R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(8.9,1.6,8.8,1.3,8.3,1.1,7.5,1.6,7.2,1.9,7.4,1.6,8.8,1.7,9.3,1.6,9.3,1.4,8.7,2.1,8.2,1.9,8.3,1.7,8.5,1.8,8.6,2,8.5,2.5,8.2,2.1,8.1,2.1,7.9,2.3,8.6,2.4,8.7,2.4,8.7,2.3,8.5,1.7,8.4,2,8.5,2.3,8.7,2,8.7,2,8.6,1.3,8.5,1.7,8.3,1.9,8,1.7,8.2,1.6,8.1,1.7,8.1,1.8,8,1.9,7.9,1.9,7.9,1.9,8,2,8,2.1,7.9,1.9,8,1.9,7.7,1.3,7.2,1.3,7.5,1.4,7.3,1.2,7,1.3,7,1.8,7,2.2,7.2,2.6,7.3,2.8,7.1,3.1,6.8,3.9,6.4,3.7,6.1,4.6,6.5,5.1,7.7,5.2,7.9,4.9,7.5,5.1,6.9,4.8,6.6,3.9,6.9,3.5),dim=c(2,60),dimnames=list(c('TWIB','GI'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('TWIB','GI'),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
TWIB GI M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.9 1.6 1 0 0 0 0 0 0 0 0 0 0 1
2 8.8 1.3 0 1 0 0 0 0 0 0 0 0 0 2
3 8.3 1.1 0 0 1 0 0 0 0 0 0 0 0 3
4 7.5 1.6 0 0 0 1 0 0 0 0 0 0 0 4
5 7.2 1.9 0 0 0 0 1 0 0 0 0 0 0 5
6 7.4 1.6 0 0 0 0 0 1 0 0 0 0 0 6
7 8.8 1.7 0 0 0 0 0 0 1 0 0 0 0 7
8 9.3 1.6 0 0 0 0 0 0 0 1 0 0 0 8
9 9.3 1.4 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 2.1 0 0 0 0 0 0 0 0 0 1 0 10
11 8.2 1.9 0 0 0 0 0 0 0 0 0 0 1 11
12 8.3 1.7 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 1.8 1 0 0 0 0 0 0 0 0 0 0 13
14 8.6 2.0 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 2.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 2.1 0 0 0 1 0 0 0 0 0 0 0 16
17 8.1 2.1 0 0 0 0 1 0 0 0 0 0 0 17
18 7.9 2.3 0 0 0 0 0 1 0 0 0 0 0 18
19 8.6 2.4 0 0 0 0 0 0 1 0 0 0 0 19
20 8.7 2.4 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 2.3 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 1.7 0 0 0 0 0 0 0 0 0 1 0 22
23 8.4 2.0 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 2.3 0 0 0 0 0 0 0 0 0 0 0 24
25 8.7 2.0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.7 2.0 0 1 0 0 0 0 0 0 0 0 0 26
27 8.6 1.3 0 0 1 0 0 0 0 0 0 0 0 27
28 8.5 1.7 0 0 0 1 0 0 0 0 0 0 0 28
29 8.3 1.9 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 1.7 0 0 0 0 0 1 0 0 0 0 0 30
31 8.2 1.6 0 0 0 0 0 0 1 0 0 0 0 31
32 8.1 1.7 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 1.8 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 1.9 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 1.9 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 1.9 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 2.0 1 0 0 0 0 0 0 0 0 0 0 37
38 8.0 2.1 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 1.9 0 0 1 0 0 0 0 0 0 0 0 39
40 8.0 1.9 0 0 0 1 0 0 0 0 0 0 0 40
41 7.7 1.3 0 0 0 0 1 0 0 0 0 0 0 41
42 7.2 1.3 0 0 0 0 0 1 0 0 0 0 0 42
43 7.5 1.4 0 0 0 0 0 0 1 0 0 0 0 43
44 7.3 1.2 0 0 0 0 0 0 0 1 0 0 0 44
45 7.0 1.3 0 0 0 0 0 0 0 0 1 0 0 45
46 7.0 1.8 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 2.2 0 0 0 0 0 0 0 0 0 0 1 47
48 7.2 2.6 0 0 0 0 0 0 0 0 0 0 0 48
49 7.3 2.8 1 0 0 0 0 0 0 0 0 0 0 49
50 7.1 3.1 0 1 0 0 0 0 0 0 0 0 0 50
51 6.8 3.9 0 0 1 0 0 0 0 0 0 0 0 51
52 6.4 3.7 0 0 0 1 0 0 0 0 0 0 0 52
53 6.1 4.6 0 0 0 0 1 0 0 0 0 0 0 53
54 6.5 5.1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 5.2 0 0 0 0 0 0 1 0 0 0 0 55
56 7.9 4.9 0 0 0 0 0 0 0 1 0 0 0 56
57 7.5 5.1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.9 4.8 0 0 0 0 0 0 0 0 0 1 0 58
59 6.6 3.9 0 0 0 0 0 0 0 0 0 0 1 59
60 6.9 3.5 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) GI M1 M2 M3 M4
8.945703 -0.052407 0.177267 0.169854 -0.018608 -0.286021
