R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(12
+ ,24
+ ,14
+ ,8
+ ,25
+ ,11
+ ,8
+ ,17
+ ,6
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+ ,5
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+ ,8
+ ,8
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+ ,6
+ ,6
+ ,17
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+ ,4
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+ ,11
+ ,4
+ ,29
+ ,11
+ ,7
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+ ,11
+ ,11
+ ,22
+ ,14
+ ,6
+ ,15
+ ,8
+ ,7
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+ ,8
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+ ,11
+ ,4
+ ,15
+ ,8
+ ,8
+ ,20
+ ,11
+ ,9
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+ ,10
+ ,8
+ ,33
+ ,14
+ ,11
+ ,22
+ ,11
+ ,8
+ ,16
+ ,9
+ ,5
+ ,17
+ ,9
+ ,4
+ ,16
+ ,8
+ ,8
+ ,21
+ ,10
+ ,10
+ ,26
+ ,13
+ ,6
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+ ,9
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+ ,8
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+ ,22
+ ,13
+ ,9
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+ ,30
+ ,12
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+ ,14
+ ,5
+ ,21
+ ,15
+ ,11
+ ,21
+ ,13
+ ,6
+ ,29
+ ,16
+ ,9
+ ,31
+ ,9
+ ,7
+ ,20
+ ,9
+ ,9
+ ,16
+ ,9
+ ,10
+ ,22
+ ,8
+ ,9
+ ,20
+ ,7
+ ,8
+ ,28
+ ,16
+ ,7
+ ,38
+ ,11
+ ,6
+ ,22
+ ,9
+ ,13
+ ,20
+ ,11
+ ,6
+ ,17
+ ,9
+ ,8
+ ,28
+ ,14
+ ,10
+ ,22
+ ,13
+ ,16
+ ,31
+ ,16)
+ ,dim=c(3
+ ,159)
+ ,dimnames=list(c('ParCritism'
+ ,'ParConcern'
+ ,'ParDoubt')
+ ,1:159))
> y <- array(NA,dim=c(3,159),dimnames=list(c('ParCritism','ParConcern','ParDoubt'),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 = '3'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
ParDoubt ParCritism ParConcern
1 14 12 24
2 11 8 25
3 6 8 17
4 12 8 18
5 8 9 18
6 10 7 16
7 10 4 20
8 11 11 16
9 16 7 18
10 11 7 17
11 13 12 23
12 12 10 30
13 8 10 23
14 12 8 18
15 11 8 15
16 4 4 12
17 9 9 21
18 8 8 15
19 8 7 20
20 14 11 31
21 15 9 27
22 16 11 34
23 9 13 21
24 14 8 31
25 11 8 19
26 8 9 16
27 9 6 20
28 9 9 21
29 9 9 22
30 9 6 17
31 10 6 24
32 16 16 25
33 11 5 26
34 8 7 25
35 9 9 17
36 16 6 32
37 11 6 33
38 16 5 13
39 12 12 32
40 12 7 25
41 14 10 29
42 9 9 22
43 10 8 18
44 9 5 17
45 10 8 20
46 12 8 15
47 14 10 20
48 14 6 33
49 10 8 29
50 14 7 23
51 16 4 26
52 9 8 18
53 10 8 20
54 6 4 11
55 8 20 28
56 13 8 26
57 10 8 22
58 8 6 17
59 7 4 12
60 15 8 14
61 9 9 17
62 10 6 21
63 12 7 19
64 13 9 18
65 10 5 10
66 11 5 29
67 8 8 31
68 9 8 19
69 13 6 9
70 11 8 20
71 8 7 28
72 9 7 19
73 9 9 30
74 15 11 29
75 9 6 26
76 10 8 23
77 14 6 13
78 12 9 21
79 12 8 19
80 11 6 28
81 14 10 23
82 6 8 18
83 12 8 21
84 8 10 20
85 14 5 23
86 11 7 21
87 10 5 21
88 14 8 15
89 12 14 28
90 10 7 19
91 14 8 26
92 5 6 10
93 11 5 16
94 10 6 22
95 9 10 19
96 10 12 31
97 16 9 31
98 13 12 29
99 9 7 19
100 10 8 22
101 10 10 23
102 7 6 15
103 9 10 20
104 8 10 18
105 14 10 23
106 14 5 25
107 8 7 21
108 9 10 24
109 14 11 25
110 14 6 17
111 8 7 13
112 8 12 28
113 8 11 21
114 7 11 25
115 6 11 9
116 8 5 16
117 6 8 19
118 11 6 17
119 14 9 25
120 11 4 20
121 11 4 29
122 11 7 14
123 14 11 22
124 8 6 15
125 20 7 19
126 11 8 20
127 8 4 15
128 11 8 20
129 10 9 18
130 14 8 33
131 11 11 22
132 9 8 16
133 9 5 17
134 8 4 16
135 10 8 21
136 13 10 26
137 13 6 18
138 12 9 18
139 8 9 17
140 13 13 22
141 14 9 30
142 12 10 30
143 14 20 24
144 15 5 21
145 13 11 21
146 16 6 29
147 9 9 31
