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.
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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(11
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+ ,47)
+ ,dim=c(5
+ ,159)
+ ,dimnames=list(c('ParentalExpectations'
+ ,'ParentalCriticism'
+ ,'PersonalStandards'
+ ,'Organization'
+ ,'CM+D')
+ ,1:159))
> y <- array(NA,dim=c(5,159),dimnames=list(c('ParentalExpectations','ParentalCriticism','PersonalStandards','Organization','CM+D'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> #'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
CM+D ParentalExpectations ParentalCriticism PersonalStandards Organization
1 38 11 12 24 26
2 36 7 8 25 23
3 23 17 8 30 25
4 30 10 8 19 23
5 26 12 9 22 19
6 26 12 7 22 29
7 30 11 4 25 25
8 27 11 11 23 21
9 34 12 7 17 22
10 28 13 7 21 25
11 36 14 12 19 24
12 42 16 10 19 18
13 31 11 10 15 22
14 30 10 8 16 15
15 26 11 8 23 22
16 16 15 4 27 28
17 30 9 9 22 20
18 23 11 8 14 12
19 28 17 7 22 24
20 45 17 11 23 20
21 42 11 9 23 21
22 50 18 11 21 20
23 30 14 13 19 21
24 45 10 8 18 23
25 30 11 8 20 28
26 24 15 9 23 24
27 29 15 6 25 24
28 30 13 9 19 24
29 31 16 9 24 23
30 26 13 6 22 23
31 34 9 6 25 29
32 41 18 16 26 24
33 37 18 5 29 18
34 33 12 7 32 25
35 26 17 9 25 21
36 48 9 6 29 26
37 44 9 6 28 22
38 29 12 5 17 22
39 44 18 12 28 22
40 37 12 7 29 23
41 43 18 10 26 30
42 31 14 9 25 23
43 28 15 8 14 17
44 26 16 5 25 23
45 30 10 8 26 23
46 27 11 8 20 25
47 34 14 10 18 24
48 47 9 6 32 24
49 39 12 8 25 23
50 37 17 7 25 21
51 42 5 4 23 24
52 27 12 8 21 24
53 30 12 8 20 28
54 17 6 4 15 16
55 36 24 20 30 20
56 39 12 8 24 29
57 32 12 8 26 27
58 25 14 6 24 22
59 19 7 4 22 28
60 29 13 8 14 16
61 26 12 9 24 25
62 31 13 6 24 24
63 31 14 7 24 28
64 31 8 9 24 24
65 20 11 5 19 23
66 40 9 5 31 30
67 39 11 8 22 24
68 28 13 8 27 21
69 22 10 6 19 25
70 31 11 8 25 25
71 36 12 7 20 22
72 28 9 7 21 23
73 39 15 9 27 26
74 44 18 11 23 23
75 35 15 6 25 25
76 33 12 8 20 21
77 27 13 6 21 25
78 33 14 9 22 24
79 31 10 8 23 29
80 39 13 6 25 22
81 37 13 10 25 27
82 24 11 8 17 26
83 33 13 8 19 22
84 28 16 10 25 24
85 37 8 5 19 27
86 32 16 7 20 24
87 31 11 5 26 24
88 29 9 8 23 29
89 40 16 14 27 22
90 29 12 7 17 21
91 40 14 8 17 24
92 15 8 6 19 24
93 27 9 5 17 23
94 32 15 6 22 20
95 28 11 10 21 27
96 41 21 12 32 26
97 47 14 9 21 25
98 42 18 12 21 21
99 28 12 7 18 21
100 32 13 8 18 19
101 33 15 10 23 21
102 22 12 6 19 21
103 29 19 10 20 16
104 26 15 10 21 22
105 37 11 10 20 29
106 39 11 5 17 15
107 29 10 7 18 17
108 33 13 10 19 15
109 39 15 11 22 21
110 31 12 6 15 21
111 21 12 7 14 19
112 36 16 12 18 24
113 29 9 11 24 20
114 32 18 11 35 17
115 15 8 11 29 23
116 24 13 5 21 24
117 25 17 8 25 14
118 28 9 6 20 19
119 39 15 9 22 24
120 31 8 4 13 13
121 40 7 4 26 22
122 25 12 7 17 16
123 36 14 11 25 19
124 23 6 6 20 25
125 39 8 7 19 25
126 31 17 8 21 23
127 23 10 4 22 24
128 31 11 8 24 26
129 28 14 9 21 26
130 47 11 8 26 25
131 33 13 11 24 18
132 25 12 8 16 21
133 26 11 5 23 26
134 24 9 4 18 23
135 31 12 8 16 23
136 39 20 10 26 22
137 31 12 6 19 20
138 30 13 9 21 13
139 25 12 9 21 24
140 35 12 13 22 15
141 44 9 9 23 14
142 42 15 10 29 22
143 38 24 20 21 10
144 36 7 5 21 24
145 34 17 11 23 22
146 45 11 6 27 24
147 40 17 9 25 19
148 29 11 7 21 20
149 25 12 9 10 13
150 30 14 10 20 20
151 27 11 9 26 22
152 44 16 8 24 24
153 49 21 7 29 29
154 31 14 6 19 12
155 31 20 13 24 20
156 26 13 6 19 21
157 42 11 8 24 24
