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(9
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+ ,20)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('Maand'
+ ,'DoubtsAboutActions'
+ ,'ParentalExpectations'
+ ,'ParentalCriticism'
+ ,'PersonalStandards'
+ ,'Organization
')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('Maand','DoubtsAboutActions','ParentalExpectations','ParentalCriticism','PersonalStandards','Organization
'),1:159))
> 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 = '5'
> #'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
PersonalStandards Maand DoubtsAboutActions ParentalExpectations
1 24 9 14 11
2 25 9 11 7
3 30 9 6 17
4 19 9 12 10
5 22 9 8 12
6 22 9 10 12
7 25 10 10 11
8 23 10 11 11
9 17 10 16 12
10 21 10 11 13
11 19 10 13 14
12 19 10 12 16
13 15 10 8 11
14 16 10 12 10
15 23 10 11 11
16 27 10 4 15
17 22 10 9 9
18 14 10 8 11
19 22 10 8 17
20 23 10 14 17
21 23 10 15 11
22 21 10 16 18
23 19 10 9 14
24 18 10 14 10
25 20 10 11 11
26 23 10 8 15
27 25 10 9 15
28 19 10 9 13
29 24 10 9 16
30 22 10 9 13
31 25 10 10 9
32 26 10 16 18
33 29 10 11 18
34 32 10 8 12
35 25 10 9 17
36 29 10 16 9
37 28 10 11 9
38 17 10 16 12
39 28 10 12 18
40 29 10 12 12
41 26 10 14 18
42 25 10 9 14
43 14 10 10 15
44 25 10 9 16
45 26 10 10 10
46 20 10 12 11
47 18 10 14 14
48 32 10 14 9
49 25 10 10 12
50 25 10 14 17
51 23 10 16 5
52 21 10 9 12
53 20 10 10 12
54 15 10 6 6
55 30 10 8 24
56 24 10 13 12
57 26 10 10 12
58 24 10 8 14
59 22 10 7 7
60 14 10 15 13
61 24 10 9 12
62 24 10 10 13
63 24 10 12 14
64 24 10 13 8
65 19 10 10 11
66 31 10 11 9
67 22 10 8 11
68 27 10 9 13
69 19 10 13 10
70 25 10 11 11
71 20 10 8 12
72 21 10 9 9
73 27 10 9 15
74 23 10 15 18
75 25 10 9 15
76 20 10 10 12
77 21 10 14 13
78 22 10 12 14
79 23 10 12 10
80 25 10 11 13
81 25 10 14 13
82 17 10 6 11
83 19 10 12 13
84 25 10 8 16
85 19 10 14 8
86 20 10 11 16
87 26 10 10 11
88 23 10 14 9
89 27 10 12 16
90 17 10 10 12
91 17 10 14 14
92 19 10 5 8
93 17 10 11 9
94 22 10 10 15
95 21 10 9 11
96 32 10 10 21
97 21 10 16 14
98 21 10 13 18
99 18 10 9 12
100 18 10 10 13
101 23 10 10 15
102 19 10 7 12
103 20 10 9 19
104 21 10 8 15
105 20 10 14 11
106 17 10 14 11
107 18 10 8 10
108 19 10 9 13
109 22 10 14 15
110 15 10 14 12
111 14 10 8 12
112 18 10 8 16
113 24 10 8 9
114 35 10 7 18
115 29 10 6 8
116 21 10 8 13
117 25 10 6 17
118 20 10 11 9
119 22 10 14 15
120 13 10 11 8
121 26 10 11 7
122 17 10 11 12
123 25 10 14 14
124 20 10 8 6
125 19 10 20 8
126 21 10 11 17
127 22 10 8 10
128 24 10 11 11
129 21 10 10 14
130 26 10 14 11
131 24 10 11 13
132 16 10 9 12
133 23 10 9 11
134 18 10 8 9
135 16 10 10 12
136 26 10 13 20
137 19 10 13 12
138 21 10 12 13
139 21 10 8 12
140 22 10 13 12
141 23 10 14 9
142 29 10 12 15
143 21 10 14 24
144 21 10 15 7
145 23 10 13 17
146 27 10 16 11
147 25 10 9 17
148 21 10 9 11
149 10 10 9 12
150 20 10 8 14
151 26 10 7 11
152 24 10 16 16
153 29 10 11 21
154 19 10 9 14
155 24 10 11 20
156 19 10 9 13
157 24 10 14 11
158 22 10 13 15
159 17 10 16 19
ParentalCriticism Organization\r t
1 12 26 1
2 8 23 2
3 8 25 3
4 8 23 4
5 9 19 5
6 7 29 6
7 4 25 7
8 11 21 8
9 7 22 9
10 7 25 10
11 12 24 11
12 10 18 12
13 10 22 13
14 8 15 14
15 8 22 15
16 4 28 16
17 9 20 17
18 8 12 18
19 7 24 19
20 11 20 20
21 9 21 21
22 11 20 22
23 13 21 23
24 8 23 24
25 8 28 25
26 9 24 26
27 6 24 27
28 9 24 28
29 9 23 29
30 6 23 30
31 6 29 31
32 16 24 32
33 5 18 33
34 7 25 34
35 9 21 35
36 6 26 36
37 6 22 37
38 5 22 38
39 12 22 39
40 7 23 40
41 10 30 41
42 9 23 42
43 8 17 43
44 5 23 44
45 8 23 45
46 8 25 46
47 10 24 47
48 6 24 48
49 8 23 49
50 7 21 50
51 4 24 51
52 8 24 52
