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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'citation()' on how to cite R or R packages in publications.
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(170588
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+ ,49288)
+ ,dim=c(5
+ ,164)
+ ,dimnames=list(c('Total_Time_RFC'
+ ,'Blogged_Computations'
+ ,'Reviewed_Compendiums'
+ ,'Long_feedback_messages'
+ ,'number_characters_compendium
')
+ ,1:164))
> y <- array(NA,dim=c(5,164),dimnames=list(c('Total_Time_RFC','Blogged_Computations','Reviewed_Compendiums','Long_feedback_messages','number_characters_compendium
'),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 = '1'
> 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
Total_Time_RFC Blogged_Computations Reviewed_Compendiums
1 170588 65 26
2 86621 54 20
3 113514 58 24
4 152510 99 25
5 86206 41 15
6 37257 0 16
7 306055 111 20
8 32750 1 18
9 116502 37 19
10 130539 60 20
11 161876 64 30
12 128274 71 37
13 104367 38 23
14 193024 76 36
15 141574 62 29
16 253559 126 35
17 181110 85 24
18 198432 74 22
19 113853 78 19
20 159940 100 30
21 166822 79 27
22 286675 76 26
23 91657 40 15
24 108278 81 30
25 146342 103 28
26 145142 70 24
27 161740 75 21
28 160905 93 27
29 106888 42 21
30 188150 95 30
31 189401 87 30
32 129484 44 33
33 204030 88 30
34 68538 29 20
35 243625 89 27
36 167255 71 25
37 264528 70 30
38 122024 50 20
39 80964 30 8
40 209795 87 24
41 224205 78 25
42 115971 48 25
43 138191 57 21
44 81106 31 21
45 93125 30 21
46 305756 70 26
47 78800 20 26
48 158835 84 30
49 223590 81 34
50 131108 79 30
51 128734 72 18
52 24188 8 4
53 257662 67 31
54 65029 21 18
55 98066 30 14
56 173587 70 20
57 180042 87 36
58 197266 87 24
59 212060 116 26
60 141582 54 22
61 245107 96 31
62 206879 94 21
63 145696 51 31
64 173535 51 26
65 142064 38 24
66 117926 65 15
67 113461 64 19
68 145285 66 28
69 150999 98 24
70 91812 100 18
71 118807 56 25
72 69471 22 20
73 126630 51 25
74 145908 61 24
75 98393 94 23
76 190926 98 25
77 198797 76 20
78 106193 57 23
79 89318 75 22
80 120362 48 25
81 98791 48 18
82 274953 109 30
83 132798 27 22
84 135251 85 25
85 80953 49 8
86 109237 24 21
87 96634 46 22
88 226191 44 24
89 171286 49 30
90 117815 108 27
91 133561 42 24
92 152193 110 25
93 112004 28 21
94 169613 79 24
95 187483 49 24
96 130533 64 20
97 142339 75 20
98 189764 118 24
99 201744 95 40
100 246834 106 22
101 155947 73 31
102 182581 108 26
103 106351 30 20
104 43287 13 19
105 127493 69 15
106 127930 75 21
107 149006 82 22
108 187714 108 24
109 74112 28 19
110 94006 83 24
111 176625 51 23
112 141933 90 27
113 22938 12 1
114 125927 87 24
115 61857 23 11
116 91290 57 27
117 255100 93 22
118 21054 4 0
119 169102 56 17
120 31414 18 8
121 188701 86 24
122 137544 40 31
123 77166 16 24
124 74567 18 20
125 38214 16 8
126 90961 42 22
127 194652 78 33
128 135261 31 33
129 244272 104 31
130 201748 121 33
131 256402 111 35
132 139144 57 21
133 76470 28 20
134 193518 56 24
135 280334 82 29
136 50999 2 20
137 253274 91 27
138 103239 41 24
139 168059 84 26
140 128768 55 26
141 78256 3 12
142 249232 68 21
143 152366 93 24
144 173260 41 21
145 197197 94 30
146 68388 105 32
147 139409 70 24
148 185366 114 29
149 0 0 0
