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('Concern_over_Mistakes'
+ ,'Doubts_about_actions'
+ ,'Parental_Expectations'
+ ,'Parental_Criticism'
+ ,'Personal_Standards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('Concern_over_Mistakes','Doubts_about_actions','Parental_Expectations','Parental_Criticism','Personal_Standards','Organization'),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
Personal_Standards Concern_over_Mistakes Doubts_about_actions
1 24 24 14
2 25 25 11
3 30 17 6
4 19 18 12
5 22 18 8
6 22 16 10
7 25 20 10
8 23 16 11
9 17 18 16
10 21 17 11
11 19 23 13
12 19 30 12
13 15 23 8
14 16 18 12
15 23 15 11
16 27 12 4
17 22 21 9
18 14 15 8
19 22 20 8
20 23 31 14
21 23 27 15
22 21 34 16
23 19 21 9
24 18 31 14
25 20 19 11
26 23 16 8
27 25 20 9
28 19 21 9
29 24 22 9
30 22 17 9
31 25 24 10
32 26 25 16
33 29 26 11
34 32 25 8
35 25 17 9
36 29 32 16
37 28 33 11
38 17 13 16
39 28 32 12
40 29 25 12
41 26 29 14
42 25 22 9
43 14 18 10
44 25 17 9
45 26 20 10
46 20 15 12
47 18 20 14
48 32 33 14
49 25 29 10
50 25 23 14
51 23 26 16
52 21 18 9
53 20 20 10
54 15 11 6
55 30 28 8
56 24 26 13
57 26 22 10
58 24 17 8
59 22 12 7
60 14 14 15
61 24 17 9
62 24 21 10
63 24 19 12
64 24 18 13
65 19 10 10
66 31 29 11
67 22 31 8
68 27 19 9
69 19 9 13
70 25 20 11
71 20 28 8
72 21 19 9
73 27 30 9
74 23 29 15
75 25 26 9
76 20 23 10
77 21 13 14
78 22 21 12
79 23 19 12
80 25 28 11
81 25 23 14
82 17 18 6
83 19 21 12
84 25 20 8
85 19 23 14
86 20 21 11
87 26 21 10
88 23 15 14
89 27 28 12
90 17 19 10
91 17 26 14
92 19 10 5
93 17 16 11
94 22 22 10
95 21 19 9
96 32 31 10
97 21 31 16
98 21 29 13
99 18 19 9
100 18 22 10
101 23 23 10
102 19 15 7
103 20 20 9
104 21 18 8
105 20 23 14
106 17 25 14
107 18 21 8
108 19 24 9
109 22 25 14
110 15 17 14
111 14 13 8
112 18 28 8
113 24 21 8
114 35 25 7
115 29 9 6
116 21 16 8
117 25 19 6
118 20 17 11
119 22 25 14
120 13 20 11
121 26 29 11
122 17 14 11
123 25 22 14
124 20 15 8
125 19 19 20
126 21 20 11
127 22 15 8
128 24 20 11
129 21 18 10
130 26 33 14
131 24 22 11
132 16 16 9
133 23 17 9
134 18 16 8
135 16 21 10
136 26 26 13
137 19 18 13
138 21 18 12
139 21 17 8
140 22 22 13
141 23 30 14
142 29 30 12
143 21 24 14
144 21 21 15
145 23 21 13
146 27 29 16
147 25 31 9
148 21 20 9
149 10 16 9
150 20 22 8
151 26 20 7
152 24 28 16
153 29 38 11
154 19 22 9
155 24 20 11
156 19 17 9
157 24 28 14
158 22 22 13
159 17 31 16
Parental_Expectations Parental_Criticism Organization
1 11 12 26
2 7 8 23
3 17 8 25
4 10 8 23
5 12 9 19
6 12 7 29
7 11 4 25
8 11 11 21
9 12 7 22
10 13 7 25
11 14 12 24
12 16 10 18
13 11 10 22
14 10 8 15
15 11 8 22
16 15 4 28
17 9 9 20
18 11 8 12
19 17 7 24
20 17 11 20
21 11 9 21
22 18 11 20
23 14 13 21
24 10 8 23
25 11 8 28
26 15 9 24
27 15 6 24
28 13 9 24
29 16 9 23
30 13 6 23
31 9 6 29
32 18 16 24
33 18 5 18
34 12 7 25
35 17 9 21
36 9 6 26
37 9 6 22
38 12 5 22
39 18 12 22
40 12 7 23
41 18 10 30
42 14 9 23
43 15 8 17
44 16 5 23
45 10 8 23
46 11 8 25
47 14 10 24
48 9 6 24
49 12 8 23
50 17 7 21
51 5 4 24
52 12 8 24
53 12 8 28
54 6 4 16
55 24 20 20
56 12 8 29
57 12 8 27
58 14 6 22
59 7 4 28
60 13 8 16
61 12 9 25
62 13 6 24
63 14 7 28
64 8 9 24
65 11 5 23
66 9 5 30
67 11 8 24
68 13 8 21
69 10 6 25
70 11 8 25
71 12 7 22
72 9 7 23
73 15 9 26
74 18 11 23
75 15 6 25
76 12 8 21
77 13 6 25
78 14 9 24
79 10 8 29
80 13 6 22
81 13 10 27
82 11 8 26
83 13 8 22
84 16 10 24
85 8 5 27
86 16 7 24
