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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(115.6,37.2,111.9,37.2,107,34.7,107.1,32.5,100.6,33.5,99.2,31.5,108.4,31.2,103,27,99.8,26.7,115,26.5,90.8,26,95.9,27.2,114.4,30.5,108.2,33.7,112.6,34.2,109.1,36.7,105,36.2,105,38.5,118.5,40,103.7,42.5,112.5,43.5,116.6,43.3,96.6,45.5,101.9,44.3,116.5,43,119.3,43.5,115.4,41.5,108.5,42.5,111.5,41.3,108.8,39.5,121.8,38.5,109.6,41,112.2,44.5,119.6,46,104.1,44,105.3,41.5,115,41.3,124.1,38,116.8,38,107.5,36.2,115.6,38.7,116.2,38.7,116.3,39.2,119,35.7,111.9,36.5,118.6,36.7,106.9,34.7,103.2,35,118.6,28.2,118.7,23.7,102.8,15,100.6,8.7,94.9,11,94.5,7.5,102.9,5.7,95.3,9.3,92.5,10.2,102.7,15.7,91.5,18.1,89.5,20.8),dim=c(2,60),dimnames=list(c('Ipzb','Cvn'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Ipzb','Cvn'),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 = 'No 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
Ipzb Cvn
1 115.6 37.2
2 111.9 37.2
3 107.0 34.7
4 107.1 32.5
5 100.6 33.5
6 99.2 31.5
7 108.4 31.2
8 103.0 27.0
9 99.8 26.7
10 115.0 26.5
11 90.8 26.0
12 95.9 27.2
13 114.4 30.5
14 108.2 33.7
15 112.6 34.2
16 109.1 36.7
17 105.0 36.2
18 105.0 38.5
19 118.5 40.0
20 103.7 42.5
21 112.5 43.5
22 116.6 43.3
23 96.6 45.5
24 101.9 44.3
25 116.5 43.0
26 119.3 43.5
27 115.4 41.5
28 108.5 42.5
29 111.5 41.3
30 108.8 39.5
31 121.8 38.5
32 109.6 41.0
33 112.2 44.5
34 119.6 46.0
35 104.1 44.0
36 105.3 41.5
37 115.0 41.3
38 124.1 38.0
39 116.8 38.0
40 107.5 36.2
41 115.6 38.7
42 116.2 38.7
43 116.3 39.2
44 119.0 35.7
45 111.9 36.5
46 118.6 36.7
47 106.9 34.7
48 103.2 35.0
49 118.6 28.2
50 118.7 23.7
51 102.8 15.0
52 100.6 8.7
53 94.9 11.0
54 94.5 7.5
55 102.9 5.7
56 95.3 9.3
57 92.5 10.2
58 102.7 15.7
59 91.5 18.1
60 89.5 20.8
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Cvn
92.7883 0.4668
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.4263 -4.7763 -0.5793 5.2458 14.8493
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 92.78831 2.96556 31.289 < 2e-16 ***
Cvn 0.46677 0.08585 5.437 1.13e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.155 on 58 degrees of freedom
Multiple R-squared: 0.3376, Adjusted R-squared: 0.3262
F-statistic: 29.56 on 1 and 58 DF, p-value: 1.131e-06
> 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,] 0.16140286 0.3228057 0.83859714
[2,] 0.07380666 0.1476133 0.92619334
[3,] 0.12901040 0.2580208 0.87098960
[4,] 0.10202281 0.2040456 0.89797719
[5,] 0.05390310 0.1078062 0.94609690
[6,] 0.27541138 0.5508228 0.72458862
[7,] 0.45904743 0.9180949 0.54095257
[8,] 0.43648856 0.8729771 0.56351144
[9,] 0.51037564 0.9792487 0.48962436
[10,] 0.41471591 0.8294318 0.58528409
[11,] 0.34962070 0.6992414 0.65037930
[12,] 0.27562783 0.5512557 0.72437217
[13,] 0.24499213 0.4899843 0.75500787
[14,] 0.23401702 0.4680340 0.76598298
[15,] 0.21861670 0.4372334 0.78138330
[16,] 0.29019921 0.5803984 0.70980079
