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(101.3,0,106.3,0,94,0,102.8,0,102,0,105.1,1,92.4,0,81.4,0,105.8,0,120.3,1,100.7,0,88.8,0,94.3,0,99.9,0,103.4,0,103.3,0,98.8,0,104.2,0,91.2,0,74.7,0,108.5,0,114.5,0,96.9,0,89.6,0,97.1,0,100.3,0,122.6,0,115.4,1,109,0,129.1,1,102.8,1,96.2,0,127.7,1,128.9,1,126.5,1,119.8,1,113.2,1,114.1,1,134.1,1,130,1,121.8,1,132.1,1,105.3,1,103,1,117.1,1,126.3,1,138.1,1,119.5,1,138,1,135.5,1,178.6,1,162.2,1,176.9,1,204.9,1,132.2,1,142.5,1,164.3,1,174.9,1,175.4,1,143,1),dim=c(2,60),dimnames=list(c('Omzet','Uitvoer'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Omzet','Uitvoer'),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 = '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
Omzet Uitvoer M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 101.3 0 1 0 0 0 0 0 0 0 0 0 0 1
2 106.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 94.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 102.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 102.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 105.1 1 0 0 0 0 0 1 0 0 0 0 0 6
7 92.4 0 0 0 0 0 0 0 1 0 0 0 0 7
8 81.4 0 0 0 0 0 0 0 0 1 0 0 0 8
9 105.8 0 0 0 0 0 0 0 0 0 1 0 0 9
10 120.3 1 0 0 0 0 0 0 0 0 0 1 0 10
11 100.7 0 0 0 0 0 0 0 0 0 0 0 1 11
12 88.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 94.3 0 1 0 0 0 0 0 0 0 0 0 0 13
14 99.9 0 0 1 0 0 0 0 0 0 0 0 0 14
15 103.4 0 0 0 1 0 0 0 0 0 0 0 0 15
16 103.3 0 0 0 0 1 0 0 0 0 0 0 0 16
17 98.8 0 0 0 0 0 1 0 0 0 0 0 0 17
18 104.2 0 0 0 0 0 0 1 0 0 0 0 0 18
19 91.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 74.7 0 0 0 0 0 0 0 0 1 0 0 0 20
21 108.5 0 0 0 0 0 0 0 0 0 1 0 0 21
22 114.5 0 0 0 0 0 0 0 0 0 0 1 0 22
23 96.9 0 0 0 0 0 0 0 0 0 0 0 1 23
24 89.6 0 0 0 0 0 0 0 0 0 0 0 0 24
25 97.1 0 1 0 0 0 0 0 0 0 0 0 0 25
26 100.3 0 0 1 0 0 0 0 0 0 0 0 0 26
27 122.6 0 0 0 1 0 0 0 0 0 0 0 0 27
28 115.4 1 0 0 0 1 0 0 0 0 0 0 0 28
29 109.0 0 0 0 0 0 1 0 0 0 0 0 0 29
30 129.1 1 0 0 0 0 0 1 0 0 0 0 0 30
31 102.8 1 0 0 0 0 0 0 1 0 0 0 0 31
32 96.2 0 0 0 0 0 0 0 0 1 0 0 0 32
33 127.7 1 0 0 0 0 0 0 0 0 1 0 0 33
34 128.9 1 0 0 0 0 0 0 0 0 0 1 0 34
35 126.5 1 0 0 0 0 0 0 0 0 0 0 1 35
36 119.8 1 0 0 0 0 0 0 0 0 0 0 0 36
37 113.2 1 1 0 0 0 0 0 0 0 0 0 0 37
38 114.1 1 0 1 0 0 0 0 0 0 0 0 0 38
39 134.1 1 0 0 1 0 0 0 0 0 0 0 0 39
40 130.0 1 0 0 0 1 0 0 0 0 0 0 0 40
41 121.8 1 0 0 0 0 1 0 0 0 0 0 0 41
42 132.1 1 0 0 0 0 0 1 0 0 0 0 0 42
43 105.3 1 0 0 0 0 0 0 1 0 0 0 0 43
44 103.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 117.1 1 0 0 0 0 0 0 0 0 1 0 0 45
46 126.3 1 0 0 0 0 0 0 0 0 0 1 0 46
47 138.1 1 0 0 0 0 0 0 0 0 0 0 1 47
48 119.5 1 0 0 0 0 0 0 0 0 0 0 0 48
49 138.0 1 1 0 0 0 0 0 0 0 0 0 0 49
50 135.5 1 0 1 0 0 0 0 0 0 0 0 0 50
51 178.6 1 0 0 1 0 0 0 0 0 0 0 0 51
52 162.2 1 0 0 0 1 0 0 0 0 0 0 0 52
53 176.9 1 0 0 0 0 1 0 0 0 0 0 0 53
54 204.9 1 0 0 0 0 0 1 0 0 0 0 0 54
55 132.2 1 0 0 0 0 0 0 1 0 0 0 0 55
56 142.5 1 0 0 0 0 0 0 0 1 0 0 0 56
57 164.3 1 0 0 0 0 0 0 0 0 1 0 0 57
58 174.9 1 0 0 0 0 0 0 0 0 0 1 0 58
59 175.4 1 0 0 0 0 0 0 0 0 0 0 1 59
60 143.0 1 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) Uitvoer M1 M2 M3 M4
69.426 2.717 9.737 11.036 25.214 19.730
