R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(67
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+ ,0
+ ,63
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+ ,2
+ ,100350)
+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('logins'
+ ,'blogged_computations'
+ ,'reviewed_compendiums'
+ ,'long_feedback_messages'
+ ,'shared_compendiums'
+ ,'number_characters')
+ ,1:164))
> y <- array(NA,dim=c(6,164),dimnames=list(c('logins','blogged_computations','reviewed_compendiums','long_feedback_messages','shared_compendiums','number_characters'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '6'
> #'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
number_characters logins blogged_computations reviewed_compendiums
1 140824 67 96 38
2 110459 63 67 34
3 105079 69 70 42
4 112098 103 134 38
5 43929 49 59 27
6 76173 28 8 35
7 187326 113 145 33
8 22807 19 1 18
9 144408 57 71 34
10 66485 43 82 33
11 79089 102 92 42
12 81625 110 106 55
13 68788 65 50 35
14 103297 74 113 51
15 69446 79 70 42
16 114948 174 168 59
17 167949 66 111 36
18 125081 154 96 39
19 125818 52 102 29
20 136588 82 135 46
21 112431 68 122 45
22 103037 102 86 39
23 82317 39 50 25
24 118906 54 97 52
25 83515 110 127 41
26 104581 112 86 38
27 103129 126 99 41
28 83243 84 117 39
29 37110 51 57 32
30 113344 63 125 41
31 139165 73 120 45
32 86652 72 44 46
33 112302 83 133 48
34 69652 35 43 37
35 119442 90 117 39
36 69867 56 83 42
37 101629 118 105 41
38 70168 79 79 36
39 31081 32 33 17
40 103925 180 116 39
41 92622 78 121 37
42 79011 62 67 38
43 93487 72 73 36
44 64520 56 68 42
45 93473 82 50 45
46 114360 146 101 38
47 33032 42 20 26
48 96125 75 101 52
49 151911 113 137 47
50 89256 54 99 45
51 95671 72 94 40
52 5950 24 8 4
53 149695 303 85 44
54 32551 17 21 18
55 31701 64 30 14
56 100087 56 96 37
57 169707 82 122 56
58 150491 171 115 36
59 120192 131 139 41
60 95893 82 89 36
61 151715 136 147 46
62 176225 113 135 28
63 59900 102 77 42
64 104767 86 72 38
65 114799 64 47 37
66 72128 65 96 30
67 143592 125 79 35
68 89626 139 85 44
69 131072 77 135 36
70 126817 66 143 28
71 81351 67 99 45
72 22618 32 22 23
73 88977 80 78 45
74 92059 52 77 38
75 81897 59 110 38
76 108146 76 132 42
77 126372 89 112 36
78 249771 106 78 41
79 71154 60 126 38
80 71571 60 73 37
81 55918 46 62 28
82 160141 111 143 45
83 38692 68 30 26
84 102812 103 117 44
85 56622 25 49 8
86 15986 53 26 27
87 123534 53 71 35
88 108535 175 59 37
89 93879 110 114 57
90 144551 102 161 41
91 56750 88 74 37
92 127654 73 151 38
93 65594 61 41 31
94 59938 72 121 36
95 146975 76 66 36
96 143372 36 83 36
97 168553 50 94 35
98 183500 74 154 39
99 165986 144 151 58
100 184923 105 164 30
101 140358 121 116 45
102 149959 62 140 41
103 57224 175 73 36
104 43750 14 13 19
105 48029 79 89 23
106 104978 130 90 40
107 100046 46 128 40
108 101047 87 169 40
109 197426 64 28 30
110 160902 86 116 41
111 147172 67 76 40
112 109432 85 145 45
113 1168 11 12 1
114 83248 70 120 36
115 25162 25 23 11
116 45724 48 83 45
117 110529 114 131 38
118 855 16 4 0
119 101382 52 81 30
120 14116 22 18 8
121 89506 110 103 39
122 135356 63 76 44
123 116066 83 55 44
124 144244 51 43 29
125 8773 34 16 8
126 102153 39 66 39
127 117440 80 137 47
128 104128 57 50 48
129 134238 77 134 46
130 134047 96 152 48
131 279488 121 137 50
132 79756 35 71 40
133 66089 42 42 36
134 102070 319 84 40
135 146760 164 103 46
136 154771 50 55 39
137 165933 127 127 42
138 64593 76 55 39
139 92280 46 104 41
140 67150 87 95 42
141 128692 111 35 32
142 124089 115 95 39
143 125386 83 121 35
144 37238 63 41 21
145 140015 98 143 45
146 150047 57 147 50
147 154451 81 97 36
148 156349 100 170 44
149 0 0 0 0
150 6023 10 4 0
151 0 1 0 0
152 0 2 0 0
153 0 0 0 0
154 0 0 0 0
155 84601 82 61 37
156 68946 139 130 47
157 0 0 0 0
158 0 4 0 0
159 1644 5 7 0
160 6179 20 12 5
161 3926 5 0 1
162 52789 42 37 43
163 0 2 0 0
164 100350 63 48 31
long_feedback_messages shared_compendiums
1 116 3
2 127 4
3 106 16
4 133 2
5 64 1
6 89 3
7 122 0
8 22 0
9 117 7
10 82 0
11 136 0
12 184 7
