R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(14
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+ ,9)
+ ,dim=c(9
+ ,145)
+ ,dimnames=list(c('Happines'
+ ,'Concern_over_Mistakes'
+ ,'Doubts_about_actions'
+ ,'Parental_Expectations'
+ ,'Parental_Criticism'
+ ,'Personal_Standards'
+ ,'Organization'
+ ,'Popularity'
+ ,'Depression')
+ ,1:145))
> y <- array(NA,dim=c(9,145),dimnames=list(c('Happines','Concern_over_Mistakes','Doubts_about_actions','Parental_Expectations','Parental_Criticism','Personal_Standards','Organization','Popularity','Depression'),1:145))
> 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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happines Concern_over_Mistakes Doubts_about_actions Parental_Expectations
1 14 26 9 15
2 18 20 9 15
3 11 21 9 14
4 12 31 14 10
5 16 21 8 10
6 18 18 8 12
7 14 26 11 18
8 14 22 10 12
9 15 22 9 14
10 15 29 15 18
11 17 15 14 9
12 19 16 11 11
13 10 24 14 11
14 18 17 6 17
15 14 19 20 8
16 14 22 9 16
17 17 31 10 21
18 14 28 8 24
19 16 38 11 21
20 18 26 14 14
21 14 25 11 7
22 12 25 16 18
23 17 29 14 18
24 9 28 11 13
25 16 15 11 11
26 14 18 12 13
27 11 21 9 13
28 16 25 7 18
29 13 23 13 14
30 17 23 10 12
31 15 19 9 9
32 14 18 9 12
33 16 18 13 8
34 9 26 16 5
35 15 18 12 10
36 17 18 6 11
37 13 28 14 11
38 15 17 14 12
39 16 29 10 12
40 16 12 4 15
41 12 28 12 16
42 11 20 14 14
43 15 17 9 17
44 17 17 9 13
45 13 20 10 10
46 16 31 14 17
47 14 21 10 12
48 11 19 9 13
49 12 23 14 13
50 12 15 8 11
51 15 24 9 13
52 16 28 8 12
53 15 16 9 12
54 12 19 9 12
55 12 21 9 9
56 8 21 15 7
57 13 20 8 17
58 11 16 10 12
59 14 25 8 12
60 15 30 14 9
61 10 29 11 9
62 11 22 10 13
63 12 19 12 10
64 15 33 14 11
65 15 17 9 12
66 14 9 13 10
67 16 14 15 13
68 15 15 8 6
69 15 12 7 7
70 13 21 10 13
71 17 20 10 11
72 13 29 13 18
73 15 33 11 9
74 13 21 8 9
75 15 15 12 11
76 16 19 9 11
77 15 23 10 15
78 16 20 11 8
79 15 20 11 11
80 14 18 10 14
81 15 31 16 14
82 7 18 16 12
83 17 13 8 12
84 13 9 6 8
85 15 20 11 11
86 14 18 12 10
87 13 23 14 17
88 16 17 9 16
89 12 17 11 13
90 14 16 8 15
91 17 31 8 11
92 15 15 7 12
93 17 28 16 16
94 12 26 13 20
95 16 20 8 16
96 11 19 11 11
97 15 25 14 15
98 9 18 10 15
99 16 20 10 12
100 10 33 14 9
101 10 24 14 24
102 15 22 10 15
103 11 32 12 18
104 13 31 9 17
105 14 13 16 12
106 18 18 8 15
107 16 17 9 11
108 14 29 16 11
109 14 22 13 15
110 14 18 13 12
111 14 22 8 14
112 12 25 14 11
113 14 20 11 20
114 15 20 9 11
115 15 17 8 12
116 13 26 13 12
117 17 10 10 11
118 17 15 8 10
119 19 20 7 11
120 15 14 11 12
121 13 16 11 9
122 9 23 14 8
123 15 11 6 6
124 15 19 10 12
125 16 30 9 15
126 11 21 12 13
127 14 20 11 17
128 11 22 14 14
129 15 30 12 16
130 13 25 14 15
131 16 23 14 11
132 14 23 8 11
133 15 21 11 16
134 16 30 12 15
135 16 22 9 14
136 11 32 16 9
137 13 22 11 13
138 16 15 11 11
139 12 21 12 14
140 9 27 15 11
141 13 22 13 12
142 13 9 6 8
143 14 29 11 7
144 19 20 7 11
145 13 16 8 13
Parental_Criticism Personal_Standards Organization Popularity Depression
1 6 25 25 11 12
2 6 25 24 12 11
3 13 19 21 15 14
4 8 18 23 10 12
5 7 18 17 12 21
6 9 22 19 11 12
7 5 29 18 5 22
8 8 26 27 16 11
9 9 25 23 11 10
10 11 23 23 15 13
11 8 23 29 12 10
12 11 23 21 9 8
13 12 24 26 11 15
14 8 30 25 15 10
15 7 19 25 12 14
16 9 24 23 16 14
17 12 32 26 14 11
18 20 30 20 11 10
19 7 29 29 10 13
20 8 17 24 7 7
21 8 25 23 11 12
22 16 26 24 10 14
23 10 26 30 11 11
24 6 25 22 16 9
25 8 23 22 14 11
26 9 21 13 12 15
27 9 19 24 12 13
28 11 35 17 11 9
