R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
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(13
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+ ,13
+ ,34
+ ,36)
+ ,dim=c(3
+ ,162)
+ ,dimnames=list(c('Learning'
+ ,'Connected'
+ ,'Separate')
+ ,1:162))
> y <- array(NA,dim=c(3,162),dimnames=list(c('Learning','Connected','Separate'),1:162))
> 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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
Learning Connected Separate
1 13 41 38
2 16 39 32
3 19 30 35
4 15 31 33
5 14 34 37
6 13 35 29
7 19 39 31
8 15 34 36
9 14 36 35
10 15 37 38
11 16 38 31
12 16 36 34
13 16 38 35
14 16 39 38
15 17 33 37
16 15 32 33
17 15 36 32
18 20 38 38
19 18 39 38
20 16 32 32
21 16 32 33
22 16 31 31
23 19 39 38
24 16 37 39
25 17 39 32
26 17 41 32
27 16 36 35
28 15 33 37
29 16 33 33
30 14 34 33
31 15 31 28
32 12 27 32
33 14 37 31
34 16 34 37
35 14 34 30
36 7 32 33
37 10 29 31
38 14 36 33
39 16 29 31
40 16 35 33
41 16 37 32
42 14 34 33
43 20 38 32
44 14 35 33
45 14 38 28
46 11 37 35
47 14 38 39
48 15 33 34
49 16 36 38
50 14 38 32
51 16 32 38
52 14 32 30
53 12 32 33
54 16 34 38
55 9 32 32
56 14 37 32
57 16 39 34
58 16 29 34
59 15 37 36
60 16 35 34
61 12 30 28
62 16 38 34
63 16 34 35
64 14 31 35
65 16 34 31
66 17 35 37
67 18 36 35
68 18 30 27
69 12 39 40
70 16 35 37
71 10 38 36
72 14 31 38
73 18 34 39
74 18 38 41
75 16 34 27
76 17 39 30
77 16 37 37
78 16 34 31
79 13 28 31
80 16 37 27
81 16 33 36
82 20 37 38
83 16 35 37
84 15 37 33
85 15 32 34
86 16 33 31
87 14 38 39
88 16 33 34
89 16 29 32
90 15 33 33
91 12 31 36
92 17 36 32
93 16 35 41
94 15 32 28
95 13 29 30
96 16 39 36
97 16 37 35
98 16 35 31
99 16 37 34
100 14 32 36
101 16 38 36
102 16 37 35
103 20 36 37
104 15 32 28
105 16 33 39
106 13 40 32
107 17 38 35
108 16 41 39
109 16 36 35
110 12 43 42
111 16 30 34
112 16 31 33
113 17 32 41
114 13 32 33
115 12 37 34
116 18 37 32
117 14 33 40
118 14 34 40
119 13 33 35
120 16 38 36
121 13 33 37
122 16 31 27
123 13 38 39
124 16 37 38
125 15 33 31
126 16 31 33
127 15 39 32
128 17 44 39
129 15 33 36
130 12 35 33
131 16 32 33
132 10 28 32
133 16 40 37
134 12 27 30
135 14 37 38
136 15 32 29
137 13 28 22
138 15 34 35
139 11 30 35
140 12 35 34
141 8 31 35
142 16 32 34
143 15 30 34
144 17 30 35
145 16 31 23
146 10 40 31
147 18 32 27
148 13 36 36
149 16 32 31
150 13 35 32
151 10 38 39
152 15 42 37
153 16 34 38
154 16 35 39
155 14 35 34
156 10 33 31
157 17 36 32
158 13 32 37
159 15 33 36
160 16 34 32
161 12 32 35
162 13 34 36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate
9.9902907 0.1425775 0.0006981
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.5758 -1.1472 0.4277 1.2806 4.8511
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.9902907 2.1079283 4.739 4.73e-06 ***
Connected 0.1425775 0.0557030 2.560 0.0114 *
Separate 0.0006981 0.0529251 0.013 0.9895
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.218 on 159 degrees of freedom
Multiple R-squared: 0.04566, Adjusted R-squared: 0.03366
F-statistic: 3.804 on 2 and 159 DF, p-value: 0.02434
> 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,] 0.69675041 0.60649918 0.3032496
[2,] 0.88728252 0.22543495 0.1127175
[3,] 0.81267877 0.37464245 0.1873212
