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(8,5560,8.1,3922,7.7,3759,7.5,4138,7.6,4634,7.8,3996,7.8,4308,7.8,4143,7.5,4429,7.5,5219,7.1,4929,7.5,5755,7.5,5592,7.6,4163,7.7,4962,7.7,5208,7.9,4755,8.1,4491,8.2,5732,8.2,5731,8.2,5040,7.9,6102,7.3,4904,6.9,5369,6.7,5578,6.7,4619,6.9,4731,7,5011,7.1,5299,7.2,4146,7.1,4625,6.9,4736,7,4219,6.8,5116,6.4,4205,6.7,4121,6.6,5103,6.4,4300,6.3,4578,6.2,3809,6.5,5526,6.8,4247,6.8,3830,6.4,4394,6.1,4826,5.8,4409,6.1,4569,7.2,4106,7.3,4794,6.9,3914,6.1,3793,5.8,4405,6.2,4022,7.1,4100,7.7,4788,7.9,3163,7.7,3585,7.4,3903,7.5,4178,8,3863,8.1,4187),dim=c(2,61),dimnames=list(c('WerklM','Bouwv'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('WerklM','Bouwv'),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 = '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
WerklM Bouwv M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.0 5560 1 0 0 0 0 0 0 0 0 0 0
2 8.1 3922 0 1 0 0 0 0 0 0 0 0 0
3 7.7 3759 0 0 1 0 0 0 0 0 0 0 0
4 7.5 4138 0 0 0 1 0 0 0 0 0 0 0
5 7.6 4634 0 0 0 0 1 0 0 0 0 0 0
6 7.8 3996 0 0 0 0 0 1 0 0 0 0 0
7 7.8 4308 0 0 0 0 0 0 1 0 0 0 0
8 7.8 4143 0 0 0 0 0 0 0 1 0 0 0
9 7.5 4429 0 0 0 0 0 0 0 0 1 0 0
10 7.5 5219 0 0 0 0 0 0 0 0 0 1 0
11 7.1 4929 0 0 0 0 0 0 0 0 0 0 1
12 7.5 5755 0 0 0 0 0 0 0 0 0 0 0
13 7.5 5592 1 0 0 0 0 0 0 0 0 0 0
14 7.6 4163 0 1 0 0 0 0 0 0 0 0 0
15 7.7 4962 0 0 1 0 0 0 0 0 0 0 0
16 7.7 5208 0 0 0 1 0 0 0 0 0 0 0
17 7.9 4755 0 0 0 0 1 0 0 0 0 0 0
18 8.1 4491 0 0 0 0 0 1 0 0 0 0 0
19 8.2 5732 0 0 0 0 0 0 1 0 0 0 0
20 8.2 5731 0 0 0 0 0 0 0 1 0 0 0
21 8.2 5040 0 0 0 0 0 0 0 0 1 0 0
22 7.9 6102 0 0 0 0 0 0 0 0 0 1 0
23 7.3 4904 0 0 0 0 0 0 0 0 0 0 1
24 6.9 5369 0 0 0 0 0 0 0 0 0 0 0
25 6.7 5578 1 0 0 0 0 0 0 0 0 0 0
26 6.7 4619 0 1 0 0 0 0 0 0 0 0 0
27 6.9 4731 0 0 1 0 0 0 0 0 0 0 0
28 7.0 5011 0 0 0 1 0 0 0 0 0 0 0
29 7.1 5299 0 0 0 0 1 0 0 0 0 0 0
30 7.2 4146 0 0 0 0 0 1 0 0 0 0 0
31 7.1 4625 0 0 0 0 0 0 1 0 0 0 0
32 6.9 4736 0 0 0 0 0 0 0 1 0 0 0
33 7.0 4219 0 0 0 0 0 0 0 0 1 0 0
34 6.8 5116 0 0 0 0 0 0 0 0 0 1 0
35 6.4 4205 0 0 0 0 0 0 0 0 0 0 1
36 6.7 4121 0 0 0 0 0 0 0 0 0 0 0
37 6.6 5103 1 0 0 0 0 0 0 0 0 0 0
38 6.4 4300 0 1 0 0 0 0 0 0 0 0 0
39 6.3 4578 0 0 1 0 0 0 0 0 0 0 0
40 6.2 3809 0 0 0 1 0 0 0 0 0 0 0
41 6.5 5526 0 0 0 0 1 0 0 0 0 0 0
42 6.8 4247 0 0 0 0 0 1 0 0 0 0 0
43 6.8 3830 0 0 0 0 0 0 1 0 0 0 0
44 6.4 4394 0 0 0 0 0 0 0 1 0 0 0
45 6.1 4826 0 0 0 0 0 0 0 0 1 0 0
46 5.8 4409 0 0 0 0 0 0 0 0 0 1 0
47 6.1 4569 0 0 0 0 0 0 0 0 0 0 1
48 7.2 4106 0 0 0 0 0 0 0 0 0 0 0
49 7.3 4794 1 0 0 0 0 0 0 0 0 0 0
50 6.9 3914 0 1 0 0 0 0 0 0 0 0 0
51 6.1 3793 0 0 1 0 0 0 0 0 0 0 0
52 5.8 4405 0 0 0 1 0 0 0 0 0 0 0
53 6.2 4022 0 0 0 0 1 0 0 0 0 0 0
54 7.1 4100 0 0 0 0 0 1 0 0 0 0 0
55 7.7 4788 0 0 0 0 0 0 1 0 0 0 0
56 7.9 3163 0 0 0 0 0 0 0 1 0 0 0
57 7.7 3585 0 0 0 0 0 0 0 0 1 0 0
58 7.4 3903 0 0 0 0 0 0 0 0 0 1 0
59 7.5 4178 0 0 0 0 0 0 0 0 0 0 1
60 8.0 3863 0 0 0 0 0 0 0 0 0 0 0
61 8.1 4187 1 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) Bouwv M1 M2 M3 M4
6.5236295 0.0001586 0.0284956 -0.0471687 -0.2758761 -0.3996034
M5 M6 M7 M8 M9 M10
-0.2324188 0.2108646 0.2578113 0.2132119 0.0753689 -0.2286917
M11
-0.3663917
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.26443 -0.52815 0.06096 0.55419 1.00149
