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(8.2,25.5,8.3,25.5,8.1,25.5,7.4,20.9,7.3,20.9,7.7,20.9,8,22.3,8,22.3,7.7,22.3,6.9,19.9,6.6,19.9,6.9,19.9,7.5,24.1,7.9,24.1,7.7,24.1,6.5,13.8,6.1,13.8,6.4,13.8,6.8,16.2,7.1,16.2,7.3,16.2,7.2,18.6,7,18.6,7,18.6,7,22.4,7.3,22.4,7.5,22.4,7.2,22.6,7.7,22.6,8,22.6,7.9,20,8,20,8,20,7.9,21.8,7.9,21.8,8,21.8,8.1,28.7,8.1,28.7,8.2,28.7,8,19.5,8.3,19.5,8.5,19.5,8.6,19.4,8.7,19.4,8.7,19.4,8.5,21.7,8.4,21.7,8.5,21.7,8.7,26.2,8.7,26.2,8.6,26.2,7.9,19.1,8.1,19.1,8.2,19.1,8.5,21.3,8.6,21.3,8.5,21.3,8.3,24.1,8.2,24.1,8.7,24.1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 = 'No 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
1 8.2 25.5 1 0 0 0 0 0 0 0 0 0 0
2 8.3 25.5 0 1 0 0 0 0 0 0 0 0 0
3 8.1 25.5 0 0 1 0 0 0 0 0 0 0 0
4 7.4 20.9 0 0 0 1 0 0 0 0 0 0 0
5 7.3 20.9 0 0 0 0 1 0 0 0 0 0 0
6 7.7 20.9 0 0 0 0 0 1 0 0 0 0 0
7 8.0 22.3 0 0 0 0 0 0 1 0 0 0 0
8 8.0 22.3 0 0 0 0 0 0 0 1 0 0 0
9 7.7 22.3 0 0 0 0 0 0 0 0 1 0 0
10 6.9 19.9 0 0 0 0 0 0 0 0 0 1 0
11 6.6 19.9 0 0 0 0 0 0 0 0 0 0 1
12 6.9 19.9 0 0 0 0 0 0 0 0 0 0 0
13 7.5 24.1 1 0 0 0 0 0 0 0 0 0 0
14 7.9 24.1 0 1 0 0 0 0 0 0 0 0 0
15 7.7 24.1 0 0 1 0 0 0 0 0 0 0 0
16 6.5 13.8 0 0 0 1 0 0 0 0 0 0 0
17 6.1 13.8 0 0 0 0 1 0 0 0 0 0 0
18 6.4 13.8 0 0 0 0 0 1 0 0 0 0 0
19 6.8 16.2 0 0 0 0 0 0 1 0 0 0 0
20 7.1 16.2 0 0 0 0 0 0 0 1 0 0 0
21 7.3 16.2 0 0 0 0 0 0 0 0 1 0 0
22 7.2 18.6 0 0 0 0 0 0 0 0 0 1 0
23 7.0 18.6 0 0 0 0 0 0 0 0 0 0 1
24 7.0 18.6 0 0 0 0 0 0 0 0 0 0 0
25 7.0 22.4 1 0 0 0 0 0 0 0 0 0 0
26 7.3 22.4 0 1 0 0 0 0 0 0 0 0 0
27 7.5 22.4 0 0 1 0 0 0 0 0 0 0 0
28 7.2 22.6 0 0 0 1 0 0 0 0 0 0 0
29 7.7 22.6 0 0 0 0 1 0 0 0 0 0 0
30 8.0 22.6 0 0 0 0 0 1 0 0 0 0 0
31 7.9 20.0 0 0 0 0 0 0 1 0 0 0 0
32 8.0 20.0 0 0 0 0 0 0 0 1 0 0 0
33 8.0 20.0 0 0 0 0 0 0 0 0 1 0 0
34 7.9 21.8 0 0 0 0 0 0 0 0 0 1 0
35 7.9 21.8 0 0 0 0 0 0 0 0 0 0 1
36 8.0 21.8 0 0 0 0 0 0 0 0 0 0 0
37 8.1 28.7 1 0 0 0 0 0 0 0 0 0 0
38 8.1 28.7 0 1 0 0 0 0 0 0 0 0 0
39 8.2 28.7 0 0 1 0 0 0 0 0 0 0 0
40 8.0 19.5 0 0 0 1 0 0 0 0 0 0 0
41 8.3 19.5 0 0 0 0 1 0 0 0 0 0 0
42 8.5 19.5 0 0 0 0 0 1 0 0 0 0 0
43 8.6 19.4 0 0 0 0 0 0 1 0 0 0 0
44 8.7 19.4 0 0 0 0 0 0 0 1 0 0 0
45 8.7 19.4 0 0 0 0 0 0 0 0 1 0 0
46 8.5 21.7 0 0 0 0 0 0 0 0 0 1 0
47 8.4 21.7 0 0 0 0 0 0 0 0 0 0 1
48 8.5 21.7 0 0 0 0 0 0 0 0 0 0 0
49 8.7 26.2 1 0 0 0 0 0 0 0 0 0 0
50 8.7 26.2 0 1 0 0 0 0 0 0 0 0 0
51 8.6 26.2 0 0 1 0 0 0 0 0 0 0 0
52 7.9 19.1 0 0 0 1 0 0 0 0 0 0 0
53 8.1 19.1 0 0 0 0 1 0 0 0 0 0 0
54 8.2 19.1 0 0 0 0 0 1 0 0 0 0 0
55 8.5 21.3 0 0 0 0 0 0 1 0 0 0 0
56 8.6 21.3 0 0 0 0 0 0 0 1 0 0 0
57 8.5 21.3 0 0 0 0 0 0 0 0 1 0 0
58 8.3 24.1 0 0 0 0 0 0 0 0 0 1 0
59 8.2 24.1 0 0 0 0 0 0 0 0 0 0 1
60 8.7 24.1 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
3.8901 0.1852 -0.6904 -0.5304 -0.5704 -0.0422
M5 M6 M7 M8 M9 M10
0.0578 0.3178 0.3956 0.5156 0.4756 -0.0600
M11
-0.2000
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.83337 -0.38225 -0.02963 0.36701 0.74149
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.89013 0.64600 6.022 2.49e-07 ***
X 0.18520 0.02851 6.495 4.77e-08 ***
M1 -0.69042 0.34141 -2.022 0.0489 *
M2 -0.53042 0.34141 -1.554 0.1270
M3 -0.57042 0.34141 -1.671 0.1014
M4 -0.04220 0.32538 -0.130 0.8974
M5 0.05780 0.32538 0.178 0.8598