[4,] 0.74207707 0.51584587 0.2579229
[5,] 0.64549154 0.70901692 0.3545085
[6,] 0.54348449 0.91303103 0.4565155
[7,] 0.44785507 0.89571014 0.5521449
[8,] 0.36723616 0.73447232 0.6327638
[9,] 0.30755999 0.61511998 0.6924400
[10,] 0.26838460 0.53676920 0.7316154
[11,] 0.21480273 0.42960546 0.7851973
[12,] 0.16217152 0.32434304 0.8378285
[13,] 0.39158177 0.78316354 0.6084182
[14,] 0.37761765 0.75523531 0.6223823
[15,] 0.31222653 0.62445307 0.6877735
[16,] 0.25248433 0.50496866 0.7475157
[17,] 0.20182303 0.40364607 0.7981770
[18,] 0.23874550 0.47749101 0.7612545
[19,] 0.19237451 0.38474902 0.8076255
[20,] 0.16196802 0.32393604 0.8380320
[21,] 0.13081230 0.26162460 0.8691877
[22,] 0.09933590 0.19867179 0.9006641
[23,] 0.07938742 0.15877483 0.9206126
[24,] 0.05915394 0.11830788 0.9408461
[25,] 0.05423706 0.10847412 0.9457629
[26,] 0.03901964 0.07803928 0.9609804
[27,] 0.05005280 0.10010560 0.9499472
[28,] 0.04573403 0.09146805 0.9542660
[29,] 0.03351518 0.06703037 0.9664848
[30,] 0.02635510 0.05271021 0.9736449
[31,] 0.47444146 0.94888292 0.5255585
[32,] 0.57992849 0.84014302 0.4200715
[33,] 0.54559288 0.90881424 0.4544071
[34,] 0.54929782 0.90140435 0.4507022
[35,] 0.50352107 0.99295786 0.4964789
[36,] 0.45386130 0.90772260 0.5461387
[37,] 0.41196830 0.82393661 0.5880317
[38,] 0.55691665 0.88616671 0.4430834
[39,] 0.52136370 0.95727260 0.4786363
[40,] 0.48994899 0.97989798 0.5100510
[41,] 0.66145335 0.67709331 0.3385467
[42,] 0.65669055 0.68661891 0.3433095
[43,] 0.60997065 0.78005871 0.3900294
[44,] 0.56480201 0.87039598 0.4351980
[45,] 0.53929835 0.92140330 0.4607017
[46,] 0.50306511 0.99386977 0.4969349
[47,] 0.45548147 0.91096293 0.5445185
[48,] 0.46958445 0.93916890 0.5304155
[49,] 0.42855842 0.85711684 0.5714416
[50,] 0.65826341 0.68347319 0.3417366
[51,] 0.62783753 0.74432493 0.3721625
[52,] 0.58308223 0.83383555 0.4169178
[53,] 0.57474471 0.85051057 0.4252553
[54,] 0.53178071 0.93643859 0.4682193
[55,] 0.49318226 0.98636452 0.5068177
[56,] 0.47933053 0.95866105 0.5206695
[57,] 0.43493741 0.86987483 0.5650626
[58,] 0.39926615 0.79853231 0.6007338
[59,] 0.35671311 0.71342621 0.6432869
[60,] 0.33040752 0.66081503 0.6695925
[61,] 0.31479633 0.62959267 0.6852037
[62,] 0.33601109 0.67202218 0.6639889
[63,] 0.45558884 0.91117768 0.5444112
[64,] 0.55600590 0.88798820 0.4439941
[65,] 0.51783149 0.96433702 0.4821685
[66,] 0.73490416 0.53019168 0.2650958
[67,] 0.69858077 0.60283846 0.3014192
[68,] 0.73078339 0.53843322 0.2692166
[69,] 0.73876464 0.52247071 0.2612354
[70,] 0.71393118 0.57213764 0.2860688
[71,] 0.69179497 0.61641007 0.3082050
[72,] 0.65548552 0.68902897 0.3445145
[73,] 0.62486549 0.75026902 0.3751345
[74,] 0.58950175 0.82099651 0.4104983
[75,] 0.55089870 0.89820260 0.4491013
[76,] 0.52036294 0.95927412 0.4796371
[77,] 0.67286422 0.65427156 0.3271358
[78,] 0.64124288 0.71751423 0.3587571
[79,] 0.59921224 0.80157553 0.4007878
[80,] 0.55674558 0.88650883 0.4432544
[81,] 0.52837605 0.94324790 0.4716239
[82,] 0.50610116 0.98779768 0.4938988
[83,] 0.47726407 0.95452814 0.5227359
[84,] 0.46480037 0.92960074 0.5351996
[85,] 0.42121116 0.84242233 0.5787888
[86,] 0.43010174 0.86020349 0.5698983
[87,] 0.42072483 0.84144966 0.5792752
