R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(12
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+ ,10)
+ ,dim=c(8
+ ,145)
+ ,dimnames=list(c('Depressie'
+ ,'Leeftijd'
+ ,'Sportgerelateerde_groep'
+ ,'Stress'
+ ,'Veranderingen_verleden'
+ ,'Alcoholgebruik'
+ ,'Depressie_mannen'
+ ,'Depressie_oktober')
+ ,1:145))
> y <- array(NA,dim=c(8,145),dimnames=list(c('Depressie','Leeftijd','Sportgerelateerde_groep','Stress','Veranderingen_verleden','Alcoholgebruik','Depressie_mannen','Depressie_oktober'),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 = '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
Depressie Leeftijd Sportgerelateerde_groep Stress Veranderingen_verleden
1 12 2 53 10 7
2 11 2 86 12 5
3 14 4 66 11 7
4 12 3 67 10 3
5 21 4 76 12 7
6 12 3 78 12 7
7 22 3 53 14 7
8 11 4 80 14 1
9 10 3 74 11 4
10 13 4 76 11 5
11 10 3 79 13 6
12 8 2 54 11 4
13 15 3 67 10 7
14 10 3 87 14 6
15 14 3 58 14 2
16 14 2 75 12 2
17 11 3 88 11 6
18 10 2 64 10 7
19 13 4 57 12 5
20 7 5 66 10 2
21 12 3 54 14 7
22 14 3 56 12 4
23 11 1 86 13 5
24 9 4 80 13 5
25 11 3 76 12 5
26 15 4 69 14 3
27 13 3 67 11 5
28 9 3 80 12 1
29 15 1 54 13 1
30 10 4 71 11 3
31 11 4 84 11 2
32 13 2 74 14 3
33 8 2 71 12 2
34 20 1 63 13 5
35 12 3 71 11 2
36 10 4 76 13 3
37 10 1 69 13 4
38 9 3 74 13 6
39 14 3 75 12 2
40 8 2 54 14 7
41 14 4 52 14 6
42 11 3 69 8 5
43 13 3 68 13 3
44 11 2 75 11 3
45 11 3 75 13 4
46 10 2 72 10 5
47 14 1 67 10 2
48 18 3 63 13 7
49 14 3 62 12 6
50 11 5 63 16 5
51 12 1 76 13 6
52 13 3 74 12 5
53 9 4 67 11 2
54 10 3 73 12 3
55 15 4 70 12 5
56 20 2 53 14 7
57 12 3 77 13 4
58 12 4 77 13 7
59 14 1 52 12 5
60 13 1 54 13 6
61 11 1 80 12 6
62 17 4 66 13 3
63 12 2 73 14 5
64 13 3 63 13 7
65 14 4 69 13 7
66 13 2 67 12 5
67 15 5 54 10 6
68 13 3 81 13 5
69 10 3 69 11 5
70 11 3 84 11 2
71 13 4 70 13 5
72 17 4 69 11 4
73 13 3 77 15 6
74 9 1 54 13 5
75 11 3 79 13 3
76 10 1 30 12 3
77 9 3 71 11 4
78 12 5 73 12 2
79 12 3 72 13 2
80 13 3 77 12 5
81 13 4 75 13 4
82 22 5 70 15 6
83 13 4 73 13 4
84 15 4 54 11 6
85 13 4 77 11 4
86 15 4 82 14 2
87 10 4 80 15 5
88 11 3 80 12 2
89 16 4 69 10 7
90 11 3 78 12 1
91 11 3 81 11 3
92 10 3 76 11 5
93 10 4 76 11 6
94 16 3 73 14 6
95 12 4 85 14 2
96 11 2 66 13 5
97 16 5 79 13 5
98 19 3 68 13 3
99 11 4 76 12 6
100 15 2 54 12 5
101 24 4 46 16 7
102 14 3 82 13 1
103 15 4 74 15 6
104 11 3 88 11 4
105 15 1 38 14 7
106 12 4 76 14 2
107 10 4 86 10 6
108 14 2 54 12 7
109 9 5 69 12 5
110 15 4 90 14 2
111 15 4 54 10 1
112 14 3 76 10 3
113 11 4 89 13 3
114 8 4 76 13 3
115 11 4 79 11 5
116 8 3 90 11 2
117 10 5 74 13 4
118 11 3 81 13 6
119 13 4 72 13 5
120 11 4 71 13 5
121 20 4 66 13 2
122 10 4 77 13 3
123 12 4 74 13 2
124 14 5 82 14 6
125 23 3 54 13 5
126 14 1 63 14 4
127 16 4 54 11 6
128 11 4 64 13 4
129 12 3 69 11 6
130 10 4 54 11 2
131 14 4 84 16 0
132 12 4 86 8 1
133 12 4 77 11 5
134 11 4 89 14 2
