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.
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'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(1,0,2,0,2,0,2,0,2,0,2,0,2,0,1,0,2,0,2,0,1,0,2,0,2,0,1,0,2,0,1,0,1,1,1,0,2,0,1,1,2,0,2,0,2,0,2,0,1,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,1,0,2,0,2,0,1,0,2,0,2,0,1,0,2,1,2,0,2,0,1,0,2,0,2,0,2,0,2,0,2,0,2,0,1,0,1,1,2,0,2,1,2,0,1,0,2,0,2,0,2,0,1,1,1,0,2,0,2,0,1,0,2,0,2,0,1,1,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,1,0,2,0,2,0,1,1,1,0,2,0,2,0,2,0,2,1,2,0,2,0,4,0,3,0,4,0,4,0,4,0,3,0,4,0,4,0,3,0,4,0,3,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,3,0,4,0,4,0,3,0,4,0,4,0,3,0,3,0,4,0,3,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,3,0,4,0,4,0,3,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,3,0,3,0,4,0,4,1,3,0,4,0,4,0,4,0,3,0,3,0,3,0,4,0,4,0,4,0,4,1,4,1,4,0),dim=c(2,154),dimnames=list(c('Treatment','CorrectAnalysis'),1:154))
> y <- array(NA,dim=c(2,154),dimnames=list(c('Treatment','CorrectAnalysis'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> 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
CorrectAnalysis Treatment
1 0 1
2 0 2
3 0 2
4 0 2
5 0 2
6 0 2
7 0 2
8 0 1
9 0 2
10 0 2
11 0 1
12 0 2
13 0 2
14 0 1
15 0 2
16 0 1
17 1 1
18 0 1
19 0 2
20 1 1
21 0 2
22 0 2
23 0 2
24 0 2
25 0 1
26 0 2
27 0 2
28 0 2
29 0 2
30 0 2
31 0 2
32 0 2
33 0 2
34 0 1
35 0 2
36 0 2
37 0 1
38 0 2
39 0 2
40 0 1
41 1 2
42 0 2
43 0 2
44 0 1
45 0 2
46 0 2
47 0 2
48 0 2
49 0 2
50 0 2
51 0 1
52 1 1
53 0 2
54 1 2
55 0 2
56 0 1
57 0 2
58 0 2
59 0 2
60 1 1
61 0 1
62 0 2
63 0 2
64 0 1
65 0 2
66 0 2
67 1 1
68 0 2
69 0 2
70 0 2
71 0 2
72 0 2
73 0 2
74 0 2
75 0 2
76 0 1
77 0 2
78 0 2
79 1 1
80 0 1
81 0 2
82 0 2
83 0 2
84 1 2
85 0 2
86 0 2
87 0 4
88 0 3
89 0 4
90 0 4
91 0 4
92 0 3
93 0 4
94 0 4
95 0 3
96 0 4
97 0 3
98 0 4
99 0 4
100 0 4
101 0 4
102 0 4
103 0 4
104 0 4
105 0 3
106 0 4
107 0 4
108 0 3
109 0 4
110 0 4
111 0 3
112 0 3
113 0 4
114 0 3
115 0 4
116 0 4
117 0 4
118 0 4
119 0 4
120 0 4
121 0 4
122 0 4
123 0 3
124 0 4
125 0 4
126 0 3
127 0 4
128 0 4
129 0 4
130 0 4
131 0 4
132 0 4
133 0 4
134 0 4
135 0 4
136 0 4
137 0 4
138 0 3
139 0 3
140 0 4
141 1 4
142 0 3
143 0 4
144 0 4
145 0 4
146 0 3
147 0 3
148 0 3
149 0 4
150 0 4
151 0 4
152 1 4
153 1 4
154 0 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Treatment
0.18449 -0.04062
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.14386 -0.10324 -0.08293 -0.02200 0.97800
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.18449 0.05573 3.311 0.00116 **
Treatment -0.04062 0.01961 -2.072 0.03998 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2661 on 152 degrees of freedom
Multiple R-squared: 0.02746, Adjusted R-squared: 0.02106
F-statistic: 4.292 on 1 and 152 DF, p-value: 0.03998
> 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,] 0.0000000000 0.0000000000 1.0000000000
[2,] 0.0000000000 0.0000000000 1.0000000000
[3,] 0.0000000000 0.0000000000 1.0000000000
[4,] 0.0000000000 0.0000000000 1.0000000000
[5,] 0.0000000000 0.0000000000 1.0000000000
[6,] 0.0000000000 0.0000000000 1.0000000000
[7,] 0.0000000000 0.0000000000 1.0000000000
[8,] 0.0000000000 0.0000000000 1.0000000000
[9,] 0.0000000000 0.0000000000 1.0000000000
[10,] 0.0000000000 0.0000000000 1.0000000000
[11,] 0.0000000000 0.0000000000 1.0000000000
[12,] 0.0000000000 0.0000000000 1.0000000000
