R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(90.7,0,94.3,0,104.6,0,111.1,0,110.8,0,107.2,0,99,0,99,0,91,0,96.2,0,96.9,0,96.2,0,100.1,0,99,0,115.4,0,106.9,0,107.1,0,99.3,0,99.2,0,108.3,0,105.6,0,99.5,0,107.4,0,93.1,0,88.1,0,110.7,0,113.1,0,99.6,0,93.6,0,98.6,0,99.6,0,114.3,0,107.8,0,101.2,0,112.5,0,100.5,0,93.9,0,116.2,0,112,0,106.4,0,95.7,0,96,0,95.8,0,103,0,102.2,0,98.4,0,111.4,1,86.6,1,91.3,1,107.9,1,101.8,1,104.4,1,93.4,1,100.1,1,98.5,1,112.9,1,101.4,1,107.1,1,110.8,1,90.3,1,95.5,1,111.4,1,113,1,107.5,1,95.9,1,106.3,1,105.2,1,117.2,1,106.9,1,108.2,1,113,1,97.2,1,99.9,1,108.1,1,118.1,1,109.1,1,93.3,1,112.1,1,111.8,1,112.5,1,116.3,1,110.3,1,117.1,1,103.4,1,96.2,1),dim=c(2,85),dimnames=list(c('Prodintergoed','invest'),1:85))
> y <- array(NA,dim=c(2,85),dimnames=list(c('Prodintergoed','invest'),1:85))
> 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
Prodintergoed invest M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 90.7 0 1 0 0 0 0 0 0 0 0 0 0 1
2 94.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 104.6 0 0 0 1 0 0 0 0 0 0 0 0 3
4 111.1 0 0 0 0 1 0 0 0 0 0 0 0 4
5 110.8 0 0 0 0 0 1 0 0 0 0 0 0 5
6 107.2 0 0 0 0 0 0 1 0 0 0 0 0 6
7 99.0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 99.0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 91.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 96.2 0 0 0 0 0 0 0 0 0 0 1 0 10
11 96.9 0 0 0 0 0 0 0 0 0 0 0 1 11
12 96.2 0 0 0 0 0 0 0 0 0 0 0 0 12
13 100.1 0 1 0 0 0 0 0 0 0 0 0 0 13
14 99.0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 115.4 0 0 0 1 0 0 0 0 0 0 0 0 15
16 106.9 0 0 0 0 1 0 0 0 0 0 0 0 16
17 107.1 0 0 0 0 0 1 0 0 0 0 0 0 17
18 99.3 0 0 0 0 0 0 1 0 0 0 0 0 18
19 99.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 108.3 0 0 0 0 0 0 0 0 1 0 0 0 20
21 105.6 0 0 0 0 0 0 0 0 0 1 0 0 21
22 99.5 0 0 0 0 0 0 0 0 0 0 1 0 22
23 107.4 0 0 0 0 0 0 0 0 0 0 0 1 23
24 93.1 0 0 0 0 0 0 0 0 0 0 0 0 24
25 88.1 0 1 0 0 0 0 0 0 0 0 0 0 25
26 110.7 0 0 1 0 0 0 0 0 0 0 0 0 26
27 113.1 0 0 0 1 0 0 0 0 0 0 0 0 27
28 99.6 0 0 0 0 1 0 0 0 0 0 0 0 28
29 93.6 0 0 0 0 0 1 0 0 0 0 0 0 29
30 98.6 0 0 0 0 0 0 1 0 0 0 0 0 30
31 99.6 0 0 0 0 0 0 0 1 0 0 0 0 31
32 114.3 0 0 0 0 0 0 0 0 1 0 0 0 32
33 107.8 0 0 0 0 0 0 0 0 0 1 0 0 33
34 101.2 0 0 0 0 0 0 0 0 0 0 1 0 34
35 112.5 0 0 0 0 0 0 0 0 0 0 0 1 35
36 100.5 0 0 0 0 0 0 0 0 0 0 0 0 36
37 93.9 0 1 0 0 0 0 0 0 0 0 0 0 37
38 116.2 0 0 1 0 0 0 0 0 0 0 0 0 38
39 112.0 0 0 0 1 0 0 0 0 0 0 0 0 39
40 106.4 0 0 0 0 1 0 0 0 0 0 0 0 40
41 95.7 0 0 0 0 0 1 0 0 0 0 0 0 41
42 96.0 0 0 0 0 0 0 1 0 0 0 0 0 42
43 95.8 0 0 0 0 0 0 0 1 0 0 0 0 43
44 103.0 0 0 0 0 0 0 0 0 1 0 0 0 44
45 102.2 0 0 0 0 0 0 0 0 0 1 0 0 45
46 98.4 0 0 0 0 0 0 0 0 0 0 1 0 46
47 111.4 1 0 0 0 0 0 0 0 0 0 0 1 47
48 86.6 1 0 0 0 0 0 0 0 0 0 0 0 48
49 91.3 1 1 0 0 0 0 0 0 0 0 0 0 49
50 107.9 1 0 1 0 0 0 0 0 0 0 0 0 50
51 101.8 1 0 0 1 0 0 0 0 0 0 0 0 51
52 104.4 1 0 0 0 1 0 0 0 0 0 0 0 52
