R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(2
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+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('G'
+ ,'COM'
+ ,'DA'
+ ,'PE'
+ ,'PC'
+ ,'PS'
+ ,'O
')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('G','COM','DA','PE','PC','PS','O
'),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 = '2'
> #'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
COM G DA PE PC PS O\r
1 24 2 14 11 12 24 26
2 25 2 11 7 8 25 23
3 17 2 6 17 8 30 25
4 18 1 12 10 8 19 23
5 18 2 8 12 9 22 19
6 16 2 10 12 7 22 29
7 20 2 10 11 4 25 25
8 16 2 11 11 11 23 21
9 18 2 16 12 7 17 22
10 17 2 11 13 7 21 25
11 23 1 13 14 12 19 24
12 30 2 12 16 10 19 18
13 23 1 8 11 10 15 22
14 18 2 12 10 8 16 15
15 15 2 11 11 8 23 22
16 12 1 4 15 4 27 28
17 21 1 9 9 9 22 20
18 15 2 8 11 8 14 12
19 20 1 8 17 7 22 24
20 31 2 14 17 11 23 20
21 27 1 15 11 9 23 21
22 34 2 16 18 11 21 20
23 21 2 9 14 13 19 21
24 31 2 14 10 8 18 23
25 19 1 11 11 8 20 28
26 16 2 8 15 9 23 24
27 20 1 9 15 6 25 24
28 21 2 9 13 9 19 24
29 22 2 9 16 9 24 23
30 17 1 9 13 6 22 23
31 24 2 10 9 6 25 29
32 25 1 16 18 16 26 24
33 26 2 11 18 5 29 18
34 25 2 8 12 7 32 25
35 17 1 9 17 9 25 21
36 32 1 16 9 6 29 26
37 33 1 11 9 6 28 22
38 13 1 16 12 5 17 22
39 32 2 12 18 12 28 22
40 25 1 12 12 7 29 23
41 29 1 14 18 10 26 30
42 22 2 9 14 9 25 23
43 18 1 10 15 8 14 17
44 17 1 9 16 5 25 23
45 20 2 10 10 8 26 23
46 15 2 12 11 8 20 25
47 20 2 14 14 10 18 24
48 33 2 14 9 6 32 24
49 29 2 10 12 8 25 23
50 23 1 14 17 7 25 21
51 26 2 16 5 4 23 24
52 18 1 9 12 8 21 24
53 20 1 10 12 8 20 28
54 11 2 6 6 4 15 16
55 28 1 8 24 20 30 20
56 26 2 13 12 8 24 29
57 22 2 10 12 8 26 27
58 17 2 8 14 6 24 22
59 12 1 7 7 4 22 28
60 14 2 15 13 8 14 16
61 17 1 9 12 9 24 25
62 21 1 10 13 6 24 24
63 19 2 12 14 7 24 28
64 18 2 13 8 9 24 24
65 10 2 10 11 5 19 23
66 29 1 11 9 5 31 30
67 31 2 8 11 8 22 24
68 19 1 9 13 8 27 21
69 9 2 13 10 6 19 25
70 20 1 11 11 8 25 25
71 28 1 8 12 7 20 22
72 19 2 9 9 7 21 23
73 30 2 9 15 9 27 26
74 29 1 15 18 11 23 23
75 26 1 9 15 6 25 25
76 23 2 10 12 8 20 21
77 13 2 14 13 6 21 25
78 21 2 12 14 9 22 24
79 19 1 12 10 8 23 29
80 28 1 11 13 6 25 22
81 23 1 14 13 10 25 27
82 18 1 6 11 8 17 26
83 21 2 12 13 8 19 22
84 20 1 8 16 10 25 24
85 23 2 14 8 5 19 27
86 21 2 11 16 7 20 24
87 21 1 10 11 5 26 24
88 15 2 14 9 8 23 29
89 28 2 12 16 14 27 22
90 19 2 10 12 7 17 21
91 26 2 14 14 8 17 24
92 10 2 5 8 6 19 24
93 16 2 11 9 5 17 23
94 22 2 10 15 6 22 20
95 19 2 9 11 10 21 27
96 31 2 10 21 12 32 26
97 31 2 16 14 9 21 25
98 29 2 13 18 12 21 21
99 19 1 9 12 7 18 21
100 22 1 10 13 8 18 19
101 23 2 10 15 10 23 21
102 15 1 7 12 6 19 21
103 20 2 9 19 10 20 16
104 18 1 8 15 10 21 22
105 23 2 14 11 10 20 29
106 25 1 14 11 5 17 15
107 21 2 8 10 7 18 17
108 24 1 9 13 10 19 15
109 25 1 14 15 11 22 21
110 17 2 14 12 6 15 21
111 13 2 8 12 7 14 19
112 28 2 8 16 12 18 24
113 21 2 8 9 11 24 20
114 25 1 7 18 11 35 17
115 9 2 6 8 11 29 23
116 16 1 8 13 5 21 24
117 19 2 6 17 8 25 14
118 17 2 11 9 6 20 19
119 25 2 14 15 9 22 24
120 20 2 11 8 4 13 13
121 29 2 11 7 4 26 22
122 14 2 11 12 7 17 16
123 22 2 14 14 11 25 19
124 15 2 8 6 6 20 25
125 19 2 20 8 7 19 25
126 20 2 11 17 8 21 23
127 15 1 8 10 4 22 24
128 20 2 11 11 8 24 26
129 18 2 10 14 9 21 26
130 33 2 14 11 8 26 25
131 22 1 11 13 11 24 18
132 16 1 9 12 8 16 21
133 17 2 9 11 5 23 26
134 16 1 8 9 4 18 23
135 21 1 10 12 8 16 23
136 26 2 13 20 10 26 22
137 18 1 13 12 6 19 20
138 18 1 12 13 9 21 13
