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(7.6,1.62,8.3,1.49,8.4,1.79,8.4,1.8,8.4,1.58,8.4,1.86,8.6,1.74,8.9,1.59,8.8,1.26,8.3,1.13,7.5,1.92,7.2,2.61,7.4,2.26,8.8,2.41,9.3,2.26,9.3,2.03,8.7,2.86,8.2,2.55,8.3,2.27,8.5,2.26,8.6,2.57,8.5,3.07,8.2,2.76,8.1,2.51,7.9,2.87,8.6,3.14,8.7,3.11,8.7,3.16,8.5,2.47,8.4,2.57,8.5,2.89,8.7,2.63,8.7,2.38,8.6,1.69,8.5,1.96,8.3,2.19,8,1.87,8.2,1.6,8.1,1.63,8.1,1.22,8,1.21,7.9,1.49,7.9,1.64,8,1.66,8,1.77,7.9,1.82,8,1.78,7.7,1.28,7.2,1.29,7.5,1.37,7.3,1.12,7,1.51,7,2.24,7,2.94,7.2,3.09,7.3,3.46,7.1,3.64,6.8,4.39,6.4,4.15,6.1,5.21,6.5,5.8,7.7,5.91,7.9,5.39,7.5,5.46,6.9,4.72,6.6,3.14,6.9,2.63,7.7,2.32,8,1.93,8,0.62,7.7,0.6,7.3,-0.37,7.4,-1.1),dim=c(2,73),dimnames=list(c('TWG','Infl'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('TWG','Infl'),1:73))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
TWG Infl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.6 1.62 1 0 0 0 0 0 0 0 0 0 0 1
2 8.3 1.49 0 1 0 0 0 0 0 0 0 0 0 2
3 8.4 1.79 0 0 1 0 0 0 0 0 0 0 0 3
4 8.4 1.80 0 0 0 1 0 0 0 0 0 0 0 4
5 8.4 1.58 0 0 0 0 1 0 0 0 0 0 0 5
6 8.4 1.86 0 0 0 0 0 1 0 0 0 0 0 6
7 8.6 1.74 0 0 0 0 0 0 1 0 0 0 0 7
8 8.9 1.59 0 0 0 0 0 0 0 1 0 0 0 8
9 8.8 1.26 0 0 0 0 0 0 0 0 1 0 0 9
10 8.3 1.13 0 0 0 0 0 0 0 0 0 1 0 10
11 7.5 1.92 0 0 0 0 0 0 0 0 0 0 1 11
12 7.2 2.61 0 0 0 0 0 0 0 0 0 0 0 12
13 7.4 2.26 1 0 0 0 0 0 0 0 0 0 0 13
14 8.8 2.41 0 1 0 0 0 0 0 0 0 0 0 14
15 9.3 2.26 0 0 1 0 0 0 0 0 0 0 0 15
16 9.3 2.03 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 2.86 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 2.55 0 0 0 0 0 1 0 0 0 0 0 18
19 8.3 2.27 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 2.26 0 0 0 0 0 0 0 1 0 0 0 20
21 8.6 2.57 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 3.07 0 0 0 0 0 0 0 0 0 1 0 22
23 8.2 2.76 0 0 0 0 0 0 0 0 0 0 1 23
24 8.1 2.51 0 0 0 0 0 0 0 0 0 0 0 24
25 7.9 2.87 1 0 0 0 0 0 0 0 0 0 0 25
26 8.6 3.14 0 1 0 0 0 0 0 0 0 0 0 26
27 8.7 3.11 0 0 1 0 0 0 0 0 0 0 0 27
28 8.7 3.16 0 0 0 1 0 0 0 0 0 0 0 28
29 8.5 2.47 0 0 0 0 1 0 0 0 0 0 0 29
30 8.4 2.57 0 0 0 0 0 1 0 0 0 0 0 30
31 8.5 2.89 0 0 0 0 0 0 1 0 0 0 0 31
32 8.7 2.63 0 0 0 0 0 0 0 1 0 0 0 32
33 8.7 2.38 0 0 0 0 0 0 0 0 1 0 0 33
34 8.6 1.69 0 0 0 0 0 0 0 0 0 1 0 34