[13,] 0.2866545973 0.5733091947 0.7133454027
[14,] 0.2453761943 0.4907523886 0.7546238057
[15,] 0.1889033442 0.3778066884 0.8110966558
[16,] 0.7432643298 0.5134713405 0.2567356702
[17,] 0.6852197178 0.6295605643 0.3147802822
[18,] 0.6232651606 0.7534696788 0.3767348394
[19,] 0.5589973607 0.8820052786 0.4410026393
[20,] 0.4941218077 0.9882436154 0.5058781923
[21,] 0.4758240211 0.9516480422 0.5241759789
[22,] 0.4135532514 0.8271065028 0.5864467486
[23,] 0.3541602150 0.7083204301 0.6458397850
[24,] 0.2988065439 0.5976130878 0.7011934561
[25,] 0.2483544669 0.4967089337 0.7516455331
[26,] 0.2033485443 0.4066970886 0.7966514557
[27,] 0.1640268916 0.3280537831 0.8359731084
[28,] 0.1303556883 0.2607113766 0.8696443117
[29,] 0.1020791851 0.2041583701 0.8979208149
[30,] 0.0939816240 0.1879632479 0.9060183760
[31,] 0.0724137491 0.1448274983 0.9275862509
[32,] 0.0550174733 0.1100349467 0.9449825267
[33,] 0.0488651650 0.0977303301 0.9511348350
[34,] 0.0365168276 0.0730336553 0.9634831724
[35,] 0.0269299016 0.0538598032 0.9730700984
[36,] 0.0231481111 0.0462962222 0.9768518889
[37,] 0.4289748735 0.8579497471 0.5710251265
[38,] 0.3812185814 0.7624371627 0.6187814186
[39,] 0.3355609601 0.6711219203 0.6644390399
[40,] 0.3078270107 0.6156540214 0.6921729893
[41,] 0.2670185104 0.5340370208 0.7329814896
[42,] 0.2294326704 0.4588653408 0.7705673296
[43,] 0.1952807226 0.3905614452 0.8047192774
[44,] 0.1646563550 0.3293127101 0.8353436450
[45,] 0.1375468449 0.2750936897 0.8624531551
[46,] 0.1138482968 0.2276965936 0.8861517032
[47,] 0.1008544076 0.2017088152 0.8991455924
[48,] 0.4348459978 0.8696919955 0.5651540022
[49,] 0.3913618946 0.7827237892 0.6086381054
[50,] 0.8581493106 0.2837013787 0.1418506894
[51,] 0.8324536248 0.3350927504 0.1675463752
[52,] 0.8161040549 0.3677918902 0.1838959451
[53,] 0.7865868269 0.4268263463 0.2134131731
[54,] 0.7547292618 0.4905414764 0.2452707382
[55,] 0.7207595844 0.5584808313 0.2792404156
[56,] 0.9383832477 0.1232335046 0.0616167523
[57,] 0.9312412625 0.1375174749 0.0687587375
[58,] 0.9161201726 0.1677596547 0.0838798274
[59,] 0.8987607861 0.2024784279 0.1012392139
[60,] 0.8886198934 0.2227602131 0.1113801066
[61,] 0.8679901791 0.2640196419 0.1320098209
[62,] 0.8451114448 0.3097771104 0.1548885552
[63,] 0.9768703582 0.0462592836 0.0231296418
[64,] 0.9703258370 0.0593483260 0.0296741630
[65,] 0.9623667183 0.0752665634 0.0376332817
[66,] 0.9528156629 0.0943686742 0.0471843371
[67,] 0.9415070179 0.1169859643 0.0584929821
[68,] 0.9282970212 0.1434059577 0.0717029788
[69,] 0.9130750886 0.1738498227 0.0869249114
[70,] 0.8957757683 0.2084484634 0.1042242317
[71,] 0.8763908978 0.2472182044 0.1236091022
[72,] 0.8680810816 0.2638378368 0.1319189184
[73,] 0.8467870052 0.3064259897 0.1532129948
[74,] 0.8240728473 0.3518543055 0.1759271527
[75,] 0.9744093973 0.0511812055 0.0255906027
[76,] 0.9705085348 0.0589829303 0.0294914652
[77,] 0.9624254626 0.0751490748 0.0375745374
[78,] 0.9527914252 0.0944171496 0.0472085748
[79,] 0.9415724636 0.1168550727 0.0584275364
[80,] 0.9992672371 0.0014655258 0.0007327629
[81,] 0.9989150141 0.0021699719 0.0010849859
[82,] 0.9984188072 0.0031623856 0.0015811928
[83,] 0.9981138086 0.0037723828 0.0018861914
[84,] 0.9973055051 0.0053889897 0.0026944949
[85,] 0.9965387239 0.0069225523 0.0034612761
[86,] 0.9954435050 0.0091129899 0.0045564950
[87,] 0.9939423986 0.0121152028 0.0060576014
