R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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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+ ,2)
+ ,dim=c(5
+ ,162)
+ ,dimnames=list(c('Learning'
+ ,'Belonging'
+ ,'Connected'
+ ,'Age'
+ ,'Gender')
+ ,1:162))
> y <- array(NA,dim=c(5,162),dimnames=list(c('Learning','Belonging','Connected','Age','Gender'),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'
> #'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
> 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 Belonging Connected Age Gender
1 13 53 41 7 2
2 16 86 39 5 2
3 19 66 30 5 2
4 15 67 31 5 1
5 14 76 34 8 2
6 13 78 35 6 2
7 19 53 39 5 2
8 15 80 34 6 2
9 14 74 36 5 2
10 15 76 37 4 2
11 16 79 38 6 1
12 16 54 36 5 2
13 16 67 38 5 1
14 16 54 39 6 2
15 17 87 33 7 2
16 15 58 32 6 1
17 15 75 36 7 1
18 20 88 38 6 2
19 18 64 39 8 1
20 16 57 32 7 2
21 16 66 32 5 1
22 16 68 31 5 2
23 19 54 39 7 2
24 16 56 37 7 2
25 17 86 39 5 1
26 17 80 41 4 2
27 16 76 36 10 1
28 15 69 33 6 2
29 16 78 33 5 2
30 14 67 34 5 1
31 15 80 31 5 2
32 12 54 27 5 1
33 14 71 37 6 2
34 16 84 34 5 2
35 14 74 34 5 1
36 7 71 32 5 1
37 10 63 29 5 1
38 14 71 36 5 1
39 16 76 29 5 2
40 16 69 35 5 1
41 16 74 37 5 1
42 14 75 34 7 2
43 20 54 38 5 1
44 14 52 35 6 1
45 14 69 38 7 2
46 11 68 37 7 2
47 14 65 38 5 2
48 15 75 33 5 2
49 16 74 36 4 2
50 14 75 38 5 1
51 16 72 32 4 2
52 14 67 32 5 1
53 12 63 32 5 1
54 16 62 34 7 2
55 9 63 32 5 1
56 14 76 37 5 2
57 16 74 39 6 2
58 16 67 29 4 2
59 15 73 37 6 1
60 16 70 35 6 2
61 12 53 30 5 1
62 16 77 38 7 1
63 16 77 34 6 2
64 14 52 31 8 2
65 16 54 34 7 2
66 17 80 35 5 1
67 18 66 36 6 2
68 18 73 30 6 1
69 12 63 39 5 2
70 16 69 35 5 1
71 10 67 38 5 1
72 14 54 31 5 2
73 18 81 34 4 2
74 18 69 38 6 1
75 16 84 34 6 1
76 17 80 39 6 2
77 16 70 37 6 2
78 16 69 34 7 2
79 13 77 28 5 1
80 16 54 37 7 1
81 16 79 33 6 1
82 20 30 37 5 1
83 16 71 35 5 2
84 15 73 37 4 1
85 15 72 32 8 2
86 16 77 33 8 2
87 14 75 38 5 1
88 16 69 33 5 2
89 16 54 29 6 2
90 15 70 33 4 2
91 12 73 31 5 2
92 17 54 36 5 2
93 16 77 35 5 2
94 15 82 32 5 2
95 13 80 29 6 2
96 16 80 39 6 2
97 16 69 37 5 2
98 16 78 35 6 2
99 16 81 37 5 1
100 14 76 32 7 1
101 16 76 38 5 2
102 16 73 37 6 1
103 20 85 36 6 2
104 15 66 32 6 1
105 16 79 33 4 2
106 13 68 40 5 1
107 17 76 38 5 2
108 16 71 41 7 1
109 16 54 36 6 1
110 12 46 43 9 2
111 16 82 30 6 2
112 16 74 31 6 2
113 17 88 32 5 2
114 13 38 32 6 1
115 12 76 37 5 2
116 18 86 37 8 1
117 14 54 33 7 2
118 14 70 34 5 2
119 13 69 33 7 2
120 16 90 38 6 2
121 13 54 33 6 2
122 16 76 31 9 2
123 13 89 38 7 2
124 16 76 37 6 2
125 15 73 33 5 2
126 16 79 31 5 2
127 15 90 39 6 1
128 17 74 44 6 2
129 15 81 33 7 2
130 12 72 35 5 2
131 16 71 32 5 1
132 10 66 28 5 1
133 16 77 40 6 2
134 12 65 27 4 1
135 14 74 37 5 1
136 15 82 32 7 2
137 13 54 28 5 1
138 15 63 34 7 1
139 11 54 30 7 2
140 12 64 35 6 2
141 8 69 31 5 1
142 16 54 32 8 2
143 15 84 30 5 1
144 17 86 30 5 2
145 16 77 31 5 1
146 10 89 40 6 2
147 18 76 32 4 2
148 13 60 36 5 1
149 16 75 32 5 1
150 13 73 35 7 1
151 10 85 38 6 2
152 15 79 42 7 2
153 16 71 34 10 1
154 16 72 35 6 2
155 14 69 35 8 2
156 10 78 33 4 2
157 17 54 36 5 2
158 13 69 32 6 2
159 15 81 33 7 2
160 16 84 34 7 2
161 12 84 32 6 2
162 13 69 34 6 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Belonging Connected Age Gender
8.50282 0.01269 0.13374 0.03401 0.44539
