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(12
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+ ,dim=c(10
+ ,145)
+ ,dimnames=list(c('Depression'
+ ,'CriticParents'
+ ,'ExpecParents'
+ ,'FutureWorrying'
+ ,'SleepDepri'
+ ,'ChangesLastYear'
+ ,'FreqSmoking'
+ ,'FreqHighAlc'
+ ,'FreqBeerOrWine'
+ ,'Month')
+ ,1:145))
> y <- array(NA,dim=c(10,145),dimnames=list(c('Depression','CriticParents','ExpecParents','FutureWorrying','SleepDepri','ChangesLastYear','FreqSmoking','FreqHighAlc','FreqBeerOrWine','Month'),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
Depression CriticParents ExpecParents FutureWorrying SleepDepri
1 12 6 15 4 7
2 11 6 15 3 5
3 14 13 14 5 7
4 12 8 10 3 3
5 21 7 10 6 7
6 12 9 12 5 7
7 22 5 18 6 7
8 11 8 12 6 1
9 10 9 14 5 4
10 13 11 18 5 5
11 10 8 9 3 6
12 8 11 11 5 4
13 15 12 11 7 7
14 10 8 17 5 6
15 14 7 8 5 2
16 14 9 16 3 2
17 11 12 21 5 6
18 10 20 24 6 7
19 13 7 21 5 5
20 7 8 14 2 2
21 12 8 7 5 7
22 14 16 18 4 4
23 11 10 18 6 5
24 9 6 13 3 5
25 11 8 11 5 5
26 15 9 13 4 3
27 13 9 13 5 5
28 9 11 18 2 1
29 15 12 14 2 1
30 10 8 12 5 3
31 11 7 9 2 2
32 13 8 12 2 3
33 8 9 8 2 2
34 20 4 5 5 5
35 12 8 10 5 2
36 10 8 11 1 3
37 10 8 11 5 4
38 9 6 12 2 6
39 14 8 12 6 2
40 8 4 15 1 7
41 14 7 12 4 6
42 11 14 16 3 5
43 13 10 14 2 3
44 11 9 17 5 3
45 11 8 10 3 4
46 10 11 17 4 5
47 14 8 12 3 2
48 18 8 13 6 7
49 14 10 13 4 6
50 11 8 11 5 5
51 12 10 13 2 6
52 13 7 12 5 5
53 9 8 12 5 2
54 10 7 12 3 3
55 15 9 9 5 5
56 20 5 7 7 7
57 12 7 17 4 4
58 12 7 12 2 7
59 14 7 12 3 5
60 13 9 9 6 6
61 11 5 9 7 6
62 17 8 13 4 3
63 12 8 10 4 5
64 13 8 11 4 7
65 14 9 12 5 7
66 13 6 10 2 5
67 15 8 13 3 6
68 13 6 6 3 5
69 10 4 7 4 5
70 11 6 13 3 2
71 13 4 11 4 5
72 17 12 18 6 4
73 13 6 9 2 6
74 9 11 9 4 5
75 11 8 11 5 3
76 10 10 11 2 3
77 9 10 15 1 4
78 12 4 8 2 2
79 12 8 11 5 2
80 13 9 14 4 5
81 13 9 14 4 4
82 22 7 12 6 6
83 13 7 12 1 4
84 15 11 8 4 6
85 13 8 11 5 4
86 15 8 10 2 2
87 10 7 17 3 5
88 11 5 16 3 2
89 16 7 13 6 7
90 11 9 15 5 1
91 11 8 11 4 3
92 10 6 12 4 5
93 10 8 16 5 6
94 16 10 20 5 6
95 12 10 16 6 2
96 11 8 11 6 5
97 16 11 15 5 5
98 19 8 15 7 3
99 11 8 12 5 6
100 15 6 9 5 5
101 24 20 24 7 7
102 14 6 15 5 1
103 15 12 18 6 6
104 11 9 17 6 4
105 15 5 12 4 7
106 12 10 15 5 2
107 10 5 11 1 6
108 14 6 11 6 7
109 9 6 12 5 5
110 15 10 14 2 2
111 15 5 11 1 1
112 14 13 20 5 3
113 11 7 11 6 3
114 8 9 12 5 3
115 11 8 12 5 5
116 8 5 11 4 2
117 10 4 10 2 4
118 11 9 11 3 6
119 13 7 12 3 5
120 11 5 9 5 5
121 20 5 8 3 2
122 10 4 6 2 3
123 12 7 12 2 2
124 14 9 15 3 6
125 23 8 13 6 5
126 14 8 17 5 4
127 16 11 14 6 6
128 11 10 16 2 4
129 12 9 15 5 6
130 10 12 16 5 2
131 14 10 11 5 0
132 12 10 11 1 1
133 12 7 16 4 5
134 11 10 15 2 2
135 12 6 14 2 5
136 13 6 9 7 6
137 17 11 13 6 7
138 11 8 11 5 5
139 12 9 14 5 5
140 19 9 11 5 5
141 15 11 8 4 6
142 14 4 7 3 6
143 11 9 11 3 6
144 9 5 13 3 1
145 18 4 9 2 3
ChangesLastYear FreqSmoking FreqHighAlc FreqBeerOrWine Month t
1 2 2 2 2 9 1
2 4 1 2 2 9 2
3 7 4 3 4 9 3
4 3 1 2 3 9 4
5 7 5 4 4 9 5
6 2 1 2 3 9 6
7 7 1 2 3 9 7
8 2 1 3 4 9 8
9 1 1 2 3 9 9
10 2 1 2 4 9 10
11 6 2 3 3 9 11
12 1 1 2 2 9 12
13 1 3 3 3 9 13
14 1 1 1 3 9 14
15 2 1 3 3 9 15
16 2 1 1 2 9 16
17 2 1 3 3 9 17
18 1 1 2 2 9 18
19 7 2 3 4 9 19
20 1 4 4 5 9 20
21 2 1 3 3 9 21
22 4 2 3 3 9 22
23 2 1 1 1 9 23
24 1 2 2 4 9 24
25 1 3 1 3 9 25
26 5 1 3 4 9 26
27 2 1 3 3 9 27
28 1 1 2 3 9 28
29 3 1 2 1 9 29
30 1 1 3 4 9 30
31 2 2 2 4 9 31
32 5 1 2 2 9 32
33 2 1 2 2 9 33
34 6 1 1 1 9 34
35 4 1 2 3 9 35
36 1 1 3 4 9 36
37 3 1 1 1 9 37
38 6 1 2 3 9 38
39 7 2 3 3 9 39
40 4 1 2 2 9 40
