R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(5.4,2.7,5.4,2.5,5.6,2.2,5.7,2.9,5.8,3.1,5.8,3,5.8,2.8,5.9,2.5,6.1,1.9,6.4,1.9,6.4,1.8,6.3,2,6.2,2.6,6.2,2.5,6.3,2.5,6.4,1.6,6.5,1.4,6.6,0.8,6.6,1.1,6.6,1.3,6.8,1.2,7,1.3,7.2,1.1,7.3,1.3,7.5,1.2,7.6,1.6,7.6,1.7,7.7,1.5,7.7,0.9,7.7,1.5,7.7,1.4,7.6,1.6,7.7,1.7,7.9,1.4,7.9,1.8,7.9,1.7,7.8,1.4,7.6,1.2,7.4,1,7,1.7,7,2.4,7.2,2,7.5,2.1,7.8,2,7.8,1.8,7.7,2.7,7.6,2.3,7.6,1.9,7.5,2,7.5,2.3,7.6,2.8,7.6,2.4,7.9,2.3,7.6,2.7,7.5,2.7,7.5,2.9,7.6,3,7.7,2.2,7.8,2.3,7.9,2.8,7.9,2.8),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 5.4 2.7 1 0 0 0 0 0 0 0 0 0 0 1
2 5.4 2.5 0 1 0 0 0 0 0 0 0 0 0 2
3 5.6 2.2 0 0 1 0 0 0 0 0 0 0 0 3
4 5.7 2.9 0 0 0 1 0 0 0 0 0 0 0 4
5 5.8 3.1 0 0 0 0 1 0 0 0 0 0 0 5
6 5.8 3.0 0 0 0 0 0 1 0 0 0 0 0 6
7 5.8 2.8 0 0 0 0 0 0 1 0 0 0 0 7
8 5.9 2.5 0 0 0 0 0 0 0 1 0 0 0 8
9 6.1 1.9 0 0 0 0 0 0 0 0 1 0 0 9
10 6.4 1.9 0 0 0 0 0 0 0 0 0 1 0 10
11 6.4 1.8 0 0 0 0 0 0 0 0 0 0 1 11
12 6.3 2.0 0 0 0 0 0 0 0 0 0 0 0 12
13 6.2 2.6 1 0 0 0 0 0 0 0 0 0 0 13
14 6.2 2.5 0 1 0 0 0 0 0 0 0 0 0 14
15 6.3 2.5 0 0 1 0 0 0 0 0 0 0 0 15
16 6.4 1.6 0 0 0 1 0 0 0 0 0 0 0 16
17 6.5 1.4 0 0 0 0 1 0 0 0 0 0 0 17
18 6.6 0.8 0 0 0 0 0 1 0 0 0 0 0 18
19 6.6 1.1 0 0 0 0 0 0 1 0 0 0 0 19
20 6.6 1.3 0 0 0 0 0 0 0 1 0 0 0 20
21 6.8 1.2 0 0 0 0 0 0 0 0 1 0 0 21
22 7.0 1.3 0 0 0 0 0 0 0 0 0 1 0 22
23 7.2 1.1 0 0 0 0 0 0 0 0 0 0 1 23
24 7.3 1.3 0 0 0 0 0 0 0 0 0 0 0 24
25 7.5 1.2 1 0 0 0 0 0 0 0 0 0 0 25
26 7.6 1.6 0 1 0 0 0 0 0 0 0 0 0 26
27 7.6 1.7 0 0 1 0 0 0 0 0 0 0 0 27
28 7.7 1.5 0 0 0 1 0 0 0 0 0 0 0 28
29 7.7 0.9 0 0 0 0 1 0 0 0 0 0 0 29
30 7.7 1.5 0 0 0 0 0 1 0 0 0 0 0 30
31 7.7 1.4 0 0 0 0 0 0 1 0 0 0 0 31
32 7.6 1.6 0 0 0 0 0 0 0 1 0 0 0 32
33 7.7 1.7 0 0 0 0 0 0 0 0 1 0 0 33
34 7.9 1.4 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 1.8 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 1.7 0 0 0 0 0 0 0 0 0 0 0 36
37 7.8 1.4 1 0 0 0 0 0 0 0 0 0 0 37
38 7.6 1.2 0 1 0 0 0 0 0 0 0 0 0 38
39 7.4 1.0 0 0 1 0 0 0 0 0 0 0 0 39
40 7.0 1.7 0 0 0 1 0 0 0 0 0 0 0 40
41 7.0 2.4 0 0 0 0 1 0 0 0 0 0 0 41
42 7.2 2.0 0 0 0 0 0 1 0 0 0 0 0 42
43 7.5 2.1 0 0 0 0 0 0 1 0 0 0 0 43
44 7.8 2.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.8 1.8 0 0 0 0 0 0 0 0 1 0 0 45
46 7.7 2.7 0 0 0 0 0 0 0 0 0 1 0 46
47 7.6 2.3 0 0 0 0 0 0 0 0 0 0 1 47
48 7.6 1.9 0 0 0 0 0 0 0 0 0 0 0 48
49 7.5 2.0 1 0 0 0 0 0 0 0 0 0 0 49
50 7.5 2.3 0 1 0 0 0 0 0 0 0 0 0 50
51 7.6 2.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.6 2.4 0 0 0 1 0 0 0 0 0 0 0 52
53 7.9 2.3 0 0 0 0 1 0 0 0 0 0 0 53
54 7.6 2.7 0 0 0 0 0 1 0 0 0 0 0 54
