R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(14
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+ ,69)
+ ,dim=c(3
+ ,162)
+ ,dimnames=list(c('Happiness'
+ ,'Depression'
+ ,'Belonging')
+ ,1:162))
> y <- array(NA,dim=c(3,162),dimnames=list(c('Happiness','Depression','Belonging'),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 = '3'
> #'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.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
Belonging Happiness Depression
1 53 14 12
2 86 18 11
3 66 11 14
4 67 12 12
5 76 16 21
6 78 18 12
7 53 14 22
8 80 14 11
9 74 15 10
10 76 15 13
11 79 17 10
12 54 19 8
13 67 10 15
14 54 16 14
15 87 18 10
16 58 14 14
17 75 14 14
18 88 17 11
19 64 14 10
20 57 16 13
21 66 18 7
22 68 11 14
23 54 14 12
24 56 12 14
25 86 17 11
26 80 9 9
27 76 16 11
28 69 14 15
29 78 15 14
30 67 11 13
31 80 16 9
32 54 13 15
33 71 17 10
34 84 15 11
35 74 14 13
36 71 16 8
37 63 9 20
38 71 15 12
39 76 17 10
40 69 13 10
41 74 15 9
42 75 16 14
43 54 16 8
44 52 12 14
45 69 12 11
46 68 11 13
47 65 15 9
48 75 15 11
49 74 17 15
50 75 13 11
51 72 16 10
52 67 14 14
53 63 11 18
54 62 12 14
55 63 12 11
56 76 15 12
57 74 16 13
58 67 15 9
59 73 12 10
60 70 12 15
61 53 8 20
62 77 13 12
63 77 11 12
64 52 14 14
65 54 15 13
66 80 10 11
67 66 11 17
68 73 12 12
69 63 15 13
70 69 15 14
71 67 14 13
72 54 16 15
73 81 15 13
74 69 15 10
75 84 13 11
76 80 12 19
77 70 17 13
78 69 13 17
79 77 15 13
80 54 13 9
81 79 15 11
82 30 16 10
83 71 15 9
84 73 16 12
85 72 15 12
86 77 14 13
87 75 15 13
88 69 14 12
89 54 13 15
90 70 7 22
91 73 17 13
92 54 13 15
93 77 15 13
94 82 14 15
95 80 13 10
96 80 16 11
97 69 12 16
98 78 14 11
99 81 17 11
100 76 15 10
101 76 17 10
102 73 12 16
103 85 16 12
104 66 11 11
105 79 15 16
106 68 9 19
107 76 16 11
108 71 15 16
109 54 10 15
110 46 10 24
111 82 15 14
112 74 11 15
113 88 13 11
114 38 14 15
115 76 18 12
116 86 16 10
117 54 14 14
118 70 14 13
119 69 14 9
120 90 14 15
121 54 12 15
122 76 14 14
123 89 15 11
124 76 15 8
125 73 15 11
126 79 13 11
127 90 17 8
128 74 17 10
129 81 19 11
130 72 15 13
131 71 13 11
132 66 9 20
133 77 15 10
134 65 15 15
135 74 15 12
136 82 16 14
137 54 11 23
138 63 14 14
139 54 11 16
140 64 15 11
141 69 13 12
142 54 15 10
143 84 16 14
144 86 14 12
145 77 15 12
146 89 16 11
147 76 16 12
148 60 11 13
149 75 12 11
150 73 9 19
151 85 16 12
152 79 13 17
153 71 16 9
154 72 12 12
155 69 9 19
156 78 13 18
157 54 13 15
158 69 14 14
159 81 19 11
160 84 13 9
161 84 12 18
162 69 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Happiness Depression
65.5916 0.8897 -0.5705
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-44.122 -4.177 1.215 6.361 20.510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 65.5916 8.5983 7.628 2.05e-12 ***
Happiness 0.8897 0.4110 2.165 0.0319 *
Depression -0.5705 0.3035 -1.880 0.0619 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.23 on 159 degrees of freedom
Multiple R-squared: 0.1016, Adjusted R-squared: 0.09025
F-statistic: 8.986 on 2 and 159 DF, p-value: 0.0002007
> 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]
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[2,] 0.66640621 0.66718759 0.3335938
[3,] 0.63591843 0.72816313 0.3640816
[4,] 0.51073579 0.97852841 0.4892642
[5,] 0.40714807 0.81429614 0.5928519
[6,] 0.30386508 0.60773015 0.6961349
[7,] 0.81476963 0.37046074 0.1852304
[8,] 0.74931509 0.50136982 0.2506849
