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(7.7,10,8.1,7.5,9.2,7.7,7.6,9.2,7.5,7.8,9.5,7.6,7.8,9.6,7.8,7.8,9.5,7.8,7.5,9.1,7.8,7.5,8.9,7.5,7.1,9,7.5,7.5,10.1,7.1,7.5,10.3,7.5,7.6,10.2,7.5,7.7,9.6,7.6,7.7,9.2,7.7,7.9,9.3,7.7,8.1,9.4,7.9,8.2,9.4,8.1,8.2,9.2,8.2,8.2,9,8.2,7.9,9,8.2,7.3,9,7.9,6.9,9.8,7.3,6.6,10,6.9,6.7,9.8,6.6,6.9,9.3,6.7,7,9,6.9,7.1,9,7,7.2,9.1,7.1,7.1,9.1,7.2,6.9,9.1,7.1,7,9.2,6.9,6.8,8.8,7,6.4,8.3,6.8,6.7,8.4,6.4,6.6,8.1,6.7,6.4,7.7,6.6,6.3,7.9,6.4,6.2,7.9,6.3,6.5,8,6.2,6.8,7.9,6.5,6.8,7.6,6.8,6.4,7.1,6.8,6.1,6.8,6.4,5.8,6.5,6.1,6.1,6.9,5.8,7.2,8.2,6.1,7.3,8.7,7.2,6.9,8.3,7.3,6.1,7.9,6.9,5.8,7.5,6.1,6.2,7.8,5.8,7.1,8.3,6.2,7.7,8.4,7.1,7.9,8.2,7.7,7.7,7.7,7.9,7.4,7.2,7.7,7.5,7.3,7.4,8,8.1,7.5,8.1,8.5,8),dim=c(3,59),dimnames=list(c('Y','X','Y1'),1:59))
> y <- array(NA,dim=c(3,59),dimnames=list(c('Y','X','Y1'),1:59))
> 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
Y X Y1 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.7 10.0 8.1 1 0 0 0 0 0 0 0 0 0 0 1
2 7.5 9.2 7.7 0 1 0 0 0 0 0 0 0 0 0 2
3 7.6 9.2 7.5 0 0 1 0 0 0 0 0 0 0 0 3
4 7.8 9.5 7.6 0 0 0 1 0 0 0 0 0 0 0 4
5 7.8 9.6 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 7.8 9.5 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.5 9.1 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 8.9 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 7.1 9.0 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 10.1 7.1 0 0 0 0 0 0 0 0 0 1 0 10
11 7.5 10.3 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 7.6 10.2 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 7.7 9.6 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 7.7 9.2 7.7 0 1 0 0 0 0 0 0 0 0 0 14
15 7.9 9.3 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 8.1 9.4 7.9 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 9.4 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 9.2 8.2 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 9.0 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 7.9 9.0 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 7.3 9.0 7.9 0 0 0 0 0 0 0 0 1 0 0 21
22 6.9 9.8 7.3 0 0 0 0 0 0 0 0 0 1 0 22
23 6.6 10.0 6.9 0 0 0 0 0 0 0 0 0 0 1 23
24 6.7 9.8 6.6 0 0 0 0 0 0 0 0 0 0 0 24
25 6.9 9.3 6.7 1 0 0 0 0 0 0 0 0 0 0 25
26 7.0 9.0 6.9 0 1 0 0 0 0 0 0 0 0 0 26
27 7.1 9.0 7.0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.2 9.1 7.1 0 0 0 1 0 0 0 0 0 0 0 28
29 7.1 9.1 7.2 0 0 0 0 1 0 0 0 0 0 0 29
30 6.9 9.1 7.1 0 0 0 0 0 1 0 0 0 0 0 30
31 7.0 9.2 6.9 0 0 0 0 0 0 1 0 0 0 0 31
32 6.8 8.8 7.0 0 0 0 0 0 0 0 1 0 0 0 32
33 6.4 8.3 6.8 0 0 0 0 0 0 0 0 1 0 0 33
34 6.7 8.4 6.4 0 0 0 0 0 0 0 0 0 1 0 34
35 6.6 8.1 6.7 0 0 0 0 0 0 0 0 0 0 1 35
36 6.4 7.7 6.6 0 0 0 0 0 0 0 0 0 0 0 36
37 6.3 7.9 6.4 1 0 0 0 0 0 0 0 0 0 0 37
38 6.2 7.9 6.3 0 1 0 0 0 0 0 0 0 0 0 38
39 6.5 8.0 6.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 7.9 6.5 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 7.6 6.8 0 0 0 0 1 0 0 0 0 0 0 41
42 6.4 7.1 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.1 6.8 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 5.8 6.5 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.1 6.9 5.8 0 0 0 0 0 0 0 0 1 0 0 45
46 7.2 8.2 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.3 8.7 7.2 0 0 0 0 0 0 0 0 0 0 1 47
48 6.9 8.3 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 7.9 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 5.8 7.5 6.1 0 1 0 0 0 0 0 0 0 0 0 50