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.2989 -1.1710 0.4483 1.2563 5.5528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.50282 2.19490 3.874 0.000157 ***
Belonging 0.01269 0.01656 0.766 0.444606
Connected 0.13374 0.05255 2.545 0.011901 *
Age 0.03401 0.15309 0.222 0.824477
Gender 0.44539 0.36466 1.221 0.223777
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.215 on 157 degrees of freedom
Multiple R-squared: 0.06041, Adjusted R-squared: 0.03647
F-statistic: 2.523 on 4 and 157 DF, p-value: 0.04314
> 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,] 0.47339816 0.94679632 0.5266018
[2,] 0.52707866 0.94584269 0.4729213
[3,] 0.46391665 0.92783329 0.5360834
[4,] 0.55706396 0.88587207 0.4429360
[5,] 0.45328625 0.90657250 0.5467137
[6,] 0.34517732 0.69035464 0.6548227
[7,] 0.25454908 0.50909815 0.7454509
[8,] 0.31113045 0.62226090 0.6888695
[9,] 0.24650060 0.49300121 0.7534994
[10,] 0.18349604 0.36699209 0.8165040
[11,] 0.45106146 0.90212292 0.5489385
[12,] 0.52911153 0.94177694 0.4708885
[13,] 0.45640603 0.91281206 0.5435940
[14,] 0.38519600 0.77039200 0.6148040
[15,] 0.31757126 0.63514251 0.6824287
[16,] 0.38435614 0.76871228 0.6156439
[17,] 0.31923152 0.63846305 0.6807685
[18,] 0.27076068 0.54152135 0.7292393
[19,] 0.21878598 0.43757196 0.7812140
[20,] 0.17361148 0.34722297 0.8263885
[21,] 0.13927966 0.27855933 0.8607203
[22,] 0.10641919 0.21283837 0.8935808
[23,] 0.09821261 0.19642522 0.9017874
[24,] 0.07475658 0.14951316 0.9252434
[25,] 0.08294017 0.16588035 0.9170598
[26,] 0.08472886 0.16945773 0.9152711
[27,] 0.06368616 0.12737232 0.9363138
[28,] 0.05232240 0.10464479 0.9476776
[29,] 0.50883058 0.98233885 0.4911694
[30,] 0.58065723 0.83868554 0.4193428
[31,] 0.53043993 0.93912014 0.4695601
[32,] 0.50156768 0.99686464 0.4984323
[33,] 0.47240541 0.94481083 0.5275946
[34,] 0.42918469 0.85836937 0.5708153
[35,] 0.40658383 0.81316767 0.5934162
[36,] 0.59801427 0.80397147 0.4019857
[37,] 0.55369506 0.89260988 0.4463049
[38,] 0.55512909 0.88974181 0.4448709
[39,] 0.72510151 0.54979699 0.2748985
[40,] 0.71471513 0.57056974 0.2852849
[41,] 0.66997041 0.66005919 0.3300296
[42,] 0.62600363 0.74799274 0.3739964
[43,] 0.59635575 0.80728850 0.4036442
[44,] 0.56073623 0.87852754 0.4392638
[45,] 0.51145495 0.97709010 0.4885451
[46,] 0.50697844 0.98604313 0.4930216
[47,] 0.46643582 0.93287165 0.5335642
[48,] 0.66036299 0.67927401 0.3396370
[49,] 0.64271737 0.71456525 0.3572826
[50,] 0.59883519 0.80232962 0.4011648
[51,] 0.58235713 0.83528574 0.4176429
[52,] 0.53484700 0.93030601 0.4651530
[53,] 0.49237790 0.98475580 0.5076221
[54,] 0.47011003 0.94022006 0.5298900
[55,] 0.42868857 0.85737714 0.5713114
[56,] 0.38862304 0.77724608 0.6113770
[57,] 0.34559355 0.69118709 0.6544065
[58,] 0.31269994 0.62539989 0.6873001
[59,] 0.31405853 0.62811707 0.6859415
[60,] 0.33100765 0.66201531 0.6689923
[61,] 0.43649699 0.87299398 0.5635030
[62,] 0.51854252 0.96291495 0.4814575
[63,] 0.48848780 0.97697560 0.5115122
[64,] 0.66847027 0.66305945 0.3315297
[65,] 0.62655645 0.74688710 0.3734435
[66,] 0.65120783 0.69758433 0.3487922
[67,] 0.67925709 0.64148583 0.3207429
[68,] 0.64759655 0.70480690 0.3524034
[69,] 0.61653523 0.76692954 0.3834648
[70,] 0.57585059 0.84829881 0.4241494
[71,] 0.53817842 0.92364315 0.4618216
[72,] 0.50028694 0.99942612 0.4997131
[73,] 0.46896308 0.93792616 0.5310369
[74,] 0.43983299 0.87966599 0.5601670
[75,] 0.70755717 0.58488566 0.2924428
[76,] 0.67625099 0.64749802 0.3237490