53 8 28 53
54 4 16 54
55 20 20 55
56 8 29 56
57 8 27 57
58 6 22 58
59 4 28 59
60 8 16 60
61 9 25 61
62 6 24 62
63 7 28 63
64 9 24 64
65 5 23 65
66 5 30 66
67 8 24 67
68 8 21 68
69 6 25 69
70 8 25 70
71 7 22 71
72 7 23 72
73 9 26 73
74 11 23 74
75 6 25 75
76 8 21 76
77 6 25 77
78 9 24 78
79 8 29 79
80 6 22 80
81 10 27 81
82 8 26 82
83 8 22 83
84 10 24 84
85 5 27 85
86 7 24 86
87 5 24 87
88 8 29 88
89 14 22 89
90 7 21 90
91 8 24 91
92 6 24 92
93 5 23 93
94 6 20 94
95 10 27 95
96 12 26 96
97 9 25 97
98 12 21 98
99 7 21 99
100 8 19 100
101 10 21 101
102 6 21 102
103 10 16 103
104 10 22 104
105 10 29 105
106 5 15 106
107 7 17 107
108 10 15 108
109 11 21 109
110 6 21 110
111 7 19 111
112 12 24 112
113 11 20 113
114 11 17 114
115 11 23 115
116 5 24 116
117 8 14 117
118 6 19 118
119 9 24 119
120 4 13 120
121 4 22 121
122 7 16 122
123 11 19 123
124 6 25 124
125 7 25 125
126 8 23 126
127 4 24 127
128 8 26 128
129 9 26 129
130 8 25 130
131 11 18 131
132 8 21 132
133 5 26 133
134 4 23 134
135 8 23 135
136 10 22 136
137 6 20 137
138 9 13 138
139 9 24 139
140 13 15 140
141 9 14 141
142 10 22 142
143 20 10 143
144 5 24 144
145 11 22 145
146 6 24 146
147 9 19 147
148 7 20 148
149 9 13 149
150 10 20 150
151 9 22 151
152 8 24 152
153 7 29 153
154 6 12 154
155 13 20 155
156 6 21 156
157 8 24 157
158 10 22 158
159 16 20 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maand DoubtsAboutActions
18.482679 -0.994362 -0.112328
ParentalExpectations ParentalCriticism `Organization\r`
0.339704 0.093101 0.437231
t
-0.001326
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.9289 -2.5653 -0.3047 2.2669 12.8267
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.482679 16.782381 1.101 0.27250
Maand -0.994362 1.685380 -0.590 0.55607
DoubtsAboutActions -0.112328 0.110148 -1.020 0.30945
ParentalExpectations 0.339704 0.109803 3.094 0.00235 **
ParentalCriticism 0.093101 0.143074 0.651 0.51621
`Organization\r` 0.437231 0.081516 5.364 2.98e-07 ***
t -0.001326 0.007110 -0.186 0.85234
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.784 on 152 degrees of freedom
Multiple R-squared: 0.2252, Adjusted R-squared: 0.1946
F-statistic: 7.364 on 6 and 152 DF, p-value: 6.292e-07
> 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,] 0.04309352 0.08618704 0.9569065
[2,] 0.01247722 0.02495445 0.9875228
[3,] 0.01098171 0.02196341 0.9890183
[4,] 0.02115175 0.04230351 0.9788482
[5,] 0.03771369 0.07542737 0.9622863
[6,] 0.33525479 0.67050959 0.6647452
[7,] 0.28072445 0.56144890 0.7192755
[8,] 0.30008472 0.60016944 0.6999153
[9,] 0.25979713 0.51959426 0.7402029
[10,] 0.19995211 0.39990421 0.8000479
[11,] 0.29916769 0.59833537 0.7008323
[12,] 0.32250506 0.64501012 0.6774949
[13,] 0.25508317 0.51016634 0.7449168
[14,] 0.21465736 0.42931472 0.7853426
[15,] 0.18256036 0.36512072 0.8174396
[16,] 0.15496928 0.30993855 0.8450307
[17,] 0.11841353 0.23682707 0.8815865
[18,] 0.09810110 0.19620220 0.9018989
[19,] 0.08618772 0.17237543 0.9138123
[20,] 0.06820238 0.13640476 0.9317976
[21,] 0.04839911 0.09679821 0.9516009
[22,] 0.04042707 0.08085414 0.9595729
[23,] 0.04426364 0.08852727 0.9557364
[24,] 0.07372730 0.14745459 0.9262727
[25,] 0.20558637 0.41117274 0.7944136
[26,] 0.16491379 0.32982758 0.8350862
[27,] 0.18870177 0.37740354 0.8112982
[28,] 0.20413490 0.40826980 0.7958651
[29,] 0.38802330 0.77604660 0.6119767
[30,] 0.36116110 0.72232221 0.6388389
[31,] 0.38393221 0.76786442 0.6160678
[32,] 0.36222254 0.72444508 0.6377775
[33,] 0.32019751 0.64039501 0.6798025
[34,] 0.58830028 0.82339943 0.4116997
[35,] 0.54684694 0.90630611 0.4531531
[36,] 0.51619287 0.96761426 0.4838071