51 6.2 7.8 5.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.1 8.3 6.2 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 8.4 7.1 0 0 0 0 1 0 0 0 0 0 0 53
54 7.9 8.2 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 7.7 7.9 0 0 0 0 0 0 1 0 0 0 0 55
56 7.4 7.2 7.7 0 0 0 0 0 0 0 1 0 0 0 56
57 7.5 7.3 7.4 0 0 0 0 0 0 0 0 1 0 0 57
58 8.0 8.1 7.5 0 0 0 0 0 0 0 0 0 1 0 58
59 8.1 8.5 8.0 0 0 0 0 0 0 0 0 0 0 1 59
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 M1 M2 M3
-0.88951 0.15826 0.87656 -0.03502 0.09279 0.37698
M4 M5 M6 M7 M8 M9
0.48801 0.30550 0.14433 0.10796 0.04735 0.02939
M10 M11 t
0.44729 0.03490 0.00764
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.77575 -0.09995 0.01053 0.14453 0.64599
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.88951 0.85323 -1.043 0.3029
X 0.15826 0.08717 1.816 0.0763 .
Y1 0.87656 0.07770 11.281 1.43e-14 ***
M1 -0.03502 0.19031 -0.184 0.8548
M2 0.09279 0.19491 0.476 0.6364
M3 0.37698 0.19200 1.963 0.0559 .
M4 0.48801 0.18965 2.573 0.0135 *
M5 0.30550 0.19287 1.584 0.1204
M6 0.14433 0.19828 0.728 0.4705
M7 0.10796 0.20253 0.533 0.5967
M8 0.04735 0.20758 0.228 0.8206
M9 0.02939 0.20191 0.146 0.8849
M10 0.44729 0.18876 2.370 0.0223 *
M11 0.03490 0.19107 0.183 0.8559
t 0.00764 0.00399 1.915 0.0620 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2807 on 44 degrees of freedom
Multiple R-squared: 0.8603, Adjusted R-squared: 0.8159
F-statistic: 19.36 on 14 and 44 DF, p-value: 2.483e-14
> 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,] 0.0009685046 0.001937009 0.99903150
[2,] 0.0213092971 0.042618594 0.97869070
[3,] 0.0150244385 0.030048877 0.98497556
[4,] 0.0095161224 0.019032245 0.99048388
[5,] 0.4130758260 0.826151652 0.58692417
[6,] 0.5750702537 0.849859493 0.42492975
[7,] 0.4652515212 0.930503042 0.53474848
[8,] 0.4970020040 0.994004008 0.50299800
[9,] 0.5372266401 0.925546720 0.46277336
[10,] 0.5002862797 0.999427441 0.49971372
[11,] 0.4194639652 0.838927930 0.58053603
[12,] 0.3605781791 0.721156358 0.63942182
[13,] 0.3293892441 0.658778488 0.67061076
[14,] 0.2435850076 0.487170015 0.75641499
[15,] 0.1745563313 0.349112663 0.82544367
[16,] 0.2898044297 0.579608859 0.71019557
[17,] 0.5928923055 0.814215389 0.40710769
[18,] 0.4995866753 0.999173351 0.50041332
[19,] 0.6288212059 0.742357588 0.37117879
[20,] 0.9608005379 0.078398924 0.03919946
[21,] 0.9512697227 0.097460555 0.04873028
[22,] 0.9454622795 0.109075441 0.05453772
[23,] 0.9038364424 0.192327115 0.09616356
[24,] 0.8771399355 0.245720129 0.12286006
> postscript(file="/var/www/html/rcomp/tmp/19xf31259259959.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2umip1259259959.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34da41259259959.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4owua1259259959.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kfbt1259259959.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 = 59
Frequency = 1
1 2 3 4 5 6
-0.065896865 0.075886174 0.059368714 0.005564533 -0.010708410 0.158652678
7 8 9 10 11 12
-0.049315394 0.298273441 -0.107231263 0.043767359 0.066236120 0.209324280
13 14 15 16 17 18
0.344008557 0.184205243 0.076549049 -0.033258636 0.066294767 0.163826006
19 20 21 22 23 24
0.224205243 -0.022827196 -0.349536971 -0.775746925 -0.352028609 0.069854480
25 26 27 28 29 30
0.288712412 0.125426558 -0.154059485 -0.276210976 -0.289001378 -0.247810442
31 32 33 34 35 36
0.040402148 -0.130981104 -0.266215347 -0.056953271 0.032303411 0.010526802
37 38 39 40 41 42
0.081568899 -0.066227408 0.013772592 -0.052038596 -0.092662351 -0.259995882