41 1 2 1 4 9 41
42 5 1 1 3 9 42
43 3 1 3 3 9 43
44 2 2 3 2 9 44
45 2 1 3 3 9 45
46 2 1 3 2 9 46
47 2 1 2 1 9 47
48 1 1 3 3 9 48
49 2 1 2 3 9 49
50 1 4 3 5 9 50
51 2 2 4 1 9 51
52 2 1 3 3 9 52
53 5 1 3 4 9 53
54 5 4 3 3 9 54
55 2 2 3 4 9 55
56 1 1 2 2 9 56
57 1 1 3 3 9 57
58 2 1 3 4 9 58
59 3 1 1 1 9 59
60 7 1 1 1 9 60
61 4 1 1 1 10 61
62 4 2 4 4 10 62
63 1 1 3 2 10 63
64 2 1 2 3 10 64
65 2 2 3 4 10 65
66 2 1 1 2 10 66
67 5 2 4 5 10 67
68 1 2 3 3 10 68
69 6 4 2 3 10 69
70 2 1 3 3 10 70
71 2 1 3 4 10 71
72 4 3 3 4 10 72
73 6 1 2 3 10 73
74 2 1 1 1 10 74
75 2 1 1 3 10 75
76 2 1 1 1 10 76
77 1 1 3 3 10 77
78 1 1 4 5 10 78
79 2 1 2 3 10 79
80 2 1 2 3 10 80
81 3 4 2 4 10 81
82 3 1 2 5 10 82
83 5 1 3 4 10 83
84 2 2 4 4 10 84
85 5 1 2 4 10 85
86 3 1 3 4 10 86
87 1 1 3 4 10 87
88 2 1 2 3 10 88
89 2 1 2 4 10 89
90 1 1 3 3 10 90
91 2 1 3 3 10 91
92 2 1 3 3 10 92
93 5 1 3 4 10 93
94 5 1 3 3 10 94
95 2 1 3 4 10 95
96 3 1 2 2 10 96
97 5 5 3 5 10 97
98 5 1 3 3 10 98
99 6 1 2 4 10 99
100 2 1 1 2 10 100
101 7 3 3 4 10 101
102 1 1 2 3 10 102
103 1 1 2 4 10 103
104 6 1 3 3 10 104
105 6 1 1 1 10 105
106 2 1 3 4 10 106
107 1 1 2 4 10 107
108 2 1 2 2 10 108
109 1 4 2 5 10 109
110 2 4 2 4 10 110
111 1 1 2 4 10 111
112 3 1 3 3 10 112
113 3 1 3 4 10 113
114 6 4 3 4 10 114
115 4 2 3 4 10 115
116 1 1 3 3 10 116
117 2 1 1 5 10 117
118 5 1 3 3 10 118
119 6 1 4 4 10 119
120 3 1 2 4 10 120
121 5 1 2 4 10 121
122 3 2 4 4 10 122
123 2 4 3 4 10 123
124 3 4 2 5 10 124
125 2 1 3 3 10 125
126 5 1 1 1 10 126
127 5 1 2 4 10 127
128 7 2 4 4 10 128
129 4 1 3 3 10 129
130 4 1 3 4 10 130
131 5 1 3 4 10 131
132 1 3 2 4 10 132
133 4 2 4 4 10 133
134 1 2 1 4 10 134
135 4 1 3 4 10 135
136 6 1 1 3 10 136
137 7 2 2 5 10 137
138 1 3 1 3 9 138
139 3 1 2 4 10 139
140 5 1 4 4 9 140
141 2 2 4 4 10 141
142 4 2 3 4 10 142
143 5 1 3 3 10 143
144 1 1 1 4 10 144
145 2 1 4 4 10 145
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CriticParents ExpecParents FutureWorrying
7.114274 0.046818 -0.061072 0.586102
SleepDepri ChangesLastYear FreqSmoking FreqHighAlc
0.214964 0.335216 -0.086396 0.284326
FreqBeerOrWine Month t
0.192260 0.002011 0.006438
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.4199 -1.9334 -0.2093 1.3471 8.8751
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.114274 7.483087 0.951 0.343461
CriticParents 0.046818 0.116614 0.401 0.688707
ExpecParents -0.061072 0.087154 -0.701 0.484685
FutureWorrying 0.586102 0.168914 3.470 0.000701 ***
SleepDepri 0.214964 0.142253 1.511 0.133107
ChangesLastYear 0.335216 0.136964 2.447 0.015681 *
FreqSmoking -0.086396 0.276683 -0.312 0.755332
FreqHighAlc 0.284326 0.316307 0.899 0.370322
FreqBeerOrWine 0.192260 0.297069 0.647 0.518617
Month 0.002011 0.830247 0.002 0.998071
t 0.006438 0.009936 0.648 0.518125
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.91 on 134 degrees of freedom
Multiple R-squared: 0.2118, Adjusted R-squared: 0.153
F-statistic: 3.602 on 10 and 134 DF, p-value: 0.0002913
> 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,] 0.68311728 0.63376544 0.31688272
[2,] 0.64768638 0.70462724 0.35231362
[3,] 0.74095533 0.51808933 0.25904467
[4,] 0.63479671 0.73040657 0.36520329
[5,] 0.54027387 0.91945226 0.45972613
[6,] 0.66502947 0.66994105 0.33497053
[7,] 0.58788329 0.82423342 0.41211671
[8,] 0.50723166 0.98553668 0.49276834
[9,] 0.53107511 0.93784979 0.46892489
[10,] 0.58052719 0.83894562 0.41947281
[11,] 0.50007083 0.99985835 0.49992917
[12,] 0.42643383 0.85286766 0.57356617
[13,] 0.38676496 0.77352993 0.61323504
[14,] 0.32520347 0.65040694 0.67479653
