.46 -2.74 10.88 27.07 26.13 19.08
M5 M6 M7 M8 M9 M10
11.85 12.83 14.26 22.74 16.01 14.74
M11
23.68
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.388 -4.200 0.819 3.595 9.373
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 109.456 10.200 10.731 2.39e-14 ***
wrk -2.740 1.224 -2.239 0.029820 *
M1 10.879 3.773 2.883 0.005875 **
M2 27.074 3.930 6.888 1.09e-08 ***
M3 26.132 3.936 6.639 2.63e-08 ***
M4 19.084 3.959 4.821 1.48e-05 ***
M5 11.848 3.941 3.006 0.004202 **
M6 12.835 3.929 3.267 0.002011 **
M7 14.264 3.929 3.630 0.000686 ***
M8 22.736 3.929 5.786 5.31e-07 ***
M9 16.008 3.941 4.061 0.000179 ***
M10 14.735 3.980 3.703 0.000550 ***
M11 23.681 4.001 5.919 3.34e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.211 on 48 degrees of freedom
Multiple R-squared: 0.6648, Adjusted R-squared: 0.581
F-statistic: 7.934 on 12 and 48 DF, p-value: 6.916e-08
> 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,] 0.047385412 0.094770824 0.9526146
[2,] 0.012849517 0.025699035 0.9871505
[3,] 0.006426104 0.012852208 0.9935739
[4,] 0.004119542 0.008239084 0.9958805
[5,] 0.022710884 0.045421768 0.9772891
[6,] 0.012082773 0.024165546 0.9879172
[7,] 0.029979890 0.059959779 0.9700201
[8,] 0.015327253 0.030654505 0.9846727
[9,] 0.012775500 0.025551001 0.9872245
[10,] 0.010883008 0.021766016 0.9891170
[11,] 0.005268607 0.010537214 0.9947314
[12,] 0.005802512 0.011605023 0.9941975
[13,] 0.005944237 0.011888474 0.9940558
[14,] 0.003779479 0.007558959 0.9962205
[15,] 0.004418851 0.008837703 0.9955811
[16,] 0.002877123 0.005754246 0.9971229
[17,] 0.005110496 0.010220992 0.9948895
[18,] 0.003678270 0.007356541 0.9963217
[19,] 0.013184570 0.026369140 0.9868154
[20,] 0.014402997 0.028805993 0.9855970
[21,] 0.009126132 0.018252263 0.9908739
[22,] 0.004741723 0.009483446 0.9952583
[23,] 0.012718609 0.025437219 0.9872814
[24,] 0.012172707 0.024345414 0.9878273
[25,] 0.073938168 0.147876337 0.9260618
[26,] 0.116927702 0.233855403 0.8830723
[27,] 0.318728945 0.637457891 0.6812711
[28,] 0.323988678 0.647977357 0.6760113
[29,] 0.754643167 0.490713667 0.2453568
[30,] 0.614914766 0.770170468 0.3850852
> postscript(file="/var/www/html/rcomp/tmp/1rpb71258649067.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rha21258649067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qjc41258649067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48usq1258649067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nd601258649067.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
1.9482118 3.0534419 -1.4486196 -2.1706811 3.0397107 -4.3995057
7 8 9 10 11 12
-4.2550934 -4.2002893 -0.1943098 -3.8958770 2.4101025 -4.9906811
13 14 15 16 17 18
-0.7959112 0.5093189 -6.3966606 3.2773599 4.2877517 0.5485353
19 20 21 22 23 24
0.8189271 6.8737312 -1.4722483 8.4521640 3.3841230 -1.5867631
25 26 27 28 29 30
2.3599658 0.4651959 2.8332369 6.0072574 2.3436287 5.6303918
31 32 33 34 35 36
3.5007836 8.2555877 2.5576492 6.2080410 3.7919590 3.5950934
37 38 39 40 41 42
3.8678019 -4.8490296 5.9930319 3.9410729 -1.5745148 8.4122483
43 44 45 46 47 48
8.5345991 -3.7586378 9.3733213 1.6237131 0.5738155 7.6431344
49 50 51 52 53 54