139 17 2 8 12 9 21 24
140 22 2 13 12 13 22 15
141 30 1 14 9 9 23 14
142 30 2 12 15 10 29 22
143 24 1 14 24 20 21 10
144 21 2 15 7 5 21 24
145 21 1 13 17 11 23 22
146 29 2 16 11 6 27 24
147 31 2 9 17 9 25 19
148 20 1 9 11 7 21 20
149 16 1 9 12 9 10 13
150 22 1 8 14 10 20 20
151 20 2 7 11 9 26 22
152 28 2 16 16 8 24 24
153 38 1 11 21 7 29 29
154 22 2 9 14 6 19 12
155 20 2 11 20 13 24 20
156 17 2 9 13 6 19 21
157 28 2 14 11 8 24 24
158 22 2 13 15 10 22 22
159 31 2 16 19 16 17 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G DA PE PC PS
-1.7649 -0.1314 0.8128 0.2485 0.1903 0.5659
`O\r`
-0.1157
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.6811 -2.5454 -0.4093 2.7864 12.5959
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.76494 3.27874 -0.538 0.5912
G -0.13139 0.74448 -0.176 0.8601
DA 0.81285 0.13166 6.174 5.80e-09 ***
PE 0.24845 0.13412 1.852 0.0659 .
PC 0.19033 0.16911 1.126 0.2621
PS 0.56590 0.09612 5.887 2.42e-08 ***
`O\r` -0.11568 0.10335 -1.119 0.2648
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.492 on 152 degrees of freedom
Multiple R-squared: 0.4073, Adjusted R-squared: 0.3839
F-statistic: 17.41 on 6 and 152 DF, p-value: 2.815e-15
> 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,] 0.2144658 0.42893166 0.78553417
[2,] 0.2663909 0.53278174 0.73360913
[3,] 0.7472689 0.50546225 0.25273112
[4,] 0.7532429 0.49351424 0.24675712
[5,] 0.7180392 0.56392151 0.28196075
[6,] 0.7175828 0.56483440 0.28241720
[7,] 0.6614078 0.67718434 0.33859217
[8,] 0.5736850 0.85262992 0.42631496
[9,] 0.5261676 0.94766478 0.47383239
[10,] 0.4452702 0.89054044 0.55472978
[11,] 0.4630583 0.92611655 0.53694173
[12,] 0.3839558 0.76791157 0.61604422
[13,] 0.3898463 0.77969250 0.61015375
[14,] 0.3177975 0.63559504 0.68220248
[15,] 0.6282750 0.74345003 0.37172502
[16,] 0.5588288 0.88234230 0.44117115
[17,] 0.5162503 0.96749941 0.48374970
[18,] 0.4490477 0.89809535 0.55095233
[19,] 0.4140437 0.82808734 0.58595633
[20,] 0.3540558 0.70811153 0.64594424
[21,] 0.3038982 0.60779644 0.69610178
[22,] 0.3801686 0.76033730 0.61983135
[23,] 0.4432964 0.88659277 0.55670361
[24,] 0.3971714 0.79434270 0.60282865
[25,] 0.4042855 0.80857094 0.59571453
[26,] 0.4086265 0.81725296 0.59137352
[27,] 0.4079787 0.81595731 0.59202134
[28,] 0.5855720 0.82885608 0.41442804
[29,] 0.7805943 0.43881139 0.21940569
[30,] 0.7749891 0.45002172 0.22501086
[31,] 0.7333678 0.53326436 0.26663218
[32,] 0.7101911 0.57961772 0.28980886
[33,] 0.6615039 0.67699228 0.33849614
[34,] 0.6143737 0.77125265 0.38562633
[35,] 0.5997505 0.80049906 0.40024953
[36,] 0.5659476 0.86810488 0.43405244
[37,] 0.5878941 0.82421172 0.41210586
[38,] 0.5463841 0.90723172 0.45361586
[39,] 0.5365914 0.92681718 0.46340859
[40,] 0.5998328 0.80033440 0.40016720
[41,] 0.5789092 0.84218168 0.42109084
[42,] 0.5432061 0.91358781 0.45679390
[43,] 0.4940078 0.98801561 0.50599220
[44,] 0.4561601 0.91232017 0.54383991
[45,] 0.4084775 0.81695501 0.59152250
[46,] 0.3641220 0.72824408 0.63587796
[47,] 0.3346956 0.66939111 0.66530445
[48,] 0.2904447 0.58088931 0.70955534
[49,] 0.2650276 0.53005517 0.73497241
[50,] 0.2441927 0.48838549 0.75580725
[51,] 0.3048603 0.60972053 0.69513974
[52,] 0.2922150 0.58442996 0.70778502
[53,] 0.2550569 0.51011388 0.74494306
[54,] 0.2419586 0.48391727 0.75804136
[55,] 0.2765487 0.55309732 0.72345134
[56,] 0.3513751 0.70275024 0.64862488
[57,] 0.3511078 0.70221562 0.64889219
[58,] 0.6841773 0.63164531 0.31582265
[59,] 0.6772180 0.64556410 0.32278205
