R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(14929388,0,0,14717825,0,0,15826281,0,0,16301310,0,0,15033017,0,0,16998461,0,0,14066463,0,0,13328937,0,0,17319718,0,0,17586427,0,0,15887037,0,0,17935679,0,0,15869489,0,0,15892511,0,0,17556558,0,0,16791643,0,1,15953689,0,1,18144914,0,1,14390881,0,1,13885709,0,1,17332572,0,1,17152596,0,1,16003877,0,1,16841467,0,1,14783398,0,1,14667848,0,1,17714362,0,1,16282088,0,1,15014866,1,0,17722582,1,0,13876509,1,0,15495490,1,0,17799521,1,0,17920079,1,0,17248022,1,0,18813782,1,0,16249688,0,0,17823359,0,0,20424438,0,0,17814219,0,0,19699960,0,0,19776328,0,0,15679833,0,0,17119267,0,0,20092613,0,0,20863688,0,0,20925203,0,0,21032593,0,0,20664684,0,0,19711511,0,0,22553293,0,0,19498333,0,0,20722828,0,0,21321275,0,0,17960848,0,0,17789655,0,0,20003709,0,0,21169852,0,0,20422839,0,0,19810562,0,0),dim=c(3,60),dimnames=list(c('omzet','D1','D2'),1:60))
> y <- array(NA,dim=c(3,60),dimnames=list(c('omzet','D1','D2'),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 D1 D2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 14929388 0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 14717825 0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 15826281 0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 16301310 0 0 0 0 0 1 0 0 0 0 0 0 0 4
5 15033017 0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 16998461 0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 14066463 0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 13328937 0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 17319718 0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 17586427 0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 15887037 0 0 0 0 0 0 0 0 0 0 0 0 1 11
12 17935679 0 0 0 0 0 0 0 0 0 0 0 0 0 12
13 15869489 0 0 1 0 0 0 0 0 0 0 0 0 0 13
14 15892511 0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 17556558 0 0 0 0 1 0 0 0 0 0 0 0 0 15
16 16791643 0 1 0 0 0 1 0 0 0 0 0 0 0 16
17 15953689 0 1 0 0 0 0 1 0 0 0 0 0 0 17
18 18144914 0 1 0 0 0 0 0 1 0 0 0 0 0 18
19 14390881 0 1 0 0 0 0 0 0 1 0 0 0 0 19
20 13885709 0 1 0 0 0 0 0 0 0 1 0 0 0 20
21 17332572 0 1 0 0 0 0 0 0 0 0 1 0 0 21
22 17152596 0 1 0 0 0 0 0 0 0 0 0 1 0 22
23 16003877 0 1 0 0 0 0 0 0 0 0 0 0 1 23
24 16841467 0 1 0 0 0 0 0 0 0 0 0 0 0 24
25 14783398 0 1 1 0 0 0 0 0 0 0 0 0 0 25
26 14667848 0 1 0 1 0 0 0 0 0 0 0 0 0 26
27 17714362 0 1 0 0 1 0 0 0 0 0 0 0 0 27
28 16282088 0 1 0 0 0 1 0 0 0 0 0 0 0 28
29 15014866 1 0 0 0 0 0 1 0 0 0 0 0 0 29
30 17722582 1 0 0 0 0 0 0 1 0 0 0 0 0 30
31 13876509 1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 15495490 1 0 0 0 0 0 0 0 0 1 0 0 0 32
33 17799521 1 0 0 0 0 0 0 0 0 0 1 0 0 33
34 17920079 1 0 0 0 0 0 0 0 0 0 0 1 0 34
35 17248022 1 0 0 0 0 0 0 0 0 0 0 0 1 35
36 18813782 1 0 0 0 0 0 0 0 0 0 0 0 0 36
37 16249688 0 0 1 0 0 0 0 0 0 0 0 0 0 37
38 17823359 0 0 0 1 0 0 0 0 0 0 0 0 0 38
39 20424438 0 0 0 0 1 0 0 0 0 0 0 0 0 39
40 17814219 0 0 0 0 0 1 0 0 0 0 0 0 0 40
41 19699960 0 0 0 0 0 0 1 0 0 0 0 0 0 41
42 19776328 0 0 0 0 0 0 0 1 0 0 0 0 0 42
43 15679833 0 0 0 0 0 0 0 0 1 0 0 0 0 43
44 17119267 0 0 0 0 0 0 0 0 0 1 0 0 0 44
45 20092613 0 0 0 0 0 0 0 0 0 0 1 0 0 45
46 20863688 0 0 0 0 0 0 0 0 0 0 0 1 0 46
47 20925203 0 0 0 0 0 0 0 0 0 0 0 0 1 47
48 21032593 0 0 0 0 0 0 0 0 0 0 0 0 0 48