135 12 4 76 12 5
136 13 3 60 13 6
137 17 5 79 13 7
138 9 3 71 14 5
139 12 4 72 14 5
140 19 4 69 11 5
141 15 4 54 11 6
142 14 4 69 14 6
143 11 3 81 13 6
144 9 4 84 15 1
145 18 4 84 14 3
Alcoholgebruik Depressie_mannen Depressie_oktober t
1 6 0 9 1
2 6 0 9 2
3 11 0 9 3
4 7 1 9 4
5 12 0 9 5
6 8 0 9 6
7 7 0 9 7
8 11 0 9 8
9 8 0 9 9
10 9 0 9 10
11 9 1 9 11
12 6 0 9 12
13 9 1 9 13
14 5 0 9 14
15 9 1 9 15
16 4 1 9 16
17 9 0 9 17
18 6 1 9 18
19 8 0 9 19
20 12 1 9 20
21 7 0 9 21
22 8 0 9 22
23 3 1 9 23
24 9 0 9 24
25 7 1 9 25
26 9 0 9 26
27 9 1 9 27
28 7 0 9 28
29 5 1 9 29
30 8 0 9 30
31 7 0 9 31
32 6 1 9 32
33 6 1 9 33
34 4 1 9 34
35 8 1 9 35
36 8 0 9 36
37 3 1 9 37
38 8 1 9 38
39 9 0 9 39
40 6 1 9 40
41 9 1 9 41
42 5 0 9 42
43 8 0 9 43
44 6 0 9 44
45 9 1 9 45
46 8 0 9 46
47 5 1 9 47
48 9 1 9 48
49 8 0 9 49
50 11 1 9 50
51 7 0 9 51
52 9 0 9 52
53 11 0 9 53
54 9 1 9 54
55 10 0 9 55
56 6 1 9 56
57 9 1 9 57
58 9 0 9 58
59 3 0 9 59
60 3 0 9 60
61 3 1 10 61
62 12 0 10 62
63 8 1 10 63
64 9 0 10 64
65 10 1 10 65
66 4 1 10 66
67 14 0 10 67
68 8 0 10 68
69 6 1 10 69
70 9 1 10 70
71 10 0 10 71
72 10 0 10 72
73 7 1 10 73
74 3 1 10 74
75 6 1 10 75
76 4 1 10 76
77 9 0 10 77
78 11 1 10 78
79 6 0 10 79
80 7 0 10 80
81 8 1 10 81
82 11 0 10 82
83 9 0 10 83
84 12 0 10 84
85 7 0 10 85
86 9 0 10 86
87 10 0 10 87
88 8 0 10 88
89 9 0 10 89
90 9 0 10 90
91 9 1 10 91
92 9 1 10 92
93 9 0 10 93
94 7 1 10 94
95 11 0 10 95
96 6 1 10 96
97 11 0 10 97
98 9 1 10 98
99 7 0 10 99
100 5 1 10 100
101 9 0 10 101
102 7 0 10 102
103 9 0 10 103
104 9 0 10 104
105 3 1 10 105
106 11 0 10 106
107 7 1 10 107
108 6 0 10 108
109 10 0 10 109
110 8 0 10 110
111 9 0 10 111
112 8 0 10 112
113 10 0 10 113
114 10 0 10 114
115 9 0 10 115
116 9 1 10 116
117 7 0 10 117
118 9 0 10 118
119 12 0 10 119
120 10 1 10 120
121 9 1 10 121
122 12 0 10 122
123 10 1 10 123
124 10 0 10 124
125 9 1 10 125
126 3 1 10 126
127 7 0 10 127
128 10 0 10 128
129 9 1 10 129
130 9 0 10 130
131 11 1 10 131
132 10 0 10 132
133 11 1 10 133
134 7 0 10 134
135 10 0 10 135
136 5 1 10 136
137 8 0 10 137
138 7 1 9 138
139 10 0 10 139
140 11 0 9 140
141 12 0 10 141
142 8 0 10 142
143 9 0 10 143
144 7 0 10 144
145 12 0 10 145
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Leeftijd Sportgerelateerde_groep
6.838200 -0.114673 -0.093746
Stress Veranderingen_verleden Alcoholgebruik
0.466816 0.260748 0.297659
Depressie_mannen Depressie_oktober t
-0.357603 0.369370 0.002356
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7541 -1.5481 -0.3388 1.2766 7.8863
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.838200 7.734283 0.884 0.378180
Leeftijd -0.114673 0.411141 -0.279 0.780734
Sportgerelateerde_groep -0.093746 0.023830 -3.934 0.000133 ***
Stress 0.466816 0.165556 2.820 0.005526 **
Veranderingen_verleden 0.260748 0.138261 1.886 0.061440 .
Alcoholgebruik 0.297659 0.173245 1.718 0.088046 .