[9,] 0.81935826 0.36128348 0.1806417
[10,] 0.84051998 0.31896005 0.1594800
[11,] 0.83199133 0.33601734 0.1680087
[12,] 0.80039435 0.39921129 0.1996056
[13,] 0.83379471 0.33241058 0.1662053
[14,] 0.80531004 0.38937992 0.1946900
[15,] 0.83746463 0.32507074 0.1625354
[16,] 0.83073448 0.33853104 0.1692655
[17,] 0.78810245 0.42379509 0.2118975
[18,] 0.82767785 0.34464429 0.1723221
[19,] 0.81619204 0.36761592 0.1838080
[20,] 0.83923902 0.32152195 0.1607610
[21,] 0.86791444 0.26417113 0.1320856
[22,] 0.83743945 0.32512110 0.1625605
[23,] 0.79834711 0.40330578 0.2016529
[24,] 0.78372676 0.43254648 0.2162732
[25,] 0.73765302 0.52469397 0.2623470
[26,] 0.70449973 0.59100054 0.2955003
[27,] 0.72873245 0.54253510 0.2712675
[28,] 0.68394699 0.63210602 0.3160530
[29,] 0.70217764 0.59564472 0.2978224
[30,] 0.66185116 0.67629767 0.3381488
[31,] 0.61797720 0.76404560 0.3820228
[32,] 0.56977336 0.86045328 0.4302266
[33,] 0.51563098 0.96873805 0.4843690
[34,] 0.46297117 0.92594234 0.5370288
[35,] 0.41154264 0.82308527 0.5884574
[36,] 0.36038389 0.72076778 0.6396161
[37,] 0.32074540 0.64149079 0.6792546
[38,] 0.47426341 0.94852682 0.5257366
[39,] 0.53221509 0.93556981 0.4677849
[40,] 0.48175712 0.96351423 0.5182429
[41,] 0.43280537 0.86561073 0.5671946
[42,] 0.41146802 0.82293604 0.5885320
[43,] 0.36962492 0.73924985 0.6303751
[44,] 0.32704965 0.65409929 0.6729504
[45,] 0.29588389 0.59176779 0.7041161
[46,] 0.25533475 0.51066951 0.7446652
[47,] 0.21883800 0.43767600 0.7811620
[48,] 0.18458103 0.36916206 0.8154190
[49,] 0.16139872 0.32279743 0.8386013
[50,] 0.14252560 0.28505119 0.8574744
[51,] 0.12325161 0.24650321 0.8767484
[52,] 0.10170389 0.20340779 0.8982961
[53,] 0.08862735 0.17725469 0.9113727
[54,] 0.07369648 0.14739296 0.9263035
[55,] 0.06022646 0.12045293 0.9397735
[56,] 0.05320241 0.10640482 0.9467976
[57,] 0.04867118 0.09734237 0.9513288
[58,] 0.04783978 0.09567957 0.9521602
[59,] 0.07598862 0.15197723 0.9240114
[60,] 0.11048926 0.22097851 0.8895107
[61,] 0.12219674 0.24439348 0.8778033
[62,] 0.10095752 0.20191505 0.8990425
[63,] 0.08497602 0.16995204 0.9150240
[64,] 0.07805554 0.15611108 0.9219445
[65,] 0.06313982 0.12627963 0.9368602
[66,] 0.05126378 0.10252756 0.9487362
[67,] 0.07489509 0.14979018 0.9251049
[68,] 0.07691698 0.15383396 0.9230830
[69,] 0.06395249 0.12790498 0.9360475
[70,] 0.07669773 0.15339546 0.9233023
[71,] 0.10267434 0.20534869 0.8973257
[72,] 0.08637860 0.17275719 0.9136214
[73,] 0.07080207 0.14160415 0.9291979
[74,] 0.06160228 0.12320456 0.9383977
[75,] 0.09657389 0.19314777 0.9034261
[76,] 0.08637814 0.17275629 0.9136219
[77,] 0.75752770 0.48494460 0.2424723
[78,] 0.72797087 0.54405826 0.2720291
[79,] 0.69269912 0.61460177 0.3073009
[80,] 0.65464971 0.69070058 0.3453503
[81,] 0.62878836 0.74242328 0.3712116
[82,] 0.59138771 0.81722458 0.4086123
[83,] 0.55199501 0.89600997 0.4480050
[84,] 0.60781242 0.78437515 0.3921876
[85,] 0.62202343 0.75595315 0.3779766
[86,] 0.58457094 0.83085812 0.4154291
[87,] 0.64194726 0.71610547 0.3580527
[88,] 0.61037910 0.77924180 0.3896209
[89,] 0.62890459 0.74219083 0.3710954
[90,] 0.61280105 0.77439790 0.3871990
[91,] 0.58382061 0.83235877 0.4161794
[92,] 0.53922430 0.92155141 0.4607757
[93,] 0.50739986 0.98520027 0.4926001
[94,] 0.47720574 0.95441148 0.5227943
[95,] 0.43463174 0.86926349 0.5653683
[96,] 0.39458031 0.78916062 0.6054197
[97,] 0.36333390 0.72666779 0.6366661
