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(13
+ ,26
+ ,9
+ ,6
+ ,25
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+ ,9
+ ,13
+ ,6
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+ ,11
+ ,7
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+ ,30
+ ,12
+ ,10
+ ,29
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+ ,18
+ ,22
+ ,9
+ ,6
+ ,19
+ ,12
+ ,13
+ ,32
+ ,16
+ ,6
+ ,29
+ ,26
+ ,15
+ ,22
+ ,11
+ ,11
+ ,24
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+ ,16
+ ,15
+ ,11
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+ ,16
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+ ,27
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+ ,9
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+ ,21
+ ,10
+ ,22
+ ,13
+ ,13
+ ,22
+ ,15
+ ,17
+ ,9
+ ,6
+ ,11
+ ,29
+ ,23
+ ,13
+ ,29
+ ,11
+ ,4
+ ,26
+ ,22
+ ,15
+ ,20
+ ,7
+ ,9
+ ,26
+ ,22
+ ,16
+ ,16
+ ,8
+ ,5
+ ,21
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+ ,12
+ ,16
+ ,8
+ ,4
+ ,18
+ ,23
+ ,13
+ ,16
+ ,9
+ ,9
+ ,10
+ ,13)
+ ,dim=c(6
+ ,150)
+ ,dimnames=list(c('Selfconfidence'
+ ,'ConcernMistakes'
+ ,'DoubtsActions'
+ ,'ParentalCriticism'
+ ,'PersonalStandards'
+ ,'Organization')
+ ,1:150))
> y <- array(NA,dim=c(6,150),dimnames=list(c('Selfconfidence','ConcernMistakes','DoubtsActions','ParentalCriticism','PersonalStandards','Organization'),1:150))
> 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
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
Selfconfidence ConcernMistakes DoubtsActions ParentalCriticism
1 13 26 9 6
2 16 20 9 6
3 19 21 9 13
4 15 31 14 8
5 14 21 8 7
6 13 18 8 9
7 19 26 11 5
8 15 22 10 8
9 14 22 9 9
10 15 29 15 11
11 16 15 14 8
12 16 16 11 11
13 16 24 14 12
14 17 17 6 8
15 15 19 20 7
16 15 22 9 9
17 20 31 10 12
18 18 28 8 20
19 16 38 11 7
20 16 26 14 8
21 19 25 11 8
22 16 25 16 16
23 17 29 14 10
24 17 28 11 6
25 16 15 11 8
26 15 18 12 9
27 14 21 9 9
28 15 25 7 11
29 12 23 13 12
30 14 23 10 8
31 16 19 9 7
32 14 18 9 8
33 7 18 13 9
34 10 26 16 4
35 14 18 12 8
36 16 18 6 8
37 16 28 14 8
38 16 17 14 6
39 14 29 10 8
40 20 12 4 4
41 14 25 12 7
42 14 28 12 14
43 11 20 14 10
44 15 17 9 9
45 16 17 9 6
46 14 20 10 8
47 16 31 14 11
48 14 21 10 8
49 12 19 9 8
50 16 23 14 10
51 9 15 8 8
52 14 24 9 10
53 16 28 8 7
54 16 16 9 8
55 15 19 9 7
56 16 21 9 9
57 12 21 15 5
58 16 20 8 7
59 16 16 10 7
60 14 25 8 7
61 16 30 14 9
62 17 29 11 5
63 18 22 10 8
64 18 19 12 8
65 12 33 14 8
66 16 17 9 9
67 10 9 13 6
68 14 14 15 8
69 18 15 8 6
70 18 12 7 4
71 16 21 10 6
72 16 20 10 4
73 16 29 13 12
74 13 33 11 6
75 16 21 8 11
76 16 15 12 8
77 20 19 9 10
78 16 23 10 10
79 15 20 11 4
80 15 20 11 8
81 16 18 10 9
82 14 31 16 9
83 15 18 16 7
84 12 13 8 7
85 17 9 6 11
86 16 20 11 8
87 15 18 12 8
88 13 23 14 7
89 16 17 9 5
90 16 17 11 7
91 16 16 8 9
92 16 31 8 8
93 14 15 7 6
94 16 28 16 8
95 16 26 13 10
96 20 20 8 10
97 15 19 11 8
98 16 25 14 11
99 13 18 10 8
100 17 20 10 8
101 16 33 14 6
102 12 24 14 20
103 16 22 10 6
104 16 32 12 12
105 17 31 9 9
106 13 13 16 5
107 12 18 8 10
108 18 17 9 5
109 14 29 16 6
110 14 22 13 10
111 13 18 13 6
112 16 22 8 10
113 13 25 14 5
114 16 20 11 13
115 13 20 9 7
116 16 17 8 9
117 15 21 13 11
118 16 26 13 8
119 15 10 10 5
120 17 15 8 4
121 15 20 7 9
122 12 14 11 7
123 16 16 11 5
124 10 23 14 5
125 16 11 6 4
126 14 19 10 7
127 15 30 9 9
128 13 21 12 8
129 15 20 11 8
130 11 22 14 11
131 12 30 12 10
132 8 25 14 9
133 16 28 8 12
134 15 23 14 10
135 17 23 8 10
136 16 21 11 7
137 10 30 12 10
138 18 22 9 6
139 13 32 16 6
140 15 22 11 11
141 16 15 11 8
