R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1
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+ ,20)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Week'
+ ,'Consern'
+ ,'Doubts'
+ ,'PExpect'
+ ,'PCritisism'
+ ,'PStandards'
+ ,'Organisation')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Week','Consern','Doubts','PExpect','PCritisism','PStandards','Organisation'),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 = '6'
> #'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
PStandards Week Consern Doubts PExpect PCritisism Organisation
1 24 1 24 14 11 12 26
2 25 1 25 11 7 8 23
3 30 1 17 6 17 8 25
4 19 1 18 12 10 8 23
5 22 1 18 8 12 9 19
6 22 1 16 10 12 7 29
7 25 1 20 10 11 4 25
8 23 1 16 11 11 11 21
9 17 1 18 16 12 7 22
10 21 2 17 11 13 7 25
11 19 2 23 13 14 12 24
12 19 2 30 12 16 10 18
13 15 2 23 8 11 10 22
14 16 2 18 12 10 8 15
15 23 2 15 11 11 8 22
16 27 2 12 4 15 4 28
17 22 2 21 9 9 9 20
18 14 2 15 8 11 8 12
19 22 2 20 8 17 7 24
20 23 3 31 14 17 11 20
21 23 3 27 15 11 9 21
22 21 3 34 16 18 11 20
23 19 3 21 9 14 13 21
24 18 3 31 14 10 8 23
25 20 3 19 11 11 8 28
26 23 3 16 8 15 9 24
27 25 3 20 9 15 6 24
28 19 3 21 9 13 9 24
29 24 3 22 9 16 9 23
30 22 3 17 9 13 6 23
31 25 3 24 10 9 6 29
32 26 3 25 16 18 16 24
33 29 3 26 11 18 5 18
34 32 3 25 8 12 7 25
35 25 3 17 9 17 9 21
36 29 3 32 16 9 6 26
37 28 3 33 11 9 6 22
38 17 3 13 16 12 5 22
39 28 3 32 12 18 12 22
40 29 3 25 12 12 7 23
41 26 3 29 14 18 10 30
42 25 3 22 9 14 9 23
43 14 3 18 10 15 8 17
44 25 3 17 9 16 5 23
45 26 3 20 10 10 8 23
46 20 3 15 12 11 8 25
47 18 3 20 14 14 10 24
48 32 3 33 14 9 6 24
49 25 3 29 10 12 8 23
50 25 3 23 14 17 7 21
51 23 3 26 16 5 4 24
52 21 3 18 9 12 8 24
53 20 3 20 10 12 8 28
54 15 3 11 6 6 4 16
55 30 3 28 8 24 20 20
56 24 3 26 13 12 8 29
57 26 3 22 10 12 8 27
58 24 3 17 8 14 6 22
59 22 3 12 7 7 4 28
60 14 3 14 15 13 8 16
61 24 3 17 9 12 9 25
62 24 3 21 10 13 6 24
63 24 3 19 12 14 7 28
64 24 3 18 13 8 9 24
65 19 3 10 10 11 5 23
66 31 3 29 11 9 5 30
67 22 3 31 8 11 8 24
68 27 3 19 9 13 8 21
69 19 3 9 13 10 6 25
70 25 3 20 11 11 8 25
71 20 3 28 8 12 7 22
72 21 3 19 9 9 7 23
73 27 3 30 9 15 9 26
74 23 3 29 15 18 11 23
75 25 3 26 9 15 6 25
76 20 3 23 10 12 8 21
77 21 3 13 14 13 6 25
78 22 3 21 12 14 9 24
79 23 3 19 12 10 8 29
80 25 3 28 11 13 6 22
81 25 3 23 14 13 10 27
82 17 3 18 6 11 8 26
83 19 3 21 12 13 8 22
84 25 3 20 8 16 10 24
85 19 4 23 14 8 5 27
86 20 4 21 11 16 7 24
87 26 4 21 10 11 5 24
88 23 4 15 14 9 8 29
89 27 4 28 12 16 14 22
90 17 4 19 10 12 7 21
91 17 4 26 14 14 8 24
92 19 4 10 5 8 6 24
93 17 4 16 11 9 5 23
94 22 4 22 10 15 6 20
95 21 4 19 9 11 10 27
96 32 4 31 10 21 12 26
97 21 4 31 16 14 9 25
98 21 4 29 13 18 12 21
99 18 4 19 9 12 7 21
100 18 4 22 10 13 8 19
101 23 4 23 10 15 10 21
102 19 4 15 7 12 6 21
103 20 4 20 9 19 10 16
104 21 4 18 8 15 10 22
105 20 4 23 14 11 10 29
106 17 4 25 14 11 5 15
107 18 4 21 8 10 7 17
108 19 4 24 9 13 10 15
109 22 4 25 14 15 11 21
110 15 4 17 14 12 6 21
111 14 4 13 8 12 7 19
112 18 4 28 8 16 12 24
113 24 4 21 8 9 11 20
114 35 4 25 7 18 11 17
115 29 4 9 6 8 11 23
116 21 4 16 8 13 5 24
117 25 4 19 6 17 8 14
118 20 4 17 11 9 6 19
119 22 4 25 14 15 9 24
120 13 4 20 11 8 4 13
121 26 4 29 11 7 4 22
122 17 4 14 11 12 7 16
123 25 4 22 14 14 11 19
124 20 4 15 8 6 6 25
125 19 4 19 20 8 7 25
126 21 4 20 11 17 8 23
127 22 4 15 8 10 4 24
128 24 4 20 11 11 8 26
129 21 4 18 10 14 9 26
130 26 4 33 14 11 8 25
131 24 4 22 11 13 11 18
132 16 4 16 9 12 8 21