[88,] 0.9915341359 0.0169317281 0.0084658641
[89,] 0.9888256203 0.0223487595 0.0111743797
[90,] 0.9852964314 0.0294071371 0.0147035686
[91,] 0.9800948208 0.0398103584 0.0199051792
[92,] 0.9742274179 0.0515451642 0.0257725821
[93,] 0.9659057074 0.0681885851 0.0340942926
[94,] 0.9566533183 0.0866933634 0.0433466817
[95,] 0.9453542334 0.1092915333 0.0546457666
[96,] 0.9317311198 0.1365377604 0.0682688802
[97,] 0.9155161816 0.1689676368 0.0844838184
[98,] 0.8964655188 0.2070689624 0.1035344812
[99,] 0.8743749623 0.2512500754 0.1256250377
[100,] 0.8490965077 0.3018069845 0.1509034923
[101,] 0.8174261653 0.3651476694 0.1825738347
[102,] 0.7852274981 0.4295450038 0.2147725019
[103,] 0.7498589924 0.5002820152 0.2501410076
[104,] 0.7072539790 0.5854920420 0.2927460210
[105,] 0.6658931727 0.6682136547 0.3341068273
[106,] 0.6223318164 0.7553363672 0.3776681836
[107,] 0.5718212213 0.8563575574 0.4281787787
[108,] 0.5198257865 0.9603484270 0.4801742135
[109,] 0.4725462522 0.9450925044 0.5274537478
[110,] 0.4200900219 0.8401800437 0.5799099781
[111,] 0.3740201945 0.7480403890 0.6259798055
[112,] 0.3296293035 0.6592586069 0.6703706965
[113,] 0.2874871080 0.5749742159 0.7125128920
[114,] 0.2480726472 0.4961452943 0.7519273528
[115,] 0.2117577129 0.4235154259 0.7882422871
[116,] 0.1787974469 0.3575948937 0.8212025531
[117,] 0.1493283659 0.2986567319 0.8506716341
[118,] 0.1233734505 0.2467469011 0.8766265495
[119,] 0.0968058178 0.1936116355 0.9031941822
[120,] 0.0778522470 0.1557044939 0.9221477530
[121,] 0.0619390146 0.1238780293 0.9380609854
[122,] 0.0460542765 0.0921085530 0.9539457235
[123,] 0.0356129997 0.0712259993 0.9643870003
[124,] 0.0272649080 0.0545298160 0.9727350920
[125,] 0.0206881700 0.0413763399 0.9793118300
[126,] 0.0155811161 0.0311622323 0.9844188839
[127,] 0.0116706363 0.0233412725 0.9883293637
[128,] 0.0087171194 0.0174342388 0.9912828806
[129,] 0.0065164081 0.0130328161 0.9934835919
[130,] 0.0048994184 0.0097988369 0.9951005816
[131,] 0.0037301879 0.0074603758 0.9962698121
[132,] 0.0029032027 0.0058064054 0.9970967973
[133,] 0.0023410708 0.0046821415 0.9976589292
[134,] 0.0013385040 0.0026770080 0.9986614960
[135,] 0.0007349385 0.0014698771 0.9992650615
[136,] 0.0005960321 0.0011920641 0.9994039679
[137,] 0.0154701583 0.0309403167 0.9845298417
[138,] 0.0090830615 0.0181661230 0.9909169385
[139,] 0.0066800458 0.0133600915 0.9933199542
[140,] 0.0051783482 0.0103566964 0.9948216518
[141,] 0.0044531275 0.0089062549 0.9955468725
[142,] 0.0021264079 0.0042528158 0.9978735921
[143,] 0.0009187059 0.0018374118 0.9990812941
[144,] 0.0003504076 0.0007008151 0.9996495924
[145,] 0.0002877655 0.0005755310 0.9997122345
> postscript(file="/var/fisher/rcomp/tmp/1nhpe1356082968.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/2n0sl1356082968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/39jlu1356082968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/400su1356082968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5vku01356082968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 154
Frequency = 1
1 2 3 4 5 6
-0.14386460 -0.10324401 -0.10324401 -0.10324401 -0.10324401 -0.10324401
7 8 9 10 11 12
-0.10324401 -0.14386460 -0.10324401 -0.10324401 -0.14386460 -0.10324401
13 14 15 16 17 18
-0.10324401 -0.14386460 -0.10324401 -0.14386460 0.85613540 -0.14386460
19 20 21 22 23 24