M5 M6 M7 M8 M9 M10
18.092 29.244 -1.654 -7.472 15.964 22.579
M11 t
16.521 1.141
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-22.361 -8.376 -2.732 8.852 41.887
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 69.4259 7.6561 9.068 8.31e-12 ***
Uitvoer 2.7171 6.3949 0.425 0.67290
M1 9.7368 9.1876 1.060 0.29478
M2 11.0356 9.1706 1.203 0.23499
M3 25.2144 9.1571 2.754 0.00841 **
M4 19.7297 9.2163 2.141 0.03763 *
M5 18.0919 9.1405 1.979 0.05378 .
M6 29.2439 9.3732 3.120 0.00312 **
M7 -1.6539 9.1480 -0.181 0.85732
M8 -7.4717 9.1421 -0.817 0.41798
M9 15.9637 9.1198 1.750 0.08671 .
M10 22.5790 9.2397 2.444 0.01843 *
M11 16.5212 9.1057 1.814 0.07614 .
t 1.1412 0.1794 6.361 8.31e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.39 on 46 degrees of freedom
Multiple R-squared: 0.7712, Adjusted R-squared: 0.7066
F-statistic: 11.93 on 13 and 46 DF, p-value: 1.041e-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,] 9.548595e-02 1.909719e-01 0.9045140
[2,] 3.366195e-02 6.732389e-02 0.9663381
[3,] 1.132123e-02 2.264245e-02 0.9886788
[4,] 4.565023e-03 9.130046e-03 0.9954350
[5,] 1.845661e-03 3.691322e-03 0.9981543
[6,] 6.743160e-04 1.348632e-03 0.9993257
[7,] 1.979483e-04 3.958967e-04 0.9998021
[8,] 5.520068e-05 1.104014e-04 0.9999448
[9,] 1.355943e-05 2.711886e-05 0.9999864
[10,] 3.208132e-06 6.416264e-06 0.9999968
[11,] 1.667470e-03 3.334941e-03 0.9983325
[12,] 7.342682e-04 1.468536e-03 0.9992657
[13,] 3.706971e-04 7.413942e-04 0.9996293
[14,] 7.110718e-04 1.422144e-03 0.9992889
[15,] 4.913059e-04 9.826117e-04 0.9995087
[16,] 4.633873e-04 9.267746e-04 0.9995366
[17,] 5.809259e-04 1.161852e-03 0.9994191
[18,] 5.099591e-04 1.019918e-03 0.9994900
[19,] 6.174024e-04 1.234805e-03 0.9993826
[20,] 2.216266e-02 4.432533e-02 0.9778373
[21,] 2.069106e-02 4.138212e-02 0.9793089
[22,] 3.324341e-02 6.648682e-02 0.9667566
[23,] 2.017485e-02 4.034971e-02 0.9798251
[24,] 1.469742e-02 2.939484e-02 0.9853026
[25,] 1.010341e-02 2.020681e-02 0.9898966
[26,] 2.113156e-01 4.226311e-01 0.7886844
[27,] 2.132798e-01 4.265596e-01 0.7867202
> postscript(file="/var/www/html/rcomp/tmp/1v0x71259316715.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qiip1259316715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bjg21259316715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4stwo1259316715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vgky1259316715.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 = 60
Frequency = 1
1 2 3 4 5 6
20.9960526 23.5560526 -4.0639474 9.0794737 8.7760526 -3.1342105
7 8 9 10 11 12
16.6394737 10.3160526 10.1394737 14.1657895 2.1994737 5.6794737
13 14 15 16 17 18
0.3014474 3.4614474 -8.3585526 -4.1151316 -8.1185526 -15.0117105
19 20 21 22 23 24
1.7448684 -10.0785526 -0.8551316 -2.6117105 -15.2951316 -7.2151316
25 26 27 28 29 30
-10.5931579 -9.8331579 -2.8531579 -8.4268421 -11.6131579 -6.5234211
31 32 33 34 35 36
-3.0668421 -2.2731579 1.9331579 -4.6234211 -2.1068421 6.5731579
37 38 39 40 41 42
-10.9048684 -12.4448684 -7.7648684 -7.5214474 -15.2248684 -17.2180263
43 44 45 46 47 48
-14.2614474 -11.8848684 -22.3614474 -20.9180263 -4.2014474 -7.4214474
49 50 51 52 53 54
0.2005263 -4.7394737 23.0405263 10.9839474 26.1805263 41.8873684