133 23 4 17 9 11 5 26
134 18 4 16 8 9 4 23
135 16 4 21 10 12 8 23
136 26 4 26 13 20 10 22
137 19 4 18 13 12 6 20
138 21 4 18 12 13 9 13
139 21 4 17 8 12 9 24
140 22 4 22 13 12 13 15
141 23 4 30 14 9 9 14
142 29 4 30 12 15 10 22
143 21 4 24 14 24 20 10
144 21 4 21 15 7 5 24
145 23 4 21 13 17 11 22
146 27 4 29 16 11 6 24
147 25 4 31 9 17 9 19
148 21 4 20 9 11 7 20
149 10 4 16 9 12 9 13
150 20 4 22 8 14 10 20
151 26 4 20 7 11 9 22
152 24 4 28 16 16 8 24
153 29 4 38 11 21 7 29
154 19 4 22 9 14 6 12
155 24 4 20 11 20 13 20
156 19 4 17 9 13 6 21
157 24 4 28 14 11 8 24
158 22 4 22 13 15 10 22
159 17 4 31 16 19 16 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Week Consern Doubts PExpect
8.79625 -0.35103 0.33286 -0.35923 0.19286
PCritisism Organisation
0.01500 0.38646
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.6497 -2.2730 0.1529 2.0825 11.5947
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.79625 2.58999 3.396 0.000872 ***
Week -0.35103 0.33822 -1.038 0.300986
Consern 0.33286 0.05571 5.974 1.58e-08 ***
Doubts -0.35923 0.10714 -3.353 0.001010 **
PExpect 0.19286 0.10130 1.904 0.058813 .
PCritisism 0.01500 0.12884 0.116 0.907501
Organisation 0.38646 0.07316 5.283 4.34e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.408 on 152 degrees of freedom
Multiple R-squared: 0.3715, Adjusted R-squared: 0.3467
F-statistic: 14.98 on 6 and 152 DF, p-value: 2.027e-13
> 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,] 0.29876690 0.59753379 0.7012331
[2,] 0.29286282 0.58572564 0.7071372
[3,] 0.23411408 0.46822816 0.7658859
[4,] 0.51772804 0.96454391 0.4822720
[5,] 0.48971989 0.97943979 0.5102801
[6,] 0.63342599 0.73314802 0.3665740
[7,] 0.54641036 0.90717927 0.4535896
[8,] 0.49858527 0.99717054 0.5014147
[9,] 0.48037633 0.96075265 0.5196237
[10,] 0.40165194 0.80330388 0.5983481
[11,] 0.52164896 0.95670209 0.4783510
[12,] 0.56616078 0.86767845 0.4338392
[13,] 0.50176644 0.99646711 0.4982336
[14,] 0.44390579 0.88781157 0.5560942
[15,] 0.46075035 0.92150070 0.5392497
[16,] 0.41048025 0.82096050 0.5895198
[17,] 0.36017316 0.72034632 0.6398268
[18,] 0.33394100 0.66788201 0.6660590
[19,] 0.32149875 0.64299750 0.6785013
[20,] 0.27952460 0.55904920 0.7204754
[21,] 0.22964202 0.45928405 0.7703580
[22,] 0.19188196 0.38376392 0.8081180
[23,] 0.23328444 0.46656888 0.7667156
[24,] 0.39401036 0.78802072 0.6059896
[25,] 0.64769106 0.70461788 0.3523089
[26,] 0.62707322 0.74585355 0.3729268
[27,] 0.68835450 0.62329099 0.3116455
[28,] 0.67727269 0.64545461 0.3227273
[29,] 0.63020517 0.73958966 0.3697948
[30,] 0.59596727 0.80806545 0.4040327
[31,] 0.67621398 0.64757204 0.3237860
[32,] 0.64675042 0.70649916 0.3532496
[33,] 0.60178513 0.79642974 0.3982149
[34,] 0.68802382 0.62395235 0.3119762
[35,] 0.65709047 0.68581907 0.3429095
[36,] 0.67984587 0.64030827 0.3201541
[37,] 0.63190849 0.73618302 0.3680915
[38,] 0.62630270 0.74739459 0.3736973
[39,] 0.72391806 0.55216388 0.2760819
[40,] 0.68339700 0.63320599 0.3166030
[41,] 0.66739394 0.66521213 0.3326061
[42,] 0.62637480 0.74725039 0.3736252
[43,] 0.58519058 0.82961884 0.4148094
[44,] 0.61855539 0.76288922 0.3814446
[45,] 0.58325714 0.83348572 0.4167429
[46,] 0.63560158 0.72879685 0.3643984
[47,] 0.60225320 0.79549361 0.3977468
[48,] 0.56152332 0.87695336 0.4384767
[49,] 0.52749309 0.94501382 0.4725069
[50,] 0.47924019 0.95848037 0.5207598
[51,] 0.44197626 0.88395251 0.5580237
[52,] 0.40860381 0.81720762 0.5913962
[53,] 0.36537018 0.73074036 0.6346298
[54,] 0.32218548 0.64437097 0.6778145
