R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(12
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+ ,4)
+ ,dim=c(9
+ ,145)
+ ,dimnames=list(c('Depression'
+ ,'CriticParents'
+ ,'ExpecParents'
+ ,'FutureWorrying'
+ ,'SleepDepri'
+ ,'ChangesLastYear'
+ ,'FreqSmoking'
+ ,'FreqHighAlc'
+ ,'FreqBeerOrWine
')
+ ,1:145))
> y <- array(NA,dim=c(9,145),dimnames=list(c('Depression','CriticParents','ExpecParents','FutureWorrying','SleepDepri','ChangesLastYear','FreqSmoking','FreqHighAlc','FreqBeerOrWine
'),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
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
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\r t
1 2 2 2 2 1
2 4 1 2 2 2
3 7 4 3 4 3
4 3 1 2 3 4
5 7 5 4 4 5
6 2 1 2 3 6
7 7 1 2 3 7
8 2 1 3 4 8
9 1 1 2 3 9
10 2 1 2 4 10
11 6 2 3 3 11
12 1 1 2 2 12
13 1 3 3 3 13
14 1 1 1 3 14
15 2 1 3 3 15
16 2 1 1 2 16
17 2 1 3 3 17
18 1 1 2 2 18
19 7 2 3 4 19
20 1 4 4 5 20
21 2 1 3 3 21
22 4 2 3 3 22
23 2 1 1 1 23
24 1 2 2 4 24
25 1 3 1 3 25
26 5 1 3 4 26
27 2 1 3 3 27
28 1 1 2 3 28
29 3 1 2 1 29
30 1 1 3 4 30
31 2 2 2 4 31
32 5 1 2 2 32
33 2 1 2 2 33
34 6 1 1 1 34
35 4 1 2 3 35
36 1 1 3 4 36
37 3 1 1 1 37
38 6 1 2 3 38
39 7 2 3 3 39
40 4 1 2 2 40
41 1 2 1 4 41
42 5 1 1 3 42
43 3 1 3 3 43
44 2 2 3 2 44
45 2 1 3 3 45
46 2 1 3 2 46
47 2 1 2 1 47
48 1 1 3 3 48
49 2 1 2 3 49
50 1 4 3 5 50
51 2 2 4 1 51
52 2 1 3 3 52
53 5 1 3 4 53
54 5 4 3 3 54
55 2 2 3 4 55
56 1 1 2 2 56
57 1 1 3 3 57
58 2 1 3 4 58
59 3 1 1 1 59
60 7 1 1 1 60
61 4 1 1 1 61
62 4 2 4 4 62
63 1 1 3 2 63
64 2 1 2 3 64
65 2 2 3 4 65
66 2 1 1 2 66
67 5 2 4 5 67
68 1 2 3 3 68
69 6 4 2 3 69
70 2 1 3 3 70
71 2 1 3 4 71
72 4 3 3 4 72
73 6 1 2 3 73
74 2 1 1 1 74
75 2 1 1 3 75
76 2 1 1 1 76
77 1 1 3 3 77
78 1 1 4 5 78
79 2 1 2 3 79
80 2 1 2 3 80
81 3 4 2 4 81
82 3 1 2 5 82
83 5 1 3 4 83
84 2 2 4 4 84
85 5 1 2 4 85
86 3 1 3 4 86
87 1 1 3 4 87
88 2 1 2 3 88
89 2 1 2 4 89
90 1 1 3 3 90
91 2 1 3 3 91
92 2 1 3 3 92
93 5 1 3 4 93
94 5 1 3 3 94
95 2 1 3 4 95
96 3 1 2 2 96
97 5 5 3 5 97
98 5 1 3 3 98
99 6 1 2 4 99
100 2 1 1 2 100
101 7 3 3 4 101
102 1 1 2 3 102
103 1 1 2 4 103
104 6 1 3 3 104
105 6 1 1 1 105
106 2 1 3 4 106
107 1 1 2 4 107
108 2 1 2 2 108
109 1 4 2 5 109
110 2 4 2 4 110
111 1 1 2 4 111
112 3 1 3 3 112
113 3 1 3 4 113
114 6 4 3 4 114
115 4 2 3 4 115
116 1 1 3 3 116
117 2 1 1 5 117
118 5 1 3 3 118
119 6 1 4 4 119
120 3 1 2 4 120
121 5 1 2 4 121
122 3 2 4 4 122
123 2 4 3 4 123
124 3 4 2 5 124
125 2 1 3 3 125
126 5 1 1 1 126
127 5 1 2 4 127
128 7 2 4 4 128
129 4 1 3 3 129
130 4 1 3 4 130
131 5 1 3 4 131
132 1 3 2 4 132
133 4 2 4 4 133
134 1 2 1 4 134
135 4 1 3 4 135
136 6 1 1 3 136
137 7 2 2 5 137
138 1 3 1 3 138
139 3 1 2 4 139
140 5 1 4 4 140
141 2 2 4 4 141
142 4 2 3 4 142
143 5 1 3 3 143
144 1 1 1 4 144
145 2 1 4 4 145
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CriticParents ExpecParents FutureWorrying
7.132034 0.046797 -0.061062 0.586113
SleepDepri ChangesLastYear FreqSmoking FreqHighAlc
0.214968 0.335210 -0.086418 0.284315
`FreqBeerOrWine\r` t
0.192320 0.006456
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.4198 -1.9342 -0.2082 1.3473 8.8749
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.132034 1.486388 4.798 4.18e-06 ***