29 12 19 24 6 15
30 8 20 21 14 10
31 7 21 23 9 11
32 8 21 24 15 13
33 9 24 24 12 8
34 4 23 24 12 20
35 8 19 23 9 12
36 8 17 26 13 10
37 8 24 24 15 10
38 6 15 21 11 9
39 8 25 23 10 14
40 4 27 28 13 8
41 14 27 22 16 11
42 10 18 24 13 13
43 9 25 21 14 11
44 6 22 23 14 15
45 8 26 23 16 11
46 11 23 20 9 10
47 8 16 23 8 14
48 8 27 21 8 18
49 10 25 27 12 14
50 8 14 12 10 11
51 10 19 15 16 12
52 7 20 22 13 13
53 8 16 21 11 9
54 7 18 21 14 10
55 9 22 20 15 15
56 5 21 24 8 20
57 7 22 24 9 12
58 7 22 29 17 12
59 7 32 25 9 14
60 9 23 14 13 13
61 5 31 30 6 11
62 8 18 19 13 17
63 8 23 29 8 12
64 8 26 25 12 13
65 9 24 25 13 14
66 6 19 25 14 13
67 8 14 16 11 15
68 6 20 25 15 13
69 4 22 28 7 10
70 6 24 24 16 11
71 4 25 25 16 13
72 12 21 21 14 17
73 6 28 22 11 13
74 11 24 20 13 9
75 8 20 25 13 11
76 10 21 27 7 10
77 10 23 21 15 9
78 4 13 13 11 12
79 8 24 26 15 12
80 9 21 26 13 13
81 9 21 25 11 13
82 7 17 22 12 22
83 7 14 19 10 13
84 11 29 23 12 15
85 8 25 25 12 13
86 8 16 15 12 15
87 7 25 21 14 10
88 5 25 23 6 11
89 7 21 25 14 16
90 9 23 24 15 11
91 8 22 24 8 11
92 6 19 21 12 10
93 8 24 24 10 10
94 10 26 22 15 16
95 10 25 24 11 12
96 8 20 28 9 11
97 11 22 21 14 16
98 8 14 17 10 19
99 8 20 28 16 11
100 6 32 24 5 15
101 20 21 10 8 24
102 6 22 20 13 14
103 12 28 22 16 15
104 9 25 19 16 11
105 5 17 22 14 15
106 10 21 22 14 12
107 5 23 26 10 10
108 6 27 24 9 14
109 10 22 22 14 13
110 6 19 20 8 9
111 10 20 20 8 15
112 5 17 15 16 15
113 13 24 20 12 14
114 7 21 20 9 11
115 9 21 24 15 8
116 8 24 29 12 11
117 5 19 23 14 8
118 4 22 24 12 10
119 9 26 22 16 11
120 7 17 16 12 13
121 5 17 23 14 11
122 5 19 27 8 20
123 4 15 16 15 10
124 7 17 21 16 12
125 9 27 26 12 14
126 8 19 22 4 23
127 8 21 23 8 14
128 11 25 19 11 16
129 10 19 18 4 11
130 9 22 24 14 12
131 10 20 29 14 14
132 10 15 22 13 12
133 7 20 24 14 12
134 10 29 22 7 11
135 6 19 12 19 12
136 6 29 26 12 13
137 11 24 18 10 17
138 8 23 22 14 9
139 9 22 24 16 12
140 9 23 21 11 19
141 13 22 15 16 18
142 11 29 23 12 15
143 4 26 22 12 14
144 9 26 22 16 11
145 5 21 24 12 9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Concern_over_Mistakes Doubts_about_actions
21.734743 -0.018001 -0.147557
Parental_Expectations Parental_Criticism Personal_Standards
0.099226 -0.080226 0.008969
Organization Popularity Depression
-0.057369 -0.028245 -0.376606
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.5369 -1.2957 0.1202 1.2892 4.4545
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.734743 1.877910 11.574 < 2e-16 ***
Concern_over_Mistakes -0.018001 0.037028 -0.486 0.6276
Doubts_about_actions -0.147557 0.071178 -2.073 0.0401 *
Parental_Expectations 0.099226 0.060301 1.646 0.1022
Parental_Criticism -0.080226 0.076655 -1.047 0.2971
Personal_Standards 0.008969 0.049532 0.181 0.8566
Organization -0.057369 0.050067 -1.146 0.2539
Popularity -0.028245 0.056806 -0.497 0.6198
Depression -0.376606 0.057887 -6.506 1.37e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.969 on 136 degrees of freedom
Multiple R-squared: 0.351, Adjusted R-squared: 0.3129
F-statistic: 9.195 on 8 and 136 DF, p-value: 4.57e-10
> 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,] 0.1391683 0.27833657 0.86083171
[2,] 0.1641987 0.32839744 0.83580128
[3,] 0.1033451 0.20669015 0.89665493
[4,] 0.4730510 0.94610193 0.52694904
[5,] 0.3779140 0.75582809 0.62208596
[6,] 0.6744528 0.65109442 0.32554721
[7,] 0.5868321 0.82633575 0.41316788
[8,] 0.7443704 0.51125915 0.25562957
