R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(94.6,116.1,95.9,107.5,104.7,116.7,102.8,112.5,98.1,113,113.9,126.4,80.9,114.1,95.7,112.5,113.2,112.4,105.9,113.1,108.8,116.3,102.3,111.7,99,118.8,100.7,116.5,115.5,125.1,100.7,113.1,109.9,119.6,114.6,114.4,85.4,114,100.5,117.8,114.8,117,116.5,120.9,112.9,115,102,117.3,106,119.4,105.3,114.9,118.8,125.8,106.1,117.6,109.3,117.6,117.2,114.9,92.5,121.9,104.2,117,112.5,106.4,122.4,110.5,113.3,113.6,100,114.2,110.7,125.4,112.8,124.6,109.8,120.2,117.3,120.8,109.1,111.4,115.9,124.1,96,120.2,99.8,125.5,116.8,116,115.7,117,99.4,105.7,94.3,102,91,106.4,93.2,96.9,103.1,107.6,94.1,98.8,91.8,101.1,102.7,105.7,82.6,104.6,89.1,103.2,104.5,101.6,105.1,106.7,95.1,99.5,88.7,101),dim=c(2,60),dimnames=list(c('T.I.P.','I.P.C.N.'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('T.I.P.','I.P.C.N.'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '2'
> #'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
> 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
I.P.C.N. T.I.P. M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 116.1 94.6 1 0 0 0 0 0 0 0 0 0 0 1
2 107.5 95.9 0 1 0 0 0 0 0 0 0 0 0 2
3 116.7 104.7 0 0 1 0 0 0 0 0 0 0 0 3
4 112.5 102.8 0 0 0 1 0 0 0 0 0 0 0 4
5 113.0 98.1 0 0 0 0 1 0 0 0 0 0 0 5
6 126.4 113.9 0 0 0 0 0 1 0 0 0 0 0 6
7 114.1 80.9 0 0 0 0 0 0 1 0 0 0 0 7
8 112.5 95.7 0 0 0 0 0 0 0 1 0 0 0 8
9 112.4 113.2 0 0 0 0 0 0 0 0 1 0 0 9
10 113.1 105.9 0 0 0 0 0 0 0 0 0 1 0 10
11 116.3 108.8 0 0 0 0 0 0 0 0 0 0 1 11
12 111.7 102.3 0 0 0 0 0 0 0 0 0 0 0 12
13 118.8 99.0 1 0 0 0 0 0 0 0 0 0 0 13
14 116.5 100.7 0 1 0 0 0 0 0 0 0 0 0 14
15 125.1 115.5 0 0 1 0 0 0 0 0 0 0 0 15
16 113.1 100.7 0 0 0 1 0 0 0 0 0 0 0 16
17 119.6 109.9 0 0 0 0 1 0 0 0 0 0 0 17
18 114.4 114.6 0 0 0 0 0 1 0 0 0 0 0 18
19 114.0 85.4 0 0 0 0 0 0 1 0 0 0 0 19
20 117.8 100.5 0 0 0 0 0 0 0 1 0 0 0 20
21 117.0 114.8 0 0 0 0 0 0 0 0 1 0 0 21
22 120.9 116.5 0 0 0 0 0 0 0 0 0 1 0 22
23 115.0 112.9 0 0 0 0 0 0 0 0 0 0 1 23
24 117.3 102.0 0 0 0 0 0 0 0 0 0 0 0 24
25 119.4 106.0 1 0 0 0 0 0 0 0 0 0 0 25
26 114.9 105.3 0 1 0 0 0 0 0 0 0 0 0 26
27 125.8 118.8 0 0 1 0 0 0 0 0 0 0 0 27
28 117.6 106.1 0 0 0 1 0 0 0 0 0 0 0 28
29 117.6 109.3 0 0 0 0 1 0 0 0 0 0 0 29
30 114.9 117.2 0 0 0 0 0 1 0 0 0 0 0 30
31 121.9 92.5 0 0 0 0 0 0 1 0 0 0 0 31
32 117.0 104.2 0 0 0 0 0 0 0 1 0 0 0 32
33 106.4 112.5 0 0 0 0 0 0 0 0 1 0 0 33
34 110.5 122.4 0 0 0 0 0 0 0 0 0 1 0 34
35 113.6 113.3 0 0 0 0 0 0 0 0 0 0 1 35
36 114.2 100.0 0 0 0 0 0 0 0 0 0 0 0 36
37 125.4 110.7 1 0 0 0 0 0 0 0 0 0 0 37
38 124.6 112.8 0 1 0 0 0 0 0 0 0 0 0 38
39 120.2 109.8 0 0 1 0 0 0 0 0 0 0 0 39
40 120.8 117.3 0 0 0 1 0 0 0 0 0 0 0 40
41 111.4 109.1 0 0 0 0 1 0 0 0 0 0 0 41
42 124.1 115.9 0 0 0 0 0 1 0 0 0 0 0 42
43 120.2 96.0 0 0 0 0 0 0 1 0 0 0 0 43
44 125.5 99.8 0 0 0 0 0 0 0 1 0 0 0 44
45 116.0 116.8 0 0 0 0 0 0 0 0 1 0 0 45
46 117.0 115.7 0 0 0 0 0 0 0 0 0 1 0 46
47 105.7 99.4 0 0 0 0 0 0 0 0 0 0 1 47
48 102.0 94.3 0 0 0 0 0 0 0 0 0 0 0 48
49 106.4 91.0 1 0 0 0 0 0 0 0 0 0 0 49
50 96.9 93.2 0 1 0 0 0 0 0 0 0 0 0 50
51 107.6 103.1 0 0 1 0 0 0 0 0 0 0 0 51
52 98.8 94.1 0 0 0 1 0 0 0 0 0 0 0 52
53 101.1 91.8 0 0 0 0 1 0 0 0 0 0 0 53
54 105.7 102.7 0 0 0 0 0 1 0 0 0 0 0 54
55 104.6 82.6 0 0 0 0 0 0 1 0 0 0 0 55
56 103.2 89.1 0 0 0 0 0 0 0 1 0 0 0 56
57 101.6 104.5 0 0 0 0 0 0 0 0 1 0 0 57
58 106.7 105.1 0 0 0 0 0 0 0 0 0 1 0 58