150 14688 4 0
151 98 0 0
152 455 0 0
153 0 0 0
154 0 0 0
155 137885 42 20
156 185288 97 27
157 0 0 0
158 203 0 0
159 7199 7 0
160 46660 12 5
161 17547 0 1
162 73567 37 23
163 969 0 0
164 105477 39 16
Long_feedback_messages number_characters_compendium\r\r
1 84 95556
2 72 54565
3 37 63016
4 85 79774
5 30 31258
6 53 52491
7 74 91256
8 22 22807
9 68 77411
10 47 48821
11 102 52295
12 123 63262
13 69 50466
14 108 62932
15 59 38439
16 122 70817
17 91 105965
18 45 73795
19 53 82043
20 112 74349
21 82 82204
22 92 55709
23 51 37137
24 120 70780
25 99 55027
26 86 56699
27 59 65911
28 98 56316
29 71 26982
30 100 54628
31 113 96750
32 92 53009
33 107 64664
34 75 36990
35 100 85224
36 69 37048
37 106 59635
38 51 42051
39 18 26998
40 91 63717
41 75 55071
42 63 40001
43 72 54506
44 59 35838
45 29 50838
46 85 86997
47 66 33032
48 106 61704
49 113 117986
50 101 56733
51 65 55064
52 7 5950
53 111 84607
54 61 32551
55 41 31701
56 70 71170
57 136 101773
58 87 101653
59 90 81493
60 76 55901
61 101 109104
62 57 114425
63 61 36311
64 92 70027
65 80 73713
66 35 40671
67 72 89041
68 88 57231
69 80 68608
70 62 59155
71 81 55827
72 63 22618
73 91 58425
74 65 65724
75 79 56979
76 85 72369
77 75 79194
78 70 202316
79 78 44970
80 75 49319
81 55 36252
82 80 75741
83 83 38417
84 38 64102
85 27 56622
86 62 15430
87 82 72571
88 88 67271
89 59 43460
90 92 99501
91 40 28340
92 91 76013
93 63 37361
94 88 48204
95 85 76168
96 76 85168
97 67 125410
98 69 123328
99 150 83038
100 77 120087
101 103 91939
102 81 103646
103 37 29467
104 64 43750
105 22 34497
106 35 66477
107 61 71181
108 80 74482
109 54 174949
110 76 46765
111 87 90257
112 75 51370
113 0 1168
114 61 51360
115 30 25162
116 66 21067
117 56 58233
118 0 855
119 32 85903
120 9 14116
121 82 57637
122 110 94137
123 71 62147
124 50 62832
125 21 8773
126 78 63785
127 118 65196
128 102 73087
129 109 72631
130 104 86281
131 124 162365
132 76 56530
133 57 35606
134 91 70111
135 101 92046
136 66 63989
137 98 104911
138 63 43448
139 85 60029
140 74 38650
141 19 47261
142 57 73586
143 74 83042
144 78 37238
145 91 63958
146 112 78956
147 79 99518
148 100 111436
149 0 0
150 0 6023
151 0 0
152 0 0
153 0 0
154 0 0
155 48 42564
156 55 38885
157 0 0
158 0 0
159 0 1644
160 13 6179
161 4 3926
162 31 23238
163 0 0
164 29 49288
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Blogged_Computations
7842.4799 982.3467
Reviewed_Compendiums Long_feedback_messages
1960.0273 153.9625
`number_characters_compendium\r\r`
0.2762
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-144375 -22230 -5350 15607 141071
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7842.4799 8742.2905 0.897 0.3710
Blogged_Computations 982.3467 146.0002 6.728 2.94e-10 ***
Reviewed_Compendiums 1960.0273 889.9740 2.202 0.0291 *
Long_feedback_messages 153.9625 241.0475 0.639 0.5239
`number_characters_compendium\r\r` 0.2762 0.1302 2.121 0.0355 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 40270 on 159 degrees of freedom
Multiple R-squared: 0.6724, Adjusted R-squared: 0.6641
F-statistic: 81.57 on 4 and 159 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,] 0.4389096 8.778192e-01 5.610904e-01
[2,] 0.3251938 6.503877e-01 6.748062e-01
[3,] 0.2119272 4.238543e-01 7.880728e-01