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.5236295 0.7953523 8.202 1.09e-10 ***
Bouwv 0.0001586 0.0001579 1.005 0.320
M1 0.0284956 0.4251413 0.067 0.947
M2 -0.0471687 0.4425236 -0.107 0.916
M3 -0.2758761 0.4387485 -0.629 0.532
M4 -0.3996034 0.4370163 -0.914 0.365
M5 -0.2324188 0.4377356 -0.531 0.598
M6 0.2108646 0.4422070 0.477 0.636
M7 0.2578113 0.4365499 0.591 0.558
M8 0.2132119 0.4377945 0.487 0.628
M9 0.0753689 0.4379619 0.172 0.864
M10 -0.2286917 0.4392269 -0.521 0.605
M11 -0.3663917 0.4367545 -0.839 0.406
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6902 on 48 degrees of freedom
Multiple R-squared: 0.1292, Adjusted R-squared: -0.08848
F-statistic: 0.5936 on 12 and 48 DF, p-value: 0.8365
> 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,] 0.094120144 0.188240288 0.9058799
[2,] 0.048342964 0.096685927 0.9516570
[3,] 0.023394796 0.046789591 0.9766052
[4,] 0.010121454 0.020242909 0.9898785
[5,] 0.004187597 0.008375193 0.9958124
[6,] 0.006523898 0.013047796 0.9934761
[7,] 0.005009993 0.010019986 0.9949900
[8,] 0.002722178 0.005444355 0.9972778
[9,] 0.002368704 0.004737408 0.9976313
[10,] 0.023220681 0.046441361 0.9767793
[11,] 0.102686044 0.205372088 0.8973140
[12,] 0.140065090 0.280130180 0.8599349
[13,] 0.200974094 0.401948188 0.7990259
[14,] 0.245533951 0.491067901 0.7544660
[15,] 0.234516146 0.469032291 0.7654839
[16,] 0.234526839 0.469053678 0.7654732
[17,] 0.266258277 0.532516555 0.7337417
[18,] 0.236897105 0.473794209 0.7631029
[19,] 0.251662274 0.503324548 0.7483377
[20,] 0.221264118 0.442528236 0.7787359
[21,] 0.198706227 0.397412453 0.8012938
[22,] 0.195106620 0.390213241 0.8048934
[23,] 0.195413365 0.390826729 0.8045866
[24,] 0.216643900 0.433287801 0.7833561
[25,] 0.172570832 0.345141664 0.8274292
[26,] 0.329573971 0.659147942 0.6704260
[27,] 0.257887808 0.515775616 0.7421122
[28,] 0.792647534 0.414704933 0.2073525
[29,] 0.734202476 0.531595049 0.2657975
[30,] 0.807894496 0.384211007 0.1921055
> postscript(file="/var/www/html/rcomp/tmp/15zy91258736114.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gqau1258736114.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3trru1258736114.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/485731258736114.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54tji1258736114.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
0.56603202 1.00149102 0.85605108 0.71966713 0.57381455 0.43172096
7 8 9 10 11 12
0.33528964 0.40605884 0.19854084 0.37730358 0.16099901 0.06359971
13 14 15 16 17 18
0.06095667 0.46326726 0.66524948 0.74995997 0.85462336 0.65321158
19 20 21 22 23 24
0.50943637 0.55419438 0.80163329 0.63725552 0.36496413 -0.47517882
25 26 27 28 29 30
-0.73682287 -0.50905654 -0.09811281 0.08120512 -0.03165766 -0.19206976
31 32 33 34 35 36
-0.41498809 -0.58799382 -0.26815215 -0.30636012 -0.42417110 -0.47724000
37 38 39 40 41 42
-0.76148558 -0.75846160 -0.67384627 -0.52815188 -0.66766095 -0.60808885
43 44 45 46 47 48
-0.58889725 -1.03375097 -1.26442528 -1.19422651 -0.78190326 0.02513907
49 50 51 52 53 54
-0.01247668 -0.19724014 -0.74934148 -1.02268035 -0.72911930 -0.28477394
55 56 57 58 59 60
0.15915933 0.66149157 0.53240331 0.48602753 0.68011123 0.86368004
61
0.88379644
