R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
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+ ,1)
+ ,dim=c(6
+ ,85)
+ ,dimnames=list(c('UseLimit'
+ ,'T40'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome')
+ ,1:85))
> y <- array(NA,dim=c(6,85),dimnames=list(c('UseLimit','T40','Used','CorrectAnalysis','Useful','Outcome'),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 = 'Do not include Seasonal Dummies'
> par1 = '2'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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, 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
T40 UseLimit Used CorrectAnalysis Useful Outcome t
1 1 1 1 1 1 1 1
2 0 0 1 1 1 0 2
3 0 0 1 1 1 0 3
4 0 0 1 1 1 0 4
5 0 0 1 1 1 0 5
6 0 1 1 1 0 1 6
7 0 0 1 1 1 0 7
8 1 0 1 1 1 0 8
9 0 0 1 1 1 1 9
10 0 1 1 1 1 0 10
11 1 1 1 1 1 0 11
12 0 0 1 1 1 0 12
13 0 0 0 1 0 0 13
14 1 1 1 1 1 0 14
15 0 0 0 1 0 1 15
16 1 0 0 1 0 1 16
17 1 1 0 0 0 0 17
18 1 1 1 1 1 0 18
19 0 0 1 1 1 1 19
20 1 0 0 0 0 1 20
21 0 1 1 1 0 0 21
22 0 1 0 1 0 1 22
23 0 0 1 1 0 1 23
24 0 1 1 1 0 1 24
25 1 0 0 1 1 1 25
26 0 0 0 1 0 0 26
27 0 1 1 1 1 1 27
28 0 0 0 1 1 0 28
29 0 0 1 1 1 1 29
30 0 0 1 1 0 0 30
31 0 0 1 1 1 0 31
32 0 1 1 1 1 0 32
33 0 1 1 1 0 0 33
34 1 0 1 1 1 1 34
35 0 0 1 1 1 0 35
36 0 0 1 1 1 0 36
37 1 1 0 1 0 0 37
38 0 0 0 1 1 1 38
39 0 0 1 1 0 1 39
40 1 0 1 1 0 0 40
41 0 0 0 0 0 1 41
42 0 0 0 1 1 1 42
43 0 1 1 1 0 1 43
44 1 1 1 1 1 0 44
45 0 0 1 1 0 0 45
46 0 0 1 1 0 1 46
47 0 0 1 1 1 0 47
48 0 0 1 1 1 1 48
49 0 0 1 1 0 1 49
50 0 0 1 1 1 0 50
51 1 0 0 1 1 0 51
52 1 1 0 0 0 0 52
53 0 0 1 1 1 1 53
54 0 0 0 0 1 0 54
55 0 0 1 1 1 0 55
56 1 0 0 1 1 1 56
57 0 0 0 1 0 1 57
58 0 0 1 1 1 1 58
59 0 0 1 1 1 1 59
60 1 1 0 0 0 1 60
61 1 1 1 1 1 1 61
62 0 0 0 1 0 0 62
63 0 0 1 1 1 0 63
64 1 1 1 1 1 1 64
65 0 0 1 1 1 0 65
66 0 0 1 1 1 0 66
67 1 0 0 0 0 0 67
68 0 1 1 1 1 0 68
69 0 0 1 1 1 1 69
70 0 0 0 1 1 0 70
71 0 0 1 1 1 0 71
72 0 0 1 1 1 1 72
73 0 0 0 1 1 1 73
74 0 1 0 1 1 0 74
75 0 0 1 1 1 1 75
76 1 0 1 1 0 1 76
77 0 0 1 1 1 1 77
78 0 0 0 1 0 1 78
79 1 0 0 0 1 1 79
80 1 0 1 1 0 0 80
81 0 0 1 1 1 0 81
82 0 1 0 1 1 1 82
83 0 0 1 1 1 0 83
84 0 0 0 0 1 0 84
85 0 0 1 1 0 1 85
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit Used CorrectAnalysis
0.626002 0.265017 -0.076233 -0.385442
Useful Outcome t
0.012820 0.025478 -0.001201
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6022 -0.1976 -0.1339 0.1556 0.9318
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.626002 0.185259 3.379 0.00114 **
UseLimit 0.265017 0.105418 2.514 0.01400 *
Used -0.076233 0.114676 -0.665 0.50816
CorrectAnalysis -0.385442 0.173557 -2.221 0.02926 *
Useful 0.012820 0.102105 0.126 0.90040
Outcome 0.025478 0.093864 0.271 0.78678
t -0.001201 0.001945 -0.618 0.53860
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.421 on 78 degrees of freedom
Multiple R-squared: 0.1759, Adjusted R-squared: 0.1125
F-statistic: 2.775 on 6 and 78 DF, p-value: 0.01692
> 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,] 0.9120316 0.1759369 0.08796843
[2,] 0.9100833 0.1798333 0.08991666
[3,] 0.8473757 0.3052487 0.15262434
[4,] 0.7655482 0.4689036 0.23445181
[5,] 0.7269428 0.5461144 0.27305720
