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)
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Type 'q()' to quit R.
> x <- array(list(1
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+ ,dim=c(6
+ ,154)
+ ,dimnames=list(c('UseLimit'
+ ,'Used'
+ ,'Useful'
+ ,'Outcome'
+ ,'CorrectAnalysis'
+ ,'T20')
+ ,1:154))
> y <- array(NA,dim=c(6,154),dimnames=list(c('UseLimit','Used','Useful','Outcome','CorrectAnalysis','T20'),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 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'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
CorrectAnalysis UseLimit Used Useful Outcome T20
1 0 1 0 0 1 0
2 0 0 0 0 0 0
3 0 0 0 0 0 0
4 0 0 0 0 0 0
5 0 0 0 0 0 0
6 0 1 0 1 1 0
7 0 0 0 0 0 0
8 0 0 0 0 0 0
9 0 0 0 0 1 0
10 0 1 0 0 0 0
11 0 1 0 0 0 0
12 0 0 0 0 0 0
13 0 0 1 1 0 0
14 0 1 0 0 0 0
15 0 0 1 1 1 0
16 0 0 1 1 1 0
17 1 1 1 1 0 0
18 0 1 0 0 0 0
19 0 0 0 0 1 0
20 1 0 1 1 1 0
21 0 1 0 1 0 0
22 0 1 1 1 1 0
23 0 0 0 1 1 0
24 0 1 0 1 1 0
25 0 0 1 0 1 0
26 0 0 1 1 0 0
27 0 1 0 0 1 0
28 0 0 1 0 0 0
29 0 0 0 0 1 0
30 0 0 0 1 0 0
31 0 0 0 0 0 0
32 0 1 0 0 0 0
33 0 1 0 1 0 0
34 0 0 0 0 1 0
35 0 0 0 0 0 0
36 0 0 0 0 0 0
37 0 1 1 1 0 0
38 0 0 1 0 1 0
39 0 0 0 1 1 0
40 0 0 0 1 0 0
41 1 0 1 1 1 0
42 0 0 1 0 1 0
43 0 1 0 1 1 0
44 0 1 0 0 0 0
45 0 0 0 1 0 0
46 0 0 0 1 1 0
47 0 0 0 0 0 0
48 0 0 0 0 1 0
49 0 0 0 1 1 0
50 0 0 0 0 0 0
51 0 0 1 0 0 0
52 1 1 1 1 0 0
53 0 0 0 0 1 0
54 1 0 1 0 0 0
55 0 0 0 0 0 0
56 0 0 1 0 1 0
57 0 0 1 1 1 0
58 0 0 0 0 1 0
59 0 0 0 0 1 0
60 1 1 1 1 1 0
61 0 1 0 0 1 0
62 0 0 1 1 0 0
63 0 0 0 0 0 0
64 0 1 0 0 1 0
65 0 0 0 0 0 0
66 0 0 0 0 0 0
67 1 0 1 1 0 0
68 0 1 0 0 0 0
69 0 0 0 0 1 0
70 0 0 1 0 0 0
71 0 0 0 0 0 0
72 0 0 0 0 1 0
73 0 0 1 0 1 0
74 0 1 1 0 0 0
75 0 0 0 0 1 0
76 0 0 0 1 1 0
77 0 0 0 0 1 0
78 0 0 1 1 1 0
79 1 0 1 0 1 0
80 0 0 0 1 0 0
81 0 0 0 0 0 0
82 0 1 1 0 1 0
83 0 0 0 0 0 0
84 1 0 1 0 0 0
85 0 0 0 1 1 0
86 0 1 0 0 0 0
87 0 1 0 0 1 1
88 0 1 1 0 1 0
89 0 0 0 0 0 1
90 0 0 0 0 1 1
91 0 0 0 1 0 1
92 0 1 0 0 0 0
93 0 1 0 1 0 1
94 0 0 0 0 0 1
95 0 0 0 0 0 0
96 0 0 0 0 1 1
97 0 1 0 0 0 0
98 0 0 0 0 0 1
99 0 1 0 0 0 1
100 0 0 0 0 1 1
101 0 1 0 0 1 1
102 0 0 0 0 0 1
103 0 0 0 0 0 1
104 0 0 0 0 0 1
105 0 0 1 0 0 0
106 0 0 0 0 0 1
107 0 0 0 0 0 1
108 0 1 1 0 0 0
109 0 0 0 0 0 1
110 0 1 0 0 0 1
111 0 1 1 1 0 0
112 0 0 0 0 0 0
113 0 0 1 0 0 1
114 0 1 1 0 0 0
115 0 1 0 0 0 1
116 0 0 0 0 0 1
117 0 1 0 0 1 1
118 0 1 0 0 0 1
119 0 0 0 0 0 1
120 0 0 0 0 1 1
121 0 1 0 0 0 1
122 0 0 0 0 0 1
123 0 1 1 0 0 0
124 0 0 1 1 1 1
125 0 0 0 0 1 1
126 0 0 0 0 0 0
127 0 0 0 1 0 1
128 0 0 0 0 1 1
129 0 0 0 0 0 1
130 0 0 0 0 1 1
131 0 1 0 0 0 1
132 0 1 0 0 1 1
133 0 1 1 0 0 1
134 0 0 0 0 0 1
135 0 0 0 0 0 1
136 0 0 0 0 0 1
137 0 1 1 1 1 1
138 0 1 1 1 1 0
139 0 0 0 0 0 0
140 0 0 0 0 0 1
141 1 0 1 0 1 1
142 0 0 1 0 1 0
143 0 1 0 0 0 1
144 0 0 0 1 1 1
145 0 0 0 1 0 1
146 0 0 0 0 1 0
147 0 0 1 0 0 0
148 0 0 0 0 0 0
149 0 1 0 0 0 1
150 0 0 0 1 1 1
151 0 0 0 0 1 1
152 1 1 1 0 0 1
153 1 1 1 1 0 1
154 0 1 1 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit Used Useful Outcome T20
-0.0163557 0.0001335 0.2588355 0.0666029 -0.0233601 0.0305637
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.31642 -0.05568 -0.01421 0.01636 0.78088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0163557 0.0358435 -0.456 0.649
UseLimit 0.0001335 0.0421168 0.003 0.997
Used 0.2588355 0.0453849 5.703 6.19e-08 ***
Useful 0.0666029 0.0464028 1.435 0.153
Outcome -0.0233601 0.0407324 -0.574 0.567
