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(8.803,3,0,0.000,3,2,1.219,1,0,-0.083,3,0,7.843,4,1.8,2.356,4,0.7,-3.772,1,3.9,5.075,4,1,1.194,1,3.6,3.954,1,1.4,-0.856,4,1.5,6.142,5,0.7,-0.598,2,2.7,5.232,5,0,-2.590,1,2.1,1.099,2,0,-0.242,2,4.1,-1.609,2,1.2,0.344,1,1.3,4.094,1,6.1,6.271,5,0.3,3.320,5,0.5,-2.120,2,3.4,5.333,1,0,4.443,1,1.5,3.593,1,0,-2.293,3,3.4,0.039,4,0.8,6.256,5,0.8,4.605,1,0,3.555,4,0,-5.298,4,1.4,-4.605,1,2,4.127,1,1.9,-2.104,1,2.4,0.300,3,2.8,-3.772,3,1.3,-3.037,3,2,0.531,1,5.6,1.253,1,3.1,5.521,5,1,-0.734,2,1.8,2.303,4,0.9,0.482,2,1.8,5.257,4,1.9,0.916,5,0.9,8.364,2,0,-1.273,3,2.6,8.351,1,2.4,1.917,2,1.2,-0.288,2,0.9,1.281,3,0.5,2.697,5,0,4.016,5,0.6,0.336,2,0,-2.813,2,2.2,-0.105,2,2.3,0.693,3,0.5,-2.263,2,2.6,1.433,4,0.6,1.253,1,6.6,1.399,1,0),dim=c(3,62),dimnames=list(c('gewicht','gevaar','slowsleep'),1:62))
> y <- array(NA,dim=c(3,62),dimnames=list(c('gewicht','gevaar','slowsleep'),1:62))
> 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 = '3'
> #'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
slowsleep gewicht gevaar
1 0.0 8.803 3
2 2.0 0.000 3
3 0.0 1.219 1
4 0.0 -0.083 3
5 1.8 7.843 4
6 0.7 2.356 4
7 3.9 -3.772 1
8 1.0 5.075 4
9 3.6 1.194 1
10 1.4 3.954 1
11 1.5 -0.856 4
12 0.7 6.142 5
13 2.7 -0.598 2
14 0.0 5.232 5
15 2.1 -2.590 1
16 0.0 1.099 2
17 4.1 -0.242 2
18 1.2 -1.609 2
19 1.3 0.344 1
20 6.1 4.094 1
21 0.3 6.271 5
22 0.5 3.320 5
23 3.4 -2.120 2
24 0.0 5.333 1
25 1.5 4.443 1
26 0.0 3.593 1
27 3.4 -2.293 3
28 0.8 0.039 4
29 0.8 6.256 5
30 0.0 4.605 1
31 0.0 3.555 4
32 1.4 -5.298 4
33 2.0 -4.605 1
34 1.9 4.127 1
35 2.4 -2.104 1
36 2.8 0.300 3
37 1.3 -3.772 3
38 2.0 -3.037 3
39 5.6 0.531 1
40 3.1 1.253 1
41 1.0 5.521 5
42 1.8 -0.734 2
43 0.9 2.303 4
44 1.8 0.482 2
45 1.9 5.257 4
46 0.9 0.916 5
47 0.0 8.364 2
48 2.6 -1.273 3
49 2.4 8.351 1
50 1.2 1.917 2
51 0.9 -0.288 2
52 0.5 1.281 3
53 0.0 2.697 5
54 0.6 4.016 5
55 0.0 0.336 2
56 2.2 -2.813 2
57 2.3 -0.105 2
58 0.5 0.693 3
59 2.6 -2.263 2
60 0.6 1.433 4
61 6.6 1.253 1
62 0.0 1.399 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) gewicht gevaar
2.7016 -0.1055 -0.3623
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.2107 -0.7970 -0.1178 0.6374 4.3929
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.70159 0.36080 7.488 4.03e-10 ***
gewicht -0.10550 0.05356 -1.970 0.05358 .