M6 0.31780 0.32538 0.977 0.3337
M7 0.39557 0.32255 1.226 0.2262
M8 0.51557 0.32255 1.598 0.1166
M9 0.47557 0.32255 1.474 0.1470
M10 -0.06000 0.32014 -0.187 0.8521
M11 -0.20000 0.32014 -0.625 0.5352
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5062 on 47 degrees of freedom
Multiple R-squared: 0.531, Adjusted R-squared: 0.4113
F-statistic: 4.435 on 12 and 47 DF, p-value: 0.0001032
> 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,] 0.118676967 0.237353934 0.881323033
[2,] 0.048309837 0.096619674 0.951690163
[3,] 0.019993609 0.039987219 0.980006391
[4,] 0.009517612 0.019035224 0.990482388
[5,] 0.004212473 0.008424945 0.995787527
[6,] 0.011433099 0.022866198 0.988566901
[7,] 0.014090780 0.028181559 0.985909220
[8,] 0.023999520 0.047999041 0.976000480
[9,] 0.027109694 0.054219388 0.972890306
[10,] 0.062345121 0.124690241 0.937654879
[11,] 0.120963183 0.241926365 0.879036817
[12,] 0.227648668 0.455297335 0.772351332
[13,] 0.310450212 0.620900424 0.689549788
[14,] 0.264059011 0.528118022 0.735940989
[15,] 0.204264006 0.408528011 0.795735994
[16,] 0.281314918 0.562629836 0.718685082
[17,] 0.381560864 0.763121728 0.618439136
[18,] 0.509017728 0.981964544 0.490982272
[19,] 0.651362337 0.697275327 0.348637663
[20,] 0.802563226 0.394873547 0.197436774
[21,] 0.974610225 0.050779550 0.025389775
[22,] 0.978254676 0.043490647 0.021745324
[23,] 0.990992366 0.018015269 0.009007634
[24,] 0.991240632 0.017518737 0.008759368
[25,] 0.989835726 0.020328548 0.010164274
[26,] 0.992392792 0.015214416 0.007607208
[27,] 0.996049123 0.007901754 0.003950877
[28,] 0.988104348 0.023791304 0.011895652
[29,] 0.961547315 0.076905369 0.038452685
> postscript(file="/var/www/html/rcomp/tmp/12u221258555437.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2o4f61258555437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33ad31258555437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/420it1258555437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/558ml1258555437.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
0.277776424 0.217776424 0.057776424 -0.318537923 -0.518537923 -0.378537923
7 8 9 10 11 12
-0.415583308 -0.535583308 -0.795583308 -0.615540664 -0.775540664 -0.675540664
13 14 15 16 17 18
-0.162948523 0.077051477 -0.082948523 0.096356991 -0.403643009 -0.363643009
19 20 21 22 23 24
-0.485884861 -0.305884861 -0.065884861 -0.074785257 -0.134785257 -0.334785257
25 26 27 28 29 30
-0.348114529 -0.208114529 0.031885471 -0.833371916 -0.433371916 -0.393371916
31 32 33 34 35 36
-0.089631435 -0.109631435 -0.069631435 0.032586049 0.172586049 0.072586049
37 38 39 40 41 42
-0.414852269 -0.574852269 -0.434852269 0.540737131 0.740737131 0.680737131
43 44 45 46 47 48
0.721486445 0.701486445 0.741486445 0.651105696 0.691105696 0.591105696
49 50 51 52 53 54
0.648138897 0.488138897 0.428138897 0.514815717 0.614815717 0.454815717
55 56 57 58 59 60
0.269613159 0.249613159 0.189613159 0.006634176 0.046634176 0.346634176
> postscript(file="/var/www/html/rcomp/tmp/67pih1258555437.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 0.277776424 NA
1 0.217776424 0.277776424
2 0.057776424 0.217776424
3 -0.318537923 0.057776424
4 -0.518537923 -0.318537923
5 -0.378537923 -0.518537923
6 -0.415583308 -0.378537923