13 106 7
14 159 4
15 86 10
16 199 0
17 139 4
18 92 4
19 85 3
20 174 8
21 148 0
22 144 1
23 84 5
24 208 9
25 144 0
26 139 0
27 127 5
28 136 0
29 99 0
30 135 0
31 165 3
32 135 5
33 178 1
34 137 4
35 148 3
36 127 0
37 141 0
38 89 2
39 46 1
40 143 2
41 116 10
42 103 8
43 108 5
44 126 6
45 45 1
46 122 2
47 66 2
48 180 0
49 165 10
50 146 3
51 137 0
52 7 0
53 157 8
54 61 5
55 41 3
56 120 1
57 208 5
58 127 5
59 147 0
60 127 12
61 161 10
62 73 12
63 94 10
64 129 8
65 125 2
66 87 0
67 128 6
68 148 9
69 116 2
70 89 5
71 154 13
72 67 6
73 171 7
74 90 2
75 133 1
76 137 4
77 133 3
78 125 6
79 134 2
80 110 0
81 89 1
82 138 0
83 99 5
84 92 2
85 27 0
86 77 0
87 127 5
88 137 1
89 122 0
90 143 1
91 85 1
92 131 3
93 90 6
94 135 1
95 132 4
96 139 3
97 127 5
98 104 0
99 221 12
100 106 13
101 153 8
102 130 0
103 59 0
104 64 4
105 36 4
106 88 0
107 125 0
108 124 0
109 83 0
110 127 0
111 143 4
112 115 0
113 0 0
114 94 0
115 30 4
116 119 0
117 102 1
118 0 0
119 77 5
120 9 0
121 137 3
122 150 7
123 137 13
124 84 3
125 21 0
126 139 2
127 168 0
128 155 0
129 161 4
130 145 0
131 175 3
132 137 0
133 100 0
134 150 4
135 163 4
136 137 15
137 149 0
138 112 4
139 135 1
140 114 1
141 45 0
142 120 9
143 111 1
144 78 3
145 136 11
146 179 5
147 118 2
148 147 1
149 0 9
150 0 0
151 0 0
152 0 0
153 0 1
154 0 0
155 88 2
156 115 3
157 0 0
158 0 0
159 0 0
160 13 0
161 4 0
162 76 0
163 0 0
164 63 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logins blogged_computations
6243.7 113.7 450.3
reviewed_compendiums long_feedback_messages shared_compendiums
158.2 290.3 1809.1
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-59626 -19696 -6513 11653 142731
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6243.70 7097.56 0.880 0.3804
logins 113.69 70.25 1.618 0.1076
blogged_computations 450.31 92.21 4.884 2.53e-06 ***
reviewed_compendiums 158.17 484.78 0.326 0.7446
long_feedback_messages 290.25 135.14 2.148 0.0333 *
shared_compendiums 1809.14 754.25 2.399 0.0176 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 33460 on 158 degrees of freedom
Multiple R-squared: 0.5987, Adjusted R-squared: 0.586
F-statistic: 47.15 on 5 and 158 DF, p-value: < 2.2e-16
> 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,] 0.7663721304 4.672557e-01 2.336279e-01
[2,] 0.6468190823 7.063618e-01 3.531809e-01
[3,] 0.5859053828 8.281892e-01 4.140946e-01
[4,] 0.5765940209 8.468120e-01 4.234060e-01
[5,] 0.5012853683 9.974293e-01 4.987146e-01
[6,] 0.3965643959 7.931288e-01 6.034356e-01
[7,] 0.3418817984 6.837636e-01 6.581182e-01
[8,] 0.2801517476 5.603035e-01 7.198483e-01
[9,] 0.2186164994 4.372330e-01 7.813835e-01
[10,] 0.3197038822 6.394078e-01 6.802961e-01
[11,] 0.2501600122 5.003200e-01 7.498400e-01
[12,] 0.2190004615 4.380009e-01 7.809995e-01
[13,] 0.1679820408 3.359641e-01 8.320180e-01
[14,] 0.1246766336 2.493533e-01 8.753234e-01
[15,] 0.1136112819 2.272226e-01 8.863887e-01
[16,] 0.0826363944 1.652728e-01 9.173636e-01
[17,] 0.1110736918 2.221474e-01 8.889263e-01
[18,] 0.0800264201 1.600528e-01 9.199736e-01
[19,] 0.0580585966 1.161172e-01 9.419414e-01
[20,] 0.0604411695 1.208823e-01 9.395588e-01
[21,] 0.0687603272 1.375207e-01 9.312397e-01
[22,] 0.0496007118 9.920142e-02 9.503993e-01
[23,] 0.0400431930 8.008639e-02 9.599568e-01
[24,] 0.0404981844 8.099637e-02 9.595018e-01
[25,] 0.0298019852 5.960397e-02 9.701980e-01
[26,] 0.0218626056 4.372521e-02 9.781374e-01
[27,] 0.0154955900 3.099118e-02 9.845044e-01
[28,] 0.0107653624 2.153072e-02 9.892346e-01
[29,] 0.0071654536 1.433091e-02 9.928345e-01
[30,] 0.0048410238 9.682048e-03 9.951590e-01
[31,] 0.0073721620 1.474432e-02 9.926278e-01
[32,] 0.0060698750 1.213975e-02 9.939301e-01
[33,] 0.0110540863 2.210817e-02 9.889459e-01
[34,] 0.0077947852 1.558957e-02 9.922052e-01
[35,] 0.0052218829 1.044377e-02 9.947781e-01
[36,] 0.0044012284 8.802457e-03 9.955988e-01
[37,] 0.0122934418 2.458688e-02 9.877066e-01
[38,] 0.0088396518 1.767930e-02 9.911603e-01
[39,] 0.0070098446 1.401969e-02 9.929902e-01
[40,] 0.0051953011 1.039060e-02 9.948047e-01