CriticParents 0.046797 0.115867 0.404 0.686934
ExpecParents -0.061062 0.086747 -0.704 0.482700
FutureWorrying 0.586113 0.168218 3.484 0.000666 ***
SleepDepri 0.214968 0.141718 1.517 0.131638
ChangesLastYear 0.335210 0.136435 2.457 0.015282 *
FreqSmoking -0.086418 0.275507 -0.314 0.754258
FreqHighAlc 0.284315 0.315102 0.902 0.368507
`FreqBeerOrWine\r` 0.192320 0.294925 0.652 0.515447
t 0.006456 0.006255 1.032 0.303860
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.899 on 135 degrees of freedom
Multiple R-squared: 0.2118, Adjusted R-squared: 0.1593
F-statistic: 4.032 on 9 and 135 DF, p-value: 0.0001363
> 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,] 0.40267112 0.80534225 0.5973289
[2,] 0.53327041 0.93345917 0.4667296
[3,] 0.51658008 0.96683984 0.4834199
[4,] 0.64049874 0.71900252 0.3595013
[5,] 0.53105046 0.93789908 0.4689495
[6,] 0.44100493 0.88200986 0.5589951
[7,] 0.58119052 0.83761896 0.4188095
[8,] 0.50489013 0.99021975 0.4951099
[9,] 0.42780879 0.85561758 0.5721912
[10,] 0.45683756 0.91367513 0.5431624
[11,] 0.51199515 0.97600969 0.4880048
[12,] 0.43369630 0.86739261 0.5663037
[13,] 0.36433555 0.72867109 0.6356645
[14,] 0.32905886 0.65811772 0.6709411
[15,] 0.27327502 0.54655004 0.7267250
[16,] 0.23082407 0.46164814 0.7691759
[17,] 0.28346386 0.56692773 0.7165361
[18,] 0.24116168 0.48232337 0.7588383
[19,] 0.19524552 0.39049104 0.8047545
[20,] 0.15753836 0.31507673 0.8424616
[21,] 0.15460319 0.30920638 0.8453968
[22,] 0.17032015 0.34064030 0.8296799
[23,] 0.17767879 0.35535758 0.8223212
[24,] 0.17541509 0.35083017 0.8245849
[25,] 0.22481333 0.44962666 0.7751867
[26,] 0.26953219 0.53906439 0.7304678
[27,] 0.27468995 0.54937991 0.7253100
[28,] 0.24839818 0.49679635 0.7516018
[29,] 0.29062855 0.58125710 0.7093714
[30,] 0.25398043 0.50796086 0.7460196
[31,] 0.26373945 0.52747890 0.7362605
[32,] 0.22177307 0.44354613 0.7782269
[33,] 0.18652214 0.37304427 0.8134779
[34,] 0.16215171 0.32430341 0.8378483
[35,] 0.17241064 0.34482128 0.8275894
[36,] 0.27325342 0.54650683 0.7267466
[37,] 0.24799895 0.49599790 0.7520010
[38,] 0.22308005 0.44616010 0.7769200
[39,] 0.18719383 0.37438767 0.8128062
[40,] 0.15288660 0.30577320 0.8471134
[41,] 0.22773714 0.45547427 0.7722629
[42,] 0.22005546 0.44011092 0.7799445
[43,] 0.20144384 0.40288769 0.7985562
[44,] 0.28879335 0.57758671 0.7112066
[45,] 0.24758268 0.49516537 0.7524173
[46,] 0.21295413 0.42590825 0.7870459
[47,] 0.19435716 0.38871432 0.8056428
[48,] 0.21691994 0.43383987 0.7830801
[49,] 0.26989740 0.53979479 0.7301026
[50,] 0.30516403 0.61032805 0.6948360
[51,] 0.26248124 0.52496249 0.7375188
[52,] 0.22269374 0.44538749 0.7773063
[53,] 0.18723352 0.37446703 0.8127665
[54,] 0.17248563 0.34497126 0.8275144
[55,] 0.14612636 0.29225272 0.8538736
[56,] 0.12199531 0.24399062 0.8780047
[57,] 0.13398499 0.26796998 0.8660150
[58,] 0.10870505 0.21741010 0.8912949
[59,] 0.08699157 0.17398315 0.9130084
[60,] 0.08924237 0.17848473 0.9107576
[61,] 0.07087559 0.14175118 0.9291244
[62,] 0.07245922 0.14491845 0.9275408
[63,] 0.05987882 0.11975763 0.9401212
[64,] 0.04776085 0.09552169 0.9522392
[65,] 0.04187450 0.08374901 0.9581255
[66,] 0.03201875 0.06403750 0.9679812
[67,] 0.02441824 0.04883648 0.9755818
[68,] 0.01840519 0.03681037 0.9815948
