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(149,0,139,0,135,0,130,0,127,0,122,0,117,0,112,0,113,0,149,0,157,0,157,0,147,0,137,0,132,0,125,0,123,0,117,0,114,0,111,0,112,0,144,0,150,0,149,0,134,0,123,0,116,0,117,0,111,0,105,0,102,0,95,0,93,0,124,0,130,0,124,0,115,0,106,0,105,0,105,0,101,0,95,0,93,0,84,0,87,0,116,0,120,0,117,1,109,1,105,1,107,1,109,1,109,1,108,1,107,1,99,1,103,1,131,1,137,1,135,1),dim=c(2,60),dimnames=list(c('WLH','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WLH','X'),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 = '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
WLH X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 149 0 1 0 0 0 0 0 0 0 0 0 0 1
2 139 0 0 1 0 0 0 0 0 0 0 0 0 2
3 135 0 0 0 1 0 0 0 0 0 0 0 0 3
4 130 0 0 0 0 1 0 0 0 0 0 0 0 4
5 127 0 0 0 0 0 1 0 0 0 0 0 0 5
6 122 0 0 0 0 0 0 1 0 0 0 0 0 6
7 117 0 0 0 0 0 0 0 1 0 0 0 0 7
8 112 0 0 0 0 0 0 0 0 1 0 0 0 8
9 113 0 0 0 0 0 0 0 0 0 1 0 0 9
10 149 0 0 0 0 0 0 0 0 0 0 1 0 10
11 157 0 0 0 0 0 0 0 0 0 0 0 1 11
12 157 0 0 0 0 0 0 0 0 0 0 0 0 12
13 147 0 1 0 0 0 0 0 0 0 0 0 0 13
14 137 0 0 1 0 0 0 0 0 0 0 0 0 14
15 132 0 0 0 1 0 0 0 0 0 0 0 0 15
16 125 0 0 0 0 1 0 0 0 0 0 0 0 16
17 123 0 0 0 0 0 1 0 0 0 0 0 0 17
18 117 0 0 0 0 0 0 1 0 0 0 0 0 18
19 114 0 0 0 0 0 0 0 1 0 0 0 0 19
20 111 0 0 0 0 0 0 0 0 1 0 0 0 20
21 112 0 0 0 0 0 0 0 0 0 1 0 0 21
22 144 0 0 0 0 0 0 0 0 0 0 1 0 22
23 150 0 0 0 0 0 0 0 0 0 0 0 1 23
24 149 0 0 0 0 0 0 0 0 0 0 0 0 24
25 134 0 1 0 0 0 0 0 0 0 0 0 0 25
26 123 0 0 1 0 0 0 0 0 0 0 0 0 26
27 116 0 0 0 1 0 0 0 0 0 0 0 0 27
28 117 0 0 0 0 1 0 0 0 0 0 0 0 28
29 111 0 0 0 0 0 1 0 0 0 0 0 0 29
30 105 0 0 0 0 0 0 1 0 0 0 0 0 30
31 102 0 0 0 0 0 0 0 1 0 0 0 0 31
32 95 0 0 0 0 0 0 0 0 1 0 0 0 32
33 93 0 0 0 0 0 0 0 0 0 1 0 0 33
34 124 0 0 0 0 0 0 0 0 0 0 1 0 34
35 130 0 0 0 0 0 0 0 0 0 0 0 1 35
36 124 0 0 0 0 0 0 0 0 0 0 0 0 36
37 115 0 1 0 0 0 0 0 0 0 0 0 0 37
38 106 0 0 1 0 0 0 0 0 0 0 0 0 38
39 105 0 0 0 1 0 0 0 0 0 0 0 0 39
40 105 0 0 0 0 1 0 0 0 0 0 0 0 40
41 101 0 0 0 0 0 1 0 0 0 0 0 0 41
42 95 0 0 0 0 0 0 1 0 0 0 0 0 42
43 93 0 0 0 0 0 0 0 1 0 0 0 0 43
44 84 0 0 0 0 0 0 0 0 1 0 0 0 44
45 87 0 0 0 0 0 0 0 0 0 1 0 0 45
46 116 0 0 0 0 0 0 0 0 0 0 1 0 46
47 120 0 0 0 0 0 0 0 0 0 0 0 1 47
48 117 1 0 0 0 0 0 0 0 0 0 0 0 48
49 109 1 1 0 0 0 0 0 0 0 0 0 0 49
50 105 1 0 1 0 0 0 0 0 0 0 0 0 50
51 107 1 0 0 1 0 0 0 0 0 0 0 0 51
52 109 1 0 0 0 1 0 0 0 0 0 0 0 52
53 109 1 0 0 0 0 1 0 0 0 0 0 0 53
54 108 1 0 0 0 0 0 1 0 0 0 0 0 54
55 107 1 0 0 0 0 0 0 1 0 0 0 0 55
56 99 1 0 0 0 0 0 0 0 1 0 0 0 56
57 103 1 0 0 0 0 0 0 0 0 1 0 0 57
58 131 1 0 0 0 0 0 0 0 0 0 1 0 58
59 137 1 0 0 0 0 0 0 0 0 0 0 1 59
60 135 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
160.4803 16.6904 -11.6597 -19.6054 -21.7510 -22.6967
M5 M6 M7 M8 M9 M10
-24.8423 -28.7880 -30.7337 -36.2793 -34.0250 -1.9706
M11 t
4.8837 -0.8543
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-19.16209 -3.49887 0.06417 4.28591 9.28591
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 160.48035 3.39687 47.244 < 2e-16 ***
X 16.69043 2.90391 5.748 6.91e-07 ***
M1 -11.65974 4.05540 -2.875 0.0061 **
M2 -19.60539 4.04999 -4.841 1.50e-05 ***