> postscript(file="/var/www/html/rcomp/tmp/6jjfr1258736114.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 0.56603202 NA
1 1.00149102 0.56603202
2 0.85605108 1.00149102
3 0.71966713 0.85605108
4 0.57381455 0.71966713
5 0.43172096 0.57381455
6 0.33528964 0.43172096
7 0.40605884 0.33528964
8 0.19854084 0.40605884
9 0.37730358 0.19854084
10 0.16099901 0.37730358
11 0.06359971 0.16099901
12 0.06095667 0.06359971
13 0.46326726 0.06095667
14 0.66524948 0.46326726
15 0.74995997 0.66524948
16 0.85462336 0.74995997
17 0.65321158 0.85462336
18 0.50943637 0.65321158
19 0.55419438 0.50943637
20 0.80163329 0.55419438
21 0.63725552 0.80163329
22 0.36496413 0.63725552
23 -0.47517882 0.36496413
24 -0.73682287 -0.47517882
25 -0.50905654 -0.73682287
26 -0.09811281 -0.50905654
27 0.08120512 -0.09811281
28 -0.03165766 0.08120512
29 -0.19206976 -0.03165766
30 -0.41498809 -0.19206976
31 -0.58799382 -0.41498809
32 -0.26815215 -0.58799382
33 -0.30636012 -0.26815215
34 -0.42417110 -0.30636012
35 -0.47724000 -0.42417110
36 -0.76148558 -0.47724000
37 -0.75846160 -0.76148558
38 -0.67384627 -0.75846160
39 -0.52815188 -0.67384627
40 -0.66766095 -0.52815188
41 -0.60808885 -0.66766095
42 -0.58889725 -0.60808885
43 -1.03375097 -0.58889725
44 -1.26442528 -1.03375097
45 -1.19422651 -1.26442528
46 -0.78190326 -1.19422651
47 0.02513907 -0.78190326
48 -0.01247668 0.02513907
49 -0.19724014 -0.01247668
50 -0.74934148 -0.19724014
51 -1.02268035 -0.74934148
52 -0.72911930 -1.02268035
53 -0.28477394 -0.72911930
54 0.15915933 -0.28477394
55 0.66149157 0.15915933
56 0.53240331 0.66149157
57 0.48602753 0.53240331
58 0.68011123 0.48602753
59 0.86368004 0.68011123
60 0.88379644 0.86368004
61 NA 0.88379644
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.00149102 0.56603202
[2,] 0.85605108 1.00149102
[3,] 0.71966713 0.85605108
[4,] 0.57381455 0.71966713
[5,] 0.43172096 0.57381455
[6,] 0.33528964 0.43172096
[7,] 0.40605884 0.33528964
[8,] 0.19854084 0.40605884
[9,] 0.37730358 0.19854084
[10,] 0.16099901 0.37730358
[11,] 0.06359971 0.16099901
[12,] 0.06095667 0.06359971
[13,] 0.46326726 0.06095667
[14,] 0.66524948 0.46326726
[15,] 0.74995997 0.66524948
[16,] 0.85462336 0.74995997
[17,] 0.65321158 0.85462336
[18,] 0.50943637 0.65321158
[19,] 0.55419438 0.50943637
[20,] 0.80163329 0.55419438
[21,] 0.63725552 0.80163329
[22,] 0.36496413 0.63725552
[23,] -0.47517882 0.36496413
[24,] -0.73682287 -0.47517882
[25,] -0.50905654 -0.73682287
[26,] -0.09811281 -0.50905654
[27,] 0.08120512 -0.09811281
[28,] -0.03165766 0.08120512
[29,] -0.19206976 -0.03165766
[30,] -0.41498809 -0.19206976
[31,] -0.58799382 -0.41498809
[32,] -0.26815215 -0.58799382
[33,] -0.30636012 -0.26815215
[34,] -0.42417110 -0.30636012
[35,] -0.47724000 -0.42417110
[36,] -0.76148558 -0.47724000
[37,] -0.75846160 -0.76148558
[38,] -0.67384627 -0.75846160
[39,] -0.52815188 -0.67384627
[40,] -0.66766095 -0.52815188
[41,] -0.60808885 -0.66766095
[42,] -0.58889725 -0.60808885
[43,] -1.03375097 -0.58889725
[44,] -1.26442528 -1.03375097
[45,] -1.19422651 -1.26442528
[46,] -0.78190326 -1.19422651
[47,] 0.02513907 -0.78190326
[48,] -0.01247668 0.02513907
[49,] -0.19724014 -0.01247668
[50,] -0.74934148 -0.19724014
[51,] -1.02268035 -0.74934148
[52,] -0.72911930 -1.02268035
[53,] -0.28477394 -0.72911930