1.1119248 0.8210729 -0.9809886 -11.0550091 -8.0965763 -10.1916697
55 56 57 58 59 60
-8.5992164 -7.1703918 -10.2644123 -12.3880410 -10.1600000 -4.6607836
61
-8.4919932
> postscript(file="/var/www/html/rcomp/tmp/6uih81258649067.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 1.9482118 NA
1 3.0534419 1.9482118
2 -1.4486196 3.0534419
3 -2.1706811 -1.4486196
4 3.0397107 -2.1706811
5 -4.3995057 3.0397107
6 -4.2550934 -4.3995057
7 -4.2002893 -4.2550934
8 -0.1943098 -4.2002893
9 -3.8958770 -0.1943098
10 2.4101025 -3.8958770
11 -4.9906811 2.4101025
12 -0.7959112 -4.9906811
13 0.5093189 -0.7959112
14 -6.3966606 0.5093189
15 3.2773599 -6.3966606
16 4.2877517 3.2773599
17 0.5485353 4.2877517
18 0.8189271 0.5485353
19 6.8737312 0.8189271
20 -1.4722483 6.8737312
21 8.4521640 -1.4722483
22 3.3841230 8.4521640
23 -1.5867631 3.3841230
24 2.3599658 -1.5867631
25 0.4651959 2.3599658
26 2.8332369 0.4651959
27 6.0072574 2.8332369
28 2.3436287 6.0072574
29 5.6303918 2.3436287
30 3.5007836 5.6303918
31 8.2555877 3.5007836
32 2.5576492 8.2555877
33 6.2080410 2.5576492
34 3.7919590 6.2080410
35 3.5950934 3.7919590
36 3.8678019 3.5950934
37 -4.8490296 3.8678019
38 5.9930319 -4.8490296
39 3.9410729 5.9930319
40 -1.5745148 3.9410729
41 8.4122483 -1.5745148
42 8.5345991 8.4122483
43 -3.7586378 8.5345991
44 9.3733213 -3.7586378
45 1.6237131 9.3733213
46 0.5738155 1.6237131
47 7.6431344 0.5738155
48 1.1119248 7.6431344
49 0.8210729 1.1119248
50 -0.9809886 0.8210729
51 -11.0550091 -0.9809886
52 -8.0965763 -11.0550091
53 -10.1916697 -8.0965763
54 -8.5992164 -10.1916697
55 -7.1703918 -8.5992164
56 -10.2644123 -7.1703918
57 -12.3880410 -10.2644123
58 -10.1600000 -12.3880410
59 -4.6607836 -10.1600000
60 -8.4919932 -4.6607836
61 NA -8.4919932
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.0534419 1.9482118
[2,] -1.4486196 3.0534419
[3,] -2.1706811 -1.4486196
[4,] 3.0397107 -2.1706811
[5,] -4.3995057 3.0397107
[6,] -4.2550934 -4.3995057
[7,] -4.2002893 -4.2550934
[8,] -0.1943098 -4.2002893
[9,] -3.8958770 -0.1943098
[10,] 2.4101025 -3.8958770
[11,] -4.9906811 2.4101025
[12,] -0.7959112 -4.9906811
[13,] 0.5093189 -0.7959112
[14,] -6.3966606 0.5093189
[15,] 3.2773599 -6.3966606
[16,] 4.2877517 3.2773599
[17,] 0.5485353 4.2877517
[18,] 0.8189271 0.5485353
[19,] 6.8737312 0.8189271
[20,] -1.4722483 6.8737312
[21,] 8.4521640 -1.4722483
[22,] 3.3841230 8.4521640
[23,] -1.5867631 3.3841230
[24,] 2.3599658 -1.5867631
[25,] 0.4651959 2.3599658
[26,] 2.8332369 0.4651959
[27,] 6.0072574 2.8332369
[28,] 2.3436287 6.0072574
[29,] 5.6303918 2.3436287
[30,] 3.5007836 5.6303918
[31,] 8.2555877 3.5007836
[32,] 2.5576492 8.2555877
[33,] 6.2080410 2.5576492
[34,] 3.7919590 6.2080410
[35,] 3.5950934 3.7919590
[36,] 3.8678019 3.5950934
[37,] -4.8490296 3.8678019
[38,] 5.9930319 -4.8490296
[39,] 3.9410729 5.9930319
[40,] -1.5745148 3.9410729
[41,] 8.4122483 -1.5745148
[42,] 8.5345991 8.4122483
[43,] -3.7586378 8.5345991
[44,] 9.3733213 -3.7586378
[45,] 1.6237131 9.3733213
[46,] 0.5738155 1.6237131
[47,] 7.6431344 0.5738155
[48,] 1.1119248 7.6431344
[49,] 0.8210729 1.1119248
[50,] -0.9809886 0.8210729
[51,] -11.0550091 -0.9809886
[52,] -8.0965763 -11.0550091
