R version 2.12.1 (2010-12-16)
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ISBN 3-900051-07-0
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> x <- array(list(7
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+ ,dim=c(8
+ ,161)
+ ,dimnames=list(c('age'
+ ,'connected'
+ ,'separated'
+ ,'learning'
+ ,'software'
+ ,'hapiness'
+ ,'depression'
+ ,'belonging')
+ ,1:161))
> y <- array(NA,dim=c(8,161),dimnames=list(c('age','connected','separated','learning','software','hapiness','depression','belonging'),1:161))
> 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 = '8'
> #'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
belonging age connected separated learning software hapiness depression
1 53 7 41 38 13 12 14 12
2 86 5 39 32 16 11 18 11
3 66 5 30 35 19 15 11 14
4 67 5 31 33 15 6 12 12
5 76 8 34 37 14 13 16 21
6 78 6 35 29 13 10 18 12
7 53 5 39 31 19 12 14 22
8 80 6 34 36 15 14 14 11
9 74 5 36 35 14 12 15 10
10 76 4 37 38 15 6 15 13
11 79 6 38 31 16 10 17 10
12 54 5 36 34 16 12 19 8
13 67 5 38 35 16 12 10 15
14 54 6 39 38 16 11 16 14
15 87 7 33 37 17 15 18 10
16 58 6 32 33 15 12 14 14
17 75 7 36 32 15 10 14 14
18 88 6 38 38 20 12 17 11
19 64 8 39 38 18 11 14 10
20 57 7 32 32 16 12 16 13
21 66 5 32 33 16 11 18 7
22 68 5 31 31 16 12 11 14
23 54 7 39 38 19 13 14 12
24 56 7 37 39 16 11 12 14
25 86 5 39 32 17 9 17 11
26 80 4 41 32 17 13 9 9
27 76 10 36 35 16 10 16 11
28 69 6 33 37 15 14 14 15
29 78 5 33 33 16 12 15 14
30 67 5 34 33 14 10 11 13
31 80 5 31 28 15 12 16 9
32 54 5 27 32 12 8 13 15
33 71 6 37 31 14 10 17 10
34 84 5 34 37 16 12 15 11
35 74 5 34 30 14 12 14 13
36 71 5 32 33 7 7 16 8
37 63 5 29 31 10 6 9 20
38 71 5 36 33 14 12 15 12
39 76 5 29 31 16 10 17 10
40 69 5 35 33 16 10 13 10
41 74 5 37 32 16 10 15 9
42 75 7 34 33 14 12 16 14
43 54 5 38 32 20 15 16 8
44 52 6 35 33 14 10 12 14
45 69 7 38 28 14 10 12 11
46 68 7 37 35 11 12 11 13
47 65 5 38 39 14 13 15 9
48 75 5 33 34 15 11 15 11
49 74 4 36 38 16 11 17 15
50 75 5 38 32 14 12 13 11
51 72 4 32 38 16 14 16 10
52 67 5 32 30 14 10 14 14
53 63 5 32 33 12 12 11 18
54 62 7 34 38 16 13 12 14
55 63 5 32 32 9 5 12 11
56 76 5 37 32 14 6 15 12
57 74 6 39 34 16 12 16 13
58 67 4 29 34 16 12 15 9
59 73 6 37 36 15 11 12 10
60 70 6 35 34 16 10 12 15
61 53 5 30 28 12 7 8 20
62 77 7 38 34 16 12 13 12
63 77 6 34 35 16 14 11 12
64 52 8 31 35 14 11 14 14
65 54 7 34 31 16 12 15 13
66 80 5 35 37 17 13 10 11
67 66 6 36 35 18 14 11 17
68 73 6 30 27 18 11 12 12
69 63 5 39 40 12 12 15 13
70 69 5 35 37 16 12 15 14
71 67 5 38 36 10 8 14 13
72 54 5 31 38 14 11 16 15
73 81 4 34 39 18 14 15 13
74 69 6 38 41 18 14 15 10
75 84 6 34 27 16 12 13 11
76 80 6 39 30 17 9 12 19
77 70 6 37 37 16 13 17 13
78 69 7 34 31 16 11 13 17
79 77 5 28 31 13 12 15 13
80 54 7 37 27 16 12 13 9
81 79 6 33 36 16 12 15 11
82 30 5 37 38 20 12 16 10
83 71 5 35 37 16 12 15 9
84 73 4 37 33 15 12 16 12
85 72 8 32 34 15 11 15 12
86 77 8 33 31 16 10 14 13
87 75 5 38 39 14 9 15 13
88 69 5 33 34 16 12 14 12
89 54 6 29 32 16 12 13 15
90 70 4 33 33 15 12 7 22
91 73 5 31 36 12 9 17 13
92 54 5 36 32 17 15 13 15
93 77 5 35 41 16 12 15 13
94 82 5 32 28 15 12 14 15
95 80 6 29 30 13 12 13 10
96 80 6 39 36 16 10 16 11
97 69 5 37 35 16 13 12 16
98 78 6 35 31 16 9 14 11
99 81 5 37 34 16 12 17 11
100 76 7 32 36 14 10 15 10
101 76 5 38 36 16 14 17 10
102 73 6 37 35 16 11 12 16
103 85 6 36 37 20 15 16 12
104 66 6 32 28 15 11 11 11
105 79 4 33 39 16 11 15 16
106 68 5 40 32 13 12 9 19
107 76 5 38 35 17 12 16 11
108 71 7 41 39 16 12 15 16
109 54 6 36 35 16 11 10 15
110 46 9 43 42 12 7 10 24
111 82 6 30 34 16 12 15 14
112 74 6 31 33 16 14 11 15
113 88 5 32 41 17 11 13 11
114 38 6 32 33 13 11 14 15