142 16 21 12 9
143 14 27 15 9
144 10 22 13 13
145 17 9 6 11
146 13 29 11 4
147 15 20 7 9
148 16 16 8 5
149 12 16 8 4
150 13 16 9 9
PersonalStandards Organization t
1 25 25 1
2 25 24 2
3 19 21 3
4 18 23 4
5 18 17 5
6 22 19 6
7 29 18 7
8 26 27 8
9 25 23 9
10 23 23 10
11 23 29 11
12 23 21 12
13 24 26 13
14 30 25 14
15 19 25 15
16 24 23 16
17 32 26 17
18 30 20 18
19 29 29 19
20 17 24 20
21 25 23 21
22 26 24 22
23 26 30 23
24 25 22 24
25 23 22 25
26 21 13 26
27 19 24 27
28 35 17 28
29 19 24 29
30 20 21 30
31 21 23 31
32 21 24 32
33 24 24 33
34 23 24 34
35 19 23 35
36 17 26 36
37 24 24 37
38 15 21 38
39 25 23 39
40 27 28 40
41 29 23 41
42 27 22 42
43 18 24 43
44 25 21 44
45 22 23 45
46 26 23 46
47 23 20 47
48 16 23 48
49 27 21 49
50 25 27 50
51 14 12 51
52 19 15 52
53 20 22 53
54 16 21 54
55 18 21 55
56 22 20 56
57 21 24 57
58 22 24 58
59 22 29 59
60 32 25 60
61 23 14 61
62 31 30 62
63 18 19 63
64 23 29 64
65 26 25 65
66 24 25 66
67 19 25 67
68 14 16 68
69 20 25 69
70 22 28 70
71 24 24 71
72 25 25 72
73 21 21 73
74 28 22 74
75 24 20 75
76 20 25 76
77 21 27 77
78 23 21 78
79 13 13 79
80 24 26 80
81 21 26 81
82 21 25 82
83 17 22 83
84 14 19 84
85 29 23 85
86 25 25 86
87 16 15 87
88 25 21 88
89 25 23 89
90 21 25 90
91 23 24 91
92 22 24 92
93 19 21 93
94 24 24 94
95 26 22 95
96 25 24 96
97 20 28 97
98 22 21 98
99 14 17 99
100 20 28 100
101 32 24 101
102 21 10 102
103 22 20 103
104 28 22 104
105 25 19 105
106 17 22 106
107 21 22 107
108 23 26 108
109 27 24 109
110 22 22 110
111 19 20 111
112 20 20 112
113 17 15 113
114 24 20 114
115 21 20 115
116 21 24 116
117 23 22 117
118 24 29 118
119 19 23 119
120 22 24 120
121 26 22 121
122 17 16 122
123 17 23 123
124 19 27 124
125 15 16 125
126 17 21 126
127 27 26 127
128 19 22 128
129 21 23 129
130 25 19 130
131 19 18 131
132 22 24 132
133 18 24 133
134 20 29 134
135 15 22 135
136 20 24 136
137 29 22 137
138 19 12 138
139 29 26 139
140 24 18 140
141 23 22 141
142 22 24 142
143 23 21 143
144 22 15 144
145 29 23 145
146 26 22 146
147 26 22 147
148 21 24 148
149 18 23 149
150 10 13 150
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ConcernMistakes DoubtsActions ParentalCriticism
13.793479 0.018016 -0.291928 0.065747
PersonalStandards Organization t
0.028720 0.143703 -0.005942
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.8565 -1.1297 0.3678 1.3593 4.5930
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.793479 1.534575 8.988 1.36e-15 ***
ConcernMistakes 0.018016 0.036983 0.487 0.62691
DoubtsActions -0.291928 0.068806 -4.243 3.95e-05 ***
ParentalCriticism 0.065747 0.068650 0.958 0.33983
PersonalStandards 0.028720 0.049129 0.585 0.55975
Organization 0.143703 0.050059 2.871 0.00472 **
t -0.005942 0.003996 -1.487 0.13923
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.069 on 143 degrees of freedom
Multiple R-squared: 0.2045, Adjusted R-squared: 0.1711
F-statistic: 6.127 on 6 and 143 DF, p-value: 9.747e-06
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.82709129 0.34581741 0.17290871
[2,] 0.72538530 0.54922940 0.27461470
[3,] 0.60668399 0.78663201 0.39331601
[4,] 0.48559843 0.97119686 0.51440157
[5,] 0.61450435 0.77099130 0.38549565
[6,] 0.51502552 0.96994897 0.48497448
[7,] 0.43019317 0.86038633 0.56980683
[8,] 0.43901604 0.87803207 0.56098396
[9,] 0.44481931 0.88963862 0.55518069
[10,] 0.36016678 0.72033357 0.63983322
[11,] 0.34842474 0.69684947 0.65157526
[12,] 0.39560577 0.79121154 0.60439423
[13,] 0.39668974 0.79337949 0.60331026
[14,] 0.33053156 0.66106311 0.66946844
[15,] 0.27076801 0.54153601 0.72923199