53 93.4 1 0 0 0 0 1 0 0 0 0 0 0 53
54 100.1 1 0 0 0 0 0 1 0 0 0 0 0 54
55 98.5 1 0 0 0 0 0 0 1 0 0 0 0 55
56 112.9 1 0 0 0 0 0 0 0 1 0 0 0 56
57 101.4 1 0 0 0 0 0 0 0 0 1 0 0 57
58 107.1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 110.8 1 0 0 0 0 0 0 0 0 0 0 1 59
60 90.3 1 0 0 0 0 0 0 0 0 0 0 0 60
61 95.5 1 1 0 0 0 0 0 0 0 0 0 0 61
62 111.4 1 0 1 0 0 0 0 0 0 0 0 0 62
63 113.0 1 0 0 1 0 0 0 0 0 0 0 0 63
64 107.5 1 0 0 0 1 0 0 0 0 0 0 0 64
65 95.9 1 0 0 0 0 1 0 0 0 0 0 0 65
66 106.3 1 0 0 0 0 0 1 0 0 0 0 0 66
67 105.2 1 0 0 0 0 0 0 1 0 0 0 0 67
68 117.2 1 0 0 0 0 0 0 0 1 0 0 0 68
69 106.9 1 0 0 0 0 0 0 0 0 1 0 0 69
70 108.2 1 0 0 0 0 0 0 0 0 0 1 0 70
71 113.0 1 0 0 0 0 0 0 0 0 0 0 1 71
72 97.2 1 0 0 0 0 0 0 0 0 0 0 0 72
73 99.9 1 1 0 0 0 0 0 0 0 0 0 0 73
74 108.1 1 0 1 0 0 0 0 0 0 0 0 0 74
75 118.1 1 0 0 1 0 0 0 0 0 0 0 0 75
76 109.1 1 0 0 0 1 0 0 0 0 0 0 0 76
77 93.3 1 0 0 0 0 1 0 0 0 0 0 0 77
78 112.1 1 0 0 0 0 0 1 0 0 0 0 0 78
79 111.8 1 0 0 0 0 0 0 1 0 0 0 0 79
80 112.5 1 0 0 0 0 0 0 0 1 0 0 0 80
81 116.3 1 0 0 0 0 0 0 0 0 1 0 0 81
82 110.3 1 0 0 0 0 0 0 0 0 0 1 0 82
83 117.1 1 0 0 0 0 0 0 0 0 0 0 1 83
84 103.4 1 0 0 0 0 0 0 0 0 0 0 0 84
85 96.2 1 1 0 0 0 0 0 0 0 0 0 0 85
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) invest M1 M2 M3 M4
90.0120 -2.5061 -0.3421 12.5194 16.7216 11.8668
M5 M6 M7 M8 M9 M10
3.8404 7.9570 6.3164 14.4758 9.1923 7.5803
M11 t
14.6835 0.1406
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.5979 -3.3122 -0.2402 3.2721 16.2445
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 90.01203 2.50466 35.938 < 2e-16 ***
invest -2.50614 2.43508 -1.029 0.306888
M1 -0.34210 2.87998 -0.119 0.905781
M2 12.51937 2.98278 4.197 7.71e-05 ***
M3 16.72163 2.97997 5.611 3.63e-07 ***
M4 11.86675 2.97798 3.985 0.000162 ***
M5 3.84044 2.97681 1.290 0.201196
M6 7.95699 2.97647 2.673 0.009312 **
M7 6.31639 2.97695 2.122 0.037348 *
M8 14.47579 2.97825 4.861 6.78e-06 ***
M9 9.19234 2.98038 3.084 0.002906 **
M10 7.58032 2.98333 2.541 0.013244 *
M11 14.68345 2.97179 4.941 4.99e-06 ***
t 0.14060 0.04954 2.838 0.005911 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.559 on 71 degrees of freedom
Multiple R-squared: 0.5718, Adjusted R-squared: 0.4934
F-statistic: 7.293 on 13 and 71 DF, p-value: 8.202e-09
> 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,] 0.8933608 0.2132783671 0.1066391835
[2,] 0.9396587 0.1206826847 0.0603413424
[3,] 0.8922287 0.2155425834 0.1077712917
[4,] 0.8926656 0.2146687138 0.1073343569
[5,] 0.9471641 0.1056717925 0.0528358963
[6,] 0.9121426 0.1757147876 0.0878573938
[7,] 0.8990780 0.2018440308 0.1009220154
[8,] 0.8925059 0.2149881559 0.1074940780
[9,] 0.9465220 0.1069559136 0.0534779568
[10,] 0.9651793 0.0696414610 0.0348207305
[11,] 0.9546474 0.0907052834 0.0453526417
[12,] 0.9804584 0.0390831810 0.0195415905
[13,] 0.9958887 0.0082225029 0.0041112514
[14,] 0.9938341 0.0123317450 0.0061658725
[15,] 0.9894155 0.0211689612 0.0105844806