[41,] 0.0036193506 7.238701e-03 9.963806e-01
[42,] 0.0026820466 5.364093e-03 9.973180e-01
[43,] 0.0017934231 3.586846e-03 9.982066e-01
[44,] 0.0019764138 3.952828e-03 9.980236e-01
[45,] 0.0016005800 3.201160e-03 9.983994e-01
[46,] 0.0012844685 2.568937e-03 9.987155e-01
[47,] 0.0010854431 2.170886e-03 9.989146e-01
[48,] 0.0007169501 1.433900e-03 9.992830e-01
[49,] 0.0009420942 1.884188e-03 9.990579e-01
[50,] 0.0007734394 1.546879e-03 9.992266e-01
[51,] 0.0005235812 1.047162e-03 9.994764e-01
[52,] 0.0004350644 8.701288e-04 9.995649e-01
[53,] 0.0002800594 5.601189e-04 9.997199e-01
[54,] 0.0003928797 7.857593e-04 9.996071e-01
[55,] 0.0005952285 1.190457e-03 9.994048e-01
[56,] 0.0003922131 7.844263e-04 9.996078e-01
[57,] 0.0006125146 1.225029e-03 9.993875e-01
[58,] 0.0004506106 9.012212e-04 9.995494e-01
[59,] 0.0005197349 1.039470e-03 9.994803e-01
[60,] 0.0005568208 1.113642e-03 9.994432e-01
[61,] 0.0003810138 7.620276e-04 9.996190e-01
[62,] 0.0002517972 5.035945e-04 9.997482e-01
[63,] 0.0004878067 9.756133e-04 9.995122e-01
[64,] 0.0005195678 1.039136e-03 9.994804e-01
[65,] 0.0004998537 9.997073e-04 9.995001e-01
[66,] 0.0003526163 7.052325e-04 9.996474e-01
[67,] 0.0003148141 6.296282e-04 9.996852e-01
[68,] 0.0002403930 4.807859e-04 9.997596e-01
[69,] 0.0001627258 3.254515e-04 9.998373e-01
[70,] 0.1014109465 2.028219e-01 8.985891e-01
[71,] 0.1264951503 2.529903e-01 8.735048e-01
[72,] 0.1082188173 2.164376e-01 8.917812e-01
[73,] 0.0939985134 1.879970e-01 9.060015e-01
[74,] 0.0914921708 1.829843e-01 9.085078e-01
[75,] 0.0934259046 1.868518e-01 9.065741e-01
[76,] 0.0761013866 1.522028e-01 9.238986e-01
[77,] 0.0637466517 1.274933e-01 9.362533e-01
[78,] 0.0682438948 1.364878e-01 9.317561e-01
[79,] 0.0650104729 1.300209e-01 9.349895e-01
[80,] 0.0524968349 1.049937e-01 9.475032e-01
[81,] 0.0453038524 9.060770e-02 9.546961e-01
[82,] 0.0354654442 7.093089e-02 9.645346e-01
[83,] 0.0324727476 6.494550e-02 9.675273e-01
[84,] 0.0250845790 5.016916e-02 9.749154e-01
[85,] 0.0200764131 4.015283e-02 9.799236e-01
[86,] 0.0356556424 7.131128e-02 9.643444e-01
[87,] 0.0479904039 9.598081e-02 9.520096e-01
[88,] 0.0555343173 1.110686e-01 9.444657e-01
[89,] 0.0903587326 1.807175e-01 9.096413e-01
[90,] 0.1539749000 3.079498e-01 8.460251e-01
[91,] 0.1495222430 2.990445e-01 8.504778e-01
[92,] 0.1659517992 3.319036e-01 8.340482e-01
[93,] 0.1390616843 2.781234e-01 8.609383e-01
[94,] 0.1342033637 2.684067e-01 8.657966e-01
[95,] 0.1215525708 2.431051e-01 8.784474e-01
[96,] 0.1008338375 2.016677e-01 8.991662e-01
[97,] 0.0936513506 1.873027e-01 9.063486e-01
[98,] 0.0774526141 1.549052e-01 9.225474e-01
[99,] 0.0633498333 1.266997e-01 9.366502e-01
[100,] 0.0597638756 1.195278e-01 9.402361e-01
[101,] 0.6205625137 7.588750e-01 3.794375e-01
[102,] 0.6687026904 6.625946e-01 3.312973e-01
[103,] 0.6779918234 6.440164e-01 3.220082e-01
[104,] 0.6358995194 7.282010e-01 3.641005e-01
[105,] 0.5981763511 8.036473e-01 4.018236e-01
[106,] 0.5612060655 8.775879e-01 4.387939e-01
[107,] 0.5191654554 9.616691e-01 4.808345e-01
[108,] 0.5963862968 8.072274e-01 4.036137e-01
[109,] 0.5464792193 9.070416e-01 4.535208e-01
[110,] 0.5002210137 9.995580e-01 4.997790e-01
[111,] 0.4565601670 9.131203e-01 5.434398e-01
[112,] 0.4070555461 8.141111e-01 5.929445e-01
[113,] 0.4010105750 8.020211e-01 5.989894e-01
[114,] 0.3621418909 7.242838e-01 6.378581e-01
[115,] 0.3190721509 6.381443e-01 6.809278e-01
[116,] 0.5093025068 9.813950e-01 4.906975e-01
[117,] 0.4640688025 9.281376e-01 5.359312e-01
[118,] 0.4110811030 8.221622e-01 5.889189e-01
[119,] 0.3898388451 7.796777e-01 6.101612e-01
[120,] 0.3425908140 6.851816e-01 6.574092e-01
[121,] 0.3016654201 6.033308e-01 6.983346e-01
[122,] 0.2597726133 5.195452e-01 7.402274e-01
[123,] 0.9183165558 1.633669e-01 8.168344e-02
[124,] 0.9001304544 1.997391e-01 9.986955e-02
[125,] 0.8734787142 2.530426e-01 1.265213e-01
[126,] 0.9004138176 1.991724e-01 9.958618e-02
[127,] 0.8805266341 2.389467e-01 1.194734e-01
[128,] 0.9206724954 1.586550e-01 7.932750e-02