[77,] 0.63564958 0.72870085 0.3643504
[78,] 0.59182089 0.81635822 0.4081791
[79,] 0.55361737 0.89276527 0.4463826
[80,] 0.51847978 0.96304044 0.4815202
[81,] 0.48858477 0.97716953 0.5114152
[82,] 0.48293599 0.96587198 0.5170640
[83,] 0.44035588 0.88071176 0.5596441
[84,] 0.45581033 0.91162065 0.5441897
[85,] 0.47338284 0.94676569 0.5266172
[86,] 0.43776874 0.87553748 0.5622313
[87,] 0.39269987 0.78539973 0.6073001
[88,] 0.37053149 0.74106298 0.6294685
[89,] 0.33129117 0.66258234 0.6687088
[90,] 0.30437716 0.60875433 0.6956228
[91,] 0.27185065 0.54370130 0.7281493
[92,] 0.24247751 0.48495503 0.7575225
[93,] 0.20980119 0.41960237 0.7901988
[94,] 0.18569184 0.37138369 0.8143082
[95,] 0.16464844 0.32929688 0.8353516
[96,] 0.29042152 0.58084303 0.7095785
[97,] 0.25692182 0.51384363 0.7430782
[98,] 0.23922449 0.47844899 0.7607755
[99,] 0.22899665 0.45799329 0.7710034
[100,] 0.23329503 0.46659006 0.7667050
[101,] 0.20754717 0.41509434 0.7924528
[102,] 0.21030579 0.42061157 0.7896942
[103,] 0.25424043 0.50848085 0.7457596
[104,] 0.22879356 0.45758711 0.7712064
[105,] 0.21082716 0.42165431 0.7891728
[106,] 0.21953813 0.43907625 0.7804619
[107,] 0.18582321 0.37164642 0.8141768
[108,] 0.20644485 0.41288969 0.7935552
[109,] 0.22558534 0.45117068 0.7744147
[110,] 0.19097286 0.38194573 0.8090271
[111,] 0.16152586 0.32305172 0.8384741
[112,] 0.14741850 0.29483700 0.8525815
[113,] 0.12619098 0.25238197 0.8738090
[114,] 0.10801065 0.21602129 0.8919894
[115,] 0.09017193 0.18034385 0.9098281
[116,] 0.09209292 0.18418584 0.9079071
[117,] 0.07850740 0.15701480 0.9214926
[118,] 0.06362739 0.12725479 0.9363726
[119,] 0.06002743 0.12005487 0.9399726
[120,] 0.04624875 0.09249751 0.9537512
[121,] 0.04685602 0.09371203 0.9531440
[122,] 0.03528183 0.07056365 0.9647182
[123,] 0.03433555 0.06867110 0.9656644
[124,] 0.03500821 0.07001643 0.9649918
[125,] 0.05177114 0.10354229 0.9482289
[126,] 0.04954087 0.09908174 0.9504591
[127,] 0.04279929 0.08559858 0.9572007
[128,] 0.03258057 0.06516114 0.9674194
[129,] 0.02303162 0.04606324 0.9769684
[130,] 0.01709297 0.03418594 0.9829070
[131,] 0.01206243 0.02412487 0.9879376
[132,] 0.02751026 0.05502053 0.9724897
[133,] 0.02868534 0.05737067 0.9713147
[134,] 0.26395755 0.52791509 0.7360425
[135,] 0.21526450 0.43052899 0.7847355
[136,] 0.16496854 0.32993709 0.8350315
[137,] 0.18313104 0.36626208 0.8168690
[138,] 0.16369650 0.32739300 0.8363035
[139,] 0.17783915 0.35567829 0.8221609
[140,] 0.42829223 0.85658445 0.5717078
[141,] 0.39575385 0.79150769 0.6042462
[142,] 0.56982434 0.86035133 0.4301757
[143,] 0.46317167 0.92634334 0.5368283
[144,] 0.57873966 0.84252068 0.4212603
[145,] 0.61974449 0.76051101 0.3802555
[146,] 0.47380694 0.94761388 0.5261931
[147,] 0.34103528 0.68207057 0.6589647
> postscript(file="/var/www/rcomp/tmp/11yr51323885330.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2pr7n1323885330.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3nthw1323885330.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4vgf01323885330.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5aymd1323885330.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
-2.78749027 0.12919565 4.58664607 0.88560519 -1.17724268 -2.26834021
7 8 9 10 11 12
3.54800364 -0.15998613 -1.31730141 -0.44240951 0.76314616 0.93652162
13 14 15 16 17 18
0.94945055 0.50130191 1.85090161 0.83207860 0.04737294 4.20353914
19 20 21 22 23 24
2.75175589 1.36537252 1.76455997 1.42752739 3.46729133 0.70938179