59 99.5 95.1 0 0 0 0 0 0 0 0 0 0 1 59
60 101.0 88.7 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T.I.P. M1 M2 M3 M4
35.3607 0.8025 4.4077 -1.6712 -1.6131 -3.0529
M5 M6 M7 M8 M9 M10
-2.5030 -5.2220 13.1271 5.1571 -10.8794 -8.4088
M11 t
-6.1140 -0.1205
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.5876 -2.3758 0.2923 2.3693 10.1887
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.36071 8.43955 4.190 0.000125 ***
T.I.P. 0.80255 0.08140 9.859 6.38e-13 ***
M1 4.40769 2.47930 1.778 0.082048 .
M2 -1.67120 2.48515 -0.672 0.504646
M3 -1.61314 2.65794 -0.607 0.546891
M4 -3.05293 2.51327 -1.215 0.230669
M5 -2.50304 2.50343 -1.000 0.322617
M6 -5.22205 2.74159 -1.905 0.063076 .
M7 13.12707 2.60007 5.049 7.46e-06 ***
M8 5.15710 2.45626 2.100 0.041281 *
M9 -10.87936 2.72884 -3.987 0.000237 ***
M10 -8.40883 2.75888 -3.048 0.003812 **
M11 -6.11397 2.54605 -2.401 0.020433 *
t -0.12047 0.03036 -3.968 0.000252 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.879 on 46 degrees of freedom
Multiple R-squared: 0.7907, Adjusted R-squared: 0.7316
F-statistic: 13.37 on 13 and 46 DF, p-value: 1.525e-11
> 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,] 0.11791512 0.23583024 0.8820849
[2,] 0.66911990 0.66176020 0.3308801
[3,] 0.53420164 0.93159672 0.4657984
[4,] 0.42609212 0.85218425 0.5739079
[5,] 0.36898804 0.73797608 0.6310120
[6,] 0.28636666 0.57273332 0.7136333
[7,] 0.21954386 0.43908771 0.7804561
[8,] 0.22745523 0.45491046 0.7725448
[9,] 0.18111273 0.36222546 0.8188873
[10,] 0.11998069 0.23996138 0.8800193
[11,] 0.07379944 0.14759888 0.9262006
[12,] 0.06930296 0.13860592 0.9306970
[13,] 0.04676867 0.09353733 0.9532313
[14,] 0.06579192 0.13158384 0.9342081
[15,] 0.07258375 0.14516751 0.9274162
[16,] 0.05430347 0.10860693 0.9456965
[17,] 0.04292491 0.08584982 0.9570751
[18,] 0.45284152 0.90568304 0.5471585
[19,] 0.52008949 0.95982103 0.4799105
[20,] 0.43276117 0.86552234 0.5672388
[21,] 0.35361459 0.70722917 0.6463854
[22,] 0.43191190 0.86382380 0.5680881
[23,] 0.35562174 0.71124347 0.6443783
[24,] 0.24788637 0.49577275 0.7521136
[25,] 0.49875158 0.99750316 0.5012484
[26,] 0.40415173 0.80830346 0.5958483
[27,] 0.27644623 0.55289246 0.7235538
> postscript(file="/var/www/rcomp/tmp/1txk91292669188.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2txk91292669188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/337jb1292669188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/437jb1292669188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/537jb1292669188.ps",horizontal=F,onefile=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 = 60
Frequency = 1
1 2 3 4 5 6
0.53112001 -2.91282905 -0.71282905 -1.82773061 2.01481436 5.57405448
7 8 9 10 11 12
1.52946333 -3.85779482 -1.84543610 2.36309347 1.06131688 -4.31562448
13 14 15 16 17 18
1.14556107 3.68059317 0.46531061 1.90326656 0.59040692 -5.54208022
19 20 21 22 23 24
-0.73635032 -0.96437260 2.91613682 3.10174255 -2.08347793 2.97078792
25 26 27 28 29 30
-2.42662032 -0.16547519 -0.03744653 3.51516052 0.51758345 -5.68305439
31 32 33 34 35 36
2.91121359 -3.28814858 -4.39235659 -10.58763703 -2.35884849 2.92153038
37 38 39 40 41 42
1.24705661 4.96106988 3.03112559 -0.82771866 -4.07625886 6.00590511
43 44 45 46 47 48
-0.15205297 10.18870691 3.20233918 2.73507677 2.34220438 -3.25830291
49 50 51 52 53 54
-0.49711737 -5.56335881 -2.74616061 -2.76297781 0.95345414 -0.35482498