115 76 5 37 34 12 10 18 12
116 86 8 37 32 18 13 16 10
117 54 7 33 40 14 13 14 14
118 70 5 34 40 14 8 14 13
119 69 7 33 35 13 11 14 9
120 90 6 38 36 16 12 14 15
121 54 6 33 37 13 11 12 15
122 76 9 31 27 16 13 14 14
123 89 7 38 39 13 12 15 11
124 76 6 37 38 16 14 15 8
125 73 5 33 31 15 13 15 11
126 79 5 31 33 16 15 13 11
127 90 6 39 32 15 10 17 8
128 74 6 44 39 17 11 17 10
129 81 7 33 36 15 9 19 11
130 72 5 35 33 12 11 15 13
131 71 5 32 33 16 10 13 11
132 66 5 28 32 10 11 9 20
133 77 6 40 37 16 8 15 10
134 65 4 27 30 12 11 15 15
135 74 5 37 38 14 12 15 12
136 82 7 32 29 15 12 16 14
137 54 5 28 22 13 9 11 23
138 63 7 34 35 15 11 14 14
139 54 7 30 35 11 10 11 16
140 64 6 35 34 12 8 15 11
141 69 5 31 35 8 9 13 12
142 54 8 32 34 16 8 15 10
143 84 5 30 34 15 9 16 14
144 86 5 30 35 17 15 14 12
145 77 5 31 23 16 11 15 12
146 89 6 40 31 10 8 16 11
147 76 4 32 27 18 13 16 12
148 60 5 36 36 13 12 11 13
149 75 5 32 31 16 12 12 11
150 73 7 35 32 13 9 9 19
151 85 6 38 39 10 7 16 12
152 79 7 42 37 15 13 13 17
153 71 10 34 38 16 9 16 9
154 72 6 35 39 16 6 12 12
155 69 8 35 34 14 8 9 19
156 78 4 33 31 10 8 13 18
157 54 5 36 32 17 15 13 15
158 69 6 32 37 13 6 14 14
159 81 7 33 36 15 9 19 11
160 84 7 34 32 16 11 13 9
161 84 6 32 35 12 8 12 18
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) age connected separated learning software
68.67914 -0.63961 0.22919 -0.26103 0.07341 -0.04058
hapiness depression
0.92489 -0.53748
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-44.447 -4.164 0.947 6.703 20.273
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.67914 13.83679 4.964 1.83e-06 ***
age -0.63961 0.71680 -0.892 0.3736
connected 0.22919 0.26836 0.854 0.3944
separated -0.26103 0.25000 -1.044 0.2981
learning 0.07341 0.45125 0.163 0.8710
software -0.04058 0.46097 -0.088 0.9300
hapiness 0.92489 0.42126 2.196 0.0296 *
depression -0.53748 0.31252 -1.720 0.0875 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.35 on 153 degrees of freedom
Multiple R-squared: 0.1143, Adjusted R-squared: 0.07378
F-statistic: 2.821 on 7 and 153 DF, p-value: 0.008607
> 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,] 0.10409363 0.20818726 0.89590637
[2,] 0.96425297 0.07149406 0.03574703
[3,] 0.93807280 0.12385439 0.06192720
[4,] 0.91992450 0.16015100 0.08007550
[5,] 0.93633312 0.12733377 0.06366688
[6,] 0.95255402 0.09489196 0.04744598
[7,] 0.92985560 0.14028880 0.07014440
[8,] 0.93241128 0.13517744 0.06758872
[9,] 0.92690575 0.14618851 0.07309425
[10,] 0.95205546 0.09588908 0.04794454
[11,] 0.95053196 0.09893607 0.04946804
[12,] 0.92964409 0.14071182 0.07035591
[13,] 0.93905905 0.12188190 0.06094095
[14,] 0.92696017 0.14607967 0.07303983
[15,] 0.93364929 0.13270142 0.06635071
[16,] 0.93720031 0.12559938 0.06279969
[17,] 0.92413604 0.15172793 0.07586396
[18,] 0.90044774 0.19910452 0.09955226
[19,] 0.88342549 0.23314902 0.11657451
[20,] 0.84891898 0.30216205 0.15108102
[21,] 0.81105698 0.37788605 0.18894302
[22,] 0.81820977 0.36358047 0.18179023
[23,] 0.78154794 0.43690412 0.21845206
[24,] 0.80041670 0.39916661 0.19958330
[25,] 0.75894024 0.48211952 0.24105976
[26,] 0.71185058 0.57629884 0.28814942
[27,] 0.67248048 0.65503903 0.32751952
[28,] 0.61911201 0.76177597 0.38088799
[29,] 0.56306780 0.87386441 0.43693220
[30,] 0.50973222 0.98053556 0.49026778
[31,] 0.45355794 0.90711587 0.54644206
[32,] 0.41040143 0.82080285 0.58959857
[33,] 0.61001837 0.77996325 0.38998163
[34,] 0.66028892 0.67942217 0.33971108
[35,] 0.61245388 0.77509224 0.38754612
[36,] 0.56401990 0.87196020 0.43598010
[37,] 0.53448834 0.93102333 0.46551166
[38,] 0.48746392 0.97492783 0.51253608
[39,] 0.43909355 0.87818710 0.56090645
[40,] 0.39751048 0.79502097 0.60248952