-0.10324401 0.85613540 -0.10324401 -0.10324401 -0.10324401 -0.10324401
25 26 27 28 29 30
-0.14386460 -0.10324401 -0.10324401 -0.10324401 -0.10324401 -0.10324401
31 32 33 34 35 36
-0.10324401 -0.10324401 -0.10324401 -0.14386460 -0.10324401 -0.10324401
37 38 39 40 41 42
-0.14386460 -0.10324401 -0.10324401 -0.14386460 0.89675599 -0.10324401
43 44 45 46 47 48
-0.10324401 -0.14386460 -0.10324401 -0.10324401 -0.10324401 -0.10324401
49 50 51 52 53 54
-0.10324401 -0.10324401 -0.14386460 0.85613540 -0.10324401 0.89675599
55 56 57 58 59 60
-0.10324401 -0.14386460 -0.10324401 -0.10324401 -0.10324401 0.85613540
61 62 63 64 65 66
-0.14386460 -0.10324401 -0.10324401 -0.14386460 -0.10324401 -0.10324401
67 68 69 70 71 72
0.85613540 -0.10324401 -0.10324401 -0.10324401 -0.10324401 -0.10324401
73 74 75 76 77 78
-0.10324401 -0.10324401 -0.10324401 -0.14386460 -0.10324401 -0.10324401
79 80 81 82 83 84
0.85613540 -0.14386460 -0.10324401 -0.10324401 -0.10324401 0.89675599
85 86 87 88 89 90
-0.10324401 -0.10324401 -0.02200282 -0.06262341 -0.02200282 -0.02200282
91 92 93 94 95 96
-0.02200282 -0.06262341 -0.02200282 -0.02200282 -0.06262341 -0.02200282
97 98 99 100 101 102
-0.06262341 -0.02200282 -0.02200282 -0.02200282 -0.02200282 -0.02200282
103 104 105 106 107 108
-0.02200282 -0.02200282 -0.06262341 -0.02200282 -0.02200282 -0.06262341
109 110 111 112 113 114
-0.02200282 -0.02200282 -0.06262341 -0.06262341 -0.02200282 -0.06262341
115 116 117 118 119 120
-0.02200282 -0.02200282 -0.02200282 -0.02200282 -0.02200282 -0.02200282
121 122 123 124 125 126
-0.02200282 -0.02200282 -0.06262341 -0.02200282 -0.02200282 -0.06262341
127 128 129 130 131 132
-0.02200282 -0.02200282 -0.02200282 -0.02200282 -0.02200282 -0.02200282
133 134 135 136 137 138
-0.02200282 -0.02200282 -0.02200282 -0.02200282 -0.02200282 -0.06262341
139 140 141 142 143 144
-0.06262341 -0.02200282 0.97799718 -0.06262341 -0.02200282 -0.02200282
145 146 147 148 149 150
-0.02200282 -0.06262341 -0.06262341 -0.06262341 -0.02200282 -0.02200282
151 152 153 154
-0.02200282 0.97799718 0.97799718 -0.02200282
> postscript(file="/var/fisher/rcomp/tmp/6rvwh1356082968.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.14386460 NA
1 -0.10324401 -0.14386460
2 -0.10324401 -0.10324401
3 -0.10324401 -0.10324401
4 -0.10324401 -0.10324401
5 -0.10324401 -0.10324401
6 -0.10324401 -0.10324401
7 -0.14386460 -0.10324401
8 -0.10324401 -0.14386460
9 -0.10324401 -0.10324401
10 -0.14386460 -0.10324401
11 -0.10324401 -0.14386460
12 -0.10324401 -0.10324401
13 -0.14386460 -0.10324401
14 -0.10324401 -0.14386460
15 -0.14386460 -0.10324401
16 0.85613540 -0.14386460
17 -0.14386460 0.85613540
18 -0.10324401 -0.14386460
19 0.85613540 -0.10324401
20 -0.10324401 0.85613540
21 -0.10324401 -0.10324401
22 -0.10324401 -0.10324401
23 -0.10324401 -0.10324401
24 -0.14386460 -0.10324401
25 -0.10324401 -0.14386460
26 -0.10324401 -0.10324401
27 -0.10324401 -0.10324401
28 -0.10324401 -0.10324401
29 -0.10324401 -0.10324401
30 -0.10324401 -0.10324401
31 -0.10324401 -0.10324401
32 -0.10324401 -0.10324401
33 -0.14386460 -0.10324401
34 -0.10324401 -0.14386460
35 -0.10324401 -0.10324401
36 -0.14386460 -0.10324401
37 -0.10324401 -0.14386460
38 -0.10324401 -0.10324401
39 -0.14386460 -0.10324401
40 0.89675599 -0.14386460
41 -0.10324401 0.89675599
42 -0.10324401 -0.10324401
43 -0.14386460 -0.10324401
44 -0.10324401 -0.14386460
45 -0.10324401 -0.10324401