55 56 57 58 59 60
-3.55227364 -2.07839091 0.11931670 2.38772424 1.03880516 1.68160909
> postscript(file="/var/www/rcomp/tmp/6wy0f1292669188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.53112001 NA
1 -2.91282905 0.53112001
2 -0.71282905 -2.91282905
3 -1.82773061 -0.71282905
4 2.01481436 -1.82773061
5 5.57405448 2.01481436
6 1.52946333 5.57405448
7 -3.85779482 1.52946333
8 -1.84543610 -3.85779482
9 2.36309347 -1.84543610
10 1.06131688 2.36309347
11 -4.31562448 1.06131688
12 1.14556107 -4.31562448
13 3.68059317 1.14556107
14 0.46531061 3.68059317
15 1.90326656 0.46531061
16 0.59040692 1.90326656
17 -5.54208022 0.59040692
18 -0.73635032 -5.54208022
19 -0.96437260 -0.73635032
20 2.91613682 -0.96437260
21 3.10174255 2.91613682
22 -2.08347793 3.10174255
23 2.97078792 -2.08347793
24 -2.42662032 2.97078792
25 -0.16547519 -2.42662032
26 -0.03744653 -0.16547519
27 3.51516052 -0.03744653
28 0.51758345 3.51516052
29 -5.68305439 0.51758345
30 2.91121359 -5.68305439
31 -3.28814858 2.91121359
32 -4.39235659 -3.28814858
33 -10.58763703 -4.39235659
34 -2.35884849 -10.58763703
35 2.92153038 -2.35884849
36 1.24705661 2.92153038
37 4.96106988 1.24705661
38 3.03112559 4.96106988
39 -0.82771866 3.03112559
40 -4.07625886 -0.82771866
41 6.00590511 -4.07625886
42 -0.15205297 6.00590511
43 10.18870691 -0.15205297
44 3.20233918 10.18870691
45 2.73507677 3.20233918
46 2.34220438 2.73507677
47 -3.25830291 2.34220438
48 -0.49711737 -3.25830291
49 -5.56335881 -0.49711737
50 -2.74616061 -5.56335881
51 -2.76297781 -2.74616061
52 0.95345414 -2.76297781
53 -0.35482498 0.95345414
54 -3.55227364 -0.35482498
55 -2.07839091 -3.55227364
56 0.11931670 -2.07839091
57 2.38772424 0.11931670
58 1.03880516 2.38772424
59 1.68160909 1.03880516
60 NA 1.68160909
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.91282905 0.53112001
[2,] -0.71282905 -2.91282905
[3,] -1.82773061 -0.71282905
[4,] 2.01481436 -1.82773061
[5,] 5.57405448 2.01481436
[6,] 1.52946333 5.57405448
[7,] -3.85779482 1.52946333
[8,] -1.84543610 -3.85779482
[9,] 2.36309347 -1.84543610
[10,] 1.06131688 2.36309347
[11,] -4.31562448 1.06131688
[12,] 1.14556107 -4.31562448
[13,] 3.68059317 1.14556107
[14,] 0.46531061 3.68059317
[15,] 1.90326656 0.46531061
[16,] 0.59040692 1.90326656
[17,] -5.54208022 0.59040692
[18,] -0.73635032 -5.54208022
[19,] -0.96437260 -0.73635032
[20,] 2.91613682 -0.96437260
[21,] 3.10174255 2.91613682
[22,] -2.08347793 3.10174255
[23,] 2.97078792 -2.08347793
[24,] -2.42662032 2.97078792
[25,] -0.16547519 -2.42662032
[26,] -0.03744653 -0.16547519
[27,] 3.51516052 -0.03744653
[28,] 0.51758345 3.51516052
[29,] -5.68305439 0.51758345
[30,] 2.91121359 -5.68305439
[31,] -3.28814858 2.91121359
[32,] -4.39235659 -3.28814858
[33,] -10.58763703 -4.39235659
[34,] -2.35884849 -10.58763703
[35,] 2.92153038 -2.35884849
[36,] 1.24705661 2.92153038
[37,] 4.96106988 1.24705661
[38,] 3.03112559 4.96106988
[39,] -0.82771866 3.03112559
[40,] -4.07625886 -0.82771866
[41,] 6.00590511 -4.07625886
[42,] -0.15205297 6.00590511
[43,] 10.18870691 -0.15205297
[44,] 3.20233918 10.18870691
[45,] 2.73507677 3.20233918
[46,] 2.34220438 2.73507677
[47,] -3.25830291 2.34220438
[48,] -0.49711737 -3.25830291
[49,] -5.56335881 -0.49711737
[50,] -2.74616061 -5.56335881
[51,] -2.76297781 -2.74616061
[52,] 0.95345414 -2.76297781
[53,] -0.35482498 0.95345414
[54,] -3.55227364 -0.35482498
[55,] -2.07839091 -3.55227364