[60,] 0.8491083 0.30178337 0.15089168
[61,] 0.8304067 0.33918662 0.16959331
[62,] 0.9312160 0.13756794 0.06878397
[63,] 0.9150520 0.16989609 0.08494805
[64,] 0.9386148 0.12277049 0.06138525
[65,] 0.9265155 0.14696907 0.07348454
[66,] 0.9272698 0.14546045 0.07273022
[67,] 0.9212959 0.15740820 0.07870410
[68,] 0.9693104 0.06137927 0.03068964
[69,] 0.9615954 0.07680928 0.03840464
[70,] 0.9544089 0.09118220 0.04559110
[71,] 0.9560883 0.08782338 0.04391169
[72,] 0.9486539 0.10269210 0.05134605
[73,] 0.9472900 0.10542005 0.05271002
[74,] 0.9339765 0.13204696 0.06602348
[75,] 0.9210433 0.15791340 0.07895670
[76,] 0.9125891 0.17482187 0.08741094
[77,] 0.8968603 0.20627938 0.10313969
[78,] 0.8755597 0.24888060 0.12444030
[79,] 0.9196765 0.16064708 0.08032354
[80,] 0.9030478 0.19390437 0.09695219
[81,] 0.8832504 0.23349920 0.11674960
[82,] 0.8858734 0.22825312 0.11412656
[83,] 0.8737780 0.25244393 0.12622196
[84,] 0.8512619 0.29747619 0.14873810
[85,] 0.8235989 0.35280220 0.17640110
[86,] 0.7905692 0.41886163 0.20943082
[87,] 0.7617144 0.47657110 0.23828555
[88,] 0.7815175 0.43696506 0.21848253
[89,] 0.7771351 0.44572985 0.22286493
[90,] 0.7416725 0.51665503 0.25832752
[91,] 0.7155676 0.56886470 0.28443235
[92,] 0.6733622 0.65327557 0.32663778
[93,] 0.6360667 0.72786659 0.36393329
[94,] 0.5956197 0.80876069 0.40438034
[95,] 0.5545065 0.89098701 0.44549350
[96,] 0.5084612 0.98307752 0.49153876
[97,] 0.4854425 0.97088500 0.51455750
[98,] 0.4845709 0.96914179 0.51542910
[99,] 0.4913362 0.98267248 0.50866376
[100,] 0.4410943 0.88218851 0.55890574
[101,] 0.4167834 0.83356689 0.58321656
[102,] 0.3769381 0.75387630 0.62306185
[103,] 0.6025866 0.79482688 0.39741344
[104,] 0.5965803 0.80683946 0.40341973
[105,] 0.5725362 0.85492752 0.42746376
[106,] 0.7721525 0.45569492 0.22784746
[107,] 0.7548822 0.49023559 0.24511780
[108,] 0.7364412 0.52711752 0.26355876
[109,] 0.7054290 0.58914197 0.29457098
[110,] 0.6558368 0.68832644 0.34416322
[111,] 0.6781286 0.64374278 0.32187139
[112,] 0.7363370 0.52732607 0.26366304
[113,] 0.7279613 0.54407739 0.27203869
[114,] 0.7289882 0.54202361 0.27101181
[115,] 0.6765521 0.64689580 0.32344790
[116,] 0.7190643 0.56187143 0.28093571
[117,] 0.6856991 0.62860174 0.31430087
[118,] 0.6825035 0.63499303 0.31749651
[119,] 0.6443443 0.71131146 0.35565573
[120,] 0.6123617 0.77527651 0.38763825
[121,] 0.6861137 0.62777264 0.31388632
[122,] 0.6334238 0.73315235 0.36657617
[123,] 0.5720686 0.85586273 0.42793137
[124,] 0.5622292 0.87554158 0.43777079
[125,] 0.5050842 0.98983165 0.49491582
[126,] 0.4553481 0.91069614 0.54465193
[127,] 0.4217537 0.84350747 0.57824626
[128,] 0.4385729 0.87714580 0.56142710
[129,] 0.5088202 0.98235964 0.49117982
[130,] 0.4332802 0.86656038 0.56671981
[131,] 0.3556699 0.71133985 0.64433008
[132,] 0.4246820 0.84936400 0.57531800
[133,] 0.3703252 0.74065036 0.62967482
[134,] 0.3047116 0.60942326 0.69528837
[135,] 0.2285587 0.45711742 0.77144129
[136,] 0.3682241 0.73644813 0.63177594
[137,] 0.2702763 0.54055257 0.72972372
[138,] 0.3937190 0.78743802 0.60628099
[139,] 0.3364928 0.67298567 0.66350716
[140,] 0.2529608 0.50592163 0.74703918
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ntbm1292264454.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ntbm1292264454.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3g3a71292264454.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4g3a71292264454.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5g3a71292264454.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