35 8.5 1.96 0 0 0 0 0 0 0 0 0 0 1 35
36 8.3 2.19 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 1.87 1 0 0 0 0 0 0 0 0 0 0 37
38 8.2 1.60 0 1 0 0 0 0 0 0 0 0 0 38
39 8.1 1.63 0 0 1 0 0 0 0 0 0 0 0 39
40 8.1 1.22 0 0 0 1 0 0 0 0 0 0 0 40
41 8.0 1.21 0 0 0 0 1 0 0 0 0 0 0 41
42 7.9 1.49 0 0 0 0 0 1 0 0 0 0 0 42
43 7.9 1.64 0 0 0 0 0 0 1 0 0 0 0 43
44 8.0 1.66 0 0 0 0 0 0 0 1 0 0 0 44
45 8.0 1.77 0 0 0 0 0 0 0 0 1 0 0 45
46 7.9 1.82 0 0 0 0 0 0 0 0 0 1 0 46
47 8.0 1.78 0 0 0 0 0 0 0 0 0 0 1 47
48 7.7 1.28 0 0 0 0 0 0 0 0 0 0 0 48
49 7.2 1.29 1 0 0 0 0 0 0 0 0 0 0 49
50 7.5 1.37 0 1 0 0 0 0 0 0 0 0 0 50
51 7.3 1.12 0 0 1 0 0 0 0 0 0 0 0 51
52 7.0 1.51 0 0 0 1 0 0 0 0 0 0 0 52
53 7.0 2.24 0 0 0 0 1 0 0 0 0 0 0 53
54 7.0 2.94 0 0 0 0 0 1 0 0 0 0 0 54
55 7.2 3.09 0 0 0 0 0 0 1 0 0 0 0 55
56 7.3 3.46 0 0 0 0 0 0 0 1 0 0 0 56
57 7.1 3.64 0 0 0 0 0 0 0 0 1 0 0 57
58 6.8 4.39 0 0 0 0 0 0 0 0 0 1 0 58
59 6.4 4.15 0 0 0 0 0 0 0 0 0 0 1 59
60 6.1 5.21 0 0 0 0 0 0 0 0 0 0 0 60
61 6.5 5.80 1 0 0 0 0 0 0 0 0 0 0 61
62 7.7 5.91 0 1 0 0 0 0 0 0 0 0 0 62
63 7.9 5.39 0 0 1 0 0 0 0 0 0 0 0 63
64 7.5 5.46 0 0 0 1 0 0 0 0 0 0 0 64
65 6.9 4.72 0 0 0 0 1 0 0 0 0 0 0 65
66 6.6 3.14 0 0 0 0 0 1 0 0 0 0 0 66
67 6.9 2.63 0 0 0 0 0 0 1 0 0 0 0 67
68 7.7 2.32 0 0 0 0 0 0 0 1 0 0 0 68
69 8.0 1.93 0 0 0 0 0 0 0 0 1 0 0 69
70 8.0 0.62 0 0 0 0 0 0 0 0 0 1 0 70
71 7.7 0.60 0 0 0 0 0 0 0 0 0 0 1 71
72 7.3 -0.37 0 0 0 0 0 0 0 0 0 0 0 72
73 7.4 -1.10 1 0 0 0 0 0 0 0 0 0 0 73
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl M1 M2 M3 M4
8.53000 -0.11729 -0.13648 0.58737 0.69472 0.59517
M5 M6 M7 M8 M9 M10
0.36268 0.20511 0.36891 0.66506 0.69396 0.51386
M11 t
0.24212 -0.01946
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.93596 -0.38638 0.04142 0.35697 0.72755
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.529998 0.238943 35.699 < 2e-16 ***
Infl -0.117288 0.045680 -2.568 0.0128 *
M1 -0.136479 0.267113 -0.511 0.6113
M2 0.587373 0.279226 2.104 0.0397 *
M3 0.694716 0.278557 2.494 0.0155 *
M4 0.595168 0.278234 2.139 0.0366 *
M5 0.362676 0.277955 1.305 0.1970
M6 0.205113 0.277546 0.739 0.4628
M7 0.368907 0.277295 1.330 0.1885
M8 0.665058 0.277090 2.400 0.0196 *
M9 0.693955 0.276944 2.506 0.0150 *
M10 0.513861 0.276898 1.856 0.0685 .