158 35 15 10 22 22
159 47 19 16 17 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ParentalExpectations ParentalCriticism
13.86485 0.15281 0.62937
PersonalStandards Organization
0.44966 0.06778
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-21.6094 -4.3694 -0.9992 3.8344 15.6631
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.86485 4.24417 3.267 0.00134 **
ParentalExpectations 0.15281 0.19868 0.769 0.44301
ParentalCriticism 0.62937 0.24764 2.542 0.01203 *
PersonalStandards 0.44966 0.14304 3.144 0.00200 **
Organization 0.06778 0.15312 0.443 0.65864
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.711 on 154 degrees of freedom
Multiple R-squared: 0.1759, Adjusted R-squared: 0.1545
F-statistic: 8.22 on 4 and 154 DF, p-value: 4.912e-06
> 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,] 0.1819108 0.3638216 0.81808921
[2,] 0.3007261 0.6014521 0.69927393
[3,] 0.1786663 0.3573325 0.82133373
[4,] 0.1530773 0.3061547 0.84692266
[5,] 0.3916308 0.7832615 0.60836925
[6,] 0.3469259 0.6938518 0.65307411
[7,] 0.2843452 0.5686904 0.71565482
[8,] 0.2377809 0.4755618 0.76221910
[9,] 0.2750491 0.5500983 0.72495086
[10,] 0.2078609 0.4157219 0.79213906
[11,] 0.2472087 0.4944175 0.75279125
[12,] 0.1909513 0.3819025 0.80904873
[13,] 0.3581886 0.7163771 0.64181143
[14,] 0.4829026 0.9658051 0.51709744
[15,] 0.6676675 0.6646650 0.33233249
[16,] 0.7378861 0.5242279 0.26211394
[17,] 0.8931325 0.2137349 0.10686746
[18,] 0.8599760 0.2800481 0.14002405
[19,] 0.8795589 0.2408821 0.12044106
[20,] 0.8504802 0.2990396 0.14951982
[21,] 0.8160152 0.3679695 0.18398477
[22,] 0.7755113 0.4489773 0.22448866
[23,] 0.7355763 0.5288474 0.26442372
[24,] 0.7326421 0.5347158 0.26735791
[25,] 0.6830751 0.6338498 0.31692488
[26,] 0.7101528 0.5796943 0.28984715
[27,] 0.6700130 0.6599740 0.32998702
[28,] 0.6897277 0.6205446 0.31027228
[29,] 0.8797093 0.2405814 0.12029070
[30,] 0.9099132 0.1801736 0.09008679
[31,] 0.8882933 0.2234135 0.11170673
[32,] 0.8813295 0.2373411 0.11867054
[33,] 0.8548350 0.2903300 0.14516499
[34,] 0.8724649 0.2550703 0.12753514
[35,] 0.8512957 0.2974086 0.14870428
[36,] 0.8187376 0.3625248 0.18126241
[37,] 0.8035925 0.3928150 0.19640751
[38,] 0.7822099 0.4355803 0.21779013
[39,] 0.7561326 0.4877348 0.24386739
[40,] 0.7185363 0.5629273 0.28146366
[41,] 0.7866182 0.4267636 0.21338182
[42,] 0.7696042 0.4607915 0.23039576
[43,] 0.7465734 0.5068532 0.25342661
[44,] 0.8255372 0.3489255 0.17446276
[45,] 0.8089210 0.3821579 0.19107897
[46,] 0.7746793 0.4506414 0.22532068
[47,] 0.8010722 0.3978556 0.19892778
[48,] 0.8360728 0.3278545 0.16392723
[49,] 0.8259950 0.3480099 0.17400497
[50,] 0.7978384 0.4043232 0.20216160
[51,] 0.7975178 0.4049643 0.20248217
[52,] 0.8410589 0.3178821 0.15894105
[53,] 0.8112226 0.3775549 0.18877744
[54,] 0.8229207 0.3541586 0.17707928
[55,] 0.7917286 0.4165429 0.20827143
[56,] 0.7609497 0.4781006 0.23905030
[57,] 0.7334421 0.5331158 0.26655788
[58,] 0.7557930 0.4884140 0.24420702
[59,] 0.7425499 0.5149001 0.25745006
[60,] 0.7452333 0.5095333 0.25476666
[61,] 0.7449425 0.5101150 0.25505750
[62,] 0.7501296 0.4997408 0.24987041
[63,] 0.7175845 0.5648310 0.28241550
[64,] 0.7063589 0.5872822 0.29364111
[65,] 0.6721168 0.6557665 0.32788325
[66,] 0.6408978 0.7182043 0.35910217
[67,] 0.6732761 0.6534478 0.32672389
[68,] 0.6390301 0.7219399 0.36096994
[69,] 0.5981833 0.8036333 0.40181665
[70,] 0.5688979 0.8622042 0.43110212
[71,] 0.5232627 0.9534746 0.47673731