-0.94299635 3.33775014 -5.68066888 -1.95645010 -1.42133490 -3.50952876
7 8 9 10 11 12
-0.85051001 -6.32663160 -4.36691141 -3.46766645 0.59123491 7.72513651
13 14 15 16 17 18
7.81374564 -1.05282575 -6.63994599 -5.88336757 1.49546584 -0.26513054
19 20 21 22 23 24
0.16409212 4.62841556 1.67124412 6.88606366 1.43664181 10.11514407
25 26 27 28 29 30
-0.37953525 -4.15417640 -1.65921838 2.79348223 0.10293635 -2.58029422
31 32 33 34 35 36
3.72846380 -5.56376641 -0.66625501 0.99443124 -6.07417816 4.10931907
37 38 39 40 41 42
9.27673312 -9.11763672 4.21719314 -0.92202716 1.89803940 0.03394252
43 44 45 46 47 48
0.56238240 -4.83302151 -2.16066048 -5.40804309 -2.14365328 4.93734290
49 50 51 52 53 54
6.90833334 -3.75775896 2.77922850 -1.03092460 1.18486119 -0.73899408
55 56 57 58 59 60
-1.03936097 2.72978720 -0.19483201 -3.13198923 -3.50478814 -6.98925024
61 62 63 64 65 66
-3.80327609 -0.40926107 -3.87961950 -5.04515239 -7.87680887 4.69483593
67 68 69 70 71 72
12.59587122 -4.02183186 -11.02587119 -2.55608876 10.30679198 0.92047871
73 74 75 76 77 78
7.00073965 1.78277062 4.45646539 3.50646799 -9.71588172 -1.59122161
79 80 81 82 83 84
-2.52594922 4.98062121 -2.64084494 4.15104723 0.31389942 -1.85615774
85 86 87 88 89 90
3.07988718 0.23719029 -0.85382138 -7.77180218 0.89933229 1.39450326
91 92 93 94 95 96
4.80291505 -3.14185043 -1.06095112 0.89429132 0.31530534 2.29666603
97 98 99 100 101 102
5.83896364 4.24996270 1.51005935 3.02705481 0.68273886 -1.23980766
103 104 105 106 107 108
-1.37894217 -1.57547022 1.04832462 3.94672965 4.48847382 4.43060112
109 110 111 112 113 114
-0.32448682 -2.53476087 -1.51346349 9.85587065 0.92724002 -3.19933936
115 116 117 118 119 120
-11.68105807 -1.89552632 -2.12368818 -2.41172148 0.53462560 4.48460017
121 122 123 124 125 126
7.41750172 -4.99676534 -4.87370917 -0.53370970 -6.40924670 -1.88318117
127 128 129 130 131 132
-2.52573289 -1.74311133 -2.16825415 7.57085480 -1.86788319 -0.54847372
133 134 135 136 137 138
-1.98050957 0.87063840 3.87004408 -1.58009444 -3.23258988 -5.18078257
139 140 141 142 143 144
-1.27701560 -2.70965448 5.17121407 2.77732052 -5.98019318 -0.96336145
145 146 147 148 149 150
-4.45876036 1.64423900 7.82584751 0.94512756 1.73112478 3.00751598
151 152 153 154 155 156
-0.27658229 0.71900589 11.34884546 2.72782550 -5.62496347 -0.98256724
157 158 159
3.58697190 -2.07422615 4.94954236
> postscript(file="/var/www/html/freestat/rcomp/tmp/6qcss1292264454.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 -0.94299635 NA
1 3.33775014 -0.94299635
2 -5.68066888 3.33775014
3 -1.95645010 -5.68066888
4 -1.42133490 -1.95645010
5 -3.50952876 -1.42133490
6 -0.85051001 -3.50952876
7 -6.32663160 -0.85051001
8 -4.36691141 -6.32663160
9 -3.46766645 -4.36691141
10 0.59123491 -3.46766645
11 7.72513651 0.59123491
12 7.81374564 7.72513651
13 -1.05282575 7.81374564
14 -6.63994599 -1.05282575
15 -5.88336757 -6.63994599
16 1.49546584 -5.88336757
17 -0.26513054 1.49546584
18 0.16409212 -0.26513054
19 4.62841556 0.16409212
20 1.67124412 4.62841556
21 6.88606366 1.67124412
22 1.43664181 6.88606366
23 10.11514407 1.43664181
24 -0.37953525 10.11514407
25 -4.15417640 -0.37953525
26 -1.65921838 -4.15417640
27 2.79348223 -1.65921838
28 0.10293635 2.79348223
29 -2.58029422 0.10293635
30 3.72846380 -2.58029422
31 -5.56376641 3.72846380
32 -0.66625501 -5.56376641
33 0.99443124 -0.66625501
34 -6.07417816 0.99443124
35 4.10931907 -6.07417816
36 9.27673312 4.10931907
37 -9.11763672 9.27673312