[15,] 0.27715487 0.55430975 0.72284513
[16,] 0.33116925 0.66233850 0.66883075
[17,] 0.28417813 0.56835626 0.71582187
[18,] 0.23261708 0.46523415 0.76738292
[19,] 0.18945085 0.37890169 0.81054915
[20,] 0.18492290 0.36984580 0.81507710
[21,] 0.20071539 0.40143079 0.79928461
[22,] 0.20744876 0.41489752 0.79255124
[23,] 0.20378804 0.40757608 0.79621196
[24,] 0.25558002 0.51116004 0.74441998
[25,] 0.30164547 0.60329094 0.69835453
[26,] 0.30557336 0.61114672 0.69442664
[27,] 0.27744161 0.55488323 0.72255839
[28,] 0.31983721 0.63967442 0.68016279
[29,] 0.28153537 0.56307075 0.71846463
[30,] 0.29021798 0.58043596 0.70978202
[31,] 0.24584979 0.49169959 0.75415021
[32,] 0.20852565 0.41705129 0.79147435
[33,] 0.18373691 0.36747381 0.81626309
[34,] 0.19197939 0.38395878 0.80802061
[35,] 0.29475249 0.58950498 0.70524751
[36,] 0.26770114 0.53540229 0.73229886
[37,] 0.24273042 0.48546085 0.75726958
[38,] 0.20460513 0.40921025 0.79539487
[39,] 0.16836777 0.33673554 0.83163223
[40,] 0.25623297 0.51246593 0.74376703
[41,] 0.25444630 0.50889261 0.74555370
[42,] 0.23446477 0.46892955 0.76553523
[43,] 0.30857082 0.61714164 0.69142918
[44,] 0.26758618 0.53517236 0.73241382
[45,] 0.23814316 0.47628631 0.76185684
[46,] 0.20848488 0.41696977 0.79151512
[47,] 0.23563256 0.47126512 0.76436744
[48,] 0.20036091 0.40072182 0.79963909
[49,] 0.32134038 0.64268076 0.67865962
[50,] 0.27606000 0.55212000 0.72394000
[51,] 0.23634562 0.47269124 0.76365438
[52,] 0.20008085 0.40016170 0.79991915
[53,] 0.18418714 0.36837428 0.81581286
[54,] 0.15535483 0.31070965 0.84464517
[55,] 0.12976795 0.25953589 0.87023205
[56,] 0.14305223 0.28610445 0.85694777
[57,] 0.11630248 0.23260496 0.88369752
[58,] 0.09319604 0.18639208 0.90680396
[59,] 0.09499810 0.18999619 0.90500190
[60,] 0.07546007 0.15092014 0.92453993
[61,] 0.07648789 0.15297577 0.92351211
[62,] 0.06321419 0.12642838 0.93678581
[63,] 0.05051232 0.10102464 0.94948768
[64,] 0.04452400 0.08904800 0.95547600
[65,] 0.03404895 0.06809790 0.96595105
[66,] 0.02599383 0.05198766 0.97400617
[67,] 0.01963392 0.03926785 0.98036608
[68,] 0.01483490 0.02966979 0.98516510
[69,] 0.06788011 0.13576023 0.93211989
[70,] 0.05306352 0.10612704 0.94693648
[71,] 0.04127527 0.08255055 0.95872473
[72,] 0.03357542 0.06715085 0.96642458
[73,] 0.03360835 0.06721669 0.96639165
[74,] 0.02879964 0.05759928 0.97120036
[75,] 0.02127360 0.04254720 0.97872640
[76,] 0.01845977 0.03691954 0.98154023
[77,] 0.01439521 0.02879041 0.98560479
[78,] 0.01164000 0.02328001 0.98836000
[79,] 0.01152213 0.02304426 0.98847787
[80,] 0.01782286 0.03564573 0.98217714
[81,] 0.01375589 0.02751178 0.98624411
[82,] 0.01086319 0.02172639 0.98913681
[83,] 0.01169790 0.02339581 0.98830210
[84,] 0.00981310 0.01962620 0.99018690
[85,] 0.01466812 0.02933625 0.98533188
[86,] 0.01748879 0.03497759 0.98251121
[87,] 0.01387276 0.02774553 0.98612724
[88,] 0.06661063 0.13322126 0.93338937
[89,] 0.05921331 0.11842662 0.94078669
[90,] 0.04895446 0.09790892 0.95104554
[91,] 0.05053498 0.10106996 0.94946502
[92,] 0.04182678 0.08365356 0.95817322
[93,] 0.03118546 0.06237092 0.96881454
[94,] 0.02494519 0.04989038 0.97505481
[95,] 0.01807416 0.03614831 0.98192584
[96,] 0.01658436 0.03316873 0.98341564
[97,] 0.02494335 0.04988669 0.97505665
[98,] 0.03635726 0.07271451 0.96364274
[99,] 0.02927812 0.05855624 0.97072188
[100,] 0.02562922 0.05125844 0.97437078
[101,] 0.04498449 0.08996898 0.95501551
[102,] 0.04141445 0.08282890 0.95858555
[103,] 0.06197242 0.12394484 0.93802758
[104,] 0.04687732 0.09375464 0.95312268
[105,] 0.04060988 0.08121975 0.95939012