[16,] 0.21844270 0.43688539 0.78155730
[17,] 0.20679007 0.41358014 0.79320993
[18,] 0.17703147 0.35406294 0.82296853
[19,] 0.24239518 0.48479035 0.75760482
[20,] 0.32775109 0.65550218 0.67224891
[21,] 0.27309520 0.54619041 0.72690480
[22,] 0.24911350 0.49822700 0.75088650
[23,] 0.20827565 0.41655130 0.79172435
[24,] 0.88094488 0.23811023 0.11905512
[25,] 0.92392849 0.15214303 0.07607151
[26,] 0.90747541 0.18504917 0.09252459
[27,] 0.90839522 0.18320955 0.09160478
[28,] 0.89921536 0.20156928 0.10078464
[29,] 0.92287968 0.15424063 0.07712032
[30,] 0.90884875 0.18230249 0.09115125
[31,] 0.95329258 0.09341483 0.04670742
[32,] 0.94141132 0.11717736 0.05858868
[33,] 0.92934343 0.14131315 0.07065657
[34,] 0.94277520 0.11444960 0.05722480
[35,] 0.92684769 0.14630463 0.07315231
[36,] 0.91462939 0.17074122 0.08537061
[37,] 0.89925701 0.20148599 0.10074299
[38,] 0.89620709 0.20758581 0.10379291
[39,] 0.87718213 0.24563573 0.12281787
[40,] 0.90561431 0.18877139 0.09438569
[41,] 0.89334393 0.21331215 0.10665607
[42,] 0.95487234 0.09025531 0.04512766
[43,] 0.94747187 0.10505626 0.05252813
[44,] 0.93930138 0.12139724 0.06069862
[45,] 0.93820524 0.12358952 0.06179476
[46,] 0.92772341 0.14455318 0.07227659
[47,] 0.91719979 0.16560042 0.08280021
[48,] 0.91194104 0.17611792 0.08805896
[49,] 0.89707380 0.20585241 0.10292620
[50,] 0.87839767 0.24320467 0.12160233
[51,] 0.89555883 0.20888234 0.10444117
[52,] 0.90930095 0.18139809 0.09069905
[53,] 0.89093065 0.21813869 0.10906935
[54,] 0.92272189 0.15455622 0.07727811
[55,] 0.92945851 0.14108298 0.07054149
[56,] 0.94856884 0.10286232 0.05143116
[57,] 0.93679553 0.12640895 0.06320447
[58,] 0.97596390 0.04807220 0.02403610
[59,] 0.97273718 0.05452563 0.02726282
[60,] 0.97370983 0.05258034 0.02629017
[61,] 0.97095434 0.05809133 0.02904566
[62,] 0.96303266 0.07393468 0.03696734
[63,] 0.95355068 0.09289865 0.04644932
[64,] 0.94447040 0.11105920 0.05552960
[65,] 0.95457362 0.09085275 0.04542638
[66,] 0.94339579 0.11320842 0.05660421
[67,] 0.93153349 0.13693303 0.06846651
[68,] 0.95433144 0.09133711 0.04566856
[69,] 0.94178230 0.11643541 0.05821770
[70,] 0.93501869 0.12996261 0.06498131
[71,] 0.92214769 0.15570462 0.07785231
[72,] 0.90287331 0.19425338 0.09712669
[73,] 0.88101402 0.23797197 0.11898598
[74,] 0.87153155 0.25693691 0.12846845
[75,] 0.91219247 0.17561506 0.08780753
[76,] 0.89530171 0.20939657 0.10469829
[77,] 0.87165775 0.25668450 0.12834225
[78,] 0.85322162 0.29355676 0.14677838
[79,] 0.83844917 0.32310165 0.16155083
[80,] 0.81135557 0.37728887 0.18864443
[81,] 0.77730940 0.44538119 0.22269060
[82,] 0.74620327 0.50759347 0.25379673
[83,] 0.71265861 0.57468278 0.28734139
[84,] 0.74641850 0.50716301 0.25358150
[85,] 0.74030837 0.51938325 0.25969163
[86,] 0.70565373 0.58869255 0.29434627
[87,] 0.75517492 0.48965017 0.24482508
[88,] 0.72211841 0.55576317 0.27788159
[89,] 0.71712284 0.56575433 0.28287716
[90,] 0.70318336 0.59363327 0.29681664
[91,] 0.66265565 0.67468870 0.33734435
[92,] 0.63148491 0.73703017 0.36851509
[93,] 0.59791100 0.80417800 0.40208900
[94,] 0.55264552 0.89470897 0.44735448
[95,] 0.52110553 0.95778894 0.47889447
[96,] 0.50731721 0.98536559 0.49268279
[97,] 0.45318014 0.90636028 0.54681986
[98,] 0.61683166 0.76633668 0.38316834
[99,] 0.60410669 0.79178662 0.39589331
[100,] 0.58104179 0.83791641 0.41895821
[101,] 0.52861779 0.94276441 0.47138221
[102,] 0.47580106 0.95160211 0.52419894
[103,] 0.41971196 0.83942392 0.58028804
[104,] 0.37759052 0.75518104 0.62240948
[105,] 0.35569670 0.71139340 0.64430330
[106,] 0.34978424 0.69956847 0.65021576