25 26 27 28 29 30
1.57458230 0.97188037 0.93265006 0.11335291 1.03314312 -0.51560394
31 32 33 34 35 36
0.27523357 -1.41446433 -1.44697491 0.82325984 -0.60444200 -7.29889579
37 38 39 40 41 42
-3.79615745 -0.83384130 1.59347094 1.32527738 0.99434887 -1.13054095
43 44 45 46 47 48
5.11443552 -0.49298363 -1.58933956 -4.44291203 -1.47055380 0.07121658
49 50 51 52 53 54
0.71670917 -1.15207866 1.27703698 -0.24813119 -2.19736658 1.03444401
55 56 57 58 59 60
-5.19736658 -1.47642009 0.24747888 1.74170187 -0.02697056 0.83318900
61 62 63 64 65 66
-1.80298231 0.75451788 0.87808732 -0.47144592 1.13597322 2.18567471
67 68 69 70 71 72
2.75021723 3.90918409 -3.57890787 1.32527738 -5.05054945 -0.39479649
73 74 75 76 77 78
2.89534387 2.89005767 1.23463591 1.17133198 0.56571625 0.94560595
79 80 81 82 83 84
-0.84009719 1.18015074 1.43182804 5.55275952 0.85450843 0.04105059
85 86 87 88 89 90
0.14099468 0.94380254 -1.15207866 1.14736348 1.83866569 0.16868291
91 92 93 94 95 96
-2.63592837 1.93652162 0.77836152 0.11611489 -1.49130425 0.17133198
97 98 99 100 101 102
0.61241797 0.73165979 0.90551081 -0.43037270 0.38984354 0.97302944
103 104 105 106 107 108
4.50908535 0.73054939 1.05446255 -2.33071336 1.38984354 0.42945566
109 110 111 112 113 114
1.34789769 -4.03414612 1.34957707 1.31736991 2.03996799 -0.91409837
115 116 117 118 119 120
-3.47642009 2.74002332 -0.73029040 -0.99906405 -1.92065767 0.17815684
121 122 123 124 125 126
-1.69627982 1.18995587 -2.84316258 0.48956934 0.09659888 1.28792473
127 128 129 130 131 132
-0.51019289 0.57879700 -0.07295148 -3.15818273 1.70110421 -3.70049452
133 134 135 136 137 138
0.07566905 -1.52005642 -1.00565113 0.04809374 -0.54820071 0.46713951
139 140 141 142 143 144
-3.32908127 -3.09066409 -6.13977711 1.36943540 0.80359200 2.33282304
145 146 147 148 149 150
1.75869368 -6.07662476 3.22627238 -1.69423864 1.65033960 -1.79350838
151 152 153 154 155 156
-5.75838740 -1.25119658 1.26357857 0.80780670 -1.22214100 -4.93284630
157 158 159 160 161 162
1.93652162 -1.75291072 -0.07295148 0.75523868 -2.94327798 -2.02038347
> postscript(file="/var/www/rcomp/tmp/6cbl11323885330.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 -2.78749027 NA
1 0.12919565 -2.78749027
2 4.58664607 0.12919565
3 0.88560519 4.58664607
4 -1.17724268 0.88560519
5 -2.26834021 -1.17724268
6 3.54800364 -2.26834021
7 -0.15998613 3.54800364
8 -1.31730141 -0.15998613
9 -0.44240951 -1.31730141
10 0.76314616 -0.44240951
11 0.93652162 0.76314616
12 0.94945055 0.93652162
13 0.50130191 0.94945055
14 1.85090161 0.50130191
15 0.83207860 1.85090161
16 0.04737294 0.83207860
17 4.20353914 0.04737294
18 2.75175589 4.20353914
19 1.36537252 2.75175589
20 1.76455997 1.36537252
21 1.42752739 1.76455997
22 3.46729133 1.42752739
23 0.70938179 3.46729133
24 1.57458230 0.70938179
25 0.97188037 1.57458230
26 0.93265006 0.97188037
27 0.11335291 0.93265006
28 1.03314312 0.11335291
29 -0.51560394 1.03314312
30 0.27523357 -0.51560394
31 -1.41446433 0.27523357
32 -1.44697491 -1.41446433
33 0.82325984 -1.44697491
34 -0.60444200 0.82325984
35 -7.29889579 -0.60444200
36 -3.79615745 -7.29889579
37 -0.83384130 -3.79615745
38 1.59347094 -0.83384130
39 1.32527738 1.59347094
40 0.99434887 1.32527738
41 -1.13054095 0.99434887
42 5.11443552 -1.13054095
43 -0.49298363 5.11443552
44 -1.58933956 -0.49298363
45 -4.44291203 -1.58933956
46 -1.47055380 -4.44291203
47 0.07121658 -1.47055380