[129,] 0.9102612039 1.794776e-01 8.973880e-02
[130,] 0.9005634889 1.988730e-01 9.943651e-02
[131,] 0.8715886728 2.568227e-01 1.284113e-01
[132,] 0.9119989644 1.760021e-01 8.800104e-02
[133,] 0.9889536543 2.209269e-02 1.104635e-02
[134,] 0.9844723112 3.105538e-02 1.552769e-02
[135,] 0.9772061371 4.558773e-02 2.279386e-02
[136,] 0.9886474997 2.270500e-02 1.135250e-02
[137,] 0.9839314400 3.213712e-02 1.606856e-02
[138,] 0.9976691895 4.661621e-03 2.330810e-03
[139,] 0.9952151407 9.569719e-03 4.784859e-03
[140,] 0.9998036807 3.926385e-04 1.963193e-04
[141,] 0.9999747396 5.052077e-05 2.526039e-05
[142,] 0.9999149755 1.700490e-04 8.502452e-05
[143,] 0.9996411240 7.177520e-04 3.588760e-04
[144,] 0.9984930296 3.013941e-03 1.506970e-03
[145,] 0.9999078206 1.843587e-04 9.217936e-05
[146,] 0.9992352852 1.529430e-03 7.647148e-04
[147,] 0.9956986471 8.602706e-03 4.301353e-03
> postscript(file="/var/wessaorg/rcomp/tmp/11lmc1323963396.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2njfn1323963396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3yzz61323963396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ge9r1323963396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/521qa1323963396.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 = 164
Frequency = 1
1 2 3 4 5 6
38626.82572 17406.19796 -6886.58300 -14428.66420 -19109.24674 26347.82095
7 8 9 10 11 12
62311.00629 4720.34030 47711.20011 -10592.56646 -26296.30222 -59626.36348
13 14 15 16 17 18
-16327.27783 -23697.36434 -26996.57138 -53820.75799 50942.33084 17991.78992
19 20 21 22 23 24
33045.64242 -12022.07529 -6555.63974 -3303.11751 11743.11014 -22035.95907
25 26 27 28 29 30
-40704.33807 522.58182 -14412.19187 -30879.06239 -34395.54307 -2019.16430
31 32 33 34 35 36
10148.82946 -3096.15283 -24334.51947 -12787.26223 -4272.63055 -23623.67722
37 38 39 40 41 42
-12722.35800 -15775.93940 -11510.38880 -26310.76320 -34589.17939 -14831.30211
43 44 45 46 47 48
98.49259 -32780.68281 33403.56530 997.64478 -13879.99815 -24596.29039
49 50 51 52 53 54
-2288.16662 -22628.91010 -7178.21309 -9289.07640 3725.97551 -14679.92825
55 56 57 58 59 60
-14869.96982 1755.82483 20928.04665 21419.84938 -12689.18923 -24016.03641
61 62 63 64 65 66
-8282.84641 49016.58434 -44631.53005 -1601.87541 34362.89564 -14731.71100
67 68 69 70 71 72
34020.23049 -36895.21122 12301.52050 9369.37974 -52425.25921 -31110.04122
73 74 75 76 77 78
-30900.22301 9478.63176 -27010.96055 -19822.46870 9850.87492 142731.32935
79 80 81 82 83 84
-47171.92735 -12146.23629 -15544.57497 29711.92148 -30684.65791 -5108.15136
85 86 87 88 89 90
16368.99070 -34611.05229 27849.53742 8402.07644 -20631.53420 4411.88474
91 92 93 94 95 96
-25153.61702 -4345.88004 -7927.92741 -55665.41802 51126.97119 44193.63703
97 98 99 100 101 102
62852.57280 63141.57444 -19654.00907 33861.42254 2123.39646 29406.20137
103 104 105 106 107 108
-24606.22383 1242.79075 -28596.70538 11558.51191 -11674.70903 -33507.15236
109 110 111 112 113 114
142461.75692 49298.79884 44018.64822 -12266.02576 -11888.09213 -17968.26072
115 116 117 118 119 120
-11964.89221 -45009.67227 -5090.34960 -9008.91417 16611.62490 -6611.93981
121 122 123 124 125 126
-26985.32234 24565.53314 5376.64410 78443.60466 -15901.59969 11623.39984
127 128 129 130 131 132
-15786.84908 16307.63861 -2343.49664 -1235.79523 133666.28610 -8529.76456
133 134 135 136 137 138
1438.29221 -35366.70913 13666.69131 45005.88661 38171.55093 -20971.13477
139 140 141 142 143 144
-13503.20864 -33304.50512 75945.56697 4711.13247 15656.24486 -26019.15957
145 146 147 148 149 150
-8256.18169 2218.91710 51756.87587 10748.99217 -22525.95848 -3158.79159
151 152 153 154 155 156
-6357.38581 -6471.07291 -8052.83869 -6243.69871 6553.58035 -57880.30612
157 158 159 160 161 162
-6243.69871 -6698.44710 -8320.27252 -12306.23238 -4205.31240 -3751.31663
163 164
-6471.07291 38521.92769
> postscript(file="/var/wessaorg/rcomp/tmp/6hgjc1323963396.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 38626.82572 NA
1 17406.19796 38626.82572