46 -0.10324401 -0.10324401
47 -0.10324401 -0.10324401
48 -0.10324401 -0.10324401
49 -0.10324401 -0.10324401
50 -0.14386460 -0.10324401
51 0.85613540 -0.14386460
52 -0.10324401 0.85613540
53 0.89675599 -0.10324401
54 -0.10324401 0.89675599
55 -0.14386460 -0.10324401
56 -0.10324401 -0.14386460
57 -0.10324401 -0.10324401
58 -0.10324401 -0.10324401
59 0.85613540 -0.10324401
60 -0.14386460 0.85613540
61 -0.10324401 -0.14386460
62 -0.10324401 -0.10324401
63 -0.14386460 -0.10324401
64 -0.10324401 -0.14386460
65 -0.10324401 -0.10324401
66 0.85613540 -0.10324401
67 -0.10324401 0.85613540
68 -0.10324401 -0.10324401
69 -0.10324401 -0.10324401
70 -0.10324401 -0.10324401
71 -0.10324401 -0.10324401
72 -0.10324401 -0.10324401
73 -0.10324401 -0.10324401
74 -0.10324401 -0.10324401
75 -0.14386460 -0.10324401
76 -0.10324401 -0.14386460
77 -0.10324401 -0.10324401
78 0.85613540 -0.10324401
79 -0.14386460 0.85613540
80 -0.10324401 -0.14386460
81 -0.10324401 -0.10324401
82 -0.10324401 -0.10324401
83 0.89675599 -0.10324401
84 -0.10324401 0.89675599
85 -0.10324401 -0.10324401
86 -0.02200282 -0.10324401
87 -0.06262341 -0.02200282
88 -0.02200282 -0.06262341
89 -0.02200282 -0.02200282
90 -0.02200282 -0.02200282
91 -0.06262341 -0.02200282
92 -0.02200282 -0.06262341
93 -0.02200282 -0.02200282
94 -0.06262341 -0.02200282
95 -0.02200282 -0.06262341
96 -0.06262341 -0.02200282
97 -0.02200282 -0.06262341
98 -0.02200282 -0.02200282
99 -0.02200282 -0.02200282
100 -0.02200282 -0.02200282
101 -0.02200282 -0.02200282
102 -0.02200282 -0.02200282
103 -0.02200282 -0.02200282
104 -0.06262341 -0.02200282
105 -0.02200282 -0.06262341
106 -0.02200282 -0.02200282
107 -0.06262341 -0.02200282
108 -0.02200282 -0.06262341
109 -0.02200282 -0.02200282
110 -0.06262341 -0.02200282
111 -0.06262341 -0.06262341
112 -0.02200282 -0.06262341
113 -0.06262341 -0.02200282
114 -0.02200282 -0.06262341
115 -0.02200282 -0.02200282
116 -0.02200282 -0.02200282
117 -0.02200282 -0.02200282
118 -0.02200282 -0.02200282
119 -0.02200282 -0.02200282
120 -0.02200282 -0.02200282
121 -0.02200282 -0.02200282
122 -0.06262341 -0.02200282
123 -0.02200282 -0.06262341
124 -0.02200282 -0.02200282
125 -0.06262341 -0.02200282
126 -0.02200282 -0.06262341
127 -0.02200282 -0.02200282
128 -0.02200282 -0.02200282
129 -0.02200282 -0.02200282
130 -0.02200282 -0.02200282
131 -0.02200282 -0.02200282
132 -0.02200282 -0.02200282
133 -0.02200282 -0.02200282
134 -0.02200282 -0.02200282
135 -0.02200282 -0.02200282
136 -0.02200282 -0.02200282
137 -0.06262341 -0.02200282
138 -0.06262341 -0.06262341
139 -0.02200282 -0.06262341
140 0.97799718 -0.02200282
141 -0.06262341 0.97799718
142 -0.02200282 -0.06262341
143 -0.02200282 -0.02200282
144 -0.02200282 -0.02200282
145 -0.06262341 -0.02200282
146 -0.06262341 -0.06262341
147 -0.06262341 -0.06262341
148 -0.02200282 -0.06262341
149 -0.02200282 -0.02200282
150 -0.02200282 -0.02200282
151 0.97799718 -0.02200282
152 0.97799718 0.97799718
153 -0.02200282 0.97799718
154 NA -0.02200282
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.10324401 -0.14386460
[2,] -0.10324401 -0.10324401
[3,] -0.10324401 -0.10324401
[4,] -0.10324401 -0.10324401
[5,] -0.10324401 -0.10324401
[6,] -0.10324401 -0.10324401
[7,] -0.14386460 -0.10324401
[8,] -0.10324401 -0.14386460
[9,] -0.10324401 -0.10324401
[10,] -0.14386460 -0.10324401
[11,] -0.10324401 -0.14386460
[12,] -0.10324401 -0.10324401
