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.
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Type 'license()' or 'licence()' for distribution details.
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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(14.2,-0.8,13.5,-0.2,11.9,0.2,14.6,1,15.6,0,14.1,-0.2,14.9,1,14.2,0.4,14.6,1,17.2,1.7,15.4,3.1,14.3,3.3,17.5,3.1,14.5,3.5,14.4,6,16.6,5.7,16.7,4.7,16.6,4.2,16.9,3.6,15.7,4.4,16.4,2.5,18.4,-0.6,16.9,-1.9,16.5,-1.9,18.3,0.7,15.1,-0.9,15.7,-1.7,18.1,-3.1,16.8,-2.1,18.9,0.2,19,1.2,18.1,3.8,17.8,4,21.5,6.6,17.1,5.3,18.7,7.6,19,4.7,16.4,6.6,16.9,4.4,18.6,4.6,19.3,6,19.4,4.8,17.6,4,18.6,2.7,18.1,3,20.4,4.1,18.1,4,19.6,2.7,19.9,2.6,19.2,3.1,17.8,4.4,19.2,3,22,2,21.1,1.3,19.5,1.5,22.2,1.3,20.9,3.2,22.2,1.8,23.5,3.3,21.5,1,24.3,2.4,22.8,0.4,20.3,-0.1,23.7,1.3,23.3,-1.1,19.6,-4.4,18,-7.5,17.3,-12.2,16.8,-14.5,18.2,-16,16.5,-16.7,16,-16.3,18.4,-16.9),dim=c(2,73),dimnames=list(c('Y','X'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('Y','X'),1:73))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X
1 14.2 -0.8
2 13.5 -0.2
3 11.9 0.2
4 14.6 1.0
5 15.6 0.0
6 14.1 -0.2
7 14.9 1.0
8 14.2 0.4
9 14.6 1.0
10 17.2 1.7
11 15.4 3.1
12 14.3 3.3
13 17.5 3.1
14 14.5 3.5
15 14.4 6.0
16 16.6 5.7
17 16.7 4.7
18 16.6 4.2
19 16.9 3.6
20 15.7 4.4
21 16.4 2.5
22 18.4 -0.6
23 16.9 -1.9
24 16.5 -1.9
25 18.3 0.7
26 15.1 -0.9
27 15.7 -1.7
28 18.1 -3.1
29 16.8 -2.1
30 18.9 0.2
31 19.0 1.2
32 18.1 3.8
33 17.8 4.0
34 21.5 6.6
35 17.1 5.3
36 18.7 7.6
37 19.0 4.7
38 16.4 6.6
39 16.9 4.4
40 18.6 4.6
41 19.3 6.0
42 19.4 4.8
43 17.6 4.0
44 18.6 2.7
45 18.1 3.0
46 20.4 4.1
47 18.1 4.0
48 19.6 2.7
49 19.9 2.6
50 19.2 3.1
51 17.8 4.4
52 19.2 3.0
53 22.0 2.0
54 21.1 1.3
55 19.5 1.5
56 22.2 1.3
57 20.9 3.2
58 22.2 1.8
59 23.5 3.3
60 21.5 1.0
61 24.3 2.4
62 22.8 0.4
63 20.3 -0.1
64 23.7 1.3
65 23.3 -1.1
66 19.6 -4.4
67 18.0 -7.5
68 17.3 -12.2
69 16.8 -14.5
70 18.2 -16.0
71 16.5 -16.7
72 16.0 -16.3
73 18.4 -16.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
17.95438 0.05081
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.06454 -1.64397 -0.04744 1.30423 6.22369
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.95438 0.31481 57.032 <2e-16 ***
X 0.05081 0.05696 0.892 0.375
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.674 on 71 degrees of freedom
Multiple R-squared: 0.01108, Adjusted R-squared: -0.002846
F-statistic: 0.7957 on 1 and 71 DF, p-value: 0.3754
> 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,] 0.249267006 0.49853401 0.750732994
[2,] 0.131170652 0.26234130 0.868829348
[3,] 0.070958037 0.14191607 0.929041963
[4,] 0.034765603 0.06953121 0.965234397
[5,] 0.016475049 0.03295010 0.983524951
[6,] 0.021778229 0.04355646 0.978221771
[7,] 0.015671991 0.03134398 0.984328009
[8,] 0.016488750 0.03297750 0.983511250
[9,] 0.018139758 0.03627952 0.981860242
[10,] 0.018675896 0.03735179 0.981324104