[17,] 0.22436182 0.4487236 0.77563818
[18,] 0.17876929 0.3575386 0.82123071
[19,] 0.52845317 0.9430937 0.47154683
[20,] 0.62807625 0.7438475 0.37192375
[21,] 0.59570746 0.8085851 0.40429254
[22,] 0.59360168 0.8127966 0.40639832
[23,] 0.53955066 0.9208987 0.46044934
[24,] 0.49475343 0.9895069 0.50524657
[25,] 0.42571403 0.8514281 0.57428597
[26,] 0.36842335 0.7368467 0.63157665
[27,] 0.46808294 0.9361659 0.53191706
[28,] 0.40971464 0.8194293 0.59028536
[29,] 0.35025452 0.7005090 0.64974548
[30,] 0.30877339 0.6175468 0.69122661
[31,] 0.40637032 0.8127406 0.59362968
[32,] 0.46737109 0.9347422 0.53262891
[33,] 0.40714033 0.8142807 0.59285967
[34,] 0.54407862 0.9118428 0.45592138
[35,] 0.49863201 0.9972640 0.50136799
[36,] 0.45380002 0.9076000 0.54619998
[37,] 0.38891239 0.7778248 0.61108761
[38,] 0.32919333 0.6583867 0.67080667
[39,] 0.27068942 0.5413788 0.72931058
[40,] 0.27788471 0.5557694 0.72211529
[41,] 0.21085361 0.4217072 0.78914639
[42,] 0.21330158 0.4266032 0.78669842
[43,] 0.15770594 0.3154119 0.84229406
[44,] 0.17204554 0.3440911 0.82795446
[45,] 0.23739912 0.4747982 0.76260088
[46,] 0.86608324 0.2678335 0.13391676
[47,] 0.90155676 0.1968865 0.09844324
[48,] 0.85318143 0.2936371 0.14681857
[49,] 0.76265467 0.4746907 0.23734533
[50,] 0.67754789 0.6449042 0.32245211
[51,] 0.57935963 0.8412807 0.42064037
> postscript(file="/var/www/html/rcomp/tmp/1i7o81258727714.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2m4x41258727714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3babs1258727714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46zk81258727714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xk7y1258727714.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
5.4478862 1.7478862 -1.9851916 -0.8583001 -7.8250690 -8.2915312
7 8 9 10 11 12
1.0484994 -2.3910713 -5.4510407 9.8423131 -14.1243025 -9.5844251
13 14 15 16 17 18
7.3752376 -0.3184228 3.8481928 -0.8187294 -4.6853449 -5.7589133
19 20 21 22 23 24
7.0409334 -8.9259888 -0.5927577 3.6005961 -17.4262954 -11.5661728
25 26 27 28 29 30
3.6406268 6.2072423 3.2407801 -4.1259888 -0.5658662 -2.4256822
31 32 33 34 35 36
11.0410867 -2.3258355 -1.3595266 5.3403201 -9.2261421 -6.8592199
37 38 39 40 41 42
2.9341338 13.5744711 6.2744711 -2.1853449 4.7477329 5.3477329
43 44 45 46 47 48
5.2143485 9.5480395 2.0746244 8.6812706 -2.0851916 -5.9252223
49 50 51 52 53 54
12.6488060 14.8492659 3.0101551 3.7507990 -3.0227694 -1.7890784
55 56 57 58 59 60
7.4511056 -1.8292623 -5.0493543 2.5834169 -9.7368284 -12.9971043
> postscript(file="/var/www/html/rcomp/tmp/6jr0u1258727714.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 5.4478862 NA
1 1.7478862 5.4478862
2 -1.9851916 1.7478862
3 -0.8583001 -1.9851916
4 -7.8250690 -0.8583001
5 -8.2915312 -7.8250690
6 1.0484994 -8.2915312
7 -2.3910713 1.0484994
8 -5.4510407 -2.3910713
9 9.8423131 -5.4510407
10 -14.1243025 9.8423131