[37,] 0.54834870 0.90330261 0.4516513
[38,] 0.63298090 0.73403819 0.3670191
[39,] 0.81794493 0.36411014 0.1820551
[40,] 0.78935799 0.42128402 0.2106420
[41,] 0.76097385 0.47805230 0.2390261
[42,] 0.74727810 0.50544380 0.2527219
[43,] 0.74721032 0.50557935 0.2527897
[44,] 0.79817431 0.40365139 0.2018257
[45,] 0.80598650 0.38802700 0.1940135
[46,] 0.82124280 0.35751440 0.1787572
[47,] 0.79814243 0.40371513 0.2018576
[48,] 0.76746198 0.46507604 0.2325380
[49,] 0.73586539 0.52826923 0.2641346
[50,] 0.70835483 0.58329034 0.2916452
[51,] 0.76366936 0.47266128 0.2363306
[52,] 0.72583298 0.54833405 0.2741670
[53,] 0.68986835 0.62026330 0.3101317
[54,] 0.65913188 0.68173624 0.3408681
[55,] 0.63275470 0.73449059 0.3672453
[56,] 0.62470831 0.75058339 0.3752917
[57,] 0.71306676 0.57386649 0.2869332
[58,] 0.67689677 0.64620646 0.3231032
[59,] 0.70689956 0.58620088 0.2931004
[60,] 0.70599939 0.58800122 0.2940006
[61,] 0.68168139 0.63663723 0.3183186
[62,] 0.65699688 0.68600624 0.3430031
[63,] 0.61722097 0.76555806 0.3827790
[64,] 0.59154103 0.81691795 0.4084590
[65,] 0.55260486 0.89479028 0.4473951
[66,] 0.51957733 0.96084533 0.4804227
[67,] 0.48126238 0.96252477 0.5187376
[68,] 0.45356059 0.90712119 0.5464394
[69,] 0.41406605 0.82813210 0.5859340
[70,] 0.37717511 0.75435021 0.6228249
[71,] 0.37738985 0.75477969 0.6226102
[72,] 0.34319174 0.68638348 0.6568083
[73,] 0.42509105 0.85018211 0.5749089
[74,] 0.39960904 0.79921809 0.6003910
[75,] 0.35976361 0.71952721 0.6402364
[76,] 0.33947648 0.67895297 0.6605235
[77,] 0.32882163 0.65764327 0.6711784
[78,] 0.35444225 0.70888449 0.6455578
[79,] 0.31499920 0.62999840 0.6850008
[80,] 0.32518081 0.65036162 0.6748192
[81,] 0.31918662 0.63837324 0.6808134
[82,] 0.35024080 0.70048160 0.6497592
[83,] 0.31913491 0.63826981 0.6808651
[84,] 0.30279514 0.60559028 0.6972049
[85,] 0.26663135 0.53326270 0.7333686
[86,] 0.24202653 0.48405306 0.7579735
[87,] 0.30092994 0.60185989 0.6990701
[88,] 0.26495134 0.52990268 0.7350487
[89,] 0.23129390 0.46258779 0.7687061
[90,] 0.20968766 0.41937532 0.7903123
[91,] 0.18436663 0.36873326 0.8156334
[92,] 0.15702487 0.31404974 0.8429751
[93,] 0.13411153 0.26822306 0.8658885
[94,] 0.11063259 0.22126518 0.8893674
[95,] 0.09230451 0.18460901 0.9076955
[96,] 0.09163063 0.18326126 0.9083694
[97,] 0.07343731 0.14687462 0.9265627
[98,] 0.05925958 0.11851915 0.9407404
[99,] 0.04761078 0.09522157 0.9523892
[100,] 0.03691391 0.07382781 0.9630861
[101,] 0.04856024 0.09712049 0.9514398
[102,] 0.08208735 0.16417469 0.9179127
[103,] 0.15433674 0.30867348 0.8456633
[104,] 0.14899548 0.29799095 0.8510045
[105,] 0.55429771 0.89140458 0.4457023
[106,] 0.69284022 0.61431957 0.3071598
[107,] 0.65305444 0.69389111 0.3469456
[108,] 0.70594638 0.58810724 0.2940536
[109,] 0.65900151 0.68199698 0.3409985
[110,] 0.61469235 0.77061530 0.3853076
[111,] 0.61503388 0.76993223 0.3849661
[112,] 0.69511704 0.60976592 0.3048830
[113,] 0.66161791 0.67676418 0.3383821
[114,] 0.66002138 0.67995723 0.3399786
[115,] 0.60380838 0.79238325 0.3961916
[116,] 0.62829294 0.74341413 0.3717071
[117,] 0.60173830 0.79652341 0.3982617
[118,] 0.54080060 0.91839880 0.4591994
[119,] 0.48100343 0.96200686 0.5189966
[120,] 0.45297133 0.90594266 0.5470287
[121,] 0.41493684 0.82987369 0.5850632
[122,] 0.40725477 0.81450954 0.5927452
[123,] 0.44917940 0.89835881 0.5508206
[124,] 0.38180160 0.76360321 0.6181984
[125,] 0.37068977 0.74137954 0.6293102
[126,] 0.59553364 0.80893272 0.4044664
[127,] 0.52972157 0.94055686 0.4702784
[128,] 0.61244190 0.77511619 0.3875581
[129,] 0.54019755 0.91960490 0.4598025