2 -6886.58300 17406.19796
3 -14428.66420 -6886.58300
4 -19109.24674 -14428.66420
5 26347.82095 -19109.24674
6 62311.00629 26347.82095
7 4720.34030 62311.00629
8 47711.20011 4720.34030
9 -10592.56646 47711.20011
10 -26296.30222 -10592.56646
11 -59626.36348 -26296.30222
12 -16327.27783 -59626.36348
13 -23697.36434 -16327.27783
14 -26996.57138 -23697.36434
15 -53820.75799 -26996.57138
16 50942.33084 -53820.75799
17 17991.78992 50942.33084
18 33045.64242 17991.78992
19 -12022.07529 33045.64242
20 -6555.63974 -12022.07529
21 -3303.11751 -6555.63974
22 11743.11014 -3303.11751
23 -22035.95907 11743.11014
24 -40704.33807 -22035.95907
25 522.58182 -40704.33807
26 -14412.19187 522.58182
27 -30879.06239 -14412.19187
28 -34395.54307 -30879.06239
29 -2019.16430 -34395.54307
30 10148.82946 -2019.16430
31 -3096.15283 10148.82946
32 -24334.51947 -3096.15283
33 -12787.26223 -24334.51947
34 -4272.63055 -12787.26223
35 -23623.67722 -4272.63055
36 -12722.35800 -23623.67722
37 -15775.93940 -12722.35800
38 -11510.38880 -15775.93940
39 -26310.76320 -11510.38880
40 -34589.17939 -26310.76320
41 -14831.30211 -34589.17939
42 98.49259 -14831.30211
43 -32780.68281 98.49259
44 33403.56530 -32780.68281
45 997.64478 33403.56530
46 -13879.99815 997.64478
47 -24596.29039 -13879.99815
48 -2288.16662 -24596.29039
49 -22628.91010 -2288.16662
50 -7178.21309 -22628.91010
51 -9289.07640 -7178.21309
52 3725.97551 -9289.07640
53 -14679.92825 3725.97551
54 -14869.96982 -14679.92825
55 1755.82483 -14869.96982
56 20928.04665 1755.82483
57 21419.84938 20928.04665
58 -12689.18923 21419.84938
59 -24016.03641 -12689.18923
60 -8282.84641 -24016.03641
61 49016.58434 -8282.84641
62 -44631.53005 49016.58434
63 -1601.87541 -44631.53005
64 34362.89564 -1601.87541
65 -14731.71100 34362.89564
66 34020.23049 -14731.71100
67 -36895.21122 34020.23049
68 12301.52050 -36895.21122
69 9369.37974 12301.52050
70 -52425.25921 9369.37974
71 -31110.04122 -52425.25921
72 -30900.22301 -31110.04122
73 9478.63176 -30900.22301
74 -27010.96055 9478.63176
75 -19822.46870 -27010.96055
76 9850.87492 -19822.46870
77 142731.32935 9850.87492
78 -47171.92735 142731.32935
79 -12146.23629 -47171.92735
80 -15544.57497 -12146.23629
81 29711.92148 -15544.57497
82 -30684.65791 29711.92148
83 -5108.15136 -30684.65791
84 16368.99070 -5108.15136
85 -34611.05229 16368.99070
86 27849.53742 -34611.05229
87 8402.07644 27849.53742
88 -20631.53420 8402.07644
89 4411.88474 -20631.53420
90 -25153.61702 4411.88474
91 -4345.88004 -25153.61702
92 -7927.92741 -4345.88004
93 -55665.41802 -7927.92741
94 51126.97119 -55665.41802
95 44193.63703 51126.97119
96 62852.57280 44193.63703
97 63141.57444 62852.57280
98 -19654.00907 63141.57444
99 33861.42254 -19654.00907
100 2123.39646 33861.42254
101 29406.20137 2123.39646
102 -24606.22383 29406.20137
103 1242.79075 -24606.22383
104 -28596.70538 1242.79075
105 11558.51191 -28596.70538
106 -11674.70903 11558.51191
107 -33507.15236 -11674.70903
108 142461.75692 -33507.15236
109 49298.79884 142461.75692
110 44018.64822 49298.79884
111 -12266.02576 44018.64822
112 -11888.09213 -12266.02576
113 -17968.26072 -11888.09213
114 -11964.89221 -17968.26072
115 -45009.67227 -11964.89221
116 -5090.34960 -45009.67227
117 -9008.91417 -5090.34960
118 16611.62490 -9008.91417
119 -6611.93981 16611.62490
120 -26985.32234 -6611.93981
121 24565.53314 -26985.32234
122 5376.64410 24565.53314
123 78443.60466 5376.64410
124 -15901.59969 78443.60466
125 11623.39984 -15901.59969
126 -15786.84908 11623.39984
127 16307.63861 -15786.84908
128 -2343.49664 16307.63861
129 -1235.79523 -2343.49664
130 133666.28610 -1235.79523
131 -8529.76456 133666.28610
132 1438.29221 -8529.76456
133 -35366.70913 1438.29221
134 13666.69131 -35366.70913
135 45005.88661 13666.69131
136 38171.55093 45005.88661
137 -20971.13477 38171.55093
138 -13503.20864 -20971.13477
139 -33304.50512 -13503.20864
140 75945.56697 -33304.50512
141 4711.13247 75945.56697
142 15656.24486 4711.13247
143 -26019.15957 15656.24486
144 -8256.18169 -26019.15957