11 -9.5844251 -14.1243025
12 7.3752376 -9.5844251
13 -0.3184228 7.3752376
14 3.8481928 -0.3184228
15 -0.8187294 3.8481928
16 -4.6853449 -0.8187294
17 -5.7589133 -4.6853449
18 7.0409334 -5.7589133
19 -8.9259888 7.0409334
20 -0.5927577 -8.9259888
21 3.6005961 -0.5927577
22 -17.4262954 3.6005961
23 -11.5661728 -17.4262954
24 3.6406268 -11.5661728
25 6.2072423 3.6406268
26 3.2407801 6.2072423
27 -4.1259888 3.2407801
28 -0.5658662 -4.1259888
29 -2.4256822 -0.5658662
30 11.0410867 -2.4256822
31 -2.3258355 11.0410867
32 -1.3595266 -2.3258355
33 5.3403201 -1.3595266
34 -9.2261421 5.3403201
35 -6.8592199 -9.2261421
36 2.9341338 -6.8592199
37 13.5744711 2.9341338
38 6.2744711 13.5744711
39 -2.1853449 6.2744711
40 4.7477329 -2.1853449
41 5.3477329 4.7477329
42 5.2143485 5.3477329
43 9.5480395 5.2143485
44 2.0746244 9.5480395
45 8.6812706 2.0746244
46 -2.0851916 8.6812706
47 -5.9252223 -2.0851916
48 12.6488060 -5.9252223
49 14.8492659 12.6488060
50 3.0101551 14.8492659
51 3.7507990 3.0101551
52 -3.0227694 3.7507990
53 -1.7890784 -3.0227694
54 7.4511056 -1.7890784
55 -1.8292623 7.4511056
56 -5.0493543 -1.8292623
57 2.5834169 -5.0493543
58 -9.7368284 2.5834169
59 -12.9971043 -9.7368284
60 NA -12.9971043
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.7478862 5.4478862
[2,] -1.9851916 1.7478862
[3,] -0.8583001 -1.9851916
[4,] -7.8250690 -0.8583001
[5,] -8.2915312 -7.8250690
[6,] 1.0484994 -8.2915312
[7,] -2.3910713 1.0484994
[8,] -5.4510407 -2.3910713
[9,] 9.8423131 -5.4510407
[10,] -14.1243025 9.8423131
[11,] -9.5844251 -14.1243025
[12,] 7.3752376 -9.5844251
[13,] -0.3184228 7.3752376
[14,] 3.8481928 -0.3184228
[15,] -0.8187294 3.8481928
[16,] -4.6853449 -0.8187294
[17,] -5.7589133 -4.6853449
[18,] 7.0409334 -5.7589133
[19,] -8.9259888 7.0409334
[20,] -0.5927577 -8.9259888
[21,] 3.6005961 -0.5927577
[22,] -17.4262954 3.6005961
[23,] -11.5661728 -17.4262954
[24,] 3.6406268 -11.5661728
[25,] 6.2072423 3.6406268
[26,] 3.2407801 6.2072423
[27,] -4.1259888 3.2407801
[28,] -0.5658662 -4.1259888
[29,] -2.4256822 -0.5658662
[30,] 11.0410867 -2.4256822
[31,] -2.3258355 11.0410867
[32,] -1.3595266 -2.3258355
[33,] 5.3403201 -1.3595266
[34,] -9.2261421 5.3403201
[35,] -6.8592199 -9.2261421
[36,] 2.9341338 -6.8592199
[37,] 13.5744711 2.9341338
[38,] 6.2744711 13.5744711
[39,] -2.1853449 6.2744711
[40,] 4.7477329 -2.1853449
[41,] 5.3477329 4.7477329
[42,] 5.2143485 5.3477329
[43,] 9.5480395 5.2143485
[44,] 2.0746244 9.5480395
[45,] 8.6812706 2.0746244
[46,] -2.0851916 8.6812706
[47,] -5.9252223 -2.0851916
[48,] 12.6488060 -5.9252223
[49,] 14.8492659 12.6488060
[50,] 3.0101551 14.8492659
[51,] 3.7507990 3.0101551
[52,] -3.0227694 3.7507990
[53,] -1.7890784 -3.0227694
[54,] 7.4511056 -1.7890784
[55,] -1.8292623 7.4511056
[56,] -5.0493543 -1.8292623
[57,] 2.5834169 -5.0493543
[58,] -9.7368284 2.5834169
[59,] -12.9971043 -9.7368284