[55,] 0.35553378 0.71106756 0.6444662
[56,] 0.31241214 0.62482429 0.6875879
[57,] 0.32344574 0.64689147 0.6765543
[58,] 0.38638023 0.77276045 0.6136198
[59,] 0.46099881 0.92199761 0.5390012
[60,] 0.42244316 0.84488632 0.5775568
[61,] 0.40785967 0.81571934 0.5921403
[62,] 0.47120103 0.94240207 0.5287990
[63,] 0.42535832 0.85071665 0.5746417
[64,] 0.38375234 0.76750467 0.6162477
[65,] 0.34767507 0.69535014 0.6523249
[66,] 0.31513839 0.63027677 0.6848616
[67,] 0.29091553 0.58183105 0.7090845
[68,] 0.26811171 0.53622342 0.7318883
[69,] 0.23198031 0.46396063 0.7680197
[70,] 0.20001843 0.40003686 0.7999816
[71,] 0.17715626 0.35431252 0.8228437
[72,] 0.16599283 0.33198565 0.8340072
[73,] 0.24121645 0.48243289 0.7587836
[74,] 0.22502040 0.45004079 0.7749796
[75,] 0.19318478 0.38636956 0.8068152
[76,] 0.19024908 0.38049816 0.8097509
[77,] 0.18042949 0.36085898 0.8195705
[78,] 0.19146451 0.38292902 0.8085355
[79,] 0.18113832 0.36227664 0.8188617
[80,] 0.17203433 0.34406866 0.8279657
[81,] 0.17320338 0.34640676 0.8267966
[82,] 0.24037843 0.48075686 0.7596216
[83,] 0.20570865 0.41141730 0.7942913
[84,] 0.18530524 0.37061048 0.8146948
[85,] 0.15623007 0.31246014 0.8437699
[86,] 0.13948937 0.27897873 0.8605106
[87,] 0.14563497 0.29126995 0.8543650
[88,] 0.14336500 0.28673001 0.8566350
[89,] 0.13627700 0.27255400 0.8637230
[90,] 0.12573139 0.25146279 0.8742686
[91,] 0.11756289 0.23512578 0.8824371
[92,] 0.09655430 0.19310860 0.9034457
[93,] 0.07855383 0.15710766 0.9214462
[94,] 0.06247882 0.12495764 0.9375212
[95,] 0.04970549 0.09941099 0.9502945
[96,] 0.05086416 0.10172832 0.9491358
[97,] 0.04064119 0.08128237 0.9593588
[98,] 0.03492125 0.06984249 0.9650788
[99,] 0.03019078 0.06038156 0.9698092
[100,] 0.02303473 0.04606946 0.9769653
[101,] 0.02161853 0.04323706 0.9783815
[102,] 0.02614662 0.05229324 0.9738534
[103,] 0.11491111 0.22982221 0.8850889
[104,] 0.11105943 0.22211886 0.8889406
[105,] 0.52504665 0.94990670 0.4749533
[106,] 0.84543439 0.30913121 0.1545656
[107,] 0.81053064 0.37893872 0.1894694
[108,] 0.86896611 0.26206778 0.1310339
[109,] 0.84447164 0.31105673 0.1555284
[110,] 0.81506346 0.36987308 0.1849365
[111,] 0.84776468 0.30447063 0.1522353
[112,] 0.82546294 0.34907413 0.1745371
[113,] 0.78493349 0.43013302 0.2150665
[114,] 0.81884772 0.36230456 0.1811523
[115,] 0.77573655 0.44852690 0.2242635
[116,] 0.73694895 0.52610209 0.2630510
[117,] 0.68721332 0.62557337 0.3127867
[118,] 0.65056692 0.69886616 0.3494331
[119,] 0.60887904 0.78224191 0.3911210
[120,] 0.54898020 0.90203960 0.4510198
[121,] 0.48613354 0.97226707 0.5138665
[122,] 0.48512865 0.97025730 0.5148714
[123,] 0.48149607 0.96299214 0.5185039
[124,] 0.43123296 0.86246593 0.5687670
[125,] 0.38200738 0.76401476 0.6179926
[126,] 0.52732071 0.94535858 0.4726793
[127,] 0.48907893 0.97815787 0.5109211
[128,] 0.41582812 0.83165623 0.5841719
[129,] 0.42674166 0.85348331 0.5732583
[130,] 0.35494925 0.70989850 0.6450508
[131,] 0.31958093 0.63916186 0.6804191
[132,] 0.28576568 0.57153136 0.7142343
[133,] 0.33728639 0.67457277 0.6627136
[134,] 0.47898332 0.95796664 0.5210167
[135,] 0.40210252 0.80420505 0.5978975
[136,] 0.32104877 0.64209754 0.6789512
[137,] 0.30964152 0.61928305 0.6903585
[138,] 0.25613186 0.51226371 0.7438681
[139,] 0.16010925 0.32021849 0.8398908
[140,] 0.30481128 0.60962257 0.6951887