[16,] 0.9932635 0.0134729019 0.0067364510
[17,] 0.9939605 0.0120789118 0.0060394559
[18,] 0.9898328 0.0203343298 0.0101671649
[19,] 0.9891213 0.0217573265 0.0108786633
[20,] 0.9942723 0.0114553433 0.0057276716
[21,] 0.9918855 0.0162290526 0.0081145263
[22,] 0.9986290 0.0027419356 0.0013709678
[23,] 0.9988308 0.0023383560 0.0011691780
[24,] 0.9989175 0.0021649726 0.0010824863
[25,] 0.9998487 0.0003025160 0.0001512580
[26,] 0.9997898 0.0004204522 0.0002102261
[27,] 0.9996319 0.0007361008 0.0003680504
[28,] 0.9994527 0.0010945442 0.0005472721
[29,] 0.9990825 0.0018349585 0.0009174792
[30,] 0.9982537 0.0034926150 0.0017463075
[31,] 0.9978318 0.0043363814 0.0021681907
[32,] 0.9979911 0.0040177443 0.0020088721
[33,] 0.9962791 0.0074418933 0.0037209466
[34,] 0.9946949 0.0106102477 0.0053051238
[35,] 0.9978019 0.0043961560 0.0021980780
[36,] 0.9958242 0.0083515584 0.0041757792
[37,] 0.9946342 0.0107316514 0.0053658257
[38,] 0.9926171 0.0147658772 0.0073829386
[39,] 0.9920002 0.0159995710 0.0079997855
[40,] 0.9892676 0.0214647230 0.0107323615
[41,] 0.9895716 0.0208567776 0.0104283888
[42,] 0.9849849 0.0300302272 0.0150151136
[43,] 0.9727578 0.0544844731 0.0272422365
[44,] 0.9763892 0.0472216656 0.0236108328
[45,] 0.9567301 0.0865398411 0.0432699205
[46,] 0.9522083 0.0955833665 0.0477916833
[47,] 0.9207932 0.1584136017 0.0792068008
[48,] 0.8636229 0.2727542138 0.1363771069
[49,] 0.8381985 0.3236030533 0.1618015266
[50,] 0.7559427 0.4881146659 0.2440573329
[51,] 0.6675120 0.6649760362 0.3324880181
[52,] 0.6913494 0.6173011386 0.3086505693
> postscript(file="/var/www/html/rcomp/tmp/1xkos1227512684.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2agdo1227512684.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3x1qf1227512684.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kmzj1227512684.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yddd1227512684.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 = 85
Frequency = 1
1 2 3 4 5 6
0.88947368 -8.51259398 -2.55545113 8.65883459 16.24454887 8.38740602
7 8 9 10 11 12
1.68740602 -6.61259398 -9.46973684 -2.79830827 -9.34204261 4.50081454
13 14 15 16 17 18
8.60231830 -5.49974937 6.55739348 2.77167920 10.85739348 -1.19974937
19 20 21 22 23 24
0.20025063 1.00025063 3.44310777 -1.18546366 -0.52919799 -0.28634085
25 26 27 28 29 30
-5.08483709 4.51309524 2.57023810 -6.21547619 -4.32976190 -3.58690476
31 32 33 34 35 36
-1.08690476 5.31309524 3.95595238 -1.17261905 2.88364662 5.42650376
37 38 39 40 41 42
-0.97199248 8.32593985 -0.21691729 -1.10263158 -3.91691729 -7.87406015
43 44 45 46 47 48
-6.57406015 -7.67406015 -3.33120301 -5.65977444 2.60263158 -7.65451128
49 50 51 52 53 54
-2.75300752 0.84492481 -9.59793233 -2.28364662 -5.39793233 -2.95507519
55 56 57 58 59 60
-3.05507519 3.04492481 -3.31221805 3.85921053 0.31547619 -5.64166667
61 62 63 64 65 66
-0.24016291 2.65776942 -0.08508772 -0.87080201 -4.58508772 1.55776942
67 68 69 70 71 72