[98,] 0.36877349 0.73754698 0.6312265
[99,] 0.33024374 0.66048748 0.6697563
[100,] 0.31626028 0.63252056 0.6837397
[101,] 0.28962908 0.57925817 0.7103709
[102,] 0.25279287 0.50558573 0.7472071
[103,] 0.21644495 0.43288990 0.7835551
[104,] 0.22848789 0.45697579 0.7715121
[105,] 0.26223881 0.52447762 0.7377612
[106,] 0.25893689 0.51787377 0.7410631
[107,] 0.23635540 0.47271081 0.7636446
[108,] 0.29589950 0.59179900 0.7041005
[109,] 0.74366345 0.51267310 0.2563365
[110,] 0.70842901 0.58314198 0.2915710
[111,] 0.70757601 0.58484797 0.2924240
[112,] 0.80207253 0.39585494 0.1979275
[113,] 0.76744633 0.46510735 0.2325537
[114,] 0.73628108 0.52743783 0.2637189
[115,] 0.82911470 0.34177060 0.1708853
[116,] 0.87313510 0.25372980 0.1268649
[117,] 0.84760702 0.30478595 0.1523930
[118,] 0.88137081 0.23725838 0.1186292
[119,] 0.85147580 0.29704840 0.1485242
[120,] 0.81822312 0.36355376 0.1817769
[121,] 0.79937732 0.40124535 0.2006227
[122,] 0.81925513 0.36148974 0.1807449
[123,] 0.78350666 0.43298667 0.2164933
[124,] 0.74040603 0.51918795 0.2595940
[125,] 0.69272831 0.61454339 0.3072717
[126,] 0.63819788 0.72360424 0.3618021
[127,] 0.58411083 0.83177835 0.4158892
[128,] 0.52729860 0.94540281 0.4727014
[129,] 0.51289580 0.97420840 0.4871042
[130,] 0.45117006 0.90234012 0.5488299
[131,] 0.41403162 0.82806324 0.5859684
[132,] 0.49019563 0.98039126 0.5098044
[133,] 0.49610178 0.99220356 0.5038982
[134,] 0.59525878 0.80948244 0.4047412
[135,] 0.60428472 0.79143056 0.3957153
[136,] 0.54101772 0.91796456 0.4589823
[137,] 0.80318420 0.39363159 0.1968158
[138,] 0.76634044 0.46731913 0.2336596
[139,] 0.78464627 0.43070747 0.2153537
[140,] 0.72071630 0.55856740 0.2792837
[141,] 0.75959200 0.48081599 0.2404080
[142,] 0.68632590 0.62734821 0.3136741
[143,] 0.71716147 0.56567707 0.2828385
[144,] 0.63413064 0.73173872 0.3658694
[145,] 0.54964136 0.90071728 0.4503586
[146,] 0.52699341 0.94601317 0.4730066
[147,] 0.45925181 0.91850361 0.5407482
[148,] 0.37181121 0.74362243 0.6281888
[149,] 0.26410660 0.52821321 0.7358934
[150,] 0.16744989 0.33489978 0.8325501
[151,] 0.11528682 0.23057364 0.8847132
> postscript(file="/var/www/html/rcomp/tmp/1mdix1290557264.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2e4hi1290557264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3e4hi1290557264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4e4hi1290557264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57vg31290557264.ps",horizontal=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
-18.20164758 10.66895918 -1.39147144 -2.42220344 8.15345300 3.23946415
7 8 9 10 11 12
-12.49659790 8.22784745 0.76762042 4.47913532 3.98817628 -23.93227779
13 14 15 16 17 18
1.06875560 -17.84008178 11.09845421 -12.06063764 4.93936236 13.55868125
19 20 21 22 23 24
-8.34265752 -15.41058675 -11.61306069 0.60852856 -17.20164758 -12.28119351
25 26 27 28 29 30
11.55868125 11.53544786 2.44840332 -0.49013267 7.04964029 -0.96197641
31 32 33 34 35 36
5.30739338 -14.60041061 -4.01182372 11.33812538 3.36885739 -4.26311159
37 38 39 40 41 42
0.81100251 -1.09136965 0.98817628 -2.45293545 0.19711545 3.15991822
43 44 45 46 47 48
-21.26311159 -16.28119351 -0.99270841 0.03802359 -8.80288455 2.33812538
49 50 51 52 53 54
1.84070112 4.11756952 -2.12210165 -3.06063764 -2.10945157 -6.28119351
55 56 57 58 59 60
-6.99270841 3.90863035 1.58941325 -6.80288455 2.43678662 2.28931146
61 62 63 64 65 66