T20 0.0305637 0.0426774 0.716 0.475
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2417 on 148 degrees of freedom
Multiple R-squared: 0.2184, Adjusted R-squared: 0.192
F-statistic: 8.271 on 5 and 148 DF, p-value: 6.485e-07
> 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,] 0.000000000 0.000000000 1.0000000000
[2,] 0.000000000 0.000000000 1.0000000000
[3,] 0.000000000 0.000000000 1.0000000000
[4,] 0.000000000 0.000000000 1.0000000000
[5,] 0.000000000 0.000000000 1.0000000000
[6,] 0.000000000 0.000000000 1.0000000000
[7,] 0.000000000 0.000000000 1.0000000000
[8,] 0.000000000 0.000000000 1.0000000000
[9,] 0.323818217 0.647636435 0.6761817827
[10,] 0.276727067 0.553454134 0.7232729332
[11,] 0.246584628 0.493169257 0.7534153717
[12,] 0.824518662 0.350962677 0.1754813384
[13,] 0.770736581 0.458526839 0.2292634194
[14,] 0.841941901 0.316116199 0.1580580994
[15,] 0.798138463 0.403723074 0.2018615372
[16,] 0.744389452 0.511221096 0.2556105479
[17,] 0.728140352 0.543719296 0.2718596480
[18,] 0.754853964 0.490292072 0.2451460358
[19,] 0.699711581 0.600576839 0.3002884193
[20,] 0.678835587 0.642328825 0.3211644127
[21,] 0.623184011 0.753631978 0.3768159890
[22,] 0.562987520 0.874024960 0.4370124799
[23,] 0.500897944 0.998204112 0.4991020560
[24,] 0.439756194 0.879512388 0.5602438062
[25,] 0.387250119 0.774500237 0.6127498815
[26,] 0.332603806 0.665207611 0.6673961944
[27,] 0.279998368 0.559996736 0.7200016318
[28,] 0.232132094 0.464264188 0.7678679060
[29,] 0.247362254 0.494724508 0.7526377458
[30,] 0.222017301 0.444034602 0.7779826989
[31,] 0.181697249 0.363394497 0.8183027514
[32,] 0.147236457 0.294472913 0.8527635433
[33,] 0.568327798 0.863344403 0.4316722016
[34,] 0.546026033 0.907947934 0.4539739670
[35,] 0.496074468 0.992148936 0.5039255321
[36,] 0.442552043 0.885104085 0.5574479574
[37,] 0.392660004 0.785320007 0.6073399964
[38,] 0.344203345 0.688406690 0.6557966550
[39,] 0.297169889 0.594339779 0.7028301107
[40,] 0.253676685 0.507353370 0.7463233149
[41,] 0.214662715 0.429325430 0.7853372851
[42,] 0.178743911 0.357487822 0.8212560890
[43,] 0.168013122 0.336026244 0.8319868782
[44,] 0.486660003 0.973320006 0.5133399970
[45,] 0.438996210 0.877992419 0.5610037905
[46,] 0.815420104 0.369159792 0.1845798959
[47,] 0.780617178 0.438765645 0.2193828224
[48,] 0.773247641 0.453504717 0.2267523585
[49,] 0.782875732 0.434248537 0.2171242685
[50,] 0.747370882 0.505258236 0.2526291181
[51,] 0.708844022 0.582311956 0.2911559782
[52,] 0.921990404 0.156019193 0.0780095964
[53,] 0.904137429 0.191725142 0.0958625712
[54,] 0.913718358 0.172563284 0.0862816420
[55,] 0.893444371 0.213111258 0.1065556288
[56,] 0.872326086 0.255347828 0.1276739139
[57,] 0.845846291 0.308307418 0.1541537088
[58,] 0.815981990 0.368036021 0.1840180105
[59,] 0.964360419 0.071279163 0.0356395814
[60,] 0.954896148 0.090207705 0.0451038523
[61,] 0.943195839 0.113608322 0.0568041611
[62,] 0.943882879 0.112234243 0.0561171214
[63,] 0.929334767 0.141330466 0.0706652329
[64,] 0.912846327 0.174307346 0.0871536731
[65,] 0.910095393 0.179809215 0.0899046073
[66,] 0.910971593 0.178056813 0.0890284067
[67,] 0.891251287 0.217497425 0.1087487126
[68,] 0.869543349 0.260913301 0.1304566505
[69,] 0.844080309 0.311839383 0.1559196913
[70,] 0.847431764 0.305136473 0.1525682364
[71,] 0.985823241 0.028353519 0.0141767593
[72,] 0.981182925 0.037634149 0.0188170747
[73,] 0.975187559 0.049624882 0.0248124408
[74,] 0.973197656 0.053604687 0.0268023437