M11 0.242121 0.276830 0.875 0.3853
t -0.019464 0.002754 -7.067 2.09e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4795 on 59 degrees of freedom
Multiple R-squared: 0.6125, Adjusted R-squared: 0.5271
F-statistic: 7.174 on 13 and 59 DF, p-value: 4.037e-08
> 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,] 0.38921043 0.77842086 0.61078957
[2,] 0.41611441 0.83222882 0.58388559
[3,] 0.45541794 0.91083589 0.54458206
[4,] 0.45895803 0.91791605 0.54104197
[5,] 0.34344456 0.68688911 0.65655544
[6,] 0.27387522 0.54775045 0.72612478
[7,] 0.25065086 0.50130172 0.74934914
[8,] 0.20789505 0.41579009 0.79210495
[9,] 0.14224761 0.28449521 0.85775239
[10,] 0.10510235 0.21020471 0.89489765
[11,] 0.09538868 0.19077736 0.90461132
[12,] 0.07986766 0.15973532 0.92013234
[13,] 0.07617203 0.15234406 0.92382797
[14,] 0.05795490 0.11590980 0.94204510
[15,] 0.04330003 0.08660006 0.95669997
[16,] 0.03103988 0.06207977 0.96896012
[17,] 0.02185451 0.04370902 0.97814549
[18,] 0.01413736 0.02827472 0.98586264
[19,] 0.01318288 0.02636576 0.98681712
[20,] 0.01567004 0.03134008 0.98432996
[21,] 0.01302981 0.02605963 0.98697019
[22,] 0.02500537 0.05001075 0.97499463
[23,] 0.04772835 0.09545670 0.95227165
[24,] 0.05503369 0.11006738 0.94496631
[25,] 0.05116455 0.10232911 0.94883545
[26,] 0.05477316 0.10954632 0.94522684
[27,] 0.05653181 0.11306361 0.94346819
[28,] 0.05119649 0.10239298 0.94880351
[29,] 0.04603793 0.09207586 0.95396207
[30,] 0.04105255 0.08210509 0.95894745
[31,] 0.08216675 0.16433350 0.91783325
[32,] 0.30318281 0.60636561 0.69681719
[33,] 0.41662265 0.83324529 0.58337735
[34,] 0.37040758 0.74081517 0.62959242
[35,] 0.47324253 0.94648506 0.52675747
[36,] 0.72390836 0.55218328 0.27609164
[37,] 0.70568690 0.58862621 0.29431310
[38,] 0.72899427 0.54201146 0.27100573
[39,] 0.90210017 0.19579967 0.09789983
[40,] 0.95388257 0.09223486 0.04611743
> postscript(file="/var/www/html/rcomp/tmp/1dirh1261068863.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2uekb1261068863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/335mx1261068864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43f931261068864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x1yz1261068864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 73
Frequency = 1
1 2 3 4 5
-0.5840484721 -0.6036840968 -0.5563779038 -0.4361925947 -0.2100411713
6 7 8 9 10
-0.0001733945 0.0414209597 0.0471407387 -0.1009982300 -0.4166874960
11 12 13 14 15
-0.8328265174 -0.7903135577 -0.4754218484 0.2377831898 0.6323097460
16 17 18 19 20
0.7243459155 0.4736498248 0.1143176333 0.0371458943 -0.0407139952
21 22 23 24 25
0.0862114086 0.2444136342 0.1992577227 0.3315198853 0.3296861329
26 27 28 29 30
0.3569657409 0.3655668669 0.4904436993 0.4614697241 0.5502256462
31 32 33 34 35
0.5434267564 0.4362448464 0.3974889243 0.4161183325 0.6389895084
36 37 38 39 40
0.7275499504 0.5459603022 0.0099043460 -0.1744572431 -0.1035329283
41 42 43 44 45
0.0472489922 0.1571167690 0.0303789053 -0.1439623418 -0.1404945544
46 47 48 49 50