[13,] -0.14386460 -0.10324401
[14,] -0.10324401 -0.14386460
[15,] -0.14386460 -0.10324401
[16,] 0.85613540 -0.14386460
[17,] -0.14386460 0.85613540
[18,] -0.10324401 -0.14386460
[19,] 0.85613540 -0.10324401
[20,] -0.10324401 0.85613540
[21,] -0.10324401 -0.10324401
[22,] -0.10324401 -0.10324401
[23,] -0.10324401 -0.10324401
[24,] -0.14386460 -0.10324401
[25,] -0.10324401 -0.14386460
[26,] -0.10324401 -0.10324401
[27,] -0.10324401 -0.10324401
[28,] -0.10324401 -0.10324401
[29,] -0.10324401 -0.10324401
[30,] -0.10324401 -0.10324401
[31,] -0.10324401 -0.10324401
[32,] -0.10324401 -0.10324401
[33,] -0.14386460 -0.10324401
[34,] -0.10324401 -0.14386460
[35,] -0.10324401 -0.10324401
[36,] -0.14386460 -0.10324401
[37,] -0.10324401 -0.14386460
[38,] -0.10324401 -0.10324401
[39,] -0.14386460 -0.10324401
[40,] 0.89675599 -0.14386460
[41,] -0.10324401 0.89675599
[42,] -0.10324401 -0.10324401
[43,] -0.14386460 -0.10324401
[44,] -0.10324401 -0.14386460
[45,] -0.10324401 -0.10324401
[46,] -0.10324401 -0.10324401
[47,] -0.10324401 -0.10324401
[48,] -0.10324401 -0.10324401
[49,] -0.10324401 -0.10324401
[50,] -0.14386460 -0.10324401
[51,] 0.85613540 -0.14386460
[52,] -0.10324401 0.85613540
[53,] 0.89675599 -0.10324401
[54,] -0.10324401 0.89675599
[55,] -0.14386460 -0.10324401
[56,] -0.10324401 -0.14386460
[57,] -0.10324401 -0.10324401
[58,] -0.10324401 -0.10324401
[59,] 0.85613540 -0.10324401
[60,] -0.14386460 0.85613540
[61,] -0.10324401 -0.14386460
[62,] -0.10324401 -0.10324401
[63,] -0.14386460 -0.10324401
[64,] -0.10324401 -0.14386460
[65,] -0.10324401 -0.10324401
[66,] 0.85613540 -0.10324401
[67,] -0.10324401 0.85613540
[68,] -0.10324401 -0.10324401
[69,] -0.10324401 -0.10324401
[70,] -0.10324401 -0.10324401
[71,] -0.10324401 -0.10324401
[72,] -0.10324401 -0.10324401
[73,] -0.10324401 -0.10324401
[74,] -0.10324401 -0.10324401
[75,] -0.14386460 -0.10324401
[76,] -0.10324401 -0.14386460
[77,] -0.10324401 -0.10324401
[78,] 0.85613540 -0.10324401
[79,] -0.14386460 0.85613540
[80,] -0.10324401 -0.14386460
[81,] -0.10324401 -0.10324401
[82,] -0.10324401 -0.10324401
[83,] 0.89675599 -0.10324401
[84,] -0.10324401 0.89675599
[85,] -0.10324401 -0.10324401
[86,] -0.02200282 -0.10324401
[87,] -0.06262341 -0.02200282
[88,] -0.02200282 -0.06262341
[89,] -0.02200282 -0.02200282
[90,] -0.02200282 -0.02200282
[91,] -0.06262341 -0.02200282
[92,] -0.02200282 -0.06262341
[93,] -0.02200282 -0.02200282
[94,] -0.06262341 -0.02200282
[95,] -0.02200282 -0.06262341
[96,] -0.06262341 -0.02200282
[97,] -0.02200282 -0.06262341
[98,] -0.02200282 -0.02200282
[99,] -0.02200282 -0.02200282
[100,] -0.02200282 -0.02200282
[101,] -0.02200282 -0.02200282
[102,] -0.02200282 -0.02200282
[103,] -0.02200282 -0.02200282
[104,] -0.06262341 -0.02200282
[105,] -0.02200282 -0.06262341
[106,] -0.02200282 -0.02200282
[107,] -0.06262341 -0.02200282
[108,] -0.02200282 -0.06262341
[109,] -0.02200282 -0.02200282
[110,] -0.06262341 -0.02200282
[111,] -0.06262341 -0.06262341
[112,] -0.02200282 -0.06262341
[113,] -0.06262341 -0.02200282
[114,] -0.02200282 -0.06262341
[115,] -0.02200282 -0.02200282
[116,] -0.02200282 -0.02200282
[117,] -0.02200282 -0.02200282
[118,] -0.02200282 -0.02200282
[119,] -0.02200282 -0.02200282
[120,] -0.02200282 -0.02200282
[121,] -0.02200282 -0.02200282
[122,] -0.06262341 -0.02200282
[123,] -0.02200282 -0.06262341
[124,] -0.02200282 -0.02200282