[75,] 0.965183656 0.069632689 0.0348163444
[76,] 0.999147953 0.001704093 0.0008520467
[77,] 0.998827543 0.002344915 0.0011724573
[78,] 0.998367714 0.003264572 0.0016322860
[79,] 0.997586378 0.004827244 0.0024136220
[80,] 0.997139088 0.005721824 0.0028609118
[81,] 0.995862577 0.008274847 0.0041374234
[82,] 0.994083780 0.011832440 0.0059162202
[83,] 0.991746315 0.016507370 0.0082536851
[84,] 0.989212666 0.021574669 0.0107873344
[85,] 0.985255753 0.029488495 0.0147442474
[86,] 0.979943912 0.040112176 0.0200560879
[87,] 0.974801776 0.050396449 0.0251982243
[88,] 0.966543760 0.066912480 0.0334562401
[89,] 0.959572341 0.080855319 0.0404276594
[90,] 0.947330962 0.105338076 0.0526690382
[91,] 0.932141738 0.135716523 0.0678582616
[92,] 0.913949929 0.172100142 0.0860500712
[93,] 0.891942606 0.216114787 0.1080573935
[94,] 0.866257249 0.267485501 0.1337427506
[95,] 0.836565568 0.326868863 0.1634344317
[96,] 0.802791676 0.394416648 0.1972083240
[97,] 0.786674214 0.426651573 0.2133257864
[98,] 0.747085131 0.505829739 0.2529148694
[99,] 0.703822471 0.592355058 0.2961775290
[100,] 0.682003924 0.635992151 0.3179960757
[101,] 0.633797694 0.732404612 0.3662023060
[102,] 0.582193429 0.835613141 0.4178065706
[103,] 0.563784682 0.872430635 0.4362153176
[104,] 0.521671005 0.956657989 0.4783289947
[105,] 0.571562408 0.856875185 0.4284375924
[106,] 0.544811771 0.910376458 0.4551882291
[107,] 0.488856992 0.977713985 0.5111430077
[108,] 0.435290372 0.870580744 0.5647096282
[109,] 0.380492974 0.760985947 0.6195070265
[110,] 0.327076655 0.654153311 0.6729233446
[111,] 0.279293045 0.558586091 0.7207069545
[112,] 0.232398899 0.464797798 0.7676011009
[113,] 0.189910153 0.379820307 0.8100898466
[114,] 0.154529868 0.309059735 0.8454701325
[115,] 0.139577350 0.279154700 0.8604226502
[116,] 0.171111801 0.342223601 0.8288881993
[117,] 0.134836324 0.269672647 0.8651636764
[118,] 0.111860774 0.223721547 0.8881392263
[119,] 0.085827234 0.171654467 0.9141727663
[120,] 0.063468329 0.126936658 0.9365316712
[121,] 0.046863472 0.093726944 0.9531365282
[122,] 0.033023802 0.066047605 0.9669761975
[123,] 0.022334119 0.044668237 0.9776658814
[124,] 0.014921144 0.029842288 0.9850788558
[125,] 0.026558408 0.053116816 0.9734415920
[126,] 0.018541414 0.037082828 0.9814585861
[127,] 0.012939443 0.025878887 0.9870605565
[128,] 0.009245306 0.018490611 0.9907546943
[129,] 0.015910862 0.031821725 0.9840891377
[130,] 0.014519285 0.029038570 0.9854807150
[131,] 0.012170421 0.024340842 0.9878295788
[132,] 0.006663008 0.013326016 0.9933369919
[133,] 0.060517751 0.121035502 0.9394822489
[134,] 0.053166035 0.106332070 0.9468339650
[135,] 0.032755393 0.065510786 0.9672446070
[136,] 0.018209196 0.036418391 0.9817908044
[137,] 0.007641397 0.015282794 0.9923586030
> postscript(file="/var/fisher/rcomp/tmp/15cbp1356099123.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2ryx01356099123.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yzvu1356099123.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4w9ou1356099123.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5mn9k1356099123.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
0.039582216 0.016355681 0.016355681 0.016355681 0.016355681 -0.027020689
7 8 9 10 11 12
0.016355681 0.016355681 0.039715762 0.016222135 0.016222135 0.016355681
13 14 15 16 17 18
-0.309082691 0.016222135 -0.285722610 -0.285722610 0.690783763 0.016222135
19 20 21 22 23 24
0.039715762 0.714277390 -0.050380770 -0.285856156 -0.026887143 -0.027020689
25 26 27 28 29 30
