R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(7.2,102.9,7.4,97.4,8.8,111.4,9.3,87.4,9.3,96.8,8.7,114.1,8.2,110.3,8.3,103.9,8.5,101.6,8.6,94.6,8.5,95.9,8.2,104.7,8.1,102.8,7.9,98.1,8.6,113.9,8.7,80.9,8.7,95.7,8.5,113.2,8.4,105.9,8.5,108.8,8.7,102.3,8.7,99,8.6,100.7,8.5,115.5,8.3,100.7,8,109.9,8.2,114.6,8.1,85.4,8.1,100.5,8,114.8,7.9,116.5,7.9,112.9,8,102,8,106,7.9,105.3,8,118.8,7.7,106.1,7.2,109.3,7.5,117.2,7.3,92.5,7,104.2,7,112.5,7,122.4,7.2,113.3,7.3,100,7.1,110.7,6.8,112.8,6.4,109.8,6.1,117.3,6.5,109.1,7.7,115.9,7.9,96,7.5,99.8,6.9,116.8,6.6,115.7,6.9,99.4,7.7,94.3,8,91,8,93.2,7.7,103.1,7.3,94.1,7.4,91.8,8.1,102.7,8.3,82.6,8.2,89.1),dim=c(2,65),dimnames=list(c('Werkl.graad','Industr.prod.'),1:65))
> y <- array(NA,dim=c(2,65),dimnames=list(c('Werkl.graad','Industr.prod.'),1:65))
> 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
Werkl.graad Industr.prod. M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 7.2 102.9 1 0 0 0 0 0 0 0 0 0 0
2 7.4 97.4 0 1 0 0 0 0 0 0 0 0 0
3 8.8 111.4 0 0 1 0 0 0 0 0 0 0 0
4 9.3 87.4 0 0 0 1 0 0 0 0 0 0 0
5 9.3 96.8 0 0 0 0 1 0 0 0 0 0 0
6 8.7 114.1 0 0 0 0 0 1 0 0 0 0 0
7 8.2 110.3 0 0 0 0 0 0 1 0 0 0 0
8 8.3 103.9 0 0 0 0 0 0 0 1 0 0 0
9 8.5 101.6 0 0 0 0 0 0 0 0 1 0 0
10 8.6 94.6 0 0 0 0 0 0 0 0 0 1 0
11 8.5 95.9 0 0 0 0 0 0 0 0 0 0 1
12 8.2 104.7 0 0 0 0 0 0 0 0 0 0 0
13 8.1 102.8 1 0 0 0 0 0 0 0 0 0 0
14 7.9 98.1 0 1 0 0 0 0 0 0 0 0 0
15 8.6 113.9 0 0 1 0 0 0 0 0 0 0 0
16 8.7 80.9 0 0 0 1 0 0 0 0 0 0 0
17 8.7 95.7 0 0 0 0 1 0 0 0 0 0 0
18 8.5 113.2 0 0 0 0 0 1 0 0 0 0 0
19 8.4 105.9 0 0 0 0 0 0 1 0 0 0 0
20 8.5 108.8 0 0 0 0 0 0 0 1 0 0 0
21 8.7 102.3 0 0 0 0 0 0 0 0 1 0 0
22 8.7 99.0 0 0 0 0 0 0 0 0 0 1 0
23 8.6 100.7 0 0 0 0 0 0 0 0 0 0 1
24 8.5 115.5 0 0 0 0 0 0 0 0 0 0 0
25 8.3 100.7 1 0 0 0 0 0 0 0 0 0 0
26 8.0 109.9 0 1 0 0 0 0 0 0 0 0 0
27 8.2 114.6 0 0 1 0 0 0 0 0 0 0 0
28 8.1 85.4 0 0 0 1 0 0 0 0 0 0 0
29 8.1 100.5 0 0 0 0 1 0 0 0 0 0 0
30 8.0 114.8 0 0 0 0 0 1 0 0 0 0 0
31 7.9 116.5 0 0 0 0 0 0 1 0 0 0 0
32 7.9 112.9 0 0 0 0 0 0 0 1 0 0 0
33 8.0 102.0 0 0 0 0 0 0 0 0 1 0 0
34 8.0 106.0 0 0 0 0 0 0 0 0 0 1 0
35 7.9 105.3 0 0 0 0 0 0 0 0 0 0 1
36 8.0 118.8 0 0 0 0 0 0 0 0 0 0 0
37 7.7 106.1 1 0 0 0 0 0 0 0 0 0 0
38 7.2 109.3 0 1 0 0 0 0 0 0 0 0 0
39 7.5 117.2 0 0 1 0 0 0 0 0 0 0 0
40 7.3 92.5 0 0 0 1 0 0 0 0 0 0 0
41 7.0 104.2 0 0 0 0 1 0 0 0 0 0 0
42 7.0 112.5 0 0 0 0 0 1 0 0 0 0 0
43 7.0 122.4 0 0 0 0 0 0 1 0 0 0 0
44 7.2 113.3 0 0 0 0 0 0 0 1 0 0 0
45 7.3 100.0 0 0 0 0 0 0 0 0 1 0 0
46 7.1 110.7 0 0 0 0 0 0 0 0 0 1 0
47 6.8 112.8 0 0 0 0 0 0 0 0 0 0 1
48 6.4 109.8 0 0 0 0 0 0 0 0 0 0 0
49 6.1 117.3 1 0 0 0 0 0 0 0 0 0 0
50 6.5 109.1 0 1 0 0 0 0 0 0 0 0 0
51 7.7 115.9 0 0 1 0 0 0 0 0 0 0 0
52 7.9 96.0 0 0 0 1 0 0 0 0 0 0 0
53 7.5 99.8 0 0 0 0 1 0 0 0 0 0 0