[125,] -0.06262341 -0.02200282
[126,] -0.02200282 -0.06262341
[127,] -0.02200282 -0.02200282
[128,] -0.02200282 -0.02200282
[129,] -0.02200282 -0.02200282
[130,] -0.02200282 -0.02200282
[131,] -0.02200282 -0.02200282
[132,] -0.02200282 -0.02200282
[133,] -0.02200282 -0.02200282
[134,] -0.02200282 -0.02200282
[135,] -0.02200282 -0.02200282
[136,] -0.02200282 -0.02200282
[137,] -0.06262341 -0.02200282
[138,] -0.06262341 -0.06262341
[139,] -0.02200282 -0.06262341
[140,] 0.97799718 -0.02200282
[141,] -0.06262341 0.97799718
[142,] -0.02200282 -0.06262341
[143,] -0.02200282 -0.02200282
[144,] -0.02200282 -0.02200282
[145,] -0.06262341 -0.02200282
[146,] -0.06262341 -0.06262341
[147,] -0.06262341 -0.06262341
[148,] -0.02200282 -0.06262341
[149,] -0.02200282 -0.02200282
[150,] -0.02200282 -0.02200282
[151,] 0.97799718 -0.02200282
[152,] 0.97799718 0.97799718
[153,] -0.02200282 0.97799718
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.10324401 -0.14386460
2 -0.10324401 -0.10324401
3 -0.10324401 -0.10324401
4 -0.10324401 -0.10324401
5 -0.10324401 -0.10324401
6 -0.10324401 -0.10324401
7 -0.14386460 -0.10324401
8 -0.10324401 -0.14386460
9 -0.10324401 -0.10324401
10 -0.14386460 -0.10324401
11 -0.10324401 -0.14386460
12 -0.10324401 -0.10324401
13 -0.14386460 -0.10324401
14 -0.10324401 -0.14386460
15 -0.14386460 -0.10324401
16 0.85613540 -0.14386460
17 -0.14386460 0.85613540
18 -0.10324401 -0.14386460
19 0.85613540 -0.10324401
20 -0.10324401 0.85613540
21 -0.10324401 -0.10324401
22 -0.10324401 -0.10324401
23 -0.10324401 -0.10324401
24 -0.14386460 -0.10324401
25 -0.10324401 -0.14386460
26 -0.10324401 -0.10324401
27 -0.10324401 -0.10324401
28 -0.10324401 -0.10324401
29 -0.10324401 -0.10324401
30 -0.10324401 -0.10324401
31 -0.10324401 -0.10324401
32 -0.10324401 -0.10324401
33 -0.14386460 -0.10324401
34 -0.10324401 -0.14386460
35 -0.10324401 -0.10324401
36 -0.14386460 -0.10324401
37 -0.10324401 -0.14386460
38 -0.10324401 -0.10324401
39 -0.14386460 -0.10324401
40 0.89675599 -0.14386460
41 -0.10324401 0.89675599
42 -0.10324401 -0.10324401
43 -0.14386460 -0.10324401
44 -0.10324401 -0.14386460
45 -0.10324401 -0.10324401
46 -0.10324401 -0.10324401
47 -0.10324401 -0.10324401
48 -0.10324401 -0.10324401
49 -0.10324401 -0.10324401
50 -0.14386460 -0.10324401
51 0.85613540 -0.14386460
52 -0.10324401 0.85613540
53 0.89675599 -0.10324401
54 -0.10324401 0.89675599
55 -0.14386460 -0.10324401
56 -0.10324401 -0.14386460
57 -0.10324401 -0.10324401
58 -0.10324401 -0.10324401
59 0.85613540 -0.10324401
60 -0.14386460 0.85613540
61 -0.10324401 -0.14386460
62 -0.10324401 -0.10324401
63 -0.14386460 -0.10324401
64 -0.10324401 -0.14386460
65 -0.10324401 -0.10324401
66 0.85613540 -0.10324401
67 -0.10324401 0.85613540
68 -0.10324401 -0.10324401
69 -0.10324401 -0.10324401
70 -0.10324401 -0.10324401
71 -0.10324401 -0.10324401
72 -0.10324401 -0.10324401
73 -0.10324401 -0.10324401
74 -0.10324401 -0.10324401
75 -0.14386460 -0.10324401
76 -0.10324401 -0.14386460
77 -0.10324401 -0.10324401
78 0.85613540 -0.10324401
79 -0.14386460 0.85613540
80 -0.10324401 -0.14386460
81 -0.10324401 -0.10324401
82 -0.10324401 -0.10324401
83 0.89675599 -0.10324401
84 -0.10324401 0.89675599
85 -0.10324401 -0.10324401
86 -0.02200282 -0.10324401
87 -0.06262341 -0.02200282
88 -0.02200282 -0.06262341
89 -0.02200282 -0.02200282
90 -0.02200282 -0.02200282
