R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24
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+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('Mistakes'
+ ,'doubts'
+ ,'p-expactations'
+ ,'p-criticism'
+ ,'standards'
+ ,'organisation')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('Mistakes','doubts','p-expactations','p-criticism','standards','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 = '5'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
standards Mistakes doubts p-expactations p-criticism organisation
1 24 24 14 11 12 26
2 25 25 11 7 8 23
3 30 17 6 17 8 25
4 19 18 12 10 8 23
5 22 18 8 12 9 19
6 22 16 10 12 7 29
7 25 20 10 11 4 25
8 23 16 11 11 11 21
9 17 18 16 12 7 22
10 21 17 11 13 7 25
11 19 23 13 14 12 24
12 19 30 12 16 10 18
13 15 23 8 11 10 22
14 16 18 12 10 8 15
15 23 15 11 11 8 22
16 27 12 4 15 4 28
17 22 21 9 9 9 20
18 14 15 8 11 8 12
19 22 20 8 17 7 24
20 23 31 14 17 11 20
21 23 27 15 11 9 21
22 21 34 16 18 11 20
23 19 21 9 14 13 21
24 18 31 14 10 8 23
25 20 19 11 11 8 28
26 23 16 8 15 9 24
27 25 20 9 15 6 24
28 19 21 9 13 9 24
29 24 22 9 16 9 23
30 22 17 9 13 6 23
31 25 24 10 9 6 29
32 26 25 16 18 16 24
33 29 26 11 18 5 18
34 32 25 8 12 7 25
35 25 17 9 17 9 21
36 29 32 16 9 6 26
37 28 33 11 9 6 22
38 17 13 16 12 5 22
39 28 32 12 18 12 22
40 29 25 12 12 7 23
41 26 29 14 18 10 30
42 25 22 9 14 9 23
43 14 18 10 15 8 17
44 25 17 9 16 5 23
45 26 20 10 10 8 23
46 20 15 12 11 8 25
47 18 20 14 14 10 24
48 32 33 14 9 6 24
49 25 29 10 12 8 23
50 25 23 14 17 7 21
51 23 26 16 5 4 24
52 21 18 9 12 8 24
53 20 20 10 12 8 28
54 15 11 6 6 4 16
55 30 28 8 24 20 20
56 24 26 13 12 8 29
57 26 22 10 12 8 27
58 24 17 8 14 6 22
59 22 12 7 7 4 28
60 14 14 15 13 8 16
61 24 17 9 12 9 25
62 24 21 10 13 6 24
63 24 19 12 14 7 28
64 24 18 13 8 9 24
65 19 10 10 11 5 23
66 31 29 11 9 5 30
67 22 31 8 11 8 24
68 27 19 9 13 8 21
69 19 9 13 10 6 25
70 25 20 11 11 8 25
71 20 28 8 12 7 22
72 21 19 9 9 7 23
73 27 30 9 15 9 26
74 23 29 15 18 11 23
75 25 26 9 15 6 25
76 20 23 10 12 8 21
77 21 13 14 13 6 25
78 22 21 12 14 9 24
79 23 19 12 10 8 29
80 25 28 11 13 6 22
81 25 23 14 13 10 27
82 17 18 6 11 8 26
83 19 21 12 13 8 22
84 25 20 8 16 10 24
85 19 23 14 8 5 27
86 20 21 11 16 7 24
87 26 21 10 11 5 24
88 23 15 14 9 8 29
89 27 28 12 16 14 22
90 17 19 10 12 7 21
91 17 26 14 14 8 24
92 19 10 5 8 6 24
93 17 16 11 9 5 23
94 22 22 10 15 6 20
95 21 19 9 11 10 27
96 32 31 10 21 12 26
97 21 31 16 14 9 25
98 21 29 13 18 12 21
99 18 19 9 12 7 21
100 18 22 10 13 8 19
101 23 23 10 15 10 21
102 19 15 7 12 6 21
103 20 20 9 19 10 16
104 21 18 8 15 10 22
105 20 23 14 11 10 29
106 17 25 14 11 5 15
107 18 21 8 10 7 17
108 19 24 9 13 10 15
109 22 25 14 15 11 21
110 15 17 14 12 6 21
111 14 13 8 12 7 19
112 18 28 8 16 12 24
113 24 21 8 9 11 20
114 35 25 7 18 11 17
115 29 9 6 8 11 23
116 21 16 8 13 5 24
117 25 19 6 17 8 14
118 20 17 11 9 6 19
119 22 25 14 15 9 24
120 13 20 11 8 4 13
121 26 29 11 7 4 22
122 17 14 11 12 7 16
123 25 22 14 14 11 19
124 20 15 8 6 6 25
125 19 19 20 8 7 25
126 21 20 11 17 8 23
127 22 15 8 10 4 24
128 24 20 11 11 8 26
129 21 18 10 14 9 26
130 26 33 14 11 8 25
131 24 22 11 13 11 18
132 16 16 9 12 8 21
133 23 17 9 11 5 26
134 18 16 8 9 4 23
135 16 21 10 12 8 23
136 26 26 13 20 10 22
137 19 18 13 12 6 20
138 21 18 12 13 9 13
139 21 17 8 12 9 24
140 22 22 13 12 13 15
141 23 30 14 9 9 14
142 29 30 12 15 10 22
143 21 24 14 24 20 10
144 21 21 15 7 5 24
145 23 21 13 17 11 22
146 27 29 16 11 6 24
147 25 31 9 17 9 19
148 21 20 9 11 7 20
149 10 16 9 12 9 13
150 20 22 8 14 10 20
151 26 20 7 11 9 22
152 24 28 16 16 8 24
153 29 38 11 21 7 29
154 19 22 9 14 6 12
155 24 20 11 20 13 20
156 19 17 9 13 6 21
157 24 28 14 11 8 24
158 22 22 13 15 10 22
159 17 31 16 19 16 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Mistakes doubts `p-expactations`
7.46043 0.32815 -0.36274 0.18656
`p-criticism` organisation
0.02338 0.40127
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.6429143 -2.1602008 -0.0001409 2.1735704 11.4379673
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.46043 2.24811 3.319 0.00113 **
Mistakes 0.32815 0.05554 5.908 2.17e-08 ***
doubts -0.36274 0.10712 -3.386 0.00090 ***
`p-expactations` 0.18656 0.10114 1.845 0.06703 .
