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(101.5,0,99.2,0,107.8,0,92.3,0,99.2,0,101.6,0,87,0,71.4,0,104.7,0,115.1,0,102.5,0,75.3,0,96.7,1,94.6,1,98.6,1,99.5,1,92,1,93.6,1,89.3,1,66.9,1,108.8,1,113.2,1,105.5,1,77.8,1,102.1,1,97,1,95.5,1,99.3,1,86.4,1,92.4,1,85.7,1,61.9,1,104.9,1,107.9,1,95.6,1,79.8,1,94.8,1,93.7,1,108.1,1,96.9,1,88.8,1,106.7,1,86.8,1,69.8,1,110.9,1,105.4,1,99.2,1,84.4,1,87.2,1,91.9,1,97.9,1,94.5,1,85,1,100.3,1,78.7,1,65.8,1,104.8,1,96,1,103.3,1,82.9,1,91.4,1,94.5,1,109.3,1,92.1,1,99.3,1,109.6,1,87.5,1,73.1,1,110.7,1,111.6,1,110.7,1,84,1,101.6,1,102.1,1,113.9,1,99,1,100.4,1,109.5,1,93,1,76.8,1,105.3,1),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
1 101.5 0 1 0 0 0 0 0 0 0 0 0 0 1
2 99.2 0 0 1 0 0 0 0 0 0 0 0 0 2
3 107.8 0 0 0 1 0 0 0 0 0 0 0 0 3
4 92.3 0 0 0 0 1 0 0 0 0 0 0 0 4
5 99.2 0 0 0 0 0 1 0 0 0 0 0 0 5
6 101.6 0 0 0 0 0 0 1 0 0 0 0 0 6
7 87.0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 71.4 0 0 0 0 0 0 0 0 1 0 0 0 8
9 104.7 0 0 0 0 0 0 0 0 0 1 0 0 9
10 115.1 0 0 0 0 0 0 0 0 0 0 1 0 10
11 102.5 0 0 0 0 0 0 0 0 0 0 0 1 11
12 75.3 0 0 0 0 0 0 0 0 0 0 0 0 12
13 96.7 1 1 0 0 0 0 0 0 0 0 0 0 13
14 94.6 1 0 1 0 0 0 0 0 0 0 0 0 14
15 98.6 1 0 0 1 0 0 0 0 0 0 0 0 15
16 99.5 1 0 0 0 1 0 0 0 0 0 0 0 16
17 92.0 1 0 0 0 0 1 0 0 0 0 0 0 17
18 93.6 1 0 0 0 0 0 1 0 0 0 0 0 18
19 89.3 1 0 0 0 0 0 0 1 0 0 0 0 19
20 66.9 1 0 0 0 0 0 0 0 1 0 0 0 20
21 108.8 1 0 0 0 0 0 0 0 0 1 0 0 21
22 113.2 1 0 0 0 0 0 0 0 0 0 1 0 22
23 105.5 1 0 0 0 0 0 0 0 0 0 0 1 23
24 77.8 1 0 0 0 0 0 0 0 0 0 0 0 24
25 102.1 1 1 0 0 0 0 0 0 0 0 0 0 25
26 97.0 1 0 1 0 0 0 0 0 0 0 0 0 26
27 95.5 1 0 0 1 0 0 0 0 0 0 0 0 27
28 99.3 1 0 0 0 1 0 0 0 0 0 0 0 28
29 86.4 1 0 0 0 0 1 0 0 0 0 0 0 29
30 92.4 1 0 0 0 0 0 1 0 0 0 0 0 30
31 85.7 1 0 0 0 0 0 0 1 0 0 0 0 31
32 61.9 1 0 0 0 0 0 0 0 1 0 0 0 32
33 104.9 1 0 0 0 0 0 0 0 0 1 0 0 33
34 107.9 1 0 0 0 0 0 0 0 0 0 1 0 34
35 95.6 1 0 0 0 0 0 0 0 0 0 0 1 35
36 79.8 1 0 0 0 0 0 0 0 0 0 0 0 36
37 94.8 1 1 0 0 0 0 0 0 0 0 0 0 37
38 93.7 1 0 1 0 0 0 0 0 0 0 0 0 38
39 108.1 1 0 0 1 0 0 0 0 0 0 0 0 39
40 96.9 1 0 0 0 1 0 0 0 0 0 0 0 40
41 88.8 1 0 0 0 0 1 0 0 0 0 0 0 41
42 106.7 1 0 0 0 0 0 1 0 0 0 0 0 42
43 86.8 1 0 0 0 0 0 0 1 0 0 0 0 43
44 69.8 1 0 0 0 0 0 0 0 1 0 0 0 44
45 110.9 1 0 0 0 0 0 0 0 0 1 0 0 45
46 105.4 1 0 0 0 0 0 0 0 0 0 1 0 46
47 99.2 1 0 0 0 0 0 0 0 0 0 0 1 47
48 84.4 1 0 0 0 0 0 0 0 0 0 0 0 48
49 87.2 1 1 0 0 0 0 0 0 0 0 0 0 49
50 91.9 1 0 1 0 0 0 0 0 0 0 0 0 50
51 97.9 1 0 0 1 0 0 0 0 0 0 0 0 51
52 94.5 1 0 0 0 1 0 0 0 0 0 0 0 52
53 85.0 1 0 0 0 0 1 0 0 0 0 0 0 53
54 100.3 1 0 0 0 0 0 1 0 0 0 0 0 54