> postscript(file="/var/www/rcomp/tmp/1tl4h1290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2tl4h1290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3tl4h1290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/43cm21290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/53cm21290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
0.24594711 1.82618847 4.99140275 -2.06312474 0.64508627 -1.80537478
7 8 9 10 11 12
1.64688144 2.77844804 -2.61044568 -1.07497688 -4.23504403 -4.96124377
13 14 15 16 17 18
-8.64972137 -1.62038595 3.12085681 2.57456959 0.54887246 -3.09220375
19 20 21 22 23 24
-2.53622667 -1.20539175 1.28595700 -3.67836432 -3.51083401 -5.96979533
25 26 27 28 29 30
-3.17834451 0.50196757 1.57474371 -4.41738769 0.05764288 0.34550535
31 32 33 34 35 36
-0.17264081 2.69655064 6.05125316 6.72831927 3.30201754 4.47926713
37 38 39 40 41 42
2.89609396 -0.21409849 1.76249908 5.93818227 -1.58217303 1.44335799
43 44 45 46 47 48
-5.72504298 2.78192791 4.25473881 -0.32827533 -3.49621107 7.20086700
49 50 51 52 53 54
-0.12672368 3.13101168 2.05078086 -1.21095245 -4.06329660 -1.64978912
55 56 57 58 59 60
3.15282840 -1.36922442 1.65744582 2.17987813 0.54615751 -1.82525193
61 62 63 64 65 66
1.72044908 0.98683173 0.61731178 3.98241742 0.43547515 4.15081999
67 68 69 70 71 72
-4.70451926 5.42272136 1.25097133 2.64818685 -5.11087101 -0.56378130
73 74 75 76 77 78
-0.57177830 -1.53268612 -0.80888526 -2.35663064 1.70018910 -0.53254409
79 80 81 82 83 84
-0.01271729 0.78896783 1.53867185 -6.86872417 -2.55176318 0.96267150
85 86 87 88 89 90
-3.07103789 -2.89647614 3.73856839 2.58107729 2.80069332 -3.65916582
91 92 93 94 95 96
-6.11235920 -0.83255376 -2.46571462 0.16513835 -2.18931204 3.60348987
97 98 99 100 101 102
-3.45965516 -3.14219436 -3.01839954 -3.09267295 0.38583253 -1.39042847
103 104 105 106 107 108
-0.81392866 -1.05479487 -3.49751484 -1.67776348 -2.11178355 -1.60176254
109 110 111 112 113 114
0.14205054 -3.54151407 -4.60754022 -8.37917921 2.86170090 11.59469807
115 116 117 118 119 120
10.17102867 -0.70131007 4.62986471 1.73228556 -0.98735118 -3.72466452
121 122 123 124 125 126
2.99426913 0.29669230 5.10641867 -0.41990565 1.15874536 -1.38500405
127 128 129 130 131 132
1.22511866 1.61274910 -1.67433088 0.74972367 3.60803913 -4.03481231
133 134 135 136 137 138
0.93784983 -2.52842055 -6.11281081 2.11419939 0.15285545 4.26102689
139 140 141 142 143 144
-0.90129427 3.64876583 3.37013066 4.38781017 1.85534090 1.30616721
145 146 147 148 149 150
1.34208092 4.21608891 -0.23407950 0.22806126 -6.95809507 -2.42045247
151 152 153 154 155 156
3.70667523 0.55467148 -2.45171816 0.09047466 2.12083929 -1.53054022
157 158 159
0.80049183 0.40993045 -6.59658928
> postscript(file="/var/www/rcomp/tmp/63cm21290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 0.24594711 NA
1 1.82618847 0.24594711
2 4.99140275 1.82618847
3 -2.06312474 4.99140275
4 0.64508627 -2.06312474
5 -1.80537478 0.64508627
6 1.64688144 -1.80537478
7 2.77844804 1.64688144
8 -2.61044568 2.77844804
9 -1.07497688 -2.61044568
10 -4.23504403 -1.07497688
11 -4.96124377 -4.23504403
12 -8.64972137 -4.96124377
13 -1.62038595 -8.64972137
14 3.12085681 -1.62038595
15 2.57456959 3.12085681
16 0.54887246 2.57456959
17 -3.09220375 0.54887246
18 -2.53622667 -3.09220375
19 -1.20539175 -2.53622667
20 1.28595700 -1.20539175
21 -3.67836432 1.28595700
22 -3.51083401 -3.67836432
23 -5.96979533 -3.51083401
24 -3.17834451 -5.96979533
25 0.50196757 -3.17834451
26 1.57474371 0.50196757
27 -4.41738769 1.57474371
28 0.05764288 -4.41738769
29 0.34550535 0.05764288
30 -0.17264081 0.34550535
31 2.69655064 -0.17264081