-8.29927542 6.68807449 8.46751862 -18.06063764 -17.52086468 11.78673572
67 68 69 70 71 72
0.32004347 3.57779656 -8.52086468 -1.95035971 -3.63114261 -17.26957681
73 74 75 76 77 78
9.47913532 -4.23237958 13.11756952 14.57133134 -3.30030882 1.54059933
79 80 81 82 83 84
5.47913532 -18.02344042 6.33812538 -44.12210165 -2.80288455 0.01890828
85 86 87 88 89 90
-0.09136965 6.36885739 3.47913532 -2.20164758 -14.60041061 10.73145658
91 92 93 94 95 96
-0.30030882 -14.60041061 5.47913532 12.50986733 8.54706455 6.44840332
97 98 99 100 101 102
1.85981643 6.22784745 6.55868125 2.76762042 0.98817628 5.85981643
103 104 105 106 107 108
12.01890828 -3.10298634 9.19065023 5.24049754 2.44840332 1.19065023
109 110 111 112 113 114
-11.93124440 -14.79669969 11.04964029 7.17903353 17.11756952 -31.49013267
115 116 117 118 119 120
1.23946415 11.87789835 -16.06063764 -0.63114261 -3.91316248 20.50986733
121 122 123 124 125 126
-13.71068854 5.93936236 16.33812538 1.62661048 0.33812538 8.11756952
127 128 129 130 131 132
13.84716634 -1.01182372 4.77923711 0.47913532 0.11756952 3.81100251
133 134 135 136 137 138
3.76762042 -5.37985474 1.90863035 10.15991822 -8.25692672 -7.06063764
139 140 141 142 143 144
-12.25046150 -8.66187462 -1.31192551 -19.23237958 12.15991822 14.79835242
145 146 147 148 149 150
4.90863035 15.44840332 3.01890828 -7.96197641 5.00729159 10.24049754
151 152 153 154 155 156
12.01890828 11.54059933 -3.69260662 2.57779656 6.24049754 11.11110430
157 158 159 160 161 162
-14.60041061 -1.06063764 4.77923711 11.97655958 18.00082637 0.97009436
> postscript(file="/var/www/html/rcomp/tmp/67vg31290557264.ps",horizontal=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 -18.20164758 NA
1 10.66895918 -18.20164758
2 -1.39147144 10.66895918
3 -2.42220344 -1.39147144
4 8.15345300 -2.42220344
5 3.23946415 8.15345300
6 -12.49659790 3.23946415
7 8.22784745 -12.49659790
8 0.76762042 8.22784745
9 4.47913532 0.76762042
10 3.98817628 4.47913532
11 -23.93227779 3.98817628
12 1.06875560 -23.93227779
13 -17.84008178 1.06875560
14 11.09845421 -17.84008178
15 -12.06063764 11.09845421
16 4.93936236 -12.06063764
17 13.55868125 4.93936236
18 -8.34265752 13.55868125
19 -15.41058675 -8.34265752
20 -11.61306069 -15.41058675
21 0.60852856 -11.61306069
22 -17.20164758 0.60852856
23 -12.28119351 -17.20164758
24 11.55868125 -12.28119351
25 11.53544786 11.55868125
26 2.44840332 11.53544786
27 -0.49013267 2.44840332
28 7.04964029 -0.49013267
29 -0.96197641 7.04964029
30 5.30739338 -0.96197641
31 -14.60041061 5.30739338
32 -4.01182372 -14.60041061
33 11.33812538 -4.01182372
34 3.36885739 11.33812538
35 -4.26311159 3.36885739
36 0.81100251 -4.26311159
37 -1.09136965 0.81100251
38 0.98817628 -1.09136965
39 -2.45293545 0.98817628
40 0.19711545 -2.45293545
41 3.15991822 0.19711545
42 -21.26311159 3.15991822
43 -16.28119351 -21.26311159
44 -0.99270841 -16.28119351
45 0.03802359 -0.99270841
46 -8.80288455 0.03802359
47 2.33812538 -8.80288455
48 1.84070112 2.33812538
49 4.11756952 1.84070112
50 -2.12210165 4.11756952
51 -3.06063764 -2.12210165
52 -2.10945157 -3.06063764
53 -6.28119351 -2.10945157
54 -6.99270841 -6.28119351
55 3.90863035 -6.99270841
56 1.58941325 3.90863035
57 -6.80288455 1.58941325
58 2.43678662 -6.80288455
59 2.28931146 2.43678662
60 -8.29927542 2.28931146
61 6.68807449 -8.29927542
62 8.46751862 6.68807449
63 -18.06063764 8.46751862
64 -17.52086468 -18.06063764
65 11.78673572 -17.52086468