55 78.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 65.8 1 0 0 0 0 0 0 0 1 0 0 0 56
57 104.8 1 0 0 0 0 0 0 0 0 1 0 0 57
58 96.0 1 0 0 0 0 0 0 0 0 0 1 0 58
59 103.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 82.9 1 0 0 0 0 0 0 0 0 0 0 0 60
61 91.4 1 1 0 0 0 0 0 0 0 0 0 0 61
62 94.5 1 0 1 0 0 0 0 0 0 0 0 0 62
63 109.3 1 0 0 1 0 0 0 0 0 0 0 0 63
64 92.1 1 0 0 0 1 0 0 0 0 0 0 0 64
65 99.3 1 0 0 0 0 1 0 0 0 0 0 0 65
66 109.6 1 0 0 0 0 0 1 0 0 0 0 0 66
67 87.5 1 0 0 0 0 0 0 1 0 0 0 0 67
68 73.1 1 0 0 0 0 0 0 0 1 0 0 0 68
69 110.7 1 0 0 0 0 0 0 0 0 1 0 0 69
70 111.6 1 0 0 0 0 0 0 0 0 0 1 0 70
71 110.7 1 0 0 0 0 0 0 0 0 0 0 1 71
72 84.0 1 0 0 0 0 0 0 0 0 0 0 0 72
73 101.6 1 1 0 0 0 0 0 0 0 0 0 0 73
74 102.1 1 0 1 0 0 0 0 0 0 0 0 0 74
75 113.9 1 0 0 1 0 0 0 0 0 0 0 0 75
76 99.0 1 0 0 0 1 0 0 0 0 0 0 0 76
77 100.4 1 0 0 0 0 1 0 0 0 0 0 0 77
78 109.5 1 0 0 0 0 0 1 0 0 0 0 0 78
79 93.0 1 0 0 0 0 0 0 1 0 0 0 0 79
80 76.8 1 0 0 0 0 0 0 0 1 0 0 0 80
81 105.3 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
81.09781 -5.16078 16.35893 15.93743 24.14451 15.83730
M5 M6 M7 M8 M9 M10
12.53009 21.38002 6.18709 -11.37726 26.30124 27.68585
M11 t
22.19292 0.09292
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.0125 -3.2974 0.6952 3.9503 7.5328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 81.09781 2.44663 33.147 < 2e-16 ***
x -5.16078 2.00077 -2.579 0.012098 *
M1 16.35893 2.80131 5.840 1.68e-07 ***
M2 15.93743 2.79949 5.693 3.01e-07 ***
M3 24.14451 2.79800 8.629 1.77e-12 ***
M4 15.83730 2.79685 5.663 3.39e-07 ***
M5 12.53009 2.79602 4.481 2.97e-05 ***
M6 21.38002 2.79553 7.648 1.04e-10 ***
M7 6.18709 2.79538 2.213 0.030285 *
M8 -11.37726 2.79556 -4.070 0.000127 ***
M9 26.30124 2.79607 9.407 7.20e-14 ***
M10 27.68585 2.90129 9.543 4.12e-14 ***
M11 22.19292 2.90080 7.651 1.03e-10 ***
t 0.09292 0.03054 3.043 0.003344 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.024 on 67 degrees of freedom
Multiple R-squared: 0.8554, Adjusted R-squared: 0.8273
F-statistic: 30.49 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,] 0.58493357 0.8301329 0.4150664
[2,] 0.46668898 0.9333780 0.5333110
[3,] 0.43262659 0.8652532 0.5673734
[4,] 0.30404484 0.6080897 0.6959552
[5,] 0.31471420 0.6294284 0.6852858
[6,] 0.26294090 0.5258818 0.7370591
[7,] 0.24793955 0.4958791 0.7520605
[8,] 0.19933775 0.3986755 0.8006623
[9,] 0.19342087 0.3868417 0.8065791
[10,] 0.15549774 0.3109955 0.8445023
[11,] 0.21852239 0.4370448 0.7814776
[12,] 0.23185949 0.4637190 0.7681405
[13,] 0.28456775 0.5691355 0.7154323
[14,] 0.27931325 0.5586265 0.7206867
[15,] 0.22490313 0.4498063 0.7750969
[16,] 0.20552966 0.4110593 0.7944703