[88,] 0.38980518 0.77961036 0.6101948
[89,] 0.34874743 0.69749485 0.6512526
[90,] 0.31695849 0.63391698 0.6830415
[91,] 0.28229598 0.56459197 0.7177040
[92,] 0.25162881 0.50325762 0.7483712
[93,] 0.22552108 0.45104216 0.7744789
[94,] 0.19876470 0.39752940 0.8012353
[95,] 0.16998901 0.33997803 0.8300110
[96,] 0.14738297 0.29476594 0.8526170
[97,] 0.12767343 0.25534686 0.8723266
[98,] 0.26565121 0.53130242 0.7343488
[99,] 0.23093809 0.46187617 0.7690619
[100,] 0.21474528 0.42949056 0.7852547
[101,] 0.22198746 0.44397492 0.7780125
[102,] 0.21662515 0.43325030 0.7833748
[103,] 0.19407776 0.38815551 0.8059222
[104,] 0.17466565 0.34933131 0.8253343
[105,] 0.22952599 0.45905198 0.7704740
[106,] 0.22034518 0.44069036 0.7796548
[107,] 0.20926994 0.41853987 0.7907301
[108,] 0.24765615 0.49531230 0.7523438
[109,] 0.22298081 0.44596162 0.7770192
[110,] 0.25211683 0.50423366 0.7478832
[111,] 0.28850819 0.57701638 0.7114918
[112,] 0.25363115 0.50726230 0.7463689
[113,] 0.22076046 0.44152091 0.7792395
[114,] 0.19679118 0.39358237 0.8032088
[115,] 0.17436689 0.34873378 0.8256331
[116,] 0.15236869 0.30473738 0.8476313
[117,] 0.13892220 0.27784440 0.8610778
[118,] 0.12850815 0.25701630 0.8714918
[119,] 0.11480289 0.22960579 0.8851971
[120,] 0.09338352 0.18676704 0.9066165
[121,] 0.09040743 0.18081485 0.9095926
[122,] 0.07082248 0.14164496 0.9291775
[123,] 0.06703977 0.13407954 0.9329602
[124,] 0.05518215 0.11036429 0.9448179
[125,] 0.05613587 0.11227175 0.9438641
[126,] 0.05303672 0.10607344 0.9469633
[127,] 0.08164331 0.16328661 0.9183567
[128,] 0.07569444 0.15138888 0.9243056
[129,] 0.07344898 0.14689796 0.9265510
[130,] 0.05756049 0.11512099 0.9424395
[131,] 0.04307985 0.08615971 0.9569201
[132,] 0.04200917 0.08401833 0.9579908
[133,] 0.03247019 0.06494038 0.9675298
[134,] 0.04400759 0.08801517 0.9559924
[135,] 0.04122247 0.08244494 0.9587775
[136,] 0.30293296 0.60586591 0.6970670
[137,] 0.25976506 0.51953012 0.7402349
[138,] 0.20624038 0.41248077 0.7937596
[139,] 0.19465296 0.38930591 0.8053470
[140,] 0.14977188 0.29954376 0.8502281
[141,] 0.29169431 0.58338863 0.7083057
[142,] 0.33142749 0.66285497 0.6685725
[143,] 0.27402064 0.54804128 0.7259794
[144,] 0.25901192 0.51802384 0.7409881
[145,] 0.20281847 0.40563695 0.7971815
[146,] 0.53169929 0.93660141 0.4683007
[147,] 0.78589393 0.42821214 0.2141061
[148,] 0.70349807 0.59300387 0.2965019
[149,] 0.58797119 0.82405763 0.4120288
[150,] 0.53355599 0.93288802 0.4664440
[151,] 0.77848040 0.44303919 0.2215196
> postscript(file="/var/fisher/rcomp/tmp/1yd2i1351949560.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2asaw1351949560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35lkm1351949560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4uqeq1351949560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/57pq91351949560.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 = 162
Frequency = 1
1 2 3 4 5 6 7
-2.8624967 0.4268470 4.7079502 0.5667689 -0.8637561 -2.0007486 3.4275451
8 9 10 11 12 13 14
0.1369420 -1.1475149 -0.2921867 0.5701226 0.8531833 0.5673301 0.4226583
15 16 17 18 19 20 21
2.2788214 0.4241914 -0.1454205 4.5652358 2.4226583 1.4248895 1.4241914