48 0.71670917 0.07121658
49 -1.15207866 0.71670917
50 1.27703698 -1.15207866
51 -0.24813119 1.27703698
52 -2.19736658 -0.24813119
53 1.03444401 -2.19736658
54 -5.19736658 1.03444401
55 -1.47642009 -5.19736658
56 0.24747888 -1.47642009
57 1.74170187 0.24747888
58 -0.02697056 1.74170187
59 0.83318900 -0.02697056
60 -1.80298231 0.83318900
61 0.75451788 -1.80298231
62 0.87808732 0.75451788
63 -0.47144592 0.87808732
64 1.13597322 -0.47144592
65 2.18567471 1.13597322
66 2.75021723 2.18567471
67 3.90918409 2.75021723
68 -3.57890787 3.90918409
69 1.32527738 -3.57890787
70 -5.05054945 1.32527738
71 -0.39479649 -5.05054945
72 2.89534387 -0.39479649
73 2.89005767 2.89534387
74 1.23463591 2.89005767
75 1.17133198 1.23463591
76 0.56571625 1.17133198
77 0.94560595 0.56571625
78 -0.84009719 0.94560595
79 1.18015074 -0.84009719
80 1.43182804 1.18015074
81 5.55275952 1.43182804
82 0.85450843 5.55275952
83 0.04105059 0.85450843
84 0.14099468 0.04105059
85 0.94380254 0.14099468
86 -1.15207866 0.94380254
87 1.14736348 -1.15207866
88 1.83866569 1.14736348
89 0.16868291 1.83866569
90 -2.63592837 0.16868291
91 1.93652162 -2.63592837
92 0.77836152 1.93652162
93 0.11611489 0.77836152
94 -1.49130425 0.11611489
95 0.17133198 -1.49130425
96 0.61241797 0.17133198
97 0.73165979 0.61241797
98 0.90551081 0.73165979
99 -0.43037270 0.90551081
100 0.38984354 -0.43037270
101 0.97302944 0.38984354
102 4.50908535 0.97302944
103 0.73054939 4.50908535
104 1.05446255 0.73054939
105 -2.33071336 1.05446255
106 1.38984354 -2.33071336
107 0.42945566 1.38984354
108 1.34789769 0.42945566
109 -4.03414612 1.34789769
110 1.34957707 -4.03414612
111 1.31736991 1.34957707
112 2.03996799 1.31736991
113 -0.91409837 2.03996799
114 -3.47642009 -0.91409837
115 2.74002332 -3.47642009
116 -0.73029040 2.74002332
117 -0.99906405 -0.73029040
118 -1.92065767 -0.99906405
119 0.17815684 -1.92065767
120 -1.69627982 0.17815684
121 1.18995587 -1.69627982
122 -2.84316258 1.18995587
123 0.48956934 -2.84316258
124 0.09659888 0.48956934
125 1.28792473 0.09659888
126 -0.51019289 1.28792473
127 0.57879700 -0.51019289
128 -0.07295148 0.57879700
129 -3.15818273 -0.07295148
130 1.70110421 -3.15818273
131 -3.70049452 1.70110421
132 0.07566905 -3.70049452
133 -1.52005642 0.07566905
134 -1.00565113 -1.52005642
135 0.04809374 -1.00565113
136 -0.54820071 0.04809374
137 0.46713951 -0.54820071
138 -3.32908127 0.46713951
139 -3.09066409 -3.32908127
140 -6.13977711 -3.09066409
141 1.36943540 -6.13977711
142 0.80359200 1.36943540
143 2.33282304 0.80359200
144 1.75869368 2.33282304
145 -6.07662476 1.75869368
146 3.22627238 -6.07662476
147 -1.69423864 3.22627238
148 1.65033960 -1.69423864
149 -1.79350838 1.65033960
150 -5.75838740 -1.79350838
151 -1.25119658 -5.75838740
152 1.26357857 -1.25119658
153 0.80780670 1.26357857
154 -1.22214100 0.80780670
155 -4.93284630 -1.22214100
156 1.93652162 -4.93284630
157 -1.75291072 1.93652162
158 -0.07295148 -1.75291072
159 0.75523868 -0.07295148
160 -2.94327798 0.75523868
161 -2.02038347 -2.94327798
162 NA -2.02038347
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.12919565 -2.78749027
[2,] 4.58664607 0.12919565
[3,] 0.88560519 4.58664607
[4,] -1.17724268 0.88560519
[5,] -2.26834021 -1.17724268
[6,] 3.54800364 -2.26834021
[7,] -0.15998613 3.54800364
[8,] -1.31730141 -0.15998613
[9,] -0.44240951 -1.31730141
[10,] 0.76314616 -0.44240951
[11,] 0.93652162 0.76314616
[12,] 0.94945055 0.93652162