gevaar -0.36230 0.12503 -2.898 0.00527 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.364 on 59 degrees of freedom
Multiple R-squared: 0.2143, Adjusted R-squared: 0.1876
F-statistic: 8.044 on 2 and 59 DF, p-value: 0.000814
> 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,] 0.364505089 0.72901018 0.6354949
[2,] 0.728299028 0.54340194 0.2717010
[3,] 0.599067378 0.80186524 0.4009326
[4,] 0.659890213 0.68021957 0.3401098
[5,] 0.549878575 0.90024285 0.4501214
[6,] 0.434234228 0.86846846 0.5657658
[7,] 0.333169229 0.66633846 0.6668308
[8,] 0.263740565 0.52748113 0.7362594
[9,] 0.195856573 0.39171315 0.8041434
[10,] 0.140636416 0.28127283 0.8593636
[11,] 0.187946711 0.37589342 0.8120533
[12,] 0.305427671 0.61085534 0.6945723
[13,] 0.265821597 0.53164319 0.7341784
[14,] 0.222412434 0.44482487 0.7775876
[15,] 0.805689975 0.38862005 0.1943100
[16,] 0.745162956 0.50967409 0.2548370
[17,] 0.676539581 0.64692084 0.3234604
[18,] 0.659911037 0.68017793 0.3400890
[19,] 0.717362144 0.56527571 0.2826379
[20,] 0.652846225 0.69430755 0.3471538
[21,] 0.712357645 0.57528471 0.2876424
[22,] 0.717866742 0.56426652 0.2821333
[23,] 0.660651485 0.67869703 0.3393485
[24,] 0.599933585 0.80013283 0.4000664
[25,] 0.656378890 0.68724222 0.3436211
[26,] 0.614867508 0.77026498 0.3851325
[27,] 0.554240046 0.89151991 0.4457600
[28,] 0.504993198 0.99001360 0.4950068
[29,] 0.435515578 0.87103116 0.5644844
[30,] 0.365125457 0.73025091 0.6348745
[31,] 0.346668614 0.69333723 0.6533314
[32,] 0.293904821 0.58780964 0.7060952
[33,] 0.230744409 0.46148882 0.7692556
[34,] 0.541791943 0.91641611 0.4582081
[35,] 0.492680438 0.98536088 0.5073196
[36,] 0.433219314 0.86643863 0.5667807
[37,] 0.355728526 0.71145705 0.6442715
[38,] 0.282019665 0.56403933 0.7179803
[39,] 0.215548332 0.43109666 0.7844517
[40,] 0.207655815 0.41531163 0.7923442
[41,] 0.159468277 0.31893655 0.8405317
[42,] 0.140146415 0.28029283 0.8598536
[43,] 0.123174448 0.24634890 0.8768256
[44,] 0.086780109 0.17356022 0.9132199
[45,] 0.059052721 0.11810544 0.9409473
[46,] 0.044285894 0.08857179 0.9557141
[47,] 0.029625670 0.05925134 0.9703743
[48,] 0.015982240 0.03196448 0.9840178
[49,] 0.009632876 0.01926575 0.9903671
[50,] 0.013267874 0.02653575 0.9867321
[51,] 0.005330708 0.01066142 0.9946693
> postscript(file="/var/www/html/rcomp/tmp/1sayq1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2sayq1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3sayq1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43jxb1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53jxb1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 62
Frequency = 1
1 2 3 4 5 6
-0.685992571 0.385301270 -2.210692545 -1.623455132 1.375026998 -0.303845056
7 8 9 10 11 12
1.162762819 0.283006249 1.386669985 -0.522153257 0.157292717 0.457871937
13 14 15 16 17 18
0.659914531 -0.338131994 -0.512537570 -1.861053962 2.097472113 -0.946744781
19 20 21 22 23 24
-1.003004017 4.192616578 0.071481286 -0.039845748 1.199345319 -1.776670377
25 26 27 28 29 30
-0.370564332 -1.960238333 1.543392464 -0.448285835 0.569898803 -1.853473522
31 32 33 34 35 36
-0.877351965 -0.411333065 -0.825117703 -0.003901961 -0.161265141 1.216950918
37 38 39 40 41 42
-0.712640299 0.064901338 3.316724264 0.892894416 0.692357167 -0.254433309
43 44 45 46 47 48
-0.109436494 -0.126146737 1.202207036 0.106535076 -1.094604997 0.851001265
49 50 51 52 53 54
0.941725077 -0.574755923 -1.107380833 -0.979554735 -0.605571516 0.133581435
55 56 57 58 59 60
-1.941549566 -0.073765367 0.311925452 -1.041588044 0.384258987 -0.501220472
61 62
4.392894416 -2.191702756
