R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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(100.0
+ ,114.1
+ ,141.7
+ ,93.5
+ ,110.3
+ ,153.4
+ ,88.2
+ ,103.9
+ ,145
+ ,89.2
+ ,101.6
+ ,137.7
+ ,91.4
+ ,94.6
+ ,148.3
+ ,92.5
+ ,95.9
+ ,152.2
+ ,91.4
+ ,104.7
+ ,169.4
+ ,88.2
+ ,102.8
+ ,168.6
+ ,87.1
+ ,98.1
+ ,161.1
+ ,84.9
+ ,113.9
+ ,174.1
+ ,92.5
+ ,80.9
+ ,179
+ ,93.5
+ ,95.7
+ ,190.6
+ ,93.5
+ ,113.2
+ ,190
+ ,91.4
+ ,105.9
+ ,181.6
+ ,90.3
+ ,108.8
+ ,174.8
+ ,91.4
+ ,102.3
+ ,180.5
+ ,93.5
+ ,99
+ ,196.8
+ ,93.5
+ ,100.7
+ ,193.8
+ ,92.5
+ ,115.5
+ ,197
+ ,91.4
+ ,100.7
+ ,216.3
+ ,89.2
+ ,109.9
+ ,221.4
+ ,86.0
+ ,114.6
+ ,217.9
+ ,88.2
+ ,85.4
+ ,229.7
+ ,87.1
+ ,100.5
+ ,227.4
+ ,87.1
+ ,114.8
+ ,204.2
+ ,86.0
+ ,116.5
+ ,196.6
+ ,84.9
+ ,112.9
+ ,198.8
+ ,84.9
+ ,102
+ ,207.5
+ ,86.0
+ ,106
+ ,190.7
+ ,86.0
+ ,105.3
+ ,201.6
+ ,84.9
+ ,118.8
+ ,210.5
+ ,86.0
+ ,106.1
+ ,223.5
+ ,82.8
+ ,109.3
+ ,223.8
+ ,77.4
+ ,117.2
+ ,231.2
+ ,80.6
+ ,92.5
+ ,244
+ ,78.5
+ ,104.2
+ ,234.7
+ ,75.3
+ ,112.5
+ ,250.2
+ ,75.3
+ ,122.4
+ ,265.7
+ ,75.3
+ ,113.3
+ ,287.6
+ ,77.4
+ ,100
+ ,283.3
+ ,78.5
+ ,110.7
+ ,295.4
+ ,76.3
+ ,112.8
+ ,312.3
+ ,73.1
+ ,109.8
+ ,333.8
+ ,68.8
+ ,117.3
+ ,347.7
+ ,65.6
+ ,109.1
+ ,383.2
+ ,69.9
+ ,115.9
+ ,407.1
+ ,82.8
+ ,96
+ ,413.6
+ ,84.9
+ ,99.8
+ ,362.7
+ ,80.6
+ ,116.8
+ ,321.9
+ ,74.2
+ ,115.7
+ ,239.4
+ ,71.0
+ ,99.4
+ ,191
+ ,74.2
+ ,94.3
+ ,159.7
+ ,82.8
+ ,91
+ ,163.4
+ ,86.0
+ ,93.2
+ ,157.6
+ ,86.0
+ ,103.1
+ ,166.2
+ ,82.8
+ ,94.1
+ ,176.7
+ ,78.5
+ ,91.8
+ ,198.3
+ ,79.6
+ ,102.7
+ ,226.2
+ ,87.1
+ ,82.6
+ ,216.2
+ ,89.2
+ ,89.1
+ ,235.9)
+ ,dim=c(3
+ ,60)
+ ,dimnames=list(c('WRKL(index)'
+ ,'IND'
+ ,'GRON')
+ ,1:60))
> y <- array(NA,dim=c(3,60),dimnames=list(c('WRKL(index)','IND','GRON'),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
WRKL(index) IND GRON M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 100.0 114.1 141.7 1 0 0 0 0 0 0 0 0 0 0
2 93.5 110.3 153.4 0 1 0 0 0 0 0 0 0 0 0
3 88.2 103.9 145.0 0 0 1 0 0 0 0 0 0 0 0
4 89.2 101.6 137.7 0 0 0 1 0 0 0 0 0 0 0
5 91.4 94.6 148.3 0 0 0 0 1 0 0 0 0 0 0
6 92.5 95.9 152.2 0 0 0 0 0 1 0 0 0 0 0
7 91.4 104.7 169.4 0 0 0 0 0 0 1 0 0 0 0
8 88.2 102.8 168.6 0 0 0 0 0 0 0 1 0 0 0
9 87.1 98.1 161.1 0 0 0 0 0 0 0 0 1 0 0
10 84.9 113.9 174.1 0 0 0 0 0 0 0 0 0 1 0
11 92.5 80.9 179.0 0 0 0 0 0 0 0 0 0 0 1
12 93.5 95.7 190.6 0 0 0 0 0 0 0 0 0 0 0
13 93.5 113.2 190.0 1 0 0 0 0 0 0 0 0 0 0
14 91.4 105.9 181.6 0 1 0 0 0 0 0 0 0 0 0
15 90.3 108.8 174.8 0 0 1 0 0 0 0 0 0 0 0
16 91.4 102.3 180.5 0 0 0 1 0 0 0 0 0 0 0
17 93.5 99.0 196.8 0 0 0 0 1 0 0 0 0 0 0
18 93.5 100.7 193.8 0 0 0 0 0 1 0 0 0 0 0
19 92.5 115.5 197.0 0 0 0 0 0 0 1 0 0 0 0
20 91.4 100.7 216.3 0 0 0 0 0 0 0 1 0 0 0
21 89.2 109.9 221.4 0 0 0 0 0 0 0 0 1 0 0
22 86.0 114.6 217.9 0 0 0 0 0 0 0 0 0 1 0
23 88.2 85.4 229.7 0 0 0 0 0 0 0 0 0 0 1
24 87.1 100.5 227.4 0 0 0 0 0 0 0 0 0 0 0
25 87.1 114.8 204.2 1 0 0 0 0 0 0 0 0 0 0
26 86.0 116.5 196.6 0 1 0 0 0 0 0 0 0 0 0