148 9 7 20
149 9 9 16
150 8 10 22
151 7 9 20
152 16 8 28
153 11 7 38
154 9 6 22
155 11 13 20
156 9 6 17
157 14 8 28
158 13 10 22
159 16 16 31
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ParCritism ParConcern
6.5359 0.0360 0.1881
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.9376 -1.8109 -0.5401 1.7715 9.6374
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.53594 0.89896 7.271 1.62e-11 ***
ParCritism 0.03600 0.08000 0.450 0.653
ParConcern 0.18814 0.03784 4.972 1.73e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.587 on 156 degrees of freedom
Multiple R-squared: 0.1573, Adjusted R-squared: 0.1465
F-statistic: 14.56 on 2 and 156 DF, p-value: 1.588e-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,] 0.6760157 0.6479685 0.32398426
[2,] 0.5374908 0.9250184 0.46250922
[3,] 0.4270463 0.8540926 0.57295369
[4,] 0.8442362 0.3115277 0.15576384
[5,] 0.7745855 0.4508290 0.22541452
[6,] 0.6921475 0.6157050 0.30785250
[7,] 0.6229938 0.7540125 0.37700624
[8,] 0.6884603 0.6230795 0.31153974
[9,] 0.6276753 0.7446495 0.37232475
[10,] 0.5475922 0.9048156 0.45240779
[11,] 0.7069714 0.5860572 0.29302861
[12,] 0.6737866 0.6524267 0.32621337
[13,] 0.6249058 0.7501883 0.37509416
[14,] 0.5946311 0.8107377 0.40536886
[15,] 0.5268390 0.9463221 0.47316104
[16,] 0.5376471 0.9247058 0.46235288
[17,] 0.4866541 0.9733082 0.51334588
[18,] 0.4933176 0.9866353 0.50668236
[19,] 0.4287316 0.8574632 0.57126840
[20,] 0.3702302 0.7404603 0.62976984
[21,] 0.3280416 0.6560832 0.67195841
[22,] 0.2837612 0.5675225 0.71623876
[23,] 0.2577019 0.5154039 0.74229806
[24,] 0.2405366 0.4810733 0.75946336
[25,] 0.1956758 0.3913517 0.80432417
[26,] 0.1640932 0.3281864 0.83590679
[27,] 0.1774191 0.3548383 0.82258087
[28,] 0.1412890 0.2825779 0.85871103
[29,] 0.1702149 0.3404297 0.82978514
[30,] 0.1384454 0.2768907 0.86155464
[31,] 0.1553260 0.3106520 0.84467401
[32,] 0.1522519 0.3045037 0.84774815
[33,] 0.5782197 0.8435607 0.42178035
[34,] 0.5484811 0.9030379 0.45151894
[35,] 0.4975609 0.9951217 0.50243915
[36,] 0.4582630 0.9165261 0.54173696
[37,] 0.4385711 0.8771421 0.56142893
[38,] 0.3872168 0.7744336 0.61278320
[39,] 0.3403388 0.6806777 0.65966116
[40,] 0.2953156 0.5906311 0.70468443
[41,] 0.2954149 0.5908299 0.70458506
[42,] 0.3190480 0.6380961 0.68095197
[43,] 0.2824066 0.5648131 0.71759343
[44,] 0.2768406 0.5536811 0.72315944
[45,] 0.2915628 0.5831256 0.70843722
[46,] 0.3910313 0.7820626 0.60896872
[47,] 0.3540294 0.7080588 0.64597060
[48,] 0.3115421 0.6230841 0.68845793
[49,] 0.3042223 0.6084446 0.69577769
[50,] 0.4175766 0.8351532 0.58242340
[51,] 0.3802957 0.7605914 0.61970431
[52,] 0.3413685 0.6827370 0.65863149
[53,] 0.3202629 0.6405257 0.67973713
[54,] 0.2972401 0.5944802 0.70275988
[55,] 0.4731492 0.9462985 0.52685075
[56,] 0.4328443 0.8656885 0.56715574
[57,] 0.3903345 0.7806690 0.60966552
[58,] 0.3636475 0.7272950 0.63635252
[59,] 0.3705181 0.7410362 0.62948188
[60,] 0.3410498 0.6820996 0.65895018
[61,] 0.3099142 0.6198284 0.69008580
[62,] 0.4079292 0.8158585 0.59207076
[63,] 0.3758751 0.7517502 0.62412489
[64,] 0.4667247 0.9334494 0.53327529
[65,] 0.4220179 0.8440358 0.57798208
[66,] 0.4853402 0.9706804 0.51465981
[67,] 0.4518686 0.9037372 0.54813142
[68,] 0.4875609 0.9751218 0.51243908
[69,] 0.4885450 0.9770900 0.51145501
[70,] 0.4898506 0.9797012 0.51014942
[71,] 0.4531050 0.9062100 0.54689501
[72,] 0.5641888 0.8716224 0.43581118
[73,] 0.5281345 0.9437310 0.47186549