[69,] 0.01390458 0.02780917 0.9860954
[70,] 0.06599974 0.13199949 0.9340003
[71,] 0.05181384 0.10362769 0.9481862
[72,] 0.04032174 0.08064349 0.9596783
[73,] 0.03281519 0.06563039 0.9671848
[74,] 0.03342417 0.06684834 0.9665758
[75,] 0.02857051 0.05714102 0.9714295
[76,] 0.02118539 0.04237077 0.9788146
[77,] 0.01848967 0.03697934 0.9815103
[78,] 0.01444005 0.02888009 0.9855600
[79,] 0.01168604 0.02337207 0.9883140
[80,] 0.01156855 0.02313709 0.9884315
[81,] 0.01761451 0.03522902 0.9823855
[82,] 0.01376000 0.02752000 0.9862400
[83,] 0.01083373 0.02166747 0.9891663
[84,] 0.01182017 0.02364035 0.9881798
[85,] 0.00997517 0.01995034 0.9900248
[86,] 0.01518251 0.03036502 0.9848175
[87,] 0.01782035 0.03564070 0.9821796
[88,] 0.01414809 0.02829617 0.9858519
[89,] 0.06901551 0.13803103 0.9309845
[90,] 0.06206004 0.12412009 0.9379400
[91,] 0.05165275 0.10330549 0.9483473
[92,] 0.05308532 0.10617063 0.9469147
[93,] 0.04453327 0.08906653 0.9554667
[94,] 0.03336501 0.06673003 0.9666350
[95,] 0.02633835 0.05267669 0.9736617
[96,] 0.01909600 0.03819200 0.9809040
[97,] 0.01789819 0.03579638 0.9821018
[98,] 0.02701689 0.05403378 0.9729831
[99,] 0.04154556 0.08309112 0.9584544
[100,] 0.03414268 0.06828536 0.9658573
[101,] 0.03020782 0.06041565 0.9697922
[102,] 0.05397846 0.10795693 0.9460215
[103,] 0.05063610 0.10127220 0.9493639
[104,] 0.07608492 0.15216984 0.9239151
[105,] 0.05807168 0.11614337 0.9419283
[106,] 0.05057874 0.10115749 0.9494213
[107,] 0.03804939 0.07609878 0.9619506
[108,] 0.07877794 0.15755588 0.9212221
[109,] 0.18105710 0.36211420 0.8189429
[110,] 0.37918805 0.75837611 0.6208119
[111,] 0.33099522 0.66199045 0.6690048
[112,] 0.25836488 0.51672976 0.7416351
[113,] 0.50272659 0.99454683 0.4972734
[114,] 0.82820967 0.34358066 0.1717903
[115,] 0.78732026 0.42535948 0.2126797
[116,] 0.70462235 0.59075531 0.2953777
[117,] 0.63435969 0.73128062 0.3656403
[118,] 0.59224888 0.81550224 0.4077511
[119,] 0.56924857 0.86150286 0.4307514
[120,] 0.46662273 0.93324545 0.5333773
> postscript(file="/var/www/html/freestat/rcomp/tmp/1vzn21290541725.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/26q451290541725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/36q451290541725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/46q451290541725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5y0lq1290541725.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
0.196579142 -0.550666215 -0.963260718 0.610339342 5.276349768 -1.024133341
7 8 9 10 11 12
7.260811264 -1.763191548 -1.941264685 0.460436323 -3.344348557 -4.045095915
13 14 15 16 17 18
0.780720613 -1.889181895 1.557626875 3.879252922 -1.755331674 -2.942215143
19 20 21 22 23 24
-1.101242058 -3.471230259 -1.663809734 1.274056070 -1.301537352 -2.107424439
25 26 27 28 29 30
-0.938773847 1.871519711 0.046964496 -0.510038302 4.906678309 -2.413844515
31 32 33 34 35 36
0.452140016 1.659698364 -2.417207639 6.359688117 -0.882278086 -0.169192096
37 38 39 40 41 42
-2.259898338 -3.457878349 -0.575619736 -2.960123559 2.158191384 -1.365453250
43 44 45 46 47 48
1.810992493 -1.109871492 -0.818446422 -2.146618777 3.389656530 4.277338646
49 50 51 52 53 54
1.513587385 -1.967037210 0.575328023 -0.081913328 -4.688213919 -2.239040789