66 0.32004347 11.78673572
67 3.57779656 0.32004347
68 -8.52086468 3.57779656
69 -1.95035971 -8.52086468
70 -3.63114261 -1.95035971
71 -17.26957681 -3.63114261
72 9.47913532 -17.26957681
73 -4.23237958 9.47913532
74 13.11756952 -4.23237958
75 14.57133134 13.11756952
76 -3.30030882 14.57133134
77 1.54059933 -3.30030882
78 5.47913532 1.54059933
79 -18.02344042 5.47913532
80 6.33812538 -18.02344042
81 -44.12210165 6.33812538
82 -2.80288455 -44.12210165
83 0.01890828 -2.80288455
84 -0.09136965 0.01890828
85 6.36885739 -0.09136965
86 3.47913532 6.36885739
87 -2.20164758 3.47913532
88 -14.60041061 -2.20164758
89 10.73145658 -14.60041061
90 -0.30030882 10.73145658
91 -14.60041061 -0.30030882
92 5.47913532 -14.60041061
93 12.50986733 5.47913532
94 8.54706455 12.50986733
95 6.44840332 8.54706455
96 1.85981643 6.44840332
97 6.22784745 1.85981643
98 6.55868125 6.22784745
99 2.76762042 6.55868125
100 0.98817628 2.76762042
101 5.85981643 0.98817628
102 12.01890828 5.85981643
103 -3.10298634 12.01890828
104 9.19065023 -3.10298634
105 5.24049754 9.19065023
106 2.44840332 5.24049754
107 1.19065023 2.44840332
108 -11.93124440 1.19065023
109 -14.79669969 -11.93124440
110 11.04964029 -14.79669969
111 7.17903353 11.04964029
112 17.11756952 7.17903353
113 -31.49013267 17.11756952
114 1.23946415 -31.49013267
115 11.87789835 1.23946415
116 -16.06063764 11.87789835
117 -0.63114261 -16.06063764
118 -3.91316248 -0.63114261
119 20.50986733 -3.91316248
120 -13.71068854 20.50986733
121 5.93936236 -13.71068854
122 16.33812538 5.93936236
123 1.62661048 16.33812538
124 0.33812538 1.62661048
125 8.11756952 0.33812538
126 13.84716634 8.11756952
127 -1.01182372 13.84716634
128 4.77923711 -1.01182372
129 0.47913532 4.77923711
130 0.11756952 0.47913532
131 3.81100251 0.11756952
132 3.76762042 3.81100251
133 -5.37985474 3.76762042
134 1.90863035 -5.37985474
135 10.15991822 1.90863035
136 -8.25692672 10.15991822
137 -7.06063764 -8.25692672
138 -12.25046150 -7.06063764
139 -8.66187462 -12.25046150
140 -1.31192551 -8.66187462
141 -19.23237958 -1.31192551
142 12.15991822 -19.23237958
143 14.79835242 12.15991822
144 4.90863035 14.79835242
145 15.44840332 4.90863035
146 3.01890828 15.44840332
147 -7.96197641 3.01890828
148 5.00729159 -7.96197641
149 10.24049754 5.00729159
150 12.01890828 10.24049754
151 11.54059933 12.01890828
152 -3.69260662 11.54059933
153 2.57779656 -3.69260662
154 6.24049754 2.57779656
155 11.11110430 6.24049754
156 -14.60041061 11.11110430
157 -1.06063764 -14.60041061
158 4.77923711 -1.06063764
159 11.97655958 4.77923711
160 18.00082637 11.97655958
161 0.97009436 18.00082637
162 NA 0.97009436
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 10.66895918 -18.20164758
[2,] -1.39147144 10.66895918
[3,] -2.42220344 -1.39147144
[4,] 8.15345300 -2.42220344
[5,] 3.23946415 8.15345300
[6,] -12.49659790 3.23946415
[7,] 8.22784745 -12.49659790
[8,] 0.76762042 8.22784745
[9,] 4.47913532 0.76762042
[10,] 3.98817628 4.47913532
[11,] -23.93227779 3.98817628
[12,] 1.06875560 -23.93227779
[13,] -17.84008178 1.06875560
[14,] 11.09845421 -17.84008178
[15,] -12.06063764 11.09845421
[16,] 4.93936236 -12.06063764
[17,] 13.55868125 4.93936236
[18,] -8.34265752 13.55868125
[19,] -15.41058675 -8.34265752
[20,] -11.61306069 -15.41058675
[21,] 0.60852856 -11.61306069
[22,] -17.20164758 0.60852856
[23,] -12.28119351 -17.20164758
[24,] 11.55868125 -12.28119351