7 -0.535583308 -0.415583308
8 -0.795583308 -0.535583308
9 -0.615540664 -0.795583308
10 -0.775540664 -0.615540664
11 -0.675540664 -0.775540664
12 -0.162948523 -0.675540664
13 0.077051477 -0.162948523
14 -0.082948523 0.077051477
15 0.096356991 -0.082948523
16 -0.403643009 0.096356991
17 -0.363643009 -0.403643009
18 -0.485884861 -0.363643009
19 -0.305884861 -0.485884861
20 -0.065884861 -0.305884861
21 -0.074785257 -0.065884861
22 -0.134785257 -0.074785257
23 -0.334785257 -0.134785257
24 -0.348114529 -0.334785257
25 -0.208114529 -0.348114529
26 0.031885471 -0.208114529
27 -0.833371916 0.031885471
28 -0.433371916 -0.833371916
29 -0.393371916 -0.433371916
30 -0.089631435 -0.393371916
31 -0.109631435 -0.089631435
32 -0.069631435 -0.109631435
33 0.032586049 -0.069631435
34 0.172586049 0.032586049
35 0.072586049 0.172586049
36 -0.414852269 0.072586049
37 -0.574852269 -0.414852269
38 -0.434852269 -0.574852269
39 0.540737131 -0.434852269
40 0.740737131 0.540737131
41 0.680737131 0.740737131
42 0.721486445 0.680737131
43 0.701486445 0.721486445
44 0.741486445 0.701486445
45 0.651105696 0.741486445
46 0.691105696 0.651105696
47 0.591105696 0.691105696
48 0.648138897 0.591105696
49 0.488138897 0.648138897
50 0.428138897 0.488138897
51 0.514815717 0.428138897
52 0.614815717 0.514815717
53 0.454815717 0.614815717
54 0.269613159 0.454815717
55 0.249613159 0.269613159
56 0.189613159 0.249613159
57 0.006634176 0.189613159
58 0.046634176 0.006634176
59 0.346634176 0.046634176
60 NA 0.346634176
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.217776424 0.277776424
[2,] 0.057776424 0.217776424
[3,] -0.318537923 0.057776424
[4,] -0.518537923 -0.318537923
[5,] -0.378537923 -0.518537923
[6,] -0.415583308 -0.378537923
[7,] -0.535583308 -0.415583308
[8,] -0.795583308 -0.535583308
[9,] -0.615540664 -0.795583308
[10,] -0.775540664 -0.615540664
[11,] -0.675540664 -0.775540664
[12,] -0.162948523 -0.675540664
[13,] 0.077051477 -0.162948523
[14,] -0.082948523 0.077051477
[15,] 0.096356991 -0.082948523
[16,] -0.403643009 0.096356991
[17,] -0.363643009 -0.403643009
[18,] -0.485884861 -0.363643009
[19,] -0.305884861 -0.485884861
[20,] -0.065884861 -0.305884861
[21,] -0.074785257 -0.065884861
[22,] -0.134785257 -0.074785257
[23,] -0.334785257 -0.134785257
[24,] -0.348114529 -0.334785257
[25,] -0.208114529 -0.348114529
[26,] 0.031885471 -0.208114529
[27,] -0.833371916 0.031885471
[28,] -0.433371916 -0.833371916
[29,] -0.393371916 -0.433371916
[30,] -0.089631435 -0.393371916
[31,] -0.109631435 -0.089631435
[32,] -0.069631435 -0.109631435
[33,] 0.032586049 -0.069631435
[34,] 0.172586049 0.032586049
[35,] 0.072586049 0.172586049
[36,] -0.414852269 0.072586049
[37,] -0.574852269 -0.414852269
[38,] -0.434852269 -0.574852269
[39,] 0.540737131 -0.434852269
[40,] 0.740737131 0.540737131
[41,] 0.680737131 0.740737131
[42,] 0.721486445 0.680737131
[43,] 0.701486445 0.721486445
[44,] 0.741486445 0.701486445
[45,] 0.651105696 0.741486445
[46,] 0.691105696 0.651105696
[47,] 0.591105696 0.691105696
[48,] 0.648138897 0.591105696
[49,] 0.488138897 0.648138897
[50,] 0.428138897 0.488138897
[51,] 0.514815717 0.428138897
[52,] 0.614815717 0.514815717
[53,] 0.454815717 0.614815717
[54,] 0.269613159 0.454815717
[55,] 0.249613159 0.269613159
