R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(99.4,0,97.5,0,94.6,0,92.6,0,92.5,0,89.8,0,88.8,0,87.4,0,85.2,0,83.1,0,84.7,0,84.8,0,85.8,0,86.3,0,89,0,89,0,89.3,0,91.9,0,94.9,0,94.4,0,96.8,0,96.9,0,98,0,97.9,0,100.9,0,103.9,0,103.1,0,102.5,0,104.3,0,102.6,0,101.7,0,102.8,0,105.4,0,110.9,1,113.5,1,116.3,1,124,1,128.8,1,133.5,1,132.6,1,128.4,1,127.3,1,126.7,1,123.3,1,123.2,1,124.4,1,128.2,1,128.7,1,135.7,1,139,1,145.4,1,142.4,1,137.7,1,137,1,137.1,1,139.3,1,139.6,1,140.4,1,142.3,1,148.3,1),dim=c(2,60),dimnames=list(c('Grondstofprijzen','Wet'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Grondstofprijzen','Wet'),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 = '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
Grondstofprijzen Wet t
1 99.4 0 1
2 97.5 0 2
3 94.6 0 3
4 92.6 0 4
5 92.5 0 5
6 89.8 0 6
7 88.8 0 7
8 87.4 0 8
9 85.2 0 9
10 83.1 0 10
11 84.7 0 11
12 84.8 0 12
13 85.8 0 13
14 86.3 0 14
15 89.0 0 15
16 89.0 0 16
17 89.3 0 17
18 91.9 0 18
19 94.9 0 19
20 94.4 0 20
21 96.8 0 21
22 96.9 0 22
23 98.0 0 23
24 97.9 0 24
25 100.9 0 25
26 103.9 0 26
27 103.1 0 27
28 102.5 0 28
29 104.3 0 29
30 102.6 0 30
31 101.7 0 31
32 102.8 0 32
33 105.4 0 33
34 110.9 1 34
35 113.5 1 35
36 116.3 1 36
37 124.0 1 37
38 128.8 1 38
39 133.5 1 39
40 132.6 1 40
41 128.4 1 41
42 127.3 1 42
43 126.7 1 43
44 123.3 1 44
45 123.2 1 45
46 124.4 1 46
47 128.2 1 47
48 128.7 1 48
49 135.7 1 49
50 139.0 1 50
51 145.4 1 51
52 142.4 1 52
53 137.7 1 53
54 137.0 1 54
55 137.1 1 55
56 139.3 1 56
57 139.6 1 57
58 140.4 1 58
59 142.3 1 59
60 148.3 1 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Wet t
83.0242 16.9368 0.6738
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.9702 -3.6747 -0.4478 2.6193 15.7020
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 83.02417 1.70383 48.728 < 2e-16 ***
Wet 16.93680 2.86257 5.917 1.97e-07 ***
t 0.67380 0.08223 8.194 3.28e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.595 on 57 degrees of freedom
Multiple R-squared: 0.9268, Adjusted R-squared: 0.9242
F-statistic: 360.9 on 2 and 57 DF, p-value: < 2.2e-16
> 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,] 1.433766e-02 2.867532e-02 0.98566234
[2,] 3.997809e-03 7.995617e-03 0.99600219
[3,] 1.094843e-03 2.189687e-03 0.99890516
[4,] 1.984465e-04 3.968931e-04 0.99980155
[5,] 3.680518e-05 7.361035e-05 0.99996319
[6,] 3.490264e-04 6.980528e-04 0.99965097
[7,] 1.586841e-03 3.173681e-03 0.99841316
[8,] 8.805632e-03 1.761126e-02 0.99119437
[9,] 2.524686e-02 5.049371e-02 0.97475314
[10,] 1.125038e-01 2.250077e-01 0.88749617
[11,] 1.880953e-01 3.761905e-01 0.81190474
[12,] 2.439476e-01 4.878952e-01 0.75605239
[13,] 3.691224e-01 7.382448e-01 0.63087759
[14,] 5.607882e-01 8.784236e-01 0.43921180
[15,] 6.198774e-01 7.602453e-01 0.38012263
[16,] 7.004124e-01 5.991753e-01 0.29958764
[17,] 7.227916e-01 5.544167e-01 0.27720836
[18,] 7.348005e-01 5.303990e-01 0.26519949