[72,] 0.4796069 0.9592137 0.52039313
[73,] 0.4765871 0.9531741 0.52341294
[74,] 0.4337251 0.8674502 0.56627489
[75,] 0.4222026 0.8444052 0.57779741
[76,] 0.3825648 0.7651296 0.61743521
[77,] 0.3956447 0.7912893 0.60435534
[78,] 0.4225693 0.8451386 0.57743072
[79,] 0.3845146 0.7690292 0.61548538
[80,] 0.3422625 0.6845250 0.65773749
[81,] 0.3120787 0.6241574 0.68792128
[82,] 0.2742052 0.5484104 0.72579479
[83,] 0.2373219 0.4746438 0.76267811
[84,] 0.2766257 0.5532514 0.72337431
[85,] 0.4212920 0.8425840 0.57870798
[86,] 0.3775684 0.7551368 0.62243160
[87,] 0.3354295 0.6708591 0.66457045
[88,] 0.3187873 0.6375746 0.68121269
[89,] 0.2787663 0.5575325 0.72123373
[90,] 0.4148606 0.8297211 0.58513945
[91,] 0.4140764 0.8281527 0.58592363
[92,] 0.3720845 0.7441690 0.62791552
[93,] 0.3294874 0.6589749 0.67051257
[94,] 0.2887506 0.5775013 0.71124937
[95,] 0.3050779 0.6101557 0.69492213
[96,] 0.2837437 0.5674875 0.71625626
[97,] 0.2964154 0.5928309 0.70358456
[98,] 0.2699540 0.5399079 0.73004604
[99,] 0.3406620 0.6813239 0.65933805
[100,] 0.2967129 0.5934258 0.70328712
[101,] 0.2581052 0.5162104 0.74189478
[102,] 0.2353128 0.4706256 0.76468719
[103,] 0.2047832 0.4095664 0.79521679
[104,] 0.2081700 0.4163400 0.79183000
[105,] 0.1765066 0.3530131 0.82349343
[106,] 0.1597583 0.3195167 0.84024166
[107,] 0.1798456 0.3596912 0.82015440
[108,] 0.6644533 0.6710933 0.33554667
[109,] 0.6649520 0.6700961 0.33504803
[110,] 0.7386789 0.5226422 0.26132112
[111,] 0.6984405 0.6031190 0.30155949
[112,] 0.6760951 0.6478098 0.32390490
[113,] 0.7053575 0.5892849 0.29464247
[114,] 0.7088404 0.5823192 0.29115962
[115,] 0.6673363 0.6653275 0.33266373
[116,] 0.6186347 0.7627306 0.38136529
[117,] 0.6236236 0.7527527 0.37637637
[118,] 0.6752541 0.6494917 0.32474586
[119,] 0.6255432 0.7489137 0.37445683
[120,] 0.6509681 0.6980637 0.34903186
[121,] 0.6196845 0.7606310 0.38031548
[122,] 0.6129431 0.7741138 0.38705691
[123,] 0.6885643 0.6228714 0.31143571
[124,] 0.6466067 0.7067866 0.35339332
[125,] 0.6027861 0.7944278 0.39721389
[126,] 0.6271191 0.7457618 0.37288088
[127,] 0.6023581 0.7952838 0.39764191
[128,] 0.5341305 0.9317391 0.46586955
[129,] 0.4693336 0.9386673 0.53066636
[130,] 0.3987072 0.7974144 0.60129282
[131,] 0.3422388 0.6844776 0.65776122
[132,] 0.4313602 0.8627204 0.56863978
[133,] 0.3568539 0.7137078 0.64314612
[134,] 0.5503236 0.8993528 0.44967639
[135,] 0.4808661 0.9617321 0.51913394
[136,] 0.4278441 0.8556881 0.57215593
[137,] 0.3572784 0.7145568 0.64272158
[138,] 0.3090945 0.6181891 0.69090546
[139,] 0.4196224 0.8392448 0.58037758
[140,] 0.3729542 0.7459084 0.62704582
[141,] 0.2723964 0.5447928 0.72760360
[142,] 0.1931607 0.3863215 0.80683926
[143,] 0.1420506 0.2841011 0.85794945
[144,] 0.1108886 0.2217773 0.88911136
> postscript(file="/var/www/html/rcomp/tmp/1b3cb1291330973.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2b3cb1291330973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3lube1291330973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lube1291330973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lube1291330973.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 = 159
Frequency = 1
1 2 3 4 5 6
2.34774901 3.23011397 -13.68180304 -0.53033770 -6.54317278 -5.96224898
7 8 9 10 11 12
-0.99920589 -7.23432097 4.76051956 -3.39427055 2.27319817 9.63300346
13 14 15 16 17 18
0.92454923 1.36089026 -6.41400483 -16.71309034 -2.15253786 -4.68925139