[25,] 11.53544786 11.55868125
[26,] 2.44840332 11.53544786
[27,] -0.49013267 2.44840332
[28,] 7.04964029 -0.49013267
[29,] -0.96197641 7.04964029
[30,] 5.30739338 -0.96197641
[31,] -14.60041061 5.30739338
[32,] -4.01182372 -14.60041061
[33,] 11.33812538 -4.01182372
[34,] 3.36885739 11.33812538
[35,] -4.26311159 3.36885739
[36,] 0.81100251 -4.26311159
[37,] -1.09136965 0.81100251
[38,] 0.98817628 -1.09136965
[39,] -2.45293545 0.98817628
[40,] 0.19711545 -2.45293545
[41,] 3.15991822 0.19711545
[42,] -21.26311159 3.15991822
[43,] -16.28119351 -21.26311159
[44,] -0.99270841 -16.28119351
[45,] 0.03802359 -0.99270841
[46,] -8.80288455 0.03802359
[47,] 2.33812538 -8.80288455
[48,] 1.84070112 2.33812538
[49,] 4.11756952 1.84070112
[50,] -2.12210165 4.11756952
[51,] -3.06063764 -2.12210165
[52,] -2.10945157 -3.06063764
[53,] -6.28119351 -2.10945157
[54,] -6.99270841 -6.28119351
[55,] 3.90863035 -6.99270841
[56,] 1.58941325 3.90863035
[57,] -6.80288455 1.58941325
[58,] 2.43678662 -6.80288455
[59,] 2.28931146 2.43678662
[60,] -8.29927542 2.28931146
[61,] 6.68807449 -8.29927542
[62,] 8.46751862 6.68807449
[63,] -18.06063764 8.46751862
[64,] -17.52086468 -18.06063764
[65,] 11.78673572 -17.52086468
[66,] 0.32004347 11.78673572
[67,] 3.57779656 0.32004347
[68,] -8.52086468 3.57779656
[69,] -1.95035971 -8.52086468
[70,] -3.63114261 -1.95035971
[71,] -17.26957681 -3.63114261
[72,] 9.47913532 -17.26957681
[73,] -4.23237958 9.47913532
[74,] 13.11756952 -4.23237958
[75,] 14.57133134 13.11756952
[76,] -3.30030882 14.57133134
[77,] 1.54059933 -3.30030882
[78,] 5.47913532 1.54059933
[79,] -18.02344042 5.47913532
[80,] 6.33812538 -18.02344042
[81,] -44.12210165 6.33812538
[82,] -2.80288455 -44.12210165
[83,] 0.01890828 -2.80288455
[84,] -0.09136965 0.01890828
[85,] 6.36885739 -0.09136965
[86,] 3.47913532 6.36885739
[87,] -2.20164758 3.47913532
[88,] -14.60041061 -2.20164758
[89,] 10.73145658 -14.60041061
[90,] -0.30030882 10.73145658
[91,] -14.60041061 -0.30030882
[92,] 5.47913532 -14.60041061
[93,] 12.50986733 5.47913532
[94,] 8.54706455 12.50986733
[95,] 6.44840332 8.54706455
[96,] 1.85981643 6.44840332
[97,] 6.22784745 1.85981643
[98,] 6.55868125 6.22784745
[99,] 2.76762042 6.55868125
[100,] 0.98817628 2.76762042
[101,] 5.85981643 0.98817628
[102,] 12.01890828 5.85981643
[103,] -3.10298634 12.01890828
[104,] 9.19065023 -3.10298634
[105,] 5.24049754 9.19065023
[106,] 2.44840332 5.24049754
[107,] 1.19065023 2.44840332
[108,] -11.93124440 1.19065023
[109,] -14.79669969 -11.93124440
[110,] 11.04964029 -14.79669969
[111,] 7.17903353 11.04964029
[112,] 17.11756952 7.17903353
[113,] -31.49013267 17.11756952
[114,] 1.23946415 -31.49013267
[115,] 11.87789835 1.23946415
[116,] -16.06063764 11.87789835
[117,] -0.63114261 -16.06063764
[118,] -3.91316248 -0.63114261
[119,] 20.50986733 -3.91316248
[120,] -13.71068854 20.50986733
[121,] 5.93936236 -13.71068854
[122,] 16.33812538 5.93936236
[123,] 1.62661048 16.33812538
[124,] 0.33812538 1.62661048
[125,] 8.11756952 0.33812538
[126,] 13.84716634 8.11756952
[127,] -1.01182372 13.84716634
[128,] 4.77923711 -1.01182372
[129,] 0.47913532 4.77923711
[130,] 0.11756952 0.47913532
[131,] 3.81100251 0.11756952
[132,] 3.76762042 3.81100251
[133,] -5.37985474 3.76762042
[134,] 1.90863035 -5.37985474
[135,] 10.15991822 1.90863035
[136,] -8.25692672 10.15991822
[137,] -7.06063764 -8.25692672
[138,] -12.25046150 -7.06063764