[9,] 0.6826097 0.63478067 0.31739034
[10,] 0.6005810 0.79883796 0.39941898
[11,] 0.5188995 0.96220096 0.48110048
[12,] 0.5863179 0.82736416 0.41368208
[13,] 0.9300279 0.13994423 0.06997211
[14,] 0.9048652 0.19026959 0.09513479
[15,] 0.8717288 0.25654247 0.12827124
[16,] 0.9607281 0.07854381 0.03927191
[17,] 0.9441820 0.11163603 0.05581801
[18,] 0.9382782 0.12344361 0.06172181
[19,] 0.9439159 0.11216820 0.05608410
[20,] 0.9276462 0.14470764 0.07235382
[21,] 0.9059943 0.18801139 0.09400570
[22,] 0.8813613 0.23727731 0.11863865
[23,] 0.8552126 0.28957483 0.14478742
[24,] 0.8257140 0.34857207 0.17428604
[25,] 0.7954126 0.40917485 0.20458743
[26,] 0.7571090 0.48578208 0.24289104
[27,] 0.7598937 0.48021269 0.24010634
[28,] 0.7902654 0.41946921 0.20973461
[29,] 0.8522047 0.29559052 0.14779526
[30,] 0.8407825 0.31843491 0.15921745
[31,] 0.8685078 0.26298435 0.13149218
[32,] 0.8413140 0.31737191 0.15868595
[33,] 0.8756396 0.24872073 0.12436037
[34,] 0.8584887 0.28302256 0.14151128
[35,] 0.8355376 0.32892486 0.16446243
[36,] 0.8091500 0.38170002 0.19085001
[37,] 0.8485406 0.30291872 0.15145936
[38,] 0.8243609 0.35127821 0.17563911
[39,] 0.8970460 0.20590807 0.10295404
[40,] 0.8887760 0.22244795 0.11122398
[41,] 0.8860057 0.22798856 0.11399428
[42,] 0.8674906 0.26501881 0.13250940
[43,] 0.9136938 0.17261236 0.08630618
[44,] 0.8989971 0.20200573 0.10100286
[45,] 0.9265088 0.14698233 0.07349117
[46,] 0.9393811 0.12123775 0.06061888
[47,] 0.9568493 0.08630134 0.04315067
[48,] 0.9455913 0.10881733 0.05440867
[49,] 0.9404504 0.11909915 0.05954958
[50,] 0.9786657 0.04266860 0.02133430
[51,] 0.9775963 0.04480733 0.02240367
[52,] 0.9762052 0.04758953 0.02379477
[53,] 0.9754177 0.04916467 0.02458234
[54,] 0.9713552 0.05728957 0.02864478
[55,] 0.9623469 0.07530622 0.03765311
[56,] 0.9709096 0.05818080 0.02909040
[57,] 0.9664486 0.06710289 0.03355144
[58,] 0.9563446 0.08731077 0.04365538
[59,] 0.9563532 0.08729364 0.04364682
[60,] 0.9684654 0.06306914 0.03153457
[61,] 0.9590885 0.08182296 0.04091148
[62,] 0.9525748 0.09485048 0.04742524
[63,] 0.9666793 0.06664139 0.03332070
[64,] 0.9568381 0.08632388 0.04316194
[65,] 0.9471719 0.10565612 0.05282806
[66,] 0.9375536 0.12489272 0.06244636
[67,] 0.9274978 0.14500432 0.07250216
[68,] 0.9129332 0.17413367 0.08706684
[69,] 0.8910905 0.21781906 0.10890953
[70,] 0.8874368 0.22512642 0.11256321
[71,] 0.9094063 0.18118736 0.09059368
[72,] 0.9159942 0.16801151 0.08400575
[73,] 0.9033509 0.19329819 0.09664909
[74,] 0.8874039 0.22519220 0.11259610
[75,] 0.8660730 0.26785392 0.13392696
[76,] 0.8729610 0.25407798 0.12703899
[77,] 0.8495789 0.30084227 0.15042113
[78,] 0.8218535 0.35629309 0.17814654
[79,] 0.8107495 0.37850100 0.18925050
[80,] 0.8172391 0.36552175 0.18276087
[81,] 0.7887776 0.42244477 0.21122239
[82,] 0.8259613 0.34807736 0.17403868
[83,] 0.8036787 0.39264262 0.19632131
[84,] 0.7697933 0.46041349 0.23020675
[85,] 0.8495451 0.30090986 0.15045493
[86,] 0.8726298 0.25474031 0.12737016
[87,] 0.9220293 0.15594149 0.07797075
[88,] 0.9081726 0.18365486 0.09182743
[89,] 0.9160470 0.16790598 0.08395299
[90,] 0.8919707 0.21605869 0.10802934
[91,] 0.8677784 0.26444314 0.13222157
[92,] 0.8900106 0.21997872 0.10998936
[93,] 0.9314889 0.13702221 0.06851111
[94,] 0.9465898 0.10682031 0.05341016
[95,] 0.9570837 0.08583254 0.04291627
[96,] 0.9429424 0.11411518 0.05705759
[97,] 0.9447002 0.11059967 0.05529984
[98,] 0.9247903 0.15041934 0.07520967