19 20 21 22 23 24
-4.38737135 9.91662846 9.02441028 15.66314457 -4.15282522 14.91932297
25 26 27 28 29 30
-1.47170731 -9.79015283 -3.80137728 -1.68589973 -3.32483797 -5.07900410
31 32 33 34 35 36
1.77655031 0.99689012 2.97761450 -3.18773265 -8.79174236 14.18124989
37 38 39 40 41 42
10.90203353 1.01925082 5.75059280 2.29681084 6.36639941 -3.46888820
43 44 45 46 47 48
-0.63937600 -6.25703613 -3.67796236 -4.26836508 1.98159009 11.96782938
49 50 51 52 53 54
5.46608787 3.46698889 12.88472750 -4.80305021 -1.62451253 -8.12854643
55 56 57 58 59 60
-8.96492338 5.50906406 -2.25469578 -7.06334990 -10.24234526 0.73401519
61 62 63 64 65 66
-7.84917858 -1.04610618 -2.09940001 -2.17017696 -8.79504604 5.64017120
67 68 69 70 71 72
6.90009434 -6.45047719 -7.40716794 -2.51666840 5.41153757 -2.64748818
73 74 75 76 77 78
3.27564302 8.56048100 2.13084197 1.84995269 -3.76490493 -0.18768695
79 80 81 82 83 84
-1.73566484 6.63979465 1.78342841 -5.98716383 2.07902739 -7.47164501
85 86 87 88 89 90
8.39224664 0.66475520 -1.01045144 -3.58285962 1.24713264 -0.17169969
91 92 93 94 95 96
9.68998201 -14.03377675 -0.59011427 0.81872770 -5.11231849 0.22241149
97 98 99 100 101 102
14.19419297 6.96599820 -1.62136035 1.73203029 -1.21617622 -7.44165539
103 104 105 106 107 108
-4.13951137 -7.38463564 4.20178069 11.64652126 -0.04462693 1.29476135
109 110 111 112 113 114
4.60411882 3.35698727 -6.68715620 2.41724839 -5.31059045 -8.42876250
115 116 117 118 119 120
-21.60943079 -6.06775855 -8.68791151 -1.29733891 5.65950783 6.66850670
121 122 123 124 125 126
9.36569656 -3.83279596 0.54350353 -6.24560772 9.26907687 -1.49929557
127 128 129 130 131 132
-6.42963793 -2.13478848 -4.87358777 13.03367093 -1.78624984 -4.35140465
133 134 135 136 137 138
-4.79703094 -3.41040931 1.51303386 2.60303494 1.62612535 -1.83963286
139 140 141 142 143 144
-7.43241584 0.21048770 11.80448595 5.01807904 -2.24016993 6.84907276
145 146 147 148 149 150
-1.21893303 11.91052227 5.34381913 -1.74975638 -1.74056032 -2.64660826
151 152 153 154 155 156
-7.39235246 10.23674691 12.51487939 1.86276088 -6.25017912 -3.59446061
157 158 159
9.00077301 1.16570370 11.16215387
> postscript(file="/var/www/html/rcomp/tmp/6w4az1291330973.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 2.34774901 NA
1 3.23011397 2.34774901
2 -13.68180304 3.23011397
3 -0.53033770 -13.68180304
4 -6.54317278 -0.53033770
5 -5.96224898 -6.54317278
6 -0.99920589 -5.96224898
7 -7.23432097 -0.99920589
8 4.76051956 -7.23432097
9 -3.39427055 4.76051956
10 2.27319817 -3.39427055
11 9.63300346 2.27319817
12 0.92454923 9.63300346
13 1.36089026 0.92454923
14 -6.41400483 1.36089026
15 -16.71309034 -6.41400483
16 -2.15253786 -16.71309034
17 -4.68925139 -2.15253786
18 -4.38737135 -4.68925139
19 9.91662846 -4.38737135
20 9.02441028 9.91662846
21 15.66314457 9.02441028
22 -4.15282522 15.66314457
23 14.91932297 -4.15282522
24 -1.47170731 14.91932297
25 -9.79015283 -1.47170731
26 -3.80137728 -9.79015283
27 -1.68589973 -3.80137728
28 -3.32483797 -1.68589973
29 -5.07900410 -3.32483797
30 1.77655031 -5.07900410
31 0.99689012 1.77655031
32 2.97761450 0.99689012
33 -3.18773265 2.97761450
34 -8.79174236 -3.18773265
35 14.18124989 -8.79174236
36 10.90203353 14.18124989
37 1.01925082 10.90203353
38 5.75059280 1.01925082
39 2.29681084 5.75059280
40 6.36639941 2.29681084
41 -3.46888820 6.36639941
42 -0.63937600 -3.46888820
43 -6.25703613 -0.63937600
44 -3.67796236 -6.25703613