[139,] -8.66187462 -12.25046150
[140,] -1.31192551 -8.66187462
[141,] -19.23237958 -1.31192551
[142,] 12.15991822 -19.23237958
[143,] 14.79835242 12.15991822
[144,] 4.90863035 14.79835242
[145,] 15.44840332 4.90863035
[146,] 3.01890828 15.44840332
[147,] -7.96197641 3.01890828
[148,] 5.00729159 -7.96197641
[149,] 10.24049754 5.00729159
[150,] 12.01890828 10.24049754
[151,] 11.54059933 12.01890828
[152,] -3.69260662 11.54059933
[153,] 2.57779656 -3.69260662
[154,] 6.24049754 2.57779656
[155,] 11.11110430 6.24049754
[156,] -14.60041061 11.11110430
[157,] -1.06063764 -14.60041061
[158,] 4.77923711 -1.06063764
[159,] 11.97655958 4.77923711
[160,] 18.00082637 11.97655958
[161,] 0.97009436 18.00082637
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 10.66895918 -18.20164758
2 -1.39147144 10.66895918
3 -2.42220344 -1.39147144
4 8.15345300 -2.42220344
5 3.23946415 8.15345300
6 -12.49659790 3.23946415
7 8.22784745 -12.49659790
8 0.76762042 8.22784745
9 4.47913532 0.76762042
10 3.98817628 4.47913532
11 -23.93227779 3.98817628
12 1.06875560 -23.93227779
13 -17.84008178 1.06875560
14 11.09845421 -17.84008178
15 -12.06063764 11.09845421
16 4.93936236 -12.06063764
17 13.55868125 4.93936236
18 -8.34265752 13.55868125
19 -15.41058675 -8.34265752
20 -11.61306069 -15.41058675
21 0.60852856 -11.61306069
22 -17.20164758 0.60852856
23 -12.28119351 -17.20164758
24 11.55868125 -12.28119351
25 11.53544786 11.55868125
26 2.44840332 11.53544786
27 -0.49013267 2.44840332
28 7.04964029 -0.49013267
29 -0.96197641 7.04964029
30 5.30739338 -0.96197641
31 -14.60041061 5.30739338
32 -4.01182372 -14.60041061
33 11.33812538 -4.01182372
34 3.36885739 11.33812538
35 -4.26311159 3.36885739
36 0.81100251 -4.26311159
37 -1.09136965 0.81100251
38 0.98817628 -1.09136965
39 -2.45293545 0.98817628
40 0.19711545 -2.45293545
41 3.15991822 0.19711545
42 -21.26311159 3.15991822
43 -16.28119351 -21.26311159
44 -0.99270841 -16.28119351
45 0.03802359 -0.99270841
46 -8.80288455 0.03802359
47 2.33812538 -8.80288455
48 1.84070112 2.33812538
49 4.11756952 1.84070112
50 -2.12210165 4.11756952
51 -3.06063764 -2.12210165
52 -2.10945157 -3.06063764
53 -6.28119351 -2.10945157
54 -6.99270841 -6.28119351
55 3.90863035 -6.99270841
56 1.58941325 3.90863035
57 -6.80288455 1.58941325
58 2.43678662 -6.80288455
59 2.28931146 2.43678662
60 -8.29927542 2.28931146
61 6.68807449 -8.29927542
62 8.46751862 6.68807449
63 -18.06063764 8.46751862
64 -17.52086468 -18.06063764
65 11.78673572 -17.52086468
66 0.32004347 11.78673572
67 3.57779656 0.32004347
68 -8.52086468 3.57779656
69 -1.95035971 -8.52086468
70 -3.63114261 -1.95035971
71 -17.26957681 -3.63114261
72 9.47913532 -17.26957681
73 -4.23237958 9.47913532
74 13.11756952 -4.23237958
75 14.57133134 13.11756952
76 -3.30030882 14.57133134
77 1.54059933 -3.30030882
78 5.47913532 1.54059933
79 -18.02344042 5.47913532
80 6.33812538 -18.02344042
81 -44.12210165 6.33812538
82 -2.80288455 -44.12210165
83 0.01890828 -2.80288455
84 -0.09136965 0.01890828
85 6.36885739 -0.09136965
86 3.47913532 6.36885739
87 -2.20164758 3.47913532
88 -14.60041061 -2.20164758
89 10.73145658 -14.60041061
90 -0.30030882 10.73145658
91 -14.60041061 -0.30030882
92 5.47913532 -14.60041061
93 12.50986733 5.47913532
94 8.54706455 12.50986733
95 6.44840332 8.54706455
96 1.85981643 6.44840332
97 6.22784745 1.85981643
98 6.55868125 6.22784745
99 2.76762042 6.55868125
100 0.98817628 2.76762042