[11,] 0.025784413 0.05156883 0.974215587
[12,] 0.018292041 0.03658408 0.981707959
[13,] 0.013631995 0.02726399 0.986368005
[14,] 0.010081726 0.02016345 0.989918274
[15,] 0.008702101 0.01740420 0.991297899
[16,] 0.006683921 0.01336784 0.993316079
[17,] 0.005944689 0.01188938 0.994055311
[18,] 0.039035974 0.07807195 0.960964026
[19,] 0.048154084 0.09630817 0.951845916
[20,] 0.046293413 0.09258683 0.953706587
[21,] 0.067125477 0.13425095 0.932874523
[22,] 0.070066940 0.14013388 0.929933060
[23,] 0.068004165 0.13600833 0.931995835
[24,] 0.084146061 0.16829212 0.915853939
[25,] 0.076625502 0.15325100 0.923374498
[26,] 0.106945671 0.21389134 0.893054329
[27,] 0.138999765 0.27799953 0.861000235
[28,] 0.145802337 0.29160467 0.854197663
[29,] 0.145697843 0.29139569 0.854302157
[30,] 0.296260320 0.59252064 0.703739680
[31,] 0.294358860 0.58871772 0.705641140
[32,] 0.274602391 0.54920478 0.725397609
[33,] 0.262394703 0.52478941 0.737605297
[34,] 0.333588296 0.66717659 0.666411704
[35,] 0.381275720 0.76255144 0.618724280
[36,] 0.381557439 0.76311488 0.618442561
[37,] 0.378040227 0.75608045 0.621959773
[38,] 0.375972734 0.75194547 0.624027266
[39,] 0.427415457 0.85483091 0.572584543
[40,] 0.443987497 0.88797499 0.556012503
[41,] 0.491849109 0.98369822 0.508150891
[42,] 0.510121446 0.97975711 0.489878554
[43,] 0.599023626 0.80195275 0.400976374
[44,] 0.620985174 0.75802965 0.379014826
[45,] 0.638566601 0.72286680 0.361433399
[46,] 0.684570967 0.63085807 0.315429033
[47,] 0.897613541 0.20477292 0.102386459
[48,] 0.956946903 0.08610619 0.043053097
[49,] 0.964685784 0.07062843 0.035314216
[50,] 0.966336112 0.06732778 0.033663888
[51,] 0.985765523 0.02846895 0.014234477
[52,] 0.985294413 0.02941117 0.014705587
[53,] 0.991258312 0.01748338 0.008741688
[54,] 0.989387681 0.02122464 0.010612319
[55,] 0.987260162 0.02547968 0.012739838
[56,] 0.983873748 0.03225250 0.016126252
[57,] 0.985835964 0.02832807 0.014164036
[58,] 0.979717101 0.04056580 0.020282899
[59,] 0.978617470 0.04276506 0.021382530
[60,] 0.972457325 0.05508535 0.027542675
[61,] 0.993546012 0.01290798 0.006453988
[62,] 0.987360162 0.02527968 0.012639838
[63,] 0.964011306 0.07197739 0.035988694
[64,] 0.903149676 0.19370065 0.096850324
> postscript(file="/var/www/html/rcomp/tmp/11vgy1258722725.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25yei1258722725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32sld1258722725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41aea1258722725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ihpl1258722725.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 = 73
Frequency = 1
1 2 3 4 5 6
-3.713734268 -4.444217543 -6.064539727 -3.405184094 -2.354378635 -3.844217543
7 8 9 10 11 12
-3.105184094 -3.774700819 -3.405184094 -0.840747915 -2.711875557 -3.822036649
13 14 15 16 17 18
-0.611875557 -3.632197741 -3.859211388 -1.643969750 -1.493164292 -1.567761562
19 20 21 22 23 24