[130,] 0.63193271 0.73613458 0.3680673
[131,] 0.55581957 0.88836087 0.4441804
[132,] 0.59713794 0.80572413 0.4028621
[133,] 0.59414226 0.81171548 0.4058577
[134,] 0.60996528 0.78006945 0.3900347
[135,] 0.56185869 0.87628263 0.4381413
[136,] 0.45876518 0.91753036 0.5412348
[137,] 0.40710741 0.81421482 0.5928926
[138,] 0.38977487 0.77954974 0.6102251
[139,] 0.26674895 0.53349790 0.7332511
[140,] 0.44450266 0.88900532 0.5554973
> postscript(file="/var/www/html/rcomp/tmp/13rh11293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23rh11293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3d0z41293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4d0z41293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5d0z41293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
-0.18146436 3.52578863 3.69397401 -3.37834315 0.15008629 -3.81004143
7 8 9 10 11 12
2.55357666 1.76444928 -4.07711614 -2.28882811 -4.43082289 -2.41164445
13 14 15 16 17 18
-6.91003692 -1.87287557 1.61580039 1.22103082 1.85456450 -3.34489563
19 20 21 22 23 24
-2.53546514 0.51635044 2.41719725 -1.59604573 -3.64563451 -3.13281148
25 26 27 28 29 30
-3.99432948 -1.03297944 1.35997678 -4.23859245 0.18085309 -0.51940753
31 32 33 34 35 36
1.32967454 1.20278260 7.28996340 8.74570781 1.72356571 7.32196498
37 38 39 40 41 42
7.51057463 -3.85246961 4.00961547 7.07743660 -1.07472432 1.87749453
43 44 45 46 47 48
-6.63206788 1.57314163 4.44571552 -2.54246858 -5.08456844 10.98767925
49 50 51 52 53 54
2.77161076 2.49129316 3.76132916 -1.77397141 -4.40924211 -2.19982961
55 56 57 58 59 60
3.67294728 -0.50551186 2.03329176 1.50291096 -0.66735030 -4.93125205
61 62 63 64 65 66
0.70762783 1.19811143 -0.75763573 2.95696346 -2.58817207 7.14427136
67 68 69 70 71 72
-0.52671046 5.21922919 -2.87374431 2.37701979 -1.89354841 -0.19801449
73 74 75 76 77 78
2.26719369 -0.95113150 0.98637862 -1.31813346 -1.76992197 -1.17502756
79 80 81 82 83 84
-0.90794167 3.20876414 0.98851529 -6.60594362 -2.86113232 0.61110582
85 86 87 88 89 90
-2.84215915 -3.76995606 4.00376191 -0.33165052 3.56910569 -4.20647303
91 92 93 94 95 96
-5.84003661 -2.62523974 -3.75931723 0.31005039 -2.87515812 5.09248764
97 98 99 100 101 102
-2.13775821 -2.36260979 -3.30686996 -2.75155848 0.50969583 -2.43444830
103 104 105 106 107 108
-1.77264030 -2.14821445 -4.17472297 -0.58665751 -0.98026057 -0.29055822
109 110 111 112 113 114
-0.12348706 -5.63754607 -6.52882758 -6.53797657 3.68330069 12.82665786
115 116 117 118 119 120
7.48930624 -1.86185690 4.64900637 0.92964862 -1.23572195 -2.91840790
121 122 123 124 125 126
6.48754143 -1.86556734 4.10923855 -1.00365706 -1.42690242 -2.71250203
127 128 129 130 131 132
0.26493773 1.01667849 -3.20653600 3.79354530 3.55979460 -5.35622311
133 134 135 136 137 138
0.07795314 -2.94884759 -6.11438019 1.75732929 -1.27684951 3.05375975
139 140 141 142 143 144
-1.86406559 3.26157750 6.20397691 6.35147407 -0.16411243 0.99978159
145 146 147 148 149 150
-0.30472908 5.66284525 2.74650486 0.53502356 -7.92893826 -1.87306686
151 152 153 154 155 156
4.25368054 0.78607898 1.43419056 1.11481622 0.15302094 -2.47790877
157 158 159
2.26656997 -0.51498684 -6.21963446
> postscript(file="/var/www/html/rcomp/tmp/6oayp1293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.18146436 NA
1 3.52578863 -0.18146436
2 3.69397401 3.52578863
3 -3.37834315 3.69397401
4 0.15008629 -3.37834315
5 -3.81004143 0.15008629
6 2.55357666 -3.81004143
7 1.76444928 2.55357666
8 -4.07711614 1.76444928
9 -2.28882811 -4.07711614
10 -4.43082289 -2.28882811
11 -2.41164445 -4.43082289
12 -6.91003692 -2.41164445
13 -1.87287557 -6.91003692
14 1.61580039 -1.87287557