101 5.85981643 0.98817628
102 12.01890828 5.85981643
103 -3.10298634 12.01890828
104 9.19065023 -3.10298634
105 5.24049754 9.19065023
106 2.44840332 5.24049754
107 1.19065023 2.44840332
108 -11.93124440 1.19065023
109 -14.79669969 -11.93124440
110 11.04964029 -14.79669969
111 7.17903353 11.04964029
112 17.11756952 7.17903353
113 -31.49013267 17.11756952
114 1.23946415 -31.49013267
115 11.87789835 1.23946415
116 -16.06063764 11.87789835
117 -0.63114261 -16.06063764
118 -3.91316248 -0.63114261
119 20.50986733 -3.91316248
120 -13.71068854 20.50986733
121 5.93936236 -13.71068854
122 16.33812538 5.93936236
123 1.62661048 16.33812538
124 0.33812538 1.62661048
125 8.11756952 0.33812538
126 13.84716634 8.11756952
127 -1.01182372 13.84716634
128 4.77923711 -1.01182372
129 0.47913532 4.77923711
130 0.11756952 0.47913532
131 3.81100251 0.11756952
132 3.76762042 3.81100251
133 -5.37985474 3.76762042
134 1.90863035 -5.37985474
135 10.15991822 1.90863035
136 -8.25692672 10.15991822
137 -7.06063764 -8.25692672
138 -12.25046150 -7.06063764
139 -8.66187462 -12.25046150
140 -1.31192551 -8.66187462
141 -19.23237958 -1.31192551
142 12.15991822 -19.23237958
143 14.79835242 12.15991822
144 4.90863035 14.79835242
145 15.44840332 4.90863035
146 3.01890828 15.44840332
147 -7.96197641 3.01890828
148 5.00729159 -7.96197641
149 10.24049754 5.00729159
150 12.01890828 10.24049754
151 11.54059933 12.01890828
152 -3.69260662 11.54059933
153 2.57779656 -3.69260662
154 6.24049754 2.57779656
155 11.11110430 6.24049754
156 -14.60041061 11.11110430
157 -1.06063764 -14.60041061
158 4.77923711 -1.06063764
159 11.97655958 4.77923711
160 18.00082637 11.97655958
161 0.97009436 18.00082637
> 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/html/rcomp/tmp/705go1290557264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/805go1290557264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bwf91290557264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bwf91290557264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11wxex1290557264.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/html/rcomp/tmp/12ixck1290557264.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/html/rcomp/tmp/136y9w1290557264.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/html/rcomp/tmp/14z78h1290557264.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/html/rcomp/tmp/15visi1290557265.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/html/rcomp/tmp/169sp91290557265.tab")
+ }
>
> try(system("convert tmp/1mdix1290557264.ps tmp/1mdix1290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/2e4hi1290557264.ps tmp/2e4hi1290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/3e4hi1290557264.ps tmp/3e4hi1290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/4e4hi1290557264.ps tmp/4e4hi1290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/57vg31290557264.ps tmp/57vg31290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/67vg31290557264.ps tmp/67vg31290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/705go1290557264.ps tmp/705go1290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/805go1290557264.ps tmp/805go1290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bwf91290557264.ps tmp/9bwf91290557264.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bwf91290557264.ps tmp/10bwf91290557264.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.966 1.775 9.598