[19,] 7.170341e-01 5.659318e-01 0.28296590
[20,] 7.351251e-01 5.297497e-01 0.26487485
[21,] 7.926244e-01 4.147513e-01 0.20737563
[22,] 7.926749e-01 4.146502e-01 0.20732511
[23,] 7.629164e-01 4.741672e-01 0.23708359
[24,] 7.468509e-01 5.062983e-01 0.25314913
[25,] 6.914895e-01 6.170209e-01 0.30851047
[26,] 6.220660e-01 7.558679e-01 0.37793396
[27,] 5.511991e-01 8.976017e-01 0.44880086
[28,] 4.883967e-01 9.767934e-01 0.51160329
[29,] 5.121314e-01 9.757372e-01 0.48786859
[30,] 5.354604e-01 9.290792e-01 0.46453960
[31,] 5.519347e-01 8.961307e-01 0.44806535
[32,] 5.498090e-01 9.003820e-01 0.45019102
[33,] 6.059103e-01 7.881794e-01 0.39408969
[34,] 7.933030e-01 4.133939e-01 0.20669697
[35,] 8.902596e-01 2.194808e-01 0.10974042
[36,] 8.808515e-01 2.382970e-01 0.11914850
[37,] 8.490246e-01 3.019509e-01 0.15097543
[38,] 7.968669e-01 4.062662e-01 0.20313308
[39,] 7.499708e-01 5.000583e-01 0.25002916
[40,] 7.437507e-01 5.124986e-01 0.25624932
[41,] 7.811208e-01 4.377584e-01 0.21887922
[42,] 7.833906e-01 4.332187e-01 0.21660937
[43,] 8.747991e-01 2.504018e-01 0.12520088
[44,] 8.424704e-01 3.150593e-01 0.15752964
[45,] 7.830048e-01 4.339904e-01 0.21699520
[46,] 9.179571e-01 1.640857e-01 0.08204287
[47,] 9.816157e-01 3.676858e-02 0.01838429
[48,] 9.737149e-01 5.257022e-02 0.02628511
[49,] 9.387771e-01 1.224457e-01 0.06122287
> postscript(file="/var/www/html/rcomp/tmp/1yfnn1227470806.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2emi81227470806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3rxjj1227470806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48iq51227470806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5h4rx1227470806.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
15.7020329 13.1282316 9.5544303 6.8806290 6.1068277 2.7330264
7 8 9 10 11 12
1.0592251 -1.0145762 -3.8883775 -6.6621788 -5.7359801 -6.3097814
13 14 15 16 17 18
-5.9835827 -6.1573840 -4.1311853 -4.8049866 -5.1787879 -3.2525892
19 20 21 22 23 24
-0.9263905 -2.1001918 -0.3739931 -0.9477944 -0.5215957 -1.2953970
25 26 27 28 29 30
1.0308018 3.3570005 1.8831992 0.6093979 1.7355966 -0.6382047
31 32 33 34 35 36
-2.2120060 -1.7858073 0.1403914 -11.9702128 -10.0440141 -7.9178154
37 38 39 40 41 42
-0.8916167 3.2345820 7.2607807 5.6869794 0.8131781 -0.9606232
43 44 45 46 47 48
-2.2344244 -6.3082257 -7.0820270 -6.5558283 -3.4296296 -3.6034309
49 50 51 52 53 54
2.7227678 5.3489665 11.0751652 7.4013639 2.0275626 0.6537613
55 56 57 58 59 60
0.0799600 1.6061587 1.2323574 1.3585561 2.5847548 7.9109535
> postscript(file="/var/www/html/rcomp/tmp/6oktz1227470806.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 15.7020329 NA
1 13.1282316 15.7020329
2 9.5544303 13.1282316
3 6.8806290 9.5544303
4 6.1068277 6.8806290
5 2.7330264 6.1068277
6 1.0592251 2.7330264
7 -1.0145762 1.0592251
8 -3.8883775 -1.0145762
9 -6.6621788 -3.8883775
10 -5.7359801 -6.6621788