15 1.22103082 1.61580039
16 1.85456450 1.22103082
17 -3.34489563 1.85456450
18 -2.53546514 -3.34489563
19 0.51635044 -2.53546514
20 2.41719725 0.51635044
21 -1.59604573 2.41719725
22 -3.64563451 -1.59604573
23 -3.13281148 -3.64563451
24 -3.99432948 -3.13281148
25 -1.03297944 -3.99432948
26 1.35997678 -1.03297944
27 -4.23859245 1.35997678
28 0.18085309 -4.23859245
29 -0.51940753 0.18085309
30 1.32967454 -0.51940753
31 1.20278260 1.32967454
32 7.28996340 1.20278260
33 8.74570781 7.28996340
34 1.72356571 8.74570781
35 7.32196498 1.72356571
36 7.51057463 7.32196498
37 -3.85246961 7.51057463
38 4.00961547 -3.85246961
39 7.07743660 4.00961547
40 -1.07472432 7.07743660
41 1.87749453 -1.07472432
42 -6.63206788 1.87749453
43 1.57314163 -6.63206788
44 4.44571552 1.57314163
45 -2.54246858 4.44571552
46 -5.08456844 -2.54246858
47 10.98767925 -5.08456844
48 2.77161076 10.98767925
49 2.49129316 2.77161076
50 3.76132916 2.49129316
51 -1.77397141 3.76132916
52 -4.40924211 -1.77397141
53 -2.19982961 -4.40924211
54 3.67294728 -2.19982961
55 -0.50551186 3.67294728
56 2.03329176 -0.50551186
57 1.50291096 2.03329176
58 -0.66735030 1.50291096
59 -4.93125205 -0.66735030
60 0.70762783 -4.93125205
61 1.19811143 0.70762783
62 -0.75763573 1.19811143
63 2.95696346 -0.75763573
64 -2.58817207 2.95696346
65 7.14427136 -2.58817207
66 -0.52671046 7.14427136
67 5.21922919 -0.52671046
68 -2.87374431 5.21922919
69 2.37701979 -2.87374431
70 -1.89354841 2.37701979
71 -0.19801449 -1.89354841
72 2.26719369 -0.19801449
73 -0.95113150 2.26719369
74 0.98637862 -0.95113150
75 -1.31813346 0.98637862
76 -1.76992197 -1.31813346
77 -1.17502756 -1.76992197
78 -0.90794167 -1.17502756
79 3.20876414 -0.90794167
80 0.98851529 3.20876414
81 -6.60594362 0.98851529
82 -2.86113232 -6.60594362
83 0.61110582 -2.86113232
84 -2.84215915 0.61110582
85 -3.76995606 -2.84215915
86 4.00376191 -3.76995606
87 -0.33165052 4.00376191
88 3.56910569 -0.33165052
89 -4.20647303 3.56910569
90 -5.84003661 -4.20647303
91 -2.62523974 -5.84003661
92 -3.75931723 -2.62523974
93 0.31005039 -3.75931723
94 -2.87515812 0.31005039
95 5.09248764 -2.87515812
96 -2.13775821 5.09248764
97 -2.36260979 -2.13775821
98 -3.30686996 -2.36260979
99 -2.75155848 -3.30686996
100 0.50969583 -2.75155848
101 -2.43444830 0.50969583
102 -1.77264030 -2.43444830
103 -2.14821445 -1.77264030
104 -4.17472297 -2.14821445
105 -0.58665751 -4.17472297
106 -0.98026057 -0.58665751
107 -0.29055822 -0.98026057
108 -0.12348706 -0.29055822
109 -5.63754607 -0.12348706
110 -6.52882758 -5.63754607
111 -6.53797657 -6.52882758
112 3.68330069 -6.53797657
113 12.82665786 3.68330069
114 7.48930624 12.82665786
115 -1.86185690 7.48930624
116 4.64900637 -1.86185690
117 0.92964862 4.64900637
118 -1.23572195 0.92964862
119 -2.91840790 -1.23572195
120 6.48754143 -2.91840790
121 -1.86556734 6.48754143
122 4.10923855 -1.86556734
123 -1.00365706 4.10923855
124 -1.42690242 -1.00365706
125 -2.71250203 -1.42690242
126 0.26493773 -2.71250203
127 1.01667849 0.26493773
128 -3.20653600 1.01667849
129 3.79354530 -3.20653600
130 3.55979460 3.79354530
131 -5.35622311 3.55979460
132 0.07795314 -5.35622311
133 -2.94884759 0.07795314
134 -6.11438019 -2.94884759
135 1.75732929 -6.11438019
136 -1.27684951 1.75732929
137 3.05375975 -1.27684951
138 -1.86406559 3.05375975
139 3.26157750 -1.86406559
140 6.20397691 3.26157750
141 6.35147407 6.20397691
142 -0.16411243 6.35147407
143 0.99978159 -0.16411243
144 -0.30472908 0.99978159
145 5.66284525 -0.30472908
146 2.74650486 5.66284525
147 0.53502356 2.74650486
148 -7.92893826 0.53502356
149 -1.87306686 -7.92893826