145 2218.91710 -8256.18169
146 51756.87587 2218.91710
147 10748.99217 51756.87587
148 -22525.95848 10748.99217
149 -3158.79159 -22525.95848
150 -6357.38581 -3158.79159
151 -6471.07291 -6357.38581
152 -8052.83869 -6471.07291
153 -6243.69871 -8052.83869
154 6553.58035 -6243.69871
155 -57880.30612 6553.58035
156 -6243.69871 -57880.30612
157 -6698.44710 -6243.69871
158 -8320.27252 -6698.44710
159 -12306.23238 -8320.27252
160 -4205.31240 -12306.23238
161 -3751.31663 -4205.31240
162 -6471.07291 -3751.31663
163 38521.92769 -6471.07291
164 NA 38521.92769
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 17406.19796 38626.82572
[2,] -6886.58300 17406.19796
[3,] -14428.66420 -6886.58300
[4,] -19109.24674 -14428.66420
[5,] 26347.82095 -19109.24674
[6,] 62311.00629 26347.82095
[7,] 4720.34030 62311.00629
[8,] 47711.20011 4720.34030
[9,] -10592.56646 47711.20011
[10,] -26296.30222 -10592.56646
[11,] -59626.36348 -26296.30222
[12,] -16327.27783 -59626.36348
[13,] -23697.36434 -16327.27783
[14,] -26996.57138 -23697.36434
[15,] -53820.75799 -26996.57138
[16,] 50942.33084 -53820.75799
[17,] 17991.78992 50942.33084
[18,] 33045.64242 17991.78992
[19,] -12022.07529 33045.64242
[20,] -6555.63974 -12022.07529
[21,] -3303.11751 -6555.63974
[22,] 11743.11014 -3303.11751
[23,] -22035.95907 11743.11014
[24,] -40704.33807 -22035.95907
[25,] 522.58182 -40704.33807
[26,] -14412.19187 522.58182
[27,] -30879.06239 -14412.19187
[28,] -34395.54307 -30879.06239
[29,] -2019.16430 -34395.54307
[30,] 10148.82946 -2019.16430
[31,] -3096.15283 10148.82946
[32,] -24334.51947 -3096.15283
[33,] -12787.26223 -24334.51947
[34,] -4272.63055 -12787.26223
[35,] -23623.67722 -4272.63055
[36,] -12722.35800 -23623.67722
[37,] -15775.93940 -12722.35800
[38,] -11510.38880 -15775.93940
[39,] -26310.76320 -11510.38880
[40,] -34589.17939 -26310.76320
[41,] -14831.30211 -34589.17939
[42,] 98.49259 -14831.30211
[43,] -32780.68281 98.49259
[44,] 33403.56530 -32780.68281
[45,] 997.64478 33403.56530
[46,] -13879.99815 997.64478
[47,] -24596.29039 -13879.99815
[48,] -2288.16662 -24596.29039
[49,] -22628.91010 -2288.16662
[50,] -7178.21309 -22628.91010
[51,] -9289.07640 -7178.21309
[52,] 3725.97551 -9289.07640
[53,] -14679.92825 3725.97551
[54,] -14869.96982 -14679.92825
[55,] 1755.82483 -14869.96982
[56,] 20928.04665 1755.82483
[57,] 21419.84938 20928.04665
[58,] -12689.18923 21419.84938
[59,] -24016.03641 -12689.18923
[60,] -8282.84641 -24016.03641
[61,] 49016.58434 -8282.84641
[62,] -44631.53005 49016.58434
[63,] -1601.87541 -44631.53005
[64,] 34362.89564 -1601.87541
[65,] -14731.71100 34362.89564
[66,] 34020.23049 -14731.71100
[67,] -36895.21122 34020.23049
[68,] 12301.52050 -36895.21122
[69,] 9369.37974 12301.52050
[70,] -52425.25921 9369.37974
[71,] -31110.04122 -52425.25921
[72,] -30900.22301 -31110.04122
[73,] 9478.63176 -30900.22301
[74,] -27010.96055 9478.63176
[75,] -19822.46870 -27010.96055
[76,] 9850.87492 -19822.46870
[77,] 142731.32935 9850.87492
[78,] -47171.92735 142731.32935
[79,] -12146.23629 -47171.92735
[80,] -15544.57497 -12146.23629
[81,] 29711.92148 -15544.57497
[82,] -30684.65791 29711.92148
[83,] -5108.15136 -30684.65791
[84,] 16368.99070 -5108.15136
[85,] -34611.05229 16368.99070
[86,] 27849.53742 -34611.05229
[87,] 8402.07644 27849.53742
[88,] -20631.53420 8402.07644
[89,] 4411.88474 -20631.53420
[90,] -25153.61702 4411.88474
[91,] -4345.88004 -25153.61702
[92,] -7927.92741 -4345.88004
[93,] -55665.41802 -7927.92741
[94,] 51126.97119 -55665.41802
[95,] 44193.63703 51126.97119
[96,] 62852.57280 44193.63703
[97,] 63141.57444 62852.57280
[98,] -19654.00907 63141.57444
[99,] 33861.42254 -19654.00907
[100,] 2123.39646 33861.42254
[101,] 29406.20137 2123.39646
[102,] -24606.22383 29406.20137
[103,] 1242.79075 -24606.22383
[104,] -28596.70538 1242.79075
[105,] 11558.51191 -28596.70538
[106,] -11674.70903 11558.51191
[107,] -33507.15236 -11674.70903
[108,] 142461.75692 -33507.15236
[109,] 49298.79884 142461.75692
[110,] 44018.64822 49298.79884
[111,] -12266.02576 44018.64822