`p-criticism` 0.02338 0.12862 0.182 0.85597
organisation 0.40127 0.07177 5.591 1.01e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.409 on 153 degrees of freedom
Multiple R-squared: 0.3671, Adjusted R-squared: 0.3464
F-statistic: 17.75 on 5 and 153 DF, p-value: 7.55e-14
> 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,] 0.30316717 0.60633435 0.6968328
[2,] 0.18327191 0.36654381 0.8167281
[3,] 0.33896575 0.67793151 0.6610342
[4,] 0.28960882 0.57921764 0.7103912
[5,] 0.86604763 0.26790473 0.1339524
[6,] 0.80493438 0.39013125 0.1950656
[7,] 0.75990210 0.48019580 0.2400979
[8,] 0.69717006 0.60565988 0.3028299
[9,] 0.61928275 0.76143450 0.3807172
[10,] 0.59103869 0.81792263 0.4089613
[11,] 0.52402539 0.95194922 0.4759746
[12,] 0.51820402 0.96359195 0.4817960
[13,] 0.49782432 0.99564864 0.5021757
[14,] 0.43087937 0.86175874 0.5691206
[15,] 0.38589468 0.77178935 0.6141053
[16,] 0.43499451 0.86998902 0.5650055
[17,] 0.44772104 0.89544209 0.5522790
[18,] 0.37960572 0.75921143 0.6203943
[19,] 0.32928175 0.65856350 0.6707183
[20,] 0.34529132 0.69058265 0.6547087
[21,] 0.29166345 0.58332690 0.7083365
[22,] 0.23850287 0.47700574 0.7614971
[23,] 0.19195255 0.38390511 0.8080474
[24,] 0.21217411 0.42434823 0.7878259
[25,] 0.36208470 0.72416941 0.6379153
[26,] 0.59668327 0.80663346 0.4033167
[27,] 0.57383665 0.85232669 0.4261633
[28,] 0.63399747 0.73200507 0.3660025
[29,] 0.62504648 0.74990705 0.3749535
[30,] 0.58120710 0.83758580 0.4187929
[31,] 0.54926504 0.90146992 0.4507350
[32,] 0.63401498 0.73197004 0.3659850
[33,] 0.60269681 0.79460639 0.3973032
[34,] 0.55749378 0.88501243 0.4425062
[35,] 0.64404552 0.71190895 0.3559545
[36,] 0.61600100 0.76799801 0.3839990
[37,] 0.63920420 0.72159159 0.3607958
[38,] 0.58970397 0.82059207 0.4102960
[39,] 0.58044335 0.83911330 0.4195567
[40,] 0.69168947 0.61662106 0.3083105
[41,] 0.65049361 0.69901279 0.3495064
[42,] 0.63968567 0.72062866 0.3603143
[43,] 0.60117621 0.79764758 0.3988238
[44,] 0.56022331 0.87955339 0.4397767
[45,] 0.59654801 0.80690398 0.4034520
[46,] 0.56340956 0.87318088 0.4365904
[47,] 0.61489342 0.77021315 0.3851066
[48,] 0.58185544 0.83628911 0.4181446
[49,] 0.54213308 0.91573385 0.4578669
[50,] 0.51104165 0.97791669 0.4889583
[51,] 0.46396714 0.92793429 0.5360329
[52,] 0.42276228 0.84552455 0.5772377
[53,] 0.38985615 0.77971230 0.6101439
[54,] 0.35005359 0.70010717 0.6499464
[55,] 0.30927596 0.61855192 0.6907240
[56,] 0.34221487 0.68442974 0.6577851
[57,] 0.30078934 0.60157868 0.6992107
[58,] 0.31462219 0.62924438 0.6853778
[59,] 0.37672230 0.75344460 0.6232777
[60,] 0.45373581 0.90747162 0.5462642
[61,] 0.41517360 0.83034720 0.5848264
[62,] 0.39896243 0.79792486 0.6010376
[63,] 0.46580513 0.93161027 0.5341949
[64,] 0.42054722 0.84109444 0.5794528
[65,] 0.37942008 0.75884017 0.6205799
[66,] 0.34398795 0.68797589 0.6560121
[67,] 0.31194164 0.62388329 0.6880584
[68,] 0.28832232 0.57664465 0.7116777
[69,] 0.26576005 0.53152011 0.7342399
[70,] 0.22996366 0.45992733 0.7700363
[71,] 0.19760962 0.39521925 0.8023904
[72,] 0.17088970 0.34177941 0.8291103
[73,] 0.14992771 0.29985542 0.8500723
[74,] 0.24395880 0.48791760 0.7560412
[75,] 0.22576963 0.45153926 0.7742304
[76,] 0.19663052 0.39326105 0.8033695
[77,] 0.19863553 0.39727105 0.8013645
[78,] 0.19410909 0.38821818 0.8058909
[79,] 0.20156676 0.40313351 0.7984332
[80,] 0.18844161 0.37688322 0.8115584
[81,] 0.17629410 0.35258820 0.8237059
[82,] 0.18130859 0.36261719 0.8186914
[83,] 0.25855265 0.51710530 0.7414473
[84,] 0.22422775 0.44845549 0.7757723
[85,] 0.20747277 0.41494554 0.7925272
[86,] 0.17664701 0.35329401 0.8233530
[87,] 0.16186053 0.32372106 0.8381395
[88,] 0.16559595 0.33119190 0.8344041