[6,] 0.6362489 0.7275022 0.36375108
[7,] 0.7783615 0.4432770 0.22163850
[8,] 0.7010945 0.5978110 0.29890549
[9,] 0.6598244 0.6803513 0.34017565
[10,] 0.6280756 0.7438488 0.37192438
[11,] 0.6038882 0.7922235 0.39611175
[12,] 0.5267128 0.9465743 0.47328716
[13,] 0.6807277 0.6385445 0.31927226
[14,] 0.6285301 0.7429398 0.37146991
[15,] 0.5802807 0.8394387 0.41971933
[16,] 0.5876176 0.8247647 0.41238236
[17,] 0.5221359 0.9557282 0.47786411
[18,] 0.5982594 0.8034813 0.40174065
[19,] 0.6040938 0.7918125 0.39590623
[20,] 0.5353735 0.9292531 0.46462654
[21,] 0.5054315 0.9891371 0.49456853
[22,] 0.4356840 0.8713681 0.56431596
[23,] 0.4141288 0.8282576 0.58587122
[24,] 0.3980754 0.7961507 0.60192464
[25,] 0.6192486 0.7615029 0.38075143
[26,] 0.5562081 0.8875838 0.44379188
[27,] 0.4915601 0.9831203 0.50843986
[28,] 0.5417017 0.9165966 0.45829828
[29,] 0.5319400 0.9361199 0.46805995
[30,] 0.4782527 0.9565054 0.52174731
[31,] 0.7238276 0.5523448 0.27617238
[32,] 0.7855477 0.4289046 0.21445228
[33,] 0.7488182 0.5023637 0.25118185
[34,] 0.7673029 0.4653941 0.23269705
[35,] 0.8057786 0.3884427 0.19422137
[36,] 0.7651171 0.4697657 0.23488287
[37,] 0.7383568 0.5232864 0.26164320
[38,] 0.6859276 0.6281448 0.31407241
[39,] 0.6345557 0.7308886 0.36544432
[40,] 0.6319702 0.7360595 0.36802976
[41,] 0.5759925 0.8480150 0.42400749
[42,] 0.7943438 0.4113124 0.20565621
[43,] 0.7413233 0.5173534 0.25867671
[44,] 0.6993456 0.6013087 0.30065436
[45,] 0.7496724 0.5006552 0.25032758
[46,] 0.6965190 0.6069619 0.30348095
[47,] 0.9221949 0.1556101 0.07780507
[48,] 0.9028800 0.1942400 0.09711999
[49,] 0.8759458 0.2481085 0.12405423
[50,] 0.8509456 0.2981088 0.14905441
[51,] 0.8832177 0.2335647 0.11678235
[52,] 0.8795606 0.2408787 0.12043937
[53,] 0.8641792 0.2716416 0.13582080
[54,] 0.8191520 0.3616960 0.18084802
[55,] 0.8813015 0.2373970 0.11869851
[56,] 0.8334337 0.3331326 0.16656628
[57,] 0.7743652 0.4512695 0.22563475
[58,] 0.7337465 0.5325069 0.26625346
[59,] 0.7430598 0.5138804 0.25694020
[60,] 0.6968861 0.6062277 0.30311386
[61,] 0.6106135 0.7787729 0.38938645
[62,] 0.5584418 0.8831165 0.44155823
[63,] 0.5352261 0.9295479 0.46477393
[64,] 0.4666019 0.9332038 0.53339810
[65,] 0.5874049 0.8251902 0.41259511
[66,] 0.5465170 0.9069660 0.45348300
> postscript(file="/var/fisher/rcomp/tmp/1yel21356185325.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2bj9z1356185325.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/39ftg1356185325.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4p4se1356185325.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5meou1356185325.ps",horizontal=F,onefile=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
0.53355923 -0.17474517 -0.17354382 -0.17234247 -0.17114112 -0.44761360
7 8 9 10 11 12
-0.16873842 0.83246294 -0.19181341 -0.43015091 0.57105044 -0.16273166
13 14 15 16 17 18
-0.22494264 0.57465450 -0.24801764 0.75318371 0.12940432 0.57945990
19 20 21 22 23 24
-0.17979989 0.37254723 -0.40411563 -0.50462473 -0.16217408 -0.42598928
25 26 27 28 29 30
0.75117547 -0.20932507 -0.43520563 -0.21974278 -0.16778638 -0.12828692
31 32 33 34 35 36
-0.13990598 -0.40372118 -0.38969942 0.83822038 -0.13510057 -0.13389922
37 38 39 40 41 42