32 6.05125316 2.69655064
33 6.72831927 6.05125316
34 3.30201754 6.72831927
35 4.47926713 3.30201754
36 2.89609396 4.47926713
37 -0.21409849 2.89609396
38 1.76249908 -0.21409849
39 5.93818227 1.76249908
40 -1.58217303 5.93818227
41 1.44335799 -1.58217303
42 -5.72504298 1.44335799
43 2.78192791 -5.72504298
44 4.25473881 2.78192791
45 -0.32827533 4.25473881
46 -3.49621107 -0.32827533
47 7.20086700 -3.49621107
48 -0.12672368 7.20086700
49 3.13101168 -0.12672368
50 2.05078086 3.13101168
51 -1.21095245 2.05078086
52 -4.06329660 -1.21095245
53 -1.64978912 -4.06329660
54 3.15282840 -1.64978912
55 -1.36922442 3.15282840
56 1.65744582 -1.36922442
57 2.17987813 1.65744582
58 0.54615751 2.17987813
59 -1.82525193 0.54615751
60 1.72044908 -1.82525193
61 0.98683173 1.72044908
62 0.61731178 0.98683173
63 3.98241742 0.61731178
64 0.43547515 3.98241742
65 4.15081999 0.43547515
66 -4.70451926 4.15081999
67 5.42272136 -4.70451926
68 1.25097133 5.42272136
69 2.64818685 1.25097133
70 -5.11087101 2.64818685
71 -0.56378130 -5.11087101
72 -0.57177830 -0.56378130
73 -1.53268612 -0.57177830
74 -0.80888526 -1.53268612
75 -2.35663064 -0.80888526
76 1.70018910 -2.35663064
77 -0.53254409 1.70018910
78 -0.01271729 -0.53254409
79 0.78896783 -0.01271729
80 1.53867185 0.78896783
81 -6.86872417 1.53867185
82 -2.55176318 -6.86872417
83 0.96267150 -2.55176318
84 -3.07103789 0.96267150
85 -2.89647614 -3.07103789
86 3.73856839 -2.89647614
87 2.58107729 3.73856839
88 2.80069332 2.58107729
89 -3.65916582 2.80069332
90 -6.11235920 -3.65916582
91 -0.83255376 -6.11235920
92 -2.46571462 -0.83255376
93 0.16513835 -2.46571462
94 -2.18931204 0.16513835
95 3.60348987 -2.18931204
96 -3.45965516 3.60348987
97 -3.14219436 -3.45965516
98 -3.01839954 -3.14219436
99 -3.09267295 -3.01839954
100 0.38583253 -3.09267295
101 -1.39042847 0.38583253
102 -0.81392866 -1.39042847
103 -1.05479487 -0.81392866
104 -3.49751484 -1.05479487
105 -1.67776348 -3.49751484
106 -2.11178355 -1.67776348
107 -1.60176254 -2.11178355
108 0.14205054 -1.60176254
109 -3.54151407 0.14205054
110 -4.60754022 -3.54151407
111 -8.37917921 -4.60754022
112 2.86170090 -8.37917921
113 11.59469807 2.86170090
114 10.17102867 11.59469807
115 -0.70131007 10.17102867
116 4.62986471 -0.70131007
117 1.73228556 4.62986471
118 -0.98735118 1.73228556
119 -3.72466452 -0.98735118
120 2.99426913 -3.72466452
121 0.29669230 2.99426913
122 5.10641867 0.29669230
123 -0.41990565 5.10641867
124 1.15874536 -0.41990565
125 -1.38500405 1.15874536
126 1.22511866 -1.38500405
127 1.61274910 1.22511866
128 -1.67433088 1.61274910
129 0.74972367 -1.67433088
130 3.60803913 0.74972367
131 -4.03481231 3.60803913
132 0.93784983 -4.03481231
133 -2.52842055 0.93784983
134 -6.11281081 -2.52842055
135 2.11419939 -6.11281081
136 0.15285545 2.11419939
137 4.26102689 0.15285545
138 -0.90129427 4.26102689
139 3.64876583 -0.90129427
140 3.37013066 3.64876583
141 4.38781017 3.37013066
142 1.85534090 4.38781017
143 1.30616721 1.85534090
144 1.34208092 1.30616721
145 4.21608891 1.34208092
146 -0.23407950 4.21608891
147 0.22806126 -0.23407950
148 -6.95809507 0.22806126
149 -2.42045247 -6.95809507
150 3.70667523 -2.42045247
151 0.55467148 3.70667523
152 -2.45171816 0.55467148
153 0.09047466 -2.45171816
154 2.12083929 0.09047466
155 -1.53054022 2.12083929
156 0.80049183 -1.53054022
157 0.40993045 0.80049183
158 -6.59658928 0.40993045
159 NA -6.59658928
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.82618847 0.24594711
[2,] 4.99140275 1.82618847