[54,] 0.15915933 -0.28477394
[55,] 0.66149157 0.15915933
[56,] 0.53240331 0.66149157
[57,] 0.48602753 0.53240331
[58,] 0.68011123 0.48602753
[59,] 0.86368004 0.68011123
[60,] 0.88379644 0.86368004
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.00149102 0.56603202
2 0.85605108 1.00149102
3 0.71966713 0.85605108
4 0.57381455 0.71966713
5 0.43172096 0.57381455
6 0.33528964 0.43172096
7 0.40605884 0.33528964
8 0.19854084 0.40605884
9 0.37730358 0.19854084
10 0.16099901 0.37730358
11 0.06359971 0.16099901
12 0.06095667 0.06359971
13 0.46326726 0.06095667
14 0.66524948 0.46326726
15 0.74995997 0.66524948
16 0.85462336 0.74995997
17 0.65321158 0.85462336
18 0.50943637 0.65321158
19 0.55419438 0.50943637
20 0.80163329 0.55419438
21 0.63725552 0.80163329
22 0.36496413 0.63725552
23 -0.47517882 0.36496413
24 -0.73682287 -0.47517882
25 -0.50905654 -0.73682287
26 -0.09811281 -0.50905654
27 0.08120512 -0.09811281
28 -0.03165766 0.08120512
29 -0.19206976 -0.03165766
30 -0.41498809 -0.19206976
31 -0.58799382 -0.41498809
32 -0.26815215 -0.58799382
33 -0.30636012 -0.26815215
34 -0.42417110 -0.30636012
35 -0.47724000 -0.42417110
36 -0.76148558 -0.47724000
37 -0.75846160 -0.76148558
38 -0.67384627 -0.75846160
39 -0.52815188 -0.67384627
40 -0.66766095 -0.52815188
41 -0.60808885 -0.66766095
42 -0.58889725 -0.60808885
43 -1.03375097 -0.58889725
44 -1.26442528 -1.03375097
45 -1.19422651 -1.26442528
46 -0.78190326 -1.19422651
47 0.02513907 -0.78190326
48 -0.01247668 0.02513907
49 -0.19724014 -0.01247668
50 -0.74934148 -0.19724014
51 -1.02268035 -0.74934148
52 -0.72911930 -1.02268035
53 -0.28477394 -0.72911930
54 0.15915933 -0.28477394
55 0.66149157 0.15915933
56 0.53240331 0.66149157
57 0.48602753 0.53240331
58 0.68011123 0.48602753
59 0.86368004 0.68011123
60 0.88379644 0.86368004
> 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/7ur6k1258736114.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8oe431258736114.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/912b71258736114.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103j4s1258736114.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fnj71258736114.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/123dlx1258736114.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/13bnw61258736114.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/14c1lz1258736114.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/15ldlp1258736114.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/16j17l1258736114.tab")
+ }
>
> system("convert tmp/15zy91258736114.ps tmp/15zy91258736114.png")
> system("convert tmp/2gqau1258736114.ps tmp/2gqau1258736114.png")
> system("convert tmp/3trru1258736114.ps tmp/3trru1258736114.png")
> system("convert tmp/485731258736114.ps tmp/485731258736114.png")
> system("convert tmp/54tji1258736114.ps tmp/54tji1258736114.png")
> system("convert tmp/6jjfr1258736114.ps tmp/6jjfr1258736114.png")
> system("convert tmp/7ur6k1258736114.ps tmp/7ur6k1258736114.png")
> system("convert tmp/8oe431258736114.ps tmp/8oe431258736114.png")
> system("convert tmp/912b71258736114.ps tmp/912b71258736114.png")
> system("convert tmp/103j4s1258736114.ps tmp/103j4s1258736114.png")
>
>
> proc.time()
user system elapsed
2.497 1.611 7.634