[74,] 0.5000233 0.9999535 0.49997673
[75,] 0.4633954 0.9267908 0.53660462
[76,] 0.4691653 0.9383305 0.53083474
[77,] 0.5424273 0.9151454 0.45757271
[78,] 0.5067166 0.9865669 0.49328344
[79,] 0.5081965 0.9836070 0.49180350
[80,] 0.5201145 0.9597710 0.47988551
[81,] 0.4745954 0.9491909 0.52540455
[82,] 0.4319948 0.8639896 0.56800520
[83,] 0.5209195 0.9581610 0.47908048
[84,] 0.4759948 0.9519897 0.52400517
[85,] 0.4309855 0.8619710 0.56901452
[86,] 0.4189691 0.8379382 0.58103091
[87,] 0.4548547 0.9097094 0.54514528
[88,] 0.4219059 0.8438118 0.57809410
[89,] 0.3818945 0.7637890 0.61810549
[90,] 0.3507055 0.7014111 0.64929447
[91,] 0.3644418 0.7288836 0.63555819
[92,] 0.3838311 0.7676622 0.61616888
[93,] 0.3412749 0.6825498 0.65872512
[94,] 0.3092080 0.6184160 0.69079202
[95,] 0.2740573 0.5481146 0.72594272
[96,] 0.2443822 0.4887644 0.75561781
[97,] 0.2397001 0.4794001 0.76029994
[98,] 0.2177355 0.4354710 0.78226448
[99,] 0.2084047 0.4168093 0.79159533
[100,] 0.2099712 0.4199424 0.79002879
[101,] 0.2065446 0.4130891 0.79345545
[102,] 0.2089797 0.4179594 0.79102028
[103,] 0.2051837 0.4103675 0.79481626
[104,] 0.1954201 0.3908401 0.80457994
[105,] 0.2464133 0.4928266 0.75358672
[106,] 0.2149295 0.4298590 0.78507049
[107,] 0.2894164 0.5788328 0.71058361
[108,] 0.3038169 0.6076339 0.69618306
[109,] 0.4312259 0.8624517 0.56877413
[110,] 0.4349184 0.8698367 0.56508163
[111,] 0.4043927 0.8087853 0.59560735
[112,] 0.5194382 0.9611236 0.48056182
[113,] 0.4731413 0.9462827 0.52685866
[114,] 0.4539818 0.9079637 0.54601817
[115,] 0.4041476 0.8082952 0.59585240
[116,] 0.3624185 0.7248369 0.63758155
[117,] 0.3279450 0.6558900 0.67205498
[118,] 0.3228679 0.6457357 0.67713214
[119,] 0.2915996 0.5831992 0.70840042
[120,] 0.9126304 0.1747392 0.08736961
[121,] 0.8871011 0.2257978 0.11289892
[122,] 0.8605825 0.2788350 0.13941748
[123,] 0.8247281 0.3505438 0.17527190
[124,] 0.7824521 0.4350957 0.21754785
[125,] 0.7365348 0.5269303 0.26346515
[126,] 0.6847539 0.6304921 0.31524605
[127,] 0.6314067 0.7371867 0.36859334
[128,] 0.5726671 0.8546658 0.42733290
[129,] 0.5267844 0.9464311 0.47321557
[130,] 0.4702449 0.9404897 0.52975515
[131,] 0.4096731 0.8193462 0.59032691
[132,] 0.4266937 0.8533874 0.57330632
[133,] 0.3883103 0.7766205 0.61168975
[134,] 0.3604053 0.7208106 0.63959468
[135,] 0.3085400 0.6170799 0.69146005
[136,] 0.2575256 0.5150511 0.74247444
[137,] 0.2058421 0.4116841 0.79415793
[138,] 0.1630123 0.3260246 0.83698770
[139,] 0.2942013 0.5884026 0.70579870
[140,] 0.2628099 0.5256197 0.73719015
[141,] 0.4329760 0.8659519 0.56702404
[142,] 0.5134841 0.9730317 0.48651586
[143,] 0.4142149 0.8284298 0.58578512
[144,] 0.3134788 0.6269576 0.68652120
[145,] 0.3438716 0.6877432 0.65612838
[146,] 0.4792291 0.9584583 0.52077087
[147,] 0.7294089 0.5411823 0.27059113
[148,] 0.8448708 0.3102585 0.15512925
> postscript(file="/var/www/html/freestat/rcomp/tmp/1qcxx1290595452.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2qcxx1290595452.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3qcxx1290595452.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/413xi1290595452.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/513xi1290595452.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
2.51668483 -0.52744446 -4.02232827 1.78953220 -2.24647036 0.20181381
7 8 9 10 11 12
-0.44273661 1.05780357 5.82553476 1.01367429 1.70482435 -0.54014719
13 14 15 16 17 18