-0.0350719656 0.3514399050 0.2543800471 -0.0885045340 -0.4835096616
51 52 53 54 55
-0.8007119136 -0.9359571333 -0.5983820321 -0.3392532609 -0.2659911246
56 57 58 59 60
-0.3992815430 -0.5876035899 -0.6000793438 -0.7370250896 -0.6511155396
61 62 63 64 65
-0.0259730333 0.4825404817 0.5336704475 0.2608930416 -0.1739453377
66 67 68 69 70
-0.4822333932 -0.3863813911 0.1005722949 0.3453960413 0.3913068386
71 72 73
0.3801644709 0.1279792145 0.2983014527
> postscript(file="/var/www/html/rcomp/tmp/6q2y11261068864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.5840484721 NA
1 -0.6036840968 -0.5840484721
2 -0.5563779038 -0.6036840968
3 -0.4361925947 -0.5563779038
4 -0.2100411713 -0.4361925947
5 -0.0001733945 -0.2100411713
6 0.0414209597 -0.0001733945
7 0.0471407387 0.0414209597
8 -0.1009982300 0.0471407387
9 -0.4166874960 -0.1009982300
10 -0.8328265174 -0.4166874960
11 -0.7903135577 -0.8328265174
12 -0.4754218484 -0.7903135577
13 0.2377831898 -0.4754218484
14 0.6323097460 0.2377831898
15 0.7243459155 0.6323097460
16 0.4736498248 0.7243459155
17 0.1143176333 0.4736498248
18 0.0371458943 0.1143176333
19 -0.0407139952 0.0371458943
20 0.0862114086 -0.0407139952
21 0.2444136342 0.0862114086
22 0.1992577227 0.2444136342
23 0.3315198853 0.1992577227
24 0.3296861329 0.3315198853
25 0.3569657409 0.3296861329
26 0.3655668669 0.3569657409
27 0.4904436993 0.3655668669
28 0.4614697241 0.4904436993
29 0.5502256462 0.4614697241
30 0.5434267564 0.5502256462
31 0.4362448464 0.5434267564
32 0.3974889243 0.4362448464
33 0.4161183325 0.3974889243
34 0.6389895084 0.4161183325
35 0.7275499504 0.6389895084
36 0.5459603022 0.7275499504
37 0.0099043460 0.5459603022
38 -0.1744572431 0.0099043460
39 -0.1035329283 -0.1744572431
40 0.0472489922 -0.1035329283
41 0.1571167690 0.0472489922
42 0.0303789053 0.1571167690
43 -0.1439623418 0.0303789053
44 -0.1404945544 -0.1439623418
45 -0.0350719656 -0.1404945544
46 0.3514399050 -0.0350719656
47 0.2543800471 0.3514399050
48 -0.0885045340 0.2543800471
49 -0.4835096616 -0.0885045340
50 -0.8007119136 -0.4835096616
51 -0.9359571333 -0.8007119136
52 -0.5983820321 -0.9359571333
53 -0.3392532609 -0.5983820321
54 -0.2659911246 -0.3392532609
55 -0.3992815430 -0.2659911246
56 -0.5876035899 -0.3992815430
57 -0.6000793438 -0.5876035899
58 -0.7370250896 -0.6000793438
59 -0.6511155396 -0.7370250896
60 -0.0259730333 -0.6511155396
61 0.4825404817 -0.0259730333
62 0.5336704475 0.4825404817
63 0.2608930416 0.5336704475
64 -0.1739453377 0.2608930416
65 -0.4822333932 -0.1739453377
66 -0.3863813911 -0.4822333932
67 0.1005722949 -0.3863813911
68 0.3453960413 0.1005722949
69 0.3913068386 0.3453960413
70 0.3801644709 0.3913068386
71 0.1279792145 0.3801644709
72 0.2983014527 0.1279792145
73 NA 0.2983014527
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.6036840968 -0.5840484721
[2,] -0.5563779038 -0.6036840968
[3,] -0.4361925947 -0.5563779038
[4,] -0.2100411713 -0.4361925947
[5,] -0.0001733945 -0.2100411713
[6,] 0.0414209597 -0.0001733945
[7,] 0.0471407387 0.0414209597