55 56 57 58 59 60
-1.0560526 13.9205263 11.1439474 13.9873684 19.4039474 2.3839474
> postscript(file="/var/www/html/rcomp/tmp/6ocyn1259316715.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 20.9960526 NA
1 23.5560526 20.9960526
2 -4.0639474 23.5560526
3 9.0794737 -4.0639474
4 8.7760526 9.0794737
5 -3.1342105 8.7760526
6 16.6394737 -3.1342105
7 10.3160526 16.6394737
8 10.1394737 10.3160526
9 14.1657895 10.1394737
10 2.1994737 14.1657895
11 5.6794737 2.1994737
12 0.3014474 5.6794737
13 3.4614474 0.3014474
14 -8.3585526 3.4614474
15 -4.1151316 -8.3585526
16 -8.1185526 -4.1151316
17 -15.0117105 -8.1185526
18 1.7448684 -15.0117105
19 -10.0785526 1.7448684
20 -0.8551316 -10.0785526
21 -2.6117105 -0.8551316
22 -15.2951316 -2.6117105
23 -7.2151316 -15.2951316
24 -10.5931579 -7.2151316
25 -9.8331579 -10.5931579
26 -2.8531579 -9.8331579
27 -8.4268421 -2.8531579
28 -11.6131579 -8.4268421
29 -6.5234211 -11.6131579
30 -3.0668421 -6.5234211
31 -2.2731579 -3.0668421
32 1.9331579 -2.2731579
33 -4.6234211 1.9331579
34 -2.1068421 -4.6234211
35 6.5731579 -2.1068421
36 -10.9048684 6.5731579
37 -12.4448684 -10.9048684
38 -7.7648684 -12.4448684
39 -7.5214474 -7.7648684
40 -15.2248684 -7.5214474
41 -17.2180263 -15.2248684
42 -14.2614474 -17.2180263
43 -11.8848684 -14.2614474
44 -22.3614474 -11.8848684
45 -20.9180263 -22.3614474
46 -4.2014474 -20.9180263
47 -7.4214474 -4.2014474
48 0.2005263 -7.4214474
49 -4.7394737 0.2005263
50 23.0405263 -4.7394737
51 10.9839474 23.0405263
52 26.1805263 10.9839474
53 41.8873684 26.1805263
54 -1.0560526 41.8873684
55 13.9205263 -1.0560526
56 11.1439474 13.9205263
57 13.9873684 11.1439474
58 19.4039474 13.9873684
59 2.3839474 19.4039474
60 NA 2.3839474
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 23.5560526 20.9960526
[2,] -4.0639474 23.5560526
[3,] 9.0794737 -4.0639474
[4,] 8.7760526 9.0794737
[5,] -3.1342105 8.7760526
[6,] 16.6394737 -3.1342105
[7,] 10.3160526 16.6394737
[8,] 10.1394737 10.3160526
[9,] 14.1657895 10.1394737
[10,] 2.1994737 14.1657895
[11,] 5.6794737 2.1994737
[12,] 0.3014474 5.6794737
[13,] 3.4614474 0.3014474
[14,] -8.3585526 3.4614474
[15,] -4.1151316 -8.3585526
[16,] -8.1185526 -4.1151316
[17,] -15.0117105 -8.1185526
[18,] 1.7448684 -15.0117105
[19,] -10.0785526 1.7448684
[20,] -0.8551316 -10.0785526
[21,] -2.6117105 -0.8551316
[22,] -15.2951316 -2.6117105
[23,] -7.2151316 -15.2951316
[24,] -10.5931579 -7.2151316
[25,] -9.8331579 -10.5931579
[26,] -2.8531579 -9.8331579
[27,] -8.4268421 -2.8531579
[28,] -11.6131579 -8.4268421
[29,] -6.5234211 -11.6131579
[30,] -3.0668421 -6.5234211
[31,] -2.2731579 -3.0668421
[32,] 1.9331579 -2.2731579
[33,] -4.6234211 1.9331579
[34,] -2.1068421 -4.6234211
[35,] 6.5731579 -2.1068421
[36,] -10.9048684 6.5731579
[37,] -12.4448684 -10.9048684
[38,] -7.7648684 -12.4448684
[39,] -7.5214474 -7.7648684
[40,] -15.2248684 -7.5214474
[41,] -17.2180263 -15.2248684
[42,] -14.2614474 -17.2180263
[43,] -11.8848684 -14.2614474
[44,] -22.3614474 -11.8848684
[45,] -20.9180263 -22.3614474
[46,] -4.2014474 -20.9180263
[47,] -7.4214474 -4.2014474
[48,] 0.2005263 -7.4214474
[49,] -4.7394737 0.2005263
[50,] 23.0405263 -4.7394737
[51,] 10.9839474 23.0405263