-0.219119705 -0.309082691 0.039582216 -0.242479786 0.039715762 -0.050247223
31 32 33 34 35 36
0.016355681 0.016222135 -0.050380770 0.039715762 0.016355681 0.016355681
37 38 39 40 41 42
-0.309216237 -0.219119705 -0.026887143 -0.050247223 0.714277390 -0.219119705
43 44 45 46 47 48
-0.027020689 0.016222135 -0.050247223 -0.026887143 0.016355681 0.039715762
49 50 51 52 53 54
-0.026887143 0.016355681 -0.242479786 0.690783763 0.039715762 0.757520214
55 56 57 58 59 60
0.016355681 -0.219119705 -0.285722610 0.039715762 0.039715762 0.714143844
61 62 63 64 65 66
0.039582216 -0.309082691 0.016355681 0.039582216 0.016355681 0.016355681
67 68 69 70 71 72
0.690917309 0.016222135 0.039715762 -0.242479786 0.016355681 0.039715762
73 74 75 76 77 78
-0.219119705 -0.242613332 0.039715762 -0.026887143 0.039715762 -0.285722610
79 80 81 82 83 84
0.780880295 -0.050247223 0.016355681 -0.219253252 0.016355681 0.757520214
85 86 87 88 89 90
-0.026887143 0.016222135 0.009018541 -0.219253252 -0.014207993 0.009152087
91 92 93 94 95 96
-0.080810898 0.016222135 -0.080944444 -0.014207993 0.016355681 0.009152087
97 98 99 100 101 102
0.016222135 -0.014207993 -0.014341539 0.009152087 0.009018541 -0.014207993
103 104 105 106 107 108
-0.014207993 -0.014207993 -0.242479786 -0.014207993 -0.014207993 -0.242613332
109 110 111 112 113 114
-0.014207993 -0.014341539 -0.309216237 0.016355681 -0.273043460 -0.242613332
115 116 117 118 119 120
-0.014341539 -0.014207993 0.009018541 -0.014341539 -0.014207993 0.009152087
121 122 123 124 125 126
-0.014341539 -0.014207993 -0.242613332 -0.316286285 0.009152087 0.016355681
127 128 129 130 131 132
-0.080810898 0.009152087 -0.014207993 0.009152087 -0.014341539 0.009018541
133 134 135 136 137 138
-0.273177007 -0.014207993 -0.014207993 -0.014207993 -0.316419831 -0.285856156
139 140 141 142 143 144
0.016355681 -0.014207993 0.750316620 -0.219119705 -0.014341539 -0.057450818
145 146 147 148 149 150
-0.080810898 0.039715762 -0.242479786 0.016355681 -0.014341539 -0.057450818
151 152 153 154
0.009152087 0.726822993 0.660220089 -0.273177007
> postscript(file="/var/fisher/rcomp/tmp/6kuow1356099123.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 0.039582216 NA
1 0.016355681 0.039582216
2 0.016355681 0.016355681
3 0.016355681 0.016355681
4 0.016355681 0.016355681
5 -0.027020689 0.016355681
6 0.016355681 -0.027020689
7 0.016355681 0.016355681
8 0.039715762 0.016355681
9 0.016222135 0.039715762
10 0.016222135 0.016222135
11 0.016355681 0.016222135
12 -0.309082691 0.016355681
13 0.016222135 -0.309082691
14 -0.285722610 0.016222135
15 -0.285722610 -0.285722610
16 0.690783763 -0.285722610
17 0.016222135 0.690783763
18 0.039715762 0.016222135
19 0.714277390 0.039715762
20 -0.050380770 0.714277390
21 -0.285856156 -0.050380770
22 -0.026887143 -0.285856156
23 -0.027020689 -0.026887143
24 -0.219119705 -0.027020689
25 -0.309082691 -0.219119705
26 0.039582216 -0.309082691
27 -0.242479786 0.039582216
28 0.039715762 -0.242479786
29 -0.050247223 0.039715762
30 0.016355681 -0.050247223
31 0.016222135 0.016355681
32 -0.050380770 0.016222135
33 0.039715762 -0.050380770
34 0.016355681 0.039715762
35 0.016355681 0.016355681
36 -0.309216237 0.016355681
37 -0.219119705 -0.309216237
38 -0.026887143 -0.219119705
39 -0.050247223 -0.026887143
40 0.714277390 -0.050247223
41 -0.219119705 0.714277390
42 -0.027020689 -0.219119705
43 0.016222135 -0.027020689
44 -0.050247223 0.016222135
45 -0.026887143 -0.050247223
46 0.016355681 -0.026887143
47 0.039715762 0.016355681