[89,] 0.16737393 0.33474785 0.8326261
[90,] 0.16345764 0.32691528 0.8365424
[91,] 0.15634952 0.31269904 0.8436505
[92,] 0.15119867 0.30239734 0.8488013
[93,] 0.12481128 0.24962256 0.8751887
[94,] 0.10469077 0.20938154 0.8953092
[95,] 0.08483472 0.16966944 0.9151653
[96,] 0.06916770 0.13833539 0.9308323
[97,] 0.07399201 0.14798402 0.9260080
[98,] 0.06100521 0.12201042 0.9389948
[99,] 0.05390697 0.10781395 0.9460930
[100,] 0.04722097 0.09444194 0.9527790
[101,] 0.03639851 0.07279702 0.9636015
[102,] 0.03589926 0.07179852 0.9641007
[103,] 0.04521252 0.09042503 0.9547875
[104,] 0.18095568 0.36191137 0.8190443
[105,] 0.16505445 0.33010890 0.8349455
[106,] 0.59730723 0.80538554 0.4026928
[107,] 0.87451015 0.25097970 0.1254899
[108,] 0.84497946 0.31004108 0.1550205
[109,] 0.89463271 0.21073457 0.1053673
[110,] 0.87279444 0.25441112 0.1272056
[111,] 0.84768956 0.30462088 0.1523104
[112,] 0.87797171 0.24405658 0.1220283
[113,] 0.85767166 0.28465669 0.1423283
[114,] 0.82194940 0.35610120 0.1780506
[115,] 0.85040073 0.29919855 0.1495993
[116,] 0.81277596 0.37444809 0.1872240
[117,] 0.77727498 0.44545004 0.2227250
[118,] 0.73284830 0.53430339 0.2671517
[119,] 0.69903198 0.60193604 0.3009680
[120,] 0.65962555 0.68074891 0.3403745
[121,] 0.60399781 0.79200438 0.3960022
[122,] 0.54261801 0.91476399 0.4573820
[123,] 0.54139697 0.91720606 0.4586030
[124,] 0.54150731 0.91698539 0.4584927
[125,] 0.49201842 0.98403684 0.5079816
[126,] 0.44429851 0.88859701 0.5557015
[127,] 0.59684311 0.80631377 0.4031569
[128,] 0.56022554 0.87954893 0.4397745
[129,] 0.48806040 0.97612079 0.5119396
[130,] 0.50046575 0.99906851 0.4995343
[131,] 0.42867736 0.85735471 0.5713226
[132,] 0.39239474 0.78478949 0.6076053
[133,] 0.35789912 0.71579824 0.6421009
[134,] 0.41756606 0.83513212 0.5824339
[135,] 0.57095049 0.85809902 0.4290495
[136,] 0.49766194 0.99532387 0.5023381
[137,] 0.41681495 0.83362990 0.5831851
[138,] 0.41193437 0.82386874 0.5880656
[139,] 0.36046092 0.72092185 0.6395391
[140,] 0.25107121 0.50214242 0.7489288
[141,] 0.45656742 0.91313484 0.5434326
[142,] 0.38552134 0.77104268 0.6144787
> postscript(file="/var/www/html/rcomp/tmp/11gwi1290509859.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2c8dl1290509859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3c8dl1290509859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4c8dl1290509859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x9x11290509860.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
0.9763823666 2.6036078927 5.7470136927 -1.2962579947 1.4613717147
6 7 8 9 10
-1.1227812852 2.4263973916 3.5431405418 -1.7937770437 -0.6696775852
11 12 13 14 15
-3.8153403514 -4.3938848920 -8.2200335337 -1.0860954232 3.5401774820
16 17 18 19 20
2.9251576179 0.9980567113 -2.5353293207 -2.0873202920 -1.0090508616
21 22 23 24 25
1.4311618221 -3.4545998179 -3.4295524471 -5.8367869290 -3.1800605325
26 27 28 29 30
0.5516474404 1.6719212671 -4.3532655212 0.1601700688 0.4307741263
31 32 33 34 35
-0.1649483335 2.7767830225 6.2997961151 6.8034404626 3.4169205823
36 37 38 39 40
4.5900504935 3.0532943958 -0.1062381096 1.9248364791 6.0569277951
41 42 43 44 45
-1.5286218564 1.5332905421 -5.5469105943 2.8944778297 4.3219606176
46 47 48 49 50
-0.3008968099 -3.4213727878 7.3389637701 -0.0045460490 3.3084486422
51 52 53 54 55
2.1545250387 -1.1588588067 -4.0575114630 -1.5269290284 3.2826097910
56 57 58 59 60
-1.3394958961 1.6874508155 2.2827475387 0.5058495286 -1.6462203516
61 62 63 64 65
1.7446404799 1.0796243914 0.6463798433 4.0149444162 0.4870938357
66 67 68 69 70
4.1791323236 -4.6010375215 5.5302378995 1.2640930548 2.6955964104
71 72 73 74 75