38 4.21719314 -9.11763672
39 -0.92202716 4.21719314
40 1.89803940 -0.92202716
41 0.03394252 1.89803940
42 0.56238240 0.03394252
43 -4.83302151 0.56238240
44 -2.16066048 -4.83302151
45 -5.40804309 -2.16066048
46 -2.14365328 -5.40804309
47 4.93734290 -2.14365328
48 6.90833334 4.93734290
49 -3.75775896 6.90833334
50 2.77922850 -3.75775896
51 -1.03092460 2.77922850
52 1.18486119 -1.03092460
53 -0.73899408 1.18486119
54 -1.03936097 -0.73899408
55 2.72978720 -1.03936097
56 -0.19483201 2.72978720
57 -3.13198923 -0.19483201
58 -3.50478814 -3.13198923
59 -6.98925024 -3.50478814
60 -3.80327609 -6.98925024
61 -0.40926107 -3.80327609
62 -3.87961950 -0.40926107
63 -5.04515239 -3.87961950
64 -7.87680887 -5.04515239
65 4.69483593 -7.87680887
66 12.59587122 4.69483593
67 -4.02183186 12.59587122
68 -11.02587119 -4.02183186
69 -2.55608876 -11.02587119
70 10.30679198 -2.55608876
71 0.92047871 10.30679198
72 7.00073965 0.92047871
73 1.78277062 7.00073965
74 4.45646539 1.78277062
75 3.50646799 4.45646539
76 -9.71588172 3.50646799
77 -1.59122161 -9.71588172
78 -2.52594922 -1.59122161
79 4.98062121 -2.52594922
80 -2.64084494 4.98062121
81 4.15104723 -2.64084494
82 0.31389942 4.15104723
83 -1.85615774 0.31389942
84 3.07988718 -1.85615774
85 0.23719029 3.07988718
86 -0.85382138 0.23719029
87 -7.77180218 -0.85382138
88 0.89933229 -7.77180218
89 1.39450326 0.89933229
90 4.80291505 1.39450326
91 -3.14185043 4.80291505
92 -1.06095112 -3.14185043
93 0.89429132 -1.06095112
94 0.31530534 0.89429132
95 2.29666603 0.31530534
96 5.83896364 2.29666603
97 4.24996270 5.83896364
98 1.51005935 4.24996270
99 3.02705481 1.51005935
100 0.68273886 3.02705481
101 -1.23980766 0.68273886
102 -1.37894217 -1.23980766
103 -1.57547022 -1.37894217
104 1.04832462 -1.57547022
105 3.94672965 1.04832462
106 4.48847382 3.94672965
107 4.43060112 4.48847382
108 -0.32448682 4.43060112
109 -2.53476087 -0.32448682
110 -1.51346349 -2.53476087
111 9.85587065 -1.51346349
112 0.92724002 9.85587065
113 -3.19933936 0.92724002
114 -11.68105807 -3.19933936
115 -1.89552632 -11.68105807
116 -2.12368818 -1.89552632
117 -2.41172148 -2.12368818
118 0.53462560 -2.41172148
119 4.48460017 0.53462560
120 7.41750172 4.48460017
121 -4.99676534 7.41750172
122 -4.87370917 -4.99676534
123 -0.53370970 -4.87370917
124 -6.40924670 -0.53370970
125 -1.88318117 -6.40924670
126 -2.52573289 -1.88318117
127 -1.74311133 -2.52573289
128 -2.16825415 -1.74311133
129 7.57085480 -2.16825415
130 -1.86788319 7.57085480
131 -0.54847372 -1.86788319
132 -1.98050957 -0.54847372
133 0.87063840 -1.98050957
134 3.87004408 0.87063840
135 -1.58009444 3.87004408
136 -3.23258988 -1.58009444
137 -5.18078257 -3.23258988
138 -1.27701560 -5.18078257
139 -2.70965448 -1.27701560
140 5.17121407 -2.70965448
141 2.77732052 5.17121407
142 -5.98019318 2.77732052
143 -0.96336145 -5.98019318
144 -4.45876036 -0.96336145
145 1.64423900 -4.45876036
146 7.82584751 1.64423900
147 0.94512756 7.82584751
148 1.73112478 0.94512756
149 3.00751598 1.73112478
150 -0.27658229 3.00751598
151 0.71900589 -0.27658229
152 11.34884546 0.71900589
153 2.72782550 11.34884546
154 -5.62496347 2.72782550
155 -0.98256724 -5.62496347
156 3.58697190 -0.98256724
157 -2.07422615 3.58697190
158 4.94954236 -2.07422615
159 NA 4.94954236
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.33775014 -0.94299635
[2,] -5.68066888 3.33775014
[3,] -1.95645010 -5.68066888
[4,] -1.42133490 -1.95645010
[5,] -3.50952876 -1.42133490
[6,] -0.85051001 -3.50952876
[7,] -6.32663160 -0.85051001