[106,] 0.03061688 0.06123376 0.96938312
[107,] 0.06779286 0.13558572 0.93220714
[108,] 0.14510988 0.29021976 0.85489012
[109,] 0.36688392 0.73376784 0.63311608
[110,] 0.31038719 0.62077438 0.68961281
[111,] 0.23821627 0.47643255 0.76178373
[112,] 0.48825140 0.97650280 0.51174860
[113,] 0.91401626 0.17196748 0.08598374
[114,] 0.86617786 0.26764427 0.13382214
[115,] 0.79735691 0.40528617 0.20264309
[116,] 0.74574545 0.50850910 0.25425455
[117,] 0.67984312 0.64031375 0.32015688
[118,] 0.61044567 0.77910866 0.38955433
> postscript(file="/var/www/rcomp/tmp/1u3ww1290542328.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2u3ww1290542328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3u3ww1290542328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4u3ww1290542328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/54ceh1290542328.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
0.196389332 -0.550845616 -0.963519201 0.610168613 5.276195073 -1.024225783
7 8 9 10 11 12
7.260858966 -1.763185332 -1.941287234 0.460485921 -3.344437841 -4.045192124
13 14 15 16 17 18
0.780661691 -1.889044936 1.557678696 3.879293622 -1.755212174 -2.942243753
19 20 21 22 23 24
-1.100975562 -3.471036383 -1.663658763 1.274105662 -1.301382848 -2.107096120
25 26 27 28 29 30
-0.938534837 1.871816059 0.047255258 -0.509756782 4.906788491 -2.413427112
31 32 33 34 35 36
0.452513132 1.659984673 -2.416946578 6.360017023 -0.881856256 -0.168719309
37 38 39 40 41 42
-2.259527071 -3.457373727 -0.575143180 -2.959569442 2.158832820 -1.364978122
43 44 45 46 47 48
1.811522024 -1.109315902 -0.817854051 -2.146050268 3.390187999 4.278066336
49 50 51 52 53 54
1.514270145 -1.966254981 0.575860163 -0.081124040 -4.687394143 -2.238328235
55 56 57 58 59 60
1.516847340 5.891072945 0.328327163 0.016365802 2.663970964 -1.933449801
61 62 63 64 65 66
-3.335080336 3.822149244 -0.209334840 0.172221511 0.203745522 2.270627996
67 68 69 70 71 72
1.203693871 1.088063304 -3.568729027 -0.274029037 0.267772155 2.859412492
73 74 75 76 77 78
0.132078866 -3.055979618 -1.340512090 -0.297762145 -1.306732077 0.715218197
79 80 81 82 83 84
-0.435625171 0.635542896 0.575780978 7.487257837 1.078758010 1.260244611
85 86 87 88 89 90
-1.102087045 3.404741641 -1.687938799 0.124450918 1.815776482 -1.043118102
91 92 93 94 95 96
-1.426067220 -2.707725487 -4.562485305 1.773987221 -1.389594453 -2.919017180
97 98 99 100 101 102
1.578524884 4.009203596 -3.896288070 2.232368169 7.427961547 2.304409501
103 104 105 106 107 108
1.347095819 -3.918176089 1.437782454 -0.935379934 -0.847922759 0.002653299
109 110 111 112 113 114
-3.909059135 4.279615213 5.201147350 0.832977453 -3.220558125 -6.419919053
115 116 117 118 119 120
-3.311827039 -3.896376743 -1.325876080 -2.711143039 -1.159710366 -2.853630218
121 122 123 124 125 126
7.225524546 -2.296926646 0.929909076 1.323942643 8.875061114 0.861500695
127 128 129 130 131 132
0.654258121 -2.561572008 -2.374658797 -3.792882008 0.083670681 2.004655318
133 134 135 136 137 138
-1.834826461 0.632931495 -0.552893634 -2.919680822 0.537549174 -1.666000948
139 140 141 142 143 144
-1.857861488 3.715412546 0.893292679 0.353506372 -2.872086869 -1.777031822
145
5.987040184
> postscript(file="/var/www/rcomp/tmp/64ceh1290542328.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 0.196389332 NA
1 -0.550845616 0.196389332
2 -0.963519201 -0.550845616
3 0.610168613 -0.963519201
4 5.276195073 0.610168613
5 -1.024225783 5.276195073
6 7.260858966 -1.024225783
7 -1.763185332 7.260858966
8 -1.941287234 -1.763185332