-3.22317053 1.78953220 1.35395077 -4.93762042 -1.81088893 -1.64604923
19 20 21 22 23 24
-2.55074428 1.23571073 3.06027394 2.67129216 -1.95489916 1.34371840
25 26 27 28 29 30
0.60139268 -1.87019131 -1.51474173 -1.81088893 -1.99902845 -0.95032316
31 32 33 34 35 36
-1.26729982 4.18453507 -0.60757631 -3.49144190 -1.05833083 3.22758400
37 38 39 40 41 42
-1.96055553 6.83823750 -0.98843136 0.50855810 1.64799233 -1.99902845
43 44 45 46 47 48
-0.21046780 -0.91432060 -0.58674684 2.35395077 3.34124804 1.03944447
49 50 51 52 53 54
-2.28000255 2.88483715 4.42842625 -1.21046780 -0.58674684 -2.74948090
55 56 57 58 59 60
-4.52389374 1.28441602 -0.96302589 -1.95032316 -1.93762042 5.54209030
61 62 63 64 65 66
-1.05833083 -0.70288125 1.63739524 2.75352964 1.40265607 -1.17199488
67 68 69 70 71 72
-4.65628160 -1.39860732 4.55479303 0.41325316 -4.05586047 -1.36260476
73 74 75 76 77 78
-3.50414463 2.61198977 -2.64357886 -1.15116541 4.80223494 1.18911107
79 80 81 82 83 84
1.60139268 -1.01985791 2.77682947 -4.21046780 1.22511363 -2.65875196
85 86 87 88 89 90
2.95684226 0.26111619 -0.66687869 4.35395077 -0.30787838 -0.36260476
91 92 93 94 95 96
2.28441602 -3.63334649 1.27381893 -0.89102077 -1.47061244 -2.80029183
97 98 99 100 101 102
3.30771584 0.57598721 -1.36260476 -0.96302589 -1.22317053 -2.57404411
103 104 105 106 107 108
-1.65875196 -2.28247291 2.77682947 2.58056322 -2.73888381 -2.41131005
109 110 111 112 113 114
2.36454786 4.04967684 -1.23376762 -4.23587326 -2.88289404 -4.63545214
115 116 117 118 119 120
-2.62521976 -1.72618107 -4.39860732 1.04967684 2.43655298 0.55726339
121 122 123 124 125 126
-1.13599232 1.57809286 2.92896643 -1.57404411 9.63739524 0.41325316
127 128 129 130 131 132
-1.50203899 0.41325316 -0.24647036 0.96743936 -0.07103357 -0.83418875
133 134 135 136 137 138
-0.91432060 -1.69017851 -0.77488637 1.21241090 2.86153732 1.75352964
139 140 141 142 143 144
-2.05833083 1.85696132 1.49585537 -0.54014719 2.22866436 4.33312131
145 146 147 148 149 150
2.11710596 3.79200257 -3.69228416 -1.55074428 -0.87019131 -3.03503101
151 152 153 154 155 156
-3.62274940 3.90813697 -2.93725570 -1.89102077 0.23324036 -0.95032316
157 158 159
1.90813697 1.96496899 3.05569793
> postscript(file="/var/www/html/freestat/rcomp/tmp/613xi1290595452.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 2.51668483 NA
1 -0.52744446 2.51668483
2 -4.02232827 -0.52744446
3 1.78953220 -4.02232827
4 -2.24647036 1.78953220
5 0.20181381 -2.24647036
6 -0.44273661 0.20181381
7 1.05780357 -0.44273661
8 5.82553476 1.05780357
9 1.01367429 5.82553476
10 1.70482435 1.01367429
11 -0.54014719 1.70482435
12 -3.22317053 -0.54014719
13 1.78953220 -3.22317053
14 1.35395077 1.78953220
15 -4.93762042 1.35395077
16 -1.81088893 -4.93762042
17 -1.64604923 -1.81088893
18 -2.55074428 -1.64604923
19 1.23571073 -2.55074428
20 3.06027394 1.23571073
21 2.67129216 3.06027394
22 -1.95489916 2.67129216
23 1.34371840 -1.95489916
24 0.60139268 1.34371840
25 -1.87019131 0.60139268
26 -1.51474173 -1.87019131
27 -1.81088893 -1.51474173
28 -1.99902845 -1.81088893
29 -0.95032316 -1.99902845
30 -1.26729982 -0.95032316
31 4.18453507 -1.26729982
32 -0.60757631 4.18453507
33 -3.49144190 -0.60757631
34 -1.05833083 -3.49144190
35 3.22758400 -1.05833083
36 -1.96055553 3.22758400
37 6.83823750 -1.96055553
38 -0.98843136 6.83823750
39 0.50855810 -0.98843136
40 1.64799233 0.50855810
41 -1.99902845 1.64799233
42 -0.21046780 -1.99902845