45 -4.26836508 -3.67796236
46 1.98159009 -4.26836508
47 11.96782938 1.98159009
48 5.46608787 11.96782938
49 3.46698889 5.46608787
50 12.88472750 3.46698889
51 -4.80305021 12.88472750
52 -1.62451253 -4.80305021
53 -8.12854643 -1.62451253
54 -8.96492338 -8.12854643
55 5.50906406 -8.96492338
56 -2.25469578 5.50906406
57 -7.06334990 -2.25469578
58 -10.24234526 -7.06334990
59 0.73401519 -10.24234526
60 -7.84917858 0.73401519
61 -1.04610618 -7.84917858
62 -2.09940001 -1.04610618
63 -2.17017696 -2.09940001
64 -8.79504604 -2.17017696
65 5.64017120 -8.79504604
66 6.90009434 5.64017120
67 -6.45047719 6.90009434
68 -7.40716794 -6.45047719
69 -2.51666840 -7.40716794
70 5.41153757 -2.51666840
71 -2.64748818 5.41153757
72 3.27564302 -2.64748818
73 8.56048100 3.27564302
74 2.13084197 8.56048100
75 1.84995269 2.13084197
76 -3.76490493 1.84995269
77 -0.18768695 -3.76490493
78 -1.73566484 -0.18768695
79 6.63979465 -1.73566484
80 1.78342841 6.63979465
81 -5.98716383 1.78342841
82 2.07902739 -5.98716383
83 -7.47164501 2.07902739
84 8.39224664 -7.47164501
85 0.66475520 8.39224664
86 -1.01045144 0.66475520
87 -3.58285962 -1.01045144
88 1.24713264 -3.58285962
89 -0.17169969 1.24713264
90 9.68998201 -0.17169969
91 -14.03377675 9.68998201
92 -0.59011427 -14.03377675
93 0.81872770 -0.59011427
94 -5.11231849 0.81872770
95 0.22241149 -5.11231849
96 14.19419297 0.22241149
97 6.96599820 14.19419297
98 -1.62136035 6.96599820
99 1.73203029 -1.62136035
100 -1.21617622 1.73203029
101 -7.44165539 -1.21617622
102 -4.13951137 -7.44165539
103 -7.38463564 -4.13951137
104 4.20178069 -7.38463564
105 11.64652126 4.20178069
106 -0.04462693 11.64652126
107 1.29476135 -0.04462693
108 4.60411882 1.29476135
109 3.35698727 4.60411882
110 -6.68715620 3.35698727
111 2.41724839 -6.68715620
112 -5.31059045 2.41724839
113 -8.42876250 -5.31059045
114 -21.60943079 -8.42876250
115 -6.06775855 -21.60943079
116 -8.68791151 -6.06775855
117 -1.29733891 -8.68791151
118 5.65950783 -1.29733891
119 6.66850670 5.65950783
120 9.36569656 6.66850670
121 -3.83279596 9.36569656
122 0.54350353 -3.83279596
123 -6.24560772 0.54350353
124 9.26907687 -6.24560772
125 -1.49929557 9.26907687
126 -6.42963793 -1.49929557
127 -2.13478848 -6.42963793
128 -4.87358777 -2.13478848
129 13.03367093 -4.87358777
130 -1.78624984 13.03367093
131 -4.35140465 -1.78624984
132 -4.79703094 -4.35140465
133 -3.41040931 -4.79703094
134 1.51303386 -3.41040931
135 2.60303494 1.51303386
136 1.62612535 2.60303494
137 -1.83963286 1.62612535
138 -7.43241584 -1.83963286
139 0.21048770 -7.43241584
140 11.80448595 0.21048770
141 5.01807904 11.80448595
142 -2.24016993 5.01807904
143 6.84907276 -2.24016993
144 -1.21893303 6.84907276
145 11.91052227 -1.21893303
146 5.34381913 11.91052227
147 -1.74975638 5.34381913
148 -1.74056032 -1.74975638
149 -2.64660826 -1.74056032
150 -7.39235246 -2.64660826
151 10.23674691 -7.39235246
152 12.51487939 10.23674691
153 1.86276088 12.51487939
154 -6.25017912 1.86276088
155 -3.59446061 -6.25017912
156 9.00077301 -3.59446061
157 1.16570370 9.00077301
158 11.16215387 1.16570370
159 NA 11.16215387
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.23011397 2.34774901
[2,] -13.68180304 3.23011397
[3,] -0.53033770 -13.68180304
[4,] -6.54317278 -0.53033770
[5,] -5.96224898 -6.54317278
[6,] -0.99920589 -5.96224898
[7,] -7.23432097 -0.99920589
[8,] 4.76051956 -7.23432097
[9,] -3.39427055 4.76051956
[10,] 2.27319817 -3.39427055
[11,] 9.63300346 2.27319817