-1.237278287 -2.477922654 -1.681392282 0.476104640 -0.957848263 -1.357848263
25 26 27 28 29 30
0.310057544 -2.808653722 -2.168009355 0.303118287 -1.047687171 0.935460273
31 32 33 34 35 36
0.984654814 -0.047439379 -0.357600470 3.210305337 -1.123647567 0.359499878
37 38 39 40 41 42
0.806835708 -1.889694663 -1.277922654 0.411916254 1.040788612 1.201755163
43 44 45 46 47 48
-0.557600470 0.508446626 -0.006795012 2.237318984 -0.057600470 1.508446626
49 50 51 52 53 54
1.813527172 1.088124443 -0.377922654 1.093204988 3.944010447 3.079574268
55 56 57 58 59 60
1.469413177 4.179574268 2.783043897 4.154171539 5.377963351 3.494815906
61 62 63 64 65 66
6.223688264 4.825299181 2.350701911 5.679574268 5.401507370 1.869165384
67 68 69 70 71 72
0.426662306 -0.034552037 -0.417699482 1.058508706 -0.605927472 -1.126249656
73
1.304233619
> postscript(file="/var/www/html/rcomp/tmp/6di1p1258722725.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.713734268 NA
1 -4.444217543 -3.713734268
2 -6.064539727 -4.444217543
3 -3.405184094 -6.064539727
4 -2.354378635 -3.405184094
5 -3.844217543 -2.354378635
6 -3.105184094 -3.844217543
7 -3.774700819 -3.105184094
8 -3.405184094 -3.774700819
9 -0.840747915 -3.405184094
10 -2.711875557 -0.840747915
11 -3.822036649 -2.711875557
12 -0.611875557 -3.822036649
13 -3.632197741 -0.611875557
14 -3.859211388 -3.632197741
15 -1.643969750 -3.859211388
16 -1.493164292 -1.643969750
17 -1.567761562 -1.493164292
18 -1.237278287 -1.567761562
19 -2.477922654 -1.237278287
20 -1.681392282 -2.477922654
21 0.476104640 -1.681392282
22 -0.957848263 0.476104640
23 -1.357848263 -0.957848263
24 0.310057544 -1.357848263
25 -2.808653722 0.310057544
26 -2.168009355 -2.808653722
27 0.303118287 -2.168009355
28 -1.047687171 0.303118287
29 0.935460273 -1.047687171
30 0.984654814 0.935460273
31 -0.047439379 0.984654814
32 -0.357600470 -0.047439379
33 3.210305337 -0.357600470
34 -1.123647567 3.210305337
35 0.359499878 -1.123647567
36 0.806835708 0.359499878
37 -1.889694663 0.806835708
38 -1.277922654 -1.889694663
39 0.411916254 -1.277922654
40 1.040788612 0.411916254
41 1.201755163 1.040788612
42 -0.557600470 1.201755163
43 0.508446626 -0.557600470
44 -0.006795012 0.508446626
45 2.237318984 -0.006795012
46 -0.057600470 2.237318984
47 1.508446626 -0.057600470
48 1.813527172 1.508446626
49 1.088124443 1.813527172
50 -0.377922654 1.088124443
51 1.093204988 -0.377922654
52 3.944010447 1.093204988
53 3.079574268 3.944010447
54 1.469413177 3.079574268
55 4.179574268 1.469413177
56 2.783043897 4.179574268
57 4.154171539 2.783043897
58 5.377963351 4.154171539
59 3.494815906 5.377963351
60 6.223688264 3.494815906
61 4.825299181 6.223688264
62 2.350701911 4.825299181
63 5.679574268 2.350701911
64 5.401507370 5.679574268
65 1.869165384 5.401507370
66 0.426662306 1.869165384
67 -0.034552037 0.426662306
68 -0.417699482 -0.034552037
69 1.058508706 -0.417699482
70 -0.605927472 1.058508706
71 -1.126249656 -0.605927472