-4.9772106375 -0.5026773833 -0.4823128306 -1.3803773473 -0.6982731831
76 77 78 79 80
-2.2330812773 1.7545329313 -0.4516157407 -0.0320339448 0.9478235564
81 82 83 84 85
1.5769144201 -6.8630492301 -2.4391304477 1.0290867044 -3.3733623295
86 87 88 89 90
-3.1407040596 3.4761292782 2.1926157225 2.5638042116 -3.8970807780
91 92 93 94 95
-6.3435280898 -1.1915635510 -2.7459731473 -0.0165688176 -2.5510323826
96 97 98 99 100
3.3627551600 -3.6834795872 -3.3266944626 -3.2598174504 -3.2889468496
101 102 103 104 105
0.1604691858 -1.6492902960 -0.9576947617 -1.3255043731 -3.8525057494
106 107 108 109 110
-1.7741072251 -2.3006604066 -1.7496791060 -0.0682765809 -3.7664416322
111 112 113 114 115
-4.8510893512 -8.6429142941 2.5885512121 11.4379672881 9.7836753973
116 117 118 119 120
-0.9816944326 4.5046791857 1.5075697036 -1.2253187185 -3.8359413687
121 122 123 124 125
2.7857997819 0.1127776089 4.9052863630 -0.7722734892 0.8714456068
126 127 128 129 130
-1.6212243668 0.9295247123 1.2943260890 -1.9951676640 0.5178041485
131 132 133 134 135
3.4049069040 -4.2987397994 0.6234680488 -2.8107987511 -6.3793138773
136 137 138 139 140
1.9301456338 -0.0560620046 4.1333800962 -1.2168258710 3.4739826230
141 142 143 144 145
3.2659758087 4.1875940589 1.7843491256 1.0360535869 1.1072120377
146 147 148 149 150
4.0039327274 -0.3746950754 -0.0001409138 -7.1119616413 -2.6490195794
151 152 153 154 155
3.4250762718 0.3525167387 -2.6584752152 0.0174173182 1.9059838204
156 157 158 159
-1.7666852308 0.5598445773 0.1755629030 -6.7736200572
> postscript(file="/var/www/html/rcomp/tmp/6x9x11290509860.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 0.9763823666 NA
1 2.6036078927 0.9763823666
2 5.7470136927 2.6036078927
3 -1.2962579947 5.7470136927
4 1.4613717147 -1.2962579947
5 -1.1227812852 1.4613717147
6 2.4263973916 -1.1227812852
7 3.5431405418 2.4263973916
8 -1.7937770437 3.5431405418
9 -0.6696775852 -1.7937770437
10 -3.8153403514 -0.6696775852
11 -4.3938848920 -3.8153403514
12 -8.2200335337 -4.3938848920
13 -1.0860954232 -8.2200335337
14 3.5401774820 -1.0860954232
15 2.9251576179 3.5401774820
16 0.9980567113 2.9251576179
17 -2.5353293207 0.9980567113
18 -2.0873202920 -2.5353293207
19 -1.0090508616 -2.0873202920
20 1.4311618221 -1.0090508616
21 -3.4545998179 1.4311618221
22 -3.4295524471 -3.4545998179
23 -5.8367869290 -3.4295524471
24 -3.1800605325 -5.8367869290
25 0.5516474404 -3.1800605325
26 1.6719212671 0.5516474404
27 -4.3532655212 1.6719212671
28 0.1601700688 -4.3532655212
29 0.4307741263 0.1601700688
30 -0.1649483335 0.4307741263
31 2.7767830225 -0.1649483335
32 6.2997961151 2.7767830225
33 6.8034404626 6.2997961151
34 3.4169205823 6.8034404626
35 4.5900504935 3.4169205823
36 3.0532943958 4.5900504935
37 -0.1062381096 3.0532943958
38 1.9248364791 -0.1062381096
39 6.0569277951 1.9248364791
40 -1.5286218564 6.0569277951
41 1.5332905421 -1.5286218564
42 -5.5469105943 1.5332905421
43 2.8944778297 -5.5469105943
44 4.3219606176 2.8944778297
45 -0.3008968099 4.3219606176
46 -3.4213727878 -0.3008968099
47 7.3389637701 -3.4213727878
48 -0.0045460490 7.3389637701
49 3.3084486422 -0.0045460490
50 2.1545250387 3.3084486422
51 -1.1588588067 2.1545250387
52 -4.0575114630 -1.1588588067
53 -1.5269290284 -4.0575114630
54 3.2826097910 -1.5269290284
55 -1.3394958961 3.2826097910
56 1.6874508155 -1.3394958961
57 2.2827475387 1.6874508155
58 0.5058495286 2.2827475387
59 -1.6462203516 0.5058495286
60 1.7446404799 -1.6462203516
61 1.0796243914 1.7446404799
62 0.6463798433 1.0796243914
63 4.0149444162 0.6463798433
64 0.4870938357 4.0149444162
65 4.1791323236 0.4870938357
66 -4.6010375215 4.1791323236
67 5.5302378995 -4.6010375215
68 1.2640930548 5.5302378995