[99,] 0.9045159 0.19096829 0.09548415
[100,] 0.8773641 0.24527182 0.12263591
[101,] 0.8431261 0.31374784 0.15687392
[102,] 0.8080611 0.38387774 0.19193887
[103,] 0.7578643 0.48427138 0.24213569
[104,] 0.7578844 0.48423130 0.24211565
[105,] 0.7203211 0.55935785 0.27967892
[106,] 0.7163171 0.56736587 0.28368294
[107,] 0.7176003 0.56479948 0.28239974
[108,] 0.7681034 0.46379322 0.23189661
[109,] 0.7782044 0.44359111 0.22179555
[110,] 0.7187491 0.56250189 0.28125094
[111,] 0.6519458 0.69610850 0.34805425
[112,] 0.5741083 0.85178333 0.42589167
[113,] 0.4951772 0.99035436 0.50482282
[114,] 0.4209434 0.84188683 0.57905659
[115,] 0.4259981 0.85199610 0.57400195
[116,] 0.4128635 0.82572702 0.58713649
[117,] 0.3311045 0.66220901 0.66889549
[118,] 0.2548457 0.50969147 0.74515427
[119,] 0.1940105 0.38802107 0.80598946
[120,] 0.5041538 0.99169241 0.49584620
[121,] 0.4771543 0.95430863 0.52284569
[122,] 0.8545100 0.29097994 0.14548997
> postscript(file="/var/www/rcomp/tmp/1z1oo1290533022.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2z1oo1290533022.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3z1oo1290533022.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/49tnr1290533022.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/59tnr1290533022.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 = 145
Frequency = 1
1 2 3 4 5 6
-0.90577072 2.58049092 -2.64444485 -1.50159999 4.45454696 3.02370968
7 8 9 10 11 12
2.17057024 -0.50170360 -0.50582210 1.52980810 2.91225057 3.23291140
13 14 15 16 17 18
-2.12957750 1.76646970 2.20141854 -0.04765966 1.92050747 -1.87209751
19 20 21 22 23 24
1.63219065 2.11006420 0.21086322 -0.72766742 2.81051140 -6.53690242
25 26 27 28 29 30
1.30264178 0.33752756 -3.15535489 -0.79388944 0.19606892 1.79280228
31 32 33 34 35 36
0.13184344 -0.12356231 0.94912595 -1.03940715 0.93205470 1.49730010
37 38 39 40 41 42
-1.26326166 -0.30192720 2.36414868 -1.35178956 -2.30994427 -2.41735999
43 44 45 46 47 48
-0.54690537 3.25738779 -1.56872967 1.04608905 0.24436632 -1.74539030
49 50 51 52 53 54
-0.80644328 -3.74597659 0.19892885 1.66641139 -0.90622916 -3.48904840
55 56 57 58 59 60
-1.17688696 -2.49023855 -2.36651432 -3.13447514 -0.05948412 1.56002702
61 62 63 64 65 66
-4.32631983 -1.81323080 -1.76985050 1.91125970 1.28924092 0.38971359
67 68 69 70 71 72
2.83460885 1.17611901 -0.48672458 -1.93355153 2.88805896 0.69629808
73 74 75 76 77 78
1.28830181 -2.49805258 0.62096636 0.97044888 -0.71969145 1.23466353
79 80 81 82 83 84
1.01800577 -0.03598370 1.96951668 -2.94503778 2.19336173 -0.55130519
85 86 87 88 89 90
1.24353984 0.71456114 -2.23817058 0.12019803 -0.86696122 -1.29572284
91 92 93 94 95 96
2.10223308 -1.00185121 2.39449813 -1.05244543 1.00297208 -3.39545679
97 98 99 100 101 102
2.60372705 -3.49407416 1.57347465 -2.60642227 -0.36398856 0.71954281
103 104 105 106 107 108
-2.09938834 -2.35313361 1.22475369 3.07206649 0.54274514 1.19944989
109 110 111 112 113 114
0.24949237 -1.60946011 0.09787814 -1.10020877 -0.14963558 -0.22071364
115 116 117 118 119 120
-1.09192271 -0.96733153 0.78782764 1.32890635 3.91223125 0.51150986
121 122 123 124 125 126
-1.61040079 -1.51097449 -0.95274407 0.47717753 2.22779946 0.63245042
127 128 129 130 131 132
-0.16705219 -1.57742901 -0.09150589 -0.89104038 3.60807934 -0.41545460
133 134 135 136 137 138
0.35254388 1.23224226 0.65542461 -1.74316515 0.37908791 0.54943056
139 140 141 142 143 144
-2.10244405 -1.94014666 1.32578330 -0.55130519 0.67708440 3.91223125