1.95776942 5.65776942 0.50062657 3.27205514 0.82832080 -0.42882206
73 74 75 76 77 78
2.47268170 -2.32938596 3.32775689 -0.95795739 -8.87224311 5.67061404
79 80 81 82 83 84
6.87061404 -0.72938596 8.21347118 3.68489975 3.24116541 4.08402256
85
-2.91447368
> postscript(file="/var/www/html/rcomp/tmp/6uwhf1227512684.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 = 85
Frequency = 1
lag(myerror, k = 1) myerror
0 0.88947368 NA
1 -8.51259398 0.88947368
2 -2.55545113 -8.51259398
3 8.65883459 -2.55545113
4 16.24454887 8.65883459
5 8.38740602 16.24454887
6 1.68740602 8.38740602
7 -6.61259398 1.68740602
8 -9.46973684 -6.61259398
9 -2.79830827 -9.46973684
10 -9.34204261 -2.79830827
11 4.50081454 -9.34204261
12 8.60231830 4.50081454
13 -5.49974937 8.60231830
14 6.55739348 -5.49974937
15 2.77167920 6.55739348
16 10.85739348 2.77167920
17 -1.19974937 10.85739348
18 0.20025063 -1.19974937
19 1.00025063 0.20025063
20 3.44310777 1.00025063
21 -1.18546366 3.44310777
22 -0.52919799 -1.18546366
23 -0.28634085 -0.52919799
24 -5.08483709 -0.28634085
25 4.51309524 -5.08483709
26 2.57023810 4.51309524
27 -6.21547619 2.57023810
28 -4.32976190 -6.21547619
29 -3.58690476 -4.32976190
30 -1.08690476 -3.58690476
31 5.31309524 -1.08690476
32 3.95595238 5.31309524
33 -1.17261905 3.95595238
34 2.88364662 -1.17261905
35 5.42650376 2.88364662
36 -0.97199248 5.42650376
37 8.32593985 -0.97199248
38 -0.21691729 8.32593985
39 -1.10263158 -0.21691729
40 -3.91691729 -1.10263158
41 -7.87406015 -3.91691729
42 -6.57406015 -7.87406015
43 -7.67406015 -6.57406015
44 -3.33120301 -7.67406015
45 -5.65977444 -3.33120301
46 2.60263158 -5.65977444
47 -7.65451128 2.60263158
48 -2.75300752 -7.65451128
49 0.84492481 -2.75300752
50 -9.59793233 0.84492481
51 -2.28364662 -9.59793233
52 -5.39793233 -2.28364662
53 -2.95507519 -5.39793233
54 -3.05507519 -2.95507519
55 3.04492481 -3.05507519
56 -3.31221805 3.04492481
57 3.85921053 -3.31221805
58 0.31547619 3.85921053
59 -5.64166667 0.31547619
60 -0.24016291 -5.64166667
61 2.65776942 -0.24016291
62 -0.08508772 2.65776942
63 -0.87080201 -0.08508772
64 -4.58508772 -0.87080201
65 1.55776942 -4.58508772
66 1.95776942 1.55776942
67 5.65776942 1.95776942
68 0.50062657 5.65776942
69 3.27205514 0.50062657
70 0.82832080 3.27205514
71 -0.42882206 0.82832080
72 2.47268170 -0.42882206
73 -2.32938596 2.47268170
74 3.32775689 -2.32938596
75 -0.95795739 3.32775689
76 -8.87224311 -0.95795739
77 5.67061404 -8.87224311
78 6.87061404 5.67061404
79 -0.72938596 6.87061404
80 8.21347118 -0.72938596
81 3.68489975 8.21347118
82 3.24116541 3.68489975
83 4.08402256 3.24116541
84 -2.91447368 4.08402256
85 NA -2.91447368
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -8.51259398 0.88947368
[2,] -2.55545113 -8.51259398
[3,] 8.65883459 -2.55545113
[4,] 16.24454887 8.65883459
[5,] 8.38740602 16.24454887
[6,] 1.68740602 8.38740602
[7,] -6.61259398 1.68740602
[8,] -9.46973684 -6.61259398
[9,] -2.79830827 -9.46973684