Depressie_mannen -0.357603 0.537147 -0.666 0.506702
Depressie_oktober 0.369370 0.820422 0.450 0.653269
t 0.002356 0.009820 0.240 0.810772
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.882 on 136 degrees of freedom
Multiple R-squared: 0.221, Adjusted R-squared: 0.1752
F-statistic: 4.823 on 8 and 136 DF, p-value: 3.041e-05
> 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,] 0.91764949 0.16470103 0.08235051
[2,] 0.92736672 0.14526657 0.07263328
[3,] 0.88673230 0.22653540 0.11326770
[4,] 0.82397613 0.35204774 0.17602387
[5,] 0.95121353 0.09757294 0.04878647
[6,] 0.93104237 0.13791527 0.06895763
[7,] 0.90477727 0.19044545 0.09522273
[8,] 0.86804446 0.26391107 0.13195554
[9,] 0.88818954 0.22362092 0.11181046
[10,] 0.87789568 0.24420864 0.12210432
[11,] 0.88399916 0.23200169 0.11600084
[12,] 0.85356472 0.29287056 0.14643528
[13,] 0.82492755 0.35014491 0.17507245
[14,] 0.77442331 0.45115338 0.22557669
[15,] 0.78464479 0.43071041 0.21535521
[16,] 0.75125963 0.49748073 0.24874037
[17,] 0.70922151 0.58155697 0.29077849
[18,] 0.69983750 0.60032500 0.30016250
[19,] 0.65273319 0.69453363 0.34726681
[20,] 0.66596375 0.66807251 0.33403625
[21,] 0.60904942 0.78190115 0.39095058
[22,] 0.58848444 0.82303112 0.41151556
[23,] 0.81752805 0.36494391 0.18247195
[24,] 0.78565078 0.42869845 0.21434922
[25,] 0.74787232 0.50425537 0.25212768
[26,] 0.72508648 0.54982703 0.27491352
[27,] 0.75202957 0.49594085 0.24797043
[28,] 0.74685917 0.50628165 0.25314083
[29,] 0.88003814 0.23992371 0.11996186
[30,] 0.85586491 0.28827017 0.14413509
[31,] 0.84310484 0.31379031 0.15689516
[32,] 0.80858848 0.38282303 0.19141152
[33,] 0.76842595 0.46314810 0.23157405
[34,] 0.72860636 0.54278729 0.27139364
[35,] 0.69301537 0.61396926 0.30698463
[36,] 0.71528503 0.56942993 0.28471497
[37,] 0.76762629 0.46474741 0.23237371
[38,] 0.72693242 0.54613516 0.27306758
[39,] 0.75471319 0.49057362 0.24528681
[40,] 0.72239470 0.55521061 0.27760530
[41,] 0.67959272 0.64081456 0.32040728
[42,] 0.68928553 0.62142894 0.31071447
[43,] 0.66330651 0.67338698 0.33669349
[44,] 0.65364423 0.69271154 0.34635577
[45,] 0.73917526 0.52164947 0.26082474
[46,] 0.69694443 0.60611114 0.30305557
[47,] 0.66135481 0.67729038 0.33864519
[48,] 0.61569781 0.76860438 0.38430219
[49,] 0.57268700 0.85462601 0.42731300
[50,] 0.52997190 0.94005619 0.47002810
[51,] 0.51249363 0.97501274 0.48750637
[52,] 0.48598694 0.97197388 0.51401306
[53,] 0.45412273 0.90824545 0.54587727
[54,] 0.40866455 0.81732911 0.59133545
[55,] 0.37095640 0.74191279 0.62904360
[56,] 0.33064109 0.66128219 0.66935891
[57,] 0.28987727 0.57975454 0.71012273
[58,] 0.25918448 0.51836895 0.74081552
[59,] 0.22199186 0.44398372 0.77800814
[60,] 0.19037848 0.38075695 0.80962152
[61,] 0.22721264 0.45442529 0.77278736
[62,] 0.19135074 0.38270149 0.80864926
[63,] 0.23254241 0.46508482 0.76745759
[64,] 0.19693379 0.39386758 0.80306621
[65,] 0.27352474 0.54704947 0.72647526
[66,] 0.29124183 0.58248366 0.70875817
[67,] 0.26869306 0.53738613 0.73130694
[68,] 0.23238650 0.46477301 0.76761350
[69,] 0.20068823 0.40137646 0.79931177
[70,] 0.17418272 0.34836544 0.82581728
[71,] 0.32083129 0.64166257 0.67916871
[72,] 0.27772587 0.55545175 0.72227413
[73,] 0.24131851 0.48263703 0.75868149
[74,] 0.21347154 0.42694307 0.78652846
[75,] 0.20176314 0.40352627 0.79823686
[76,] 0.23684844 0.47369688 0.76315156
[77,] 0.19977236 0.39954471 0.80022764
[78,] 0.20098036 0.40196071 0.79901964
[79,] 0.16890370 0.33780741 0.83109630
[80,] 0.13852301 0.27704602 0.86147699
[81,] 0.12660070 0.25320141 0.87339930
[82,] 0.11956542 0.23913084 0.88043458
[83,] 0.11047727 0.22095454 0.88952273
[84,] 0.08983629 0.17967258 0.91016371
[85,] 0.08385125 0.16770250 0.91614875
[86,] 0.07584431 0.15168861 0.92415569