9 0.460485921 -1.941287234
10 -3.344437841 0.460485921
11 -4.045192124 -3.344437841
12 0.780661691 -4.045192124
13 -1.889044936 0.780661691
14 1.557678696 -1.889044936
15 3.879293622 1.557678696
16 -1.755212174 3.879293622
17 -2.942243753 -1.755212174
18 -1.100975562 -2.942243753
19 -3.471036383 -1.100975562
20 -1.663658763 -3.471036383
21 1.274105662 -1.663658763
22 -1.301382848 1.274105662
23 -2.107096120 -1.301382848
24 -0.938534837 -2.107096120
25 1.871816059 -0.938534837
26 0.047255258 1.871816059
27 -0.509756782 0.047255258
28 4.906788491 -0.509756782
29 -2.413427112 4.906788491
30 0.452513132 -2.413427112
31 1.659984673 0.452513132
32 -2.416946578 1.659984673
33 6.360017023 -2.416946578
34 -0.881856256 6.360017023
35 -0.168719309 -0.881856256
36 -2.259527071 -0.168719309
37 -3.457373727 -2.259527071
38 -0.575143180 -3.457373727
39 -2.959569442 -0.575143180
40 2.158832820 -2.959569442
41 -1.364978122 2.158832820
42 1.811522024 -1.364978122
43 -1.109315902 1.811522024
44 -0.817854051 -1.109315902
45 -2.146050268 -0.817854051
46 3.390187999 -2.146050268
47 4.278066336 3.390187999
48 1.514270145 4.278066336
49 -1.966254981 1.514270145
50 0.575860163 -1.966254981
51 -0.081124040 0.575860163
52 -4.687394143 -0.081124040
53 -2.238328235 -4.687394143
54 1.516847340 -2.238328235
55 5.891072945 1.516847340
56 0.328327163 5.891072945
57 0.016365802 0.328327163
58 2.663970964 0.016365802
59 -1.933449801 2.663970964
60 -3.335080336 -1.933449801
61 3.822149244 -3.335080336
62 -0.209334840 3.822149244
63 0.172221511 -0.209334840
64 0.203745522 0.172221511
65 2.270627996 0.203745522
66 1.203693871 2.270627996
67 1.088063304 1.203693871
68 -3.568729027 1.088063304
69 -0.274029037 -3.568729027
70 0.267772155 -0.274029037
71 2.859412492 0.267772155
72 0.132078866 2.859412492
73 -3.055979618 0.132078866
74 -1.340512090 -3.055979618
75 -0.297762145 -1.340512090
76 -1.306732077 -0.297762145
77 0.715218197 -1.306732077
78 -0.435625171 0.715218197
79 0.635542896 -0.435625171
80 0.575780978 0.635542896
81 7.487257837 0.575780978
82 1.078758010 7.487257837
83 1.260244611 1.078758010
84 -1.102087045 1.260244611
85 3.404741641 -1.102087045
86 -1.687938799 3.404741641
87 0.124450918 -1.687938799
88 1.815776482 0.124450918
89 -1.043118102 1.815776482
90 -1.426067220 -1.043118102
91 -2.707725487 -1.426067220
92 -4.562485305 -2.707725487
93 1.773987221 -4.562485305
94 -1.389594453 1.773987221
95 -2.919017180 -1.389594453
96 1.578524884 -2.919017180
97 4.009203596 1.578524884
98 -3.896288070 4.009203596
99 2.232368169 -3.896288070
100 7.427961547 2.232368169
101 2.304409501 7.427961547
102 1.347095819 2.304409501
103 -3.918176089 1.347095819
104 1.437782454 -3.918176089
105 -0.935379934 1.437782454
106 -0.847922759 -0.935379934
107 0.002653299 -0.847922759
108 -3.909059135 0.002653299
109 4.279615213 -3.909059135
110 5.201147350 4.279615213
111 0.832977453 5.201147350
112 -3.220558125 0.832977453
113 -6.419919053 -3.220558125
114 -3.311827039 -6.419919053
115 -3.896376743 -3.311827039
116 -1.325876080 -3.896376743
117 -2.711143039 -1.325876080
118 -1.159710366 -2.711143039
119 -2.853630218 -1.159710366
120 7.225524546 -2.853630218
121 -2.296926646 7.225524546
122 0.929909076 -2.296926646
123 1.323942643 0.929909076
124 8.875061114 1.323942643
125 0.861500695 8.875061114
126 0.654258121 0.861500695
127 -2.561572008 0.654258121
128 -2.374658797 -2.561572008
129 -3.792882008 -2.374658797
130 0.083670681 -3.792882008
131 2.004655318 0.083670681
132 -1.834826461 2.004655318
133 0.632931495 -1.834826461
134 -0.552893634 0.632931495