43 -0.91432060 -0.21046780
44 -0.58674684 -0.91432060
45 2.35395077 -0.58674684
46 3.34124804 2.35395077
47 1.03944447 3.34124804
48 -2.28000255 1.03944447
49 2.88483715 -2.28000255
50 4.42842625 2.88483715
51 -1.21046780 4.42842625
52 -0.58674684 -1.21046780
53 -2.74948090 -0.58674684
54 -4.52389374 -2.74948090
55 1.28441602 -4.52389374
56 -0.96302589 1.28441602
57 -1.95032316 -0.96302589
58 -1.93762042 -1.95032316
59 5.54209030 -1.93762042
60 -1.05833083 5.54209030
61 -0.70288125 -1.05833083
62 1.63739524 -0.70288125
63 2.75352964 1.63739524
64 1.40265607 2.75352964
65 -1.17199488 1.40265607
66 -4.65628160 -1.17199488
67 -1.39860732 -4.65628160
68 4.55479303 -1.39860732
69 0.41325316 4.55479303
70 -4.05586047 0.41325316
71 -1.36260476 -4.05586047
72 -3.50414463 -1.36260476
73 2.61198977 -3.50414463
74 -2.64357886 2.61198977
75 -1.15116541 -2.64357886
76 4.80223494 -1.15116541
77 1.18911107 4.80223494
78 1.60139268 1.18911107
79 -1.01985791 1.60139268
80 2.77682947 -1.01985791
81 -4.21046780 2.77682947
82 1.22511363 -4.21046780
83 -2.65875196 1.22511363
84 2.95684226 -2.65875196
85 0.26111619 2.95684226
86 -0.66687869 0.26111619
87 4.35395077 -0.66687869
88 -0.30787838 4.35395077
89 -0.36260476 -0.30787838
90 2.28441602 -0.36260476
91 -3.63334649 2.28441602
92 1.27381893 -3.63334649
93 -0.89102077 1.27381893
94 -1.47061244 -0.89102077
95 -2.80029183 -1.47061244
96 3.30771584 -2.80029183
97 0.57598721 3.30771584
98 -1.36260476 0.57598721
99 -0.96302589 -1.36260476
100 -1.22317053 -0.96302589
101 -2.57404411 -1.22317053
102 -1.65875196 -2.57404411
103 -2.28247291 -1.65875196
104 2.77682947 -2.28247291
105 2.58056322 2.77682947
106 -2.73888381 2.58056322
107 -2.41131005 -2.73888381
108 2.36454786 -2.41131005
109 4.04967684 2.36454786
110 -1.23376762 4.04967684
111 -4.23587326 -1.23376762
112 -2.88289404 -4.23587326
113 -4.63545214 -2.88289404
114 -2.62521976 -4.63545214
115 -1.72618107 -2.62521976
116 -4.39860732 -1.72618107
117 1.04967684 -4.39860732
118 2.43655298 1.04967684
119 0.55726339 2.43655298
120 -1.13599232 0.55726339
121 1.57809286 -1.13599232
122 2.92896643 1.57809286
123 -1.57404411 2.92896643
124 9.63739524 -1.57404411
125 0.41325316 9.63739524
126 -1.50203899 0.41325316
127 0.41325316 -1.50203899
128 -0.24647036 0.41325316
129 0.96743936 -0.24647036
130 -0.07103357 0.96743936
131 -0.83418875 -0.07103357
132 -0.91432060 -0.83418875
133 -1.69017851 -0.91432060
134 -0.77488637 -1.69017851
135 1.21241090 -0.77488637
136 2.86153732 1.21241090
137 1.75352964 2.86153732
138 -2.05833083 1.75352964
139 1.85696132 -2.05833083
140 1.49585537 1.85696132
141 -0.54014719 1.49585537
142 2.22866436 -0.54014719
143 4.33312131 2.22866436
144 2.11710596 4.33312131
145 3.79200257 2.11710596
146 -3.69228416 3.79200257
147 -1.55074428 -3.69228416
148 -0.87019131 -1.55074428
149 -3.03503101 -0.87019131
150 -3.62274940 -3.03503101
151 3.90813697 -3.62274940
152 -2.93725570 3.90813697
153 -1.89102077 -2.93725570
154 0.23324036 -1.89102077
155 -0.95032316 0.23324036
156 1.90813697 -0.95032316
157 1.96496899 1.90813697
158 3.05569793 1.96496899
159 NA 3.05569793
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.52744446 2.51668483
[2,] -4.02232827 -0.52744446
[3,] 1.78953220 -4.02232827
[4,] -2.24647036 1.78953220
[5,] 0.20181381 -2.24647036
[6,] -0.44273661 0.20181381
[7,] 1.05780357 -0.44273661
[8,] 5.82553476 1.05780357
[9,] 1.01367429 5.82553476
[10,] 1.70482435 1.01367429