[87,] 0.12265749 0.24531497 0.87734251
[88,] 0.10166655 0.20333309 0.89833345
[89,] 0.08292786 0.16585571 0.91707214
[90,] 0.17429641 0.34859283 0.82570359
[91,] 0.17138547 0.34277095 0.82861453
[92,] 0.15412391 0.30824782 0.84587609
[93,] 0.12524363 0.25048726 0.87475637
[94,] 0.09932704 0.19865408 0.90067296
[95,] 0.07913636 0.15827272 0.92086364
[96,] 0.05997415 0.11994829 0.94002585
[97,] 0.04529715 0.09059430 0.95470285
[98,] 0.05839551 0.11679101 0.94160449
[99,] 0.08534541 0.17069082 0.91465459
[100,] 0.07050348 0.14100696 0.92949652
[101,] 0.08172620 0.16345241 0.91827380
[102,] 0.06640782 0.13281564 0.93359218
[103,] 0.07622121 0.15244242 0.92377879
[104,] 0.05636373 0.11272746 0.94363627
[105,] 0.04489646 0.08979292 0.95510354
[106,] 0.03665793 0.07331585 0.96334207
[107,] 0.02616478 0.05232956 0.97383522
[108,] 0.01784227 0.03568453 0.98215773
[109,] 0.02004661 0.04009322 0.97995339
[110,] 0.05488202 0.10976404 0.94511798
[111,] 0.06491415 0.12982831 0.93508585
[112,] 0.04816594 0.09633187 0.95183406
[113,] 0.03491308 0.06982616 0.96508692
[114,] 0.22636525 0.45273051 0.77363475
[115,] 0.46626654 0.93253308 0.53373346
[116,] 0.56381414 0.87237171 0.43618586
[117,] 0.47425497 0.94850994 0.52574503
[118,] 0.38934106 0.77868211 0.61065894
[119,] 0.31219853 0.62439706 0.68780147
[120,] 0.30882382 0.61764763 0.69117618
[121,] 0.20767171 0.41534342 0.79232829
[122,] 0.22178059 0.44356118 0.77821941
> postscript(file="/var/www/rcomp/tmp/1c8jo1293289483.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/250jr1293289483.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/350jr1293289483.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/450jr1293289483.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5f9iu1293289483.ps",horizontal=F,onefile=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
-1.246345012 0.432784397 0.241876009 1.276640543 7.410150117 -0.328749205
7 8 9 10 11 12
6.689271191 -1.293417410 -1.461741770 1.279660298 -2.392906813 -4.863085272
13 14 15 16 17 18
2.617127315 -1.283786571 0.205178203 4.103760565 0.012701175 -1.897585229
19 20 21 22 23 24
-0.691874199 -4.853002166 -3.249964868 0.353386980 1.052403304 -3.311968676
25 26 27 28 29 30
-0.384244958 1.706793005 0.638825453 -1.330939211 2.486066574 -1.417032219
31 32 33 34 35 36
1.357718218 1.182622814 -2.906592213 7.572669608 1.074865967 -1.896068179
37 38 39 40 41 42
-1.313514236 -3.627586247 2.318349211 -6.754137380 -1.346869437 1.024461121
43 44 45 46 47 48
0.222799061 0.290942550 -1.326494752 -1.645006420 3.802058784 3.759240937
49 50 51 52 53 54
0.330759192 -4.490394266 -0.760009793 0.411733089 -3.478431637 -1.807626212
55 56 57 58 59 60
1.846694879 5.114422671 -0.167272999 -1.194802235 0.889438352 -0.652988858
61 62 63 64 65 66
0.237101504 2.545211007 -1.470339753 -2.005425635 -0.270688668 1.084384782
67 68 69 70 71 72
-0.453963975 0.491735516 -1.749020749 0.544080237 -1.027184605 4.071092524
73 74 75 76 77 78
-0.434145178 -4.436990446 -0.237830470 -5.000955994 -3.570208358 -0.338761725
79 80 81 82 83 84
-0.000331222 0.852955739 0.631656431 6.569535489 -0.215809485 -0.480183627
85 86 87 88 89 90
1.683412970 2.675517891 -4.061048844 -0.400068553 3.013273951 -0.629183882
91 92 93 94 95 96
-0.047379127 -2.039961233 -2.545994620 2.608212878 -0.659765405 -2.142171606
97 98 99 100 101 102
2.572291261 5.783799641 -1.431626669 1.487926928 6.027950305 2.846033180
103 104 105 106 107 108
0.375692256 -0.040133438 -0.998270994 -1.529394644 -0.221779012 -0.707677883
109 110 111 112 113 114
-4.628964985 3.666605073 2.119741254 2.841289553 -0.823459683 -5.044514565
115 116 117 118 119 120
-1.055837534 -2.001813173 -2.492171088 -2.184465119 -1.548092789 -2.691273344
121 122 123 124 125 126