[3,] -2.06312474 4.99140275
[4,] 0.64508627 -2.06312474
[5,] -1.80537478 0.64508627
[6,] 1.64688144 -1.80537478
[7,] 2.77844804 1.64688144
[8,] -2.61044568 2.77844804
[9,] -1.07497688 -2.61044568
[10,] -4.23504403 -1.07497688
[11,] -4.96124377 -4.23504403
[12,] -8.64972137 -4.96124377
[13,] -1.62038595 -8.64972137
[14,] 3.12085681 -1.62038595
[15,] 2.57456959 3.12085681
[16,] 0.54887246 2.57456959
[17,] -3.09220375 0.54887246
[18,] -2.53622667 -3.09220375
[19,] -1.20539175 -2.53622667
[20,] 1.28595700 -1.20539175
[21,] -3.67836432 1.28595700
[22,] -3.51083401 -3.67836432
[23,] -5.96979533 -3.51083401
[24,] -3.17834451 -5.96979533
[25,] 0.50196757 -3.17834451
[26,] 1.57474371 0.50196757
[27,] -4.41738769 1.57474371
[28,] 0.05764288 -4.41738769
[29,] 0.34550535 0.05764288
[30,] -0.17264081 0.34550535
[31,] 2.69655064 -0.17264081
[32,] 6.05125316 2.69655064
[33,] 6.72831927 6.05125316
[34,] 3.30201754 6.72831927
[35,] 4.47926713 3.30201754
[36,] 2.89609396 4.47926713
[37,] -0.21409849 2.89609396
[38,] 1.76249908 -0.21409849
[39,] 5.93818227 1.76249908
[40,] -1.58217303 5.93818227
[41,] 1.44335799 -1.58217303
[42,] -5.72504298 1.44335799
[43,] 2.78192791 -5.72504298
[44,] 4.25473881 2.78192791
[45,] -0.32827533 4.25473881
[46,] -3.49621107 -0.32827533
[47,] 7.20086700 -3.49621107
[48,] -0.12672368 7.20086700
[49,] 3.13101168 -0.12672368
[50,] 2.05078086 3.13101168
[51,] -1.21095245 2.05078086
[52,] -4.06329660 -1.21095245
[53,] -1.64978912 -4.06329660
[54,] 3.15282840 -1.64978912
[55,] -1.36922442 3.15282840
[56,] 1.65744582 -1.36922442
[57,] 2.17987813 1.65744582
[58,] 0.54615751 2.17987813
[59,] -1.82525193 0.54615751
[60,] 1.72044908 -1.82525193
[61,] 0.98683173 1.72044908
[62,] 0.61731178 0.98683173
[63,] 3.98241742 0.61731178
[64,] 0.43547515 3.98241742
[65,] 4.15081999 0.43547515
[66,] -4.70451926 4.15081999
[67,] 5.42272136 -4.70451926
[68,] 1.25097133 5.42272136
[69,] 2.64818685 1.25097133
[70,] -5.11087101 2.64818685
[71,] -0.56378130 -5.11087101
[72,] -0.57177830 -0.56378130
[73,] -1.53268612 -0.57177830
[74,] -0.80888526 -1.53268612
[75,] -2.35663064 -0.80888526
[76,] 1.70018910 -2.35663064
[77,] -0.53254409 1.70018910
[78,] -0.01271729 -0.53254409
[79,] 0.78896783 -0.01271729
[80,] 1.53867185 0.78896783
[81,] -6.86872417 1.53867185
[82,] -2.55176318 -6.86872417
[83,] 0.96267150 -2.55176318
[84,] -3.07103789 0.96267150
[85,] -2.89647614 -3.07103789
[86,] 3.73856839 -2.89647614
[87,] 2.58107729 3.73856839
[88,] 2.80069332 2.58107729
[89,] -3.65916582 2.80069332
[90,] -6.11235920 -3.65916582
[91,] -0.83255376 -6.11235920
[92,] -2.46571462 -0.83255376
[93,] 0.16513835 -2.46571462
[94,] -2.18931204 0.16513835
[95,] 3.60348987 -2.18931204
[96,] -3.45965516 3.60348987
[97,] -3.14219436 -3.45965516
[98,] -3.01839954 -3.14219436
[99,] -3.09267295 -3.01839954
[100,] 0.38583253 -3.09267295
[101,] -1.39042847 0.38583253
[102,] -0.81392866 -1.39042847
[103,] -1.05479487 -0.81392866
[104,] -3.49751484 -1.05479487
[105,] -1.67776348 -3.49751484
[106,] -2.11178355 -1.67776348
[107,] -1.60176254 -2.11178355
[108,] 0.14205054 -1.60176254
[109,] -3.54151407 0.14205054
[110,] -4.60754022 -3.54151407
[111,] -8.37917921 -4.60754022
[112,] 2.86170090 -8.37917921
[113,] 11.59469807 2.86170090
[114,] 10.17102867 11.59469807
[115,] -0.70131007 10.17102867
[116,] 4.62986471 -0.70131007
[117,] 1.73228556 4.62986471
[118,] -0.98735118 1.73228556
[119,] -3.72466452 -0.98735118
[120,] 2.99426913 -3.72466452