[107,] 0.29633189 0.59266377 0.70366811
[108,] 0.27428906 0.54857812 0.72571094
[109,] 0.27574999 0.55149997 0.72425001
[110,] 0.22636572 0.45273145 0.77363428
[111,] 0.19840885 0.39681770 0.80159115
[112,] 0.16339631 0.32679262 0.83660369
[113,] 0.14589246 0.29178493 0.85410754
[114,] 0.13402449 0.26804899 0.86597551
[115,] 0.19309781 0.38619562 0.80690219
[116,] 0.15777354 0.31554708 0.84222646
[117,] 0.12883016 0.25766032 0.87116984
[118,] 0.09822702 0.19645403 0.90177298
[119,] 0.08098241 0.16196482 0.91901759
[120,] 0.05654128 0.11308257 0.94345872
[121,] 0.05730733 0.11461465 0.94269267
[122,] 0.04893891 0.09787783 0.95106109
[123,] 0.57450037 0.85099926 0.42549963
[124,] 0.48966548 0.97933096 0.51033452
[125,] 0.39706198 0.79412395 0.60293802
[126,] 0.34184419 0.68368837 0.65815581
[127,] 0.25303230 0.50606461 0.74696770
[128,] 0.70175146 0.59649709 0.29824854
[129,] 0.63885764 0.72228472 0.36114236
[130,] 0.53889006 0.92221987 0.46110994
[131,] 0.37823969 0.75647938 0.62176031
> postscript(file="/var/www/html/rcomp/tmp/161va1290473590.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/261va1290473590.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/361va1290473590.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4haud1290473590.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5haud1290473590.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 = 150
Frequency = 1
1 2 3 4 5 6
-3.33363759 -0.07589832 3.05523001 0.41070132 -1.22680306 -2.70059137
7 8 9 10 11 12
4.24265854 -1.37566813 -2.12386931 0.43347636 0.73473417 0.79925969
13 14 15 16 17 18
0.72388003 -0.24511985 2.19344431 -1.05355220 3.22407034 1.09388808
19 20 21 22 23 24
-0.61444270 1.48087636 3.54299518 1.31018155 1.19246884 1.78197124
25 26 27 28 29 30
0.94806430 1.47690478 -1.97027280 -1.20534048 -3.02394791 -1.22841434
31 32 33 34 35 36
0.30728445 -1.87820661 -7.85645934 -3.76140674 -0.78345365 -0.90274586
37 38 39 40 41 42
1.34482790 2.37002272 -1.71403093 2.33364482 -1.09536071 -1.40254855
43 44 45 46 47 48
-3.43456572 -0.53839924 0.46353699 -1.53901235 1.75649652 -1.25794454
49 50 51 52 53 54
-3.53641148 0.92083742 -5.07770922 -0.64818059 0.15637231 0.86326633
55 56 57 58 59 60
-0.17653146 0.69070985 -1.83488614 -0.01463495 -0.07128772 -2.52372971
61 62 63 64 65 66
2.85141586 0.73357512 3.33054778 2.39376662 -2.78000392 0.04624411
67 68 69 70 71 72
-4.29513819 1.51001169 2.12029414 1.53130122 0.63676366 0.61979231
73 74 75 76 77 78
1.50309477 -2.09714352 0.32275621 1.19810925 3.80858753 0.83917137
79 80 81 82 83 84
2.02238778 -0.41870916 0.35175001 0.01875757 1.93638448 -2.78574878
85 86 87 88 89 90
0.43980631 0.73192846 1.76133558 -0.79389324 0.70459240 0.99037133
91 92 93 94 95 96
0.09331624 -0.07651022 -1.42548382 2.26740384 1.53206713 3.92777958
97 98 99 100 101 102
-0.47219751 2.05267352 -0.98117594 1.23568648 1.53680141 -0.88781022
103 104 105 106 107 108
1.44115730 0.99659472 1.85927688 0.29463265 -3.56853713 2.44383089
109 110 111 112 113 114
0.38385860 -0.19185372 -0.47729749 0.70523767 0.54210501 1.31681816
115 116 117 118 119 120
-1.78045613 0.28130189 0.77329284 0.85175778 0.47322302 1.64111587
121 122 123 124 125 126
-0.89115448 -1.35721915 1.73826619 -4.13836843 1.50969597 -0.43396887
127 128 129 130 131 132
-1.05533171 -1.14114878 0.38973925 -2.50187474 -1.84214487 -6.04489768
133 134 135 136 137 138
0.07307125 0.27619839 1.68009259 1.36408451 -4.66849938 4.59299690
139 140 141 142 143 144
-0.83675959 0.85418928 1.63739023 1.50273427 0.67875398 -3.18113068