> postscript(file="/var/www/html/rcomp/tmp/63jxb1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 62
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.685992571 NA
1 0.385301270 -0.685992571
2 -2.210692545 0.385301270
3 -1.623455132 -2.210692545
4 1.375026998 -1.623455132
5 -0.303845056 1.375026998
6 1.162762819 -0.303845056
7 0.283006249 1.162762819
8 1.386669985 0.283006249
9 -0.522153257 1.386669985
10 0.157292717 -0.522153257
11 0.457871937 0.157292717
12 0.659914531 0.457871937
13 -0.338131994 0.659914531
14 -0.512537570 -0.338131994
15 -1.861053962 -0.512537570
16 2.097472113 -1.861053962
17 -0.946744781 2.097472113
18 -1.003004017 -0.946744781
19 4.192616578 -1.003004017
20 0.071481286 4.192616578
21 -0.039845748 0.071481286
22 1.199345319 -0.039845748
23 -1.776670377 1.199345319
24 -0.370564332 -1.776670377
25 -1.960238333 -0.370564332
26 1.543392464 -1.960238333
27 -0.448285835 1.543392464
28 0.569898803 -0.448285835
29 -1.853473522 0.569898803
30 -0.877351965 -1.853473522
31 -0.411333065 -0.877351965
32 -0.825117703 -0.411333065
33 -0.003901961 -0.825117703
34 -0.161265141 -0.003901961
35 1.216950918 -0.161265141
36 -0.712640299 1.216950918
37 0.064901338 -0.712640299
38 3.316724264 0.064901338
39 0.892894416 3.316724264
40 0.692357167 0.892894416
41 -0.254433309 0.692357167
42 -0.109436494 -0.254433309
43 -0.126146737 -0.109436494
44 1.202207036 -0.126146737
45 0.106535076 1.202207036
46 -1.094604997 0.106535076
47 0.851001265 -1.094604997
48 0.941725077 0.851001265
49 -0.574755923 0.941725077
50 -1.107380833 -0.574755923
51 -0.979554735 -1.107380833
52 -0.605571516 -0.979554735
53 0.133581435 -0.605571516
54 -1.941549566 0.133581435
55 -0.073765367 -1.941549566
56 0.311925452 -0.073765367
57 -1.041588044 0.311925452
58 0.384258987 -1.041588044
59 -0.501220472 0.384258987
60 4.392894416 -0.501220472
61 -2.191702756 4.392894416
62 NA -2.191702756
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.385301270 -0.685992571
[2,] -2.210692545 0.385301270
[3,] -1.623455132 -2.210692545
[4,] 1.375026998 -1.623455132
[5,] -0.303845056 1.375026998
[6,] 1.162762819 -0.303845056
[7,] 0.283006249 1.162762819
[8,] 1.386669985 0.283006249
[9,] -0.522153257 1.386669985
[10,] 0.157292717 -0.522153257
[11,] 0.457871937 0.157292717
[12,] 0.659914531 0.457871937
[13,] -0.338131994 0.659914531
[14,] -0.512537570 -0.338131994
[15,] -1.861053962 -0.512537570
[16,] 2.097472113 -1.861053962
[17,] -0.946744781 2.097472113
[18,] -1.003004017 -0.946744781
[19,] 4.192616578 -1.003004017
[20,] 0.071481286 4.192616578
[21,] -0.039845748 0.071481286
[22,] 1.199345319 -0.039845748
[23,] -1.776670377 1.199345319
[24,] -0.370564332 -1.776670377
[25,] -1.960238333 -0.370564332
[26,] 1.543392464 -1.960238333
[27,] -0.448285835 1.543392464
[28,] 0.569898803 -0.448285835
[29,] -1.853473522 0.569898803
[30,] -0.877351965 -1.853473522
[31,] -0.411333065 -0.877351965
[32,] -0.825117703 -0.411333065
[33,] -0.003901961 -0.825117703
[34,] -0.161265141 -0.003901961
[35,] 1.216950918 -0.161265141
[36,] -0.712640299 1.216950918
[37,] 0.064901338 -0.712640299
[38,] 3.316724264 0.064901338
[39,] 0.892894416 3.316724264
[40,] 0.692357167 0.892894416
[41,] -0.254433309 0.692357167
[42,] -0.109436494 -0.254433309
[43,] -0.126146737 -0.109436494
[44,] 1.202207036 -0.126146737
[45,] 0.106535076 1.202207036
[46,] -1.094604997 0.106535076
[47,] 0.851001265 -1.094604997
[48,] 0.941725077 0.851001265
[49,] -0.574755923 0.941725077
[50,] -1.107380833 -0.574755923
[51,] -0.979554735 -1.107380833
[52,] -0.605571516 -0.979554735
[53,] 0.133581435 -0.605571516