M3 -21.75104 4.04577 -5.376 2.46e-06 ***
M4 -22.69670 4.04275 -5.614 1.09e-06 ***
M5 -24.84235 4.04094 -6.148 1.74e-07 ***
M6 -28.78800 4.04033 -7.125 5.92e-09 ***
M7 -30.73365 4.04094 -7.606 1.13e-09 ***
M8 -36.27930 4.04275 -8.974 1.13e-11 ***
M9 -34.02496 4.04577 -8.410 7.41e-11 ***
M10 -1.97061 4.04999 -0.487 0.6289
M11 4.88374 4.05540 1.204 0.2347
t -0.85435 0.06986 -12.230 4.65e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.357 on 46 degrees of freedom
Multiple R-squared: 0.8996, Adjusted R-squared: 0.8712
F-statistic: 31.7 on 13 and 46 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,] 5.510923e-03 1.102185e-02 0.994489077
[2,] 1.341914e-03 2.683828e-03 0.998658086
[3,] 1.906092e-04 3.812184e-04 0.999809391
[4,] 8.102803e-05 1.620561e-04 0.999918972
[5,] 2.508205e-05 5.016409e-05 0.999974918
[6,] 7.761440e-06 1.552288e-05 0.999992239
[7,] 9.435471e-06 1.887094e-05 0.999990565
[8,] 2.485793e-05 4.971585e-05 0.999975142
[9,] 9.351912e-04 1.870382e-03 0.999064809
[10,] 5.191706e-03 1.038341e-02 0.994808294
[11,] 1.669892e-02 3.339785e-02 0.983301076
[12,] 1.236835e-02 2.473670e-02 0.987631652
[13,] 1.005262e-02 2.010525e-02 0.989947377
[14,] 7.473308e-03 1.494662e-02 0.992526692
[15,] 4.412105e-03 8.824211e-03 0.995587895
[16,] 4.659335e-03 9.318670e-03 0.995340665
[17,] 6.430117e-03 1.286023e-02 0.993569883
[18,] 1.525459e-02 3.050917e-02 0.984745414
[19,] 5.013333e-02 1.002667e-01 0.949866672
[20,] 2.614523e-01 5.229045e-01 0.738547729
[21,] 5.579764e-01 8.840471e-01 0.442023575
[22,] 7.545585e-01 4.908830e-01 0.245441508
[23,] 8.791755e-01 2.416489e-01 0.120824456
[24,] 9.769666e-01 4.606680e-02 0.023033400
[25,] 9.986485e-01 2.703079e-03 0.001351540
[26,] 9.980951e-01 3.809890e-03 0.001904945
[27,] 9.954503e-01 9.099421e-03 0.004549711
> postscript(file="/var/www/html/rcomp/tmp/1hrlg1258620102.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ht591258620102.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3md3i1258620102.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4v3ea1258620102.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5n0ke1258620102.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 6
1.03373913 -0.16626087 -1.16626087 -4.36626087 -4.36626087 -4.56626087
7 8 9 10 11 12
-6.76626087 -5.36626087 -5.76626087 -0.96626087 1.03373913 6.77182609
13 14 15 16 17 18
9.28591304 8.08591304 6.08591304 0.88591304 1.88591304 0.68591304
19 20 21 22 23 24
0.48591304 3.88591304 3.48591304 4.28591304 4.28591304 9.02400000
25 26 27 28 29 30
6.53808696 4.33808696 0.33808696 3.13808696 0.13808696 -1.06191304
31 32 33 34 35 36
-1.26191304 -1.86191304 -5.26191304 -5.46191304 -5.46191304 -5.72382609
37 38 39 40 41 42
-2.20973913 -2.40973913 -0.40973913 1.39026087 0.39026087 -0.80973913
43 44 45 46 47 48
-0.00973913 -2.60973913 -1.00973913 -3.20973913 -5.20973913 -19.16208696
49 50 51 52 53 54
-14.64800000 -9.84800000 -4.84800000 -1.04800000 1.95200000 5.75200000
55 56 57 58 59 60
7.55200000 5.95200000 8.55200000 5.35200000 5.35200000 9.09008696
> postscript(file="/var/www/html/rcomp/tmp/6nqzh1258620102.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 1.03373913 NA