69 2.6955964104 1.2640930548
70 -4.9772106375 2.6955964104
71 -0.5026773833 -4.9772106375
72 -0.4823128306 -0.5026773833
73 -1.3803773473 -0.4823128306
74 -0.6982731831 -1.3803773473
75 -2.2330812773 -0.6982731831
76 1.7545329313 -2.2330812773
77 -0.4516157407 1.7545329313
78 -0.0320339448 -0.4516157407
79 0.9478235564 -0.0320339448
80 1.5769144201 0.9478235564
81 -6.8630492301 1.5769144201
82 -2.4391304477 -6.8630492301
83 1.0290867044 -2.4391304477
84 -3.3733623295 1.0290867044
85 -3.1407040596 -3.3733623295
86 3.4761292782 -3.1407040596
87 2.1926157225 3.4761292782
88 2.5638042116 2.1926157225
89 -3.8970807780 2.5638042116
90 -6.3435280898 -3.8970807780
91 -1.1915635510 -6.3435280898
92 -2.7459731473 -1.1915635510
93 -0.0165688176 -2.7459731473
94 -2.5510323826 -0.0165688176
95 3.3627551600 -2.5510323826
96 -3.6834795872 3.3627551600
97 -3.3266944626 -3.6834795872
98 -3.2598174504 -3.3266944626
99 -3.2889468496 -3.2598174504
100 0.1604691858 -3.2889468496
101 -1.6492902960 0.1604691858
102 -0.9576947617 -1.6492902960
103 -1.3255043731 -0.9576947617
104 -3.8525057494 -1.3255043731
105 -1.7741072251 -3.8525057494
106 -2.3006604066 -1.7741072251
107 -1.7496791060 -2.3006604066
108 -0.0682765809 -1.7496791060
109 -3.7664416322 -0.0682765809
110 -4.8510893512 -3.7664416322
111 -8.6429142941 -4.8510893512
112 2.5885512121 -8.6429142941
113 11.4379672881 2.5885512121
114 9.7836753973 11.4379672881
115 -0.9816944326 9.7836753973
116 4.5046791857 -0.9816944326
117 1.5075697036 4.5046791857
118 -1.2253187185 1.5075697036
119 -3.8359413687 -1.2253187185
120 2.7857997819 -3.8359413687
121 0.1127776089 2.7857997819
122 4.9052863630 0.1127776089
123 -0.7722734892 4.9052863630
124 0.8714456068 -0.7722734892
125 -1.6212243668 0.8714456068
126 0.9295247123 -1.6212243668
127 1.2943260890 0.9295247123
128 -1.9951676640 1.2943260890
129 0.5178041485 -1.9951676640
130 3.4049069040 0.5178041485
131 -4.2987397994 3.4049069040
132 0.6234680488 -4.2987397994
133 -2.8107987511 0.6234680488
134 -6.3793138773 -2.8107987511
135 1.9301456338 -6.3793138773
136 -0.0560620046 1.9301456338
137 4.1333800962 -0.0560620046
138 -1.2168258710 4.1333800962
139 3.4739826230 -1.2168258710
140 3.2659758087 3.4739826230
141 4.1875940589 3.2659758087
142 1.7843491256 4.1875940589
143 1.0360535869 1.7843491256
144 1.1072120377 1.0360535869
145 4.0039327274 1.1072120377
146 -0.3746950754 4.0039327274
147 -0.0001409138 -0.3746950754
148 -7.1119616413 -0.0001409138
149 -2.6490195794 -7.1119616413
150 3.4250762718 -2.6490195794
151 0.3525167387 3.4250762718
152 -2.6584752152 0.3525167387
153 0.0174173182 -2.6584752152
154 1.9059838204 0.0174173182
155 -1.7666852308 1.9059838204
156 0.5598445773 -1.7666852308
157 0.1755629030 0.5598445773
158 -6.7736200572 0.1755629030
159 NA -6.7736200572
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.6036078927 0.9763823666
[2,] 5.7470136927 2.6036078927
[3,] -1.2962579947 5.7470136927
[4,] 1.4613717147 -1.2962579947
[5,] -1.1227812852 1.4613717147
[6,] 2.4263973916 -1.1227812852
[7,] 3.5431405418 2.4263973916
[8,] -1.7937770437 3.5431405418
[9,] -0.6696775852 -1.7937770437
[10,] -3.8153403514 -0.6696775852
[11,] -4.3938848920 -3.8153403514
[12,] -8.2200335337 -4.3938848920
[13,] -1.0860954232 -8.2200335337
[14,] 3.5401774820 -1.0860954232
[15,] 2.9251576179 3.5401774820
[16,] 0.9980567113 2.9251576179
[17,] -2.5353293207 0.9980567113
[18,] -2.0873202920 -2.5353293207
[19,] -1.0090508616 -2.0873202920
[20,] 1.4311618221 -1.0090508616
[21,] -3.4545998179 1.4311618221
[22,] -3.4295524471 -3.4545998179
[23,] -5.8367869290 -3.4295524471
[24,] -3.1800605325 -5.8367869290
[25,] 0.5516474404 -3.1800605325