[112,] -11888.09213 -12266.02576
[113,] -17968.26072 -11888.09213
[114,] -11964.89221 -17968.26072
[115,] -45009.67227 -11964.89221
[116,] -5090.34960 -45009.67227
[117,] -9008.91417 -5090.34960
[118,] 16611.62490 -9008.91417
[119,] -6611.93981 16611.62490
[120,] -26985.32234 -6611.93981
[121,] 24565.53314 -26985.32234
[122,] 5376.64410 24565.53314
[123,] 78443.60466 5376.64410
[124,] -15901.59969 78443.60466
[125,] 11623.39984 -15901.59969
[126,] -15786.84908 11623.39984
[127,] 16307.63861 -15786.84908
[128,] -2343.49664 16307.63861
[129,] -1235.79523 -2343.49664
[130,] 133666.28610 -1235.79523
[131,] -8529.76456 133666.28610
[132,] 1438.29221 -8529.76456
[133,] -35366.70913 1438.29221
[134,] 13666.69131 -35366.70913
[135,] 45005.88661 13666.69131
[136,] 38171.55093 45005.88661
[137,] -20971.13477 38171.55093
[138,] -13503.20864 -20971.13477
[139,] -33304.50512 -13503.20864
[140,] 75945.56697 -33304.50512
[141,] 4711.13247 75945.56697
[142,] 15656.24486 4711.13247
[143,] -26019.15957 15656.24486
[144,] -8256.18169 -26019.15957
[145,] 2218.91710 -8256.18169
[146,] 51756.87587 2218.91710
[147,] 10748.99217 51756.87587
[148,] -22525.95848 10748.99217
[149,] -3158.79159 -22525.95848
[150,] -6357.38581 -3158.79159
[151,] -6471.07291 -6357.38581
[152,] -8052.83869 -6471.07291
[153,] -6243.69871 -8052.83869
[154,] 6553.58035 -6243.69871
[155,] -57880.30612 6553.58035
[156,] -6243.69871 -57880.30612
[157,] -6698.44710 -6243.69871
[158,] -8320.27252 -6698.44710
[159,] -12306.23238 -8320.27252
[160,] -4205.31240 -12306.23238
[161,] -3751.31663 -4205.31240
[162,] -6471.07291 -3751.31663
[163,] 38521.92769 -6471.07291
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 17406.19796 38626.82572
2 -6886.58300 17406.19796
3 -14428.66420 -6886.58300
4 -19109.24674 -14428.66420
5 26347.82095 -19109.24674
6 62311.00629 26347.82095
7 4720.34030 62311.00629
8 47711.20011 4720.34030
9 -10592.56646 47711.20011
10 -26296.30222 -10592.56646
11 -59626.36348 -26296.30222
12 -16327.27783 -59626.36348
13 -23697.36434 -16327.27783
14 -26996.57138 -23697.36434
15 -53820.75799 -26996.57138
16 50942.33084 -53820.75799
17 17991.78992 50942.33084
18 33045.64242 17991.78992
19 -12022.07529 33045.64242
20 -6555.63974 -12022.07529
21 -3303.11751 -6555.63974
22 11743.11014 -3303.11751
23 -22035.95907 11743.11014
24 -40704.33807 -22035.95907
25 522.58182 -40704.33807
26 -14412.19187 522.58182
27 -30879.06239 -14412.19187
28 -34395.54307 -30879.06239
29 -2019.16430 -34395.54307
30 10148.82946 -2019.16430
31 -3096.15283 10148.82946
32 -24334.51947 -3096.15283
33 -12787.26223 -24334.51947
34 -4272.63055 -12787.26223
35 -23623.67722 -4272.63055
36 -12722.35800 -23623.67722
37 -15775.93940 -12722.35800
38 -11510.38880 -15775.93940
39 -26310.76320 -11510.38880
40 -34589.17939 -26310.76320
41 -14831.30211 -34589.17939
42 98.49259 -14831.30211
43 -32780.68281 98.49259
44 33403.56530 -32780.68281
45 997.64478 33403.56530
46 -13879.99815 997.64478
47 -24596.29039 -13879.99815
48 -2288.16662 -24596.29039
49 -22628.91010 -2288.16662
50 -7178.21309 -22628.91010
51 -9289.07640 -7178.21309
52 3725.97551 -9289.07640
53 -14679.92825 3725.97551
54 -14869.96982 -14679.92825
55 1755.82483 -14869.96982
56 20928.04665 1755.82483
57 21419.84938 20928.04665
58 -12689.18923 21419.84938
59 -24016.03641 -12689.18923
60 -8282.84641 -24016.03641
61 49016.58434 -8282.84641
62 -44631.53005 49016.58434
63 -1601.87541 -44631.53005
64 34362.89564 -1601.87541
65 -14731.71100 34362.89564
66 34020.23049 -14731.71100
67 -36895.21122 34020.23049
68 12301.52050 -36895.21122
69 9369.37974 12301.52050
70 -52425.25921 9369.37974
71 -31110.04122 -52425.25921
72 -30900.22301 -31110.04122
73 9478.63176 -30900.22301
74 -27010.96055 9478.63176
75 -19822.46870 -27010.96055
76 9850.87492 -19822.46870
77 142731.32935 9850.87492
78 -47171.92735 142731.32935
79 -12146.23629 -47171.92735
80 -15544.57497 -12146.23629
81 29711.92148 -15544.57497
82 -30684.65791 29711.92148
83 -5108.15136 -30684.65791