6.917543536 -3.564934150 -0.634858937 0.360016953 7.886250291 1.078151526
127 128 129 130 131 132
1.906810914 -3.463197564 -1.044098781 -3.652583716 1.107144670 2.706114332
133 134 135 136 137 138
-0.523451360 -0.186022448 -1.148668045 -0.647298246 3.849538826 -4.052819461
139 140 141 142 143 144
-2.466706975 5.721856322 -0.614468042 -0.420442006 -2.243361793 -2.884379157
145
4.570287882
> postscript(file="/var/www/rcomp/tmp/6f9iu1293289483.ps",horizontal=F,onefile=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 -1.246345012 NA
1 0.432784397 -1.246345012
2 0.241876009 0.432784397
3 1.276640543 0.241876009
4 7.410150117 1.276640543
5 -0.328749205 7.410150117
6 6.689271191 -0.328749205
7 -1.293417410 6.689271191
8 -1.461741770 -1.293417410
9 1.279660298 -1.461741770
10 -2.392906813 1.279660298
11 -4.863085272 -2.392906813
12 2.617127315 -4.863085272
13 -1.283786571 2.617127315
14 0.205178203 -1.283786571
15 4.103760565 0.205178203
16 0.012701175 4.103760565
17 -1.897585229 0.012701175
18 -0.691874199 -1.897585229
19 -4.853002166 -0.691874199
20 -3.249964868 -4.853002166
21 0.353386980 -3.249964868
22 1.052403304 0.353386980
23 -3.311968676 1.052403304
24 -0.384244958 -3.311968676
25 1.706793005 -0.384244958
26 0.638825453 1.706793005
27 -1.330939211 0.638825453
28 2.486066574 -1.330939211
29 -1.417032219 2.486066574
30 1.357718218 -1.417032219
31 1.182622814 1.357718218
32 -2.906592213 1.182622814
33 7.572669608 -2.906592213
34 1.074865967 7.572669608
35 -1.896068179 1.074865967
36 -1.313514236 -1.896068179
37 -3.627586247 -1.313514236
38 2.318349211 -3.627586247
39 -6.754137380 2.318349211
40 -1.346869437 -6.754137380
41 1.024461121 -1.346869437
42 0.222799061 1.024461121
43 0.290942550 0.222799061
44 -1.326494752 0.290942550
45 -1.645006420 -1.326494752
46 3.802058784 -1.645006420
47 3.759240937 3.802058784
48 0.330759192 3.759240937
49 -4.490394266 0.330759192
50 -0.760009793 -4.490394266
51 0.411733089 -0.760009793
52 -3.478431637 0.411733089
53 -1.807626212 -3.478431637
54 1.846694879 -1.807626212
55 5.114422671 1.846694879
56 -0.167272999 5.114422671
57 -1.194802235 -0.167272999
58 0.889438352 -1.194802235
59 -0.652988858 0.889438352
60 0.237101504 -0.652988858
61 2.545211007 0.237101504
62 -1.470339753 2.545211007
63 -2.005425635 -1.470339753
64 -0.270688668 -2.005425635
65 1.084384782 -0.270688668
66 -0.453963975 1.084384782
67 0.491735516 -0.453963975
68 -1.749020749 0.491735516
69 0.544080237 -1.749020749
70 -1.027184605 0.544080237
71 4.071092524 -1.027184605
72 -0.434145178 4.071092524
73 -4.436990446 -0.434145178
74 -0.237830470 -4.436990446
75 -5.000955994 -0.237830470
76 -3.570208358 -5.000955994
77 -0.338761725 -3.570208358
78 -0.000331222 -0.338761725
79 0.852955739 -0.000331222
80 0.631656431 0.852955739
81 6.569535489 0.631656431
82 -0.215809485 6.569535489
83 -0.480183627 -0.215809485
84 1.683412970 -0.480183627
85 2.675517891 1.683412970
86 -4.061048844 2.675517891
87 -0.400068553 -4.061048844
88 3.013273951 -0.400068553
89 -0.629183882 3.013273951
90 -0.047379127 -0.629183882
91 -2.039961233 -0.047379127
92 -2.545994620 -2.039961233
93 2.608212878 -2.545994620
94 -0.659765405 2.608212878
95 -2.142171606 -0.659765405
96 2.572291261 -2.142171606
97 5.783799641 2.572291261
98 -1.431626669 5.783799641
99 1.487926928 -1.431626669
100 6.027950305 1.487926928
101 2.846033180 6.027950305
102 0.375692256 2.846033180
103 -0.040133438 0.375692256
104 -0.998270994 -0.040133438
105 -1.529394644 -0.998270994
106 -0.221779012 -1.529394644
107 -0.707677883 -0.221779012
108 -4.628964985 -0.707677883
109 3.666605073 -4.628964985
110 2.119741254 3.666605073
111 2.841289553 2.119741254
112 -0.823459683 2.841289553
113 -5.044514565 -0.823459683
114 -1.055837534 -5.044514565