55 7.5 2.7 0 0 0 0 0 0 1 0 0 0 0 55
56 7.5 2.9 0 0 0 0 0 0 0 1 0 0 0 56
57 7.6 3.0 0 0 0 0 0 0 0 0 1 0 0 57
58 7.7 2.2 0 0 0 0 0 0 0 0 0 1 0 58
59 7.8 2.3 0 0 0 0 0 0 0 0 0 0 1 59
60 7.9 2.8 0 0 0 0 0 0 0 0 0 0 0 60
61 7.9 2.8 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
6.892555 -0.450048 -0.078750 -0.120513 -0.109861 -0.177210
M5 M6 M7 M8 M9 M10
-0.115558 -0.162907 -0.152255 -0.112601 -0.093956 -0.001305
M11 t
-0.017656 0.038348
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.48419 -0.23702 -0.02934 0.19738 0.58597
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.892555 0.201041 34.284 < 2e-16 ***
X -0.450048 0.066701 -6.747 1.98e-08 ***
M1 -0.078750 0.191341 -0.412 0.683
M2 -0.120513 0.200525 -0.601 0.551
M3 -0.109861 0.200315 -0.548 0.586
M4 -0.177210 0.200010 -0.886 0.380
M5 -0.115558 0.199794 -0.578 0.566
M6 -0.162907 0.199560 -0.816 0.418
M7 -0.152255 0.199444 -0.763 0.449
M8 -0.112601 0.199418 -0.565 0.575
M9 -0.093956 0.199101 -0.472 0.639
M10 -0.001305 0.199043 -0.007 0.995
M11 -0.017656 0.199055 -0.089 0.930
t 0.038348 0.002348 16.330 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3146 on 47 degrees of freedom
Multiple R-squared: 0.8672, Adjusted R-squared: 0.8304
F-statistic: 23.6 on 13 and 47 DF, p-value: 2.881e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.984642e-03 3.969283e-03 0.9980154
[2,] 6.175765e-04 1.235153e-03 0.9993824
[3,] 1.256030e-04 2.512061e-04 0.9998744
[4,] 3.829998e-05 7.659995e-05 0.9999617
[5,] 1.262623e-05 2.525245e-05 0.9999874
[6,] 3.258601e-05 6.517202e-05 0.9999674
[7,] 1.616403e-05 3.232806e-05 0.9999838
[8,] 3.301995e-04 6.603990e-04 0.9996698
[9,] 4.720130e-02 9.440260e-02 0.9527987
[10,] 1.593154e-01 3.186308e-01 0.8406846
[11,] 1.455922e-01 2.911843e-01 0.8544078
[12,] 1.587734e-01 3.175467e-01 0.8412266
[13,] 1.227930e-01 2.455859e-01 0.8772070
[14,] 9.980579e-02 1.996116e-01 0.9001942
[15,] 7.219783e-02 1.443957e-01 0.9278022
[16,] 4.823644e-02 9.647287e-02 0.9517636
[17,] 3.788713e-02 7.577426e-02 0.9621129
[18,] 3.627054e-02 7.254107e-02 0.9637295
[19,] 4.951490e-02 9.902979e-02 0.9504851
[20,] 6.462745e-02 1.292549e-01 0.9353725
[21,] 1.065927e-01 2.131855e-01 0.8934073
[22,] 1.838952e-01 3.677904e-01 0.8161048
[23,] 3.355920e-01 6.711840e-01 0.6644080
[24,] 6.629110e-01 6.741779e-01 0.3370890
[25,] 8.959434e-01 2.081132e-01 0.1040566
[26,] 8.972219e-01 2.055563e-01 0.1027781
[27,] 8.046570e-01 3.906861e-01 0.1953430
[28,] 7.995291e-01 4.009417e-01 0.2004709