[26,] 1.6719212671 0.5516474404
[27,] -4.3532655212 1.6719212671
[28,] 0.1601700688 -4.3532655212
[29,] 0.4307741263 0.1601700688
[30,] -0.1649483335 0.4307741263
[31,] 2.7767830225 -0.1649483335
[32,] 6.2997961151 2.7767830225
[33,] 6.8034404626 6.2997961151
[34,] 3.4169205823 6.8034404626
[35,] 4.5900504935 3.4169205823
[36,] 3.0532943958 4.5900504935
[37,] -0.1062381096 3.0532943958
[38,] 1.9248364791 -0.1062381096
[39,] 6.0569277951 1.9248364791
[40,] -1.5286218564 6.0569277951
[41,] 1.5332905421 -1.5286218564
[42,] -5.5469105943 1.5332905421
[43,] 2.8944778297 -5.5469105943
[44,] 4.3219606176 2.8944778297
[45,] -0.3008968099 4.3219606176
[46,] -3.4213727878 -0.3008968099
[47,] 7.3389637701 -3.4213727878
[48,] -0.0045460490 7.3389637701
[49,] 3.3084486422 -0.0045460490
[50,] 2.1545250387 3.3084486422
[51,] -1.1588588067 2.1545250387
[52,] -4.0575114630 -1.1588588067
[53,] -1.5269290284 -4.0575114630
[54,] 3.2826097910 -1.5269290284
[55,] -1.3394958961 3.2826097910
[56,] 1.6874508155 -1.3394958961
[57,] 2.2827475387 1.6874508155
[58,] 0.5058495286 2.2827475387
[59,] -1.6462203516 0.5058495286
[60,] 1.7446404799 -1.6462203516
[61,] 1.0796243914 1.7446404799
[62,] 0.6463798433 1.0796243914
[63,] 4.0149444162 0.6463798433
[64,] 0.4870938357 4.0149444162
[65,] 4.1791323236 0.4870938357
[66,] -4.6010375215 4.1791323236
[67,] 5.5302378995 -4.6010375215
[68,] 1.2640930548 5.5302378995
[69,] 2.6955964104 1.2640930548
[70,] -4.9772106375 2.6955964104
[71,] -0.5026773833 -4.9772106375
[72,] -0.4823128306 -0.5026773833
[73,] -1.3803773473 -0.4823128306
[74,] -0.6982731831 -1.3803773473
[75,] -2.2330812773 -0.6982731831
[76,] 1.7545329313 -2.2330812773
[77,] -0.4516157407 1.7545329313
[78,] -0.0320339448 -0.4516157407
[79,] 0.9478235564 -0.0320339448
[80,] 1.5769144201 0.9478235564
[81,] -6.8630492301 1.5769144201
[82,] -2.4391304477 -6.8630492301
[83,] 1.0290867044 -2.4391304477
[84,] -3.3733623295 1.0290867044
[85,] -3.1407040596 -3.3733623295
[86,] 3.4761292782 -3.1407040596
[87,] 2.1926157225 3.4761292782
[88,] 2.5638042116 2.1926157225
[89,] -3.8970807780 2.5638042116
[90,] -6.3435280898 -3.8970807780
[91,] -1.1915635510 -6.3435280898
[92,] -2.7459731473 -1.1915635510
[93,] -0.0165688176 -2.7459731473
[94,] -2.5510323826 -0.0165688176
[95,] 3.3627551600 -2.5510323826
[96,] -3.6834795872 3.3627551600
[97,] -3.3266944626 -3.6834795872
[98,] -3.2598174504 -3.3266944626
[99,] -3.2889468496 -3.2598174504
[100,] 0.1604691858 -3.2889468496
[101,] -1.6492902960 0.1604691858
[102,] -0.9576947617 -1.6492902960
[103,] -1.3255043731 -0.9576947617
[104,] -3.8525057494 -1.3255043731
[105,] -1.7741072251 -3.8525057494
[106,] -2.3006604066 -1.7741072251
[107,] -1.7496791060 -2.3006604066
[108,] -0.0682765809 -1.7496791060
[109,] -3.7664416322 -0.0682765809
[110,] -4.8510893512 -3.7664416322
[111,] -8.6429142941 -4.8510893512
[112,] 2.5885512121 -8.6429142941
[113,] 11.4379672881 2.5885512121
[114,] 9.7836753973 11.4379672881
[115,] -0.9816944326 9.7836753973
[116,] 4.5046791857 -0.9816944326
[117,] 1.5075697036 4.5046791857
[118,] -1.2253187185 1.5075697036
[119,] -3.8359413687 -1.2253187185
[120,] 2.7857997819 -3.8359413687
[121,] 0.1127776089 2.7857997819
[122,] 4.9052863630 0.1127776089
[123,] -0.7722734892 4.9052863630
[124,] 0.8714456068 -0.7722734892
[125,] -1.6212243668 0.8714456068
[126,] 0.9295247123 -1.6212243668
[127,] 1.2943260890 0.9295247123
[128,] -1.9951676640 1.2943260890
[129,] 0.5178041485 -1.9951676640
[130,] 3.4049069040 0.5178041485
[131,] -4.2987397994 3.4049069040
[132,] 0.6234680488 -4.2987397994
[133,] -2.8107987511 0.6234680488
[134,] -6.3793138773 -2.8107987511
[135,] 1.9301456338 -6.3793138773