[8,] -0.1009982300 0.0471407387
[9,] -0.4166874960 -0.1009982300
[10,] -0.8328265174 -0.4166874960
[11,] -0.7903135577 -0.8328265174
[12,] -0.4754218484 -0.7903135577
[13,] 0.2377831898 -0.4754218484
[14,] 0.6323097460 0.2377831898
[15,] 0.7243459155 0.6323097460
[16,] 0.4736498248 0.7243459155
[17,] 0.1143176333 0.4736498248
[18,] 0.0371458943 0.1143176333
[19,] -0.0407139952 0.0371458943
[20,] 0.0862114086 -0.0407139952
[21,] 0.2444136342 0.0862114086
[22,] 0.1992577227 0.2444136342
[23,] 0.3315198853 0.1992577227
[24,] 0.3296861329 0.3315198853
[25,] 0.3569657409 0.3296861329
[26,] 0.3655668669 0.3569657409
[27,] 0.4904436993 0.3655668669
[28,] 0.4614697241 0.4904436993
[29,] 0.5502256462 0.4614697241
[30,] 0.5434267564 0.5502256462
[31,] 0.4362448464 0.5434267564
[32,] 0.3974889243 0.4362448464
[33,] 0.4161183325 0.3974889243
[34,] 0.6389895084 0.4161183325
[35,] 0.7275499504 0.6389895084
[36,] 0.5459603022 0.7275499504
[37,] 0.0099043460 0.5459603022
[38,] -0.1744572431 0.0099043460
[39,] -0.1035329283 -0.1744572431
[40,] 0.0472489922 -0.1035329283
[41,] 0.1571167690 0.0472489922
[42,] 0.0303789053 0.1571167690
[43,] -0.1439623418 0.0303789053
[44,] -0.1404945544 -0.1439623418
[45,] -0.0350719656 -0.1404945544
[46,] 0.3514399050 -0.0350719656
[47,] 0.2543800471 0.3514399050
[48,] -0.0885045340 0.2543800471
[49,] -0.4835096616 -0.0885045340
[50,] -0.8007119136 -0.4835096616
[51,] -0.9359571333 -0.8007119136
[52,] -0.5983820321 -0.9359571333
[53,] -0.3392532609 -0.5983820321
[54,] -0.2659911246 -0.3392532609
[55,] -0.3992815430 -0.2659911246
[56,] -0.5876035899 -0.3992815430
[57,] -0.6000793438 -0.5876035899
[58,] -0.7370250896 -0.6000793438
[59,] -0.6511155396 -0.7370250896
[60,] -0.0259730333 -0.6511155396
[61,] 0.4825404817 -0.0259730333
[62,] 0.5336704475 0.4825404817
[63,] 0.2608930416 0.5336704475
[64,] -0.1739453377 0.2608930416
[65,] -0.4822333932 -0.1739453377
[66,] -0.3863813911 -0.4822333932
[67,] 0.1005722949 -0.3863813911
[68,] 0.3453960413 0.1005722949
[69,] 0.3913068386 0.3453960413
[70,] 0.3801644709 0.3913068386
[71,] 0.1279792145 0.3801644709
[72,] 0.2983014527 0.1279792145
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.6036840968 -0.5840484721
2 -0.5563779038 -0.6036840968
3 -0.4361925947 -0.5563779038
4 -0.2100411713 -0.4361925947
5 -0.0001733945 -0.2100411713
6 0.0414209597 -0.0001733945
7 0.0471407387 0.0414209597
8 -0.1009982300 0.0471407387
9 -0.4166874960 -0.1009982300
10 -0.8328265174 -0.4166874960
11 -0.7903135577 -0.8328265174
12 -0.4754218484 -0.7903135577
13 0.2377831898 -0.4754218484
14 0.6323097460 0.2377831898
15 0.7243459155 0.6323097460
16 0.4736498248 0.7243459155
17 0.1143176333 0.4736498248
18 0.0371458943 0.1143176333
19 -0.0407139952 0.0371458943
20 0.0862114086 -0.0407139952
21 0.2444136342 0.0862114086
22 0.1992577227 0.2444136342
23 0.3315198853 0.1992577227
24 0.3296861329 0.3315198853
25 0.3569657409 0.3296861329
26 0.3655668669 0.3569657409
27 0.4904436993 0.3655668669
28 0.4614697241 0.4904436993