115 -2.001813173 -1.055837534
116 -2.492171088 -2.001813173
117 -2.184465119 -2.492171088
118 -1.548092789 -2.184465119
119 -2.691273344 -1.548092789
120 6.917543536 -2.691273344
121 -3.564934150 6.917543536
122 -0.634858937 -3.564934150
123 0.360016953 -0.634858937
124 7.886250291 0.360016953
125 1.078151526 7.886250291
126 1.906810914 1.078151526
127 -3.463197564 1.906810914
128 -1.044098781 -3.463197564
129 -3.652583716 -1.044098781
130 1.107144670 -3.652583716
131 2.706114332 1.107144670
132 -0.523451360 2.706114332
133 -0.186022448 -0.523451360
134 -1.148668045 -0.186022448
135 -0.647298246 -1.148668045
136 3.849538826 -0.647298246
137 -4.052819461 3.849538826
138 -2.466706975 -4.052819461
139 5.721856322 -2.466706975
140 -0.614468042 5.721856322
141 -0.420442006 -0.614468042
142 -2.243361793 -0.420442006
143 -2.884379157 -2.243361793
144 4.570287882 -2.884379157
145 NA 4.570287882
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.432784397 -1.246345012
[2,] 0.241876009 0.432784397
[3,] 1.276640543 0.241876009
[4,] 7.410150117 1.276640543
[5,] -0.328749205 7.410150117
[6,] 6.689271191 -0.328749205
[7,] -1.293417410 6.689271191
[8,] -1.461741770 -1.293417410
[9,] 1.279660298 -1.461741770
[10,] -2.392906813 1.279660298
[11,] -4.863085272 -2.392906813
[12,] 2.617127315 -4.863085272
[13,] -1.283786571 2.617127315
[14,] 0.205178203 -1.283786571
[15,] 4.103760565 0.205178203
[16,] 0.012701175 4.103760565
[17,] -1.897585229 0.012701175
[18,] -0.691874199 -1.897585229
[19,] -4.853002166 -0.691874199
[20,] -3.249964868 -4.853002166
[21,] 0.353386980 -3.249964868
[22,] 1.052403304 0.353386980
[23,] -3.311968676 1.052403304
[24,] -0.384244958 -3.311968676
[25,] 1.706793005 -0.384244958
[26,] 0.638825453 1.706793005
[27,] -1.330939211 0.638825453
[28,] 2.486066574 -1.330939211
[29,] -1.417032219 2.486066574
[30,] 1.357718218 -1.417032219
[31,] 1.182622814 1.357718218
[32,] -2.906592213 1.182622814
[33,] 7.572669608 -2.906592213
[34,] 1.074865967 7.572669608
[35,] -1.896068179 1.074865967
[36,] -1.313514236 -1.896068179
[37,] -3.627586247 -1.313514236
[38,] 2.318349211 -3.627586247
[39,] -6.754137380 2.318349211
[40,] -1.346869437 -6.754137380
[41,] 1.024461121 -1.346869437
[42,] 0.222799061 1.024461121
[43,] 0.290942550 0.222799061
[44,] -1.326494752 0.290942550
[45,] -1.645006420 -1.326494752
[46,] 3.802058784 -1.645006420
[47,] 3.759240937 3.802058784
[48,] 0.330759192 3.759240937
[49,] -4.490394266 0.330759192
[50,] -0.760009793 -4.490394266
[51,] 0.411733089 -0.760009793
[52,] -3.478431637 0.411733089
[53,] -1.807626212 -3.478431637
[54,] 1.846694879 -1.807626212
[55,] 5.114422671 1.846694879
[56,] -0.167272999 5.114422671
[57,] -1.194802235 -0.167272999
[58,] 0.889438352 -1.194802235
[59,] -0.652988858 0.889438352
[60,] 0.237101504 -0.652988858
[61,] 2.545211007 0.237101504
[62,] -1.470339753 2.545211007
[63,] -2.005425635 -1.470339753
[64,] -0.270688668 -2.005425635
[65,] 1.084384782 -0.270688668
[66,] -0.453963975 1.084384782
[67,] 0.491735516 -0.453963975
[68,] -1.749020749 0.491735516
[69,] 0.544080237 -1.749020749
[70,] -1.027184605 0.544080237
[71,] 4.071092524 -1.027184605
[72,] -0.434145178 4.071092524
[73,] -4.436990446 -0.434145178
[74,] -0.237830470 -4.436990446
[75,] -5.000955994 -0.237830470
[76,] -3.570208358 -5.000955994
[77,] -0.338761725 -3.570208358
[78,] -0.000331222 -0.338761725
[79,] 0.852955739 -0.000331222
[80,] 0.631656431 0.852955739
[81,] 6.569535489 0.631656431
[82,] -0.215809485 6.569535489
[83,] -0.480183627 -0.215809485
[84,] 1.683412970 -0.480183627
[85,] 2.675517891 1.683412970
[86,] -4.061048844 2.675517891
[87,] -0.400068553 -4.061048844
[88,] 3.013273951 -0.400068553
[89,] -0.629183882 3.013273951
[90,] -0.047379127 -0.629183882