0.53887324 -0.23320697 -0.14295245 0.88372659 -0.60222439 -0.22840156
43 44 45 46 47 48
-0.40316360 0.61069504 -0.11026665 -0.13454299 -0.12068436 -0.14496070
49 50 51 52 53 54
-0.13093894 -0.11708030 0.80788830 0.17145162 -0.13895395 -0.57394953
55 56 57 58 59 60
-0.11107355 0.78841736 -0.19756088 -0.13294719 -0.13174584 0.15558474
61 62 63 64 65 66
0.60564032 -0.16607642 -0.10146273 0.60924437 -0.09906003 -0.09785868
67 68 69 70 71 72
0.45448845 -0.36047253 -0.11973232 -0.16928602 -0.09185192 -0.11612827
73 74 75 76 77 78
-0.19115966 -0.42949717 -0.11252421 0.90149755 -0.11012151 -0.17233250
79 80 81 82 83 84
0.43060655 0.93178065 -0.07983841 -0.44536405 -0.07743571 -0.53790899
85
-0.08769029
> postscript(file="/var/fisher/rcomp/tmp/6svdq1356185325.ps",horizontal=F,onefile=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 0.53355923 NA
1 -0.17474517 0.53355923
2 -0.17354382 -0.17474517
3 -0.17234247 -0.17354382
4 -0.17114112 -0.17234247
5 -0.44761360 -0.17114112
6 -0.16873842 -0.44761360
7 0.83246294 -0.16873842
8 -0.19181341 0.83246294
9 -0.43015091 -0.19181341
10 0.57105044 -0.43015091
11 -0.16273166 0.57105044
12 -0.22494264 -0.16273166
13 0.57465450 -0.22494264
14 -0.24801764 0.57465450
15 0.75318371 -0.24801764
16 0.12940432 0.75318371
17 0.57945990 0.12940432
18 -0.17979989 0.57945990
19 0.37254723 -0.17979989
20 -0.40411563 0.37254723
21 -0.50462473 -0.40411563
22 -0.16217408 -0.50462473
23 -0.42598928 -0.16217408
24 0.75117547 -0.42598928
25 -0.20932507 0.75117547
26 -0.43520563 -0.20932507
27 -0.21974278 -0.43520563
28 -0.16778638 -0.21974278
29 -0.12828692 -0.16778638
30 -0.13990598 -0.12828692
31 -0.40372118 -0.13990598
32 -0.38969942 -0.40372118
33 0.83822038 -0.38969942
34 -0.13510057 0.83822038
35 -0.13389922 -0.13510057
36 0.53887324 -0.13389922
37 -0.23320697 0.53887324
38 -0.14295245 -0.23320697
39 0.88372659 -0.14295245
40 -0.60222439 0.88372659
41 -0.22840156 -0.60222439
42 -0.40316360 -0.22840156
43 0.61069504 -0.40316360
44 -0.11026665 0.61069504
45 -0.13454299 -0.11026665
46 -0.12068436 -0.13454299
47 -0.14496070 -0.12068436
48 -0.13093894 -0.14496070
49 -0.11708030 -0.13093894
50 0.80788830 -0.11708030
51 0.17145162 0.80788830
52 -0.13895395 0.17145162
53 -0.57394953 -0.13895395
54 -0.11107355 -0.57394953
55 0.78841736 -0.11107355
56 -0.19756088 0.78841736
57 -0.13294719 -0.19756088
58 -0.13174584 -0.13294719
59 0.15558474 -0.13174584
60 0.60564032 0.15558474
61 -0.16607642 0.60564032
62 -0.10146273 -0.16607642
63 0.60924437 -0.10146273
64 -0.09906003 0.60924437
65 -0.09785868 -0.09906003
66 0.45448845 -0.09785868
67 -0.36047253 0.45448845
68 -0.11973232 -0.36047253
69 -0.16928602 -0.11973232
70 -0.09185192 -0.16928602
71 -0.11612827 -0.09185192
72 -0.19115966 -0.11612827
73 -0.42949717 -0.19115966
74 -0.11252421 -0.42949717
75 0.90149755 -0.11252421
76 -0.11012151 0.90149755
77 -0.17233250 -0.11012151
78 0.43060655 -0.17233250
79 0.93178065 0.43060655
80 -0.07983841 0.93178065
81 -0.44536405 -0.07983841
82 -0.07743571 -0.44536405
83 -0.53790899 -0.07743571
84 -0.08769029 -0.53790899
85 NA -0.08769029
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.17474517 0.53355923
[2,] -0.17354382 -0.17474517
[3,] -0.17234247 -0.17354382