[12,] 0.92454923 9.63300346
[13,] 1.36089026 0.92454923
[14,] -6.41400483 1.36089026
[15,] -16.71309034 -6.41400483
[16,] -2.15253786 -16.71309034
[17,] -4.68925139 -2.15253786
[18,] -4.38737135 -4.68925139
[19,] 9.91662846 -4.38737135
[20,] 9.02441028 9.91662846
[21,] 15.66314457 9.02441028
[22,] -4.15282522 15.66314457
[23,] 14.91932297 -4.15282522
[24,] -1.47170731 14.91932297
[25,] -9.79015283 -1.47170731
[26,] -3.80137728 -9.79015283
[27,] -1.68589973 -3.80137728
[28,] -3.32483797 -1.68589973
[29,] -5.07900410 -3.32483797
[30,] 1.77655031 -5.07900410
[31,] 0.99689012 1.77655031
[32,] 2.97761450 0.99689012
[33,] -3.18773265 2.97761450
[34,] -8.79174236 -3.18773265
[35,] 14.18124989 -8.79174236
[36,] 10.90203353 14.18124989
[37,] 1.01925082 10.90203353
[38,] 5.75059280 1.01925082
[39,] 2.29681084 5.75059280
[40,] 6.36639941 2.29681084
[41,] -3.46888820 6.36639941
[42,] -0.63937600 -3.46888820
[43,] -6.25703613 -0.63937600
[44,] -3.67796236 -6.25703613
[45,] -4.26836508 -3.67796236
[46,] 1.98159009 -4.26836508
[47,] 11.96782938 1.98159009
[48,] 5.46608787 11.96782938
[49,] 3.46698889 5.46608787
[50,] 12.88472750 3.46698889
[51,] -4.80305021 12.88472750
[52,] -1.62451253 -4.80305021
[53,] -8.12854643 -1.62451253
[54,] -8.96492338 -8.12854643
[55,] 5.50906406 -8.96492338
[56,] -2.25469578 5.50906406
[57,] -7.06334990 -2.25469578
[58,] -10.24234526 -7.06334990
[59,] 0.73401519 -10.24234526
[60,] -7.84917858 0.73401519
[61,] -1.04610618 -7.84917858
[62,] -2.09940001 -1.04610618
[63,] -2.17017696 -2.09940001
[64,] -8.79504604 -2.17017696
[65,] 5.64017120 -8.79504604
[66,] 6.90009434 5.64017120
[67,] -6.45047719 6.90009434
[68,] -7.40716794 -6.45047719
[69,] -2.51666840 -7.40716794
[70,] 5.41153757 -2.51666840
[71,] -2.64748818 5.41153757
[72,] 3.27564302 -2.64748818
[73,] 8.56048100 3.27564302
[74,] 2.13084197 8.56048100
[75,] 1.84995269 2.13084197
[76,] -3.76490493 1.84995269
[77,] -0.18768695 -3.76490493
[78,] -1.73566484 -0.18768695
[79,] 6.63979465 -1.73566484
[80,] 1.78342841 6.63979465
[81,] -5.98716383 1.78342841
[82,] 2.07902739 -5.98716383
[83,] -7.47164501 2.07902739
[84,] 8.39224664 -7.47164501
[85,] 0.66475520 8.39224664
[86,] -1.01045144 0.66475520
[87,] -3.58285962 -1.01045144
[88,] 1.24713264 -3.58285962
[89,] -0.17169969 1.24713264
[90,] 9.68998201 -0.17169969
[91,] -14.03377675 9.68998201
[92,] -0.59011427 -14.03377675
[93,] 0.81872770 -0.59011427
[94,] -5.11231849 0.81872770
[95,] 0.22241149 -5.11231849
[96,] 14.19419297 0.22241149
[97,] 6.96599820 14.19419297
[98,] -1.62136035 6.96599820
[99,] 1.73203029 -1.62136035
[100,] -1.21617622 1.73203029
[101,] -7.44165539 -1.21617622
[102,] -4.13951137 -7.44165539
[103,] -7.38463564 -4.13951137
[104,] 4.20178069 -7.38463564
[105,] 11.64652126 4.20178069
[106,] -0.04462693 11.64652126
[107,] 1.29476135 -0.04462693
[108,] 4.60411882 1.29476135
[109,] 3.35698727 4.60411882
[110,] -6.68715620 3.35698727
[111,] 2.41724839 -6.68715620
[112,] -5.31059045 2.41724839
[113,] -8.42876250 -5.31059045
[114,] -21.60943079 -8.42876250
[115,] -6.06775855 -21.60943079
[116,] -8.68791151 -6.06775855
[117,] -1.29733891 -8.68791151
[118,] 5.65950783 -1.29733891
[119,] 6.66850670 5.65950783
[120,] 9.36569656 6.66850670
[121,] -3.83279596 9.36569656
[122,] 0.54350353 -3.83279596
[123,] -6.24560772 0.54350353
[124,] 9.26907687 -6.24560772
[125,] -1.49929557 9.26907687
[126,] -6.42963793 -1.49929557