[8,] -4.36691141 -6.32663160
[9,] -3.46766645 -4.36691141
[10,] 0.59123491 -3.46766645
[11,] 7.72513651 0.59123491
[12,] 7.81374564 7.72513651
[13,] -1.05282575 7.81374564
[14,] -6.63994599 -1.05282575
[15,] -5.88336757 -6.63994599
[16,] 1.49546584 -5.88336757
[17,] -0.26513054 1.49546584
[18,] 0.16409212 -0.26513054
[19,] 4.62841556 0.16409212
[20,] 1.67124412 4.62841556
[21,] 6.88606366 1.67124412
[22,] 1.43664181 6.88606366
[23,] 10.11514407 1.43664181
[24,] -0.37953525 10.11514407
[25,] -4.15417640 -0.37953525
[26,] -1.65921838 -4.15417640
[27,] 2.79348223 -1.65921838
[28,] 0.10293635 2.79348223
[29,] -2.58029422 0.10293635
[30,] 3.72846380 -2.58029422
[31,] -5.56376641 3.72846380
[32,] -0.66625501 -5.56376641
[33,] 0.99443124 -0.66625501
[34,] -6.07417816 0.99443124
[35,] 4.10931907 -6.07417816
[36,] 9.27673312 4.10931907
[37,] -9.11763672 9.27673312
[38,] 4.21719314 -9.11763672
[39,] -0.92202716 4.21719314
[40,] 1.89803940 -0.92202716
[41,] 0.03394252 1.89803940
[42,] 0.56238240 0.03394252
[43,] -4.83302151 0.56238240
[44,] -2.16066048 -4.83302151
[45,] -5.40804309 -2.16066048
[46,] -2.14365328 -5.40804309
[47,] 4.93734290 -2.14365328
[48,] 6.90833334 4.93734290
[49,] -3.75775896 6.90833334
[50,] 2.77922850 -3.75775896
[51,] -1.03092460 2.77922850
[52,] 1.18486119 -1.03092460
[53,] -0.73899408 1.18486119
[54,] -1.03936097 -0.73899408
[55,] 2.72978720 -1.03936097
[56,] -0.19483201 2.72978720
[57,] -3.13198923 -0.19483201
[58,] -3.50478814 -3.13198923
[59,] -6.98925024 -3.50478814
[60,] -3.80327609 -6.98925024
[61,] -0.40926107 -3.80327609
[62,] -3.87961950 -0.40926107
[63,] -5.04515239 -3.87961950
[64,] -7.87680887 -5.04515239
[65,] 4.69483593 -7.87680887
[66,] 12.59587122 4.69483593
[67,] -4.02183186 12.59587122
[68,] -11.02587119 -4.02183186
[69,] -2.55608876 -11.02587119
[70,] 10.30679198 -2.55608876
[71,] 0.92047871 10.30679198
[72,] 7.00073965 0.92047871
[73,] 1.78277062 7.00073965
[74,] 4.45646539 1.78277062
[75,] 3.50646799 4.45646539
[76,] -9.71588172 3.50646799
[77,] -1.59122161 -9.71588172
[78,] -2.52594922 -1.59122161
[79,] 4.98062121 -2.52594922
[80,] -2.64084494 4.98062121
[81,] 4.15104723 -2.64084494
[82,] 0.31389942 4.15104723
[83,] -1.85615774 0.31389942
[84,] 3.07988718 -1.85615774
[85,] 0.23719029 3.07988718
[86,] -0.85382138 0.23719029
[87,] -7.77180218 -0.85382138
[88,] 0.89933229 -7.77180218
[89,] 1.39450326 0.89933229
[90,] 4.80291505 1.39450326
[91,] -3.14185043 4.80291505
[92,] -1.06095112 -3.14185043
[93,] 0.89429132 -1.06095112
[94,] 0.31530534 0.89429132
[95,] 2.29666603 0.31530534
[96,] 5.83896364 2.29666603
[97,] 4.24996270 5.83896364
[98,] 1.51005935 4.24996270
[99,] 3.02705481 1.51005935
[100,] 0.68273886 3.02705481
[101,] -1.23980766 0.68273886
[102,] -1.37894217 -1.23980766
[103,] -1.57547022 -1.37894217
[104,] 1.04832462 -1.57547022
[105,] 3.94672965 1.04832462
[106,] 4.48847382 3.94672965
[107,] 4.43060112 4.48847382
[108,] -0.32448682 4.43060112
[109,] -2.53476087 -0.32448682
[110,] -1.51346349 -2.53476087
[111,] 9.85587065 -1.51346349
[112,] 0.92724002 9.85587065
[113,] -3.19933936 0.92724002
[114,] -11.68105807 -3.19933936
[115,] -1.89552632 -11.68105807
[116,] -2.12368818 -1.89552632
[117,] -2.41172148 -2.12368818
[118,] 0.53462560 -2.41172148
[119,] 4.48460017 0.53462560
[120,] 7.41750172 4.48460017
[121,] -4.99676534 7.41750172
[122,] -4.87370917 -4.99676534
[123,] -0.53370970 -4.87370917
[124,] -6.40924670 -0.53370970
[125,] -1.88318117 -6.40924670