[136,] -0.0560620046 1.9301456338
[137,] 4.1333800962 -0.0560620046
[138,] -1.2168258710 4.1333800962
[139,] 3.4739826230 -1.2168258710
[140,] 3.2659758087 3.4739826230
[141,] 4.1875940589 3.2659758087
[142,] 1.7843491256 4.1875940589
[143,] 1.0360535869 1.7843491256
[144,] 1.1072120377 1.0360535869
[145,] 4.0039327274 1.1072120377
[146,] -0.3746950754 4.0039327274
[147,] -0.0001409138 -0.3746950754
[148,] -7.1119616413 -0.0001409138
[149,] -2.6490195794 -7.1119616413
[150,] 3.4250762718 -2.6490195794
[151,] 0.3525167387 3.4250762718
[152,] -2.6584752152 0.3525167387
[153,] 0.0174173182 -2.6584752152
[154,] 1.9059838204 0.0174173182
[155,] -1.7666852308 1.9059838204
[156,] 0.5598445773 -1.7666852308
[157,] 0.1755629030 0.5598445773
[158,] -6.7736200572 0.1755629030
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.6036078927 0.9763823666
2 5.7470136927 2.6036078927
3 -1.2962579947 5.7470136927
4 1.4613717147 -1.2962579947
5 -1.1227812852 1.4613717147
6 2.4263973916 -1.1227812852
7 3.5431405418 2.4263973916
8 -1.7937770437 3.5431405418
9 -0.6696775852 -1.7937770437
10 -3.8153403514 -0.6696775852
11 -4.3938848920 -3.8153403514
12 -8.2200335337 -4.3938848920
13 -1.0860954232 -8.2200335337
14 3.5401774820 -1.0860954232
15 2.9251576179 3.5401774820
16 0.9980567113 2.9251576179
17 -2.5353293207 0.9980567113
18 -2.0873202920 -2.5353293207
19 -1.0090508616 -2.0873202920
20 1.4311618221 -1.0090508616
21 -3.4545998179 1.4311618221
22 -3.4295524471 -3.4545998179
23 -5.8367869290 -3.4295524471
24 -3.1800605325 -5.8367869290
25 0.5516474404 -3.1800605325
26 1.6719212671 0.5516474404
27 -4.3532655212 1.6719212671
28 0.1601700688 -4.3532655212
29 0.4307741263 0.1601700688
30 -0.1649483335 0.4307741263
31 2.7767830225 -0.1649483335
32 6.2997961151 2.7767830225
33 6.8034404626 6.2997961151
34 3.4169205823 6.8034404626
35 4.5900504935 3.4169205823
36 3.0532943958 4.5900504935
37 -0.1062381096 3.0532943958
38 1.9248364791 -0.1062381096
39 6.0569277951 1.9248364791
40 -1.5286218564 6.0569277951
41 1.5332905421 -1.5286218564
42 -5.5469105943 1.5332905421
43 2.8944778297 -5.5469105943
44 4.3219606176 2.8944778297
45 -0.3008968099 4.3219606176
46 -3.4213727878 -0.3008968099
47 7.3389637701 -3.4213727878
48 -0.0045460490 7.3389637701
49 3.3084486422 -0.0045460490
50 2.1545250387 3.3084486422
51 -1.1588588067 2.1545250387
52 -4.0575114630 -1.1588588067
53 -1.5269290284 -4.0575114630
54 3.2826097910 -1.5269290284
55 -1.3394958961 3.2826097910
56 1.6874508155 -1.3394958961
57 2.2827475387 1.6874508155
58 0.5058495286 2.2827475387
59 -1.6462203516 0.5058495286
60 1.7446404799 -1.6462203516
61 1.0796243914 1.7446404799
62 0.6463798433 1.0796243914
63 4.0149444162 0.6463798433
64 0.4870938357 4.0149444162
65 4.1791323236 0.4870938357
66 -4.6010375215 4.1791323236
67 5.5302378995 -4.6010375215
68 1.2640930548 5.5302378995
69 2.6955964104 1.2640930548
70 -4.9772106375 2.6955964104
71 -0.5026773833 -4.9772106375
72 -0.4823128306 -0.5026773833
73 -1.3803773473 -0.4823128306
74 -0.6982731831 -1.3803773473
75 -2.2330812773 -0.6982731831
76 1.7545329313 -2.2330812773
77 -0.4516157407 1.7545329313
78 -0.0320339448 -0.4516157407
79 0.9478235564 -0.0320339448
80 1.5769144201 0.9478235564
81 -6.8630492301 1.5769144201
82 -2.4391304477 -6.8630492301
83 1.0290867044 -2.4391304477
84 -3.3733623295 1.0290867044
85 -3.1407040596 -3.3733623295
86 3.4761292782 -3.1407040596
87 2.1926157225 3.4761292782
88 2.5638042116 2.1926157225
89 -3.8970807780 2.5638042116
90 -6.3435280898 -3.8970807780
91 -1.1915635510 -6.3435280898
92 -2.7459731473 -1.1915635510
93 -0.0165688176 -2.7459731473
94 -2.5510323826 -0.0165688176
95 3.3627551600 -2.5510323826