145 146 147 148 149 150
0.79635421 -1.40829054 -0.73665039 0.75246254 -2.94598622 -0.31006294
> postscript(file="/var/www/html/rcomp/tmp/6haud1290473590.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 = 150
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.33363759 NA
1 -0.07589832 -3.33363759
2 3.05523001 -0.07589832
3 0.41070132 3.05523001
4 -1.22680306 0.41070132
5 -2.70059137 -1.22680306
6 4.24265854 -2.70059137
7 -1.37566813 4.24265854
8 -2.12386931 -1.37566813
9 0.43347636 -2.12386931
10 0.73473417 0.43347636
11 0.79925969 0.73473417
12 0.72388003 0.79925969
13 -0.24511985 0.72388003
14 2.19344431 -0.24511985
15 -1.05355220 2.19344431
16 3.22407034 -1.05355220
17 1.09388808 3.22407034
18 -0.61444270 1.09388808
19 1.48087636 -0.61444270
20 3.54299518 1.48087636
21 1.31018155 3.54299518
22 1.19246884 1.31018155
23 1.78197124 1.19246884
24 0.94806430 1.78197124
25 1.47690478 0.94806430
26 -1.97027280 1.47690478
27 -1.20534048 -1.97027280
28 -3.02394791 -1.20534048
29 -1.22841434 -3.02394791
30 0.30728445 -1.22841434
31 -1.87820661 0.30728445
32 -7.85645934 -1.87820661
33 -3.76140674 -7.85645934
34 -0.78345365 -3.76140674
35 -0.90274586 -0.78345365
36 1.34482790 -0.90274586
37 2.37002272 1.34482790
38 -1.71403093 2.37002272
39 2.33364482 -1.71403093
40 -1.09536071 2.33364482
41 -1.40254855 -1.09536071
42 -3.43456572 -1.40254855
43 -0.53839924 -3.43456572
44 0.46353699 -0.53839924
45 -1.53901235 0.46353699
46 1.75649652 -1.53901235
47 -1.25794454 1.75649652
48 -3.53641148 -1.25794454
49 0.92083742 -3.53641148
50 -5.07770922 0.92083742
51 -0.64818059 -5.07770922
52 0.15637231 -0.64818059
53 0.86326633 0.15637231
54 -0.17653146 0.86326633
55 0.69070985 -0.17653146
56 -1.83488614 0.69070985
57 -0.01463495 -1.83488614
58 -0.07128772 -0.01463495
59 -2.52372971 -0.07128772
60 2.85141586 -2.52372971
61 0.73357512 2.85141586
62 3.33054778 0.73357512
63 2.39376662 3.33054778
64 -2.78000392 2.39376662
65 0.04624411 -2.78000392
66 -4.29513819 0.04624411
67 1.51001169 -4.29513819
68 2.12029414 1.51001169
69 1.53130122 2.12029414
70 0.63676366 1.53130122
71 0.61979231 0.63676366
72 1.50309477 0.61979231
73 -2.09714352 1.50309477
74 0.32275621 -2.09714352
75 1.19810925 0.32275621
76 3.80858753 1.19810925
77 0.83917137 3.80858753
78 2.02238778 0.83917137
79 -0.41870916 2.02238778
80 0.35175001 -0.41870916
81 0.01875757 0.35175001
82 1.93638448 0.01875757
83 -2.78574878 1.93638448
84 0.43980631 -2.78574878
85 0.73192846 0.43980631
86 1.76133558 0.73192846
87 -0.79389324 1.76133558
88 0.70459240 -0.79389324
89 0.99037133 0.70459240
90 0.09331624 0.99037133
91 -0.07651022 0.09331624
92 -1.42548382 -0.07651022
93 2.26740384 -1.42548382
94 1.53206713 2.26740384
95 3.92777958 1.53206713
96 -0.47219751 3.92777958
97 2.05267352 -0.47219751
98 -0.98117594 2.05267352
99 1.23568648 -0.98117594
100 1.53680141 1.23568648
101 -0.88781022 1.53680141
102 1.44115730 -0.88781022
103 0.99659472 1.44115730
104 1.85927688 0.99659472
105 0.29463265 1.85927688
106 -3.56853713 0.29463265
107 2.44383089 -3.56853713
108 0.38385860 2.44383089
109 -0.19185372 0.38385860
110 -0.47729749 -0.19185372
111 0.70523767 -0.47729749
112 0.54210501 0.70523767
113 1.31681816 0.54210501
114 -1.78045613 1.31681816
115 0.28130189 -1.78045613
116 0.77329284 0.28130189
117 0.85175778 0.77329284
118 0.47322302 0.85175778
119 1.64111587 0.47322302
120 -0.89115448 1.64111587
121 -1.35721915 -0.89115448
122 1.73826619 -1.35721915
123 -4.13836843 1.73826619
124 1.50969597 -4.13836843
125 -0.43396887 1.50969597
126 -1.05533171 -0.43396887