27 84.9 112.9 198.8 0 0 1 0 0 0 0 0 0 0 0
28 84.9 102.0 207.5 0 0 0 1 0 0 0 0 0 0 0
29 86.0 106.0 190.7 0 0 0 0 1 0 0 0 0 0 0
30 86.0 105.3 201.6 0 0 0 0 0 1 0 0 0 0 0
31 84.9 118.8 210.5 0 0 0 0 0 0 1 0 0 0 0
32 86.0 106.1 223.5 0 0 0 0 0 0 0 1 0 0 0
33 82.8 109.3 223.8 0 0 0 0 0 0 0 0 1 0 0
34 77.4 117.2 231.2 0 0 0 0 0 0 0 0 0 1 0
35 80.6 92.5 244.0 0 0 0 0 0 0 0 0 0 0 1
36 78.5 104.2 234.7 0 0 0 0 0 0 0 0 0 0 0
37 75.3 112.5 250.2 1 0 0 0 0 0 0 0 0 0 0
38 75.3 122.4 265.7 0 1 0 0 0 0 0 0 0 0 0
39 75.3 113.3 287.6 0 0 1 0 0 0 0 0 0 0 0
40 77.4 100.0 283.3 0 0 0 1 0 0 0 0 0 0 0
41 78.5 110.7 295.4 0 0 0 0 1 0 0 0 0 0 0
42 76.3 112.8 312.3 0 0 0 0 0 1 0 0 0 0 0
43 73.1 109.8 333.8 0 0 0 0 0 0 1 0 0 0 0
44 68.8 117.3 347.7 0 0 0 0 0 0 0 1 0 0 0
45 65.6 109.1 383.2 0 0 0 0 0 0 0 0 1 0 0
46 69.9 115.9 407.1 0 0 0 0 0 0 0 0 0 1 0
47 82.8 96.0 413.6 0 0 0 0 0 0 0 0 0 0 1
48 84.9 99.8 362.7 0 0 0 0 0 0 0 0 0 0 0
49 80.6 116.8 321.9 1 0 0 0 0 0 0 0 0 0 0
50 74.2 115.7 239.4 0 1 0 0 0 0 0 0 0 0 0
51 71.0 99.4 191.0 0 0 1 0 0 0 0 0 0 0 0
52 74.2 94.3 159.7 0 0 0 1 0 0 0 0 0 0 0
53 82.8 91.0 163.4 0 0 0 0 1 0 0 0 0 0 0
54 86.0 93.2 157.6 0 0 0 0 0 1 0 0 0 0 0
55 86.0 103.1 166.2 0 0 0 0 0 0 1 0 0 0 0
56 82.8 94.1 176.7 0 0 0 0 0 0 0 1 0 0 0
57 78.5 91.8 198.3 0 0 0 0 0 0 0 0 1 0 0
58 79.6 102.7 226.2 0 0 0 0 0 0 0 0 0 1 0
59 87.1 82.6 216.2 0 0 0 0 0 0 0 0 0 0 1
60 89.2 89.1 235.9 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) IND GRON M1 M2 M3
92.19966 0.16707 -0.08754 -4.59227 -9.04060 -10.78627
M4 M5 M6 M7 M8 M9
-8.53222 -5.09549 -4.49507 -6.20523 -6.33401 -8.07746
M10 M11
-9.49496 1.88044
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.2410 -2.7293 0.3551 2.8811 8.8898
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 92.19966 12.90404 7.145 5.52e-09 ***
IND 0.16707 0.14637 1.141 0.25960
GRON -0.08754 0.01374 -6.372 8.01e-08 ***
M1 -4.59227 4.33564 -1.059 0.29504
M2 -9.04060 4.40833 -2.051 0.04601 *
M3 -10.78627 3.93381 -2.742 0.00867 **
M4 -8.53222 3.57380 -2.387 0.02113 *
M5 -5.09549 3.56204 -1.430 0.15933
M6 -4.49507 3.59070 -1.252 0.21695
M7 -6.20523 4.04678 -1.533 0.13203
M8 -6.33401 3.61892 -1.750 0.08674 .
M9 -8.07746 3.56352 -2.267 0.02815 *
M10 -9.49496 4.07040 -2.333 0.02409 *
M11 1.88044 3.77468 0.498 0.62074
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.428 on 46 degrees of freedom
Multiple R-squared: 0.576, Adjusted R-squared: 0.4561
F-statistic: 4.806 on 13 and 46 DF, p-value: 3.398e-05
> 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,] 0.088317660 0.17663532 0.9116823
[2,] 0.034066385 0.06813277 0.9659336
[3,] 0.020934119 0.04186824 0.9790659
[4,] 0.025408412 0.05081682 0.9745916
[5,] 0.013161761 0.02632352 0.9868382
[6,] 0.005935217 0.01187043 0.9940648
[7,] 0.006997614 0.01399523 0.9930024
[8,] 0.013152969 0.02630594 0.9868470
[9,] 0.052974725 0.10594945 0.9470253
[10,] 0.080055631 0.16011126 0.9199444
[11,] 0.081711055 0.16342211 0.9182889