[13,] 0.50130191 0.94945055
[14,] 1.85090161 0.50130191
[15,] 0.83207860 1.85090161
[16,] 0.04737294 0.83207860
[17,] 4.20353914 0.04737294
[18,] 2.75175589 4.20353914
[19,] 1.36537252 2.75175589
[20,] 1.76455997 1.36537252
[21,] 1.42752739 1.76455997
[22,] 3.46729133 1.42752739
[23,] 0.70938179 3.46729133
[24,] 1.57458230 0.70938179
[25,] 0.97188037 1.57458230
[26,] 0.93265006 0.97188037
[27,] 0.11335291 0.93265006
[28,] 1.03314312 0.11335291
[29,] -0.51560394 1.03314312
[30,] 0.27523357 -0.51560394
[31,] -1.41446433 0.27523357
[32,] -1.44697491 -1.41446433
[33,] 0.82325984 -1.44697491
[34,] -0.60444200 0.82325984
[35,] -7.29889579 -0.60444200
[36,] -3.79615745 -7.29889579
[37,] -0.83384130 -3.79615745
[38,] 1.59347094 -0.83384130
[39,] 1.32527738 1.59347094
[40,] 0.99434887 1.32527738
[41,] -1.13054095 0.99434887
[42,] 5.11443552 -1.13054095
[43,] -0.49298363 5.11443552
[44,] -1.58933956 -0.49298363
[45,] -4.44291203 -1.58933956
[46,] -1.47055380 -4.44291203
[47,] 0.07121658 -1.47055380
[48,] 0.71670917 0.07121658
[49,] -1.15207866 0.71670917
[50,] 1.27703698 -1.15207866
[51,] -0.24813119 1.27703698
[52,] -2.19736658 -0.24813119
[53,] 1.03444401 -2.19736658
[54,] -5.19736658 1.03444401
[55,] -1.47642009 -5.19736658
[56,] 0.24747888 -1.47642009
[57,] 1.74170187 0.24747888
[58,] -0.02697056 1.74170187
[59,] 0.83318900 -0.02697056
[60,] -1.80298231 0.83318900
[61,] 0.75451788 -1.80298231
[62,] 0.87808732 0.75451788
[63,] -0.47144592 0.87808732
[64,] 1.13597322 -0.47144592
[65,] 2.18567471 1.13597322
[66,] 2.75021723 2.18567471
[67,] 3.90918409 2.75021723
[68,] -3.57890787 3.90918409
[69,] 1.32527738 -3.57890787
[70,] -5.05054945 1.32527738
[71,] -0.39479649 -5.05054945
[72,] 2.89534387 -0.39479649
[73,] 2.89005767 2.89534387
[74,] 1.23463591 2.89005767
[75,] 1.17133198 1.23463591
[76,] 0.56571625 1.17133198
[77,] 0.94560595 0.56571625
[78,] -0.84009719 0.94560595
[79,] 1.18015074 -0.84009719
[80,] 1.43182804 1.18015074
[81,] 5.55275952 1.43182804
[82,] 0.85450843 5.55275952
[83,] 0.04105059 0.85450843
[84,] 0.14099468 0.04105059
[85,] 0.94380254 0.14099468
[86,] -1.15207866 0.94380254
[87,] 1.14736348 -1.15207866
[88,] 1.83866569 1.14736348
[89,] 0.16868291 1.83866569
[90,] -2.63592837 0.16868291
[91,] 1.93652162 -2.63592837
[92,] 0.77836152 1.93652162
[93,] 0.11611489 0.77836152
[94,] -1.49130425 0.11611489
[95,] 0.17133198 -1.49130425
[96,] 0.61241797 0.17133198
[97,] 0.73165979 0.61241797
[98,] 0.90551081 0.73165979
[99,] -0.43037270 0.90551081
[100,] 0.38984354 -0.43037270
[101,] 0.97302944 0.38984354
[102,] 4.50908535 0.97302944
[103,] 0.73054939 4.50908535
[104,] 1.05446255 0.73054939
[105,] -2.33071336 1.05446255
[106,] 1.38984354 -2.33071336
[107,] 0.42945566 1.38984354
[108,] 1.34789769 0.42945566
[109,] -4.03414612 1.34789769
[110,] 1.34957707 -4.03414612
[111,] 1.31736991 1.34957707
[112,] 2.03996799 1.31736991
[113,] -0.91409837 2.03996799
[114,] -3.47642009 -0.91409837
[115,] 2.74002332 -3.47642009
[116,] -0.73029040 2.74002332
[117,] -0.99906405 -0.73029040
[118,] -1.92065767 -0.99906405
[119,] 0.17815684 -1.92065767
[120,] -1.69627982 0.17815684
[121,] 1.18995587 -1.69627982
[122,] -2.84316258 1.18995587
[123,] 0.48956934 -2.84316258
[124,] 0.09659888 0.48956934
[125,] 1.28792473 0.09659888
[126,] -0.51019289 1.28792473
[127,] 0.57879700 -0.51019289
[128,] -0.07295148 0.57879700
[129,] -3.15818273 -0.07295148
[130,] 1.70110421 -3.15818273