49 20664684 0 0 1 0 0 0 0 0 0 0 0 0 0 49
50 19711511 0 0 0 1 0 0 0 0 0 0 0 0 0 50
51 22553293 0 0 0 0 1 0 0 0 0 0 0 0 0 51
52 19498333 0 0 0 0 0 1 0 0 0 0 0 0 0 52
53 20722828 0 0 0 0 0 0 1 0 0 0 0 0 0 53
54 21321275 0 0 0 0 0 0 0 1 0 0 0 0 0 54
55 17960848 0 0 0 0 0 0 0 0 1 0 0 0 0 55
56 17789655 0 0 0 0 0 0 0 0 0 1 0 0 0 56
57 20003709 0 0 0 0 0 0 0 0 0 0 1 0 0 57
58 21169852 0 0 0 0 0 0 0 0 0 0 0 1 0 58
59 20422839 0 0 0 0 0 0 0 0 0 0 0 0 1 59
60 19810562 0 0 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) D1 D2 M1 M2 M3
16246717 -1413728 -1068775 -1711827 -1735674 429574
M4 M5 M6 M7 M8 M9
-921266 -992050 428662 -3256271 -3014494 -115807
M10 M11 t
225967 -702293 87128
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1663822 -426873 16993 447096 1860533
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16246717 469918 34.574 < 2e-16 ***
D1 -1413728 342938 -4.122 0.000159 ***
D2 -1068776 284107 -3.762 0.000485 ***
M1 -1711827 546666 -3.131 0.003055 **
M2 -1735674 545758 -3.180 0.002664 **
M3 429574 544930 0.788 0.434648
M4 -921266 544184 -1.693 0.097382 .
M5 -992050 538847 -1.841 0.072211 .
M6 428662 538306 0.796 0.430029
M7 -3256271 537847 -6.054 2.59e-07 ***
M8 -3014494 537472 -5.609 1.19e-06 ***
M9 -115807 537180 -0.216 0.830287
M10 225967 536971 0.421 0.675891
M11 -702293 536846 -1.308 0.197454
t 87128 6696 13.011 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 848800 on 45 degrees of freedom
Multiple R-squared: 0.8929, Adjusted R-squared: 0.8596
F-statistic: 26.8 on 14 and 45 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,] 0.045592040 0.091184079 0.9544080
[2,] 0.024933468 0.049866936 0.9750665
[3,] 0.007465011 0.014930021 0.9925350
[4,] 0.006722215 0.013444429 0.9932778
[5,] 0.011442376 0.022884752 0.9885576
[6,] 0.004704684 0.009409367 0.9952953
[7,] 0.017951396 0.035902792 0.9820486
[8,] 0.039119691 0.078239382 0.9608803
[9,] 0.046893295 0.093786589 0.9531067
[10,] 0.035314009 0.070628017 0.9646860
[11,] 0.033396972 0.066793944 0.9666030
[12,] 0.046802427 0.093604853 0.9531976
[13,] 0.028976023 0.057952047 0.9710240
[14,] 0.015997858 0.031995716 0.9840021
[15,] 0.043147349 0.086294698 0.9568527
[16,] 0.024764293 0.049528586 0.9752357
[17,] 0.013718509 0.027437018 0.9862815
[18,] 0.014156066 0.028312133 0.9858439
[19,] 0.009333926 0.018667852 0.9906661
[20,] 0.071900873 0.143801746 0.9280991
[21,] 0.083567176 0.167134351 0.9164328
[22,] 0.126813673 0.253627345 0.8731863
[23,] 0.128277683 0.256555366 0.8717223
[24,] 0.124488790 0.248977579 0.8755112
[25,] 0.105179613 0.210359227 0.8948204
> postscript(file="/var/www/html/rcomp/tmp/1h8j21228562944.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fsxf1228562944.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3s85h1228562944.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4k5pc1228562944.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c7ye1228562944.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
307370.11 32525.71 -1111393.89 627347.82 -657289.10 -199685.10
7 8 9 10 11 12
466122.10 -600308.70 404657.30 242464.50 -615792.70 643428.30
13 14 15 16 17 18
201937.81 161678.41 -426650.19 1140922.97 286625.06 970010.06
19 20 21 22 23 24
813782.26 -20294.54 440753.46 -168124.34 -475710.54 -427541.54
25 26 27 28 29 30