M5 M6 M7 M8 M9 M10
-0.488193 -0.536655 0.255932 0.380134 0.270625 0.004260
M11 t
-0.170491 -0.029442
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.01073 -0.21087 0.07738 0.26285 0.79589
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.94570 0.25341 35.302 < 2e-16 ***
GI -0.05241 0.07339 -0.714 0.479
M1 0.17727 0.29599 0.599 0.552
M2 0.16985 0.29558 0.575 0.568
M3 -0.01861 0.29518 -0.063 0.950
M4 -0.28602 0.29486 -0.970 0.337
M5 -0.48819 0.29493 -1.655 0.105
M6 -0.53665 0.29466 -1.821 0.075 .
M7 0.25593 0.29452 0.869 0.389
M8 0.38013 0.29385 1.294 0.202
M9 0.27063 0.29366 0.922 0.362
M10 0.00426 0.29364 0.015 0.988
M11 -0.17049 0.29340 -0.581 0.564
t -0.02944 0.00458 -6.428 6.6e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4639 on 46 degrees of freedom
Multiple R-squared: 0.6964, Adjusted R-squared: 0.6105
F-statistic: 8.115 on 13 and 46 DF, p-value: 4.134e-08
> 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,] 0.7711443 0.4577114 0.22885572
[2,] 0.6847893 0.6304215 0.31521075
[3,] 0.6496290 0.7007419 0.35037096
[4,] 0.7232099 0.5535803 0.27679013
[5,] 0.7158795 0.5682409 0.28412047
[6,] 0.6624522 0.6750957 0.33754784
[7,] 0.5642958 0.8714084 0.43570421
[8,] 0.4878616 0.9757232 0.51213838
[9,] 0.3866373 0.7732747 0.61336267
[10,] 0.2895824 0.5791649 0.71041757
[11,] 0.2136176 0.4272352 0.78638239
[12,] 0.2006075 0.4012149 0.79939253
[13,] 0.1799760 0.3599521 0.82002396
[14,] 0.1247752 0.2495503 0.87522485
[15,] 0.1831151 0.3662303 0.81688487
[16,] 0.3308652 0.6617304 0.66913479
[17,] 0.3810898 0.7621795 0.61891023
[18,] 0.3283714 0.6567427 0.67162864
[19,] 0.2511770 0.5023540 0.74882300
[20,] 0.2131178 0.4262356 0.78688222
[21,] 0.1746275 0.3492550 0.82537251
[22,] 0.1284350 0.2568700 0.87156500
[23,] 0.0946914 0.1893828 0.90530861
[24,] 0.1377272 0.2754544 0.86227279
[25,] 0.6101463 0.7797074 0.38985370
[26,] 0.9424175 0.1151649 0.05758246
[27,] 0.8969921 0.2060158 0.10300791
> postscript(file="/var/www/html/rcomp/tmp/1sqsa1258756877.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2f4lg1258756877.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xr1p1258756877.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4r6kn1258756877.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t0k71258756877.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
-0.109677262 -0.188543642 -0.481121229 -0.958062320 -1.010725394 -0.748543642
7 8 9 10 11 12
-0.106447377 0.293552623 0.422023168 0.154515267 -0.151773526 -0.203302981
13 14 15 16 17 18
-0.145886763 0.001450162 0.145557204 0.121450162 0.253065105 0.141450162
19 20 21 22 23 24
0.083546427 0.088787088 0.222498295 0.286861799 0.406776311 0.381450162
25 26 27 28 29 30
0.417903736 0.454759339 0.535978447 0.753796694 0.795892959 0.563315372
31 32 33 34 35 36
-0.005069686 -0.194588363 -0.050395835 0.150652298 0.254844827 0.113796694
37 38 39 40 41 42
0.071212912 0.113309176 0.220731590 0.617587193 0.517758169 0.095661904
43 44 45 46 47 48
-0.362241831 -0.667482493 -0.823289964 -0.501279187 -0.276124014 -0.196209501
49 50 51 52 53 54
-0.233552623 -0.380975036 -0.421146011 -0.534771730 -0.555990838 -0.051883797
55 56 57 58 59 60
0.390212468 0.479731145 0.229164335 -0.090750177 -0.233723598 -0.095734375
> postscript(file="/var/www/html/rcomp/tmp/6eaok1258756877.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 -0.109677262 NA