72 1.304233619 -1.126249656
73 NA 1.304233619
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.444217543 -3.713734268
[2,] -6.064539727 -4.444217543
[3,] -3.405184094 -6.064539727
[4,] -2.354378635 -3.405184094
[5,] -3.844217543 -2.354378635
[6,] -3.105184094 -3.844217543
[7,] -3.774700819 -3.105184094
[8,] -3.405184094 -3.774700819
[9,] -0.840747915 -3.405184094
[10,] -2.711875557 -0.840747915
[11,] -3.822036649 -2.711875557
[12,] -0.611875557 -3.822036649
[13,] -3.632197741 -0.611875557
[14,] -3.859211388 -3.632197741
[15,] -1.643969750 -3.859211388
[16,] -1.493164292 -1.643969750
[17,] -1.567761562 -1.493164292
[18,] -1.237278287 -1.567761562
[19,] -2.477922654 -1.237278287
[20,] -1.681392282 -2.477922654
[21,] 0.476104640 -1.681392282
[22,] -0.957848263 0.476104640
[23,] -1.357848263 -0.957848263
[24,] 0.310057544 -1.357848263
[25,] -2.808653722 0.310057544
[26,] -2.168009355 -2.808653722
[27,] 0.303118287 -2.168009355
[28,] -1.047687171 0.303118287
[29,] 0.935460273 -1.047687171
[30,] 0.984654814 0.935460273
[31,] -0.047439379 0.984654814
[32,] -0.357600470 -0.047439379
[33,] 3.210305337 -0.357600470
[34,] -1.123647567 3.210305337
[35,] 0.359499878 -1.123647567
[36,] 0.806835708 0.359499878
[37,] -1.889694663 0.806835708
[38,] -1.277922654 -1.889694663
[39,] 0.411916254 -1.277922654
[40,] 1.040788612 0.411916254
[41,] 1.201755163 1.040788612
[42,] -0.557600470 1.201755163
[43,] 0.508446626 -0.557600470
[44,] -0.006795012 0.508446626
[45,] 2.237318984 -0.006795012
[46,] -0.057600470 2.237318984
[47,] 1.508446626 -0.057600470
[48,] 1.813527172 1.508446626
[49,] 1.088124443 1.813527172
[50,] -0.377922654 1.088124443
[51,] 1.093204988 -0.377922654
[52,] 3.944010447 1.093204988
[53,] 3.079574268 3.944010447
[54,] 1.469413177 3.079574268
[55,] 4.179574268 1.469413177
[56,] 2.783043897 4.179574268
[57,] 4.154171539 2.783043897
[58,] 5.377963351 4.154171539
[59,] 3.494815906 5.377963351
[60,] 6.223688264 3.494815906
[61,] 4.825299181 6.223688264
[62,] 2.350701911 4.825299181
[63,] 5.679574268 2.350701911
[64,] 5.401507370 5.679574268
[65,] 1.869165384 5.401507370
[66,] 0.426662306 1.869165384
[67,] -0.034552037 0.426662306
[68,] -0.417699482 -0.034552037
[69,] 1.058508706 -0.417699482
[70,] -0.605927472 1.058508706
[71,] -1.126249656 -0.605927472
[72,] 1.304233619 -1.126249656
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.444217543 -3.713734268
2 -6.064539727 -4.444217543
3 -3.405184094 -6.064539727
4 -2.354378635 -3.405184094
5 -3.844217543 -2.354378635
6 -3.105184094 -3.844217543
7 -3.774700819 -3.105184094
8 -3.405184094 -3.774700819
9 -0.840747915 -3.405184094
10 -2.711875557 -0.840747915
11 -3.822036649 -2.711875557
12 -0.611875557 -3.822036649
13 -3.632197741 -0.611875557
14 -3.859211388 -3.632197741
15 -1.643969750 -3.859211388
16 -1.493164292 -1.643969750
17 -1.567761562 -1.493164292
18 -1.237278287 -1.567761562
19 -2.477922654 -1.237278287