[41,] 0.34875318 0.69750635 0.65124682
[42,] 0.30725751 0.61451501 0.69274249
[43,] 0.26408824 0.52817649 0.73591176
[44,] 0.22699883 0.45399766 0.77300117
[45,] 0.20263654 0.40527308 0.79736346
[46,] 0.17450491 0.34900983 0.82549509
[47,] 0.14552575 0.29105149 0.85447425
[48,] 0.12744243 0.25488486 0.87255757
[49,] 0.10859156 0.21718312 0.89140844
[50,] 0.08973891 0.17947782 0.91026109
[51,] 0.08348749 0.16697498 0.91651251
[52,] 0.07646739 0.15293478 0.92353261
[53,] 0.07617407 0.15234814 0.92382593
[54,] 0.09384007 0.18768015 0.90615993
[55,] 0.12916481 0.25832962 0.87083519
[56,] 0.14070735 0.28141469 0.85929265
[57,] 0.11558895 0.23117789 0.88441105
[58,] 0.09737928 0.19475855 0.90262072
[59,] 0.08784190 0.17568379 0.91215810
[60,] 0.07063496 0.14126992 0.92936504
[61,] 0.05794315 0.11588630 0.94205685
[62,] 0.07425578 0.14851157 0.92574422
[63,] 0.07354658 0.14709315 0.92645342
[64,] 0.05931625 0.11863251 0.94068375
[65,] 0.06309033 0.12618066 0.93690967
[66,] 0.07092786 0.14185572 0.92907214
[67,] 0.05780599 0.11561197 0.94219401
[68,] 0.04580537 0.09161074 0.95419463
[69,] 0.03969445 0.07938889 0.96030555
[70,] 0.07626816 0.15253631 0.92373184
[71,] 0.06999691 0.13999383 0.93000309
[72,] 0.73441653 0.53116693 0.26558347
[73,] 0.70421894 0.59156212 0.29578106
[74,] 0.67125286 0.65749428 0.32874714
[75,] 0.63272334 0.73455332 0.36727666
[76,] 0.61190547 0.77618905 0.38809453
[77,] 0.57808432 0.84383137 0.42191568
[78,] 0.53959640 0.92080719 0.46040360
[79,] 0.58408843 0.83182314 0.41591157
[80,] 0.57400190 0.85199620 0.42599810
[81,] 0.53263814 0.93472372 0.46736186
[82,] 0.62764282 0.74471435 0.37235718
[83,] 0.59891255 0.80217489 0.40108745
[84,] 0.59480750 0.81038500 0.40519250
[85,] 0.58374156 0.83251688 0.41625844
[86,] 0.55260107 0.89479787 0.44739893
[87,] 0.50599010 0.98801979 0.49400990
[88,] 0.46821871 0.93643741 0.53178129
[89,] 0.43100039 0.86200078 0.56899961
[90,] 0.39401844 0.78803688 0.60598156
[91,] 0.35848244 0.71696489 0.64151756
[92,] 0.32243867 0.64487734 0.67756133
[93,] 0.31910996 0.63821993 0.68089004
[94,] 0.28378372 0.56756743 0.71621628
[95,] 0.26592834 0.53185668 0.73407166
[96,] 0.22864910 0.45729820 0.77135090
[97,] 0.19736011 0.39472022 0.80263989
[98,] 0.16577757 0.33155514 0.83422243
[99,] 0.18456978 0.36913957 0.81543022
[100,] 0.20992548 0.41985096 0.79007452
[101,] 0.21567601 0.43135201 0.78432399
[102,] 0.19574958 0.39149915 0.80425042
[103,] 0.28068541 0.56137082 0.71931459
[104,] 0.70397632 0.59204735 0.29602368
[105,] 0.67250929 0.65498142 0.32749071
[106,] 0.66195579 0.67608842 0.33804421
[107,] 0.72091356 0.55817288 0.27908644
[108,] 0.67444698 0.65110604 0.32555302
[109,] 0.62843130 0.74313740 0.37156870
[110,] 0.71202179 0.57595642 0.28797821
[111,] 0.76158686 0.47682629 0.23841314
[112,] 0.72913267 0.54173466 0.27086733
[113,] 0.77102620 0.45794759 0.22897380
[114,] 0.72372760 0.55254479 0.27627240
[115,] 0.67332354 0.65335293 0.32667646
[116,] 0.65715712 0.68568575 0.34284288
[117,] 0.65479699 0.69040602 0.34520301
[118,] 0.63660409 0.72679182 0.36339591
[119,] 0.58186796 0.83626409 0.41813204
[120,] 0.52736111 0.94527777 0.47263889
[121,] 0.46175798 0.92351596 0.53824202
[122,] 0.41073348 0.82146696 0.58926652
[123,] 0.35717080 0.71434161 0.64282920
[124,] 0.31394202 0.62788404 0.68605798
[125,] 0.26529142 0.53058284 0.73470858
[126,] 0.25593823 0.51187645 0.74406177
[127,] 0.28003080 0.56006160 0.71996920
[128,] 0.25607473 0.51214947 0.74392527
[129,] 0.28806950 0.57613901 0.71193050
[130,] 0.30814083 0.61628166 0.69185917
[131,] 0.30915637 0.61831273 0.69084363
[132,] 0.56315067 0.87369866 0.43684933
[133,] 0.50663965 0.98672070 0.49336035