[54,] -1.941549566 0.133581435
[55,] -0.073765367 -1.941549566
[56,] 0.311925452 -0.073765367
[57,] -1.041588044 0.311925452
[58,] 0.384258987 -1.041588044
[59,] -0.501220472 0.384258987
[60,] 4.392894416 -0.501220472
[61,] -2.191702756 4.392894416
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.385301270 -0.685992571
2 -2.210692545 0.385301270
3 -1.623455132 -2.210692545
4 1.375026998 -1.623455132
5 -0.303845056 1.375026998
6 1.162762819 -0.303845056
7 0.283006249 1.162762819
8 1.386669985 0.283006249
9 -0.522153257 1.386669985
10 0.157292717 -0.522153257
11 0.457871937 0.157292717
12 0.659914531 0.457871937
13 -0.338131994 0.659914531
14 -0.512537570 -0.338131994
15 -1.861053962 -0.512537570
16 2.097472113 -1.861053962
17 -0.946744781 2.097472113
18 -1.003004017 -0.946744781
19 4.192616578 -1.003004017
20 0.071481286 4.192616578
21 -0.039845748 0.071481286
22 1.199345319 -0.039845748
23 -1.776670377 1.199345319
24 -0.370564332 -1.776670377
25 -1.960238333 -0.370564332
26 1.543392464 -1.960238333
27 -0.448285835 1.543392464
28 0.569898803 -0.448285835
29 -1.853473522 0.569898803
30 -0.877351965 -1.853473522
31 -0.411333065 -0.877351965
32 -0.825117703 -0.411333065
33 -0.003901961 -0.825117703
34 -0.161265141 -0.003901961
35 1.216950918 -0.161265141
36 -0.712640299 1.216950918
37 0.064901338 -0.712640299
38 3.316724264 0.064901338
39 0.892894416 3.316724264
40 0.692357167 0.892894416
41 -0.254433309 0.692357167
42 -0.109436494 -0.254433309
43 -0.126146737 -0.109436494
44 1.202207036 -0.126146737
45 0.106535076 1.202207036
46 -1.094604997 0.106535076
47 0.851001265 -1.094604997
48 0.941725077 0.851001265
49 -0.574755923 0.941725077
50 -1.107380833 -0.574755923
51 -0.979554735 -1.107380833
52 -0.605571516 -0.979554735
53 0.133581435 -0.605571516
54 -1.941549566 0.133581435
55 -0.073765367 -1.941549566
56 0.311925452 -0.073765367
57 -1.041588044 0.311925452
58 0.384258987 -1.041588044
59 -0.501220472 0.384258987
60 4.392894416 -0.501220472
61 -2.191702756 4.392894416
> 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/7ebxe1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86keh1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96keh1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hbdk1292353255.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113uu81292353255.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/126cse1292353255.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/13k4qn1292353255.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/14vdp81292353255.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/15yw5v1292353255.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/16z9sb1292353255.tab")
+ }
>
> try(system("convert tmp/1sayq1292353255.ps tmp/1sayq1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sayq1292353255.ps tmp/2sayq1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sayq1292353255.ps tmp/3sayq1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/43jxb1292353255.ps tmp/43jxb1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/53jxb1292353255.ps tmp/53jxb1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/63jxb1292353255.ps tmp/63jxb1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ebxe1292353255.ps tmp/7ebxe1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/86keh1292353255.ps tmp/86keh1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/96keh1292353255.ps tmp/96keh1292353255.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hbdk1292353255.ps tmp/10hbdk1292353255.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.584 1.642 6.997