[56,] 0.189613159 0.249613159
[57,] 0.006634176 0.189613159
[58,] 0.046634176 0.006634176
[59,] 0.346634176 0.046634176
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.217776424 0.277776424
2 0.057776424 0.217776424
3 -0.318537923 0.057776424
4 -0.518537923 -0.318537923
5 -0.378537923 -0.518537923
6 -0.415583308 -0.378537923
7 -0.535583308 -0.415583308
8 -0.795583308 -0.535583308
9 -0.615540664 -0.795583308
10 -0.775540664 -0.615540664
11 -0.675540664 -0.775540664
12 -0.162948523 -0.675540664
13 0.077051477 -0.162948523
14 -0.082948523 0.077051477
15 0.096356991 -0.082948523
16 -0.403643009 0.096356991
17 -0.363643009 -0.403643009
18 -0.485884861 -0.363643009
19 -0.305884861 -0.485884861
20 -0.065884861 -0.305884861
21 -0.074785257 -0.065884861
22 -0.134785257 -0.074785257
23 -0.334785257 -0.134785257
24 -0.348114529 -0.334785257
25 -0.208114529 -0.348114529
26 0.031885471 -0.208114529
27 -0.833371916 0.031885471
28 -0.433371916 -0.833371916
29 -0.393371916 -0.433371916
30 -0.089631435 -0.393371916
31 -0.109631435 -0.089631435
32 -0.069631435 -0.109631435
33 0.032586049 -0.069631435
34 0.172586049 0.032586049
35 0.072586049 0.172586049
36 -0.414852269 0.072586049
37 -0.574852269 -0.414852269
38 -0.434852269 -0.574852269
39 0.540737131 -0.434852269
40 0.740737131 0.540737131
41 0.680737131 0.740737131
42 0.721486445 0.680737131
43 0.701486445 0.721486445
44 0.741486445 0.701486445
45 0.651105696 0.741486445
46 0.691105696 0.651105696
47 0.591105696 0.691105696
48 0.648138897 0.591105696
49 0.488138897 0.648138897
50 0.428138897 0.488138897
51 0.514815717 0.428138897
52 0.614815717 0.514815717
53 0.454815717 0.614815717
54 0.269613159 0.454815717
55 0.249613159 0.269613159
56 0.189613159 0.249613159
57 0.006634176 0.189613159
58 0.046634176 0.006634176
59 0.346634176 0.046634176
> 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/7uysc1258555437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ksuf1258555437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9mx611258555437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jxq01258555437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11w0561258555437.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/12fkoa1258555437.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/13c04p1258555438.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/149n151258555438.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/15oin81258555438.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/166b6y1258555438.tab")
+ }
>
> system("convert tmp/12u221258555437.ps tmp/12u221258555437.png")
> system("convert tmp/2o4f61258555437.ps tmp/2o4f61258555437.png")
> system("convert tmp/33ad31258555437.ps tmp/33ad31258555437.png")
> system("convert tmp/420it1258555437.ps tmp/420it1258555437.png")
> system("convert tmp/558ml1258555437.ps tmp/558ml1258555437.png")
> system("convert tmp/67pih1258555437.ps tmp/67pih1258555437.png")
> system("convert tmp/7uysc1258555437.ps tmp/7uysc1258555437.png")
> system("convert tmp/8ksuf1258555437.ps tmp/8ksuf1258555437.png")
> system("convert tmp/9mx611258555437.ps tmp/9mx611258555437.png")
> system("convert tmp/10jxq01258555437.ps tmp/10jxq01258555437.png")
>
>
> proc.time()
user system elapsed
2.390 1.570 7.053