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(98.5,0,97.0,0,103.3,0,99.6,0,100.1,0,102.9,0,95.9,0,94.5,0,107.4,0,116.0,0,102.8,0,99.8,0,109.6,0,103.0,0,111.6,0,106.3,0,97.9,0,108.8,0,103.9,0,101.2,0,122.9,0,123.9,0,111.7,0,120.9,0,99.6,0,103.3,0,119.4,0,106.5,0,101.9,0,124.6,0,106.5,0,107.8,0,127.4,0,120.1,0,118.5,0,127.7,0,107.7,0,104.5,0,118.8,0,110.3,0,109.6,0,119.1,0,96.5,0,106.7,0,126.3,0,116.2,0,118.8,0,115.2,0,110.0,0,111.4,0,129.6,0,108.1,0,117.8,0,122.9,0,100.6,0,111.8,0,127.0,0,128.6,0,124.8,0,118.5,0,114.7,0,112.6,0,128.7,0,111.0,0,115.8,0,126.0,0,111.1,1,113.2,1,120.1,1,130.6,1,124.0,1,119.4,1,116.7,1,116.5,1,119.6,1,126.5,1,111.3,1,123.5,1,114.2,1,103.7,1,129.5,1),dim=c(2,81),dimnames=list(c('Y','X'),1:81))
> y <- array(NA,dim=c(2,81),dimnames=list(c('Y','X'),1:81))
> 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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 98.5 0 1 0 0 0 0 0 0 0 0 0 0 1
2 97.0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 103.3 0 0 0 1 0 0 0 0 0 0 0 0 3
4 99.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 100.1 0 0 0 0 0 1 0 0 0 0 0 0 5
6 102.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 95.9 0 0 0 0 0 0 0 1 0 0 0 0 7
8 94.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 107.4 0 0 0 0 0 0 0 0 0 1 0 0 9
10 116.0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 102.8 0 0 0 0 0 0 0 0 0 0 0 1 11
12 99.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 109.6 0 1 0 0 0 0 0 0 0 0 0 0 13
14 103.0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 111.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 106.3 0 0 0 0 1 0 0 0 0 0 0 0 16
17 97.9 0 0 0 0 0 1 0 0 0 0 0 0 17
18 108.8 0 0 0 0 0 0 1 0 0 0 0 0 18
19 103.9 0 0 0 0 0 0 0 1 0 0 0 0 19
20 101.2 0 0 0 0 0 0 0 0 1 0 0 0 20
21 122.9 0 0 0 0 0 0 0 0 0 1 0 0 21
22 123.9 0 0 0 0 0 0 0 0 0 0 1 0 22
23 111.7 0 0 0 0 0 0 0 0 0 0 0 1 23
24 120.9 0 0 0 0 0 0 0 0 0 0 0 0 24
25 99.6 0 1 0 0 0 0 0 0 0 0 0 0 25
26 103.3 0 0 1 0 0 0 0 0 0 0 0 0 26
27 119.4 0 0 0 1 0 0 0 0 0 0 0 0 27
28 106.5 0 0 0 0 1 0 0 0 0 0 0 0 28
29 101.9 0 0 0 0 0 1 0 0 0 0 0 0 29
30 124.6 0 0 0 0 0 0 1 0 0 0 0 0 30
31 106.5 0 0 0 0 0 0 0 1 0 0 0 0 31