[126,] -2.52573289 -1.88318117
[127,] -1.74311133 -2.52573289
[128,] -2.16825415 -1.74311133
[129,] 7.57085480 -2.16825415
[130,] -1.86788319 7.57085480
[131,] -0.54847372 -1.86788319
[132,] -1.98050957 -0.54847372
[133,] 0.87063840 -1.98050957
[134,] 3.87004408 0.87063840
[135,] -1.58009444 3.87004408
[136,] -3.23258988 -1.58009444
[137,] -5.18078257 -3.23258988
[138,] -1.27701560 -5.18078257
[139,] -2.70965448 -1.27701560
[140,] 5.17121407 -2.70965448
[141,] 2.77732052 5.17121407
[142,] -5.98019318 2.77732052
[143,] -0.96336145 -5.98019318
[144,] -4.45876036 -0.96336145
[145,] 1.64423900 -4.45876036
[146,] 7.82584751 1.64423900
[147,] 0.94512756 7.82584751
[148,] 1.73112478 0.94512756
[149,] 3.00751598 1.73112478
[150,] -0.27658229 3.00751598
[151,] 0.71900589 -0.27658229
[152,] 11.34884546 0.71900589
[153,] 2.72782550 11.34884546
[154,] -5.62496347 2.72782550
[155,] -0.98256724 -5.62496347
[156,] 3.58697190 -0.98256724
[157,] -2.07422615 3.58697190
[158,] 4.94954236 -2.07422615
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.33775014 -0.94299635
2 -5.68066888 3.33775014
3 -1.95645010 -5.68066888
4 -1.42133490 -1.95645010
5 -3.50952876 -1.42133490
6 -0.85051001 -3.50952876
7 -6.32663160 -0.85051001
8 -4.36691141 -6.32663160
9 -3.46766645 -4.36691141
10 0.59123491 -3.46766645
11 7.72513651 0.59123491
12 7.81374564 7.72513651
13 -1.05282575 7.81374564
14 -6.63994599 -1.05282575
15 -5.88336757 -6.63994599
16 1.49546584 -5.88336757
17 -0.26513054 1.49546584
18 0.16409212 -0.26513054
19 4.62841556 0.16409212
20 1.67124412 4.62841556
21 6.88606366 1.67124412
22 1.43664181 6.88606366
23 10.11514407 1.43664181
24 -0.37953525 10.11514407
25 -4.15417640 -0.37953525
26 -1.65921838 -4.15417640
27 2.79348223 -1.65921838
28 0.10293635 2.79348223
29 -2.58029422 0.10293635
30 3.72846380 -2.58029422
31 -5.56376641 3.72846380
32 -0.66625501 -5.56376641
33 0.99443124 -0.66625501
34 -6.07417816 0.99443124
35 4.10931907 -6.07417816
36 9.27673312 4.10931907
37 -9.11763672 9.27673312
38 4.21719314 -9.11763672
39 -0.92202716 4.21719314
40 1.89803940 -0.92202716
41 0.03394252 1.89803940
42 0.56238240 0.03394252
43 -4.83302151 0.56238240
44 -2.16066048 -4.83302151
45 -5.40804309 -2.16066048
46 -2.14365328 -5.40804309
47 4.93734290 -2.14365328
48 6.90833334 4.93734290
49 -3.75775896 6.90833334
50 2.77922850 -3.75775896
51 -1.03092460 2.77922850
52 1.18486119 -1.03092460
53 -0.73899408 1.18486119
54 -1.03936097 -0.73899408
55 2.72978720 -1.03936097
56 -0.19483201 2.72978720
57 -3.13198923 -0.19483201
58 -3.50478814 -3.13198923
59 -6.98925024 -3.50478814
60 -3.80327609 -6.98925024
61 -0.40926107 -3.80327609
62 -3.87961950 -0.40926107
63 -5.04515239 -3.87961950
64 -7.87680887 -5.04515239
65 4.69483593 -7.87680887
66 12.59587122 4.69483593
67 -4.02183186 12.59587122
68 -11.02587119 -4.02183186
69 -2.55608876 -11.02587119
70 10.30679198 -2.55608876
71 0.92047871 10.30679198
72 7.00073965 0.92047871
73 1.78277062 7.00073965
74 4.45646539 1.78277062
75 3.50646799 4.45646539
76 -9.71588172 3.50646799
77 -1.59122161 -9.71588172
78 -2.52594922 -1.59122161
79 4.98062121 -2.52594922
80 -2.64084494 4.98062121
81 4.15104723 -2.64084494
82 0.31389942 4.15104723
83 -1.85615774 0.31389942
84 3.07988718 -1.85615774
85 0.23719029 3.07988718
86 -0.85382138 0.23719029
87 -7.77180218 -0.85382138
88 0.89933229 -7.77180218
89 1.39450326 0.89933229
90 4.80291505 1.39450326
91 -3.14185043 4.80291505
92 -1.06095112 -3.14185043
93 0.89429132 -1.06095112
94 0.31530534 0.89429132