145
-3.23818147
> postscript(file="/var/www/rcomp/tmp/62k4u1290533022.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 = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.90577072 NA
1 2.58049092 -0.90577072
2 -2.64444485 2.58049092
3 -1.50159999 -2.64444485
4 4.45454696 -1.50159999
5 3.02370968 4.45454696
6 2.17057024 3.02370968
7 -0.50170360 2.17057024
8 -0.50582210 -0.50170360
9 1.52980810 -0.50582210
10 2.91225057 1.52980810
11 3.23291140 2.91225057
12 -2.12957750 3.23291140
13 1.76646970 -2.12957750
14 2.20141854 1.76646970
15 -0.04765966 2.20141854
16 1.92050747 -0.04765966
17 -1.87209751 1.92050747
18 1.63219065 -1.87209751
19 2.11006420 1.63219065
20 0.21086322 2.11006420
21 -0.72766742 0.21086322
22 2.81051140 -0.72766742
23 -6.53690242 2.81051140
24 1.30264178 -6.53690242
25 0.33752756 1.30264178
26 -3.15535489 0.33752756
27 -0.79388944 -3.15535489
28 0.19606892 -0.79388944
29 1.79280228 0.19606892
30 0.13184344 1.79280228
31 -0.12356231 0.13184344
32 0.94912595 -0.12356231
33 -1.03940715 0.94912595
34 0.93205470 -1.03940715
35 1.49730010 0.93205470
36 -1.26326166 1.49730010
37 -0.30192720 -1.26326166
38 2.36414868 -0.30192720
39 -1.35178956 2.36414868
40 -2.30994427 -1.35178956
41 -2.41735999 -2.30994427
42 -0.54690537 -2.41735999
43 3.25738779 -0.54690537
44 -1.56872967 3.25738779
45 1.04608905 -1.56872967
46 0.24436632 1.04608905
47 -1.74539030 0.24436632
48 -0.80644328 -1.74539030
49 -3.74597659 -0.80644328
50 0.19892885 -3.74597659
51 1.66641139 0.19892885
52 -0.90622916 1.66641139
53 -3.48904840 -0.90622916
54 -1.17688696 -3.48904840
55 -2.49023855 -1.17688696
56 -2.36651432 -2.49023855
57 -3.13447514 -2.36651432
58 -0.05948412 -3.13447514
59 1.56002702 -0.05948412
60 -4.32631983 1.56002702
61 -1.81323080 -4.32631983
62 -1.76985050 -1.81323080
63 1.91125970 -1.76985050
64 1.28924092 1.91125970
65 0.38971359 1.28924092
66 2.83460885 0.38971359
67 1.17611901 2.83460885
68 -0.48672458 1.17611901
69 -1.93355153 -0.48672458
70 2.88805896 -1.93355153
71 0.69629808 2.88805896
72 1.28830181 0.69629808
73 -2.49805258 1.28830181
74 0.62096636 -2.49805258
75 0.97044888 0.62096636
76 -0.71969145 0.97044888
77 1.23466353 -0.71969145
78 1.01800577 1.23466353
79 -0.03598370 1.01800577
80 1.96951668 -0.03598370
81 -2.94503778 1.96951668
82 2.19336173 -2.94503778
83 -0.55130519 2.19336173
84 1.24353984 -0.55130519
85 0.71456114 1.24353984
86 -2.23817058 0.71456114
87 0.12019803 -2.23817058
88 -0.86696122 0.12019803
89 -1.29572284 -0.86696122
90 2.10223308 -1.29572284
91 -1.00185121 2.10223308
92 2.39449813 -1.00185121
93 -1.05244543 2.39449813
94 1.00297208 -1.05244543
95 -3.39545679 1.00297208
96 2.60372705 -3.39545679
97 -3.49407416 2.60372705
98 1.57347465 -3.49407416
99 -2.60642227 1.57347465
100 -0.36398856 -2.60642227
101 0.71954281 -0.36398856
102 -2.09938834 0.71954281
103 -2.35313361 -2.09938834
104 1.22475369 -2.35313361
105 3.07206649 1.22475369
106 0.54274514 3.07206649
107 1.19944989 0.54274514
108 0.24949237 1.19944989
109 -1.60946011 0.24949237
110 0.09787814 -1.60946011
111 -1.10020877 0.09787814
112 -0.14963558 -1.10020877
113 -0.22071364 -0.14963558
114 -1.09192271 -0.22071364
115 -0.96733153 -1.09192271
116 0.78782764 -0.96733153
117 1.32890635 0.78782764
118 3.91223125 1.32890635
119 0.51150986 3.91223125
120 -1.61040079 0.51150986
121 -1.51097449 -1.61040079
122 -0.95274407 -1.51097449
123 0.47717753 -0.95274407