[134,] 0.72190053 0.55619893 0.27809947
[135,] 0.65185964 0.69628073 0.34814036
[136,] 0.62630550 0.74738899 0.37369450
[137,] 0.50715622 0.98568756 0.49284378
[138,] 0.47328831 0.94657663 0.52671169
[139,] 0.35813070 0.71626141 0.64186930
[140,] 0.23066170 0.46132340 0.76933830
> postscript(file="/var/www/rcomp/tmp/1ccdq1321539000.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2yy7v1321539000.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3g5hu1321539000.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/42xey1321539000.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/539dv1321539000.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 = 161
Frequency = 1
1 2 3 4 5 6
-17.64552400 8.46961362 -0.65585150 -2.47855093 10.29207336 1.96001584
7 8 9 10 11 12
-15.35923286 9.19398759 0.36484782 3.57465843 2.42421484 -24.81751376
13 14 15 16 17 18
0.07149835 -17.86239497 12.98054194 -11.59944969 4.78122209 13.57641372
19 20 21 22 23 24
-7.03012153 -14.68153101 -11.81496440 0.16933074 -16.58702028 -10.80382130
25 26 27 28 29 30
9.23992868 8.62843487 4.94751808 0.83411631 6.53345149 -1.46800292
31 32 33 34 35 36
4.14781582 -13.83389122 -5.19978032 11.73593705 2.05541204 -3.92935785
37 38 39 40 41 42
0.89931199 -2.08224917 0.84728531 -3.30622336 -1.41288294 3.80541842
43 44 45 46 47 48
-23.19515928 -16.44498816 -2.41050785 0.94710880 -8.54631345 2.21486865
49 50 51 52 53 54
1.15852892 2.51065066 -1.56547732 -4.02990112 -2.09425196 -4.29612136
55 56 57 58 59 60
-7.10637336 2.18403894 0.59660083 -6.61577936 2.69697864 2.20669982
61 62 63 64 65 66
-9.29430143 6.70259183 9.17169976 -15.53615637 -17.47604240 12.09837331
67 68 69 70 71 72
0.25390103 2.80677446 -8.25832401 -1.88081410 -4.16386965 -15.98421514
73 74 75 76 77 78
9.62768367 -3.10022014 11.61505848 12.28176577 -3.04625057 1.48306888
79 80 81 82 83 84
5.84008054 -19.50784692 7.34371001 -44.44660103 -2.56820727 -1.94934799
85 86 87 88 89 90
1.90037343 7.23648163 3.44126928 -2.35558880 -13.78395605 9.66619522
91 92 93 94 95 96
0.55953536 -15.97953851 6.62581578 10.99328027 9.22682319 5.96253192
97 98 99 100 101 102
1.02896729 5.38334124 5.41551479 4.74068334 0.25206926 5.58741442
103 104 105 106 107 108
12.35787804 -3.78293464 9.49437555 3.12507391 1.29883583 1.62030115
109 110 111 112 113 114
-11.87109746 -12.76076638 12.12165281 7.94964257 18.97420754 -30.95573622
115 116 117 118 119 120
0.24057323 12.09347601 -13.24784196 0.50335153 -2.24812779 20.27257911
121 122 123 124 125 126
-12.29103506 6.94958774 17.84069582 2.41774604 -0.48704683 8.35091825
127 128 129 130 131 132
12.45450654 -1.89551640 6.23542367 -0.20934865 -0.08118356 4.59244093
133 134 135 136 137 138
3.30061731 -5.72358935 1.99369943 10.14627512 -10.55656674 -5.93673946
139 140 141 142 143 144
-9.91731181 -8.50541461 -0.24577544 -17.36974158 12.50880997 15.64133946
145 146 147 148 149 150
2.26601408 13.78749665 0.45078079 -7.98872085 4.40281793 10.42848640
151 152 153 154 155 156
12.83098349 11.37031001 0.07343336 3.73706776 7.47616184 9.64959997
157 158 159 160 161
-15.97953851 0.34797891 6.23542367 12.44426694 18.98019368
> postscript(file="/var/www/rcomp/tmp/66ls91321539000.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 = 161
Frequency = 1
lag(myerror, k = 1) myerror
0 -17.64552400 NA
1 8.46961362 -17.64552400
2 -0.65585150 8.46961362
3 -2.47855093 -0.65585150
4 10.29207336 -2.47855093
5 1.96001584 10.29207336
6 -15.35923286 1.96001584
7 9.19398759 -15.35923286
8 0.36484782 9.19398759
9 3.57465843 0.36484782
10 2.42421484 3.57465843
11 -24.81751376 2.42421484
12 0.07149835 -24.81751376
13 -17.86239497 0.07149835
14 12.98054194 -17.86239497
15 -11.59944969 12.98054194