48 -0.026887143 0.039715762
49 0.016355681 -0.026887143
50 -0.242479786 0.016355681
51 0.690783763 -0.242479786
52 0.039715762 0.690783763
53 0.757520214 0.039715762
54 0.016355681 0.757520214
55 -0.219119705 0.016355681
56 -0.285722610 -0.219119705
57 0.039715762 -0.285722610
58 0.039715762 0.039715762
59 0.714143844 0.039715762
60 0.039582216 0.714143844
61 -0.309082691 0.039582216
62 0.016355681 -0.309082691
63 0.039582216 0.016355681
64 0.016355681 0.039582216
65 0.016355681 0.016355681
66 0.690917309 0.016355681
67 0.016222135 0.690917309
68 0.039715762 0.016222135
69 -0.242479786 0.039715762
70 0.016355681 -0.242479786
71 0.039715762 0.016355681
72 -0.219119705 0.039715762
73 -0.242613332 -0.219119705
74 0.039715762 -0.242613332
75 -0.026887143 0.039715762
76 0.039715762 -0.026887143
77 -0.285722610 0.039715762
78 0.780880295 -0.285722610
79 -0.050247223 0.780880295
80 0.016355681 -0.050247223
81 -0.219253252 0.016355681
82 0.016355681 -0.219253252
83 0.757520214 0.016355681
84 -0.026887143 0.757520214
85 0.016222135 -0.026887143
86 0.009018541 0.016222135
87 -0.219253252 0.009018541
88 -0.014207993 -0.219253252
89 0.009152087 -0.014207993
90 -0.080810898 0.009152087
91 0.016222135 -0.080810898
92 -0.080944444 0.016222135
93 -0.014207993 -0.080944444
94 0.016355681 -0.014207993
95 0.009152087 0.016355681
96 0.016222135 0.009152087
97 -0.014207993 0.016222135
98 -0.014341539 -0.014207993
99 0.009152087 -0.014341539
100 0.009018541 0.009152087
101 -0.014207993 0.009018541
102 -0.014207993 -0.014207993
103 -0.014207993 -0.014207993
104 -0.242479786 -0.014207993
105 -0.014207993 -0.242479786
106 -0.014207993 -0.014207993
107 -0.242613332 -0.014207993
108 -0.014207993 -0.242613332
109 -0.014341539 -0.014207993
110 -0.309216237 -0.014341539
111 0.016355681 -0.309216237
112 -0.273043460 0.016355681
113 -0.242613332 -0.273043460
114 -0.014341539 -0.242613332
115 -0.014207993 -0.014341539
116 0.009018541 -0.014207993
117 -0.014341539 0.009018541
118 -0.014207993 -0.014341539
119 0.009152087 -0.014207993
120 -0.014341539 0.009152087
121 -0.014207993 -0.014341539
122 -0.242613332 -0.014207993
123 -0.316286285 -0.242613332
124 0.009152087 -0.316286285
125 0.016355681 0.009152087
126 -0.080810898 0.016355681
127 0.009152087 -0.080810898
128 -0.014207993 0.009152087
129 0.009152087 -0.014207993
130 -0.014341539 0.009152087
131 0.009018541 -0.014341539
132 -0.273177007 0.009018541
133 -0.014207993 -0.273177007
134 -0.014207993 -0.014207993
135 -0.014207993 -0.014207993
136 -0.316419831 -0.014207993
137 -0.285856156 -0.316419831
138 0.016355681 -0.285856156
139 -0.014207993 0.016355681
140 0.750316620 -0.014207993
141 -0.219119705 0.750316620
142 -0.014341539 -0.219119705
143 -0.057450818 -0.014341539
144 -0.080810898 -0.057450818
145 0.039715762 -0.080810898
146 -0.242479786 0.039715762
147 0.016355681 -0.242479786
148 -0.014341539 0.016355681
149 -0.057450818 -0.014341539
150 0.009152087 -0.057450818
151 0.726822993 0.009152087
152 0.660220089 0.726822993
153 -0.273177007 0.660220089
154 NA -0.273177007
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.016355681 0.039582216
[2,] 0.016355681 0.016355681
[3,] 0.016355681 0.016355681
[4,] 0.016355681 0.016355681
[5,] -0.027020689 0.016355681
[6,] 0.016355681 -0.027020689
[7,] 0.016355681 0.016355681
[8,] 0.039715762 0.016355681
[9,] 0.016222135 0.039715762
[10,] 0.016222135 0.016222135
[11,] 0.016355681 0.016222135
[12,] -0.309082691 0.016355681