22 23 24 25 26 27 28
1.5681651 3.4226583 0.7071152 1.4268470 1.1416920 0.8524851 0.2788214
29 30 31 32 33 34 35
1.2816139 -0.8609636 0.5702595 -1.8622230 -1.2872999 1.1362439 -0.8588693
36 37 38 39 40 41 42
-7.5758086 -4.1466799 -1.1461186 1.8533201 0.9964589 0.7120020 -0.8609636
43 44 45 46 47 48 49
4.5694245 -1.0035411 -1.4277830 -4.2900924 -1.4354623 0.2809158 0.8503908
50 51 52 53 54 55 56
-1.4305755 1.4207008 -0.5737143 -2.5758086 1.1355458 -5.5751105 -1.2879980
57 58 59 60 61 62 63
0.4254508 1.8512258 -0.2907905 0.9957608 -2.2871630 0.5680283 1.1376402
64 65 66 67 68 69 70
-0.4346273 1.1404326 1.9936664 2.8524851 3.7135351 -3.5787379 0.9936664
71 72 73 74 75 76 77
-5.4333680 -0.4367217 3.1348477 2.5631414 1.1432251 1.4282432 0.7085114
78 79 80 81 82 83 84
1.1404326 -1.0041024 0.7154926 1.2795195 4.7078133 0.9936664 -0.2886961
85 86 87 88 89 90 91
0.4234933 1.2830101 -1.4354623 1.2809158 1.8526220 0.2816139 -2.4353255
92 93 94 95 96 97 98
1.8545795 0.9908739 0.4276820 -1.1459818 0.4240545 0.7099076 0.9978551
99 100 101 102 103 104 105
0.7106058 -0.5779030 0.5666320 0.7099076 4.8510889 0.4276820 1.2774252
106 107 108 109 110 111 112
-2.7157305 1.5673301 0.1368052 0.8524851 -4.1504442 1.7086483 1.5667689
113 114 115 116 117 118 119
2.4186064 -1.5758086 -3.2893942 2.7120020 -0.7232729 -0.8658504 -1.7197823
120 121 122 123 124 125 126
0.5666320 -1.7211786 1.5709576 -2.4354623 0.7078133 0.2830101 1.5667689
127 128 129 130 131 132 133
-0.5731530 0.7090727 0.2795195 -3.0035411 1.4241914 -4.0048005 0.2807789
134 135 136 137 138 139 140
-1.8608268 -1.2921867 0.4269839 -0.9978193 0.1376402 -3.2920498 -3.0042392
141 142 143 144 145 146 147
-6.4346273 1.4234933 0.7086483 2.7079502 1.5737501 -5.7150324 3.4283801
148 149 150 151 152 153 154
-2.1482130 1.4255876 -2.0028430 -5.4354623 -1.0043761 1.1355458 0.9922702
155 156 157 158 159 160 161
-1.0042392 -4.7169899 1.8545795 -1.5786011 0.2795195 1.1397345 -2.5772048
162
-1.8630580
> postscript(file="/var/fisher/rcomp/tmp/66x541351949560.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.8624967 NA
1 0.4268470 -2.8624967
2 4.7079502 0.4268470
3 0.5667689 4.7079502
4 -0.8637561 0.5667689
5 -2.0007486 -0.8637561
6 3.4275451 -2.0007486
7 0.1369420 3.4275451
8 -1.1475149 0.1369420
9 -0.2921867 -1.1475149
10 0.5701226 -0.2921867
11 0.8531833 0.5701226
12 0.5673301 0.8531833
13 0.4226583 0.5673301
14 2.2788214 0.4226583
15 0.4241914 2.2788214
16 -0.1454205 0.4241914
17 4.5652358 -0.1454205
18 2.4226583 4.5652358
19 1.4248895 2.4226583
20 1.4241914 1.4248895
21 1.5681651 1.4241914
22 3.4226583 1.5681651
23 0.7071152 3.4226583
24 1.4268470 0.7071152
25 1.1416920 1.4268470
26 0.8524851 1.1416920
27 0.2788214 0.8524851
28 1.2816139 0.2788214
29 -0.8609636 1.2816139
30 0.5702595 -0.8609636
31 -1.8622230 0.5702595
32 -1.2872999 -1.8622230
33 1.1362439 -1.2872999
34 -0.8588693 1.1362439
35 -7.5758086 -0.8588693
36 -4.1466799 -7.5758086
37 -1.1461186 -4.1466799
38 1.8533201 -1.1461186
39 0.9964589 1.8533201
40 0.7120020 0.9964589
41 -0.8609636 0.7120020
42 4.5694245 -0.8609636
43 -1.0035411 4.5694245
44 -1.4277830 -1.0035411
45 -4.2900924 -1.4277830
46 -1.4354623 -4.2900924
47 0.2809158 -1.4354623
48 0.8503908 0.2809158