32 107.8 0 0 0 0 0 0 0 0 1 0 0 0 32
33 127.4 0 0 0 0 0 0 0 0 0 1 0 0 33
34 120.1 0 0 0 0 0 0 0 0 0 0 1 0 34
35 118.5 0 0 0 0 0 0 0 0 0 0 0 1 35
36 127.7 0 0 0 0 0 0 0 0 0 0 0 0 36
37 107.7 0 1 0 0 0 0 0 0 0 0 0 0 37
38 104.5 0 0 1 0 0 0 0 0 0 0 0 0 38
39 118.8 0 0 0 1 0 0 0 0 0 0 0 0 39
40 110.3 0 0 0 0 1 0 0 0 0 0 0 0 40
41 109.6 0 0 0 0 0 1 0 0 0 0 0 0 41
42 119.1 0 0 0 0 0 0 1 0 0 0 0 0 42
43 96.5 0 0 0 0 0 0 0 1 0 0 0 0 43
44 106.7 0 0 0 0 0 0 0 0 1 0 0 0 44
45 126.3 0 0 0 0 0 0 0 0 0 1 0 0 45
46 116.2 0 0 0 0 0 0 0 0 0 0 1 0 46
47 118.8 0 0 0 0 0 0 0 0 0 0 0 1 47
48 115.2 0 0 0 0 0 0 0 0 0 0 0 0 48
49 110.0 0 1 0 0 0 0 0 0 0 0 0 0 49
50 111.4 0 0 1 0 0 0 0 0 0 0 0 0 50
51 129.6 0 0 0 1 0 0 0 0 0 0 0 0 51
52 108.1 0 0 0 0 1 0 0 0 0 0 0 0 52
53 117.8 0 0 0 0 0 1 0 0 0 0 0 0 53
54 122.9 0 0 0 0 0 0 1 0 0 0 0 0 54
55 100.6 0 0 0 0 0 0 0 1 0 0 0 0 55
56 111.8 0 0 0 0 0 0 0 0 1 0 0 0 56
57 127.0 0 0 0 0 0 0 0 0 0 1 0 0 57
58 128.6 0 0 0 0 0 0 0 0 0 0 1 0 58
59 124.8 0 0 0 0 0 0 0 0 0 0 0 1 59
60 118.5 0 0 0 0 0 0 0 0 0 0 0 0 60
61 114.7 0 1 0 0 0 0 0 0 0 0 0 0 61
62 112.6 0 0 1 0 0 0 0 0 0 0 0 0 62
63 128.7 0 0 0 1 0 0 0 0 0 0 0 0 63
64 111.0 0 0 0 0 1 0 0 0 0 0 0 0 64
65 115.8 0 0 0 0 0 1 0 0 0 0 0 0 65
66 126.0 0 0 0 0 0 0 1 0 0 0 0 0 66
67 111.1 1 0 0 0 0 0 0 1 0 0 0 0 67
68 113.2 1 0 0 0 0 0 0 0 1 0 0 0 68
69 120.1 1 0 0 0 0 0 0 0 0 1 0 0 69
70 130.6 1 0 0 0 0 0 0 0 0 0 1 0 70
71 124.0 1 0 0 0 0 0 0 0 0 0 0 1 71
72 119.4 1 0 0 0 0 0 0 0 0 0 0 0 72
73 116.7 1 1 0 0 0 0 0 0 0 0 0 0 73
74 116.5 1 0 1 0 0 0 0 0 0 0 0 0 74
75 119.6 1 0 0 1 0 0 0 0 0 0 0 0 75
76 126.5 1 0 0 0 1 0 0 0 0 0 0 0 76
77 111.3 1 0 0 0 0 1 0 0 0 0 0 0 77
78 123.5 1 0 0 0 0 0 1 0 0 0 0 0 78
79 114.2 1 0 0 0 0 0 0 1 0 0 0 0 79
80 103.7 1 0 0 0 0 0 0 0 1 0 0 0 80
81 129.5 1 0 0 0 0 0 0 0 0 1 0 0 81
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
105.8928 -3.8627 -7.5053 -8.9974 2.5391 -6.6959
M5 M6 M7 M8 M9 M10
-8.9594 1.2485 -12.6346 -11.4553 5.6526 6.2056
M11 t
0.1278 0.2778
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.42639 -2.95181 -0.00895 2.75637 11.80637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 105.89277 2.34302 45.195 < 2e-16 ***
X -3.86265 1.95062 -1.980 0.051788 .