54 6.9 116.8 0 0 0 0 0 1 0 0 0 0 0
55 6.6 115.7 0 0 0 0 0 0 1 0 0 0 0
56 6.9 99.4 0 0 0 0 0 0 0 1 0 0 0
57 7.7 94.3 0 0 0 0 0 0 0 0 1 0 0
58 8.0 91.0 0 0 0 0 0 0 0 0 0 1 0
59 8.0 93.2 0 0 0 0 0 0 0 0 0 0 1
60 7.7 103.1 0 0 0 0 0 0 0 0 0 0 0
61 7.3 94.1 1 0 0 0 0 0 0 0 0 0 0
62 7.4 91.8 0 1 0 0 0 0 0 0 0 0 0
63 8.1 102.7 0 0 1 0 0 0 0 0 0 0 0
64 8.3 82.6 0 0 0 1 0 0 0 0 0 0 0
65 8.2 89.1 0 0 0 0 1 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Industr.prod. M1 M2 M3
12.30908 -0.04121 -0.57363 -0.68064 0.48218
M4 M5 M6 M7 M8
-0.43766 -0.14993 0.22073 0.01578 -0.11210
M9 M10 M11
-0.14614 -0.09707 -0.16267
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.38390 -0.46111 0.08275 0.48493 1.13026
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.30908 1.62984 7.552 6.53e-10 ***
Industr.prod. -0.04121 0.01452 -2.838 0.00645 **
M1 -0.57363 0.41170 -1.393 0.16945
M2 -0.68064 0.41669 -1.633 0.10842
M3 0.48218 0.40240 1.198 0.23624
M4 -0.43766 0.52111 -0.840 0.40483
M5 -0.14993 0.44142 -0.340 0.73548
M6 0.22073 0.42273 0.522 0.60378
M7 0.01578 0.42250 0.037 0.97034
M8 -0.11210 0.42078 -0.266 0.79098
M9 -0.14614 0.44501 -0.328 0.74393
M10 -0.09707 0.44394 -0.219 0.82777
M11 -0.16267 0.43797 -0.371 0.71183
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6624 on 52 degrees of freedom
Multiple R-squared: 0.2738, Adjusted R-squared: 0.1062
F-statistic: 1.634 on 12 and 52 DF, p-value: 0.1110
> 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,] 0.273608852 0.547217705 0.726391148
[2,] 0.198048991 0.396097982 0.801951009
[3,] 0.116128622 0.232257243 0.883871378
[4,] 0.087749770 0.175499539 0.912250230
[5,] 0.052653797 0.105307594 0.947346203
[6,] 0.032925656 0.065851311 0.967074344
[7,] 0.018592877 0.037185754 0.981407123
[8,] 0.010766196 0.021532392 0.989233804
[9,] 0.007938603 0.015877206 0.992061397
[10,] 0.022113178 0.044226355 0.977886822
[11,] 0.019051201 0.038102402 0.980948799
[12,] 0.021669377 0.043338754 0.978330623
[13,] 0.050263472 0.100526944 0.949736528
[14,] 0.100998324 0.201996648 0.899001676
[15,] 0.135147206 0.270294412 0.864852794
[16,] 0.157294167 0.314588333 0.842705833
[17,] 0.202203795 0.404407591 0.797796205
[18,] 0.219613435 0.439226870 0.780386565
[19,] 0.224359636 0.448719272 0.775640364
[20,] 0.220676367 0.441352734 0.779323633
[21,] 0.464846086 0.929692172 0.535153914
[22,] 0.619873703 0.760252595 0.380126297
[23,] 0.669866209 0.660267582 0.330133791
[24,] 0.688386838 0.623226325 0.311613162
[25,] 0.821965168 0.356069664 0.178034832
[26,] 0.887724500 0.224550999 0.112275500
[27,] 0.905211842 0.189576316 0.094788158
[28,] 0.917057611 0.165884777 0.082942389
[29,] 0.977403855 0.045192289 0.022596145
[30,] 0.965398861 0.069202279 0.034601139
[31,] 0.934068182 0.131863635 0.065931818