-860911.04 -1039742.44 -245604.04 -414165.33 -1352778.67 -152902.67
31 32 33 34 35 36
-401170.48 888905.73 207121.73 -101222.08 67853.72 844192.72
37 38 39 40 41 42
-1508929.79 1459.81 350163.21 -996343.08 873054.01 -558417.99
43 44 45 46 47 48
-1057107.79 53421.41 40952.41 383125.61 1285773.41 603742.41
49 50 51 52 53 54
1860532.91 844078.51 1433484.91 -357762.38 850388.71 -59004.29
55 56 57 58 59 60
178373.91 -321723.89 -1093484.89 -356243.69 -262123.89 -1663821.89
> postscript(file="/var/www/html/rcomp/tmp/6vpup1228562944.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 307370.11 NA
1 32525.71 307370.11
2 -1111393.89 32525.71
3 627347.82 -1111393.89
4 -657289.10 627347.82
5 -199685.10 -657289.10
6 466122.10 -199685.10
7 -600308.70 466122.10
8 404657.30 -600308.70
9 242464.50 404657.30
10 -615792.70 242464.50
11 643428.30 -615792.70
12 201937.81 643428.30
13 161678.41 201937.81
14 -426650.19 161678.41
15 1140922.97 -426650.19
16 286625.06 1140922.97
17 970010.06 286625.06
18 813782.26 970010.06
19 -20294.54 813782.26
20 440753.46 -20294.54
21 -168124.34 440753.46
22 -475710.54 -168124.34
23 -427541.54 -475710.54
24 -860911.04 -427541.54
25 -1039742.44 -860911.04
26 -245604.04 -1039742.44
27 -414165.33 -245604.04
28 -1352778.67 -414165.33
29 -152902.67 -1352778.67
30 -401170.48 -152902.67
31 888905.73 -401170.48
32 207121.73 888905.73
33 -101222.08 207121.73
34 67853.72 -101222.08
35 844192.72 67853.72
36 -1508929.79 844192.72
37 1459.81 -1508929.79
38 350163.21 1459.81
39 -996343.08 350163.21
40 873054.01 -996343.08
41 -558417.99 873054.01
42 -1057107.79 -558417.99
43 53421.41 -1057107.79
44 40952.41 53421.41
45 383125.61 40952.41
46 1285773.41 383125.61
47 603742.41 1285773.41
48 1860532.91 603742.41
49 844078.51 1860532.91
50 1433484.91 844078.51
51 -357762.38 1433484.91
52 850388.71 -357762.38
53 -59004.29 850388.71
54 178373.91 -59004.29
55 -321723.89 178373.91
56 -1093484.89 -321723.89
57 -356243.69 -1093484.89
58 -262123.89 -356243.69
59 -1663821.89 -262123.89
60 NA -1663821.89
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 32525.71 307370.11
[2,] -1111393.89 32525.71
[3,] 627347.82 -1111393.89
[4,] -657289.10 627347.82
[5,] -199685.10 -657289.10
[6,] 466122.10 -199685.10
[7,] -600308.70 466122.10
[8,] 404657.30 -600308.70
[9,] 242464.50 404657.30
[10,] -615792.70 242464.50
[11,] 643428.30 -615792.70
[12,] 201937.81 643428.30
[13,] 161678.41 201937.81
[14,] -426650.19 161678.41
[15,] 1140922.97 -426650.19
[16,] 286625.06 1140922.97
[17,] 970010.06 286625.06
[18,] 813782.26 970010.06
[19,] -20294.54 813782.26
[20,] 440753.46 -20294.54
[21,] -168124.34 440753.46
[22,] -475710.54 -168124.34
[23,] -427541.54 -475710.54
[24,] -860911.04 -427541.54
[25,] -1039742.44 -860911.04
[26,] -245604.04 -1039742.44
[27,] -414165.33 -245604.04
[28,] -1352778.67 -414165.33
[29,] -152902.67 -1352778.67
[30,] -401170.48 -152902.67
[31,] 888905.73 -401170.48
[32,] 207121.73 888905.73
[33,] -101222.08 207121.73
[34,] 67853.72 -101222.08
[35,] 844192.72 67853.72
[36,] -1508929.79 844192.72
[37,] 1459.81 -1508929.79
[38,] 350163.21 1459.81
[39,] -996343.08 350163.21
[40,] 873054.01 -996343.08
[41,] -558417.99 873054.01
[42,] -1057107.79 -558417.99
[43,] 53421.41 -1057107.79
[44,] 40952.41 53421.41
[45,] 383125.61 40952.41