M1 -7.50534 2.77892 -2.701 0.008751 **
M2 -8.99743 2.77761 -3.239 0.001866 **
M3 2.53906 2.77666 0.914 0.363772
M4 -6.69589 2.77609 -2.412 0.018612 *
M5 -8.95940 2.77590 -3.228 0.001934 **
M6 1.24851 2.77607 0.450 0.654350
M7 -12.63463 2.78376 -4.539 2.41e-05 ***
M8 -11.45529 2.78252 -4.117 0.000108 ***
M9 5.65262 2.78164 2.032 0.046109 *
M10 6.20560 2.88118 2.154 0.034854 *
M11 0.12780 2.88064 0.044 0.964745
t 0.27780 0.03217 8.635 1.73e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.989 on 67 degrees of freedom
Multiple R-squared: 0.7823, Adjusted R-squared: 0.7401
F-statistic: 18.52 on 13 and 67 DF, p-value: < 2.2e-16
> 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,] 0.44319796 0.8863959 0.5568020
[2,] 0.29363771 0.5872754 0.7063623
[3,] 0.18437873 0.3687575 0.8156213
[4,] 0.10173705 0.2034741 0.8982630
[5,] 0.19305481 0.3861096 0.8069452
[6,] 0.12495096 0.2499019 0.8750490
[7,] 0.07946266 0.1589253 0.9205373
[8,] 0.30330298 0.6066060 0.6966970
[9,] 0.74168446 0.5166311 0.2583155
[10,] 0.71419004 0.5716199 0.2858100
[11,] 0.64716844 0.7056631 0.3528316
[12,] 0.60744292 0.7851142 0.3925571
[13,] 0.60764551 0.7847090 0.3923545
[14,] 0.69769919 0.6046016 0.3023008
[15,] 0.64726418 0.7054716 0.3527358
[16,] 0.58027637 0.8394473 0.4197236
[17,] 0.55604490 0.8879102 0.4439551
[18,] 0.56817285 0.8636543 0.4318271
[19,] 0.49748023 0.9949605 0.5025198
[20,] 0.72210490 0.5557902 0.2778951
[21,] 0.70124958 0.5975008 0.2987504
[22,] 0.70338029 0.5932394 0.2966197
[23,] 0.64573752 0.7085250 0.3542625
[24,] 0.58715831 0.8256834 0.4128417
[25,] 0.50926725 0.9814655 0.4907327
[26,] 0.43945391 0.8789078 0.5605461
[27,] 0.65196340 0.6960732 0.3480366
[28,] 0.58462596 0.8307481 0.4153740
[29,] 0.53651445 0.9269711 0.4634855
[30,] 0.69604659 0.6079068 0.3039534
[31,] 0.63489035 0.7302193 0.3651097
[32,] 0.60993895 0.7801221 0.3900610
[33,] 0.55505676 0.8898865 0.4449432
[34,] 0.47575927 0.9515185 0.5242407
[35,] 0.49601599 0.9920320 0.5039840
[36,] 0.56080162 0.8783968 0.4391984
[37,] 0.54243790 0.9151242 0.4575621
[38,] 0.45150483 0.9030097 0.5484952
[39,] 0.61884568 0.7623086 0.3811543
[40,] 0.54717903 0.9056419 0.4528210
[41,] 0.46962286 0.9392457 0.5303771
[42,] 0.37083737 0.7416747 0.6291626
[43,] 0.28171029 0.5634206 0.7182897
[44,] 0.20895458 0.4179092 0.7910454
[45,] 0.13640529 0.2728106 0.8635947
[46,] 0.08715357 0.1743071 0.9128464
[47,] 0.09543354 0.1908671 0.9045665
[48,] 0.31043580 0.6208716 0.6895642
> postscript(file="/var/www/html/rcomp/tmp/1lqo11229434956.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ommg1229434956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30vuj1229434956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4sbdq1229434956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59bd81229434956.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 = 81
Frequency = 1
1 2 3 4 5 6
-0.165232358 -0.450946644 -5.965232358 -0.708089501 1.777624785 -5.908089501
7 8 9 10 11 12
0.697246127 -2.159896730 -6.645611015 1.123608721 -6.276391279 -9.426391279
13 14 15 16 17 18
7.601147447 2.215433161 -0.998852553 2.658290304 -3.755995410 -3.341709696
19 20 21 22 23 24
5.363625932 1.206483075 5.520768789 5.689988526 -0.710011474 8.339988526