49 -1.4305755 0.8503908
50 1.4207008 -1.4305755
51 -0.5737143 1.4207008
52 -2.5758086 -0.5737143
53 1.1355458 -2.5758086
54 -5.5751105 1.1355458
55 -1.2879980 -5.5751105
56 0.4254508 -1.2879980
57 1.8512258 0.4254508
58 -0.2907905 1.8512258
59 0.9957608 -0.2907905
60 -2.2871630 0.9957608
61 0.5680283 -2.2871630
62 1.1376402 0.5680283
63 -0.4346273 1.1376402
64 1.1404326 -0.4346273
65 1.9936664 1.1404326
66 2.8524851 1.9936664
67 3.7135351 2.8524851
68 -3.5787379 3.7135351
69 0.9936664 -3.5787379
70 -5.4333680 0.9936664
71 -0.4367217 -5.4333680
72 3.1348477 -0.4367217
73 2.5631414 3.1348477
74 1.1432251 2.5631414
75 1.4282432 1.1432251
76 0.7085114 1.4282432
77 1.1404326 0.7085114
78 -1.0041024 1.1404326
79 0.7154926 -1.0041024
80 1.2795195 0.7154926
81 4.7078133 1.2795195
82 0.9936664 4.7078133
83 -0.2886961 0.9936664
84 0.4234933 -0.2886961
85 1.2830101 0.4234933
86 -1.4354623 1.2830101
87 1.2809158 -1.4354623
88 1.8526220 1.2809158
89 0.2816139 1.8526220
90 -2.4353255 0.2816139
91 1.8545795 -2.4353255
92 0.9908739 1.8545795
93 0.4276820 0.9908739
94 -1.1459818 0.4276820
95 0.4240545 -1.1459818
96 0.7099076 0.4240545
97 0.9978551 0.7099076
98 0.7106058 0.9978551
99 -0.5779030 0.7106058
100 0.5666320 -0.5779030
101 0.7099076 0.5666320
102 4.8510889 0.7099076
103 0.4276820 4.8510889
104 1.2774252 0.4276820
105 -2.7157305 1.2774252
106 1.5673301 -2.7157305
107 0.1368052 1.5673301
108 0.8524851 0.1368052
109 -4.1504442 0.8524851
110 1.7086483 -4.1504442
111 1.5667689 1.7086483
112 2.4186064 1.5667689
113 -1.5758086 2.4186064
114 -3.2893942 -1.5758086
115 2.7120020 -3.2893942
116 -0.7232729 2.7120020
117 -0.8658504 -0.7232729
118 -1.7197823 -0.8658504
119 0.5666320 -1.7197823
120 -1.7211786 0.5666320
121 1.5709576 -1.7211786
122 -2.4354623 1.5709576
123 0.7078133 -2.4354623
124 0.2830101 0.7078133
125 1.5667689 0.2830101
126 -0.5731530 1.5667689
127 0.7090727 -0.5731530
128 0.2795195 0.7090727
129 -3.0035411 0.2795195
130 1.4241914 -3.0035411
131 -4.0048005 1.4241914
132 0.2807789 -4.0048005
133 -1.8608268 0.2807789
134 -1.2921867 -1.8608268
135 0.4269839 -1.2921867
136 -0.9978193 0.4269839
137 0.1376402 -0.9978193
138 -3.2920498 0.1376402
139 -3.0042392 -3.2920498
140 -6.4346273 -3.0042392
141 1.4234933 -6.4346273
142 0.7086483 1.4234933
143 2.7079502 0.7086483
144 1.5737501 2.7079502
145 -5.7150324 1.5737501
146 3.4283801 -5.7150324
147 -2.1482130 3.4283801
148 1.4255876 -2.1482130
149 -2.0028430 1.4255876
150 -5.4354623 -2.0028430
151 -1.0043761 -5.4354623
152 1.1355458 -1.0043761
153 0.9922702 1.1355458
154 -1.0042392 0.9922702
155 -4.7169899 -1.0042392
156 1.8545795 -4.7169899
157 -1.5786011 1.8545795
158 0.2795195 -1.5786011
159 1.1397345 0.2795195
160 -2.5772048 1.1397345
161 -1.8630580 -2.5772048
162 NA -1.8630580
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.4268470 -2.8624967
[2,] 4.7079502 0.4268470
[3,] 0.5667689 4.7079502
[4,] -0.8637561 0.5667689
[5,] -2.0007486 -0.8637561
[6,] 3.4275451 -2.0007486
[7,] 0.1369420 3.4275451
[8,] -1.1475149 0.1369420
[9,] -0.2921867 -1.1475149
[10,] 0.5701226 -0.2921867
[11,] 0.8531833 0.5701226
[12,] 0.5673301 0.8531833
[13,] 0.4226583 0.5673301
[14,] 2.2788214 0.4226583