1 -0.16626087 1.03373913
2 -1.16626087 -0.16626087
3 -4.36626087 -1.16626087
4 -4.36626087 -4.36626087
5 -4.56626087 -4.36626087
6 -6.76626087 -4.56626087
7 -5.36626087 -6.76626087
8 -5.76626087 -5.36626087
9 -0.96626087 -5.76626087
10 1.03373913 -0.96626087
11 6.77182609 1.03373913
12 9.28591304 6.77182609
13 8.08591304 9.28591304
14 6.08591304 8.08591304
15 0.88591304 6.08591304
16 1.88591304 0.88591304
17 0.68591304 1.88591304
18 0.48591304 0.68591304
19 3.88591304 0.48591304
20 3.48591304 3.88591304
21 4.28591304 3.48591304
22 4.28591304 4.28591304
23 9.02400000 4.28591304
24 6.53808696 9.02400000
25 4.33808696 6.53808696
26 0.33808696 4.33808696
27 3.13808696 0.33808696
28 0.13808696 3.13808696
29 -1.06191304 0.13808696
30 -1.26191304 -1.06191304
31 -1.86191304 -1.26191304
32 -5.26191304 -1.86191304
33 -5.46191304 -5.26191304
34 -5.46191304 -5.46191304
35 -5.72382609 -5.46191304
36 -2.20973913 -5.72382609
37 -2.40973913 -2.20973913
38 -0.40973913 -2.40973913
39 1.39026087 -0.40973913
40 0.39026087 1.39026087
41 -0.80973913 0.39026087
42 -0.00973913 -0.80973913
43 -2.60973913 -0.00973913
44 -1.00973913 -2.60973913
45 -3.20973913 -1.00973913
46 -5.20973913 -3.20973913
47 -19.16208696 -5.20973913
48 -14.64800000 -19.16208696
49 -9.84800000 -14.64800000
50 -4.84800000 -9.84800000
51 -1.04800000 -4.84800000
52 1.95200000 -1.04800000
53 5.75200000 1.95200000
54 7.55200000 5.75200000
55 5.95200000 7.55200000
56 8.55200000 5.95200000
57 5.35200000 8.55200000
58 5.35200000 5.35200000
59 9.09008696 5.35200000
60 NA 9.09008696
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.16626087 1.03373913
[2,] -1.16626087 -0.16626087
[3,] -4.36626087 -1.16626087
[4,] -4.36626087 -4.36626087
[5,] -4.56626087 -4.36626087
[6,] -6.76626087 -4.56626087
[7,] -5.36626087 -6.76626087
[8,] -5.76626087 -5.36626087
[9,] -0.96626087 -5.76626087
[10,] 1.03373913 -0.96626087
[11,] 6.77182609 1.03373913
[12,] 9.28591304 6.77182609
[13,] 8.08591304 9.28591304
[14,] 6.08591304 8.08591304
[15,] 0.88591304 6.08591304
[16,] 1.88591304 0.88591304
[17,] 0.68591304 1.88591304
[18,] 0.48591304 0.68591304
[19,] 3.88591304 0.48591304
[20,] 3.48591304 3.88591304
[21,] 4.28591304 3.48591304
[22,] 4.28591304 4.28591304
[23,] 9.02400000 4.28591304
[24,] 6.53808696 9.02400000
[25,] 4.33808696 6.53808696
[26,] 0.33808696 4.33808696
[27,] 3.13808696 0.33808696
[28,] 0.13808696 3.13808696
[29,] -1.06191304 0.13808696
[30,] -1.26191304 -1.06191304
[31,] -1.86191304 -1.26191304
[32,] -5.26191304 -1.86191304
[33,] -5.46191304 -5.26191304
[34,] -5.46191304 -5.46191304
[35,] -5.72382609 -5.46191304
[36,] -2.20973913 -5.72382609
[37,] -2.40973913 -2.20973913
[38,] -0.40973913 -2.40973913
[39,] 1.39026087 -0.40973913
[40,] 0.39026087 1.39026087
[41,] -0.80973913 0.39026087
[42,] -0.00973913 -0.80973913
[43,] -2.60973913 -0.00973913
[44,] -1.00973913 -2.60973913
[45,] -3.20973913 -1.00973913
[46,] -5.20973913 -3.20973913
[47,] -19.16208696 -5.20973913
[48,] -14.64800000 -19.16208696
[49,] -9.84800000 -14.64800000
[50,] -4.84800000 -9.84800000
[51,] -1.04800000 -4.84800000
[52,] 1.95200000 -1.04800000
[53,] 5.75200000 1.95200000
[54,] 7.55200000 5.75200000