25 26 27 28 29 30
-5.732472748 -0.818187034 3.467527252 -0.475329891 -3.089615605 9.124670109
31 32 33 34 35 36
4.630005737 4.472862880 6.687148594 -1.443631670 2.756368330 11.806368330
37 38 39 40 41 42
-0.966092943 -2.951807229 -0.466092943 -0.008950086 1.276764200 0.291049914
43 44 45 46 47 48
-8.703614458 0.039242685 2.253528399 -8.677251865 -0.277251865 -4.027251865
49 50 51 52 53 54
-1.999713138 0.614572576 7.000286862 -5.542570281 6.143144005 0.757429719
55 56 57 58 59 60
-7.937234653 1.805622490 -0.380091796 0.389127940 2.389127940 -4.060872060
61 62 63 64 65 66
-0.633333333 -1.519047619 2.766666667 -5.976190476 0.809523810 0.523809524
67 68 69 70 71 72
3.091795754 3.734652897 -6.751061388 2.918158348 2.118158348 -2.631841652
73 74 75 76 77 78
1.895697074 2.909982788 -5.804302926 10.052839931 -3.161445783 -1.447160069
79 80 81
2.858175559 -9.098967298 -0.684681583
> postscript(file="/var/www/html/rcomp/tmp/66wpg1229434956.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 = 81
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.165232358 NA
1 -0.450946644 -0.165232358
2 -5.965232358 -0.450946644
3 -0.708089501 -5.965232358
4 1.777624785 -0.708089501
5 -5.908089501 1.777624785
6 0.697246127 -5.908089501
7 -2.159896730 0.697246127
8 -6.645611015 -2.159896730
9 1.123608721 -6.645611015
10 -6.276391279 1.123608721
11 -9.426391279 -6.276391279
12 7.601147447 -9.426391279
13 2.215433161 7.601147447
14 -0.998852553 2.215433161
15 2.658290304 -0.998852553
16 -3.755995410 2.658290304
17 -3.341709696 -3.755995410
18 5.363625932 -3.341709696
19 1.206483075 5.363625932
20 5.520768789 1.206483075
21 5.689988526 5.520768789
22 -0.710011474 5.689988526
23 8.339988526 -0.710011474
24 -5.732472748 8.339988526
25 -0.818187034 -5.732472748
26 3.467527252 -0.818187034
27 -0.475329891 3.467527252
28 -3.089615605 -0.475329891
29 9.124670109 -3.089615605
30 4.630005737 9.124670109
31 4.472862880 4.630005737
32 6.687148594 4.472862880
33 -1.443631670 6.687148594
34 2.756368330 -1.443631670
35 11.806368330 2.756368330
36 -0.966092943 11.806368330
37 -2.951807229 -0.966092943
38 -0.466092943 -2.951807229
39 -0.008950086 -0.466092943
40 1.276764200 -0.008950086
41 0.291049914 1.276764200
42 -8.703614458 0.291049914
43 0.039242685 -8.703614458
44 2.253528399 0.039242685
45 -8.677251865 2.253528399
46 -0.277251865 -8.677251865
47 -4.027251865 -0.277251865
48 -1.999713138 -4.027251865
49 0.614572576 -1.999713138
50 7.000286862 0.614572576
51 -5.542570281 7.000286862
52 6.143144005 -5.542570281
53 0.757429719 6.143144005
54 -7.937234653 0.757429719
55 1.805622490 -7.937234653
56 -0.380091796 1.805622490
57 0.389127940 -0.380091796
58 2.389127940 0.389127940
59 -4.060872060 2.389127940
60 -0.633333333 -4.060872060
61 -1.519047619 -0.633333333
62 2.766666667 -1.519047619
63 -5.976190476 2.766666667
64 0.809523810 -5.976190476
65 0.523809524 0.809523810
66 3.091795754 0.523809524
67 3.734652897 3.091795754
68 -6.751061388 3.734652897
69 2.918158348 -6.751061388
70 2.118158348 2.918158348
71 -2.631841652 2.118158348
72 1.895697074 -2.631841652
73 2.909982788 1.895697074
74 -5.804302926 2.909982788
75 10.052839931 -5.804302926