[127,] -2.13478848 -6.42963793
[128,] -4.87358777 -2.13478848
[129,] 13.03367093 -4.87358777
[130,] -1.78624984 13.03367093
[131,] -4.35140465 -1.78624984
[132,] -4.79703094 -4.35140465
[133,] -3.41040931 -4.79703094
[134,] 1.51303386 -3.41040931
[135,] 2.60303494 1.51303386
[136,] 1.62612535 2.60303494
[137,] -1.83963286 1.62612535
[138,] -7.43241584 -1.83963286
[139,] 0.21048770 -7.43241584
[140,] 11.80448595 0.21048770
[141,] 5.01807904 11.80448595
[142,] -2.24016993 5.01807904
[143,] 6.84907276 -2.24016993
[144,] -1.21893303 6.84907276
[145,] 11.91052227 -1.21893303
[146,] 5.34381913 11.91052227
[147,] -1.74975638 5.34381913
[148,] -1.74056032 -1.74975638
[149,] -2.64660826 -1.74056032
[150,] -7.39235246 -2.64660826
[151,] 10.23674691 -7.39235246
[152,] 12.51487939 10.23674691
[153,] 1.86276088 12.51487939
[154,] -6.25017912 1.86276088
[155,] -3.59446061 -6.25017912
[156,] 9.00077301 -3.59446061
[157,] 1.16570370 9.00077301
[158,] 11.16215387 1.16570370
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.23011397 2.34774901
2 -13.68180304 3.23011397
3 -0.53033770 -13.68180304
4 -6.54317278 -0.53033770
5 -5.96224898 -6.54317278
6 -0.99920589 -5.96224898
7 -7.23432097 -0.99920589
8 4.76051956 -7.23432097
9 -3.39427055 4.76051956
10 2.27319817 -3.39427055
11 9.63300346 2.27319817
12 0.92454923 9.63300346
13 1.36089026 0.92454923
14 -6.41400483 1.36089026
15 -16.71309034 -6.41400483
16 -2.15253786 -16.71309034
17 -4.68925139 -2.15253786
18 -4.38737135 -4.68925139
19 9.91662846 -4.38737135
20 9.02441028 9.91662846
21 15.66314457 9.02441028
22 -4.15282522 15.66314457
23 14.91932297 -4.15282522
24 -1.47170731 14.91932297
25 -9.79015283 -1.47170731
26 -3.80137728 -9.79015283
27 -1.68589973 -3.80137728
28 -3.32483797 -1.68589973
29 -5.07900410 -3.32483797
30 1.77655031 -5.07900410
31 0.99689012 1.77655031
32 2.97761450 0.99689012
33 -3.18773265 2.97761450
34 -8.79174236 -3.18773265
35 14.18124989 -8.79174236
36 10.90203353 14.18124989
37 1.01925082 10.90203353
38 5.75059280 1.01925082
39 2.29681084 5.75059280
40 6.36639941 2.29681084
41 -3.46888820 6.36639941
42 -0.63937600 -3.46888820
43 -6.25703613 -0.63937600
44 -3.67796236 -6.25703613
45 -4.26836508 -3.67796236
46 1.98159009 -4.26836508
47 11.96782938 1.98159009
48 5.46608787 11.96782938
49 3.46698889 5.46608787
50 12.88472750 3.46698889
51 -4.80305021 12.88472750
52 -1.62451253 -4.80305021
53 -8.12854643 -1.62451253
54 -8.96492338 -8.12854643
55 5.50906406 -8.96492338
56 -2.25469578 5.50906406
57 -7.06334990 -2.25469578
58 -10.24234526 -7.06334990
59 0.73401519 -10.24234526
60 -7.84917858 0.73401519
61 -1.04610618 -7.84917858
62 -2.09940001 -1.04610618
63 -2.17017696 -2.09940001
64 -8.79504604 -2.17017696
65 5.64017120 -8.79504604
66 6.90009434 5.64017120
67 -6.45047719 6.90009434
68 -7.40716794 -6.45047719
69 -2.51666840 -7.40716794
70 5.41153757 -2.51666840
71 -2.64748818 5.41153757
72 3.27564302 -2.64748818
73 8.56048100 3.27564302
74 2.13084197 8.56048100
75 1.84995269 2.13084197
76 -3.76490493 1.84995269
77 -0.18768695 -3.76490493
78 -1.73566484 -0.18768695
79 6.63979465 -1.73566484
80 1.78342841 6.63979465
81 -5.98716383 1.78342841
82 2.07902739 -5.98716383
83 -7.47164501 2.07902739
84 8.39224664 -7.47164501
85 0.66475520 8.39224664
86 -1.01045144 0.66475520
87 -3.58285962 -1.01045144
88 1.24713264 -3.58285962
89 -0.17169969 1.24713264
90 9.68998201 -0.17169969
91 -14.03377675 9.68998201
92 -0.59011427 -14.03377675