[17,] 0.15548312 0.3109662 0.8445169
[18,] 0.15517495 0.3103499 0.8448251
[19,] 0.14695986 0.2939197 0.8530401
[20,] 0.15430099 0.3086020 0.8456990
[21,] 0.13171503 0.2634301 0.8682850
[22,] 0.09799653 0.1959931 0.9020035
[23,] 0.21772659 0.4354532 0.7822734
[24,] 0.21657619 0.4331524 0.7834238
[25,] 0.16536434 0.3307287 0.8346357
[26,] 0.34291193 0.6858239 0.6570881
[27,] 0.34081554 0.6816311 0.6591845
[28,] 0.32876435 0.6575287 0.6712357
[29,] 0.55293361 0.8941328 0.4470664
[30,] 0.66567623 0.6686475 0.3343238
[31,] 0.59066446 0.8186711 0.4093355
[32,] 0.72526730 0.5494654 0.2747327
[33,] 0.76572560 0.4685488 0.2342744
[34,] 0.70878797 0.5824241 0.2912120
[35,] 0.70368085 0.5926383 0.2963192
[36,] 0.73731930 0.5253614 0.2626807
[37,] 0.77798764 0.4440247 0.2220124
[38,] 0.71950059 0.5609988 0.2804994
[39,] 0.71232328 0.5753534 0.2876767
[40,] 0.64042004 0.7191599 0.3595800
[41,] 0.58251171 0.8349766 0.4174883
[42,] 0.85175954 0.2964809 0.1482405
[43,] 0.81817615 0.3636477 0.1818239
[44,] 0.75920549 0.4815890 0.2407945
[45,] 0.79186663 0.4162667 0.2081334
[46,] 0.76485883 0.4702823 0.2351412
[47,] 0.68755783 0.6248843 0.3124422
[48,] 0.65656995 0.6868601 0.3434301
> postscript(file="/var/www/html/rcomp/tmp/1mvgk1260961416.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23sar1260961416.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yrvy1260961416.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wbln1260961416.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58njk1260961416.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
3.9503348 1.9789063 2.2789063 -5.0068080 5.1074777 -1.4353795
7 8 9 10 11 12
-0.9353795 0.9360491 -3.5353795 5.3870908 -1.8129092 -6.9129092
13 14 15 16 17 18
3.1960193 1.4245908 -2.8754092 6.2388765 1.9531622 -5.3896949
19 20 21 22 23 24
5.4103051 0.4817336 4.6103051 7.5327753 5.2327753 -0.3672247
25 26 27 28 29 30
7.4809226 2.7094940 -7.0905060 4.9237798 -4.7619345 -7.7047917
31 32 33 34 35 36
0.6952083 -5.6333631 -0.4047917 1.1176786 -5.7823214 0.5176786
37 38 39 40 41 42
-0.9341741 -1.7056027 4.3943973 1.4086830 -3.4770313 5.4801116
43 44 45 46 47 48
0.6801116 1.1515402 4.4801116 -2.4974182 -3.2974182 4.0025818
49 50 51 52 53 54
-9.6492708 -4.6206994 -6.9206994 -2.1064137 -8.3921280 -2.0349851
55 56 57 58 59 60
-8.5349851 -3.9635565 -2.7349851 -13.0125149 -0.3125149 1.3874851
61 62 63 64 65 66
-6.5643676 -3.1357961 3.3642039 -5.6215104 4.7927753 6.1499182
67 68 69 70 71 72
-0.8500818 2.2213467 2.0499182 1.4723884 5.9723884 1.3723884
73 74 75 76 77 78
2.5205357 3.3491071 6.8491071 0.1633929 4.7776786 4.9348214