> postscript(file="/var/www/html/rcomp/tmp/19ffs1258658219.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22ctf1258658219.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3d0ot1258658219.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pfm01258658219.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wt6m1258658219.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 = 61
Frequency = 1
1 2 3 4 5 6
-0.23702316 -0.32361788 -0.30763328 0.13640136 0.22641099 0.19040714
7 8 9 10 11 12
0.05139655 -0.06161981 -0.18864194 -0.01964098 -0.08664387 -0.15263810
13 14 15 16 17 18
0.05779252 0.01620261 0.06720164 -0.20884070 -0.29885033 -0.45987824
19 20 21 22 23 24
-0.37386476 -0.36185706 -0.26385514 -0.14984936 -0.06185706 0.07214871
25 26 27 28 29 30
0.26754564 0.55097979 0.54698364 0.58597498 0.21594611 0.49497594
31 32 33 34 35 36
0.40097017 0.31297786 0.40098941 0.33497594 0.49299711 0.39198845
37 38 39 40 41 42
0.19737576 -0.08921897 -0.42822955 -0.48419491 -0.26916122 -0.24017951
43 44 45 46 47 48
0.05582434 0.23281760 0.08581472 0.25985899 -0.04215834 -0.27818143
49 50 51 52 53 54
-0.29277488 -0.15434554 0.12167755 -0.02934073 0.12565446 0.01467467
55 56 57 58 59 60
-0.13432630 -0.12231860 -0.03430705 -0.42534458 -0.30233784 -0.03331763
61
0.00708411
> postscript(file="/var/www/html/rcomp/tmp/6amq81258658219.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.23702316 NA
1 -0.32361788 -0.23702316
2 -0.30763328 -0.32361788
3 0.13640136 -0.30763328
4 0.22641099 0.13640136
5 0.19040714 0.22641099
6 0.05139655 0.19040714
7 -0.06161981 0.05139655
8 -0.18864194 -0.06161981
9 -0.01964098 -0.18864194
10 -0.08664387 -0.01964098
11 -0.15263810 -0.08664387
12 0.05779252 -0.15263810
13 0.01620261 0.05779252
14 0.06720164 0.01620261
15 -0.20884070 0.06720164
16 -0.29885033 -0.20884070
17 -0.45987824 -0.29885033
18 -0.37386476 -0.45987824
19 -0.36185706 -0.37386476
20 -0.26385514 -0.36185706
21 -0.14984936 -0.26385514
22 -0.06185706 -0.14984936
23 0.07214871 -0.06185706
24 0.26754564 0.07214871
25 0.55097979 0.26754564
26 0.54698364 0.55097979
27 0.58597498 0.54698364
28 0.21594611 0.58597498
29 0.49497594 0.21594611
30 0.40097017 0.49497594
31 0.31297786 0.40097017
32 0.40098941 0.31297786
33 0.33497594 0.40098941
34 0.49299711 0.33497594
35 0.39198845 0.49299711
36 0.19737576 0.39198845
37 -0.08921897 0.19737576
38 -0.42822955 -0.08921897
39 -0.48419491 -0.42822955
40 -0.26916122 -0.48419491
41 -0.24017951 -0.26916122
42 0.05582434 -0.24017951
43 0.23281760 0.05582434
44 0.08581472 0.23281760
45 0.25985899 0.08581472
46 -0.04215834 0.25985899
47 -0.27818143 -0.04215834
48 -0.29277488 -0.27818143