96 -3.6834795872 3.3627551600
97 -3.3266944626 -3.6834795872
98 -3.2598174504 -3.3266944626
99 -3.2889468496 -3.2598174504
100 0.1604691858 -3.2889468496
101 -1.6492902960 0.1604691858
102 -0.9576947617 -1.6492902960
103 -1.3255043731 -0.9576947617
104 -3.8525057494 -1.3255043731
105 -1.7741072251 -3.8525057494
106 -2.3006604066 -1.7741072251
107 -1.7496791060 -2.3006604066
108 -0.0682765809 -1.7496791060
109 -3.7664416322 -0.0682765809
110 -4.8510893512 -3.7664416322
111 -8.6429142941 -4.8510893512
112 2.5885512121 -8.6429142941
113 11.4379672881 2.5885512121
114 9.7836753973 11.4379672881
115 -0.9816944326 9.7836753973
116 4.5046791857 -0.9816944326
117 1.5075697036 4.5046791857
118 -1.2253187185 1.5075697036
119 -3.8359413687 -1.2253187185
120 2.7857997819 -3.8359413687
121 0.1127776089 2.7857997819
122 4.9052863630 0.1127776089
123 -0.7722734892 4.9052863630
124 0.8714456068 -0.7722734892
125 -1.6212243668 0.8714456068
126 0.9295247123 -1.6212243668
127 1.2943260890 0.9295247123
128 -1.9951676640 1.2943260890
129 0.5178041485 -1.9951676640
130 3.4049069040 0.5178041485
131 -4.2987397994 3.4049069040
132 0.6234680488 -4.2987397994
133 -2.8107987511 0.6234680488
134 -6.3793138773 -2.8107987511
135 1.9301456338 -6.3793138773
136 -0.0560620046 1.9301456338
137 4.1333800962 -0.0560620046
138 -1.2168258710 4.1333800962
139 3.4739826230 -1.2168258710
140 3.2659758087 3.4739826230
141 4.1875940589 3.2659758087
142 1.7843491256 4.1875940589
143 1.0360535869 1.7843491256
144 1.1072120377 1.0360535869
145 4.0039327274 1.1072120377
146 -0.3746950754 4.0039327274
147 -0.0001409138 -0.3746950754
148 -7.1119616413 -0.0001409138
149 -2.6490195794 -7.1119616413
150 3.4250762718 -2.6490195794
151 0.3525167387 3.4250762718
152 -2.6584752152 0.3525167387
153 0.0174173182 -2.6584752152
154 1.9059838204 0.0174173182
155 -1.7666852308 1.9059838204
156 0.5598445773 -1.7666852308
157 0.1755629030 0.5598445773
158 -6.7736200572 0.1755629030
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/780el1290509860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/880el1290509860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jaeo1290509860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jaeo1290509860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/114suc1290509860.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12qati1290509860.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13wtqu1290509860.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14737x1290509860.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15bl631290509860.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16pvlb1290509860.tab")
+ }
>
> try(system("convert tmp/11gwi1290509859.ps tmp/11gwi1290509859.png",intern=TRUE))
character(0)
> try(system("convert tmp/2c8dl1290509859.ps tmp/2c8dl1290509859.png",intern=TRUE))
character(0)
> try(system("convert tmp/3c8dl1290509859.ps tmp/3c8dl1290509859.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c8dl1290509859.ps tmp/4c8dl1290509859.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x9x11290509860.ps tmp/5x9x11290509860.png",intern=TRUE))
character(0)
> try(system("convert tmp/6x9x11290509860.ps tmp/6x9x11290509860.png",intern=TRUE))
character(0)
> try(system("convert tmp/780el1290509860.ps tmp/780el1290509860.png",intern=TRUE))
character(0)
> try(system("convert tmp/880el1290509860.ps tmp/880el1290509860.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jaeo1290509860.ps tmp/9jaeo1290509860.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jaeo1290509860.ps tmp/10jaeo1290509860.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.022 1.765 10.433