79 80 81
3.5348214 4.8062500 -4.4651786
> postscript(file="/var/www/html/rcomp/tmp/67y941260961416.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 3.9503348 NA
1 1.9789063 3.9503348
2 2.2789063 1.9789063
3 -5.0068080 2.2789063
4 5.1074777 -5.0068080
5 -1.4353795 5.1074777
6 -0.9353795 -1.4353795
7 0.9360491 -0.9353795
8 -3.5353795 0.9360491
9 5.3870908 -3.5353795
10 -1.8129092 5.3870908
11 -6.9129092 -1.8129092
12 3.1960193 -6.9129092
13 1.4245908 3.1960193
14 -2.8754092 1.4245908
15 6.2388765 -2.8754092
16 1.9531622 6.2388765
17 -5.3896949 1.9531622
18 5.4103051 -5.3896949
19 0.4817336 5.4103051
20 4.6103051 0.4817336
21 7.5327753 4.6103051
22 5.2327753 7.5327753
23 -0.3672247 5.2327753
24 7.4809226 -0.3672247
25 2.7094940 7.4809226
26 -7.0905060 2.7094940
27 4.9237798 -7.0905060
28 -4.7619345 4.9237798
29 -7.7047917 -4.7619345
30 0.6952083 -7.7047917
31 -5.6333631 0.6952083
32 -0.4047917 -5.6333631
33 1.1176786 -0.4047917
34 -5.7823214 1.1176786
35 0.5176786 -5.7823214
36 -0.9341741 0.5176786
37 -1.7056027 -0.9341741
38 4.3943973 -1.7056027
39 1.4086830 4.3943973
40 -3.4770313 1.4086830
41 5.4801116 -3.4770313
42 0.6801116 5.4801116
43 1.1515402 0.6801116
44 4.4801116 1.1515402
45 -2.4974182 4.4801116
46 -3.2974182 -2.4974182
47 4.0025818 -3.2974182
48 -9.6492708 4.0025818
49 -4.6206994 -9.6492708
50 -6.9206994 -4.6206994
51 -2.1064137 -6.9206994
52 -8.3921280 -2.1064137
53 -2.0349851 -8.3921280
54 -8.5349851 -2.0349851
55 -3.9635565 -8.5349851
56 -2.7349851 -3.9635565
57 -13.0125149 -2.7349851
58 -0.3125149 -13.0125149
59 1.3874851 -0.3125149
60 -6.5643676 1.3874851
61 -3.1357961 -6.5643676
62 3.3642039 -3.1357961
63 -5.6215104 3.3642039
64 4.7927753 -5.6215104
65 6.1499182 4.7927753
66 -0.8500818 6.1499182
67 2.2213467 -0.8500818
68 2.0499182 2.2213467
69 1.4723884 2.0499182
70 5.9723884 1.4723884
71 1.3723884 5.9723884
72 2.5205357 1.3723884
73 3.3491071 2.5205357
74 6.8491071 3.3491071
75 0.1633929 6.8491071
76 4.7776786 0.1633929
77 4.9348214 4.7776786
78 3.5348214 4.9348214
79 4.8062500 3.5348214
80 -4.4651786 4.8062500
81 NA -4.4651786
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.9789063 3.9503348
[2,] 2.2789063 1.9789063
[3,] -5.0068080 2.2789063
[4,] 5.1074777 -5.0068080
[5,] -1.4353795 5.1074777
[6,] -0.9353795 -1.4353795
[7,] 0.9360491 -0.9353795
[8,] -3.5353795 0.9360491
[9,] 5.3870908 -3.5353795
[10,] -1.8129092 5.3870908
[11,] -6.9129092 -1.8129092
[12,] 3.1960193 -6.9129092
[13,] 1.4245908 3.1960193
[14,] -2.8754092 1.4245908
[15,] 6.2388765 -2.8754092
[16,] 1.9531622 6.2388765
[17,] -5.3896949 1.9531622
[18,] 5.4103051 -5.3896949