16 4.78122209 -11.59944969
17 13.57641372 4.78122209
18 -7.03012153 13.57641372
19 -14.68153101 -7.03012153
20 -11.81496440 -14.68153101
21 0.16933074 -11.81496440
22 -16.58702028 0.16933074
23 -10.80382130 -16.58702028
24 9.23992868 -10.80382130
25 8.62843487 9.23992868
26 4.94751808 8.62843487
27 0.83411631 4.94751808
28 6.53345149 0.83411631
29 -1.46800292 6.53345149
30 4.14781582 -1.46800292
31 -13.83389122 4.14781582
32 -5.19978032 -13.83389122
33 11.73593705 -5.19978032
34 2.05541204 11.73593705
35 -3.92935785 2.05541204
36 0.89931199 -3.92935785
37 -2.08224917 0.89931199
38 0.84728531 -2.08224917
39 -3.30622336 0.84728531
40 -1.41288294 -3.30622336
41 3.80541842 -1.41288294
42 -23.19515928 3.80541842
43 -16.44498816 -23.19515928
44 -2.41050785 -16.44498816
45 0.94710880 -2.41050785
46 -8.54631345 0.94710880
47 2.21486865 -8.54631345
48 1.15852892 2.21486865
49 2.51065066 1.15852892
50 -1.56547732 2.51065066
51 -4.02990112 -1.56547732
52 -2.09425196 -4.02990112
53 -4.29612136 -2.09425196
54 -7.10637336 -4.29612136
55 2.18403894 -7.10637336
56 0.59660083 2.18403894
57 -6.61577936 0.59660083
58 2.69697864 -6.61577936
59 2.20669982 2.69697864
60 -9.29430143 2.20669982
61 6.70259183 -9.29430143
62 9.17169976 6.70259183
63 -15.53615637 9.17169976
64 -17.47604240 -15.53615637
65 12.09837331 -17.47604240
66 0.25390103 12.09837331
67 2.80677446 0.25390103
68 -8.25832401 2.80677446
69 -1.88081410 -8.25832401
70 -4.16386965 -1.88081410
71 -15.98421514 -4.16386965
72 9.62768367 -15.98421514
73 -3.10022014 9.62768367
74 11.61505848 -3.10022014
75 12.28176577 11.61505848
76 -3.04625057 12.28176577
77 1.48306888 -3.04625057
78 5.84008054 1.48306888
79 -19.50784692 5.84008054
80 7.34371001 -19.50784692
81 -44.44660103 7.34371001
82 -2.56820727 -44.44660103
83 -1.94934799 -2.56820727
84 1.90037343 -1.94934799
85 7.23648163 1.90037343
86 3.44126928 7.23648163
87 -2.35558880 3.44126928
88 -13.78395605 -2.35558880
89 9.66619522 -13.78395605
90 0.55953536 9.66619522
91 -15.97953851 0.55953536
92 6.62581578 -15.97953851
93 10.99328027 6.62581578
94 9.22682319 10.99328027
95 5.96253192 9.22682319
96 1.02896729 5.96253192
97 5.38334124 1.02896729
98 5.41551479 5.38334124
99 4.74068334 5.41551479
100 0.25206926 4.74068334
101 5.58741442 0.25206926
102 12.35787804 5.58741442
103 -3.78293464 12.35787804
104 9.49437555 -3.78293464
105 3.12507391 9.49437555
106 1.29883583 3.12507391
107 1.62030115 1.29883583
108 -11.87109746 1.62030115
109 -12.76076638 -11.87109746
110 12.12165281 -12.76076638
111 7.94964257 12.12165281
112 18.97420754 7.94964257
113 -30.95573622 18.97420754
114 0.24057323 -30.95573622
115 12.09347601 0.24057323
116 -13.24784196 12.09347601
117 0.50335153 -13.24784196
118 -2.24812779 0.50335153
119 20.27257911 -2.24812779
120 -12.29103506 20.27257911
121 6.94958774 -12.29103506
122 17.84069582 6.94958774
123 2.41774604 17.84069582
124 -0.48704683 2.41774604
125 8.35091825 -0.48704683
126 12.45450654 8.35091825
127 -1.89551640 12.45450654
128 6.23542367 -1.89551640
129 -0.20934865 6.23542367
130 -0.08118356 -0.20934865
131 4.59244093 -0.08118356
132 3.30061731 4.59244093
133 -5.72358935 3.30061731
134 1.99369943 -5.72358935
135 10.14627512 1.99369943
136 -10.55656674 10.14627512
137 -5.93673946 -10.55656674
138 -9.91731181 -5.93673946
139 -8.50541461 -9.91731181
140 -0.24577544 -8.50541461
141 -17.36974158 -0.24577544
142 12.50880997 -17.36974158
143 15.64133946 12.50880997
144 2.26601408 15.64133946
145 13.78749665 2.26601408
146 0.45078079 13.78749665
147 -7.98872085 0.45078079
148 4.40281793 -7.98872085
149 10.42848640 4.40281793
150 12.83098349 10.42848640
151 11.37031001 12.83098349
152 0.07343336 11.37031001