[10,] -9.34204261 -2.79830827
[11,] 4.50081454 -9.34204261
[12,] 8.60231830 4.50081454
[13,] -5.49974937 8.60231830
[14,] 6.55739348 -5.49974937
[15,] 2.77167920 6.55739348
[16,] 10.85739348 2.77167920
[17,] -1.19974937 10.85739348
[18,] 0.20025063 -1.19974937
[19,] 1.00025063 0.20025063
[20,] 3.44310777 1.00025063
[21,] -1.18546366 3.44310777
[22,] -0.52919799 -1.18546366
[23,] -0.28634085 -0.52919799
[24,] -5.08483709 -0.28634085
[25,] 4.51309524 -5.08483709
[26,] 2.57023810 4.51309524
[27,] -6.21547619 2.57023810
[28,] -4.32976190 -6.21547619
[29,] -3.58690476 -4.32976190
[30,] -1.08690476 -3.58690476
[31,] 5.31309524 -1.08690476
[32,] 3.95595238 5.31309524
[33,] -1.17261905 3.95595238
[34,] 2.88364662 -1.17261905
[35,] 5.42650376 2.88364662
[36,] -0.97199248 5.42650376
[37,] 8.32593985 -0.97199248
[38,] -0.21691729 8.32593985
[39,] -1.10263158 -0.21691729
[40,] -3.91691729 -1.10263158
[41,] -7.87406015 -3.91691729
[42,] -6.57406015 -7.87406015
[43,] -7.67406015 -6.57406015
[44,] -3.33120301 -7.67406015
[45,] -5.65977444 -3.33120301
[46,] 2.60263158 -5.65977444
[47,] -7.65451128 2.60263158
[48,] -2.75300752 -7.65451128
[49,] 0.84492481 -2.75300752
[50,] -9.59793233 0.84492481
[51,] -2.28364662 -9.59793233
[52,] -5.39793233 -2.28364662
[53,] -2.95507519 -5.39793233
[54,] -3.05507519 -2.95507519
[55,] 3.04492481 -3.05507519
[56,] -3.31221805 3.04492481
[57,] 3.85921053 -3.31221805
[58,] 0.31547619 3.85921053
[59,] -5.64166667 0.31547619
[60,] -0.24016291 -5.64166667
[61,] 2.65776942 -0.24016291
[62,] -0.08508772 2.65776942
[63,] -0.87080201 -0.08508772
[64,] -4.58508772 -0.87080201
[65,] 1.55776942 -4.58508772
[66,] 1.95776942 1.55776942
[67,] 5.65776942 1.95776942
[68,] 0.50062657 5.65776942
[69,] 3.27205514 0.50062657
[70,] 0.82832080 3.27205514
[71,] -0.42882206 0.82832080
[72,] 2.47268170 -0.42882206
[73,] -2.32938596 2.47268170
[74,] 3.32775689 -2.32938596
[75,] -0.95795739 3.32775689
[76,] -8.87224311 -0.95795739
[77,] 5.67061404 -8.87224311
[78,] 6.87061404 5.67061404
[79,] -0.72938596 6.87061404
[80,] 8.21347118 -0.72938596
[81,] 3.68489975 8.21347118
[82,] 3.24116541 3.68489975
[83,] 4.08402256 3.24116541
[84,] -2.91447368 4.08402256
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -8.51259398 0.88947368
2 -2.55545113 -8.51259398
3 8.65883459 -2.55545113
4 16.24454887 8.65883459
5 8.38740602 16.24454887
6 1.68740602 8.38740602
7 -6.61259398 1.68740602
8 -9.46973684 -6.61259398
9 -2.79830827 -9.46973684
10 -9.34204261 -2.79830827
11 4.50081454 -9.34204261
12 8.60231830 4.50081454
13 -5.49974937 8.60231830
14 6.55739348 -5.49974937
15 2.77167920 6.55739348
16 10.85739348 2.77167920
17 -1.19974937 10.85739348
18 0.20025063 -1.19974937
19 1.00025063 0.20025063
20 3.44310777 1.00025063
21 -1.18546366 3.44310777
22 -0.52919799 -1.18546366
23 -0.28634085 -0.52919799
24 -5.08483709 -0.28634085
25 4.51309524 -5.08483709
26 2.57023810 4.51309524
27 -6.21547619 2.57023810