124 2.22779946 0.47717753
125 0.63245042 2.22779946
126 -0.16705219 0.63245042
127 -1.57742901 -0.16705219
128 -0.09150589 -1.57742901
129 -0.89104038 -0.09150589
130 3.60807934 -0.89104038
131 -0.41545460 3.60807934
132 0.35254388 -0.41545460
133 1.23224226 0.35254388
134 0.65542461 1.23224226
135 -1.74316515 0.65542461
136 0.37908791 -1.74316515
137 0.54943056 0.37908791
138 -2.10244405 0.54943056
139 -1.94014666 -2.10244405
140 1.32578330 -1.94014666
141 -0.55130519 1.32578330
142 0.67708440 -0.55130519
143 3.91223125 0.67708440
144 -3.23818147 3.91223125
145 NA -3.23818147
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.58049092 -0.90577072
[2,] -2.64444485 2.58049092
[3,] -1.50159999 -2.64444485
[4,] 4.45454696 -1.50159999
[5,] 3.02370968 4.45454696
[6,] 2.17057024 3.02370968
[7,] -0.50170360 2.17057024
[8,] -0.50582210 -0.50170360
[9,] 1.52980810 -0.50582210
[10,] 2.91225057 1.52980810
[11,] 3.23291140 2.91225057
[12,] -2.12957750 3.23291140
[13,] 1.76646970 -2.12957750
[14,] 2.20141854 1.76646970
[15,] -0.04765966 2.20141854
[16,] 1.92050747 -0.04765966
[17,] -1.87209751 1.92050747
[18,] 1.63219065 -1.87209751
[19,] 2.11006420 1.63219065
[20,] 0.21086322 2.11006420
[21,] -0.72766742 0.21086322
[22,] 2.81051140 -0.72766742
[23,] -6.53690242 2.81051140
[24,] 1.30264178 -6.53690242
[25,] 0.33752756 1.30264178
[26,] -3.15535489 0.33752756
[27,] -0.79388944 -3.15535489
[28,] 0.19606892 -0.79388944
[29,] 1.79280228 0.19606892
[30,] 0.13184344 1.79280228
[31,] -0.12356231 0.13184344
[32,] 0.94912595 -0.12356231
[33,] -1.03940715 0.94912595
[34,] 0.93205470 -1.03940715
[35,] 1.49730010 0.93205470
[36,] -1.26326166 1.49730010
[37,] -0.30192720 -1.26326166
[38,] 2.36414868 -0.30192720
[39,] -1.35178956 2.36414868
[40,] -2.30994427 -1.35178956
[41,] -2.41735999 -2.30994427
[42,] -0.54690537 -2.41735999
[43,] 3.25738779 -0.54690537
[44,] -1.56872967 3.25738779
[45,] 1.04608905 -1.56872967
[46,] 0.24436632 1.04608905
[47,] -1.74539030 0.24436632
[48,] -0.80644328 -1.74539030
[49,] -3.74597659 -0.80644328
[50,] 0.19892885 -3.74597659
[51,] 1.66641139 0.19892885
[52,] -0.90622916 1.66641139
[53,] -3.48904840 -0.90622916
[54,] -1.17688696 -3.48904840
[55,] -2.49023855 -1.17688696
[56,] -2.36651432 -2.49023855
[57,] -3.13447514 -2.36651432
[58,] -0.05948412 -3.13447514
[59,] 1.56002702 -0.05948412
[60,] -4.32631983 1.56002702
[61,] -1.81323080 -4.32631983
[62,] -1.76985050 -1.81323080
[63,] 1.91125970 -1.76985050
[64,] 1.28924092 1.91125970
[65,] 0.38971359 1.28924092
[66,] 2.83460885 0.38971359
[67,] 1.17611901 2.83460885
[68,] -0.48672458 1.17611901
[69,] -1.93355153 -0.48672458
[70,] 2.88805896 -1.93355153
[71,] 0.69629808 2.88805896
[72,] 1.28830181 0.69629808
[73,] -2.49805258 1.28830181
[74,] 0.62096636 -2.49805258
[75,] 0.97044888 0.62096636
[76,] -0.71969145 0.97044888
[77,] 1.23466353 -0.71969145
[78,] 1.01800577 1.23466353
[79,] -0.03598370 1.01800577
[80,] 1.96951668 -0.03598370
[81,] -2.94503778 1.96951668
[82,] 2.19336173 -2.94503778
[83,] -0.55130519 2.19336173
[84,] 1.24353984 -0.55130519
[85,] 0.71456114 1.24353984
[86,] -2.23817058 0.71456114
[87,] 0.12019803 -2.23817058
[88,] -0.86696122 0.12019803
[89,] -1.29572284 -0.86696122
[90,] 2.10223308 -1.29572284
[91,] -1.00185121 2.10223308
[92,] 2.39449813 -1.00185121
[93,] -1.05244543 2.39449813
[94,] 1.00297208 -1.05244543
[95,] -3.39545679 1.00297208
[96,] 2.60372705 -3.39545679
[97,] -3.49407416 2.60372705