153 3.73706776 0.07343336
154 7.47616184 3.73706776
155 9.64959997 7.47616184
156 -15.97953851 9.64959997
157 0.34797891 -15.97953851
158 6.23542367 0.34797891
159 12.44426694 6.23542367
160 18.98019368 12.44426694
161 NA 18.98019368
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 8.46961362 -17.64552400
[2,] -0.65585150 8.46961362
[3,] -2.47855093 -0.65585150
[4,] 10.29207336 -2.47855093
[5,] 1.96001584 10.29207336
[6,] -15.35923286 1.96001584
[7,] 9.19398759 -15.35923286
[8,] 0.36484782 9.19398759
[9,] 3.57465843 0.36484782
[10,] 2.42421484 3.57465843
[11,] -24.81751376 2.42421484
[12,] 0.07149835 -24.81751376
[13,] -17.86239497 0.07149835
[14,] 12.98054194 -17.86239497
[15,] -11.59944969 12.98054194
[16,] 4.78122209 -11.59944969
[17,] 13.57641372 4.78122209
[18,] -7.03012153 13.57641372
[19,] -14.68153101 -7.03012153
[20,] -11.81496440 -14.68153101
[21,] 0.16933074 -11.81496440
[22,] -16.58702028 0.16933074
[23,] -10.80382130 -16.58702028
[24,] 9.23992868 -10.80382130
[25,] 8.62843487 9.23992868
[26,] 4.94751808 8.62843487
[27,] 0.83411631 4.94751808
[28,] 6.53345149 0.83411631
[29,] -1.46800292 6.53345149
[30,] 4.14781582 -1.46800292
[31,] -13.83389122 4.14781582
[32,] -5.19978032 -13.83389122
[33,] 11.73593705 -5.19978032
[34,] 2.05541204 11.73593705
[35,] -3.92935785 2.05541204
[36,] 0.89931199 -3.92935785
[37,] -2.08224917 0.89931199
[38,] 0.84728531 -2.08224917
[39,] -3.30622336 0.84728531
[40,] -1.41288294 -3.30622336
[41,] 3.80541842 -1.41288294
[42,] -23.19515928 3.80541842
[43,] -16.44498816 -23.19515928
[44,] -2.41050785 -16.44498816
[45,] 0.94710880 -2.41050785
[46,] -8.54631345 0.94710880
[47,] 2.21486865 -8.54631345
[48,] 1.15852892 2.21486865
[49,] 2.51065066 1.15852892
[50,] -1.56547732 2.51065066
[51,] -4.02990112 -1.56547732
[52,] -2.09425196 -4.02990112
[53,] -4.29612136 -2.09425196
[54,] -7.10637336 -4.29612136
[55,] 2.18403894 -7.10637336
[56,] 0.59660083 2.18403894
[57,] -6.61577936 0.59660083
[58,] 2.69697864 -6.61577936
[59,] 2.20669982 2.69697864
[60,] -9.29430143 2.20669982
[61,] 6.70259183 -9.29430143
[62,] 9.17169976 6.70259183
[63,] -15.53615637 9.17169976
[64,] -17.47604240 -15.53615637
[65,] 12.09837331 -17.47604240
[66,] 0.25390103 12.09837331
[67,] 2.80677446 0.25390103
[68,] -8.25832401 2.80677446
[69,] -1.88081410 -8.25832401
[70,] -4.16386965 -1.88081410
[71,] -15.98421514 -4.16386965
[72,] 9.62768367 -15.98421514
[73,] -3.10022014 9.62768367
[74,] 11.61505848 -3.10022014
[75,] 12.28176577 11.61505848
[76,] -3.04625057 12.28176577
[77,] 1.48306888 -3.04625057
[78,] 5.84008054 1.48306888
[79,] -19.50784692 5.84008054
[80,] 7.34371001 -19.50784692
[81,] -44.44660103 7.34371001
[82,] -2.56820727 -44.44660103
[83,] -1.94934799 -2.56820727
[84,] 1.90037343 -1.94934799
[85,] 7.23648163 1.90037343
[86,] 3.44126928 7.23648163
[87,] -2.35558880 3.44126928
[88,] -13.78395605 -2.35558880
[89,] 9.66619522 -13.78395605
[90,] 0.55953536 9.66619522
[91,] -15.97953851 0.55953536
[92,] 6.62581578 -15.97953851
[93,] 10.99328027 6.62581578
[94,] 9.22682319 10.99328027
[95,] 5.96253192 9.22682319
[96,] 1.02896729 5.96253192
[97,] 5.38334124 1.02896729
[98,] 5.41551479 5.38334124
[99,] 4.74068334 5.41551479
[100,] 0.25206926 4.74068334
[101,] 5.58741442 0.25206926
[102,] 12.35787804 5.58741442
[103,] -3.78293464 12.35787804
[104,] 9.49437555 -3.78293464
[105,] 3.12507391 9.49437555
[106,] 1.29883583 3.12507391
[107,] 1.62030115 1.29883583
[108,] -11.87109746 1.62030115
[109,] -12.76076638 -11.87109746
[110,] 12.12165281 -12.76076638
[111,] 7.94964257 12.12165281
[112,] 18.97420754 7.94964257
[113,] -30.95573622 18.97420754
[114,] 0.24057323 -30.95573622
[115,] 12.09347601 0.24057323