76 -3.161445783 10.052839931
77 -1.447160069 -3.161445783
78 2.858175559 -1.447160069
79 -9.098967298 2.858175559
80 -0.684681583 -9.098967298
81 NA -0.684681583
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.450946644 -0.165232358
[2,] -5.965232358 -0.450946644
[3,] -0.708089501 -5.965232358
[4,] 1.777624785 -0.708089501
[5,] -5.908089501 1.777624785
[6,] 0.697246127 -5.908089501
[7,] -2.159896730 0.697246127
[8,] -6.645611015 -2.159896730
[9,] 1.123608721 -6.645611015
[10,] -6.276391279 1.123608721
[11,] -9.426391279 -6.276391279
[12,] 7.601147447 -9.426391279
[13,] 2.215433161 7.601147447
[14,] -0.998852553 2.215433161
[15,] 2.658290304 -0.998852553
[16,] -3.755995410 2.658290304
[17,] -3.341709696 -3.755995410
[18,] 5.363625932 -3.341709696
[19,] 1.206483075 5.363625932
[20,] 5.520768789 1.206483075
[21,] 5.689988526 5.520768789
[22,] -0.710011474 5.689988526
[23,] 8.339988526 -0.710011474
[24,] -5.732472748 8.339988526
[25,] -0.818187034 -5.732472748
[26,] 3.467527252 -0.818187034
[27,] -0.475329891 3.467527252
[28,] -3.089615605 -0.475329891
[29,] 9.124670109 -3.089615605
[30,] 4.630005737 9.124670109
[31,] 4.472862880 4.630005737
[32,] 6.687148594 4.472862880
[33,] -1.443631670 6.687148594
[34,] 2.756368330 -1.443631670
[35,] 11.806368330 2.756368330
[36,] -0.966092943 11.806368330
[37,] -2.951807229 -0.966092943
[38,] -0.466092943 -2.951807229
[39,] -0.008950086 -0.466092943
[40,] 1.276764200 -0.008950086
[41,] 0.291049914 1.276764200
[42,] -8.703614458 0.291049914
[43,] 0.039242685 -8.703614458
[44,] 2.253528399 0.039242685
[45,] -8.677251865 2.253528399
[46,] -0.277251865 -8.677251865
[47,] -4.027251865 -0.277251865
[48,] -1.999713138 -4.027251865
[49,] 0.614572576 -1.999713138
[50,] 7.000286862 0.614572576
[51,] -5.542570281 7.000286862
[52,] 6.143144005 -5.542570281
[53,] 0.757429719 6.143144005
[54,] -7.937234653 0.757429719
[55,] 1.805622490 -7.937234653
[56,] -0.380091796 1.805622490
[57,] 0.389127940 -0.380091796
[58,] 2.389127940 0.389127940
[59,] -4.060872060 2.389127940
[60,] -0.633333333 -4.060872060
[61,] -1.519047619 -0.633333333
[62,] 2.766666667 -1.519047619
[63,] -5.976190476 2.766666667
[64,] 0.809523810 -5.976190476
[65,] 0.523809524 0.809523810
[66,] 3.091795754 0.523809524
[67,] 3.734652897 3.091795754
[68,] -6.751061388 3.734652897
[69,] 2.918158348 -6.751061388
[70,] 2.118158348 2.918158348
[71,] -2.631841652 2.118158348
[72,] 1.895697074 -2.631841652
[73,] 2.909982788 1.895697074
[74,] -5.804302926 2.909982788
[75,] 10.052839931 -5.804302926
[76,] -3.161445783 10.052839931
[77,] -1.447160069 -3.161445783
[78,] 2.858175559 -1.447160069
[79,] -9.098967298 2.858175559
[80,] -0.684681583 -9.098967298
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.450946644 -0.165232358
2 -5.965232358 -0.450946644
3 -0.708089501 -5.965232358
4 1.777624785 -0.708089501
5 -5.908089501 1.777624785
6 0.697246127 -5.908089501
7 -2.159896730 0.697246127
8 -6.645611015 -2.159896730
9 1.123608721 -6.645611015
10 -6.276391279 1.123608721
11 -9.426391279 -6.276391279
12 7.601147447 -9.426391279
13 2.215433161 7.601147447
14 -0.998852553 2.215433161
15 2.658290304 -0.998852553
16 -3.755995410 2.658290304
17 -3.341709696 -3.755995410