[55,] 5.95200000 7.55200000
[56,] 8.55200000 5.95200000
[57,] 5.35200000 8.55200000
[58,] 5.35200000 5.35200000
[59,] 9.09008696 5.35200000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.16626087 1.03373913
2 -1.16626087 -0.16626087
3 -4.36626087 -1.16626087
4 -4.36626087 -4.36626087
5 -4.56626087 -4.36626087
6 -6.76626087 -4.56626087
7 -5.36626087 -6.76626087
8 -5.76626087 -5.36626087
9 -0.96626087 -5.76626087
10 1.03373913 -0.96626087
11 6.77182609 1.03373913
12 9.28591304 6.77182609
13 8.08591304 9.28591304
14 6.08591304 8.08591304
15 0.88591304 6.08591304
16 1.88591304 0.88591304
17 0.68591304 1.88591304
18 0.48591304 0.68591304
19 3.88591304 0.48591304
20 3.48591304 3.88591304
21 4.28591304 3.48591304
22 4.28591304 4.28591304
23 9.02400000 4.28591304
24 6.53808696 9.02400000
25 4.33808696 6.53808696
26 0.33808696 4.33808696
27 3.13808696 0.33808696
28 0.13808696 3.13808696
29 -1.06191304 0.13808696
30 -1.26191304 -1.06191304
31 -1.86191304 -1.26191304
32 -5.26191304 -1.86191304
33 -5.46191304 -5.26191304
34 -5.46191304 -5.46191304
35 -5.72382609 -5.46191304
36 -2.20973913 -5.72382609
37 -2.40973913 -2.20973913
38 -0.40973913 -2.40973913
39 1.39026087 -0.40973913
40 0.39026087 1.39026087
41 -0.80973913 0.39026087
42 -0.00973913 -0.80973913
43 -2.60973913 -0.00973913
44 -1.00973913 -2.60973913
45 -3.20973913 -1.00973913
46 -5.20973913 -3.20973913
47 -19.16208696 -5.20973913
48 -14.64800000 -19.16208696
49 -9.84800000 -14.64800000
50 -4.84800000 -9.84800000
51 -1.04800000 -4.84800000
52 1.95200000 -1.04800000
53 5.75200000 1.95200000
54 7.55200000 5.75200000
55 5.95200000 7.55200000
56 8.55200000 5.95200000
57 5.35200000 8.55200000
58 5.35200000 5.35200000
59 9.09008696 5.35200000
> 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/72iow1258620102.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sgms1258620102.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97bgr1258620102.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10b88g1258620102.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u3f31258620102.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/12t9m51258620102.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/1364lm1258620102.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/14imxk1258620102.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/15r6z71258620102.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/162v4j1258620102.tab")
+ }
>
> system("convert tmp/1hrlg1258620102.ps tmp/1hrlg1258620102.png")
> system("convert tmp/2ht591258620102.ps tmp/2ht591258620102.png")
> system("convert tmp/3md3i1258620102.ps tmp/3md3i1258620102.png")
> system("convert tmp/4v3ea1258620102.ps tmp/4v3ea1258620102.png")
> system("convert tmp/5n0ke1258620102.ps tmp/5n0ke1258620102.png")
> system("convert tmp/6nqzh1258620102.ps tmp/6nqzh1258620102.png")
> system("convert tmp/72iow1258620102.ps tmp/72iow1258620102.png")
> system("convert tmp/8sgms1258620102.ps tmp/8sgms1258620102.png")
> system("convert tmp/97bgr1258620102.ps tmp/97bgr1258620102.png")
> system("convert tmp/10b88g1258620102.ps tmp/10b88g1258620102.png")
>
>
> proc.time()
user system elapsed
2.352 1.499 3.300