43 44 45 46 47 48
-0.133165522 -0.069750342 0.440234501 0.645987072 0.107383436 -0.289705562
49 50 51 52 53 54
-0.648393003 -0.319290568 0.004369130 0.355943675 0.326077372 0.185327639
55 56 57 58 59
-0.082126476 -0.074714800 0.282749079 0.142945765 0.146105641
> postscript(file="/var/www/html/rcomp/tmp/60a9o1259259959.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.065896865 NA
1 0.075886174 -0.065896865
2 0.059368714 0.075886174
3 0.005564533 0.059368714
4 -0.010708410 0.005564533
5 0.158652678 -0.010708410
6 -0.049315394 0.158652678
7 0.298273441 -0.049315394
8 -0.107231263 0.298273441
9 0.043767359 -0.107231263
10 0.066236120 0.043767359
11 0.209324280 0.066236120
12 0.344008557 0.209324280
13 0.184205243 0.344008557
14 0.076549049 0.184205243
15 -0.033258636 0.076549049
16 0.066294767 -0.033258636
17 0.163826006 0.066294767
18 0.224205243 0.163826006
19 -0.022827196 0.224205243
20 -0.349536971 -0.022827196
21 -0.775746925 -0.349536971
22 -0.352028609 -0.775746925
23 0.069854480 -0.352028609
24 0.288712412 0.069854480
25 0.125426558 0.288712412
26 -0.154059485 0.125426558
27 -0.276210976 -0.154059485
28 -0.289001378 -0.276210976
29 -0.247810442 -0.289001378
30 0.040402148 -0.247810442
31 -0.130981104 0.040402148
32 -0.266215347 -0.130981104
33 -0.056953271 -0.266215347
34 0.032303411 -0.056953271
35 0.010526802 0.032303411
36 0.081568899 0.010526802
37 -0.066227408 0.081568899
38 0.013772592 -0.066227408
39 -0.052038596 0.013772592
40 -0.092662351 -0.052038596
41 -0.259995882 -0.092662351
42 -0.133165522 -0.259995882
43 -0.069750342 -0.133165522
44 0.440234501 -0.069750342
45 0.645987072 0.440234501
46 0.107383436 0.645987072
47 -0.289705562 0.107383436
48 -0.648393003 -0.289705562
49 -0.319290568 -0.648393003
50 0.004369130 -0.319290568
51 0.355943675 0.004369130
52 0.326077372 0.355943675
53 0.185327639 0.326077372
54 -0.082126476 0.185327639
55 -0.074714800 -0.082126476
56 0.282749079 -0.074714800
57 0.142945765 0.282749079
58 0.146105641 0.142945765
59 NA 0.146105641
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.075886174 -0.065896865
[2,] 0.059368714 0.075886174
[3,] 0.005564533 0.059368714
[4,] -0.010708410 0.005564533
[5,] 0.158652678 -0.010708410
[6,] -0.049315394 0.158652678
[7,] 0.298273441 -0.049315394
[8,] -0.107231263 0.298273441
[9,] 0.043767359 -0.107231263
[10,] 0.066236120 0.043767359
[11,] 0.209324280 0.066236120
[12,] 0.344008557 0.209324280
[13,] 0.184205243 0.344008557
[14,] 0.076549049 0.184205243
[15,] -0.033258636 0.076549049
[16,] 0.066294767 -0.033258636
[17,] 0.163826006 0.066294767
[18,] 0.224205243 0.163826006
[19,] -0.022827196 0.224205243
[20,] -0.349536971 -0.022827196
[21,] -0.775746925 -0.349536971
[22,] -0.352028609 -0.775746925
[23,] 0.069854480 -0.352028609
[24,] 0.288712412 0.069854480
[25,] 0.125426558 0.288712412
[26,] -0.154059485 0.125426558
[27,] -0.276210976 -0.154059485
[28,] -0.289001378 -0.276210976
[29,] -0.247810442 -0.289001378
[30,] 0.040402148 -0.247810442
[31,] -0.130981104 0.040402148
[32,] -0.266215347 -0.130981104
[33,] -0.056953271 -0.266215347
[34,] 0.032303411 -0.056953271
[35,] 0.010526802 0.032303411
[36,] 0.081568899 0.010526802
[37,] -0.066227408 0.081568899
[38,] 0.013772592 -0.066227408
[39,] -0.052038596 0.013772592
[40,] -0.092662351 -0.052038596
[41,] -0.259995882 -0.092662351
[42,] -0.133165522 -0.259995882
[43,] -0.069750342 -0.133165522
[44,] 0.440234501 -0.069750342
[45,] 0.645987072 0.440234501