[121,] 0.29669230 2.99426913
[122,] 5.10641867 0.29669230
[123,] -0.41990565 5.10641867
[124,] 1.15874536 -0.41990565
[125,] -1.38500405 1.15874536
[126,] 1.22511866 -1.38500405
[127,] 1.61274910 1.22511866
[128,] -1.67433088 1.61274910
[129,] 0.74972367 -1.67433088
[130,] 3.60803913 0.74972367
[131,] -4.03481231 3.60803913
[132,] 0.93784983 -4.03481231
[133,] -2.52842055 0.93784983
[134,] -6.11281081 -2.52842055
[135,] 2.11419939 -6.11281081
[136,] 0.15285545 2.11419939
[137,] 4.26102689 0.15285545
[138,] -0.90129427 4.26102689
[139,] 3.64876583 -0.90129427
[140,] 3.37013066 3.64876583
[141,] 4.38781017 3.37013066
[142,] 1.85534090 4.38781017
[143,] 1.30616721 1.85534090
[144,] 1.34208092 1.30616721
[145,] 4.21608891 1.34208092
[146,] -0.23407950 4.21608891
[147,] 0.22806126 -0.23407950
[148,] -6.95809507 0.22806126
[149,] -2.42045247 -6.95809507
[150,] 3.70667523 -2.42045247
[151,] 0.55467148 3.70667523
[152,] -2.45171816 0.55467148
[153,] 0.09047466 -2.45171816
[154,] 2.12083929 0.09047466
[155,] -1.53054022 2.12083929
[156,] 0.80049183 -1.53054022
[157,] 0.40993045 0.80049183
[158,] -6.59658928 0.40993045
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.82618847 0.24594711
2 4.99140275 1.82618847
3 -2.06312474 4.99140275
4 0.64508627 -2.06312474
5 -1.80537478 0.64508627
6 1.64688144 -1.80537478
7 2.77844804 1.64688144
8 -2.61044568 2.77844804
9 -1.07497688 -2.61044568
10 -4.23504403 -1.07497688
11 -4.96124377 -4.23504403
12 -8.64972137 -4.96124377
13 -1.62038595 -8.64972137
14 3.12085681 -1.62038595
15 2.57456959 3.12085681
16 0.54887246 2.57456959
17 -3.09220375 0.54887246
18 -2.53622667 -3.09220375
19 -1.20539175 -2.53622667
20 1.28595700 -1.20539175
21 -3.67836432 1.28595700
22 -3.51083401 -3.67836432
23 -5.96979533 -3.51083401
24 -3.17834451 -5.96979533
25 0.50196757 -3.17834451
26 1.57474371 0.50196757
27 -4.41738769 1.57474371
28 0.05764288 -4.41738769
29 0.34550535 0.05764288
30 -0.17264081 0.34550535
31 2.69655064 -0.17264081
32 6.05125316 2.69655064
33 6.72831927 6.05125316
34 3.30201754 6.72831927
35 4.47926713 3.30201754
36 2.89609396 4.47926713
37 -0.21409849 2.89609396
38 1.76249908 -0.21409849
39 5.93818227 1.76249908
40 -1.58217303 5.93818227
41 1.44335799 -1.58217303
42 -5.72504298 1.44335799
43 2.78192791 -5.72504298
44 4.25473881 2.78192791
45 -0.32827533 4.25473881
46 -3.49621107 -0.32827533
47 7.20086700 -3.49621107
48 -0.12672368 7.20086700
49 3.13101168 -0.12672368
50 2.05078086 3.13101168
51 -1.21095245 2.05078086
52 -4.06329660 -1.21095245
53 -1.64978912 -4.06329660
54 3.15282840 -1.64978912
55 -1.36922442 3.15282840
56 1.65744582 -1.36922442
57 2.17987813 1.65744582
58 0.54615751 2.17987813
59 -1.82525193 0.54615751
60 1.72044908 -1.82525193
61 0.98683173 1.72044908
62 0.61731178 0.98683173
63 3.98241742 0.61731178
64 0.43547515 3.98241742
65 4.15081999 0.43547515
66 -4.70451926 4.15081999
67 5.42272136 -4.70451926
68 1.25097133 5.42272136
69 2.64818685 1.25097133
70 -5.11087101 2.64818685
71 -0.56378130 -5.11087101
72 -0.57177830 -0.56378130
73 -1.53268612 -0.57177830
74 -0.80888526 -1.53268612
75 -2.35663064 -0.80888526
76 1.70018910 -2.35663064
77 -0.53254409 1.70018910
78 -0.01271729 -0.53254409
79 0.78896783 -0.01271729
80 1.53867185 0.78896783
81 -6.86872417 1.53867185
82 -2.55176318 -6.86872417
83 0.96267150 -2.55176318
84 -3.07103789 0.96267150
85 -2.89647614 -3.07103789
86 3.73856839 -2.89647614
87 2.58107729 3.73856839
88 2.80069332 2.58107729
89 -3.65916582 2.80069332