[98,] 1.57347465 -3.49407416
[99,] -2.60642227 1.57347465
[100,] -0.36398856 -2.60642227
[101,] 0.71954281 -0.36398856
[102,] -2.09938834 0.71954281
[103,] -2.35313361 -2.09938834
[104,] 1.22475369 -2.35313361
[105,] 3.07206649 1.22475369
[106,] 0.54274514 3.07206649
[107,] 1.19944989 0.54274514
[108,] 0.24949237 1.19944989
[109,] -1.60946011 0.24949237
[110,] 0.09787814 -1.60946011
[111,] -1.10020877 0.09787814
[112,] -0.14963558 -1.10020877
[113,] -0.22071364 -0.14963558
[114,] -1.09192271 -0.22071364
[115,] -0.96733153 -1.09192271
[116,] 0.78782764 -0.96733153
[117,] 1.32890635 0.78782764
[118,] 3.91223125 1.32890635
[119,] 0.51150986 3.91223125
[120,] -1.61040079 0.51150986
[121,] -1.51097449 -1.61040079
[122,] -0.95274407 -1.51097449
[123,] 0.47717753 -0.95274407
[124,] 2.22779946 0.47717753
[125,] 0.63245042 2.22779946
[126,] -0.16705219 0.63245042
[127,] -1.57742901 -0.16705219
[128,] -0.09150589 -1.57742901
[129,] -0.89104038 -0.09150589
[130,] 3.60807934 -0.89104038
[131,] -0.41545460 3.60807934
[132,] 0.35254388 -0.41545460
[133,] 1.23224226 0.35254388
[134,] 0.65542461 1.23224226
[135,] -1.74316515 0.65542461
[136,] 0.37908791 -1.74316515
[137,] 0.54943056 0.37908791
[138,] -2.10244405 0.54943056
[139,] -1.94014666 -2.10244405
[140,] 1.32578330 -1.94014666
[141,] -0.55130519 1.32578330
[142,] 0.67708440 -0.55130519
[143,] 3.91223125 0.67708440
[144,] -3.23818147 3.91223125
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.58049092 -0.90577072
2 -2.64444485 2.58049092
3 -1.50159999 -2.64444485
4 4.45454696 -1.50159999
5 3.02370968 4.45454696
6 2.17057024 3.02370968
7 -0.50170360 2.17057024
8 -0.50582210 -0.50170360
9 1.52980810 -0.50582210
10 2.91225057 1.52980810
11 3.23291140 2.91225057
12 -2.12957750 3.23291140
13 1.76646970 -2.12957750
14 2.20141854 1.76646970
15 -0.04765966 2.20141854
16 1.92050747 -0.04765966
17 -1.87209751 1.92050747
18 1.63219065 -1.87209751
19 2.11006420 1.63219065
20 0.21086322 2.11006420
21 -0.72766742 0.21086322
22 2.81051140 -0.72766742
23 -6.53690242 2.81051140
24 1.30264178 -6.53690242
25 0.33752756 1.30264178
26 -3.15535489 0.33752756
27 -0.79388944 -3.15535489
28 0.19606892 -0.79388944
29 1.79280228 0.19606892
30 0.13184344 1.79280228
31 -0.12356231 0.13184344
32 0.94912595 -0.12356231
33 -1.03940715 0.94912595
34 0.93205470 -1.03940715
35 1.49730010 0.93205470
36 -1.26326166 1.49730010
37 -0.30192720 -1.26326166
38 2.36414868 -0.30192720
39 -1.35178956 2.36414868
40 -2.30994427 -1.35178956
41 -2.41735999 -2.30994427
42 -0.54690537 -2.41735999
43 3.25738779 -0.54690537
44 -1.56872967 3.25738779
45 1.04608905 -1.56872967
46 0.24436632 1.04608905
47 -1.74539030 0.24436632
48 -0.80644328 -1.74539030
49 -3.74597659 -0.80644328
50 0.19892885 -3.74597659
51 1.66641139 0.19892885
52 -0.90622916 1.66641139
53 -3.48904840 -0.90622916
54 -1.17688696 -3.48904840
55 -2.49023855 -1.17688696
56 -2.36651432 -2.49023855
57 -3.13447514 -2.36651432
58 -0.05948412 -3.13447514
59 1.56002702 -0.05948412
60 -4.32631983 1.56002702
61 -1.81323080 -4.32631983
62 -1.76985050 -1.81323080
63 1.91125970 -1.76985050
64 1.28924092 1.91125970
65 0.38971359 1.28924092
66 2.83460885 0.38971359
67 1.17611901 2.83460885
68 -0.48672458 1.17611901
69 -1.93355153 -0.48672458
70 2.88805896 -1.93355153
71 0.69629808 2.88805896
72 1.28830181 0.69629808
73 -2.49805258 1.28830181
74 0.62096636 -2.49805258
75 0.97044888 0.62096636
76 -0.71969145 0.97044888