55 56 57 58 59 60
1.516033137 5.890227011 0.327407812 0.015432839 2.663179541 -1.934206336
61 62 63 64 65 66
-3.333956792 3.823198731 -0.208212720 0.173243478 0.204721071 2.271660438
67 68 69 70 71 72
1.204597033 1.089061239 -3.567750132 -0.273139122 0.268537928 2.860296377
73 74 75 76 77 78
0.132960195 -3.054946634 -1.339704090 -0.296775205 -1.305897250 0.715859899
79 80 81 82 83 84
-0.434877510 0.636266345 0.576500847 7.487779059 1.079409253 1.260970437
85 86 87 88 89 90
-1.101501153 3.405359735 -1.687458675 0.124945776 1.816208964 -1.042583393
91 92 93 94 95 96
-1.425524194 -2.707258800 -4.562091010 1.774427662 -1.389211291 -2.918542658
97 98 99 100 101 102
1.578947453 4.009560939 -3.895973668 2.232740307 7.428410277 2.304647311
103 104 105 106 107 108
1.347322372 -3.917914261 1.438101158 -0.935180854 -0.847792244 0.002849802
109 110 111 112 113 114
-3.908992394 4.279839731 5.201220530 0.833143260 -3.220524329 -6.419776332
115 116 117 118 119 120
-3.311786302 -3.896364593 -1.326013818 -2.711064009 -1.159741032 -2.853755881
121 122 123 124 125 126
7.225435051 -2.296996574 0.929858182 1.323795876 8.874920967 0.861436485
127 128 129 130 131 132
0.654076474 -2.561702182 -2.374851374 -3.793086710 0.083464794 2.004484738
133 134 135 136 137 138
-1.835156607 0.632638451 -0.553272550 -2.920044361 0.537160133 -1.668347467
139 140 141 142 143 144
-1.858304763 3.713000644 0.892955426 0.353026973 -2.872474102 -1.777627068
145
5.986485684
> postscript(file="/var/www/html/freestat/rcomp/tmp/6y0lq1290541725.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 0.196579142 NA
1 -0.550666215 0.196579142
2 -0.963260718 -0.550666215
3 0.610339342 -0.963260718
4 5.276349768 0.610339342
5 -1.024133341 5.276349768
6 7.260811264 -1.024133341
7 -1.763191548 7.260811264
8 -1.941264685 -1.763191548
9 0.460436323 -1.941264685
10 -3.344348557 0.460436323
11 -4.045095915 -3.344348557
12 0.780720613 -4.045095915
13 -1.889181895 0.780720613
14 1.557626875 -1.889181895
15 3.879252922 1.557626875
16 -1.755331674 3.879252922
17 -2.942215143 -1.755331674
18 -1.101242058 -2.942215143
19 -3.471230259 -1.101242058
20 -1.663809734 -3.471230259
21 1.274056070 -1.663809734
22 -1.301537352 1.274056070
23 -2.107424439 -1.301537352
24 -0.938773847 -2.107424439
25 1.871519711 -0.938773847
26 0.046964496 1.871519711
27 -0.510038302 0.046964496
28 4.906678309 -0.510038302
29 -2.413844515 4.906678309
30 0.452140016 -2.413844515
31 1.659698364 0.452140016
32 -2.417207639 1.659698364
33 6.359688117 -2.417207639
34 -0.882278086 6.359688117
35 -0.169192096 -0.882278086
36 -2.259898338 -0.169192096
37 -3.457878349 -2.259898338
38 -0.575619736 -3.457878349
39 -2.960123559 -0.575619736
40 2.158191384 -2.960123559
41 -1.365453250 2.158191384
42 1.810992493 -1.365453250
43 -1.109871492 1.810992493
44 -0.818446422 -1.109871492
45 -2.146618777 -0.818446422
46 3.389656530 -2.146618777
47 4.277338646 3.389656530
48 1.513587385 4.277338646
49 -1.967037210 1.513587385
50 0.575328023 -1.967037210
51 -0.081913328 0.575328023
52 -4.688213919 -0.081913328
53 -2.239040789 -4.688213919
54 1.516033137 -2.239040789
55 5.890227011 1.516033137
56 0.327407812 5.890227011
57 0.015432839 0.327407812
58 2.663179541 0.015432839