[13,] 0.016222135 -0.309082691
[14,] -0.285722610 0.016222135
[15,] -0.285722610 -0.285722610
[16,] 0.690783763 -0.285722610
[17,] 0.016222135 0.690783763
[18,] 0.039715762 0.016222135
[19,] 0.714277390 0.039715762
[20,] -0.050380770 0.714277390
[21,] -0.285856156 -0.050380770
[22,] -0.026887143 -0.285856156
[23,] -0.027020689 -0.026887143
[24,] -0.219119705 -0.027020689
[25,] -0.309082691 -0.219119705
[26,] 0.039582216 -0.309082691
[27,] -0.242479786 0.039582216
[28,] 0.039715762 -0.242479786
[29,] -0.050247223 0.039715762
[30,] 0.016355681 -0.050247223
[31,] 0.016222135 0.016355681
[32,] -0.050380770 0.016222135
[33,] 0.039715762 -0.050380770
[34,] 0.016355681 0.039715762
[35,] 0.016355681 0.016355681
[36,] -0.309216237 0.016355681
[37,] -0.219119705 -0.309216237
[38,] -0.026887143 -0.219119705
[39,] -0.050247223 -0.026887143
[40,] 0.714277390 -0.050247223
[41,] -0.219119705 0.714277390
[42,] -0.027020689 -0.219119705
[43,] 0.016222135 -0.027020689
[44,] -0.050247223 0.016222135
[45,] -0.026887143 -0.050247223
[46,] 0.016355681 -0.026887143
[47,] 0.039715762 0.016355681
[48,] -0.026887143 0.039715762
[49,] 0.016355681 -0.026887143
[50,] -0.242479786 0.016355681
[51,] 0.690783763 -0.242479786
[52,] 0.039715762 0.690783763
[53,] 0.757520214 0.039715762
[54,] 0.016355681 0.757520214
[55,] -0.219119705 0.016355681
[56,] -0.285722610 -0.219119705
[57,] 0.039715762 -0.285722610
[58,] 0.039715762 0.039715762
[59,] 0.714143844 0.039715762
[60,] 0.039582216 0.714143844
[61,] -0.309082691 0.039582216
[62,] 0.016355681 -0.309082691
[63,] 0.039582216 0.016355681
[64,] 0.016355681 0.039582216
[65,] 0.016355681 0.016355681
[66,] 0.690917309 0.016355681
[67,] 0.016222135 0.690917309
[68,] 0.039715762 0.016222135
[69,] -0.242479786 0.039715762
[70,] 0.016355681 -0.242479786
[71,] 0.039715762 0.016355681
[72,] -0.219119705 0.039715762
[73,] -0.242613332 -0.219119705
[74,] 0.039715762 -0.242613332
[75,] -0.026887143 0.039715762
[76,] 0.039715762 -0.026887143
[77,] -0.285722610 0.039715762
[78,] 0.780880295 -0.285722610
[79,] -0.050247223 0.780880295
[80,] 0.016355681 -0.050247223
[81,] -0.219253252 0.016355681
[82,] 0.016355681 -0.219253252
[83,] 0.757520214 0.016355681
[84,] -0.026887143 0.757520214
[85,] 0.016222135 -0.026887143
[86,] 0.009018541 0.016222135
[87,] -0.219253252 0.009018541
[88,] -0.014207993 -0.219253252
[89,] 0.009152087 -0.014207993
[90,] -0.080810898 0.009152087
[91,] 0.016222135 -0.080810898
[92,] -0.080944444 0.016222135
[93,] -0.014207993 -0.080944444
[94,] 0.016355681 -0.014207993
[95,] 0.009152087 0.016355681
[96,] 0.016222135 0.009152087
[97,] -0.014207993 0.016222135
[98,] -0.014341539 -0.014207993
[99,] 0.009152087 -0.014341539
[100,] 0.009018541 0.009152087
[101,] -0.014207993 0.009018541
[102,] -0.014207993 -0.014207993
[103,] -0.014207993 -0.014207993
[104,] -0.242479786 -0.014207993
[105,] -0.014207993 -0.242479786
[106,] -0.014207993 -0.014207993
[107,] -0.242613332 -0.014207993
[108,] -0.014207993 -0.242613332
[109,] -0.014341539 -0.014207993
[110,] -0.309216237 -0.014341539
[111,] 0.016355681 -0.309216237
[112,] -0.273043460 0.016355681
[113,] -0.242613332 -0.273043460
[114,] -0.014341539 -0.242613332
[115,] -0.014207993 -0.014341539
[116,] 0.009018541 -0.014207993
[117,] -0.014341539 0.009018541
[118,] -0.014207993 -0.014341539
[119,] 0.009152087 -0.014207993
[120,] -0.014341539 0.009152087
[121,] -0.014207993 -0.014341539
[122,] -0.242613332 -0.014207993
[123,] -0.316286285 -0.242613332
[124,] 0.009152087 -0.316286285