127 -1.14114878 -1.05533171
128 0.38973925 -1.14114878
129 -2.50187474 0.38973925
130 -1.84214487 -2.50187474
131 -6.04489768 -1.84214487
132 0.07307125 -6.04489768
133 0.27619839 0.07307125
134 1.68009259 0.27619839
135 1.36408451 1.68009259
136 -4.66849938 1.36408451
137 4.59299690 -4.66849938
138 -0.83675959 4.59299690
139 0.85418928 -0.83675959
140 1.63739023 0.85418928
141 1.50273427 1.63739023
142 0.67875398 1.50273427
143 -3.18113068 0.67875398
144 0.79635421 -3.18113068
145 -1.40829054 0.79635421
146 -0.73665039 -1.40829054
147 0.75246254 -0.73665039
148 -2.94598622 0.75246254
149 -0.31006294 -2.94598622
150 NA -0.31006294
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.07589832 -3.33363759
[2,] 3.05523001 -0.07589832
[3,] 0.41070132 3.05523001
[4,] -1.22680306 0.41070132
[5,] -2.70059137 -1.22680306
[6,] 4.24265854 -2.70059137
[7,] -1.37566813 4.24265854
[8,] -2.12386931 -1.37566813
[9,] 0.43347636 -2.12386931
[10,] 0.73473417 0.43347636
[11,] 0.79925969 0.73473417
[12,] 0.72388003 0.79925969
[13,] -0.24511985 0.72388003
[14,] 2.19344431 -0.24511985
[15,] -1.05355220 2.19344431
[16,] 3.22407034 -1.05355220
[17,] 1.09388808 3.22407034
[18,] -0.61444270 1.09388808
[19,] 1.48087636 -0.61444270
[20,] 3.54299518 1.48087636
[21,] 1.31018155 3.54299518
[22,] 1.19246884 1.31018155
[23,] 1.78197124 1.19246884
[24,] 0.94806430 1.78197124
[25,] 1.47690478 0.94806430
[26,] -1.97027280 1.47690478
[27,] -1.20534048 -1.97027280
[28,] -3.02394791 -1.20534048
[29,] -1.22841434 -3.02394791
[30,] 0.30728445 -1.22841434
[31,] -1.87820661 0.30728445
[32,] -7.85645934 -1.87820661
[33,] -3.76140674 -7.85645934
[34,] -0.78345365 -3.76140674
[35,] -0.90274586 -0.78345365
[36,] 1.34482790 -0.90274586
[37,] 2.37002272 1.34482790
[38,] -1.71403093 2.37002272
[39,] 2.33364482 -1.71403093
[40,] -1.09536071 2.33364482
[41,] -1.40254855 -1.09536071
[42,] -3.43456572 -1.40254855
[43,] -0.53839924 -3.43456572
[44,] 0.46353699 -0.53839924
[45,] -1.53901235 0.46353699
[46,] 1.75649652 -1.53901235
[47,] -1.25794454 1.75649652
[48,] -3.53641148 -1.25794454
[49,] 0.92083742 -3.53641148
[50,] -5.07770922 0.92083742
[51,] -0.64818059 -5.07770922
[52,] 0.15637231 -0.64818059
[53,] 0.86326633 0.15637231
[54,] -0.17653146 0.86326633
[55,] 0.69070985 -0.17653146
[56,] -1.83488614 0.69070985
[57,] -0.01463495 -1.83488614
[58,] -0.07128772 -0.01463495
[59,] -2.52372971 -0.07128772
[60,] 2.85141586 -2.52372971
[61,] 0.73357512 2.85141586
[62,] 3.33054778 0.73357512
[63,] 2.39376662 3.33054778
[64,] -2.78000392 2.39376662
[65,] 0.04624411 -2.78000392
[66,] -4.29513819 0.04624411
[67,] 1.51001169 -4.29513819
[68,] 2.12029414 1.51001169
[69,] 1.53130122 2.12029414
[70,] 0.63676366 1.53130122
[71,] 0.61979231 0.63676366
[72,] 1.50309477 0.61979231
[73,] -2.09714352 1.50309477
[74,] 0.32275621 -2.09714352
[75,] 1.19810925 0.32275621
[76,] 3.80858753 1.19810925
[77,] 0.83917137 3.80858753
[78,] 2.02238778 0.83917137
[79,] -0.41870916 2.02238778
[80,] 0.35175001 -0.41870916
[81,] 0.01875757 0.35175001
[82,] 1.93638448 0.01875757
[83,] -2.78574878 1.93638448
[84,] 0.43980631 -2.78574878
[85,] 0.73192846 0.43980631
[86,] 1.76133558 0.73192846
[87,] -0.79389324 1.76133558
[88,] 0.70459240 -0.79389324
[89,] 0.99037133 0.70459240
[90,] 0.09331624 0.99037133
[91,] -0.07651022 0.09331624
[92,] -1.42548382 -0.07651022
[93,] 2.26740384 -1.42548382
[94,] 1.53206713 2.26740384
[95,] 3.92777958 1.53206713
[96,] -0.47219751 3.92777958
[97,] 2.05267352 -0.47219751
[98,] -0.98117594 2.05267352
[99,] 1.23568648 -0.98117594