49 -0.15434554 -0.29277488
50 0.12167755 -0.15434554
51 -0.02934073 0.12167755
52 0.12565446 -0.02934073
53 0.01467467 0.12565446
54 -0.13432630 0.01467467
55 -0.12231860 -0.13432630
56 -0.03430705 -0.12231860
57 -0.42534458 -0.03430705
58 -0.30233784 -0.42534458
59 -0.03331763 -0.30233784
60 0.00708411 -0.03331763
61 NA 0.00708411
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.32361788 -0.23702316
[2,] -0.30763328 -0.32361788
[3,] 0.13640136 -0.30763328
[4,] 0.22641099 0.13640136
[5,] 0.19040714 0.22641099
[6,] 0.05139655 0.19040714
[7,] -0.06161981 0.05139655
[8,] -0.18864194 -0.06161981
[9,] -0.01964098 -0.18864194
[10,] -0.08664387 -0.01964098
[11,] -0.15263810 -0.08664387
[12,] 0.05779252 -0.15263810
[13,] 0.01620261 0.05779252
[14,] 0.06720164 0.01620261
[15,] -0.20884070 0.06720164
[16,] -0.29885033 -0.20884070
[17,] -0.45987824 -0.29885033
[18,] -0.37386476 -0.45987824
[19,] -0.36185706 -0.37386476
[20,] -0.26385514 -0.36185706
[21,] -0.14984936 -0.26385514
[22,] -0.06185706 -0.14984936
[23,] 0.07214871 -0.06185706
[24,] 0.26754564 0.07214871
[25,] 0.55097979 0.26754564
[26,] 0.54698364 0.55097979
[27,] 0.58597498 0.54698364
[28,] 0.21594611 0.58597498
[29,] 0.49497594 0.21594611
[30,] 0.40097017 0.49497594
[31,] 0.31297786 0.40097017
[32,] 0.40098941 0.31297786
[33,] 0.33497594 0.40098941
[34,] 0.49299711 0.33497594
[35,] 0.39198845 0.49299711
[36,] 0.19737576 0.39198845
[37,] -0.08921897 0.19737576
[38,] -0.42822955 -0.08921897
[39,] -0.48419491 -0.42822955
[40,] -0.26916122 -0.48419491
[41,] -0.24017951 -0.26916122
[42,] 0.05582434 -0.24017951
[43,] 0.23281760 0.05582434
[44,] 0.08581472 0.23281760
[45,] 0.25985899 0.08581472
[46,] -0.04215834 0.25985899
[47,] -0.27818143 -0.04215834
[48,] -0.29277488 -0.27818143
[49,] -0.15434554 -0.29277488
[50,] 0.12167755 -0.15434554
[51,] -0.02934073 0.12167755
[52,] 0.12565446 -0.02934073
[53,] 0.01467467 0.12565446
[54,] -0.13432630 0.01467467
[55,] -0.12231860 -0.13432630
[56,] -0.03430705 -0.12231860
[57,] -0.42534458 -0.03430705
[58,] -0.30233784 -0.42534458
[59,] -0.03331763 -0.30233784
[60,] 0.00708411 -0.03331763
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.32361788 -0.23702316
2 -0.30763328 -0.32361788
3 0.13640136 -0.30763328
4 0.22641099 0.13640136
5 0.19040714 0.22641099
6 0.05139655 0.19040714
7 -0.06161981 0.05139655
8 -0.18864194 -0.06161981
9 -0.01964098 -0.18864194
10 -0.08664387 -0.01964098
11 -0.15263810 -0.08664387
12 0.05779252 -0.15263810
13 0.01620261 0.05779252
14 0.06720164 0.01620261
15 -0.20884070 0.06720164
16 -0.29885033 -0.20884070