[125,] 0.016355681 0.009152087
[126,] -0.080810898 0.016355681
[127,] 0.009152087 -0.080810898
[128,] -0.014207993 0.009152087
[129,] 0.009152087 -0.014207993
[130,] -0.014341539 0.009152087
[131,] 0.009018541 -0.014341539
[132,] -0.273177007 0.009018541
[133,] -0.014207993 -0.273177007
[134,] -0.014207993 -0.014207993
[135,] -0.014207993 -0.014207993
[136,] -0.316419831 -0.014207993
[137,] -0.285856156 -0.316419831
[138,] 0.016355681 -0.285856156
[139,] -0.014207993 0.016355681
[140,] 0.750316620 -0.014207993
[141,] -0.219119705 0.750316620
[142,] -0.014341539 -0.219119705
[143,] -0.057450818 -0.014341539
[144,] -0.080810898 -0.057450818
[145,] 0.039715762 -0.080810898
[146,] -0.242479786 0.039715762
[147,] 0.016355681 -0.242479786
[148,] -0.014341539 0.016355681
[149,] -0.057450818 -0.014341539
[150,] 0.009152087 -0.057450818
[151,] 0.726822993 0.009152087
[152,] 0.660220089 0.726822993
[153,] -0.273177007 0.660220089
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.016355681 0.039582216
2 0.016355681 0.016355681
3 0.016355681 0.016355681
4 0.016355681 0.016355681
5 -0.027020689 0.016355681
6 0.016355681 -0.027020689
7 0.016355681 0.016355681
8 0.039715762 0.016355681
9 0.016222135 0.039715762
10 0.016222135 0.016222135
11 0.016355681 0.016222135
12 -0.309082691 0.016355681
13 0.016222135 -0.309082691
14 -0.285722610 0.016222135
15 -0.285722610 -0.285722610
16 0.690783763 -0.285722610
17 0.016222135 0.690783763
18 0.039715762 0.016222135
19 0.714277390 0.039715762
20 -0.050380770 0.714277390
21 -0.285856156 -0.050380770
22 -0.026887143 -0.285856156
23 -0.027020689 -0.026887143
24 -0.219119705 -0.027020689
25 -0.309082691 -0.219119705
26 0.039582216 -0.309082691
27 -0.242479786 0.039582216
28 0.039715762 -0.242479786
29 -0.050247223 0.039715762
30 0.016355681 -0.050247223
31 0.016222135 0.016355681
32 -0.050380770 0.016222135
33 0.039715762 -0.050380770
34 0.016355681 0.039715762
35 0.016355681 0.016355681
36 -0.309216237 0.016355681
37 -0.219119705 -0.309216237
38 -0.026887143 -0.219119705
39 -0.050247223 -0.026887143
40 0.714277390 -0.050247223
41 -0.219119705 0.714277390
42 -0.027020689 -0.219119705
43 0.016222135 -0.027020689
44 -0.050247223 0.016222135
45 -0.026887143 -0.050247223
46 0.016355681 -0.026887143
47 0.039715762 0.016355681
48 -0.026887143 0.039715762
49 0.016355681 -0.026887143
50 -0.242479786 0.016355681
51 0.690783763 -0.242479786
52 0.039715762 0.690783763
53 0.757520214 0.039715762
54 0.016355681 0.757520214
55 -0.219119705 0.016355681
56 -0.285722610 -0.219119705
57 0.039715762 -0.285722610
58 0.039715762 0.039715762
59 0.714143844 0.039715762
60 0.039582216 0.714143844
61 -0.309082691 0.039582216
62 0.016355681 -0.309082691
63 0.039582216 0.016355681
64 0.016355681 0.039582216
65 0.016355681 0.016355681
66 0.690917309 0.016355681
67 0.016222135 0.690917309
68 0.039715762 0.016222135
69 -0.242479786 0.039715762
70 0.016355681 -0.242479786
71 0.039715762 0.016355681
72 -0.219119705 0.039715762
73 -0.242613332 -0.219119705
74 0.039715762 -0.242613332
75 -0.026887143 0.039715762
76 0.039715762 -0.026887143
77 -0.285722610 0.039715762
78 0.780880295 -0.285722610
79 -0.050247223 0.780880295
80 0.016355681 -0.050247223
81 -0.219253252 0.016355681
82 0.016355681 -0.219253252
83 0.757520214 0.016355681
84 -0.026887143 0.757520214
85 0.016222135 -0.026887143
86 0.009018541 0.016222135
87 -0.219253252 0.009018541
88 -0.014207993 -0.219253252
89 0.009152087 -0.014207993
90 -0.080810898 0.009152087