[56,] 0.11931670 -2.07839091
[57,] 2.38772424 0.11931670
[58,] 1.03880516 2.38772424
[59,] 1.68160909 1.03880516
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.91282905 0.53112001
2 -0.71282905 -2.91282905
3 -1.82773061 -0.71282905
4 2.01481436 -1.82773061
5 5.57405448 2.01481436
6 1.52946333 5.57405448
7 -3.85779482 1.52946333
8 -1.84543610 -3.85779482
9 2.36309347 -1.84543610
10 1.06131688 2.36309347
11 -4.31562448 1.06131688
12 1.14556107 -4.31562448
13 3.68059317 1.14556107
14 0.46531061 3.68059317
15 1.90326656 0.46531061
16 0.59040692 1.90326656
17 -5.54208022 0.59040692
18 -0.73635032 -5.54208022
19 -0.96437260 -0.73635032
20 2.91613682 -0.96437260
21 3.10174255 2.91613682
22 -2.08347793 3.10174255
23 2.97078792 -2.08347793
24 -2.42662032 2.97078792
25 -0.16547519 -2.42662032
26 -0.03744653 -0.16547519
27 3.51516052 -0.03744653
28 0.51758345 3.51516052
29 -5.68305439 0.51758345
30 2.91121359 -5.68305439
31 -3.28814858 2.91121359
32 -4.39235659 -3.28814858
33 -10.58763703 -4.39235659
34 -2.35884849 -10.58763703
35 2.92153038 -2.35884849
36 1.24705661 2.92153038
37 4.96106988 1.24705661
38 3.03112559 4.96106988
39 -0.82771866 3.03112559
40 -4.07625886 -0.82771866
41 6.00590511 -4.07625886
42 -0.15205297 6.00590511
43 10.18870691 -0.15205297
44 3.20233918 10.18870691
45 2.73507677 3.20233918
46 2.34220438 2.73507677
47 -3.25830291 2.34220438
48 -0.49711737 -3.25830291
49 -5.56335881 -0.49711737
50 -2.74616061 -5.56335881
51 -2.76297781 -2.74616061
52 0.95345414 -2.76297781
53 -0.35482498 0.95345414
54 -3.55227364 -0.35482498
55 -2.07839091 -3.55227364
56 0.11931670 -2.07839091
57 2.38772424 0.11931670
58 1.03880516 2.38772424
59 1.68160909 1.03880516
> 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/rcomp/tmp/77phh1292669188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/87phh1292669188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/97phh1292669188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10izh21292669188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11lhxq1292669188.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/rcomp/tmp/126hwe1292669188.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/rcomp/tmp/1329b51292669188.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/rcomp/tmp/146sat1292669188.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/rcomp/tmp/15raqg1292669188.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/rcomp/tmp/16utpm1292669188.tab")
+ }
>
> try(system("convert tmp/1txk91292669188.ps tmp/1txk91292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/2txk91292669188.ps tmp/2txk91292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/337jb1292669188.ps tmp/337jb1292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/437jb1292669188.ps tmp/437jb1292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/537jb1292669188.ps tmp/537jb1292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wy0f1292669188.ps tmp/6wy0f1292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/77phh1292669188.ps tmp/77phh1292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/87phh1292669188.ps tmp/87phh1292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/97phh1292669188.ps tmp/97phh1292669188.png",intern=TRUE))
character(0)
> try(system("convert tmp/10izh21292669188.ps tmp/10izh21292669188.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.140 1.610 4.731