150 4.25368054 -1.87306686
151 0.78607898 4.25368054
152 1.43419056 0.78607898
153 1.11481622 1.43419056
154 0.15302094 1.11481622
155 -2.47790877 0.15302094
156 2.26656997 -2.47790877
157 -0.51498684 2.26656997
158 -6.21963446 -0.51498684
159 NA -6.21963446
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.52578863 -0.18146436
[2,] 3.69397401 3.52578863
[3,] -3.37834315 3.69397401
[4,] 0.15008629 -3.37834315
[5,] -3.81004143 0.15008629
[6,] 2.55357666 -3.81004143
[7,] 1.76444928 2.55357666
[8,] -4.07711614 1.76444928
[9,] -2.28882811 -4.07711614
[10,] -4.43082289 -2.28882811
[11,] -2.41164445 -4.43082289
[12,] -6.91003692 -2.41164445
[13,] -1.87287557 -6.91003692
[14,] 1.61580039 -1.87287557
[15,] 1.22103082 1.61580039
[16,] 1.85456450 1.22103082
[17,] -3.34489563 1.85456450
[18,] -2.53546514 -3.34489563
[19,] 0.51635044 -2.53546514
[20,] 2.41719725 0.51635044
[21,] -1.59604573 2.41719725
[22,] -3.64563451 -1.59604573
[23,] -3.13281148 -3.64563451
[24,] -3.99432948 -3.13281148
[25,] -1.03297944 -3.99432948
[26,] 1.35997678 -1.03297944
[27,] -4.23859245 1.35997678
[28,] 0.18085309 -4.23859245
[29,] -0.51940753 0.18085309
[30,] 1.32967454 -0.51940753
[31,] 1.20278260 1.32967454
[32,] 7.28996340 1.20278260
[33,] 8.74570781 7.28996340
[34,] 1.72356571 8.74570781
[35,] 7.32196498 1.72356571
[36,] 7.51057463 7.32196498
[37,] -3.85246961 7.51057463
[38,] 4.00961547 -3.85246961
[39,] 7.07743660 4.00961547
[40,] -1.07472432 7.07743660
[41,] 1.87749453 -1.07472432
[42,] -6.63206788 1.87749453
[43,] 1.57314163 -6.63206788
[44,] 4.44571552 1.57314163
[45,] -2.54246858 4.44571552
[46,] -5.08456844 -2.54246858
[47,] 10.98767925 -5.08456844
[48,] 2.77161076 10.98767925
[49,] 2.49129316 2.77161076
[50,] 3.76132916 2.49129316
[51,] -1.77397141 3.76132916
[52,] -4.40924211 -1.77397141
[53,] -2.19982961 -4.40924211
[54,] 3.67294728 -2.19982961
[55,] -0.50551186 3.67294728
[56,] 2.03329176 -0.50551186
[57,] 1.50291096 2.03329176
[58,] -0.66735030 1.50291096
[59,] -4.93125205 -0.66735030
[60,] 0.70762783 -4.93125205
[61,] 1.19811143 0.70762783
[62,] -0.75763573 1.19811143
[63,] 2.95696346 -0.75763573
[64,] -2.58817207 2.95696346
[65,] 7.14427136 -2.58817207
[66,] -0.52671046 7.14427136
[67,] 5.21922919 -0.52671046
[68,] -2.87374431 5.21922919
[69,] 2.37701979 -2.87374431
[70,] -1.89354841 2.37701979
[71,] -0.19801449 -1.89354841
[72,] 2.26719369 -0.19801449
[73,] -0.95113150 2.26719369
[74,] 0.98637862 -0.95113150
[75,] -1.31813346 0.98637862
[76,] -1.76992197 -1.31813346
[77,] -1.17502756 -1.76992197
[78,] -0.90794167 -1.17502756
[79,] 3.20876414 -0.90794167
[80,] 0.98851529 3.20876414
[81,] -6.60594362 0.98851529
[82,] -2.86113232 -6.60594362
[83,] 0.61110582 -2.86113232
[84,] -2.84215915 0.61110582
[85,] -3.76995606 -2.84215915
[86,] 4.00376191 -3.76995606
[87,] -0.33165052 4.00376191
[88,] 3.56910569 -0.33165052
[89,] -4.20647303 3.56910569
[90,] -5.84003661 -4.20647303
[91,] -2.62523974 -5.84003661
[92,] -3.75931723 -2.62523974
[93,] 0.31005039 -3.75931723
[94,] -2.87515812 0.31005039
[95,] 5.09248764 -2.87515812
[96,] -2.13775821 5.09248764
[97,] -2.36260979 -2.13775821
[98,] -3.30686996 -2.36260979
[99,] -2.75155848 -3.30686996
[100,] 0.50969583 -2.75155848
[101,] -2.43444830 0.50969583
[102,] -1.77264030 -2.43444830
[103,] -2.14821445 -1.77264030
[104,] -4.17472297 -2.14821445
[105,] -0.58665751 -4.17472297
[106,] -0.98026057 -0.58665751
[107,] -0.29055822 -0.98026057
[108,] -0.12348706 -0.29055822
[109,] -5.63754607 -0.12348706
[110,] -6.52882758 -5.63754607
[111,] -6.53797657 -6.52882758
[112,] 3.68330069 -6.53797657
[113,] 12.82665786 3.68330069