87 11 5 24
88 9 8 29
89 16 14 22
90 12 7 21
91 14 8 24
92 8 6 24
93 9 5 23
94 15 6 20
95 11 10 27
96 21 12 26
97 14 9 25
98 18 12 21
99 12 7 21
100 13 8 19
101 15 10 21
102 12 6 21
103 19 10 16
104 15 10 22
105 11 10 29
106 11 5 15
107 10 7 17
108 13 10 15
109 15 11 21
110 12 6 21
111 12 7 19
112 16 12 24
113 9 11 20
114 18 11 17
115 8 11 23
116 13 5 24
117 17 8 14
118 9 6 19
119 15 9 24
120 8 4 13
121 7 4 22
122 12 7 16
123 14 11 19
124 6 6 25
125 8 7 25
126 17 8 23
127 10 4 24
128 11 8 26
129 14 9 26
130 11 8 25
131 13 11 18
132 12 8 21
133 11 5 26
134 9 4 23
135 12 8 23
136 20 10 22
137 12 6 20
138 13 9 13
139 12 9 24
140 12 13 15
141 9 9 14
142 15 10 22
143 24 20 10
144 7 5 24
145 17 11 22
146 11 6 24
147 17 9 19
148 11 7 20
149 12 9 13
150 14 10 20
151 11 9 22
152 16 8 24
153 21 7 29
154 14 6 12
155 20 13 20
156 13 6 21
157 11 8 24
158 15 10 22
159 19 16 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Concern_over_Mistakes Doubts_about_actions
7.46043 0.32815 -0.36274
Parental_Expectations Parental_Criticism Organization
0.18656 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 **
Concern_over_Mistakes 0.32815 0.05554 5.908 2.17e-08 ***
Doubts_about_actions -0.36274 0.10712 -3.386 0.00090 ***
Parental_Expectations 0.18656 0.10114 1.845 0.06703 .
Parental_Criticism 0.02338 0.12862 0.182 0.85597
Organization 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/10i931290523793.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2b9961290523793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b9961290523793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4b9961290523793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5418r1290523793.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/6418r1290523793.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/7fapc1290523793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8fapc1290523793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9pjow1290523793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10pjow1290523793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11s15k1290523793.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/12w23q1290523793.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/133l021290523793.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/14vc051290523793.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/154ynz1290523793.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/16dne11290523793.tab")
+ }
>
> try(system("convert tmp/10i931290523793.ps tmp/10i931290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/2b9961290523793.ps tmp/2b9961290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b9961290523793.ps tmp/3b9961290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b9961290523793.ps tmp/4b9961290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/5418r1290523793.ps tmp/5418r1290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/6418r1290523793.ps tmp/6418r1290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fapc1290523793.ps tmp/7fapc1290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fapc1290523793.ps tmp/8fapc1290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pjow1290523793.ps tmp/9pjow1290523793.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pjow1290523793.ps tmp/10pjow1290523793.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.048 1.761 10.803