59 -1.934206336 2.663179541
60 -3.333956792 -1.934206336
61 3.823198731 -3.333956792
62 -0.208212720 3.823198731
63 0.173243478 -0.208212720
64 0.204721071 0.173243478
65 2.271660438 0.204721071
66 1.204597033 2.271660438
67 1.089061239 1.204597033
68 -3.567750132 1.089061239
69 -0.273139122 -3.567750132
70 0.268537928 -0.273139122
71 2.860296377 0.268537928
72 0.132960195 2.860296377
73 -3.054946634 0.132960195
74 -1.339704090 -3.054946634
75 -0.296775205 -1.339704090
76 -1.305897250 -0.296775205
77 0.715859899 -1.305897250
78 -0.434877510 0.715859899
79 0.636266345 -0.434877510
80 0.576500847 0.636266345
81 7.487779059 0.576500847
82 1.079409253 7.487779059
83 1.260970437 1.079409253
84 -1.101501153 1.260970437
85 3.405359735 -1.101501153
86 -1.687458675 3.405359735
87 0.124945776 -1.687458675
88 1.816208964 0.124945776
89 -1.042583393 1.816208964
90 -1.425524194 -1.042583393
91 -2.707258800 -1.425524194
92 -4.562091010 -2.707258800
93 1.774427662 -4.562091010
94 -1.389211291 1.774427662
95 -2.918542658 -1.389211291
96 1.578947453 -2.918542658
97 4.009560939 1.578947453
98 -3.895973668 4.009560939
99 2.232740307 -3.895973668
100 7.428410277 2.232740307
101 2.304647311 7.428410277
102 1.347322372 2.304647311
103 -3.917914261 1.347322372
104 1.438101158 -3.917914261
105 -0.935180854 1.438101158
106 -0.847792244 -0.935180854
107 0.002849802 -0.847792244
108 -3.908992394 0.002849802
109 4.279839731 -3.908992394
110 5.201220530 4.279839731
111 0.833143260 5.201220530
112 -3.220524329 0.833143260
113 -6.419776332 -3.220524329
114 -3.311786302 -6.419776332
115 -3.896364593 -3.311786302
116 -1.326013818 -3.896364593
117 -2.711064009 -1.326013818
118 -1.159741032 -2.711064009
119 -2.853755881 -1.159741032
120 7.225435051 -2.853755881
121 -2.296996574 7.225435051
122 0.929858182 -2.296996574
123 1.323795876 0.929858182
124 8.874920967 1.323795876
125 0.861436485 8.874920967
126 0.654076474 0.861436485
127 -2.561702182 0.654076474
128 -2.374851374 -2.561702182
129 -3.793086710 -2.374851374
130 0.083464794 -3.793086710
131 2.004484738 0.083464794
132 -1.835156607 2.004484738
133 0.632638451 -1.835156607
134 -0.553272550 0.632638451
135 -2.920044361 -0.553272550
136 0.537160133 -2.920044361
137 -1.668347467 0.537160133
138 -1.858304763 -1.668347467
139 3.713000644 -1.858304763
140 0.892955426 3.713000644
141 0.353026973 0.892955426
142 -2.872474102 0.353026973
143 -1.777627068 -2.872474102
144 5.986485684 -1.777627068
145 NA 5.986485684
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.550666215 0.196579142
[2,] -0.963260718 -0.550666215
[3,] 0.610339342 -0.963260718
[4,] 5.276349768 0.610339342
[5,] -1.024133341 5.276349768
[6,] 7.260811264 -1.024133341
[7,] -1.763191548 7.260811264
[8,] -1.941264685 -1.763191548
[9,] 0.460436323 -1.941264685
[10,] -3.344348557 0.460436323
[11,] -4.045095915 -3.344348557
[12,] 0.780720613 -4.045095915
[13,] -1.889181895 0.780720613
[14,] 1.557626875 -1.889181895
[15,] 3.879252922 1.557626875
[16,] -1.755331674 3.879252922
[17,] -2.942215143 -1.755331674
[18,] -1.101242058 -2.942215143
[19,] -3.471230259 -1.101242058
[20,] -1.663809734 -3.471230259
[21,] 1.274056070 -1.663809734
[22,] -1.301537352 1.274056070
[23,] -2.107424439 -1.301537352