135 -2.919680822 -0.552893634
136 0.537549174 -2.919680822
137 -1.666000948 0.537549174
138 -1.857861488 -1.666000948
139 3.715412546 -1.857861488
140 0.893292679 3.715412546
141 0.353506372 0.893292679
142 -2.872086869 0.353506372
143 -1.777031822 -2.872086869
144 5.987040184 -1.777031822
145 NA 5.987040184
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.550845616 0.196389332
[2,] -0.963519201 -0.550845616
[3,] 0.610168613 -0.963519201
[4,] 5.276195073 0.610168613
[5,] -1.024225783 5.276195073
[6,] 7.260858966 -1.024225783
[7,] -1.763185332 7.260858966
[8,] -1.941287234 -1.763185332
[9,] 0.460485921 -1.941287234
[10,] -3.344437841 0.460485921
[11,] -4.045192124 -3.344437841
[12,] 0.780661691 -4.045192124
[13,] -1.889044936 0.780661691
[14,] 1.557678696 -1.889044936
[15,] 3.879293622 1.557678696
[16,] -1.755212174 3.879293622
[17,] -2.942243753 -1.755212174
[18,] -1.100975562 -2.942243753
[19,] -3.471036383 -1.100975562
[20,] -1.663658763 -3.471036383
[21,] 1.274105662 -1.663658763
[22,] -1.301382848 1.274105662
[23,] -2.107096120 -1.301382848
[24,] -0.938534837 -2.107096120
[25,] 1.871816059 -0.938534837
[26,] 0.047255258 1.871816059
[27,] -0.509756782 0.047255258
[28,] 4.906788491 -0.509756782
[29,] -2.413427112 4.906788491
[30,] 0.452513132 -2.413427112
[31,] 1.659984673 0.452513132
[32,] -2.416946578 1.659984673
[33,] 6.360017023 -2.416946578
[34,] -0.881856256 6.360017023
[35,] -0.168719309 -0.881856256
[36,] -2.259527071 -0.168719309
[37,] -3.457373727 -2.259527071
[38,] -0.575143180 -3.457373727
[39,] -2.959569442 -0.575143180
[40,] 2.158832820 -2.959569442
[41,] -1.364978122 2.158832820
[42,] 1.811522024 -1.364978122
[43,] -1.109315902 1.811522024
[44,] -0.817854051 -1.109315902
[45,] -2.146050268 -0.817854051
[46,] 3.390187999 -2.146050268
[47,] 4.278066336 3.390187999
[48,] 1.514270145 4.278066336
[49,] -1.966254981 1.514270145
[50,] 0.575860163 -1.966254981
[51,] -0.081124040 0.575860163
[52,] -4.687394143 -0.081124040
[53,] -2.238328235 -4.687394143
[54,] 1.516847340 -2.238328235
[55,] 5.891072945 1.516847340
[56,] 0.328327163 5.891072945
[57,] 0.016365802 0.328327163
[58,] 2.663970964 0.016365802
[59,] -1.933449801 2.663970964
[60,] -3.335080336 -1.933449801
[61,] 3.822149244 -3.335080336
[62,] -0.209334840 3.822149244
[63,] 0.172221511 -0.209334840
[64,] 0.203745522 0.172221511
[65,] 2.270627996 0.203745522
[66,] 1.203693871 2.270627996
[67,] 1.088063304 1.203693871
[68,] -3.568729027 1.088063304
[69,] -0.274029037 -3.568729027
[70,] 0.267772155 -0.274029037
[71,] 2.859412492 0.267772155
[72,] 0.132078866 2.859412492
[73,] -3.055979618 0.132078866
[74,] -1.340512090 -3.055979618
[75,] -0.297762145 -1.340512090
[76,] -1.306732077 -0.297762145
[77,] 0.715218197 -1.306732077
[78,] -0.435625171 0.715218197
[79,] 0.635542896 -0.435625171
[80,] 0.575780978 0.635542896
[81,] 7.487257837 0.575780978
[82,] 1.078758010 7.487257837
[83,] 1.260244611 1.078758010
[84,] -1.102087045 1.260244611
[85,] 3.404741641 -1.102087045
[86,] -1.687938799 3.404741641
[87,] 0.124450918 -1.687938799
[88,] 1.815776482 0.124450918
[89,] -1.043118102 1.815776482
[90,] -1.426067220 -1.043118102
[91,] -2.707725487 -1.426067220
[92,] -4.562485305 -2.707725487
[93,] 1.773987221 -4.562485305
[94,] -1.389594453 1.773987221
[95,] -2.919017180 -1.389594453
[96,] 1.578524884 -2.919017180
[97,] 4.009203596 1.578524884
[98,] -3.896288070 4.009203596
[99,] 2.232368169 -3.896288070
[100,] 7.427961547 2.232368169
[101,] 2.304409501 7.427961547
[102,] 1.347095819 2.304409501