[116,] -13.24784196 12.09347601
[117,] 0.50335153 -13.24784196
[118,] -2.24812779 0.50335153
[119,] 20.27257911 -2.24812779
[120,] -12.29103506 20.27257911
[121,] 6.94958774 -12.29103506
[122,] 17.84069582 6.94958774
[123,] 2.41774604 17.84069582
[124,] -0.48704683 2.41774604
[125,] 8.35091825 -0.48704683
[126,] 12.45450654 8.35091825
[127,] -1.89551640 12.45450654
[128,] 6.23542367 -1.89551640
[129,] -0.20934865 6.23542367
[130,] -0.08118356 -0.20934865
[131,] 4.59244093 -0.08118356
[132,] 3.30061731 4.59244093
[133,] -5.72358935 3.30061731
[134,] 1.99369943 -5.72358935
[135,] 10.14627512 1.99369943
[136,] -10.55656674 10.14627512
[137,] -5.93673946 -10.55656674
[138,] -9.91731181 -5.93673946
[139,] -8.50541461 -9.91731181
[140,] -0.24577544 -8.50541461
[141,] -17.36974158 -0.24577544
[142,] 12.50880997 -17.36974158
[143,] 15.64133946 12.50880997
[144,] 2.26601408 15.64133946
[145,] 13.78749665 2.26601408
[146,] 0.45078079 13.78749665
[147,] -7.98872085 0.45078079
[148,] 4.40281793 -7.98872085
[149,] 10.42848640 4.40281793
[150,] 12.83098349 10.42848640
[151,] 11.37031001 12.83098349
[152,] 0.07343336 11.37031001
[153,] 3.73706776 0.07343336
[154,] 7.47616184 3.73706776
[155,] 9.64959997 7.47616184
[156,] -15.97953851 9.64959997
[157,] 0.34797891 -15.97953851
[158,] 6.23542367 0.34797891
[159,] 12.44426694 6.23542367
[160,] 18.98019368 12.44426694
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 8.46961362 -17.64552400
2 -0.65585150 8.46961362
3 -2.47855093 -0.65585150
4 10.29207336 -2.47855093
5 1.96001584 10.29207336
6 -15.35923286 1.96001584
7 9.19398759 -15.35923286
8 0.36484782 9.19398759
9 3.57465843 0.36484782
10 2.42421484 3.57465843
11 -24.81751376 2.42421484
12 0.07149835 -24.81751376
13 -17.86239497 0.07149835
14 12.98054194 -17.86239497
15 -11.59944969 12.98054194
16 4.78122209 -11.59944969
17 13.57641372 4.78122209
18 -7.03012153 13.57641372
19 -14.68153101 -7.03012153
20 -11.81496440 -14.68153101
21 0.16933074 -11.81496440
22 -16.58702028 0.16933074
23 -10.80382130 -16.58702028
24 9.23992868 -10.80382130
25 8.62843487 9.23992868
26 4.94751808 8.62843487
27 0.83411631 4.94751808
28 6.53345149 0.83411631
29 -1.46800292 6.53345149
30 4.14781582 -1.46800292
31 -13.83389122 4.14781582
32 -5.19978032 -13.83389122
33 11.73593705 -5.19978032
34 2.05541204 11.73593705
35 -3.92935785 2.05541204
36 0.89931199 -3.92935785
37 -2.08224917 0.89931199
38 0.84728531 -2.08224917
39 -3.30622336 0.84728531
40 -1.41288294 -3.30622336
41 3.80541842 -1.41288294
42 -23.19515928 3.80541842
43 -16.44498816 -23.19515928
44 -2.41050785 -16.44498816
45 0.94710880 -2.41050785
46 -8.54631345 0.94710880
47 2.21486865 -8.54631345
48 1.15852892 2.21486865
49 2.51065066 1.15852892
50 -1.56547732 2.51065066
51 -4.02990112 -1.56547732
52 -2.09425196 -4.02990112
53 -4.29612136 -2.09425196
54 -7.10637336 -4.29612136
55 2.18403894 -7.10637336
56 0.59660083 2.18403894
57 -6.61577936 0.59660083
58 2.69697864 -6.61577936
59 2.20669982 2.69697864
60 -9.29430143 2.20669982
61 6.70259183 -9.29430143
62 9.17169976 6.70259183
63 -15.53615637 9.17169976
64 -17.47604240 -15.53615637
65 12.09837331 -17.47604240
66 0.25390103 12.09837331
67 2.80677446 0.25390103
68 -8.25832401 2.80677446
69 -1.88081410 -8.25832401
70 -4.16386965 -1.88081410
71 -15.98421514 -4.16386965
72 9.62768367 -15.98421514
73 -3.10022014 9.62768367
74 11.61505848 -3.10022014
75 12.28176577 11.61505848
76 -3.04625057 12.28176577
77 1.48306888 -3.04625057
78 5.84008054 1.48306888
79 -19.50784692 5.84008054
80 7.34371001 -19.50784692
81 -44.44660103 7.34371001
82 -2.56820727 -44.44660103
83 -1.94934799 -2.56820727
84 1.90037343 -1.94934799
85 7.23648163 1.90037343