[4,] -0.17114112 -0.17234247
[5,] -0.44761360 -0.17114112
[6,] -0.16873842 -0.44761360
[7,] 0.83246294 -0.16873842
[8,] -0.19181341 0.83246294
[9,] -0.43015091 -0.19181341
[10,] 0.57105044 -0.43015091
[11,] -0.16273166 0.57105044
[12,] -0.22494264 -0.16273166
[13,] 0.57465450 -0.22494264
[14,] -0.24801764 0.57465450
[15,] 0.75318371 -0.24801764
[16,] 0.12940432 0.75318371
[17,] 0.57945990 0.12940432
[18,] -0.17979989 0.57945990
[19,] 0.37254723 -0.17979989
[20,] -0.40411563 0.37254723
[21,] -0.50462473 -0.40411563
[22,] -0.16217408 -0.50462473
[23,] -0.42598928 -0.16217408
[24,] 0.75117547 -0.42598928
[25,] -0.20932507 0.75117547
[26,] -0.43520563 -0.20932507
[27,] -0.21974278 -0.43520563
[28,] -0.16778638 -0.21974278
[29,] -0.12828692 -0.16778638
[30,] -0.13990598 -0.12828692
[31,] -0.40372118 -0.13990598
[32,] -0.38969942 -0.40372118
[33,] 0.83822038 -0.38969942
[34,] -0.13510057 0.83822038
[35,] -0.13389922 -0.13510057
[36,] 0.53887324 -0.13389922
[37,] -0.23320697 0.53887324
[38,] -0.14295245 -0.23320697
[39,] 0.88372659 -0.14295245
[40,] -0.60222439 0.88372659
[41,] -0.22840156 -0.60222439
[42,] -0.40316360 -0.22840156
[43,] 0.61069504 -0.40316360
[44,] -0.11026665 0.61069504
[45,] -0.13454299 -0.11026665
[46,] -0.12068436 -0.13454299
[47,] -0.14496070 -0.12068436
[48,] -0.13093894 -0.14496070
[49,] -0.11708030 -0.13093894
[50,] 0.80788830 -0.11708030
[51,] 0.17145162 0.80788830
[52,] -0.13895395 0.17145162
[53,] -0.57394953 -0.13895395
[54,] -0.11107355 -0.57394953
[55,] 0.78841736 -0.11107355
[56,] -0.19756088 0.78841736
[57,] -0.13294719 -0.19756088
[58,] -0.13174584 -0.13294719
[59,] 0.15558474 -0.13174584
[60,] 0.60564032 0.15558474
[61,] -0.16607642 0.60564032
[62,] -0.10146273 -0.16607642
[63,] 0.60924437 -0.10146273
[64,] -0.09906003 0.60924437
[65,] -0.09785868 -0.09906003
[66,] 0.45448845 -0.09785868
[67,] -0.36047253 0.45448845
[68,] -0.11973232 -0.36047253
[69,] -0.16928602 -0.11973232
[70,] -0.09185192 -0.16928602
[71,] -0.11612827 -0.09185192
[72,] -0.19115966 -0.11612827
[73,] -0.42949717 -0.19115966
[74,] -0.11252421 -0.42949717
[75,] 0.90149755 -0.11252421
[76,] -0.11012151 0.90149755
[77,] -0.17233250 -0.11012151
[78,] 0.43060655 -0.17233250
[79,] 0.93178065 0.43060655
[80,] -0.07983841 0.93178065
[81,] -0.44536405 -0.07983841
[82,] -0.07743571 -0.44536405
[83,] -0.53790899 -0.07743571
[84,] -0.08769029 -0.53790899
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.17474517 0.53355923
2 -0.17354382 -0.17474517
3 -0.17234247 -0.17354382
4 -0.17114112 -0.17234247
5 -0.44761360 -0.17114112
6 -0.16873842 -0.44761360
7 0.83246294 -0.16873842
8 -0.19181341 0.83246294
9 -0.43015091 -0.19181341
10 0.57105044 -0.43015091
11 -0.16273166 0.57105044
12 -0.22494264 -0.16273166
13 0.57465450 -0.22494264
14 -0.24801764 0.57465450
15 0.75318371 -0.24801764
16 0.12940432 0.75318371
17 0.57945990 0.12940432
18 -0.17979989 0.57945990
19 0.37254723 -0.17979989
20 -0.40411563 0.37254723
21 -0.50462473 -0.40411563
22 -0.16217408 -0.50462473
23 -0.42598928 -0.16217408
24 0.75117547 -0.42598928
25 -0.20932507 0.75117547
26 -0.43520563 -0.20932507
27 -0.21974278 -0.43520563
28 -0.16778638 -0.21974278
29 -0.12828692 -0.16778638