[15,] 0.4241914 2.2788214
[16,] -0.1454205 0.4241914
[17,] 4.5652358 -0.1454205
[18,] 2.4226583 4.5652358
[19,] 1.4248895 2.4226583
[20,] 1.4241914 1.4248895
[21,] 1.5681651 1.4241914
[22,] 3.4226583 1.5681651
[23,] 0.7071152 3.4226583
[24,] 1.4268470 0.7071152
[25,] 1.1416920 1.4268470
[26,] 0.8524851 1.1416920
[27,] 0.2788214 0.8524851
[28,] 1.2816139 0.2788214
[29,] -0.8609636 1.2816139
[30,] 0.5702595 -0.8609636
[31,] -1.8622230 0.5702595
[32,] -1.2872999 -1.8622230
[33,] 1.1362439 -1.2872999
[34,] -0.8588693 1.1362439
[35,] -7.5758086 -0.8588693
[36,] -4.1466799 -7.5758086
[37,] -1.1461186 -4.1466799
[38,] 1.8533201 -1.1461186
[39,] 0.9964589 1.8533201
[40,] 0.7120020 0.9964589
[41,] -0.8609636 0.7120020
[42,] 4.5694245 -0.8609636
[43,] -1.0035411 4.5694245
[44,] -1.4277830 -1.0035411
[45,] -4.2900924 -1.4277830
[46,] -1.4354623 -4.2900924
[47,] 0.2809158 -1.4354623
[48,] 0.8503908 0.2809158
[49,] -1.4305755 0.8503908
[50,] 1.4207008 -1.4305755
[51,] -0.5737143 1.4207008
[52,] -2.5758086 -0.5737143
[53,] 1.1355458 -2.5758086
[54,] -5.5751105 1.1355458
[55,] -1.2879980 -5.5751105
[56,] 0.4254508 -1.2879980
[57,] 1.8512258 0.4254508
[58,] -0.2907905 1.8512258
[59,] 0.9957608 -0.2907905
[60,] -2.2871630 0.9957608
[61,] 0.5680283 -2.2871630
[62,] 1.1376402 0.5680283
[63,] -0.4346273 1.1376402
[64,] 1.1404326 -0.4346273
[65,] 1.9936664 1.1404326
[66,] 2.8524851 1.9936664
[67,] 3.7135351 2.8524851
[68,] -3.5787379 3.7135351
[69,] 0.9936664 -3.5787379
[70,] -5.4333680 0.9936664
[71,] -0.4367217 -5.4333680
[72,] 3.1348477 -0.4367217
[73,] 2.5631414 3.1348477
[74,] 1.1432251 2.5631414
[75,] 1.4282432 1.1432251
[76,] 0.7085114 1.4282432
[77,] 1.1404326 0.7085114
[78,] -1.0041024 1.1404326
[79,] 0.7154926 -1.0041024
[80,] 1.2795195 0.7154926
[81,] 4.7078133 1.2795195
[82,] 0.9936664 4.7078133
[83,] -0.2886961 0.9936664
[84,] 0.4234933 -0.2886961
[85,] 1.2830101 0.4234933
[86,] -1.4354623 1.2830101
[87,] 1.2809158 -1.4354623
[88,] 1.8526220 1.2809158
[89,] 0.2816139 1.8526220
[90,] -2.4353255 0.2816139
[91,] 1.8545795 -2.4353255
[92,] 0.9908739 1.8545795
[93,] 0.4276820 0.9908739
[94,] -1.1459818 0.4276820
[95,] 0.4240545 -1.1459818
[96,] 0.7099076 0.4240545
[97,] 0.9978551 0.7099076
[98,] 0.7106058 0.9978551
[99,] -0.5779030 0.7106058
[100,] 0.5666320 -0.5779030
[101,] 0.7099076 0.5666320
[102,] 4.8510889 0.7099076
[103,] 0.4276820 4.8510889
[104,] 1.2774252 0.4276820
[105,] -2.7157305 1.2774252
[106,] 1.5673301 -2.7157305
[107,] 0.1368052 1.5673301
[108,] 0.8524851 0.1368052
[109,] -4.1504442 0.8524851
[110,] 1.7086483 -4.1504442
[111,] 1.5667689 1.7086483
[112,] 2.4186064 1.5667689
[113,] -1.5758086 2.4186064
[114,] -3.2893942 -1.5758086
[115,] 2.7120020 -3.2893942
[116,] -0.7232729 2.7120020
[117,] -0.8658504 -0.7232729
[118,] -1.7197823 -0.8658504
[119,] 0.5666320 -1.7197823
[120,] -1.7211786 0.5666320
[121,] 1.5709576 -1.7211786
[122,] -2.4354623 1.5709576
[123,] 0.7078133 -2.4354623
[124,] 0.2830101 0.7078133
[125,] 1.5667689 0.2830101
[126,] -0.5731530 1.5667689
[127,] 0.7090727 -0.5731530
[128,] 0.2795195 0.7090727
[129,] -3.0035411 0.2795195
[130,] 1.4241914 -3.0035411
[131,] -4.0048005 1.4241914
[132,] 0.2807789 -4.0048005