17 -0.45987824 -0.29885033
18 -0.37386476 -0.45987824
19 -0.36185706 -0.37386476
20 -0.26385514 -0.36185706
21 -0.14984936 -0.26385514
22 -0.06185706 -0.14984936
23 0.07214871 -0.06185706
24 0.26754564 0.07214871
25 0.55097979 0.26754564
26 0.54698364 0.55097979
27 0.58597498 0.54698364
28 0.21594611 0.58597498
29 0.49497594 0.21594611
30 0.40097017 0.49497594
31 0.31297786 0.40097017
32 0.40098941 0.31297786
33 0.33497594 0.40098941
34 0.49299711 0.33497594
35 0.39198845 0.49299711
36 0.19737576 0.39198845
37 -0.08921897 0.19737576
38 -0.42822955 -0.08921897
39 -0.48419491 -0.42822955
40 -0.26916122 -0.48419491
41 -0.24017951 -0.26916122
42 0.05582434 -0.24017951
43 0.23281760 0.05582434
44 0.08581472 0.23281760
45 0.25985899 0.08581472
46 -0.04215834 0.25985899
47 -0.27818143 -0.04215834
48 -0.29277488 -0.27818143
49 -0.15434554 -0.29277488
50 0.12167755 -0.15434554
51 -0.02934073 0.12167755
52 0.12565446 -0.02934073
53 0.01467467 0.12565446
54 -0.13432630 0.01467467
55 -0.12231860 -0.13432630
56 -0.03430705 -0.12231860
57 -0.42534458 -0.03430705
58 -0.30233784 -0.42534458
59 -0.03331763 -0.30233784
60 0.00708411 -0.03331763
> 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/7g0xn1258658219.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z3yk1258658219.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/98j0y1258658219.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rvwf1258658219.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fcal1258658219.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/120i4t1258658219.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/13t1oo1258658219.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/1411e21258658219.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/15u50j1258658219.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/16pmjx1258658219.tab")
+ }
>
> system("convert tmp/19ffs1258658219.ps tmp/19ffs1258658219.png")
> system("convert tmp/22ctf1258658219.ps tmp/22ctf1258658219.png")
> system("convert tmp/3d0ot1258658219.ps tmp/3d0ot1258658219.png")
> system("convert tmp/4pfm01258658219.ps tmp/4pfm01258658219.png")
> system("convert tmp/5wt6m1258658219.ps tmp/5wt6m1258658219.png")
> system("convert tmp/6amq81258658219.ps tmp/6amq81258658219.png")
> system("convert tmp/7g0xn1258658219.ps tmp/7g0xn1258658219.png")
> system("convert tmp/8z3yk1258658219.ps tmp/8z3yk1258658219.png")
> system("convert tmp/98j0y1258658219.ps tmp/98j0y1258658219.png")
> system("convert tmp/10rvwf1258658219.ps tmp/10rvwf1258658219.png")
>
>
> proc.time()
user system elapsed
2.416 1.584 3.430