[131,] -3.70049452 1.70110421
[132,] 0.07566905 -3.70049452
[133,] -1.52005642 0.07566905
[134,] -1.00565113 -1.52005642
[135,] 0.04809374 -1.00565113
[136,] -0.54820071 0.04809374
[137,] 0.46713951 -0.54820071
[138,] -3.32908127 0.46713951
[139,] -3.09066409 -3.32908127
[140,] -6.13977711 -3.09066409
[141,] 1.36943540 -6.13977711
[142,] 0.80359200 1.36943540
[143,] 2.33282304 0.80359200
[144,] 1.75869368 2.33282304
[145,] -6.07662476 1.75869368
[146,] 3.22627238 -6.07662476
[147,] -1.69423864 3.22627238
[148,] 1.65033960 -1.69423864
[149,] -1.79350838 1.65033960
[150,] -5.75838740 -1.79350838
[151,] -1.25119658 -5.75838740
[152,] 1.26357857 -1.25119658
[153,] 0.80780670 1.26357857
[154,] -1.22214100 0.80780670
[155,] -4.93284630 -1.22214100
[156,] 1.93652162 -4.93284630
[157,] -1.75291072 1.93652162
[158,] -0.07295148 -1.75291072
[159,] 0.75523868 -0.07295148
[160,] -2.94327798 0.75523868
[161,] -2.02038347 -2.94327798
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.12919565 -2.78749027
2 4.58664607 0.12919565
3 0.88560519 4.58664607
4 -1.17724268 0.88560519
5 -2.26834021 -1.17724268
6 3.54800364 -2.26834021
7 -0.15998613 3.54800364
8 -1.31730141 -0.15998613
9 -0.44240951 -1.31730141
10 0.76314616 -0.44240951
11 0.93652162 0.76314616
12 0.94945055 0.93652162
13 0.50130191 0.94945055
14 1.85090161 0.50130191
15 0.83207860 1.85090161
16 0.04737294 0.83207860
17 4.20353914 0.04737294
18 2.75175589 4.20353914
19 1.36537252 2.75175589
20 1.76455997 1.36537252
21 1.42752739 1.76455997
22 3.46729133 1.42752739
23 0.70938179 3.46729133
24 1.57458230 0.70938179
25 0.97188037 1.57458230
26 0.93265006 0.97188037
27 0.11335291 0.93265006
28 1.03314312 0.11335291
29 -0.51560394 1.03314312
30 0.27523357 -0.51560394
31 -1.41446433 0.27523357
32 -1.44697491 -1.41446433
33 0.82325984 -1.44697491
34 -0.60444200 0.82325984
35 -7.29889579 -0.60444200
36 -3.79615745 -7.29889579
37 -0.83384130 -3.79615745
38 1.59347094 -0.83384130
39 1.32527738 1.59347094
40 0.99434887 1.32527738
41 -1.13054095 0.99434887
42 5.11443552 -1.13054095
43 -0.49298363 5.11443552
44 -1.58933956 -0.49298363
45 -4.44291203 -1.58933956
46 -1.47055380 -4.44291203
47 0.07121658 -1.47055380
48 0.71670917 0.07121658
49 -1.15207866 0.71670917
50 1.27703698 -1.15207866
51 -0.24813119 1.27703698
52 -2.19736658 -0.24813119
53 1.03444401 -2.19736658
54 -5.19736658 1.03444401
55 -1.47642009 -5.19736658
56 0.24747888 -1.47642009
57 1.74170187 0.24747888
58 -0.02697056 1.74170187
59 0.83318900 -0.02697056
60 -1.80298231 0.83318900
61 0.75451788 -1.80298231
62 0.87808732 0.75451788
63 -0.47144592 0.87808732
64 1.13597322 -0.47144592
65 2.18567471 1.13597322
66 2.75021723 2.18567471
67 3.90918409 2.75021723
68 -3.57890787 3.90918409
69 1.32527738 -3.57890787
70 -5.05054945 1.32527738
71 -0.39479649 -5.05054945
72 2.89534387 -0.39479649
73 2.89005767 2.89534387
74 1.23463591 2.89005767
75 1.17133198 1.23463591
76 0.56571625 1.17133198
77 0.94560595 0.56571625
78 -0.84009719 0.94560595
79 1.18015074 -0.84009719
80 1.43182804 1.18015074
81 5.55275952 1.43182804
82 0.85450843 5.55275952
83 0.04105059 0.85450843
84 0.14099468 0.04105059
85 0.94380254 0.14099468
86 -1.15207866 0.94380254
87 1.14736348 -1.15207866
88 1.83866569 1.14736348
89 0.16868291 1.83866569
90 -2.63592837 0.16868291
91 1.93652162 -2.63592837
92 0.77836152 1.93652162
93 0.11611489 0.77836152
94 -1.49130425 0.11611489