[46,] 1285773.41 383125.61
[47,] 603742.41 1285773.41
[48,] 1860532.91 603742.41
[49,] 844078.51 1860532.91
[50,] 1433484.91 844078.51
[51,] -357762.38 1433484.91
[52,] 850388.71 -357762.38
[53,] -59004.29 850388.71
[54,] 178373.91 -59004.29
[55,] -321723.89 178373.91
[56,] -1093484.89 -321723.89
[57,] -356243.69 -1093484.89
[58,] -262123.89 -356243.69
[59,] -1663821.89 -262123.89
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 32525.71 307370.11
2 -1111393.89 32525.71
3 627347.82 -1111393.89
4 -657289.10 627347.82
5 -199685.10 -657289.10
6 466122.10 -199685.10
7 -600308.70 466122.10
8 404657.30 -600308.70
9 242464.50 404657.30
10 -615792.70 242464.50
11 643428.30 -615792.70
12 201937.81 643428.30
13 161678.41 201937.81
14 -426650.19 161678.41
15 1140922.97 -426650.19
16 286625.06 1140922.97
17 970010.06 286625.06
18 813782.26 970010.06
19 -20294.54 813782.26
20 440753.46 -20294.54
21 -168124.34 440753.46
22 -475710.54 -168124.34
23 -427541.54 -475710.54
24 -860911.04 -427541.54
25 -1039742.44 -860911.04
26 -245604.04 -1039742.44
27 -414165.33 -245604.04
28 -1352778.67 -414165.33
29 -152902.67 -1352778.67
30 -401170.48 -152902.67
31 888905.73 -401170.48
32 207121.73 888905.73
33 -101222.08 207121.73
34 67853.72 -101222.08
35 844192.72 67853.72
36 -1508929.79 844192.72
37 1459.81 -1508929.79
38 350163.21 1459.81
39 -996343.08 350163.21
40 873054.01 -996343.08
41 -558417.99 873054.01
42 -1057107.79 -558417.99
43 53421.41 -1057107.79
44 40952.41 53421.41
45 383125.61 40952.41
46 1285773.41 383125.61
47 603742.41 1285773.41
48 1860532.91 603742.41
49 844078.51 1860532.91
50 1433484.91 844078.51
51 -357762.38 1433484.91
52 850388.71 -357762.38
53 -59004.29 850388.71
54 178373.91 -59004.29
55 -321723.89 178373.91
56 -1093484.89 -321723.89
57 -356243.69 -1093484.89
58 -262123.89 -356243.69
59 -1663821.89 -262123.89
> 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/7ymyl1228562944.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lzue1228562944.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9h6r11228562944.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10w9l81228562944.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118y931228562944.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/12v6531228562944.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/13lp8s1228562944.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/143sgr1228562944.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/15oar41228562944.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/161zxm1228562944.tab")
+ }
>
> system("convert tmp/1h8j21228562944.ps tmp/1h8j21228562944.png")
> system("convert tmp/2fsxf1228562944.ps tmp/2fsxf1228562944.png")
> system("convert tmp/3s85h1228562944.ps tmp/3s85h1228562944.png")
> system("convert tmp/4k5pc1228562944.ps tmp/4k5pc1228562944.png")
> system("convert tmp/5c7ye1228562944.ps tmp/5c7ye1228562944.png")
> system("convert tmp/6vpup1228562944.ps tmp/6vpup1228562944.png")
> system("convert tmp/7ymyl1228562944.ps tmp/7ymyl1228562944.png")
> system("convert tmp/8lzue1228562944.ps tmp/8lzue1228562944.png")
> system("convert tmp/9h6r11228562944.ps tmp/9h6r11228562944.png")
> system("convert tmp/10w9l81228562944.ps tmp/10w9l81228562944.png")
>
>
> proc.time()
user system elapsed
2.410 1.591 3.087