[24,] -0.938773847 -2.107424439
[25,] 1.871519711 -0.938773847
[26,] 0.046964496 1.871519711
[27,] -0.510038302 0.046964496
[28,] 4.906678309 -0.510038302
[29,] -2.413844515 4.906678309
[30,] 0.452140016 -2.413844515
[31,] 1.659698364 0.452140016
[32,] -2.417207639 1.659698364
[33,] 6.359688117 -2.417207639
[34,] -0.882278086 6.359688117
[35,] -0.169192096 -0.882278086
[36,] -2.259898338 -0.169192096
[37,] -3.457878349 -2.259898338
[38,] -0.575619736 -3.457878349
[39,] -2.960123559 -0.575619736
[40,] 2.158191384 -2.960123559
[41,] -1.365453250 2.158191384
[42,] 1.810992493 -1.365453250
[43,] -1.109871492 1.810992493
[44,] -0.818446422 -1.109871492
[45,] -2.146618777 -0.818446422
[46,] 3.389656530 -2.146618777
[47,] 4.277338646 3.389656530
[48,] 1.513587385 4.277338646
[49,] -1.967037210 1.513587385
[50,] 0.575328023 -1.967037210
[51,] -0.081913328 0.575328023
[52,] -4.688213919 -0.081913328
[53,] -2.239040789 -4.688213919
[54,] 1.516033137 -2.239040789
[55,] 5.890227011 1.516033137
[56,] 0.327407812 5.890227011
[57,] 0.015432839 0.327407812
[58,] 2.663179541 0.015432839
[59,] -1.934206336 2.663179541
[60,] -3.333956792 -1.934206336
[61,] 3.823198731 -3.333956792
[62,] -0.208212720 3.823198731
[63,] 0.173243478 -0.208212720
[64,] 0.204721071 0.173243478
[65,] 2.271660438 0.204721071
[66,] 1.204597033 2.271660438
[67,] 1.089061239 1.204597033
[68,] -3.567750132 1.089061239
[69,] -0.273139122 -3.567750132
[70,] 0.268537928 -0.273139122
[71,] 2.860296377 0.268537928
[72,] 0.132960195 2.860296377
[73,] -3.054946634 0.132960195
[74,] -1.339704090 -3.054946634
[75,] -0.296775205 -1.339704090
[76,] -1.305897250 -0.296775205
[77,] 0.715859899 -1.305897250
[78,] -0.434877510 0.715859899
[79,] 0.636266345 -0.434877510
[80,] 0.576500847 0.636266345
[81,] 7.487779059 0.576500847
[82,] 1.079409253 7.487779059
[83,] 1.260970437 1.079409253
[84,] -1.101501153 1.260970437
[85,] 3.405359735 -1.101501153
[86,] -1.687458675 3.405359735
[87,] 0.124945776 -1.687458675
[88,] 1.816208964 0.124945776
[89,] -1.042583393 1.816208964
[90,] -1.425524194 -1.042583393
[91,] -2.707258800 -1.425524194
[92,] -4.562091010 -2.707258800
[93,] 1.774427662 -4.562091010
[94,] -1.389211291 1.774427662
[95,] -2.918542658 -1.389211291
[96,] 1.578947453 -2.918542658
[97,] 4.009560939 1.578947453
[98,] -3.895973668 4.009560939
[99,] 2.232740307 -3.895973668
[100,] 7.428410277 2.232740307
[101,] 2.304647311 7.428410277
[102,] 1.347322372 2.304647311
[103,] -3.917914261 1.347322372
[104,] 1.438101158 -3.917914261
[105,] -0.935180854 1.438101158
[106,] -0.847792244 -0.935180854
[107,] 0.002849802 -0.847792244
[108,] -3.908992394 0.002849802
[109,] 4.279839731 -3.908992394
[110,] 5.201220530 4.279839731
[111,] 0.833143260 5.201220530
[112,] -3.220524329 0.833143260
[113,] -6.419776332 -3.220524329
[114,] -3.311786302 -6.419776332
[115,] -3.896364593 -3.311786302
[116,] -1.326013818 -3.896364593
[117,] -2.711064009 -1.326013818
[118,] -1.159741032 -2.711064009
[119,] -2.853755881 -1.159741032
[120,] 7.225435051 -2.853755881
[121,] -2.296996574 7.225435051
[122,] 0.929858182 -2.296996574
[123,] 1.323795876 0.929858182
[124,] 8.874920967 1.323795876
[125,] 0.861436485 8.874920967
[126,] 0.654076474 0.861436485