[53,] -10.1916697 -8.0965763
[54,] -8.5992164 -10.1916697
[55,] -7.1703918 -8.5992164
[56,] -10.2644123 -7.1703918
[57,] -12.3880410 -10.2644123
[58,] -10.1600000 -12.3880410
[59,] -4.6607836 -10.1600000
[60,] -8.4919932 -4.6607836
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.0534419 1.9482118
2 -1.4486196 3.0534419
3 -2.1706811 -1.4486196
4 3.0397107 -2.1706811
5 -4.3995057 3.0397107
6 -4.2550934 -4.3995057
7 -4.2002893 -4.2550934
8 -0.1943098 -4.2002893
9 -3.8958770 -0.1943098
10 2.4101025 -3.8958770
11 -4.9906811 2.4101025
12 -0.7959112 -4.9906811
13 0.5093189 -0.7959112
14 -6.3966606 0.5093189
15 3.2773599 -6.3966606
16 4.2877517 3.2773599
17 0.5485353 4.2877517
18 0.8189271 0.5485353
19 6.8737312 0.8189271
20 -1.4722483 6.8737312
21 8.4521640 -1.4722483
22 3.3841230 8.4521640
23 -1.5867631 3.3841230
24 2.3599658 -1.5867631
25 0.4651959 2.3599658
26 2.8332369 0.4651959
27 6.0072574 2.8332369
28 2.3436287 6.0072574
29 5.6303918 2.3436287
30 3.5007836 5.6303918
31 8.2555877 3.5007836
32 2.5576492 8.2555877
33 6.2080410 2.5576492
34 3.7919590 6.2080410
35 3.5950934 3.7919590
36 3.8678019 3.5950934
37 -4.8490296 3.8678019
38 5.9930319 -4.8490296
39 3.9410729 5.9930319
40 -1.5745148 3.9410729
41 8.4122483 -1.5745148
42 8.5345991 8.4122483
43 -3.7586378 8.5345991
44 9.3733213 -3.7586378
45 1.6237131 9.3733213
46 0.5738155 1.6237131
47 7.6431344 0.5738155
48 1.1119248 7.6431344
49 0.8210729 1.1119248
50 -0.9809886 0.8210729
51 -11.0550091 -0.9809886
52 -8.0965763 -11.0550091
53 -10.1916697 -8.0965763
54 -8.5992164 -10.1916697
55 -7.1703918 -8.5992164
56 -10.2644123 -7.1703918
57 -12.3880410 -10.2644123
58 -10.1600000 -12.3880410
59 -4.6607836 -10.1600000
60 -8.4919932 -4.6607836
> 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/75aiu1258649067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8w21a1258649067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/984p21258649067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nseu1258649067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11e5vr1258649067.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/12liqo1258649067.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/13ycvf1258649067.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/14q6ts1258649067.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/15k8871258649067.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/1669po1258649067.tab")
+ }
> system("convert tmp/1rpb71258649067.ps tmp/1rpb71258649067.png")
> system("convert tmp/2rha21258649067.ps tmp/2rha21258649067.png")
> system("convert tmp/3qjc41258649067.ps tmp/3qjc41258649067.png")
> system("convert tmp/48usq1258649067.ps tmp/48usq1258649067.png")
> system("convert tmp/5nd601258649067.ps tmp/5nd601258649067.png")
> system("convert tmp/6uih81258649067.ps tmp/6uih81258649067.png")
> system("convert tmp/75aiu1258649067.ps tmp/75aiu1258649067.png")
> system("convert tmp/8w21a1258649067.ps tmp/8w21a1258649067.png")
> system("convert tmp/984p21258649067.ps tmp/984p21258649067.png")
> system("convert tmp/10nseu1258649067.ps tmp/10nseu1258649067.png")
>
>
> proc.time()
user system elapsed
2.445 1.565 2.872