[11,] -0.54014719 1.70482435
[12,] -3.22317053 -0.54014719
[13,] 1.78953220 -3.22317053
[14,] 1.35395077 1.78953220
[15,] -4.93762042 1.35395077
[16,] -1.81088893 -4.93762042
[17,] -1.64604923 -1.81088893
[18,] -2.55074428 -1.64604923
[19,] 1.23571073 -2.55074428
[20,] 3.06027394 1.23571073
[21,] 2.67129216 3.06027394
[22,] -1.95489916 2.67129216
[23,] 1.34371840 -1.95489916
[24,] 0.60139268 1.34371840
[25,] -1.87019131 0.60139268
[26,] -1.51474173 -1.87019131
[27,] -1.81088893 -1.51474173
[28,] -1.99902845 -1.81088893
[29,] -0.95032316 -1.99902845
[30,] -1.26729982 -0.95032316
[31,] 4.18453507 -1.26729982
[32,] -0.60757631 4.18453507
[33,] -3.49144190 -0.60757631
[34,] -1.05833083 -3.49144190
[35,] 3.22758400 -1.05833083
[36,] -1.96055553 3.22758400
[37,] 6.83823750 -1.96055553
[38,] -0.98843136 6.83823750
[39,] 0.50855810 -0.98843136
[40,] 1.64799233 0.50855810
[41,] -1.99902845 1.64799233
[42,] -0.21046780 -1.99902845
[43,] -0.91432060 -0.21046780
[44,] -0.58674684 -0.91432060
[45,] 2.35395077 -0.58674684
[46,] 3.34124804 2.35395077
[47,] 1.03944447 3.34124804
[48,] -2.28000255 1.03944447
[49,] 2.88483715 -2.28000255
[50,] 4.42842625 2.88483715
[51,] -1.21046780 4.42842625
[52,] -0.58674684 -1.21046780
[53,] -2.74948090 -0.58674684
[54,] -4.52389374 -2.74948090
[55,] 1.28441602 -4.52389374
[56,] -0.96302589 1.28441602
[57,] -1.95032316 -0.96302589
[58,] -1.93762042 -1.95032316
[59,] 5.54209030 -1.93762042
[60,] -1.05833083 5.54209030
[61,] -0.70288125 -1.05833083
[62,] 1.63739524 -0.70288125
[63,] 2.75352964 1.63739524
[64,] 1.40265607 2.75352964
[65,] -1.17199488 1.40265607
[66,] -4.65628160 -1.17199488
[67,] -1.39860732 -4.65628160
[68,] 4.55479303 -1.39860732
[69,] 0.41325316 4.55479303
[70,] -4.05586047 0.41325316
[71,] -1.36260476 -4.05586047
[72,] -3.50414463 -1.36260476
[73,] 2.61198977 -3.50414463
[74,] -2.64357886 2.61198977
[75,] -1.15116541 -2.64357886
[76,] 4.80223494 -1.15116541
[77,] 1.18911107 4.80223494
[78,] 1.60139268 1.18911107
[79,] -1.01985791 1.60139268
[80,] 2.77682947 -1.01985791
[81,] -4.21046780 2.77682947
[82,] 1.22511363 -4.21046780
[83,] -2.65875196 1.22511363
[84,] 2.95684226 -2.65875196
[85,] 0.26111619 2.95684226
[86,] -0.66687869 0.26111619
[87,] 4.35395077 -0.66687869
[88,] -0.30787838 4.35395077
[89,] -0.36260476 -0.30787838
[90,] 2.28441602 -0.36260476
[91,] -3.63334649 2.28441602
[92,] 1.27381893 -3.63334649
[93,] -0.89102077 1.27381893
[94,] -1.47061244 -0.89102077
[95,] -2.80029183 -1.47061244
[96,] 3.30771584 -2.80029183
[97,] 0.57598721 3.30771584
[98,] -1.36260476 0.57598721
[99,] -0.96302589 -1.36260476
[100,] -1.22317053 -0.96302589
[101,] -2.57404411 -1.22317053
[102,] -1.65875196 -2.57404411
[103,] -2.28247291 -1.65875196
[104,] 2.77682947 -2.28247291
[105,] 2.58056322 2.77682947
[106,] -2.73888381 2.58056322
[107,] -2.41131005 -2.73888381
[108,] 2.36454786 -2.41131005
[109,] 4.04967684 2.36454786
[110,] -1.23376762 4.04967684
[111,] -4.23587326 -1.23376762
[112,] -2.88289404 -4.23587326
[113,] -4.63545214 -2.88289404
[114,] -2.62521976 -4.63545214
[115,] -1.72618107 -2.62521976
[116,] -4.39860732 -1.72618107
[117,] 1.04967684 -4.39860732
[118,] 2.43655298 1.04967684
[119,] 0.55726339 2.43655298
[120,] -1.13599232 0.55726339
[121,] 1.57809286 -1.13599232
[122,] 2.92896643 1.57809286
[123,] -1.57404411 2.92896643
[124,] 9.63739524 -1.57404411
[125,] 0.41325316 9.63739524
[126,] -1.50203899 0.41325316