84 16368.99070 -5108.15136
85 -34611.05229 16368.99070
86 27849.53742 -34611.05229
87 8402.07644 27849.53742
88 -20631.53420 8402.07644
89 4411.88474 -20631.53420
90 -25153.61702 4411.88474
91 -4345.88004 -25153.61702
92 -7927.92741 -4345.88004
93 -55665.41802 -7927.92741
94 51126.97119 -55665.41802
95 44193.63703 51126.97119
96 62852.57280 44193.63703
97 63141.57444 62852.57280
98 -19654.00907 63141.57444
99 33861.42254 -19654.00907
100 2123.39646 33861.42254
101 29406.20137 2123.39646
102 -24606.22383 29406.20137
103 1242.79075 -24606.22383
104 -28596.70538 1242.79075
105 11558.51191 -28596.70538
106 -11674.70903 11558.51191
107 -33507.15236 -11674.70903
108 142461.75692 -33507.15236
109 49298.79884 142461.75692
110 44018.64822 49298.79884
111 -12266.02576 44018.64822
112 -11888.09213 -12266.02576
113 -17968.26072 -11888.09213
114 -11964.89221 -17968.26072
115 -45009.67227 -11964.89221
116 -5090.34960 -45009.67227
117 -9008.91417 -5090.34960
118 16611.62490 -9008.91417
119 -6611.93981 16611.62490
120 -26985.32234 -6611.93981
121 24565.53314 -26985.32234
122 5376.64410 24565.53314
123 78443.60466 5376.64410
124 -15901.59969 78443.60466
125 11623.39984 -15901.59969
126 -15786.84908 11623.39984
127 16307.63861 -15786.84908
128 -2343.49664 16307.63861
129 -1235.79523 -2343.49664
130 133666.28610 -1235.79523
131 -8529.76456 133666.28610
132 1438.29221 -8529.76456
133 -35366.70913 1438.29221
134 13666.69131 -35366.70913
135 45005.88661 13666.69131
136 38171.55093 45005.88661
137 -20971.13477 38171.55093
138 -13503.20864 -20971.13477
139 -33304.50512 -13503.20864
140 75945.56697 -33304.50512
141 4711.13247 75945.56697
142 15656.24486 4711.13247
143 -26019.15957 15656.24486
144 -8256.18169 -26019.15957
145 2218.91710 -8256.18169
146 51756.87587 2218.91710
147 10748.99217 51756.87587
148 -22525.95848 10748.99217
149 -3158.79159 -22525.95848
150 -6357.38581 -3158.79159
151 -6471.07291 -6357.38581
152 -8052.83869 -6471.07291
153 -6243.69871 -8052.83869
154 6553.58035 -6243.69871
155 -57880.30612 6553.58035
156 -6243.69871 -57880.30612
157 -6698.44710 -6243.69871
158 -8320.27252 -6698.44710
159 -12306.23238 -8320.27252
160 -4205.31240 -12306.23238
161 -3751.31663 -4205.31240
162 -6471.07291 -3751.31663
163 38521.92769 -6471.07291
> 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/wessaorg/rcomp/tmp/77kl11323963396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8b23d1323963396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9oppb1323963396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1095y71323963396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/115ydl1323963396.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/wessaorg/rcomp/tmp/12cndf1323963396.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/wessaorg/rcomp/tmp/13o3p61323963396.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/wessaorg/rcomp/tmp/14k9jr1323963396.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/wessaorg/rcomp/tmp/15t4s51323963396.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/wessaorg/rcomp/tmp/167wiv1323963397.tab")
+ }
>
> try(system("convert tmp/11lmc1323963396.ps tmp/11lmc1323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/2njfn1323963396.ps tmp/2njfn1323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yzz61323963396.ps tmp/3yzz61323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ge9r1323963396.ps tmp/4ge9r1323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/521qa1323963396.ps tmp/521qa1323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hgjc1323963396.ps tmp/6hgjc1323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/77kl11323963396.ps tmp/77kl11323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/8b23d1323963396.ps tmp/8b23d1323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/9oppb1323963396.ps tmp/9oppb1323963396.png",intern=TRUE))
character(0)
> try(system("convert tmp/1095y71323963396.ps tmp/1095y71323963396.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.797 0.593 5.488