[19,] 0.4817336 5.4103051
[20,] 4.6103051 0.4817336
[21,] 7.5327753 4.6103051
[22,] 5.2327753 7.5327753
[23,] -0.3672247 5.2327753
[24,] 7.4809226 -0.3672247
[25,] 2.7094940 7.4809226
[26,] -7.0905060 2.7094940
[27,] 4.9237798 -7.0905060
[28,] -4.7619345 4.9237798
[29,] -7.7047917 -4.7619345
[30,] 0.6952083 -7.7047917
[31,] -5.6333631 0.6952083
[32,] -0.4047917 -5.6333631
[33,] 1.1176786 -0.4047917
[34,] -5.7823214 1.1176786
[35,] 0.5176786 -5.7823214
[36,] -0.9341741 0.5176786
[37,] -1.7056027 -0.9341741
[38,] 4.3943973 -1.7056027
[39,] 1.4086830 4.3943973
[40,] -3.4770313 1.4086830
[41,] 5.4801116 -3.4770313
[42,] 0.6801116 5.4801116
[43,] 1.1515402 0.6801116
[44,] 4.4801116 1.1515402
[45,] -2.4974182 4.4801116
[46,] -3.2974182 -2.4974182
[47,] 4.0025818 -3.2974182
[48,] -9.6492708 4.0025818
[49,] -4.6206994 -9.6492708
[50,] -6.9206994 -4.6206994
[51,] -2.1064137 -6.9206994
[52,] -8.3921280 -2.1064137
[53,] -2.0349851 -8.3921280
[54,] -8.5349851 -2.0349851
[55,] -3.9635565 -8.5349851
[56,] -2.7349851 -3.9635565
[57,] -13.0125149 -2.7349851
[58,] -0.3125149 -13.0125149
[59,] 1.3874851 -0.3125149
[60,] -6.5643676 1.3874851
[61,] -3.1357961 -6.5643676
[62,] 3.3642039 -3.1357961
[63,] -5.6215104 3.3642039
[64,] 4.7927753 -5.6215104
[65,] 6.1499182 4.7927753
[66,] -0.8500818 6.1499182
[67,] 2.2213467 -0.8500818
[68,] 2.0499182 2.2213467
[69,] 1.4723884 2.0499182
[70,] 5.9723884 1.4723884
[71,] 1.3723884 5.9723884
[72,] 2.5205357 1.3723884
[73,] 3.3491071 2.5205357
[74,] 6.8491071 3.3491071
[75,] 0.1633929 6.8491071
[76,] 4.7776786 0.1633929
[77,] 4.9348214 4.7776786
[78,] 3.5348214 4.9348214
[79,] 4.8062500 3.5348214
[80,] -4.4651786 4.8062500
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.9789063 3.9503348
2 2.2789063 1.9789063
3 -5.0068080 2.2789063
4 5.1074777 -5.0068080
5 -1.4353795 5.1074777
6 -0.9353795 -1.4353795
7 0.9360491 -0.9353795
8 -3.5353795 0.9360491
9 5.3870908 -3.5353795
10 -1.8129092 5.3870908
11 -6.9129092 -1.8129092
12 3.1960193 -6.9129092
13 1.4245908 3.1960193
14 -2.8754092 1.4245908
15 6.2388765 -2.8754092
16 1.9531622 6.2388765
17 -5.3896949 1.9531622
18 5.4103051 -5.3896949
19 0.4817336 5.4103051
20 4.6103051 0.4817336
21 7.5327753 4.6103051
22 5.2327753 7.5327753
23 -0.3672247 5.2327753
24 7.4809226 -0.3672247
25 2.7094940 7.4809226
26 -7.0905060 2.7094940
27 4.9237798 -7.0905060
28 -4.7619345 4.9237798
29 -7.7047917 -4.7619345
30 0.6952083 -7.7047917
31 -5.6333631 0.6952083
32 -0.4047917 -5.6333631
33 1.1176786 -0.4047917
34 -5.7823214 1.1176786
35 0.5176786 -5.7823214
36 -0.9341741 0.5176786
37 -1.7056027 -0.9341741
38 4.3943973 -1.7056027
39 1.4086830 4.3943973