1 -0.188543642 -0.109677262
2 -0.481121229 -0.188543642
3 -0.958062320 -0.481121229
4 -1.010725394 -0.958062320
5 -0.748543642 -1.010725394
6 -0.106447377 -0.748543642
7 0.293552623 -0.106447377
8 0.422023168 0.293552623
9 0.154515267 0.422023168
10 -0.151773526 0.154515267
11 -0.203302981 -0.151773526
12 -0.145886763 -0.203302981
13 0.001450162 -0.145886763
14 0.145557204 0.001450162
15 0.121450162 0.145557204
16 0.253065105 0.121450162
17 0.141450162 0.253065105
18 0.083546427 0.141450162
19 0.088787088 0.083546427
20 0.222498295 0.088787088
21 0.286861799 0.222498295
22 0.406776311 0.286861799
23 0.381450162 0.406776311
24 0.417903736 0.381450162
25 0.454759339 0.417903736
26 0.535978447 0.454759339
27 0.753796694 0.535978447
28 0.795892959 0.753796694
29 0.563315372 0.795892959
30 -0.005069686 0.563315372
31 -0.194588363 -0.005069686
32 -0.050395835 -0.194588363
33 0.150652298 -0.050395835
34 0.254844827 0.150652298
35 0.113796694 0.254844827
36 0.071212912 0.113796694
37 0.113309176 0.071212912
38 0.220731590 0.113309176
39 0.617587193 0.220731590
40 0.517758169 0.617587193
41 0.095661904 0.517758169
42 -0.362241831 0.095661904
43 -0.667482493 -0.362241831
44 -0.823289964 -0.667482493
45 -0.501279187 -0.823289964
46 -0.276124014 -0.501279187
47 -0.196209501 -0.276124014
48 -0.233552623 -0.196209501
49 -0.380975036 -0.233552623
50 -0.421146011 -0.380975036
51 -0.534771730 -0.421146011
52 -0.555990838 -0.534771730
53 -0.051883797 -0.555990838
54 0.390212468 -0.051883797
55 0.479731145 0.390212468
56 0.229164335 0.479731145
57 -0.090750177 0.229164335
58 -0.233723598 -0.090750177
59 -0.095734375 -0.233723598
60 NA -0.095734375
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.188543642 -0.109677262
[2,] -0.481121229 -0.188543642
[3,] -0.958062320 -0.481121229
[4,] -1.010725394 -0.958062320
[5,] -0.748543642 -1.010725394
[6,] -0.106447377 -0.748543642
[7,] 0.293552623 -0.106447377
[8,] 0.422023168 0.293552623
[9,] 0.154515267 0.422023168
[10,] -0.151773526 0.154515267
[11,] -0.203302981 -0.151773526
[12,] -0.145886763 -0.203302981
[13,] 0.001450162 -0.145886763
[14,] 0.145557204 0.001450162
[15,] 0.121450162 0.145557204
[16,] 0.253065105 0.121450162
[17,] 0.141450162 0.253065105
[18,] 0.083546427 0.141450162
[19,] 0.088787088 0.083546427
[20,] 0.222498295 0.088787088
[21,] 0.286861799 0.222498295
[22,] 0.406776311 0.286861799
[23,] 0.381450162 0.406776311
[24,] 0.417903736 0.381450162
[25,] 0.454759339 0.417903736
[26,] 0.535978447 0.454759339
[27,] 0.753796694 0.535978447
[28,] 0.795892959 0.753796694
[29,] 0.563315372 0.795892959
[30,] -0.005069686 0.563315372
[31,] -0.194588363 -0.005069686
[32,] -0.050395835 -0.194588363
[33,] 0.150652298 -0.050395835
[34,] 0.254844827 0.150652298
[35,] 0.113796694 0.254844827
[36,] 0.071212912 0.113796694
[37,] 0.113309176 0.071212912
[38,] 0.220731590 0.113309176
[39,] 0.617587193 0.220731590
[40,] 0.517758169 0.617587193
[41,] 0.095661904 0.517758169
[42,] -0.362241831 0.095661904
[43,] -0.667482493 -0.362241831
[44,] -0.823289964 -0.667482493
[45,] -0.501279187 -0.823289964
[46,] -0.276124014 -0.501279187
[47,] -0.196209501 -0.276124014
[48,] -0.233552623 -0.196209501
[49,] -0.380975036 -0.233552623
[50,] -0.421146011 -0.380975036
[51,] -0.534771730 -0.421146011
[52,] -0.555990838 -0.534771730