95 0.17133198 -1.49130425
96 0.61241797 0.17133198
97 0.73165979 0.61241797
98 0.90551081 0.73165979
99 -0.43037270 0.90551081
100 0.38984354 -0.43037270
101 0.97302944 0.38984354
102 4.50908535 0.97302944
103 0.73054939 4.50908535
104 1.05446255 0.73054939
105 -2.33071336 1.05446255
106 1.38984354 -2.33071336
107 0.42945566 1.38984354
108 1.34789769 0.42945566
109 -4.03414612 1.34789769
110 1.34957707 -4.03414612
111 1.31736991 1.34957707
112 2.03996799 1.31736991
113 -0.91409837 2.03996799
114 -3.47642009 -0.91409837
115 2.74002332 -3.47642009
116 -0.73029040 2.74002332
117 -0.99906405 -0.73029040
118 -1.92065767 -0.99906405
119 0.17815684 -1.92065767
120 -1.69627982 0.17815684
121 1.18995587 -1.69627982
122 -2.84316258 1.18995587
123 0.48956934 -2.84316258
124 0.09659888 0.48956934
125 1.28792473 0.09659888
126 -0.51019289 1.28792473
127 0.57879700 -0.51019289
128 -0.07295148 0.57879700
129 -3.15818273 -0.07295148
130 1.70110421 -3.15818273
131 -3.70049452 1.70110421
132 0.07566905 -3.70049452
133 -1.52005642 0.07566905
134 -1.00565113 -1.52005642
135 0.04809374 -1.00565113
136 -0.54820071 0.04809374
137 0.46713951 -0.54820071
138 -3.32908127 0.46713951
139 -3.09066409 -3.32908127
140 -6.13977711 -3.09066409
141 1.36943540 -6.13977711
142 0.80359200 1.36943540
143 2.33282304 0.80359200
144 1.75869368 2.33282304
145 -6.07662476 1.75869368
146 3.22627238 -6.07662476
147 -1.69423864 3.22627238
148 1.65033960 -1.69423864
149 -1.79350838 1.65033960
150 -5.75838740 -1.79350838
151 -1.25119658 -5.75838740
152 1.26357857 -1.25119658
153 0.80780670 1.26357857
154 -1.22214100 0.80780670
155 -4.93284630 -1.22214100
156 1.93652162 -4.93284630
157 -1.75291072 1.93652162
158 -0.07295148 -1.75291072
159 0.75523868 -0.07295148
160 -2.94327798 0.75523868
161 -2.02038347 -2.94327798
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7gpls1323885330.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8hrji1323885330.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9qbof1323885330.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/103v3q1323885330.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11oknk1323885330.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1213kb1323885330.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1327oe1323885330.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14iagy1323885330.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15z2qh1323885330.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16uazk1323885330.tab")
+ }
>
> try(system("convert tmp/11yr51323885330.ps tmp/11yr51323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/2pr7n1323885330.ps tmp/2pr7n1323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nthw1323885330.ps tmp/3nthw1323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vgf01323885330.ps tmp/4vgf01323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/5aymd1323885330.ps tmp/5aymd1323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cbl11323885330.ps tmp/6cbl11323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gpls1323885330.ps tmp/7gpls1323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hrji1323885330.ps tmp/8hrji1323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qbof1323885330.ps tmp/9qbof1323885330.png",intern=TRUE))
character(0)
> try(system("convert tmp/103v3q1323885330.ps tmp/103v3q1323885330.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.660 0.330 4.988