[32,] 0.879906376 0.240187248 0.120093624
[33,] 0.995047231 0.009905539 0.004952769
[34,] 0.980895494 0.038209012 0.019104506
> postscript(file="/var/www/html/rcomp/tmp/1exff1258657008.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2f9w81258657008.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xxet1258657008.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wbnf1258657008.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x4fk1258657008.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 = 65
Frequency = 1
1 2 3 4 5 6
-0.29464733 -0.21430718 0.59985762 1.03058581 1.13026192 0.87258167
7 8 9 10 11 12
0.42091813 0.38503943 0.52429215 0.28673488 0.30591062 0.20591062
13 14 15 16 17 18
0.60123138 0.31454187 0.50288991 0.16270184 0.48492771 0.63549005
19 20 21 22 23 24
0.43958129 0.78698273 0.75314120 0.56807172 0.60373263 0.95101014
25 26 27 28 29 30
0.71468425 0.90085430 0.13173895 -0.25184003 0.08274972 0.20143072
31 32 33 34 35 36
0.37643823 0.35595569 0.04077732 0.15656215 0.09331206 0.58701277
37 38 39 40 41 42
0.33723401 0.07612655 -0.46110746 -0.75922831 -0.86476248 -0.89335899
43 44 45 46 47 48
-0.28040555 -0.32755914 -0.74164852 -0.54973713 -0.69759106 -1.38390349
49 50 51 52 53 54
-0.80118130 -0.63211603 -0.31468425 -0.01498310 -0.54609932 -0.81614345
55 56 57 58 59 60
-0.95653211 -1.20041871 -0.57656215 -0.46163162 -0.30536426 -0.36003005
61 62 63 64 65
-0.55732101 -0.44509952 -0.45869477 -0.16723620 -0.28707755
> postscript(file="/var/www/html/rcomp/tmp/6a5l51258657008.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 = 65
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.29464733 NA
1 -0.21430718 -0.29464733
2 0.59985762 -0.21430718
3 1.03058581 0.59985762
4 1.13026192 1.03058581
5 0.87258167 1.13026192
6 0.42091813 0.87258167
7 0.38503943 0.42091813
8 0.52429215 0.38503943
9 0.28673488 0.52429215
10 0.30591062 0.28673488
11 0.20591062 0.30591062
12 0.60123138 0.20591062
13 0.31454187 0.60123138
14 0.50288991 0.31454187
15 0.16270184 0.50288991
16 0.48492771 0.16270184
17 0.63549005 0.48492771
18 0.43958129 0.63549005
19 0.78698273 0.43958129
20 0.75314120 0.78698273
21 0.56807172 0.75314120
22 0.60373263 0.56807172
23 0.95101014 0.60373263
24 0.71468425 0.95101014
25 0.90085430 0.71468425
26 0.13173895 0.90085430
27 -0.25184003 0.13173895
28 0.08274972 -0.25184003
29 0.20143072 0.08274972
30 0.37643823 0.20143072
31 0.35595569 0.37643823
32 0.04077732 0.35595569
33 0.15656215 0.04077732
34 0.09331206 0.15656215
35 0.58701277 0.09331206
36 0.33723401 0.58701277
37 0.07612655 0.33723401
38 -0.46110746 0.07612655
39 -0.75922831 -0.46110746
40 -0.86476248 -0.75922831
41 -0.89335899 -0.86476248
42 -0.28040555 -0.89335899
43 -0.32755914 -0.28040555
44 -0.74164852 -0.32755914
45 -0.54973713 -0.74164852