[114,] 7.48930624 12.82665786
[115,] -1.86185690 7.48930624
[116,] 4.64900637 -1.86185690
[117,] 0.92964862 4.64900637
[118,] -1.23572195 0.92964862
[119,] -2.91840790 -1.23572195
[120,] 6.48754143 -2.91840790
[121,] -1.86556734 6.48754143
[122,] 4.10923855 -1.86556734
[123,] -1.00365706 4.10923855
[124,] -1.42690242 -1.00365706
[125,] -2.71250203 -1.42690242
[126,] 0.26493773 -2.71250203
[127,] 1.01667849 0.26493773
[128,] -3.20653600 1.01667849
[129,] 3.79354530 -3.20653600
[130,] 3.55979460 3.79354530
[131,] -5.35622311 3.55979460
[132,] 0.07795314 -5.35622311
[133,] -2.94884759 0.07795314
[134,] -6.11438019 -2.94884759
[135,] 1.75732929 -6.11438019
[136,] -1.27684951 1.75732929
[137,] 3.05375975 -1.27684951
[138,] -1.86406559 3.05375975
[139,] 3.26157750 -1.86406559
[140,] 6.20397691 3.26157750
[141,] 6.35147407 6.20397691
[142,] -0.16411243 6.35147407
[143,] 0.99978159 -0.16411243
[144,] -0.30472908 0.99978159
[145,] 5.66284525 -0.30472908
[146,] 2.74650486 5.66284525
[147,] 0.53502356 2.74650486
[148,] -7.92893826 0.53502356
[149,] -1.87306686 -7.92893826
[150,] 4.25368054 -1.87306686
[151,] 0.78607898 4.25368054
[152,] 1.43419056 0.78607898
[153,] 1.11481622 1.43419056
[154,] 0.15302094 1.11481622
[155,] -2.47790877 0.15302094
[156,] 2.26656997 -2.47790877
[157,] -0.51498684 2.26656997
[158,] -6.21963446 -0.51498684
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.52578863 -0.18146436
2 3.69397401 3.52578863
3 -3.37834315 3.69397401
4 0.15008629 -3.37834315
5 -3.81004143 0.15008629
6 2.55357666 -3.81004143
7 1.76444928 2.55357666
8 -4.07711614 1.76444928
9 -2.28882811 -4.07711614
10 -4.43082289 -2.28882811
11 -2.41164445 -4.43082289
12 -6.91003692 -2.41164445
13 -1.87287557 -6.91003692
14 1.61580039 -1.87287557
15 1.22103082 1.61580039
16 1.85456450 1.22103082
17 -3.34489563 1.85456450
18 -2.53546514 -3.34489563
19 0.51635044 -2.53546514
20 2.41719725 0.51635044
21 -1.59604573 2.41719725
22 -3.64563451 -1.59604573
23 -3.13281148 -3.64563451
24 -3.99432948 -3.13281148
25 -1.03297944 -3.99432948
26 1.35997678 -1.03297944
27 -4.23859245 1.35997678
28 0.18085309 -4.23859245
29 -0.51940753 0.18085309
30 1.32967454 -0.51940753
31 1.20278260 1.32967454
32 7.28996340 1.20278260
33 8.74570781 7.28996340
34 1.72356571 8.74570781
35 7.32196498 1.72356571
36 7.51057463 7.32196498
37 -3.85246961 7.51057463
38 4.00961547 -3.85246961
39 7.07743660 4.00961547
40 -1.07472432 7.07743660
41 1.87749453 -1.07472432
42 -6.63206788 1.87749453
43 1.57314163 -6.63206788
44 4.44571552 1.57314163
45 -2.54246858 4.44571552
46 -5.08456844 -2.54246858
47 10.98767925 -5.08456844
48 2.77161076 10.98767925
49 2.49129316 2.77161076
50 3.76132916 2.49129316
51 -1.77397141 3.76132916
52 -4.40924211 -1.77397141
53 -2.19982961 -4.40924211
54 3.67294728 -2.19982961
55 -0.50551186 3.67294728
56 2.03329176 -0.50551186
57 1.50291096 2.03329176
58 -0.66735030 1.50291096
59 -4.93125205 -0.66735030
60 0.70762783 -4.93125205
61 1.19811143 0.70762783
62 -0.75763573 1.19811143
63 2.95696346 -0.75763573
64 -2.58817207 2.95696346
65 7.14427136 -2.58817207
66 -0.52671046 7.14427136
67 5.21922919 -0.52671046
68 -2.87374431 5.21922919
69 2.37701979 -2.87374431
70 -1.89354841 2.37701979
71 -0.19801449 -1.89354841
72 2.26719369 -0.19801449
73 -0.95113150 2.26719369
74 0.98637862 -0.95113150
75 -1.31813346 0.98637862
76 -1.76992197 -1.31813346
77 -1.17502756 -1.76992197
78 -0.90794167 -1.17502756
79 3.20876414 -0.90794167
80 0.98851529 3.20876414
81 -6.60594362 0.98851529
82 -2.86113232 -6.60594362
83 0.61110582 -2.86113232
84 -2.84215915 0.61110582