90 -6.11235920 -3.65916582
91 -0.83255376 -6.11235920
92 -2.46571462 -0.83255376
93 0.16513835 -2.46571462
94 -2.18931204 0.16513835
95 3.60348987 -2.18931204
96 -3.45965516 3.60348987
97 -3.14219436 -3.45965516
98 -3.01839954 -3.14219436
99 -3.09267295 -3.01839954
100 0.38583253 -3.09267295
101 -1.39042847 0.38583253
102 -0.81392866 -1.39042847
103 -1.05479487 -0.81392866
104 -3.49751484 -1.05479487
105 -1.67776348 -3.49751484
106 -2.11178355 -1.67776348
107 -1.60176254 -2.11178355
108 0.14205054 -1.60176254
109 -3.54151407 0.14205054
110 -4.60754022 -3.54151407
111 -8.37917921 -4.60754022
112 2.86170090 -8.37917921
113 11.59469807 2.86170090
114 10.17102867 11.59469807
115 -0.70131007 10.17102867
116 4.62986471 -0.70131007
117 1.73228556 4.62986471
118 -0.98735118 1.73228556
119 -3.72466452 -0.98735118
120 2.99426913 -3.72466452
121 0.29669230 2.99426913
122 5.10641867 0.29669230
123 -0.41990565 5.10641867
124 1.15874536 -0.41990565
125 -1.38500405 1.15874536
126 1.22511866 -1.38500405
127 1.61274910 1.22511866
128 -1.67433088 1.61274910
129 0.74972367 -1.67433088
130 3.60803913 0.74972367
131 -4.03481231 3.60803913
132 0.93784983 -4.03481231
133 -2.52842055 0.93784983
134 -6.11281081 -2.52842055
135 2.11419939 -6.11281081
136 0.15285545 2.11419939
137 4.26102689 0.15285545
138 -0.90129427 4.26102689
139 3.64876583 -0.90129427
140 3.37013066 3.64876583
141 4.38781017 3.37013066
142 1.85534090 4.38781017
143 1.30616721 1.85534090
144 1.34208092 1.30616721
145 4.21608891 1.34208092
146 -0.23407950 4.21608891
147 0.22806126 -0.23407950
148 -6.95809507 0.22806126
149 -2.42045247 -6.95809507
150 3.70667523 -2.42045247
151 0.55467148 3.70667523
152 -2.45171816 0.55467148
153 0.09047466 -2.45171816
154 2.12083929 0.09047466
155 -1.53054022 2.12083929
156 0.80049183 -1.53054022
157 0.40993045 0.80049183
158 -6.59658928 0.40993045
> 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/7w43n1290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/87v2p1290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/97v2p1290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10hm1s1290527232.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11350g1290527232.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/12ony41290527232.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/132fev1290527232.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/146xv11290527232.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/15ryb71290527232.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/1658rx1290527232.tab")
+ }
>
> try(system("convert tmp/1tl4h1290527232.ps tmp/1tl4h1290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tl4h1290527232.ps tmp/2tl4h1290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tl4h1290527232.ps tmp/3tl4h1290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/43cm21290527232.ps tmp/43cm21290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/53cm21290527232.ps tmp/53cm21290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/63cm21290527232.ps tmp/63cm21290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/7w43n1290527232.ps tmp/7w43n1290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/87v2p1290527232.ps tmp/87v2p1290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/97v2p1290527232.ps tmp/97v2p1290527232.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hm1s1290527232.ps tmp/10hm1s1290527232.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.530 2.200 7.749