[53,] -0.051883797 -0.555990838
[54,] 0.390212468 -0.051883797
[55,] 0.479731145 0.390212468
[56,] 0.229164335 0.479731145
[57,] -0.090750177 0.229164335
[58,] -0.233723598 -0.090750177
[59,] -0.095734375 -0.233723598
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.188543642 -0.109677262
2 -0.481121229 -0.188543642
3 -0.958062320 -0.481121229
4 -1.010725394 -0.958062320
5 -0.748543642 -1.010725394
6 -0.106447377 -0.748543642
7 0.293552623 -0.106447377
8 0.422023168 0.293552623
9 0.154515267 0.422023168
10 -0.151773526 0.154515267
11 -0.203302981 -0.151773526
12 -0.145886763 -0.203302981
13 0.001450162 -0.145886763
14 0.145557204 0.001450162
15 0.121450162 0.145557204
16 0.253065105 0.121450162
17 0.141450162 0.253065105
18 0.083546427 0.141450162
19 0.088787088 0.083546427
20 0.222498295 0.088787088
21 0.286861799 0.222498295
22 0.406776311 0.286861799
23 0.381450162 0.406776311
24 0.417903736 0.381450162
25 0.454759339 0.417903736
26 0.535978447 0.454759339
27 0.753796694 0.535978447
28 0.795892959 0.753796694
29 0.563315372 0.795892959
30 -0.005069686 0.563315372
31 -0.194588363 -0.005069686
32 -0.050395835 -0.194588363
33 0.150652298 -0.050395835
34 0.254844827 0.150652298
35 0.113796694 0.254844827
36 0.071212912 0.113796694
37 0.113309176 0.071212912
38 0.220731590 0.113309176
39 0.617587193 0.220731590
40 0.517758169 0.617587193
41 0.095661904 0.517758169
42 -0.362241831 0.095661904
43 -0.667482493 -0.362241831
44 -0.823289964 -0.667482493
45 -0.501279187 -0.823289964
46 -0.276124014 -0.501279187
47 -0.196209501 -0.276124014
48 -0.233552623 -0.196209501
49 -0.380975036 -0.233552623
50 -0.421146011 -0.380975036
51 -0.534771730 -0.421146011
52 -0.555990838 -0.534771730
53 -0.051883797 -0.555990838
54 0.390212468 -0.051883797
55 0.479731145 0.390212468
56 0.229164335 0.479731145
57 -0.090750177 0.229164335
58 -0.233723598 -0.090750177
59 -0.095734375 -0.233723598
> 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/7pl911258756877.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ruhw1258756877.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9q8ts1258756877.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hcqo1258756877.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ifam1258756877.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/12cof21258756877.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/1327yi1258756877.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/144jgy1258756877.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/15e8lk1258756877.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/16mn001258756877.tab")
+ }
>
> system("convert tmp/1sqsa1258756877.ps tmp/1sqsa1258756877.png")
> system("convert tmp/2f4lg1258756877.ps tmp/2f4lg1258756877.png")
> system("convert tmp/3xr1p1258756877.ps tmp/3xr1p1258756877.png")
> system("convert tmp/4r6kn1258756877.ps tmp/4r6kn1258756877.png")
> system("convert tmp/5t0k71258756877.ps tmp/5t0k71258756877.png")
> system("convert tmp/6eaok1258756877.ps tmp/6eaok1258756877.png")
> system("convert tmp/7pl911258756877.ps tmp/7pl911258756877.png")
> system("convert tmp/8ruhw1258756877.ps tmp/8ruhw1258756877.png")
> system("convert tmp/9q8ts1258756877.ps tmp/9q8ts1258756877.png")
> system("convert tmp/10hcqo1258756877.ps tmp/10hcqo1258756877.png")
>
>
> proc.time()
user system elapsed
2.451 1.582 3.094