[127,] -2.561702182 0.654076474
[128,] -2.374851374 -2.561702182
[129,] -3.793086710 -2.374851374
[130,] 0.083464794 -3.793086710
[131,] 2.004484738 0.083464794
[132,] -1.835156607 2.004484738
[133,] 0.632638451 -1.835156607
[134,] -0.553272550 0.632638451
[135,] -2.920044361 -0.553272550
[136,] 0.537160133 -2.920044361
[137,] -1.668347467 0.537160133
[138,] -1.858304763 -1.668347467
[139,] 3.713000644 -1.858304763
[140,] 0.892955426 3.713000644
[141,] 0.353026973 0.892955426
[142,] -2.872474102 0.353026973
[143,] -1.777627068 -2.872474102
[144,] 5.986485684 -1.777627068
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.550666215 0.196579142
2 -0.963260718 -0.550666215
3 0.610339342 -0.963260718
4 5.276349768 0.610339342
5 -1.024133341 5.276349768
6 7.260811264 -1.024133341
7 -1.763191548 7.260811264
8 -1.941264685 -1.763191548
9 0.460436323 -1.941264685
10 -3.344348557 0.460436323
11 -4.045095915 -3.344348557
12 0.780720613 -4.045095915
13 -1.889181895 0.780720613
14 1.557626875 -1.889181895
15 3.879252922 1.557626875
16 -1.755331674 3.879252922
17 -2.942215143 -1.755331674
18 -1.101242058 -2.942215143
19 -3.471230259 -1.101242058
20 -1.663809734 -3.471230259
21 1.274056070 -1.663809734
22 -1.301537352 1.274056070
23 -2.107424439 -1.301537352
24 -0.938773847 -2.107424439
25 1.871519711 -0.938773847
26 0.046964496 1.871519711
27 -0.510038302 0.046964496
28 4.906678309 -0.510038302
29 -2.413844515 4.906678309
30 0.452140016 -2.413844515
31 1.659698364 0.452140016
32 -2.417207639 1.659698364
33 6.359688117 -2.417207639
34 -0.882278086 6.359688117
35 -0.169192096 -0.882278086
36 -2.259898338 -0.169192096
37 -3.457878349 -2.259898338
38 -0.575619736 -3.457878349
39 -2.960123559 -0.575619736
40 2.158191384 -2.960123559
41 -1.365453250 2.158191384
42 1.810992493 -1.365453250
43 -1.109871492 1.810992493
44 -0.818446422 -1.109871492
45 -2.146618777 -0.818446422
46 3.389656530 -2.146618777
47 4.277338646 3.389656530
48 1.513587385 4.277338646
49 -1.967037210 1.513587385
50 0.575328023 -1.967037210
51 -0.081913328 0.575328023
52 -4.688213919 -0.081913328
53 -2.239040789 -4.688213919
54 1.516033137 -2.239040789
55 5.890227011 1.516033137
56 0.327407812 5.890227011
57 0.015432839 0.327407812
58 2.663179541 0.015432839
59 -1.934206336 2.663179541
60 -3.333956792 -1.934206336
61 3.823198731 -3.333956792
62 -0.208212720 3.823198731
63 0.173243478 -0.208212720
64 0.204721071 0.173243478
65 2.271660438 0.204721071
66 1.204597033 2.271660438
67 1.089061239 1.204597033
68 -3.567750132 1.089061239
69 -0.273139122 -3.567750132
70 0.268537928 -0.273139122
71 2.860296377 0.268537928
72 0.132960195 2.860296377
73 -3.054946634 0.132960195
74 -1.339704090 -3.054946634
75 -0.296775205 -1.339704090
76 -1.305897250 -0.296775205
77 0.715859899 -1.305897250
78 -0.434877510 0.715859899
79 0.636266345 -0.434877510
80 0.576500847 0.636266345
81 7.487779059 0.576500847
82 1.079409253 7.487779059
83 1.260970437 1.079409253
84 -1.101501153 1.260970437
85 3.405359735 -1.101501153
86 -1.687458675 3.405359735
87 0.124945776 -1.687458675
88 1.816208964 0.124945776
89 -1.042583393 1.816208964
90 -1.425524194 -1.042583393
91 -2.707258800 -1.425524194
92 -4.562091010 -2.707258800
93 1.774427662 -4.562091010