[12,] 0.083368996 0.16673799 0.9166310
[13,] 0.083378220 0.16675644 0.9166218
[14,] 0.075350748 0.15070150 0.9246493
[15,] 0.067693266 0.13538653 0.9323067
[16,] 0.076436418 0.15287284 0.9235636
[17,] 0.159769175 0.31953835 0.8402308
[18,] 0.185046913 0.37009383 0.8149531
[19,] 0.168296610 0.33659322 0.8317034
[20,] 0.278650353 0.55730071 0.7213496
[21,] 0.557866885 0.88426623 0.4421331
[22,] 0.472899221 0.94579844 0.5271008
[23,] 0.631124463 0.73775107 0.3688755
[24,] 0.787581787 0.42483643 0.2124182
[25,] 0.758088043 0.48382391 0.2419120
[26,] 0.618530338 0.76293932 0.3814697
[27,] 0.834106460 0.33178708 0.1658935
> postscript(file="/var/www/html/rcomp/tmp/1wloh1261145515.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ftr01261145515.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3kz211261145515.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47hov1261145515.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ub9l1261145515.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
5.735272303 5.342733392 2.122254446 0.613385226 1.474102067
6 7 8 9 10
2.097913202 2.743649560 -0.080180813 0.691905964 -1.592179869
11 12 13 14 15
0.574609770 1.997964821 3.614028772 6.446582469 6.012448546
16 17 18 19 20
6.443339630 7.084910634 5.937838287 4.455548738 7.646529782
21 22 23 24 25
6.099440626 3.225318963 -0.038690654 -1.982323350 -1.810148057
26 27 28 29 30
0.588835316 2.028538817 2.357159388 -2.118581608 -1.647824839
31 32 33 34 35
-2.513923632 1.974686144 0.009787645 -4.644715170 -7.572983292
36 37 38 39 40
-10.561398103 -9.198849526 -5.047541874 0.135657115 1.827161684
41 42 43 44 45
-1.237896372 -2.909661525 -2.016089766 -6.223449839 -3.202217034
46 47 48 49 50
3.471537267 8.889816118 7.779384910 1.659696507 -7.330609302
51 52 53 54 55
-10.298898923 -11.241045928 -5.202534720 -3.478265125 -2.669184900
56 57 58 59 60
-3.317585273 -3.598917200 -0.459961192 -1.852751942 2.766371721
> postscript(file="/var/www/html/rcomp/tmp/6pc0g1261145515.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 5.735272303 NA
1 5.342733392 5.735272303
2 2.122254446 5.342733392
3 0.613385226 2.122254446
4 1.474102067 0.613385226
5 2.097913202 1.474102067
6 2.743649560 2.097913202
7 -0.080180813 2.743649560
8 0.691905964 -0.080180813
9 -1.592179869 0.691905964
10 0.574609770 -1.592179869
11 1.997964821 0.574609770
12 3.614028772 1.997964821
13 6.446582469 3.614028772
14 6.012448546 6.446582469
15 6.443339630 6.012448546
16 7.084910634 6.443339630
17 5.937838287 7.084910634
18 4.455548738 5.937838287
19 7.646529782 4.455548738
20 6.099440626 7.646529782
21 3.225318963 6.099440626
22 -0.038690654 3.225318963
23 -1.982323350 -0.038690654
24 -1.810148057 -1.982323350
25 0.588835316 -1.810148057
26 2.028538817 0.588835316
27 2.357159388 2.028538817
28 -2.118581608 2.357159388
29 -1.647824839 -2.118581608
30 -2.513923632 -1.647824839
31 1.974686144 -2.513923632
32 0.009787645 1.974686144
33 -4.644715170 0.009787645
34 -7.572983292 -4.644715170