93 0.81872770 -0.59011427
94 -5.11231849 0.81872770
95 0.22241149 -5.11231849
96 14.19419297 0.22241149
97 6.96599820 14.19419297
98 -1.62136035 6.96599820
99 1.73203029 -1.62136035
100 -1.21617622 1.73203029
101 -7.44165539 -1.21617622
102 -4.13951137 -7.44165539
103 -7.38463564 -4.13951137
104 4.20178069 -7.38463564
105 11.64652126 4.20178069
106 -0.04462693 11.64652126
107 1.29476135 -0.04462693
108 4.60411882 1.29476135
109 3.35698727 4.60411882
110 -6.68715620 3.35698727
111 2.41724839 -6.68715620
112 -5.31059045 2.41724839
113 -8.42876250 -5.31059045
114 -21.60943079 -8.42876250
115 -6.06775855 -21.60943079
116 -8.68791151 -6.06775855
117 -1.29733891 -8.68791151
118 5.65950783 -1.29733891
119 6.66850670 5.65950783
120 9.36569656 6.66850670
121 -3.83279596 9.36569656
122 0.54350353 -3.83279596
123 -6.24560772 0.54350353
124 9.26907687 -6.24560772
125 -1.49929557 9.26907687
126 -6.42963793 -1.49929557
127 -2.13478848 -6.42963793
128 -4.87358777 -2.13478848
129 13.03367093 -4.87358777
130 -1.78624984 13.03367093
131 -4.35140465 -1.78624984
132 -4.79703094 -4.35140465
133 -3.41040931 -4.79703094
134 1.51303386 -3.41040931
135 2.60303494 1.51303386
136 1.62612535 2.60303494
137 -1.83963286 1.62612535
138 -7.43241584 -1.83963286
139 0.21048770 -7.43241584
140 11.80448595 0.21048770
141 5.01807904 11.80448595
142 -2.24016993 5.01807904
143 6.84907276 -2.24016993
144 -1.21893303 6.84907276
145 11.91052227 -1.21893303
146 5.34381913 11.91052227
147 -1.74975638 5.34381913
148 -1.74056032 -1.74975638
149 -2.64660826 -1.74056032
150 -7.39235246 -2.64660826
151 10.23674691 -7.39235246
152 12.51487939 10.23674691
153 1.86276088 12.51487939
154 -6.25017912 1.86276088
155 -3.59446061 -6.25017912
156 9.00077301 -3.59446061
157 1.16570370 9.00077301
158 11.16215387 1.16570370
> 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/77ds21291330973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87ds21291330973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97ds21291330973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zm941291330973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113nqa1291330973.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/12onoy1291330973.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/13dpp21291330974.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/14yq5q1291330974.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/1518mw1291330974.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/16nrk21291330974.tab")
+ }
>
> try(system("convert tmp/1b3cb1291330973.ps tmp/1b3cb1291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/2b3cb1291330973.ps tmp/2b3cb1291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lube1291330973.ps tmp/3lube1291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lube1291330973.ps tmp/4lube1291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lube1291330973.ps tmp/5lube1291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/6w4az1291330973.ps tmp/6w4az1291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/77ds21291330973.ps tmp/77ds21291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/87ds21291330973.ps tmp/87ds21291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/97ds21291330973.ps tmp/97ds21291330973.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zm941291330973.ps tmp/10zm941291330973.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.996 1.761 8.965