[133,] -1.8608268 0.2807789
[134,] -1.2921867 -1.8608268
[135,] 0.4269839 -1.2921867
[136,] -0.9978193 0.4269839
[137,] 0.1376402 -0.9978193
[138,] -3.2920498 0.1376402
[139,] -3.0042392 -3.2920498
[140,] -6.4346273 -3.0042392
[141,] 1.4234933 -6.4346273
[142,] 0.7086483 1.4234933
[143,] 2.7079502 0.7086483
[144,] 1.5737501 2.7079502
[145,] -5.7150324 1.5737501
[146,] 3.4283801 -5.7150324
[147,] -2.1482130 3.4283801
[148,] 1.4255876 -2.1482130
[149,] -2.0028430 1.4255876
[150,] -5.4354623 -2.0028430
[151,] -1.0043761 -5.4354623
[152,] 1.1355458 -1.0043761
[153,] 0.9922702 1.1355458
[154,] -1.0042392 0.9922702
[155,] -4.7169899 -1.0042392
[156,] 1.8545795 -4.7169899
[157,] -1.5786011 1.8545795
[158,] 0.2795195 -1.5786011
[159,] 1.1397345 0.2795195
[160,] -2.5772048 1.1397345
[161,] -1.8630580 -2.5772048
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.4268470 -2.8624967
2 4.7079502 0.4268470
3 0.5667689 4.7079502
4 -0.8637561 0.5667689
5 -2.0007486 -0.8637561
6 3.4275451 -2.0007486
7 0.1369420 3.4275451
8 -1.1475149 0.1369420
9 -0.2921867 -1.1475149
10 0.5701226 -0.2921867
11 0.8531833 0.5701226
12 0.5673301 0.8531833
13 0.4226583 0.5673301
14 2.2788214 0.4226583
15 0.4241914 2.2788214
16 -0.1454205 0.4241914
17 4.5652358 -0.1454205
18 2.4226583 4.5652358
19 1.4248895 2.4226583
20 1.4241914 1.4248895
21 1.5681651 1.4241914
22 3.4226583 1.5681651
23 0.7071152 3.4226583
24 1.4268470 0.7071152
25 1.1416920 1.4268470
26 0.8524851 1.1416920
27 0.2788214 0.8524851
28 1.2816139 0.2788214
29 -0.8609636 1.2816139
30 0.5702595 -0.8609636
31 -1.8622230 0.5702595
32 -1.2872999 -1.8622230
33 1.1362439 -1.2872999
34 -0.8588693 1.1362439
35 -7.5758086 -0.8588693
36 -4.1466799 -7.5758086
37 -1.1461186 -4.1466799
38 1.8533201 -1.1461186
39 0.9964589 1.8533201
40 0.7120020 0.9964589
41 -0.8609636 0.7120020
42 4.5694245 -0.8609636
43 -1.0035411 4.5694245
44 -1.4277830 -1.0035411
45 -4.2900924 -1.4277830
46 -1.4354623 -4.2900924
47 0.2809158 -1.4354623
48 0.8503908 0.2809158
49 -1.4305755 0.8503908
50 1.4207008 -1.4305755
51 -0.5737143 1.4207008
52 -2.5758086 -0.5737143
53 1.1355458 -2.5758086
54 -5.5751105 1.1355458
55 -1.2879980 -5.5751105
56 0.4254508 -1.2879980
57 1.8512258 0.4254508
58 -0.2907905 1.8512258
59 0.9957608 -0.2907905
60 -2.2871630 0.9957608
61 0.5680283 -2.2871630
62 1.1376402 0.5680283
63 -0.4346273 1.1376402
64 1.1404326 -0.4346273
65 1.9936664 1.1404326
66 2.8524851 1.9936664
67 3.7135351 2.8524851
68 -3.5787379 3.7135351
69 0.9936664 -3.5787379
70 -5.4333680 0.9936664
71 -0.4367217 -5.4333680
72 3.1348477 -0.4367217
73 2.5631414 3.1348477
74 1.1432251 2.5631414
75 1.4282432 1.1432251
76 0.7085114 1.4282432
77 1.1404326 0.7085114
78 -1.0041024 1.1404326
79 0.7154926 -1.0041024
80 1.2795195 0.7154926
81 4.7078133 1.2795195
82 0.9936664 4.7078133
83 -0.2886961 0.9936664
84 0.4234933 -0.2886961
85 1.2830101 0.4234933
86 -1.4354623 1.2830101
87 1.2809158 -1.4354623
88 1.8526220 1.2809158
89 0.2816139 1.8526220
90 -2.4353255 0.2816139
91 1.8545795 -2.4353255
92 0.9908739 1.8545795
93 0.4276820 0.9908739
94 -1.1459818 0.4276820
95 0.4240545 -1.1459818
96 0.7099076 0.4240545
97 0.9978551 0.7099076
98 0.7106058 0.9978551