46 -0.69759106 -0.54973713
47 -1.38390349 -0.69759106
48 -0.80118130 -1.38390349
49 -0.63211603 -0.80118130
50 -0.31468425 -0.63211603
51 -0.01498310 -0.31468425
52 -0.54609932 -0.01498310
53 -0.81614345 -0.54609932
54 -0.95653211 -0.81614345
55 -1.20041871 -0.95653211
56 -0.57656215 -1.20041871
57 -0.46163162 -0.57656215
58 -0.30536426 -0.46163162
59 -0.36003005 -0.30536426
60 -0.55732101 -0.36003005
61 -0.44509952 -0.55732101
62 -0.45869477 -0.44509952
63 -0.16723620 -0.45869477
64 -0.28707755 -0.16723620
65 NA -0.28707755
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.21430718 -0.29464733
[2,] 0.59985762 -0.21430718
[3,] 1.03058581 0.59985762
[4,] 1.13026192 1.03058581
[5,] 0.87258167 1.13026192
[6,] 0.42091813 0.87258167
[7,] 0.38503943 0.42091813
[8,] 0.52429215 0.38503943
[9,] 0.28673488 0.52429215
[10,] 0.30591062 0.28673488
[11,] 0.20591062 0.30591062
[12,] 0.60123138 0.20591062
[13,] 0.31454187 0.60123138
[14,] 0.50288991 0.31454187
[15,] 0.16270184 0.50288991
[16,] 0.48492771 0.16270184
[17,] 0.63549005 0.48492771
[18,] 0.43958129 0.63549005
[19,] 0.78698273 0.43958129
[20,] 0.75314120 0.78698273
[21,] 0.56807172 0.75314120
[22,] 0.60373263 0.56807172
[23,] 0.95101014 0.60373263
[24,] 0.71468425 0.95101014
[25,] 0.90085430 0.71468425
[26,] 0.13173895 0.90085430
[27,] -0.25184003 0.13173895
[28,] 0.08274972 -0.25184003
[29,] 0.20143072 0.08274972
[30,] 0.37643823 0.20143072
[31,] 0.35595569 0.37643823
[32,] 0.04077732 0.35595569
[33,] 0.15656215 0.04077732
[34,] 0.09331206 0.15656215
[35,] 0.58701277 0.09331206
[36,] 0.33723401 0.58701277
[37,] 0.07612655 0.33723401
[38,] -0.46110746 0.07612655
[39,] -0.75922831 -0.46110746
[40,] -0.86476248 -0.75922831
[41,] -0.89335899 -0.86476248
[42,] -0.28040555 -0.89335899
[43,] -0.32755914 -0.28040555
[44,] -0.74164852 -0.32755914
[45,] -0.54973713 -0.74164852
[46,] -0.69759106 -0.54973713
[47,] -1.38390349 -0.69759106
[48,] -0.80118130 -1.38390349
[49,] -0.63211603 -0.80118130
[50,] -0.31468425 -0.63211603
[51,] -0.01498310 -0.31468425
[52,] -0.54609932 -0.01498310
[53,] -0.81614345 -0.54609932
[54,] -0.95653211 -0.81614345
[55,] -1.20041871 -0.95653211
[56,] -0.57656215 -1.20041871
[57,] -0.46163162 -0.57656215
[58,] -0.30536426 -0.46163162
[59,] -0.36003005 -0.30536426
[60,] -0.55732101 -0.36003005
[61,] -0.44509952 -0.55732101
[62,] -0.45869477 -0.44509952
[63,] -0.16723620 -0.45869477
[64,] -0.28707755 -0.16723620
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.21430718 -0.29464733
2 0.59985762 -0.21430718
3 1.03058581 0.59985762
4 1.13026192 1.03058581
5 0.87258167 1.13026192
6 0.42091813 0.87258167
7 0.38503943 0.42091813
8 0.52429215 0.38503943
9 0.28673488 0.52429215
10 0.30591062 0.28673488
11 0.20591062 0.30591062
12 0.60123138 0.20591062
13 0.31454187 0.60123138