85 -3.76995606 -2.84215915
86 4.00376191 -3.76995606
87 -0.33165052 4.00376191
88 3.56910569 -0.33165052
89 -4.20647303 3.56910569
90 -5.84003661 -4.20647303
91 -2.62523974 -5.84003661
92 -3.75931723 -2.62523974
93 0.31005039 -3.75931723
94 -2.87515812 0.31005039
95 5.09248764 -2.87515812
96 -2.13775821 5.09248764
97 -2.36260979 -2.13775821
98 -3.30686996 -2.36260979
99 -2.75155848 -3.30686996
100 0.50969583 -2.75155848
101 -2.43444830 0.50969583
102 -1.77264030 -2.43444830
103 -2.14821445 -1.77264030
104 -4.17472297 -2.14821445
105 -0.58665751 -4.17472297
106 -0.98026057 -0.58665751
107 -0.29055822 -0.98026057
108 -0.12348706 -0.29055822
109 -5.63754607 -0.12348706
110 -6.52882758 -5.63754607
111 -6.53797657 -6.52882758
112 3.68330069 -6.53797657
113 12.82665786 3.68330069
114 7.48930624 12.82665786
115 -1.86185690 7.48930624
116 4.64900637 -1.86185690
117 0.92964862 4.64900637
118 -1.23572195 0.92964862
119 -2.91840790 -1.23572195
120 6.48754143 -2.91840790
121 -1.86556734 6.48754143
122 4.10923855 -1.86556734
123 -1.00365706 4.10923855
124 -1.42690242 -1.00365706
125 -2.71250203 -1.42690242
126 0.26493773 -2.71250203
127 1.01667849 0.26493773
128 -3.20653600 1.01667849
129 3.79354530 -3.20653600
130 3.55979460 3.79354530
131 -5.35622311 3.55979460
132 0.07795314 -5.35622311
133 -2.94884759 0.07795314
134 -6.11438019 -2.94884759
135 1.75732929 -6.11438019
136 -1.27684951 1.75732929
137 3.05375975 -1.27684951
138 -1.86406559 3.05375975
139 3.26157750 -1.86406559
140 6.20397691 3.26157750
141 6.35147407 6.20397691
142 -0.16411243 6.35147407
143 0.99978159 -0.16411243
144 -0.30472908 0.99978159
145 5.66284525 -0.30472908
146 2.74650486 5.66284525
147 0.53502356 2.74650486
148 -7.92893826 0.53502356
149 -1.87306686 -7.92893826
150 4.25368054 -1.87306686
151 0.78607898 4.25368054
152 1.43419056 0.78607898
153 1.11481622 1.43419056
154 0.15302094 1.11481622
155 -2.47790877 0.15302094
156 2.26656997 -2.47790877
157 -0.51498684 2.26656997
158 -6.21963446 -0.51498684
> 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/7oayp1293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hjxs1293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hjxs1293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109sev1293539222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vtv11293539222.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/12gbc71293539222.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/13cl9x1293539222.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/14g4ql1293539222.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/15j4o91293539222.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/16mnnx1293539222.tab")
+ }
>
> try(system("convert tmp/13rh11293539222.ps tmp/13rh11293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/23rh11293539222.ps tmp/23rh11293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/3d0z41293539222.ps tmp/3d0z41293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/4d0z41293539222.ps tmp/4d0z41293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/5d0z41293539222.ps tmp/5d0z41293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oayp1293539222.ps tmp/6oayp1293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/7oayp1293539222.ps tmp/7oayp1293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hjxs1293539222.ps tmp/8hjxs1293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hjxs1293539222.ps tmp/9hjxs1293539222.png",intern=TRUE))
character(0)
> try(system("convert tmp/109sev1293539222.ps tmp/109sev1293539222.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.095 1.749 9.195