35 -10.561398103 -7.572983292
36 -9.198849526 -10.561398103
37 -5.047541874 -9.198849526
38 0.135657115 -5.047541874
39 1.827161684 0.135657115
40 -1.237896372 1.827161684
41 -2.909661525 -1.237896372
42 -2.016089766 -2.909661525
43 -6.223449839 -2.016089766
44 -3.202217034 -6.223449839
45 3.471537267 -3.202217034
46 8.889816118 3.471537267
47 7.779384910 8.889816118
48 1.659696507 7.779384910
49 -7.330609302 1.659696507
50 -10.298898923 -7.330609302
51 -11.241045928 -10.298898923
52 -5.202534720 -11.241045928
53 -3.478265125 -5.202534720
54 -2.669184900 -3.478265125
55 -3.317585273 -2.669184900
56 -3.598917200 -3.317585273
57 -0.459961192 -3.598917200
58 -1.852751942 -0.459961192
59 2.766371721 -1.852751942
60 NA 2.766371721
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.342733392 5.735272303
[2,] 2.122254446 5.342733392
[3,] 0.613385226 2.122254446
[4,] 1.474102067 0.613385226
[5,] 2.097913202 1.474102067
[6,] 2.743649560 2.097913202
[7,] -0.080180813 2.743649560
[8,] 0.691905964 -0.080180813
[9,] -1.592179869 0.691905964
[10,] 0.574609770 -1.592179869
[11,] 1.997964821 0.574609770
[12,] 3.614028772 1.997964821
[13,] 6.446582469 3.614028772
[14,] 6.012448546 6.446582469
[15,] 6.443339630 6.012448546
[16,] 7.084910634 6.443339630
[17,] 5.937838287 7.084910634
[18,] 4.455548738 5.937838287
[19,] 7.646529782 4.455548738
[20,] 6.099440626 7.646529782
[21,] 3.225318963 6.099440626
[22,] -0.038690654 3.225318963
[23,] -1.982323350 -0.038690654
[24,] -1.810148057 -1.982323350
[25,] 0.588835316 -1.810148057
[26,] 2.028538817 0.588835316
[27,] 2.357159388 2.028538817
[28,] -2.118581608 2.357159388
[29,] -1.647824839 -2.118581608
[30,] -2.513923632 -1.647824839
[31,] 1.974686144 -2.513923632
[32,] 0.009787645 1.974686144
[33,] -4.644715170 0.009787645
[34,] -7.572983292 -4.644715170
[35,] -10.561398103 -7.572983292
[36,] -9.198849526 -10.561398103
[37,] -5.047541874 -9.198849526
[38,] 0.135657115 -5.047541874
[39,] 1.827161684 0.135657115
[40,] -1.237896372 1.827161684
[41,] -2.909661525 -1.237896372
[42,] -2.016089766 -2.909661525
[43,] -6.223449839 -2.016089766
[44,] -3.202217034 -6.223449839
[45,] 3.471537267 -3.202217034
[46,] 8.889816118 3.471537267
[47,] 7.779384910 8.889816118
[48,] 1.659696507 7.779384910
[49,] -7.330609302 1.659696507
[50,] -10.298898923 -7.330609302
[51,] -11.241045928 -10.298898923
[52,] -5.202534720 -11.241045928
[53,] -3.478265125 -5.202534720
[54,] -2.669184900 -3.478265125
[55,] -3.317585273 -2.669184900
[56,] -3.598917200 -3.317585273
[57,] -0.459961192 -3.598917200
[58,] -1.852751942 -0.459961192
[59,] 2.766371721 -1.852751942
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.342733392 5.735272303
2 2.122254446 5.342733392
3 0.613385226 2.122254446
4 1.474102067 0.613385226
5 2.097913202 1.474102067
6 2.743649560 2.097913202
7 -0.080180813 2.743649560
8 0.691905964 -0.080180813
9 -1.592179869 0.691905964
10 0.574609770 -1.592179869
11 1.997964821 0.574609770