14 0.50288991 0.31454187
15 0.16270184 0.50288991
16 0.48492771 0.16270184
17 0.63549005 0.48492771
18 0.43958129 0.63549005
19 0.78698273 0.43958129
20 0.75314120 0.78698273
21 0.56807172 0.75314120
22 0.60373263 0.56807172
23 0.95101014 0.60373263
24 0.71468425 0.95101014
25 0.90085430 0.71468425
26 0.13173895 0.90085430
27 -0.25184003 0.13173895
28 0.08274972 -0.25184003
29 0.20143072 0.08274972
30 0.37643823 0.20143072
31 0.35595569 0.37643823
32 0.04077732 0.35595569
33 0.15656215 0.04077732
34 0.09331206 0.15656215
35 0.58701277 0.09331206
36 0.33723401 0.58701277
37 0.07612655 0.33723401
38 -0.46110746 0.07612655
39 -0.75922831 -0.46110746
40 -0.86476248 -0.75922831
41 -0.89335899 -0.86476248
42 -0.28040555 -0.89335899
43 -0.32755914 -0.28040555
44 -0.74164852 -0.32755914
45 -0.54973713 -0.74164852
46 -0.69759106 -0.54973713
47 -1.38390349 -0.69759106
48 -0.80118130 -1.38390349
49 -0.63211603 -0.80118130
50 -0.31468425 -0.63211603
51 -0.01498310 -0.31468425
52 -0.54609932 -0.01498310
53 -0.81614345 -0.54609932
54 -0.95653211 -0.81614345
55 -1.20041871 -0.95653211
56 -0.57656215 -1.20041871
57 -0.46163162 -0.57656215
58 -0.30536426 -0.46163162
59 -0.36003005 -0.30536426
60 -0.55732101 -0.36003005
61 -0.44509952 -0.55732101
62 -0.45869477 -0.44509952
63 -0.16723620 -0.45869477
64 -0.28707755 -0.16723620
> 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/7eypx1258657008.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8x7oz1258657008.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/966y61258657008.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mtq81258657008.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118yz01258657008.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/12zvx91258657008.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/13cfvh1258657008.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/14d3ty1258657008.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/15kejh1258657008.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/16xqmo1258657009.tab")
+ }
>
> system("convert tmp/1exff1258657008.ps tmp/1exff1258657008.png")
> system("convert tmp/2f9w81258657008.ps tmp/2f9w81258657008.png")
> system("convert tmp/3xxet1258657008.ps tmp/3xxet1258657008.png")
> system("convert tmp/4wbnf1258657008.ps tmp/4wbnf1258657008.png")
> system("convert tmp/5x4fk1258657008.ps tmp/5x4fk1258657008.png")
> system("convert tmp/6a5l51258657008.ps tmp/6a5l51258657008.png")
> system("convert tmp/7eypx1258657008.ps tmp/7eypx1258657008.png")
> system("convert tmp/8x7oz1258657008.ps tmp/8x7oz1258657008.png")
> system("convert tmp/966y61258657008.ps tmp/966y61258657008.png")
> system("convert tmp/10mtq81258657008.ps tmp/10mtq81258657008.png")
>
>
> proc.time()
user system elapsed
2.480 1.548 2.877