95 2.29666603 0.31530534
96 5.83896364 2.29666603
97 4.24996270 5.83896364
98 1.51005935 4.24996270
99 3.02705481 1.51005935
100 0.68273886 3.02705481
101 -1.23980766 0.68273886
102 -1.37894217 -1.23980766
103 -1.57547022 -1.37894217
104 1.04832462 -1.57547022
105 3.94672965 1.04832462
106 4.48847382 3.94672965
107 4.43060112 4.48847382
108 -0.32448682 4.43060112
109 -2.53476087 -0.32448682
110 -1.51346349 -2.53476087
111 9.85587065 -1.51346349
112 0.92724002 9.85587065
113 -3.19933936 0.92724002
114 -11.68105807 -3.19933936
115 -1.89552632 -11.68105807
116 -2.12368818 -1.89552632
117 -2.41172148 -2.12368818
118 0.53462560 -2.41172148
119 4.48460017 0.53462560
120 7.41750172 4.48460017
121 -4.99676534 7.41750172
122 -4.87370917 -4.99676534
123 -0.53370970 -4.87370917
124 -6.40924670 -0.53370970
125 -1.88318117 -6.40924670
126 -2.52573289 -1.88318117
127 -1.74311133 -2.52573289
128 -2.16825415 -1.74311133
129 7.57085480 -2.16825415
130 -1.86788319 7.57085480
131 -0.54847372 -1.86788319
132 -1.98050957 -0.54847372
133 0.87063840 -1.98050957
134 3.87004408 0.87063840
135 -1.58009444 3.87004408
136 -3.23258988 -1.58009444
137 -5.18078257 -3.23258988
138 -1.27701560 -5.18078257
139 -2.70965448 -1.27701560
140 5.17121407 -2.70965448
141 2.77732052 5.17121407
142 -5.98019318 2.77732052
143 -0.96336145 -5.98019318
144 -4.45876036 -0.96336145
145 1.64423900 -4.45876036
146 7.82584751 1.64423900
147 0.94512756 7.82584751
148 1.73112478 0.94512756
149 3.00751598 1.73112478
150 -0.27658229 3.00751598
151 0.71900589 -0.27658229
152 11.34884546 0.71900589
153 2.72782550 11.34884546
154 -5.62496347 2.72782550
155 -0.98256724 -5.62496347
156 3.58697190 -0.98256724
157 -2.07422615 3.58697190
158 4.94954236 -2.07422615
> 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/freestat/rcomp/tmp/71lrv1292264454.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/81lrv1292264454.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/91lrv1292264454.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10cc8g1292264454.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11fv731292264454.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/freestat/rcomp/tmp/12iv5r1292264454.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/freestat/rcomp/tmp/13x53i1292264454.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/freestat/rcomp/tmp/14i61o1292264454.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/freestat/rcomp/tmp/15m60c1292264454.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/freestat/rcomp/tmp/167phi1292264454.tab")
+ }
>
> try(system("convert tmp/1ntbm1292264454.ps tmp/1ntbm1292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ntbm1292264454.ps tmp/2ntbm1292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g3a71292264454.ps tmp/3g3a71292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g3a71292264454.ps tmp/4g3a71292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/5g3a71292264454.ps tmp/5g3a71292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qcss1292264454.ps tmp/6qcss1292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/71lrv1292264454.ps tmp/71lrv1292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/81lrv1292264454.ps tmp/81lrv1292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/91lrv1292264454.ps tmp/91lrv1292264454.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cc8g1292264454.ps tmp/10cc8g1292264454.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.801 2.698 6.290