12 3.614028772 1.997964821
13 6.446582469 3.614028772
14 6.012448546 6.446582469
15 6.443339630 6.012448546
16 7.084910634 6.443339630
17 5.937838287 7.084910634
18 4.455548738 5.937838287
19 7.646529782 4.455548738
20 6.099440626 7.646529782
21 3.225318963 6.099440626
22 -0.038690654 3.225318963
23 -1.982323350 -0.038690654
24 -1.810148057 -1.982323350
25 0.588835316 -1.810148057
26 2.028538817 0.588835316
27 2.357159388 2.028538817
28 -2.118581608 2.357159388
29 -1.647824839 -2.118581608
30 -2.513923632 -1.647824839
31 1.974686144 -2.513923632
32 0.009787645 1.974686144
33 -4.644715170 0.009787645
34 -7.572983292 -4.644715170
35 -10.561398103 -7.572983292
36 -9.198849526 -10.561398103
37 -5.047541874 -9.198849526
38 0.135657115 -5.047541874
39 1.827161684 0.135657115
40 -1.237896372 1.827161684
41 -2.909661525 -1.237896372
42 -2.016089766 -2.909661525
43 -6.223449839 -2.016089766
44 -3.202217034 -6.223449839
45 3.471537267 -3.202217034
46 8.889816118 3.471537267
47 7.779384910 8.889816118
48 1.659696507 7.779384910
49 -7.330609302 1.659696507
50 -10.298898923 -7.330609302
51 -11.241045928 -10.298898923
52 -5.202534720 -11.241045928
53 -3.478265125 -5.202534720
54 -2.669184900 -3.478265125
55 -3.317585273 -2.669184900
56 -3.598917200 -3.317585273
57 -0.459961192 -3.598917200
58 -1.852751942 -0.459961192
59 2.766371721 -1.852751942
> 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/74o3m1261145515.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8omix1261145515.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99ngr1261145515.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10c2aq1261145515.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11k36i1261145515.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/124rge1261145515.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/13hdgz1261145515.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/14h2op1261145515.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/15mzdm1261145515.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/16npdj1261145515.tab")
+ }
>
> try(system("convert tmp/1wloh1261145515.ps tmp/1wloh1261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ftr01261145515.ps tmp/2ftr01261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kz211261145515.ps tmp/3kz211261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/47hov1261145515.ps tmp/47hov1261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ub9l1261145515.ps tmp/5ub9l1261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pc0g1261145515.ps tmp/6pc0g1261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/74o3m1261145515.ps tmp/74o3m1261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/8omix1261145515.ps tmp/8omix1261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/99ngr1261145515.ps tmp/99ngr1261145515.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c2aq1261145515.ps tmp/10c2aq1261145515.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.448 1.579 8.050