28 -4.32976190 -6.21547619
29 -3.58690476 -4.32976190
30 -1.08690476 -3.58690476
31 5.31309524 -1.08690476
32 3.95595238 5.31309524
33 -1.17261905 3.95595238
34 2.88364662 -1.17261905
35 5.42650376 2.88364662
36 -0.97199248 5.42650376
37 8.32593985 -0.97199248
38 -0.21691729 8.32593985
39 -1.10263158 -0.21691729
40 -3.91691729 -1.10263158
41 -7.87406015 -3.91691729
42 -6.57406015 -7.87406015
43 -7.67406015 -6.57406015
44 -3.33120301 -7.67406015
45 -5.65977444 -3.33120301
46 2.60263158 -5.65977444
47 -7.65451128 2.60263158
48 -2.75300752 -7.65451128
49 0.84492481 -2.75300752
50 -9.59793233 0.84492481
51 -2.28364662 -9.59793233
52 -5.39793233 -2.28364662
53 -2.95507519 -5.39793233
54 -3.05507519 -2.95507519
55 3.04492481 -3.05507519
56 -3.31221805 3.04492481
57 3.85921053 -3.31221805
58 0.31547619 3.85921053
59 -5.64166667 0.31547619
60 -0.24016291 -5.64166667
61 2.65776942 -0.24016291
62 -0.08508772 2.65776942
63 -0.87080201 -0.08508772
64 -4.58508772 -0.87080201
65 1.55776942 -4.58508772
66 1.95776942 1.55776942
67 5.65776942 1.95776942
68 0.50062657 5.65776942
69 3.27205514 0.50062657
70 0.82832080 3.27205514
71 -0.42882206 0.82832080
72 2.47268170 -0.42882206
73 -2.32938596 2.47268170
74 3.32775689 -2.32938596
75 -0.95795739 3.32775689
76 -8.87224311 -0.95795739
77 5.67061404 -8.87224311
78 6.87061404 5.67061404
79 -0.72938596 6.87061404
80 8.21347118 -0.72938596
81 3.68489975 8.21347118
82 3.24116541 3.68489975
83 4.08402256 3.24116541
84 -2.91447368 4.08402256
> 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/73nwj1227512684.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8chz71227512684.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ufy91227512684.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101mf01227512684.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11cjr41227512684.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/12s0uq1227512684.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/13hgxq1227512684.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/14x7wi1227512684.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/15qhxh1227512685.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/168le21227512685.tab")
+ }
>
> system("convert tmp/1xkos1227512684.ps tmp/1xkos1227512684.png")
> system("convert tmp/2agdo1227512684.ps tmp/2agdo1227512684.png")
> system("convert tmp/3x1qf1227512684.ps tmp/3x1qf1227512684.png")
> system("convert tmp/4kmzj1227512684.ps tmp/4kmzj1227512684.png")
> system("convert tmp/5yddd1227512684.ps tmp/5yddd1227512684.png")
> system("convert tmp/6uwhf1227512684.ps tmp/6uwhf1227512684.png")
> system("convert tmp/73nwj1227512684.ps tmp/73nwj1227512684.png")
> system("convert tmp/8chz71227512684.ps tmp/8chz71227512684.png")
> system("convert tmp/9ufy91227512684.ps tmp/9ufy91227512684.png")
> system("convert tmp/101mf01227512684.ps tmp/101mf01227512684.png")
>
>
> proc.time()
user system elapsed
2.769 1.626 3.751