30 -0.13990598 -0.12828692
31 -0.40372118 -0.13990598
32 -0.38969942 -0.40372118
33 0.83822038 -0.38969942
34 -0.13510057 0.83822038
35 -0.13389922 -0.13510057
36 0.53887324 -0.13389922
37 -0.23320697 0.53887324
38 -0.14295245 -0.23320697
39 0.88372659 -0.14295245
40 -0.60222439 0.88372659
41 -0.22840156 -0.60222439
42 -0.40316360 -0.22840156
43 0.61069504 -0.40316360
44 -0.11026665 0.61069504
45 -0.13454299 -0.11026665
46 -0.12068436 -0.13454299
47 -0.14496070 -0.12068436
48 -0.13093894 -0.14496070
49 -0.11708030 -0.13093894
50 0.80788830 -0.11708030
51 0.17145162 0.80788830
52 -0.13895395 0.17145162
53 -0.57394953 -0.13895395
54 -0.11107355 -0.57394953
55 0.78841736 -0.11107355
56 -0.19756088 0.78841736
57 -0.13294719 -0.19756088
58 -0.13174584 -0.13294719
59 0.15558474 -0.13174584
60 0.60564032 0.15558474
61 -0.16607642 0.60564032
62 -0.10146273 -0.16607642
63 0.60924437 -0.10146273
64 -0.09906003 0.60924437
65 -0.09785868 -0.09906003
66 0.45448845 -0.09785868
67 -0.36047253 0.45448845
68 -0.11973232 -0.36047253
69 -0.16928602 -0.11973232
70 -0.09185192 -0.16928602
71 -0.11612827 -0.09185192
72 -0.19115966 -0.11612827
73 -0.42949717 -0.19115966
74 -0.11252421 -0.42949717
75 0.90149755 -0.11252421
76 -0.11012151 0.90149755
77 -0.17233250 -0.11012151
78 0.43060655 -0.17233250
79 0.93178065 0.43060655
80 -0.07983841 0.93178065
81 -0.44536405 -0.07983841
82 -0.07743571 -0.44536405
83 -0.53790899 -0.07743571
84 -0.08769029 -0.53790899
> 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/fisher/rcomp/tmp/769ol1356185325.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8bqps1356185325.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9gdzb1356185325.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/106khq1356185325.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11j8jh1356185325.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/fisher/rcomp/tmp/12uklx1356185325.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/fisher/rcomp/tmp/1394nq1356185325.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/fisher/rcomp/tmp/140w9e1356185325.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/fisher/rcomp/tmp/153gr91356185325.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/fisher/rcomp/tmp/16exgm1356185325.tab")
+ }
>
> try(system("convert tmp/1yel21356185325.ps tmp/1yel21356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bj9z1356185325.ps tmp/2bj9z1356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/39ftg1356185325.ps tmp/39ftg1356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/4p4se1356185325.ps tmp/4p4se1356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/5meou1356185325.ps tmp/5meou1356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/6svdq1356185325.ps tmp/6svdq1356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/769ol1356185325.ps tmp/769ol1356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bqps1356185325.ps tmp/8bqps1356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gdzb1356185325.ps tmp/9gdzb1356185325.png",intern=TRUE))
character(0)
> try(system("convert tmp/106khq1356185325.ps tmp/106khq1356185325.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.420 1.776 8.242