91 -0.06262341 -0.02200282
92 -0.02200282 -0.06262341
93 -0.02200282 -0.02200282
94 -0.06262341 -0.02200282
95 -0.02200282 -0.06262341
96 -0.06262341 -0.02200282
97 -0.02200282 -0.06262341
98 -0.02200282 -0.02200282
99 -0.02200282 -0.02200282
100 -0.02200282 -0.02200282
101 -0.02200282 -0.02200282
102 -0.02200282 -0.02200282
103 -0.02200282 -0.02200282
104 -0.06262341 -0.02200282
105 -0.02200282 -0.06262341
106 -0.02200282 -0.02200282
107 -0.06262341 -0.02200282
108 -0.02200282 -0.06262341
109 -0.02200282 -0.02200282
110 -0.06262341 -0.02200282
111 -0.06262341 -0.06262341
112 -0.02200282 -0.06262341
113 -0.06262341 -0.02200282
114 -0.02200282 -0.06262341
115 -0.02200282 -0.02200282
116 -0.02200282 -0.02200282
117 -0.02200282 -0.02200282
118 -0.02200282 -0.02200282
119 -0.02200282 -0.02200282
120 -0.02200282 -0.02200282
121 -0.02200282 -0.02200282
122 -0.06262341 -0.02200282
123 -0.02200282 -0.06262341
124 -0.02200282 -0.02200282
125 -0.06262341 -0.02200282
126 -0.02200282 -0.06262341
127 -0.02200282 -0.02200282
128 -0.02200282 -0.02200282
129 -0.02200282 -0.02200282
130 -0.02200282 -0.02200282
131 -0.02200282 -0.02200282
132 -0.02200282 -0.02200282
133 -0.02200282 -0.02200282
134 -0.02200282 -0.02200282
135 -0.02200282 -0.02200282
136 -0.02200282 -0.02200282
137 -0.06262341 -0.02200282
138 -0.06262341 -0.06262341
139 -0.02200282 -0.06262341
140 0.97799718 -0.02200282
141 -0.06262341 0.97799718
142 -0.02200282 -0.06262341
143 -0.02200282 -0.02200282
144 -0.02200282 -0.02200282
145 -0.06262341 -0.02200282
146 -0.06262341 -0.06262341
147 -0.06262341 -0.06262341
148 -0.02200282 -0.06262341
149 -0.02200282 -0.02200282
150 -0.02200282 -0.02200282
151 0.97799718 -0.02200282
152 0.97799718 0.97799718
153 -0.02200282 0.97799718
> 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/7jn9v1356082968.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/8ntr11356082968.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/933ie1356082968.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/10nybj1356082968.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/114bcy1356082968.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12ecl61356082968.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13cfh81356082969.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14bs9n1356082969.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/156fte1356082969.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/164pnw1356082969.tab")
+ }
>
> try(system("convert tmp/1nhpe1356082968.ps tmp/1nhpe1356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n0sl1356082968.ps tmp/2n0sl1356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/39jlu1356082968.ps tmp/39jlu1356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/400su1356082968.ps tmp/400su1356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vku01356082968.ps tmp/5vku01356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rvwh1356082968.ps tmp/6rvwh1356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jn9v1356082968.ps tmp/7jn9v1356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ntr11356082968.ps tmp/8ntr11356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/933ie1356082968.ps tmp/933ie1356082968.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nybj1356082968.ps tmp/10nybj1356082968.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.299 1.790 9.120