94 -1.389211291 1.774427662
95 -2.918542658 -1.389211291
96 1.578947453 -2.918542658
97 4.009560939 1.578947453
98 -3.895973668 4.009560939
99 2.232740307 -3.895973668
100 7.428410277 2.232740307
101 2.304647311 7.428410277
102 1.347322372 2.304647311
103 -3.917914261 1.347322372
104 1.438101158 -3.917914261
105 -0.935180854 1.438101158
106 -0.847792244 -0.935180854
107 0.002849802 -0.847792244
108 -3.908992394 0.002849802
109 4.279839731 -3.908992394
110 5.201220530 4.279839731
111 0.833143260 5.201220530
112 -3.220524329 0.833143260
113 -6.419776332 -3.220524329
114 -3.311786302 -6.419776332
115 -3.896364593 -3.311786302
116 -1.326013818 -3.896364593
117 -2.711064009 -1.326013818
118 -1.159741032 -2.711064009
119 -2.853755881 -1.159741032
120 7.225435051 -2.853755881
121 -2.296996574 7.225435051
122 0.929858182 -2.296996574
123 1.323795876 0.929858182
124 8.874920967 1.323795876
125 0.861436485 8.874920967
126 0.654076474 0.861436485
127 -2.561702182 0.654076474
128 -2.374851374 -2.561702182
129 -3.793086710 -2.374851374
130 0.083464794 -3.793086710
131 2.004484738 0.083464794
132 -1.835156607 2.004484738
133 0.632638451 -1.835156607
134 -0.553272550 0.632638451
135 -2.920044361 -0.553272550
136 0.537160133 -2.920044361
137 -1.668347467 0.537160133
138 -1.858304763 -1.668347467
139 3.713000644 -1.858304763
140 0.892955426 3.713000644
141 0.353026973 0.892955426
142 -2.872474102 0.353026973
143 -1.777627068 -2.872474102
144 5.986485684 -1.777627068
> 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/freestat/rcomp/tmp/7rrlt1290541725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/82ikw1290541725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/92ikw1290541725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10ds1h1290541725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11ys041290541725.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/freestat/rcomp/tmp/12rjz81290541725.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/freestat/rcomp/tmp/13xkwj1290541725.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/freestat/rcomp/tmp/141lvp1290541725.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/freestat/rcomp/tmp/15buus1290541725.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/freestat/rcomp/tmp/16q4s11290541725.tab")
+ }
>
> try(system("convert tmp/1vzn21290541725.ps tmp/1vzn21290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/26q451290541725.ps tmp/26q451290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/36q451290541725.ps tmp/36q451290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/46q451290541725.ps tmp/46q451290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/5y0lq1290541725.ps tmp/5y0lq1290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y0lq1290541725.ps tmp/6y0lq1290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rrlt1290541725.ps tmp/7rrlt1290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/82ikw1290541725.ps tmp/82ikw1290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/92ikw1290541725.ps tmp/92ikw1290541725.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ds1h1290541725.ps tmp/10ds1h1290541725.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.871 2.701 24.932