29 0.5502256462 0.4614697241
30 0.5434267564 0.5502256462
31 0.4362448464 0.5434267564
32 0.3974889243 0.4362448464
33 0.4161183325 0.3974889243
34 0.6389895084 0.4161183325
35 0.7275499504 0.6389895084
36 0.5459603022 0.7275499504
37 0.0099043460 0.5459603022
38 -0.1744572431 0.0099043460
39 -0.1035329283 -0.1744572431
40 0.0472489922 -0.1035329283
41 0.1571167690 0.0472489922
42 0.0303789053 0.1571167690
43 -0.1439623418 0.0303789053
44 -0.1404945544 -0.1439623418
45 -0.0350719656 -0.1404945544
46 0.3514399050 -0.0350719656
47 0.2543800471 0.3514399050
48 -0.0885045340 0.2543800471
49 -0.4835096616 -0.0885045340
50 -0.8007119136 -0.4835096616
51 -0.9359571333 -0.8007119136
52 -0.5983820321 -0.9359571333
53 -0.3392532609 -0.5983820321
54 -0.2659911246 -0.3392532609
55 -0.3992815430 -0.2659911246
56 -0.5876035899 -0.3992815430
57 -0.6000793438 -0.5876035899
58 -0.7370250896 -0.6000793438
59 -0.6511155396 -0.7370250896
60 -0.0259730333 -0.6511155396
61 0.4825404817 -0.0259730333
62 0.5336704475 0.4825404817
63 0.2608930416 0.5336704475
64 -0.1739453377 0.2608930416
65 -0.4822333932 -0.1739453377
66 -0.3863813911 -0.4822333932
67 0.1005722949 -0.3863813911
68 0.3453960413 0.1005722949
69 0.3913068386 0.3453960413
70 0.3801644709 0.3913068386
71 0.1279792145 0.3801644709
72 0.2983014527 0.1279792145
> 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/7etlg1261068864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kdo81261068864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94s3s1261068864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10z4nh1261068864.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bgzy1261068864.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/12mfaz1261068864.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/13uorx1261068864.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/141sil1261068864.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/1574i51261068864.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/162x451261068864.tab")
+ }
>
> try(system("convert tmp/1dirh1261068863.ps tmp/1dirh1261068863.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uekb1261068863.ps tmp/2uekb1261068863.png",intern=TRUE))
character(0)
> try(system("convert tmp/335mx1261068864.ps tmp/335mx1261068864.png",intern=TRUE))
character(0)
> try(system("convert tmp/43f931261068864.ps tmp/43f931261068864.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x1yz1261068864.ps tmp/5x1yz1261068864.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q2y11261068864.ps tmp/6q2y11261068864.png",intern=TRUE))
character(0)
> try(system("convert tmp/7etlg1261068864.ps tmp/7etlg1261068864.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kdo81261068864.ps tmp/8kdo81261068864.png",intern=TRUE))
character(0)
> try(system("convert tmp/94s3s1261068864.ps tmp/94s3s1261068864.png",intern=TRUE))
character(0)
> try(system("convert tmp/10z4nh1261068864.ps tmp/10z4nh1261068864.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.634 1.608 4.785