86 3.44126928 7.23648163
87 -2.35558880 3.44126928
88 -13.78395605 -2.35558880
89 9.66619522 -13.78395605
90 0.55953536 9.66619522
91 -15.97953851 0.55953536
92 6.62581578 -15.97953851
93 10.99328027 6.62581578
94 9.22682319 10.99328027
95 5.96253192 9.22682319
96 1.02896729 5.96253192
97 5.38334124 1.02896729
98 5.41551479 5.38334124
99 4.74068334 5.41551479
100 0.25206926 4.74068334
101 5.58741442 0.25206926
102 12.35787804 5.58741442
103 -3.78293464 12.35787804
104 9.49437555 -3.78293464
105 3.12507391 9.49437555
106 1.29883583 3.12507391
107 1.62030115 1.29883583
108 -11.87109746 1.62030115
109 -12.76076638 -11.87109746
110 12.12165281 -12.76076638
111 7.94964257 12.12165281
112 18.97420754 7.94964257
113 -30.95573622 18.97420754
114 0.24057323 -30.95573622
115 12.09347601 0.24057323
116 -13.24784196 12.09347601
117 0.50335153 -13.24784196
118 -2.24812779 0.50335153
119 20.27257911 -2.24812779
120 -12.29103506 20.27257911
121 6.94958774 -12.29103506
122 17.84069582 6.94958774
123 2.41774604 17.84069582
124 -0.48704683 2.41774604
125 8.35091825 -0.48704683
126 12.45450654 8.35091825
127 -1.89551640 12.45450654
128 6.23542367 -1.89551640
129 -0.20934865 6.23542367
130 -0.08118356 -0.20934865
131 4.59244093 -0.08118356
132 3.30061731 4.59244093
133 -5.72358935 3.30061731
134 1.99369943 -5.72358935
135 10.14627512 1.99369943
136 -10.55656674 10.14627512
137 -5.93673946 -10.55656674
138 -9.91731181 -5.93673946
139 -8.50541461 -9.91731181
140 -0.24577544 -8.50541461
141 -17.36974158 -0.24577544
142 12.50880997 -17.36974158
143 15.64133946 12.50880997
144 2.26601408 15.64133946
145 13.78749665 2.26601408
146 0.45078079 13.78749665
147 -7.98872085 0.45078079
148 4.40281793 -7.98872085
149 10.42848640 4.40281793
150 12.83098349 10.42848640
151 11.37031001 12.83098349
152 0.07343336 11.37031001
153 3.73706776 0.07343336
154 7.47616184 3.73706776
155 9.64959997 7.47616184
156 -15.97953851 9.64959997
157 0.34797891 -15.97953851
158 6.23542367 0.34797891
159 12.44426694 6.23542367
160 18.98019368 12.44426694
> 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/7ic041321539000.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8e4or1321539000.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9h1hf1321539000.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/100pbg1321539000.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11usbx1321539000.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/12qpvt1321539000.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/13z8p51321539000.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/14thfc1321539000.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/15ufg91321539000.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/16k5011321539000.tab")
+ }
>
> try(system("convert tmp/1ccdq1321539000.ps tmp/1ccdq1321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yy7v1321539000.ps tmp/2yy7v1321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g5hu1321539000.ps tmp/3g5hu1321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/42xey1321539000.ps tmp/42xey1321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/539dv1321539000.ps tmp/539dv1321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/66ls91321539000.ps tmp/66ls91321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ic041321539000.ps tmp/7ic041321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/8e4or1321539000.ps tmp/8e4or1321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/9h1hf1321539000.ps tmp/9h1hf1321539000.png",intern=TRUE))
character(0)
> try(system("convert tmp/100pbg1321539000.ps tmp/100pbg1321539000.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.396 0.572 8.544