11 -6.3097814 -5.7359801
12 -5.9835827 -6.3097814
13 -6.1573840 -5.9835827
14 -4.1311853 -6.1573840
15 -4.8049866 -4.1311853
16 -5.1787879 -4.8049866
17 -3.2525892 -5.1787879
18 -0.9263905 -3.2525892
19 -2.1001918 -0.9263905
20 -0.3739931 -2.1001918
21 -0.9477944 -0.3739931
22 -0.5215957 -0.9477944
23 -1.2953970 -0.5215957
24 1.0308018 -1.2953970
25 3.3570005 1.0308018
26 1.8831992 3.3570005
27 0.6093979 1.8831992
28 1.7355966 0.6093979
29 -0.6382047 1.7355966
30 -2.2120060 -0.6382047
31 -1.7858073 -2.2120060
32 0.1403914 -1.7858073
33 -11.9702128 0.1403914
34 -10.0440141 -11.9702128
35 -7.9178154 -10.0440141
36 -0.8916167 -7.9178154
37 3.2345820 -0.8916167
38 7.2607807 3.2345820
39 5.6869794 7.2607807
40 0.8131781 5.6869794
41 -0.9606232 0.8131781
42 -2.2344244 -0.9606232
43 -6.3082257 -2.2344244
44 -7.0820270 -6.3082257
45 -6.5558283 -7.0820270
46 -3.4296296 -6.5558283
47 -3.6034309 -3.4296296
48 2.7227678 -3.6034309
49 5.3489665 2.7227678
50 11.0751652 5.3489665
51 7.4013639 11.0751652
52 2.0275626 7.4013639
53 0.6537613 2.0275626
54 0.0799600 0.6537613
55 1.6061587 0.0799600
56 1.2323574 1.6061587
57 1.3585561 1.2323574
58 2.5847548 1.3585561
59 7.9109535 2.5847548
60 NA 7.9109535
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 13.1282316 15.7020329
[2,] 9.5544303 13.1282316
[3,] 6.8806290 9.5544303
[4,] 6.1068277 6.8806290
[5,] 2.7330264 6.1068277
[6,] 1.0592251 2.7330264
[7,] -1.0145762 1.0592251
[8,] -3.8883775 -1.0145762
[9,] -6.6621788 -3.8883775
[10,] -5.7359801 -6.6621788
[11,] -6.3097814 -5.7359801
[12,] -5.9835827 -6.3097814
[13,] -6.1573840 -5.9835827
[14,] -4.1311853 -6.1573840
[15,] -4.8049866 -4.1311853
[16,] -5.1787879 -4.8049866
[17,] -3.2525892 -5.1787879
[18,] -0.9263905 -3.2525892
[19,] -2.1001918 -0.9263905
[20,] -0.3739931 -2.1001918
[21,] -0.9477944 -0.3739931
[22,] -0.5215957 -0.9477944
[23,] -1.2953970 -0.5215957
[24,] 1.0308018 -1.2953970
[25,] 3.3570005 1.0308018
[26,] 1.8831992 3.3570005
[27,] 0.6093979 1.8831992
[28,] 1.7355966 0.6093979
[29,] -0.6382047 1.7355966
[30,] -2.2120060 -0.6382047
[31,] -1.7858073 -2.2120060
[32,] 0.1403914 -1.7858073
[33,] -11.9702128 0.1403914
[34,] -10.0440141 -11.9702128
[35,] -7.9178154 -10.0440141
[36,] -0.8916167 -7.9178154
[37,] 3.2345820 -0.8916167
[38,] 7.2607807 3.2345820
[39,] 5.6869794 7.2607807
[40,] 0.8131781 5.6869794
[41,] -0.9606232 0.8131781
[42,] -2.2344244 -0.9606232
[43,] -6.3082257 -2.2344244
[44,] -7.0820270 -6.3082257
[45,] -6.5558283 -7.0820270
[46,] -3.4296296 -6.5558283
[47,] -3.6034309 -3.4296296
[48,] 2.7227678 -3.6034309
[49,] 5.3489665 2.7227678
[50,] 11.0751652 5.3489665
[51,] 7.4013639 11.0751652
[52,] 2.0275626 7.4013639
[53,] 0.6537613 2.0275626
[54,] 0.0799600 0.6537613
[55,] 1.6061587 0.0799600
[56,] 1.2323574 1.6061587
[57,] 1.3585561 1.2323574
[58,] 2.5847548 1.3585561
[59,] 7.9109535 2.5847548