40 -3.4770313 1.4086830
41 5.4801116 -3.4770313
42 0.6801116 5.4801116
43 1.1515402 0.6801116
44 4.4801116 1.1515402
45 -2.4974182 4.4801116
46 -3.2974182 -2.4974182
47 4.0025818 -3.2974182
48 -9.6492708 4.0025818
49 -4.6206994 -9.6492708
50 -6.9206994 -4.6206994
51 -2.1064137 -6.9206994
52 -8.3921280 -2.1064137
53 -2.0349851 -8.3921280
54 -8.5349851 -2.0349851
55 -3.9635565 -8.5349851
56 -2.7349851 -3.9635565
57 -13.0125149 -2.7349851
58 -0.3125149 -13.0125149
59 1.3874851 -0.3125149
60 -6.5643676 1.3874851
61 -3.1357961 -6.5643676
62 3.3642039 -3.1357961
63 -5.6215104 3.3642039
64 4.7927753 -5.6215104
65 6.1499182 4.7927753
66 -0.8500818 6.1499182
67 2.2213467 -0.8500818
68 2.0499182 2.2213467
69 1.4723884 2.0499182
70 5.9723884 1.4723884
71 1.3723884 5.9723884
72 2.5205357 1.3723884
73 3.3491071 2.5205357
74 6.8491071 3.3491071
75 0.1633929 6.8491071
76 4.7776786 0.1633929
77 4.9348214 4.7776786
78 3.5348214 4.9348214
79 4.8062500 3.5348214
80 -4.4651786 4.8062500
> 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/7stuy1260961416.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vy521260961416.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jxgm1260961416.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10soiu1260961416.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fefw1260961416.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/12vx8d1260961416.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/13q0ub1260961416.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/14042c1260961417.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/15v8hb1260961417.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/16qj9v1260961417.tab")
+ }
>
> try(system("convert tmp/1mvgk1260961416.ps tmp/1mvgk1260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/23sar1260961416.ps tmp/23sar1260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yrvy1260961416.ps tmp/3yrvy1260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wbln1260961416.ps tmp/4wbln1260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/58njk1260961416.ps tmp/58njk1260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/67y941260961416.ps tmp/67y941260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/7stuy1260961416.ps tmp/7stuy1260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vy521260961416.ps tmp/8vy521260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jxgm1260961416.ps tmp/9jxgm1260961416.png",intern=TRUE))
character(0)
> try(system("convert tmp/10soiu1260961416.ps tmp/10soiu1260961416.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.639 1.534 8.822