[100,] 1.53680141 1.23568648
[101,] -0.88781022 1.53680141
[102,] 1.44115730 -0.88781022
[103,] 0.99659472 1.44115730
[104,] 1.85927688 0.99659472
[105,] 0.29463265 1.85927688
[106,] -3.56853713 0.29463265
[107,] 2.44383089 -3.56853713
[108,] 0.38385860 2.44383089
[109,] -0.19185372 0.38385860
[110,] -0.47729749 -0.19185372
[111,] 0.70523767 -0.47729749
[112,] 0.54210501 0.70523767
[113,] 1.31681816 0.54210501
[114,] -1.78045613 1.31681816
[115,] 0.28130189 -1.78045613
[116,] 0.77329284 0.28130189
[117,] 0.85175778 0.77329284
[118,] 0.47322302 0.85175778
[119,] 1.64111587 0.47322302
[120,] -0.89115448 1.64111587
[121,] -1.35721915 -0.89115448
[122,] 1.73826619 -1.35721915
[123,] -4.13836843 1.73826619
[124,] 1.50969597 -4.13836843
[125,] -0.43396887 1.50969597
[126,] -1.05533171 -0.43396887
[127,] -1.14114878 -1.05533171
[128,] 0.38973925 -1.14114878
[129,] -2.50187474 0.38973925
[130,] -1.84214487 -2.50187474
[131,] -6.04489768 -1.84214487
[132,] 0.07307125 -6.04489768
[133,] 0.27619839 0.07307125
[134,] 1.68009259 0.27619839
[135,] 1.36408451 1.68009259
[136,] -4.66849938 1.36408451
[137,] 4.59299690 -4.66849938
[138,] -0.83675959 4.59299690
[139,] 0.85418928 -0.83675959
[140,] 1.63739023 0.85418928
[141,] 1.50273427 1.63739023
[142,] 0.67875398 1.50273427
[143,] -3.18113068 0.67875398
[144,] 0.79635421 -3.18113068
[145,] -1.40829054 0.79635421
[146,] -0.73665039 -1.40829054
[147,] 0.75246254 -0.73665039
[148,] -2.94598622 0.75246254
[149,] -0.31006294 -2.94598622
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.07589832 -3.33363759
2 3.05523001 -0.07589832
3 0.41070132 3.05523001
4 -1.22680306 0.41070132
5 -2.70059137 -1.22680306
6 4.24265854 -2.70059137
7 -1.37566813 4.24265854
8 -2.12386931 -1.37566813
9 0.43347636 -2.12386931
10 0.73473417 0.43347636
11 0.79925969 0.73473417
12 0.72388003 0.79925969
13 -0.24511985 0.72388003
14 2.19344431 -0.24511985
15 -1.05355220 2.19344431
16 3.22407034 -1.05355220
17 1.09388808 3.22407034
18 -0.61444270 1.09388808
19 1.48087636 -0.61444270
20 3.54299518 1.48087636
21 1.31018155 3.54299518
22 1.19246884 1.31018155
23 1.78197124 1.19246884
24 0.94806430 1.78197124
25 1.47690478 0.94806430
26 -1.97027280 1.47690478
27 -1.20534048 -1.97027280
28 -3.02394791 -1.20534048
29 -1.22841434 -3.02394791
30 0.30728445 -1.22841434
31 -1.87820661 0.30728445
32 -7.85645934 -1.87820661
33 -3.76140674 -7.85645934
34 -0.78345365 -3.76140674
35 -0.90274586 -0.78345365
36 1.34482790 -0.90274586
37 2.37002272 1.34482790
38 -1.71403093 2.37002272
39 2.33364482 -1.71403093
40 -1.09536071 2.33364482
41 -1.40254855 -1.09536071
42 -3.43456572 -1.40254855
43 -0.53839924 -3.43456572
44 0.46353699 -0.53839924
45 -1.53901235 0.46353699
46 1.75649652 -1.53901235
47 -1.25794454 1.75649652
48 -3.53641148 -1.25794454
49 0.92083742 -3.53641148
50 -5.07770922 0.92083742
51 -0.64818059 -5.07770922
52 0.15637231 -0.64818059
53 0.86326633 0.15637231
54 -0.17653146 0.86326633
55 0.69070985 -0.17653146
56 -1.83488614 0.69070985
57 -0.01463495 -1.83488614
58 -0.07128772 -0.01463495
59 -2.52372971 -0.07128772
60 2.85141586 -2.52372971
61 0.73357512 2.85141586
62 3.33054778 0.73357512
63 2.39376662 3.33054778
64 -2.78000392 2.39376662
65 0.04624411 -2.78000392
66 -4.29513819 0.04624411
67 1.51001169 -4.29513819
68 2.12029414 1.51001169
69 1.53130122 2.12029414
70 0.63676366 1.53130122
71 0.61979231 0.63676366
72 1.50309477 0.61979231
73 -2.09714352 1.50309477
74 0.32275621 -2.09714352
75 1.19810925 0.32275621
76 3.80858753 1.19810925
77 0.83917137 3.80858753