[103,] -3.918176089 1.347095819
[104,] 1.437782454 -3.918176089
[105,] -0.935379934 1.437782454
[106,] -0.847922759 -0.935379934
[107,] 0.002653299 -0.847922759
[108,] -3.909059135 0.002653299
[109,] 4.279615213 -3.909059135
[110,] 5.201147350 4.279615213
[111,] 0.832977453 5.201147350
[112,] -3.220558125 0.832977453
[113,] -6.419919053 -3.220558125
[114,] -3.311827039 -6.419919053
[115,] -3.896376743 -3.311827039
[116,] -1.325876080 -3.896376743
[117,] -2.711143039 -1.325876080
[118,] -1.159710366 -2.711143039
[119,] -2.853630218 -1.159710366
[120,] 7.225524546 -2.853630218
[121,] -2.296926646 7.225524546
[122,] 0.929909076 -2.296926646
[123,] 1.323942643 0.929909076
[124,] 8.875061114 1.323942643
[125,] 0.861500695 8.875061114
[126,] 0.654258121 0.861500695
[127,] -2.561572008 0.654258121
[128,] -2.374658797 -2.561572008
[129,] -3.792882008 -2.374658797
[130,] 0.083670681 -3.792882008
[131,] 2.004655318 0.083670681
[132,] -1.834826461 2.004655318
[133,] 0.632931495 -1.834826461
[134,] -0.552893634 0.632931495
[135,] -2.919680822 -0.552893634
[136,] 0.537549174 -2.919680822
[137,] -1.666000948 0.537549174
[138,] -1.857861488 -1.666000948
[139,] 3.715412546 -1.857861488
[140,] 0.893292679 3.715412546
[141,] 0.353506372 0.893292679
[142,] -2.872086869 0.353506372
[143,] -1.777031822 -2.872086869
[144,] 5.987040184 -1.777031822
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.550845616 0.196389332
2 -0.963519201 -0.550845616
3 0.610168613 -0.963519201
4 5.276195073 0.610168613
5 -1.024225783 5.276195073
6 7.260858966 -1.024225783
7 -1.763185332 7.260858966
8 -1.941287234 -1.763185332
9 0.460485921 -1.941287234
10 -3.344437841 0.460485921
11 -4.045192124 -3.344437841
12 0.780661691 -4.045192124
13 -1.889044936 0.780661691
14 1.557678696 -1.889044936
15 3.879293622 1.557678696
16 -1.755212174 3.879293622
17 -2.942243753 -1.755212174
18 -1.100975562 -2.942243753
19 -3.471036383 -1.100975562
20 -1.663658763 -3.471036383
21 1.274105662 -1.663658763
22 -1.301382848 1.274105662
23 -2.107096120 -1.301382848
24 -0.938534837 -2.107096120
25 1.871816059 -0.938534837
26 0.047255258 1.871816059
27 -0.509756782 0.047255258
28 4.906788491 -0.509756782
29 -2.413427112 4.906788491
30 0.452513132 -2.413427112
31 1.659984673 0.452513132
32 -2.416946578 1.659984673
33 6.360017023 -2.416946578
34 -0.881856256 6.360017023
35 -0.168719309 -0.881856256
36 -2.259527071 -0.168719309
37 -3.457373727 -2.259527071
38 -0.575143180 -3.457373727
39 -2.959569442 -0.575143180
40 2.158832820 -2.959569442
41 -1.364978122 2.158832820
42 1.811522024 -1.364978122
43 -1.109315902 1.811522024
44 -0.817854051 -1.109315902
45 -2.146050268 -0.817854051
46 3.390187999 -2.146050268
47 4.278066336 3.390187999
48 1.514270145 4.278066336
49 -1.966254981 1.514270145
50 0.575860163 -1.966254981
51 -0.081124040 0.575860163
52 -4.687394143 -0.081124040
53 -2.238328235 -4.687394143
54 1.516847340 -2.238328235
55 5.891072945 1.516847340
56 0.328327163 5.891072945
57 0.016365802 0.328327163
58 2.663970964 0.016365802
59 -1.933449801 2.663970964
60 -3.335080336 -1.933449801
61 3.822149244 -3.335080336
62 -0.209334840 3.822149244
63 0.172221511 -0.209334840
64 0.203745522 0.172221511
65 2.270627996 0.203745522
66 1.203693871 2.270627996
67 1.088063304 1.203693871
68 -3.568729027 1.088063304
69 -0.274029037 -3.568729027
70 0.267772155 -0.274029037
71 2.859412492 0.267772155
72 0.132078866 2.859412492
73 -3.055979618 0.132078866
74 -1.340512090 -3.055979618
75 -0.297762145 -1.340512090
76 -1.306732077 -0.297762145
77 0.715218197 -1.306732077