[52,] 26.1805263 10.9839474
[53,] 41.8873684 26.1805263
[54,] -1.0560526 41.8873684
[55,] 13.9205263 -1.0560526
[56,] 11.1439474 13.9205263
[57,] 13.9873684 11.1439474
[58,] 19.4039474 13.9873684
[59,] 2.3839474 19.4039474
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 23.5560526 20.9960526
2 -4.0639474 23.5560526
3 9.0794737 -4.0639474
4 8.7760526 9.0794737
5 -3.1342105 8.7760526
6 16.6394737 -3.1342105
7 10.3160526 16.6394737
8 10.1394737 10.3160526
9 14.1657895 10.1394737
10 2.1994737 14.1657895
11 5.6794737 2.1994737
12 0.3014474 5.6794737
13 3.4614474 0.3014474
14 -8.3585526 3.4614474
15 -4.1151316 -8.3585526
16 -8.1185526 -4.1151316
17 -15.0117105 -8.1185526
18 1.7448684 -15.0117105
19 -10.0785526 1.7448684
20 -0.8551316 -10.0785526
21 -2.6117105 -0.8551316
22 -15.2951316 -2.6117105
23 -7.2151316 -15.2951316
24 -10.5931579 -7.2151316
25 -9.8331579 -10.5931579
26 -2.8531579 -9.8331579
27 -8.4268421 -2.8531579
28 -11.6131579 -8.4268421
29 -6.5234211 -11.6131579
30 -3.0668421 -6.5234211
31 -2.2731579 -3.0668421
32 1.9331579 -2.2731579
33 -4.6234211 1.9331579
34 -2.1068421 -4.6234211
35 6.5731579 -2.1068421
36 -10.9048684 6.5731579
37 -12.4448684 -10.9048684
38 -7.7648684 -12.4448684
39 -7.5214474 -7.7648684
40 -15.2248684 -7.5214474
41 -17.2180263 -15.2248684
42 -14.2614474 -17.2180263
43 -11.8848684 -14.2614474
44 -22.3614474 -11.8848684
45 -20.9180263 -22.3614474
46 -4.2014474 -20.9180263
47 -7.4214474 -4.2014474
48 0.2005263 -7.4214474
49 -4.7394737 0.2005263
50 23.0405263 -4.7394737
51 10.9839474 23.0405263
52 26.1805263 10.9839474
53 41.8873684 26.1805263
54 -1.0560526 41.8873684
55 13.9205263 -1.0560526
56 11.1439474 13.9205263
57 13.9873684 11.1439474
58 19.4039474 13.9873684
59 2.3839474 19.4039474
> 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/7i8nh1259316715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ad9e1259316715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zh9v1259316715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ast61259316715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113f451259316715.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/12eu1f1259316715.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/138v5d1259316715.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/148aqm1259316715.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/15n88c1259316715.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/16xzk71259316715.tab")
+ }
>
> system("convert tmp/1v0x71259316715.ps tmp/1v0x71259316715.png")
> system("convert tmp/2qiip1259316715.ps tmp/2qiip1259316715.png")
> system("convert tmp/3bjg21259316715.ps tmp/3bjg21259316715.png")
> system("convert tmp/4stwo1259316715.ps tmp/4stwo1259316715.png")
> system("convert tmp/5vgky1259316715.ps tmp/5vgky1259316715.png")
> system("convert tmp/6ocyn1259316715.ps tmp/6ocyn1259316715.png")
> system("convert tmp/7i8nh1259316715.ps tmp/7i8nh1259316715.png")
> system("convert tmp/8ad9e1259316715.ps tmp/8ad9e1259316715.png")
> system("convert tmp/9zh9v1259316715.ps tmp/9zh9v1259316715.png")
> system("convert tmp/10ast61259316715.ps tmp/10ast61259316715.png")
>
>
> proc.time()
user system elapsed
2.355 1.544 3.142