[127,] 0.41325316 -1.50203899
[128,] -0.24647036 0.41325316
[129,] 0.96743936 -0.24647036
[130,] -0.07103357 0.96743936
[131,] -0.83418875 -0.07103357
[132,] -0.91432060 -0.83418875
[133,] -1.69017851 -0.91432060
[134,] -0.77488637 -1.69017851
[135,] 1.21241090 -0.77488637
[136,] 2.86153732 1.21241090
[137,] 1.75352964 2.86153732
[138,] -2.05833083 1.75352964
[139,] 1.85696132 -2.05833083
[140,] 1.49585537 1.85696132
[141,] -0.54014719 1.49585537
[142,] 2.22866436 -0.54014719
[143,] 4.33312131 2.22866436
[144,] 2.11710596 4.33312131
[145,] 3.79200257 2.11710596
[146,] -3.69228416 3.79200257
[147,] -1.55074428 -3.69228416
[148,] -0.87019131 -1.55074428
[149,] -3.03503101 -0.87019131
[150,] -3.62274940 -3.03503101
[151,] 3.90813697 -3.62274940
[152,] -2.93725570 3.90813697
[153,] -1.89102077 -2.93725570
[154,] 0.23324036 -1.89102077
[155,] -0.95032316 0.23324036
[156,] 1.90813697 -0.95032316
[157,] 1.96496899 1.90813697
[158,] 3.05569793 1.96496899
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.52744446 2.51668483
2 -4.02232827 -0.52744446
3 1.78953220 -4.02232827
4 -2.24647036 1.78953220
5 0.20181381 -2.24647036
6 -0.44273661 0.20181381
7 1.05780357 -0.44273661
8 5.82553476 1.05780357
9 1.01367429 5.82553476
10 1.70482435 1.01367429
11 -0.54014719 1.70482435
12 -3.22317053 -0.54014719
13 1.78953220 -3.22317053
14 1.35395077 1.78953220
15 -4.93762042 1.35395077
16 -1.81088893 -4.93762042
17 -1.64604923 -1.81088893
18 -2.55074428 -1.64604923
19 1.23571073 -2.55074428
20 3.06027394 1.23571073
21 2.67129216 3.06027394
22 -1.95489916 2.67129216
23 1.34371840 -1.95489916
24 0.60139268 1.34371840
25 -1.87019131 0.60139268
26 -1.51474173 -1.87019131
27 -1.81088893 -1.51474173
28 -1.99902845 -1.81088893
29 -0.95032316 -1.99902845
30 -1.26729982 -0.95032316
31 4.18453507 -1.26729982
32 -0.60757631 4.18453507
33 -3.49144190 -0.60757631
34 -1.05833083 -3.49144190
35 3.22758400 -1.05833083
36 -1.96055553 3.22758400
37 6.83823750 -1.96055553
38 -0.98843136 6.83823750
39 0.50855810 -0.98843136
40 1.64799233 0.50855810
41 -1.99902845 1.64799233
42 -0.21046780 -1.99902845
43 -0.91432060 -0.21046780
44 -0.58674684 -0.91432060
45 2.35395077 -0.58674684
46 3.34124804 2.35395077
47 1.03944447 3.34124804
48 -2.28000255 1.03944447
49 2.88483715 -2.28000255
50 4.42842625 2.88483715
51 -1.21046780 4.42842625
52 -0.58674684 -1.21046780
53 -2.74948090 -0.58674684
54 -4.52389374 -2.74948090
55 1.28441602 -4.52389374
56 -0.96302589 1.28441602
57 -1.95032316 -0.96302589
58 -1.93762042 -1.95032316
59 5.54209030 -1.93762042
60 -1.05833083 5.54209030
61 -0.70288125 -1.05833083
62 1.63739524 -0.70288125
63 2.75352964 1.63739524
64 1.40265607 2.75352964
65 -1.17199488 1.40265607
66 -4.65628160 -1.17199488
67 -1.39860732 -4.65628160
68 4.55479303 -1.39860732
69 0.41325316 4.55479303
70 -4.05586047 0.41325316
71 -1.36260476 -4.05586047
72 -3.50414463 -1.36260476
73 2.61198977 -3.50414463
74 -2.64357886 2.61198977
75 -1.15116541 -2.64357886
76 4.80223494 -1.15116541
77 1.18911107 4.80223494
78 1.60139268 1.18911107
79 -1.01985791 1.60139268
80 2.77682947 -1.01985791
81 -4.21046780 2.77682947
82 1.22511363 -4.21046780
83 -2.65875196 1.22511363
84 2.95684226 -2.65875196
85 0.26111619 2.95684226
86 -0.66687869 0.26111619
87 4.35395077 -0.66687869
88 -0.30787838 4.35395077
89 -0.36260476 -0.30787838
90 2.28441602 -0.36260476
91 -3.63334649 2.28441602
92 1.27381893 -3.63334649