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.7478862 5.4478862
2 -1.9851916 1.7478862
3 -0.8583001 -1.9851916
4 -7.8250690 -0.8583001
5 -8.2915312 -7.8250690
6 1.0484994 -8.2915312
7 -2.3910713 1.0484994
8 -5.4510407 -2.3910713
9 9.8423131 -5.4510407
10 -14.1243025 9.8423131
11 -9.5844251 -14.1243025
12 7.3752376 -9.5844251
13 -0.3184228 7.3752376
14 3.8481928 -0.3184228
15 -0.8187294 3.8481928
16 -4.6853449 -0.8187294
17 -5.7589133 -4.6853449
18 7.0409334 -5.7589133
19 -8.9259888 7.0409334
20 -0.5927577 -8.9259888
21 3.6005961 -0.5927577
22 -17.4262954 3.6005961
23 -11.5661728 -17.4262954
24 3.6406268 -11.5661728
25 6.2072423 3.6406268
26 3.2407801 6.2072423
27 -4.1259888 3.2407801
28 -0.5658662 -4.1259888
29 -2.4256822 -0.5658662
30 11.0410867 -2.4256822
31 -2.3258355 11.0410867
32 -1.3595266 -2.3258355
33 5.3403201 -1.3595266
34 -9.2261421 5.3403201
35 -6.8592199 -9.2261421
36 2.9341338 -6.8592199
37 13.5744711 2.9341338
38 6.2744711 13.5744711
39 -2.1853449 6.2744711
40 4.7477329 -2.1853449
41 5.3477329 4.7477329
42 5.2143485 5.3477329
43 9.5480395 5.2143485
44 2.0746244 9.5480395
45 8.6812706 2.0746244
46 -2.0851916 8.6812706
47 -5.9252223 -2.0851916
48 12.6488060 -5.9252223
49 14.8492659 12.6488060
50 3.0101551 14.8492659
51 3.7507990 3.0101551
52 -3.0227694 3.7507990
53 -1.7890784 -3.0227694
54 7.4511056 -1.7890784
55 -1.8292623 7.4511056
56 -5.0493543 -1.8292623
57 2.5834169 -5.0493543
58 -9.7368284 2.5834169
59 -12.9971043 -9.7368284
> 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/7zwzk1258727714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83nfr1258727714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/916ny1258727714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10u2ly1258727714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110cml1258727714.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/12kyn91258727714.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/139fp91258727714.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/14i4gr1258727714.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/156zmf1258727714.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/16mt471258727714.tab")
+ }
>
> system("convert tmp/1i7o81258727714.ps tmp/1i7o81258727714.png")
> system("convert tmp/2m4x41258727714.ps tmp/2m4x41258727714.png")
> system("convert tmp/3babs1258727714.ps tmp/3babs1258727714.png")
> system("convert tmp/46zk81258727714.ps tmp/46zk81258727714.png")
> system("convert tmp/5xk7y1258727714.ps tmp/5xk7y1258727714.png")
> system("convert tmp/6jr0u1258727714.ps tmp/6jr0u1258727714.png")
> system("convert tmp/7zwzk1258727714.ps tmp/7zwzk1258727714.png")
> system("convert tmp/83nfr1258727714.ps tmp/83nfr1258727714.png")
> system("convert tmp/916ny1258727714.ps tmp/916ny1258727714.png")
> system("convert tmp/10u2ly1258727714.ps tmp/10u2ly1258727714.png")
>
>
> proc.time()
user system elapsed
2.518 1.590 2.942