78 -0.435625171 0.715218197
79 0.635542896 -0.435625171
80 0.575780978 0.635542896
81 7.487257837 0.575780978
82 1.078758010 7.487257837
83 1.260244611 1.078758010
84 -1.102087045 1.260244611
85 3.404741641 -1.102087045
86 -1.687938799 3.404741641
87 0.124450918 -1.687938799
88 1.815776482 0.124450918
89 -1.043118102 1.815776482
90 -1.426067220 -1.043118102
91 -2.707725487 -1.426067220
92 -4.562485305 -2.707725487
93 1.773987221 -4.562485305
94 -1.389594453 1.773987221
95 -2.919017180 -1.389594453
96 1.578524884 -2.919017180
97 4.009203596 1.578524884
98 -3.896288070 4.009203596
99 2.232368169 -3.896288070
100 7.427961547 2.232368169
101 2.304409501 7.427961547
102 1.347095819 2.304409501
103 -3.918176089 1.347095819
104 1.437782454 -3.918176089
105 -0.935379934 1.437782454
106 -0.847922759 -0.935379934
107 0.002653299 -0.847922759
108 -3.909059135 0.002653299
109 4.279615213 -3.909059135
110 5.201147350 4.279615213
111 0.832977453 5.201147350
112 -3.220558125 0.832977453
113 -6.419919053 -3.220558125
114 -3.311827039 -6.419919053
115 -3.896376743 -3.311827039
116 -1.325876080 -3.896376743
117 -2.711143039 -1.325876080
118 -1.159710366 -2.711143039
119 -2.853630218 -1.159710366
120 7.225524546 -2.853630218
121 -2.296926646 7.225524546
122 0.929909076 -2.296926646
123 1.323942643 0.929909076
124 8.875061114 1.323942643
125 0.861500695 8.875061114
126 0.654258121 0.861500695
127 -2.561572008 0.654258121
128 -2.374658797 -2.561572008
129 -3.792882008 -2.374658797
130 0.083670681 -3.792882008
131 2.004655318 0.083670681
132 -1.834826461 2.004655318
133 0.632931495 -1.834826461
134 -0.552893634 0.632931495
135 -2.919680822 -0.552893634
136 0.537549174 -2.919680822
137 -1.666000948 0.537549174
138 -1.857861488 -1.666000948
139 3.715412546 -1.857861488
140 0.893292679 3.715412546
141 0.353506372 0.893292679
142 -2.872086869 0.353506372
143 -1.777031822 -2.872086869
144 5.987040184 -1.777031822
> 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/7f3d21290542328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8f3d21290542328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9qvc51290542328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10qvc51290542328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bvbb1290542328.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/12we9z1290542328.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/133x6s1290542328.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/14e6nv1290542328.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/15sz7e1290542329.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/166q451290542329.tab")
+ }
>
> try(system("convert tmp/1u3ww1290542328.ps tmp/1u3ww1290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/2u3ww1290542328.ps tmp/2u3ww1290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u3ww1290542328.ps tmp/3u3ww1290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/4u3ww1290542328.ps tmp/4u3ww1290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/54ceh1290542328.ps tmp/54ceh1290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/64ceh1290542328.ps tmp/64ceh1290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/7f3d21290542328.ps tmp/7f3d21290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/8f3d21290542328.ps tmp/8f3d21290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qvc51290542328.ps tmp/9qvc51290542328.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qvc51290542328.ps tmp/10qvc51290542328.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.610 2.070 7.687