99 -0.5779030 0.7106058
100 0.5666320 -0.5779030
101 0.7099076 0.5666320
102 4.8510889 0.7099076
103 0.4276820 4.8510889
104 1.2774252 0.4276820
105 -2.7157305 1.2774252
106 1.5673301 -2.7157305
107 0.1368052 1.5673301
108 0.8524851 0.1368052
109 -4.1504442 0.8524851
110 1.7086483 -4.1504442
111 1.5667689 1.7086483
112 2.4186064 1.5667689
113 -1.5758086 2.4186064
114 -3.2893942 -1.5758086
115 2.7120020 -3.2893942
116 -0.7232729 2.7120020
117 -0.8658504 -0.7232729
118 -1.7197823 -0.8658504
119 0.5666320 -1.7197823
120 -1.7211786 0.5666320
121 1.5709576 -1.7211786
122 -2.4354623 1.5709576
123 0.7078133 -2.4354623
124 0.2830101 0.7078133
125 1.5667689 0.2830101
126 -0.5731530 1.5667689
127 0.7090727 -0.5731530
128 0.2795195 0.7090727
129 -3.0035411 0.2795195
130 1.4241914 -3.0035411
131 -4.0048005 1.4241914
132 0.2807789 -4.0048005
133 -1.8608268 0.2807789
134 -1.2921867 -1.8608268
135 0.4269839 -1.2921867
136 -0.9978193 0.4269839
137 0.1376402 -0.9978193
138 -3.2920498 0.1376402
139 -3.0042392 -3.2920498
140 -6.4346273 -3.0042392
141 1.4234933 -6.4346273
142 0.7086483 1.4234933
143 2.7079502 0.7086483
144 1.5737501 2.7079502
145 -5.7150324 1.5737501
146 3.4283801 -5.7150324
147 -2.1482130 3.4283801
148 1.4255876 -2.1482130
149 -2.0028430 1.4255876
150 -5.4354623 -2.0028430
151 -1.0043761 -5.4354623
152 1.1355458 -1.0043761
153 0.9922702 1.1355458
154 -1.0042392 0.9922702
155 -4.7169899 -1.0042392
156 1.8545795 -4.7169899
157 -1.5786011 1.8545795
158 0.2795195 -1.5786011
159 1.1397345 0.2795195
160 -2.5772048 1.1397345
161 -1.8630580 -2.5772048
> 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/7kbgh1351949560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/80n8h1351949560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9omoa1351949560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10qd6e1351949560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116flp1351949560.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/126k611351949560.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/13qjdd1351949561.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/14jqgd1351949561.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/15r63d1351949561.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/160tok1351949561.tab")
+ }
>
> try(system("convert tmp/1yd2i1351949560.ps tmp/1yd2i1351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/2asaw1351949560.ps tmp/2asaw1351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/35lkm1351949560.ps tmp/35lkm1351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uqeq1351949560.ps tmp/4uqeq1351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/57pq91351949560.ps tmp/57pq91351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/66x541351949560.ps tmp/66x541351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kbgh1351949560.ps tmp/7kbgh1351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/80n8h1351949560.ps tmp/80n8h1351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/9omoa1351949560.ps tmp/9omoa1351949560.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qd6e1351949560.ps tmp/10qd6e1351949560.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.381 1.144 8.525