78 2.02238778 0.83917137
79 -0.41870916 2.02238778
80 0.35175001 -0.41870916
81 0.01875757 0.35175001
82 1.93638448 0.01875757
83 -2.78574878 1.93638448
84 0.43980631 -2.78574878
85 0.73192846 0.43980631
86 1.76133558 0.73192846
87 -0.79389324 1.76133558
88 0.70459240 -0.79389324
89 0.99037133 0.70459240
90 0.09331624 0.99037133
91 -0.07651022 0.09331624
92 -1.42548382 -0.07651022
93 2.26740384 -1.42548382
94 1.53206713 2.26740384
95 3.92777958 1.53206713
96 -0.47219751 3.92777958
97 2.05267352 -0.47219751
98 -0.98117594 2.05267352
99 1.23568648 -0.98117594
100 1.53680141 1.23568648
101 -0.88781022 1.53680141
102 1.44115730 -0.88781022
103 0.99659472 1.44115730
104 1.85927688 0.99659472
105 0.29463265 1.85927688
106 -3.56853713 0.29463265
107 2.44383089 -3.56853713
108 0.38385860 2.44383089
109 -0.19185372 0.38385860
110 -0.47729749 -0.19185372
111 0.70523767 -0.47729749
112 0.54210501 0.70523767
113 1.31681816 0.54210501
114 -1.78045613 1.31681816
115 0.28130189 -1.78045613
116 0.77329284 0.28130189
117 0.85175778 0.77329284
118 0.47322302 0.85175778
119 1.64111587 0.47322302
120 -0.89115448 1.64111587
121 -1.35721915 -0.89115448
122 1.73826619 -1.35721915
123 -4.13836843 1.73826619
124 1.50969597 -4.13836843
125 -0.43396887 1.50969597
126 -1.05533171 -0.43396887
127 -1.14114878 -1.05533171
128 0.38973925 -1.14114878
129 -2.50187474 0.38973925
130 -1.84214487 -2.50187474
131 -6.04489768 -1.84214487
132 0.07307125 -6.04489768
133 0.27619839 0.07307125
134 1.68009259 0.27619839
135 1.36408451 1.68009259
136 -4.66849938 1.36408451
137 4.59299690 -4.66849938
138 -0.83675959 4.59299690
139 0.85418928 -0.83675959
140 1.63739023 0.85418928
141 1.50273427 1.63739023
142 0.67875398 1.50273427
143 -3.18113068 0.67875398
144 0.79635421 -3.18113068
145 -1.40829054 0.79635421
146 -0.73665039 -1.40829054
147 0.75246254 -0.73665039
148 -2.94598622 0.75246254
149 -0.31006294 -2.94598622
> 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/791tg1290473590.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kts11290473590.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kts11290473590.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kts11290473590.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11y3qs1290473590.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/129u7v1290473590.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/13yvmo1290473590.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/1484m91290473590.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/15cnkx1290473590.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/168eio1290473590.tab")
+ }
>
> try(system("convert tmp/161va1290473590.ps tmp/161va1290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/261va1290473590.ps tmp/261va1290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/361va1290473590.ps tmp/361va1290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/4haud1290473590.ps tmp/4haud1290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/5haud1290473590.ps tmp/5haud1290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/6haud1290473590.ps tmp/6haud1290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/791tg1290473590.ps tmp/791tg1290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kts11290473590.ps tmp/8kts11290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kts11290473590.ps tmp/9kts11290473590.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kts11290473590.ps tmp/10kts11290473590.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.895 1.682 10.283