18 5.363625932 -3.341709696
19 1.206483075 5.363625932
20 5.520768789 1.206483075
21 5.689988526 5.520768789
22 -0.710011474 5.689988526
23 8.339988526 -0.710011474
24 -5.732472748 8.339988526
25 -0.818187034 -5.732472748
26 3.467527252 -0.818187034
27 -0.475329891 3.467527252
28 -3.089615605 -0.475329891
29 9.124670109 -3.089615605
30 4.630005737 9.124670109
31 4.472862880 4.630005737
32 6.687148594 4.472862880
33 -1.443631670 6.687148594
34 2.756368330 -1.443631670
35 11.806368330 2.756368330
36 -0.966092943 11.806368330
37 -2.951807229 -0.966092943
38 -0.466092943 -2.951807229
39 -0.008950086 -0.466092943
40 1.276764200 -0.008950086
41 0.291049914 1.276764200
42 -8.703614458 0.291049914
43 0.039242685 -8.703614458
44 2.253528399 0.039242685
45 -8.677251865 2.253528399
46 -0.277251865 -8.677251865
47 -4.027251865 -0.277251865
48 -1.999713138 -4.027251865
49 0.614572576 -1.999713138
50 7.000286862 0.614572576
51 -5.542570281 7.000286862
52 6.143144005 -5.542570281
53 0.757429719 6.143144005
54 -7.937234653 0.757429719
55 1.805622490 -7.937234653
56 -0.380091796 1.805622490
57 0.389127940 -0.380091796
58 2.389127940 0.389127940
59 -4.060872060 2.389127940
60 -0.633333333 -4.060872060
61 -1.519047619 -0.633333333
62 2.766666667 -1.519047619
63 -5.976190476 2.766666667
64 0.809523810 -5.976190476
65 0.523809524 0.809523810
66 3.091795754 0.523809524
67 3.734652897 3.091795754
68 -6.751061388 3.734652897
69 2.918158348 -6.751061388
70 2.118158348 2.918158348
71 -2.631841652 2.118158348
72 1.895697074 -2.631841652
73 2.909982788 1.895697074
74 -5.804302926 2.909982788
75 10.052839931 -5.804302926
76 -3.161445783 10.052839931
77 -1.447160069 -3.161445783
78 2.858175559 -1.447160069
79 -9.098967298 2.858175559
80 -0.684681583 -9.098967298
> 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/7qimu1229434956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83n2x1229434956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9v9dn1229434956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bh0o1229434956.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gd7y1229434956.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/12cycy1229434957.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/132ycl1229434957.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/14xckv1229434957.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/157kk91229434957.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/16lzi01229434957.tab")
+ }
>
> system("convert tmp/1lqo11229434956.ps tmp/1lqo11229434956.png")
> system("convert tmp/2ommg1229434956.ps tmp/2ommg1229434956.png")
> system("convert tmp/30vuj1229434956.ps tmp/30vuj1229434956.png")
> system("convert tmp/4sbdq1229434956.ps tmp/4sbdq1229434956.png")
> system("convert tmp/59bd81229434956.ps tmp/59bd81229434956.png")
> system("convert tmp/66wpg1229434956.ps tmp/66wpg1229434956.png")
> system("convert tmp/7qimu1229434956.ps tmp/7qimu1229434956.png")
> system("convert tmp/83n2x1229434956.ps tmp/83n2x1229434956.png")
> system("convert tmp/9v9dn1229434956.ps tmp/9v9dn1229434956.png")
> system("convert tmp/10bh0o1229434956.ps tmp/10bh0o1229434956.png")
>
>
> proc.time()
user system elapsed
2.707 1.591 3.163