91 0.016222135 -0.080810898
92 -0.080944444 0.016222135
93 -0.014207993 -0.080944444
94 0.016355681 -0.014207993
95 0.009152087 0.016355681
96 0.016222135 0.009152087
97 -0.014207993 0.016222135
98 -0.014341539 -0.014207993
99 0.009152087 -0.014341539
100 0.009018541 0.009152087
101 -0.014207993 0.009018541
102 -0.014207993 -0.014207993
103 -0.014207993 -0.014207993
104 -0.242479786 -0.014207993
105 -0.014207993 -0.242479786
106 -0.014207993 -0.014207993
107 -0.242613332 -0.014207993
108 -0.014207993 -0.242613332
109 -0.014341539 -0.014207993
110 -0.309216237 -0.014341539
111 0.016355681 -0.309216237
112 -0.273043460 0.016355681
113 -0.242613332 -0.273043460
114 -0.014341539 -0.242613332
115 -0.014207993 -0.014341539
116 0.009018541 -0.014207993
117 -0.014341539 0.009018541
118 -0.014207993 -0.014341539
119 0.009152087 -0.014207993
120 -0.014341539 0.009152087
121 -0.014207993 -0.014341539
122 -0.242613332 -0.014207993
123 -0.316286285 -0.242613332
124 0.009152087 -0.316286285
125 0.016355681 0.009152087
126 -0.080810898 0.016355681
127 0.009152087 -0.080810898
128 -0.014207993 0.009152087
129 0.009152087 -0.014207993
130 -0.014341539 0.009152087
131 0.009018541 -0.014341539
132 -0.273177007 0.009018541
133 -0.014207993 -0.273177007
134 -0.014207993 -0.014207993
135 -0.014207993 -0.014207993
136 -0.316419831 -0.014207993
137 -0.285856156 -0.316419831
138 0.016355681 -0.285856156
139 -0.014207993 0.016355681
140 0.750316620 -0.014207993
141 -0.219119705 0.750316620
142 -0.014341539 -0.219119705
143 -0.057450818 -0.014341539
144 -0.080810898 -0.057450818
145 0.039715762 -0.080810898
146 -0.242479786 0.039715762
147 0.016355681 -0.242479786
148 -0.014341539 0.016355681
149 -0.057450818 -0.014341539
150 0.009152087 -0.057450818
151 0.726822993 0.009152087
152 0.660220089 0.726822993
153 -0.273177007 0.660220089
> 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/7yz561356099123.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8dw9r1356099123.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/94ctt1356099123.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10amtf1356099123.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116hhd1356099123.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/12el061356099123.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/134g2j1356099123.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/14k4u61356099123.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/15n3gr1356099123.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/16jn3n1356099123.tab")
+ }
>
> try(system("convert tmp/15cbp1356099123.ps tmp/15cbp1356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ryx01356099123.ps tmp/2ryx01356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yzvu1356099123.ps tmp/3yzvu1356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/4w9ou1356099123.ps tmp/4w9ou1356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mn9k1356099123.ps tmp/5mn9k1356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kuow1356099123.ps tmp/6kuow1356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yz561356099123.ps tmp/7yz561356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dw9r1356099123.ps tmp/8dw9r1356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/94ctt1356099123.ps tmp/94ctt1356099123.png",intern=TRUE))
character(0)
> try(system("convert tmp/10amtf1356099123.ps tmp/10amtf1356099123.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.968 1.777 9.787