[91,] -2.039961233 -0.047379127
[92,] -2.545994620 -2.039961233
[93,] 2.608212878 -2.545994620
[94,] -0.659765405 2.608212878
[95,] -2.142171606 -0.659765405
[96,] 2.572291261 -2.142171606
[97,] 5.783799641 2.572291261
[98,] -1.431626669 5.783799641
[99,] 1.487926928 -1.431626669
[100,] 6.027950305 1.487926928
[101,] 2.846033180 6.027950305
[102,] 0.375692256 2.846033180
[103,] -0.040133438 0.375692256
[104,] -0.998270994 -0.040133438
[105,] -1.529394644 -0.998270994
[106,] -0.221779012 -1.529394644
[107,] -0.707677883 -0.221779012
[108,] -4.628964985 -0.707677883
[109,] 3.666605073 -4.628964985
[110,] 2.119741254 3.666605073
[111,] 2.841289553 2.119741254
[112,] -0.823459683 2.841289553
[113,] -5.044514565 -0.823459683
[114,] -1.055837534 -5.044514565
[115,] -2.001813173 -1.055837534
[116,] -2.492171088 -2.001813173
[117,] -2.184465119 -2.492171088
[118,] -1.548092789 -2.184465119
[119,] -2.691273344 -1.548092789
[120,] 6.917543536 -2.691273344
[121,] -3.564934150 6.917543536
[122,] -0.634858937 -3.564934150
[123,] 0.360016953 -0.634858937
[124,] 7.886250291 0.360016953
[125,] 1.078151526 7.886250291
[126,] 1.906810914 1.078151526
[127,] -3.463197564 1.906810914
[128,] -1.044098781 -3.463197564
[129,] -3.652583716 -1.044098781
[130,] 1.107144670 -3.652583716
[131,] 2.706114332 1.107144670
[132,] -0.523451360 2.706114332
[133,] -0.186022448 -0.523451360
[134,] -1.148668045 -0.186022448
[135,] -0.647298246 -1.148668045
[136,] 3.849538826 -0.647298246
[137,] -4.052819461 3.849538826
[138,] -2.466706975 -4.052819461
[139,] 5.721856322 -2.466706975
[140,] -0.614468042 5.721856322
[141,] -0.420442006 -0.614468042
[142,] -2.243361793 -0.420442006
[143,] -2.884379157 -2.243361793
[144,] 4.570287882 -2.884379157
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.432784397 -1.246345012
2 0.241876009 0.432784397
3 1.276640543 0.241876009
4 7.410150117 1.276640543
5 -0.328749205 7.410150117
6 6.689271191 -0.328749205
7 -1.293417410 6.689271191
8 -1.461741770 -1.293417410
9 1.279660298 -1.461741770
10 -2.392906813 1.279660298
11 -4.863085272 -2.392906813
12 2.617127315 -4.863085272
13 -1.283786571 2.617127315
14 0.205178203 -1.283786571
15 4.103760565 0.205178203
16 0.012701175 4.103760565
17 -1.897585229 0.012701175
18 -0.691874199 -1.897585229
19 -4.853002166 -0.691874199
20 -3.249964868 -4.853002166
21 0.353386980 -3.249964868
22 1.052403304 0.353386980
23 -3.311968676 1.052403304
24 -0.384244958 -3.311968676
25 1.706793005 -0.384244958
26 0.638825453 1.706793005
27 -1.330939211 0.638825453
28 2.486066574 -1.330939211
29 -1.417032219 2.486066574
30 1.357718218 -1.417032219
31 1.182622814 1.357718218
32 -2.906592213 1.182622814
33 7.572669608 -2.906592213
34 1.074865967 7.572669608
35 -1.896068179 1.074865967
36 -1.313514236 -1.896068179
37 -3.627586247 -1.313514236
38 2.318349211 -3.627586247
39 -6.754137380 2.318349211
40 -1.346869437 -6.754137380
41 1.024461121 -1.346869437
42 0.222799061 1.024461121
43 0.290942550 0.222799061
44 -1.326494752 0.290942550
45 -1.645006420 -1.326494752
46 3.802058784 -1.645006420
47 3.759240937 3.802058784
48 0.330759192 3.759240937
49 -4.490394266 0.330759192
50 -0.760009793 -4.490394266
51 0.411733089 -0.760009793
52 -3.478431637 0.411733089
53 -1.807626212 -3.478431637
54 1.846694879 -1.807626212
55 5.114422671 1.846694879
56 -0.167272999 5.114422671
57 -1.194802235 -0.167272999
58 0.889438352 -1.194802235
59 -0.652988858 0.889438352
60 0.237101504 -0.652988858
61 2.545211007 0.237101504
62 -1.470339753 2.545211007
63 -2.005425635 -1.470339753
64 -0.270688668 -2.005425635
65 1.084384782 -0.270688668
66 -0.453963975 1.084384782
67 0.491735516 -0.453963975
68 -1.749020749 0.491735516
69 0.544080237 -1.749020749
70 -1.027184605 0.544080237