[46,] 0.107383436 0.645987072
[47,] -0.289705562 0.107383436
[48,] -0.648393003 -0.289705562
[49,] -0.319290568 -0.648393003
[50,] 0.004369130 -0.319290568
[51,] 0.355943675 0.004369130
[52,] 0.326077372 0.355943675
[53,] 0.185327639 0.326077372
[54,] -0.082126476 0.185327639
[55,] -0.074714800 -0.082126476
[56,] 0.282749079 -0.074714800
[57,] 0.142945765 0.282749079
[58,] 0.146105641 0.142945765
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.075886174 -0.065896865
2 0.059368714 0.075886174
3 0.005564533 0.059368714
4 -0.010708410 0.005564533
5 0.158652678 -0.010708410
6 -0.049315394 0.158652678
7 0.298273441 -0.049315394
8 -0.107231263 0.298273441
9 0.043767359 -0.107231263
10 0.066236120 0.043767359
11 0.209324280 0.066236120
12 0.344008557 0.209324280
13 0.184205243 0.344008557
14 0.076549049 0.184205243
15 -0.033258636 0.076549049
16 0.066294767 -0.033258636
17 0.163826006 0.066294767
18 0.224205243 0.163826006
19 -0.022827196 0.224205243
20 -0.349536971 -0.022827196
21 -0.775746925 -0.349536971
22 -0.352028609 -0.775746925
23 0.069854480 -0.352028609
24 0.288712412 0.069854480
25 0.125426558 0.288712412
26 -0.154059485 0.125426558
27 -0.276210976 -0.154059485
28 -0.289001378 -0.276210976
29 -0.247810442 -0.289001378
30 0.040402148 -0.247810442
31 -0.130981104 0.040402148
32 -0.266215347 -0.130981104
33 -0.056953271 -0.266215347
34 0.032303411 -0.056953271
35 0.010526802 0.032303411
36 0.081568899 0.010526802
37 -0.066227408 0.081568899
38 0.013772592 -0.066227408
39 -0.052038596 0.013772592
40 -0.092662351 -0.052038596
41 -0.259995882 -0.092662351
42 -0.133165522 -0.259995882
43 -0.069750342 -0.133165522
44 0.440234501 -0.069750342
45 0.645987072 0.440234501
46 0.107383436 0.645987072
47 -0.289705562 0.107383436
48 -0.648393003 -0.289705562
49 -0.319290568 -0.648393003
50 0.004369130 -0.319290568
51 0.355943675 0.004369130
52 0.326077372 0.355943675
53 0.185327639 0.326077372
54 -0.082126476 0.185327639
55 -0.074714800 -0.082126476
56 0.282749079 -0.074714800
57 0.142945765 0.282749079
58 0.146105641 0.142945765
> 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/7tft71259259959.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/859og1259259959.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9u7yr1259259959.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107t651259259959.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112w351259259959.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/124ur61259259959.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/13pvxd1259259959.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/145pri1259259959.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/15fp9u1259259960.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/165omp1259259960.tab")
+ }
>
> system("convert tmp/19xf31259259959.ps tmp/19xf31259259959.png")
> system("convert tmp/2umip1259259959.ps tmp/2umip1259259959.png")
> system("convert tmp/34da41259259959.ps tmp/34da41259259959.png")
> system("convert tmp/4owua1259259959.ps tmp/4owua1259259959.png")
> system("convert tmp/5kfbt1259259959.ps tmp/5kfbt1259259959.png")
> system("convert tmp/60a9o1259259959.ps tmp/60a9o1259259959.png")
> system("convert tmp/7tft71259259959.ps tmp/7tft71259259959.png")
> system("convert tmp/859og1259259959.ps tmp/859og1259259959.png")
> system("convert tmp/9u7yr1259259959.ps tmp/9u7yr1259259959.png")
> system("convert tmp/107t651259259959.ps tmp/107t651259259959.png")
>
>
> proc.time()
user system elapsed
2.492 1.591 5.189