93 -0.89102077 1.27381893
94 -1.47061244 -0.89102077
95 -2.80029183 -1.47061244
96 3.30771584 -2.80029183
97 0.57598721 3.30771584
98 -1.36260476 0.57598721
99 -0.96302589 -1.36260476
100 -1.22317053 -0.96302589
101 -2.57404411 -1.22317053
102 -1.65875196 -2.57404411
103 -2.28247291 -1.65875196
104 2.77682947 -2.28247291
105 2.58056322 2.77682947
106 -2.73888381 2.58056322
107 -2.41131005 -2.73888381
108 2.36454786 -2.41131005
109 4.04967684 2.36454786
110 -1.23376762 4.04967684
111 -4.23587326 -1.23376762
112 -2.88289404 -4.23587326
113 -4.63545214 -2.88289404
114 -2.62521976 -4.63545214
115 -1.72618107 -2.62521976
116 -4.39860732 -1.72618107
117 1.04967684 -4.39860732
118 2.43655298 1.04967684
119 0.55726339 2.43655298
120 -1.13599232 0.55726339
121 1.57809286 -1.13599232
122 2.92896643 1.57809286
123 -1.57404411 2.92896643
124 9.63739524 -1.57404411
125 0.41325316 9.63739524
126 -1.50203899 0.41325316
127 0.41325316 -1.50203899
128 -0.24647036 0.41325316
129 0.96743936 -0.24647036
130 -0.07103357 0.96743936
131 -0.83418875 -0.07103357
132 -0.91432060 -0.83418875
133 -1.69017851 -0.91432060
134 -0.77488637 -1.69017851
135 1.21241090 -0.77488637
136 2.86153732 1.21241090
137 1.75352964 2.86153732
138 -2.05833083 1.75352964
139 1.85696132 -2.05833083
140 1.49585537 1.85696132
141 -0.54014719 1.49585537
142 2.22866436 -0.54014719
143 4.33312131 2.22866436
144 2.11710596 4.33312131
145 3.79200257 2.11710596
146 -3.69228416 3.79200257
147 -1.55074428 -3.69228416
148 -0.87019131 -1.55074428
149 -3.03503101 -0.87019131
150 -3.62274940 -3.03503101
151 3.90813697 -3.62274940
152 -2.93725570 3.90813697
153 -1.89102077 -2.93725570
154 0.23324036 -1.89102077
155 -0.95032316 0.23324036
156 1.90813697 -0.95032316
157 1.96496899 1.90813697
158 3.05569793 1.96496899
> 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/7uue31290595452.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/84lv61290595452.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/94lv61290595452.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10fvur1290595452.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110vtx1290595452.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/124wsl1290595452.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/13i6pu1290595452.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/14l66i1290595452.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/157pm51290595452.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/16ly2w1290595452.tab")
+ }
>
> try(system("convert tmp/1qcxx1290595452.ps tmp/1qcxx1290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qcxx1290595452.ps tmp/2qcxx1290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qcxx1290595452.ps tmp/3qcxx1290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/413xi1290595452.ps tmp/413xi1290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/513xi1290595452.ps tmp/513xi1290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/613xi1290595452.ps tmp/613xi1290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uue31290595452.ps tmp/7uue31290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/84lv61290595452.ps tmp/84lv61290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/94lv61290595452.ps tmp/94lv61290595452.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fvur1290595452.ps tmp/10fvur1290595452.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.365 2.631 5.833