71 4.071092524 -1.027184605
72 -0.434145178 4.071092524
73 -4.436990446 -0.434145178
74 -0.237830470 -4.436990446
75 -5.000955994 -0.237830470
76 -3.570208358 -5.000955994
77 -0.338761725 -3.570208358
78 -0.000331222 -0.338761725
79 0.852955739 -0.000331222
80 0.631656431 0.852955739
81 6.569535489 0.631656431
82 -0.215809485 6.569535489
83 -0.480183627 -0.215809485
84 1.683412970 -0.480183627
85 2.675517891 1.683412970
86 -4.061048844 2.675517891
87 -0.400068553 -4.061048844
88 3.013273951 -0.400068553
89 -0.629183882 3.013273951
90 -0.047379127 -0.629183882
91 -2.039961233 -0.047379127
92 -2.545994620 -2.039961233
93 2.608212878 -2.545994620
94 -0.659765405 2.608212878
95 -2.142171606 -0.659765405
96 2.572291261 -2.142171606
97 5.783799641 2.572291261
98 -1.431626669 5.783799641
99 1.487926928 -1.431626669
100 6.027950305 1.487926928
101 2.846033180 6.027950305
102 0.375692256 2.846033180
103 -0.040133438 0.375692256
104 -0.998270994 -0.040133438
105 -1.529394644 -0.998270994
106 -0.221779012 -1.529394644
107 -0.707677883 -0.221779012
108 -4.628964985 -0.707677883
109 3.666605073 -4.628964985
110 2.119741254 3.666605073
111 2.841289553 2.119741254
112 -0.823459683 2.841289553
113 -5.044514565 -0.823459683
114 -1.055837534 -5.044514565
115 -2.001813173 -1.055837534
116 -2.492171088 -2.001813173
117 -2.184465119 -2.492171088
118 -1.548092789 -2.184465119
119 -2.691273344 -1.548092789
120 6.917543536 -2.691273344
121 -3.564934150 6.917543536
122 -0.634858937 -3.564934150
123 0.360016953 -0.634858937
124 7.886250291 0.360016953
125 1.078151526 7.886250291
126 1.906810914 1.078151526
127 -3.463197564 1.906810914
128 -1.044098781 -3.463197564
129 -3.652583716 -1.044098781
130 1.107144670 -3.652583716
131 2.706114332 1.107144670
132 -0.523451360 2.706114332
133 -0.186022448 -0.523451360
134 -1.148668045 -0.186022448
135 -0.647298246 -1.148668045
136 3.849538826 -0.647298246
137 -4.052819461 3.849538826
138 -2.466706975 -4.052819461
139 5.721856322 -2.466706975
140 -0.614468042 5.721856322
141 -0.420442006 -0.614468042
142 -2.243361793 -0.420442006
143 -2.884379157 -2.243361793
144 4.570287882 -2.884379157
> 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/7qihe1293289483.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8qihe1293289483.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/919gi1293289483.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1019gi1293289483.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114ax51293289483.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/127tdt1293289483.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/13l2b21293289483.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/14p3a81293289483.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/15slqe1293289483.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/16w47k1293289483.tab")
+ }
>
> try(system("convert tmp/1c8jo1293289483.ps tmp/1c8jo1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/250jr1293289483.ps tmp/250jr1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/350jr1293289483.ps tmp/350jr1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/450jr1293289483.ps tmp/450jr1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/5f9iu1293289483.ps tmp/5f9iu1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/6f9iu1293289483.ps tmp/6f9iu1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qihe1293289483.ps tmp/7qihe1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qihe1293289483.ps tmp/8qihe1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/919gi1293289483.ps tmp/919gi1293289483.png",intern=TRUE))
character(0)
> try(system("convert tmp/1019gi1293289483.ps tmp/1019gi1293289483.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.490 1.490 5.998