20 -1.681392282 -2.477922654
21 0.476104640 -1.681392282
22 -0.957848263 0.476104640
23 -1.357848263 -0.957848263
24 0.310057544 -1.357848263
25 -2.808653722 0.310057544
26 -2.168009355 -2.808653722
27 0.303118287 -2.168009355
28 -1.047687171 0.303118287
29 0.935460273 -1.047687171
30 0.984654814 0.935460273
31 -0.047439379 0.984654814
32 -0.357600470 -0.047439379
33 3.210305337 -0.357600470
34 -1.123647567 3.210305337
35 0.359499878 -1.123647567
36 0.806835708 0.359499878
37 -1.889694663 0.806835708
38 -1.277922654 -1.889694663
39 0.411916254 -1.277922654
40 1.040788612 0.411916254
41 1.201755163 1.040788612
42 -0.557600470 1.201755163
43 0.508446626 -0.557600470
44 -0.006795012 0.508446626
45 2.237318984 -0.006795012
46 -0.057600470 2.237318984
47 1.508446626 -0.057600470
48 1.813527172 1.508446626
49 1.088124443 1.813527172
50 -0.377922654 1.088124443
51 1.093204988 -0.377922654
52 3.944010447 1.093204988
53 3.079574268 3.944010447
54 1.469413177 3.079574268
55 4.179574268 1.469413177
56 2.783043897 4.179574268
57 4.154171539 2.783043897
58 5.377963351 4.154171539
59 3.494815906 5.377963351
60 6.223688264 3.494815906
61 4.825299181 6.223688264
62 2.350701911 4.825299181
63 5.679574268 2.350701911
64 5.401507370 5.679574268
65 1.869165384 5.401507370
66 0.426662306 1.869165384
67 -0.034552037 0.426662306
68 -0.417699482 -0.034552037
69 1.058508706 -0.417699482
70 -0.605927472 1.058508706
71 -1.126249656 -0.605927472
72 1.304233619 -1.126249656
> 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/7lr1a1258722725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85es41258722725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9n2vh1258722725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10s6mf1258722725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11b7m61258722726.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/12sd941258722726.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/13s1lw1258722726.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/144p571258722726.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/15gwlz1258722726.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/16nbas1258722726.tab")
+ }
>
> system("convert tmp/11vgy1258722725.ps tmp/11vgy1258722725.png")
> system("convert tmp/25yei1258722725.ps tmp/25yei1258722725.png")
> system("convert tmp/32sld1258722725.ps tmp/32sld1258722725.png")
> system("convert tmp/41aea1258722725.ps tmp/41aea1258722725.png")
> system("convert tmp/5ihpl1258722725.ps tmp/5ihpl1258722725.png")
> system("convert tmp/6di1p1258722725.ps tmp/6di1p1258722725.png")
> system("convert tmp/7lr1a1258722725.ps tmp/7lr1a1258722725.png")
> system("convert tmp/85es41258722725.ps tmp/85es41258722725.png")
> system("convert tmp/9n2vh1258722725.ps tmp/9n2vh1258722725.png")
> system("convert tmp/10s6mf1258722725.ps tmp/10s6mf1258722725.png")
>
>
> proc.time()
user system elapsed
2.608 1.595 4.907