77 1.23466353 -0.71969145
78 1.01800577 1.23466353
79 -0.03598370 1.01800577
80 1.96951668 -0.03598370
81 -2.94503778 1.96951668
82 2.19336173 -2.94503778
83 -0.55130519 2.19336173
84 1.24353984 -0.55130519
85 0.71456114 1.24353984
86 -2.23817058 0.71456114
87 0.12019803 -2.23817058
88 -0.86696122 0.12019803
89 -1.29572284 -0.86696122
90 2.10223308 -1.29572284
91 -1.00185121 2.10223308
92 2.39449813 -1.00185121
93 -1.05244543 2.39449813
94 1.00297208 -1.05244543
95 -3.39545679 1.00297208
96 2.60372705 -3.39545679
97 -3.49407416 2.60372705
98 1.57347465 -3.49407416
99 -2.60642227 1.57347465
100 -0.36398856 -2.60642227
101 0.71954281 -0.36398856
102 -2.09938834 0.71954281
103 -2.35313361 -2.09938834
104 1.22475369 -2.35313361
105 3.07206649 1.22475369
106 0.54274514 3.07206649
107 1.19944989 0.54274514
108 0.24949237 1.19944989
109 -1.60946011 0.24949237
110 0.09787814 -1.60946011
111 -1.10020877 0.09787814
112 -0.14963558 -1.10020877
113 -0.22071364 -0.14963558
114 -1.09192271 -0.22071364
115 -0.96733153 -1.09192271
116 0.78782764 -0.96733153
117 1.32890635 0.78782764
118 3.91223125 1.32890635
119 0.51150986 3.91223125
120 -1.61040079 0.51150986
121 -1.51097449 -1.61040079
122 -0.95274407 -1.51097449
123 0.47717753 -0.95274407
124 2.22779946 0.47717753
125 0.63245042 2.22779946
126 -0.16705219 0.63245042
127 -1.57742901 -0.16705219
128 -0.09150589 -1.57742901
129 -0.89104038 -0.09150589
130 3.60807934 -0.89104038
131 -0.41545460 3.60807934
132 0.35254388 -0.41545460
133 1.23224226 0.35254388
134 0.65542461 1.23224226
135 -1.74316515 0.65542461
136 0.37908791 -1.74316515
137 0.54943056 0.37908791
138 -2.10244405 0.54943056
139 -1.94014666 -2.10244405
140 1.32578330 -1.94014666
141 -0.55130519 1.32578330
142 0.67708440 -0.55130519
143 3.91223125 0.67708440
144 -3.23818147 3.91223125
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7db4x1290533022.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8db4x1290533022.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9db4x1290533022.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10n2301290533022.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/111u1r1290533022.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12cl0u1290533022.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/131nfo1290533022.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14mndt1290533022.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15ifbk1290533022.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/164xsq1290533022.tab")
+ }
>
> try(system("convert tmp/1z1oo1290533022.ps tmp/1z1oo1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z1oo1290533022.ps tmp/2z1oo1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z1oo1290533022.ps tmp/3z1oo1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/49tnr1290533022.ps tmp/49tnr1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/59tnr1290533022.ps tmp/59tnr1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/62k4u1290533022.ps tmp/62k4u1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/7db4x1290533022.ps tmp/7db4x1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/8db4x1290533022.ps tmp/8db4x1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/9db4x1290533022.ps tmp/9db4x1290533022.png",intern=TRUE))
character(0)
> try(system("convert tmp/10n2301290533022.ps tmp/10n2301290533022.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.680 2.010 7.672