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 13.1282316 15.7020329
2 9.5544303 13.1282316
3 6.8806290 9.5544303
4 6.1068277 6.8806290
5 2.7330264 6.1068277
6 1.0592251 2.7330264
7 -1.0145762 1.0592251
8 -3.8883775 -1.0145762
9 -6.6621788 -3.8883775
10 -5.7359801 -6.6621788
11 -6.3097814 -5.7359801
12 -5.9835827 -6.3097814
13 -6.1573840 -5.9835827
14 -4.1311853 -6.1573840
15 -4.8049866 -4.1311853
16 -5.1787879 -4.8049866
17 -3.2525892 -5.1787879
18 -0.9263905 -3.2525892
19 -2.1001918 -0.9263905
20 -0.3739931 -2.1001918
21 -0.9477944 -0.3739931
22 -0.5215957 -0.9477944
23 -1.2953970 -0.5215957
24 1.0308018 -1.2953970
25 3.3570005 1.0308018
26 1.8831992 3.3570005
27 0.6093979 1.8831992
28 1.7355966 0.6093979
29 -0.6382047 1.7355966
30 -2.2120060 -0.6382047
31 -1.7858073 -2.2120060
32 0.1403914 -1.7858073
33 -11.9702128 0.1403914
34 -10.0440141 -11.9702128
35 -7.9178154 -10.0440141
36 -0.8916167 -7.9178154
37 3.2345820 -0.8916167
38 7.2607807 3.2345820
39 5.6869794 7.2607807
40 0.8131781 5.6869794
41 -0.9606232 0.8131781
42 -2.2344244 -0.9606232
43 -6.3082257 -2.2344244
44 -7.0820270 -6.3082257
45 -6.5558283 -7.0820270
46 -3.4296296 -6.5558283
47 -3.6034309 -3.4296296
48 2.7227678 -3.6034309
49 5.3489665 2.7227678
50 11.0751652 5.3489665
51 7.4013639 11.0751652
52 2.0275626 7.4013639
53 0.6537613 2.0275626
54 0.0799600 0.6537613
55 1.6061587 0.0799600
56 1.2323574 1.6061587
57 1.3585561 1.2323574
58 2.5847548 1.3585561
59 7.9109535 2.5847548
> 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/7hvq81227470806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81vut1227470806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9g2p71227470806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hzjj1227470806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jgtz1227470806.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/129oto1227470807.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/13vkj31227470807.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/1488721227470807.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/15nvwz1227470807.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/16rbav1227470807.tab")
+ }
>
> system("convert tmp/1yfnn1227470806.ps tmp/1yfnn1227470806.png")
> system("convert tmp/2emi81227470806.ps tmp/2emi81227470806.png")
> system("convert tmp/3rxjj1227470806.ps tmp/3rxjj1227470806.png")
> system("convert tmp/48iq51227470806.ps tmp/48iq51227470806.png")
> system("convert tmp/5h4rx1227470806.ps tmp/5h4rx1227470806.png")
> system("convert tmp/6oktz1227470806.ps tmp/6oktz1227470806.png")
> system("convert tmp/7hvq81227470806.ps tmp/7hvq81227470806.png")
> system("convert tmp/81vut1227470806.ps tmp/81vut1227470806.png")
> system("convert tmp/9g2p71227470806.ps tmp/9g2p71227470806.png")
> system("convert tmp/10hzjj1227470806.ps tmp/10hzjj1227470806.png")
>
>
> proc.time()
user system elapsed
6.744 4.484 7.269