[4,] 0.6156006 7.687988e-01 3.843994e-01
[5,] 0.5126558 9.746883e-01 4.873442e-01
[6,] 0.4152886 8.305771e-01 5.847114e-01
[7,] 0.4207865 8.415730e-01 5.792135e-01
[8,] 0.3415718 6.831437e-01 6.584282e-01
[9,] 0.2583475 5.166949e-01 7.416525e-01
[10,] 0.2124304 4.248608e-01 7.875696e-01
[11,] 0.1836945 3.673891e-01 8.163055e-01
[12,] 0.3063447 6.126894e-01 6.936553e-01
[13,] 0.3238447 6.476894e-01 6.761553e-01
[14,] 0.2582890 5.165781e-01 7.417110e-01
[15,] 0.8118536 3.762929e-01 1.881464e-01
[16,] 0.7663151 4.673697e-01 2.336849e-01
[17,] 0.8432964 3.134072e-01 1.567036e-01
[18,] 0.8686000 2.627999e-01 1.314000e-01
[19,] 0.8307978 3.384043e-01 1.692022e-01
[20,] 0.7877299 4.245402e-01 2.122701e-01
[21,] 0.7499845 5.000310e-01 2.500155e-01
[22,] 0.7043450 5.913101e-01 2.956550e-01
[23,] 0.6490568 7.018865e-01 3.509432e-01
[24,] 0.5917575 8.164849e-01 4.082425e-01
[25,] 0.5451546 9.096909e-01 4.548454e-01
[26,] 0.5071349 9.857303e-01 4.928651e-01
[27,] 0.4572596 9.145191e-01 5.427404e-01
[28,] 0.5095259 9.809482e-01 4.904741e-01
[29,] 0.4669449 9.338898e-01 5.330551e-01
[30,] 0.7627455 4.745091e-01 2.372545e-01
[31,] 0.7187965 5.624070e-01 2.812035e-01
[32,] 0.6750088 6.499824e-01 3.249912e-01
[33,] 0.6542793 6.914414e-01 3.457207e-01
[34,] 0.7035785 5.928430e-01 2.964215e-01
[35,] 0.6568554 6.862892e-01 3.431446e-01
[36,] 0.6077561 7.844878e-01 3.922439e-01
[37,] 0.5608224 8.783551e-01 4.391776e-01
[38,] 0.5092492 9.815016e-01 4.907508e-01
[39,] 0.9072936 1.854129e-01 9.270643e-02
[40,] 0.8880064 2.239872e-01 1.119936e-01
[41,] 0.8713665 2.572671e-01 1.286335e-01
[42,] 0.8461954 3.076092e-01 1.538046e-01
[43,] 0.8483793 3.032414e-01 1.516207e-01
[44,] 0.8249314 3.501372e-01 1.750686e-01
[45,] 0.7914088 4.171824e-01 2.085912e-01
[46,] 0.8794515 2.410971e-01 1.205485e-01
[47,] 0.8558997 2.882007e-01 1.441003e-01
[48,] 0.8340410 3.319179e-01 1.659590e-01
[49,] 0.8115757 3.768487e-01 1.884243e-01
[50,] 0.8061335 3.877329e-01 1.938665e-01
[51,] 0.7784945 4.430109e-01 2.215055e-01
[52,] 0.7501227 4.997546e-01 2.498773e-01
[53,] 0.7133888 5.732224e-01 2.866112e-01
[54,] 0.6938708 6.122585e-01 3.061292e-01
[55,] 0.6724978 6.550045e-01 3.275022e-01
[56,] 0.6339990 7.320021e-01 3.660010e-01
[57,] 0.6162746 7.674508e-01 3.837254e-01
[58,] 0.5772336 8.455328e-01 4.227664e-01
[59,] 0.5336225 9.327549e-01 4.663775e-01
[60,] 0.5359072 9.281855e-01 4.640928e-01
[61,] 0.4929480 9.858959e-01 5.070520e-01
[62,] 0.4865925 9.731849e-01 5.134075e-01
[63,] 0.6107882 7.784236e-01 3.892118e-01
[64,] 0.5772591 8.454818e-01 4.227409e-01
[65,] 0.5365676 9.268648e-01 4.634324e-01
[66,] 0.4931193 9.862386e-01 5.068807e-01
[67,] 0.4478606 8.957211e-01 5.521394e-01
[68,] 0.5511004 8.977992e-01 4.488996e-01
[69,] 0.5062709 9.874581e-01 4.937291e-01
[70,] 0.5096325 9.807350e-01 4.903675e-01
[71,] 0.6622159 6.755683e-01 3.377841e-01
[72,] 0.7063653 5.872694e-01 2.936347e-01
[73,] 0.6677401 6.645198e-01 3.322599e-01
[74,] 0.6273620 7.452760e-01 3.726380e-01
[75,] 0.6955350 6.089301e-01 3.044650e-01
[76,] 0.6847456 6.305088e-01 3.152544e-01
[77,] 0.6672466 6.655068e-01 3.327534e-01
[78,] 0.6264584 7.470832e-01 3.735416e-01
[79,] 0.5980465 8.039071e-01 4.019535e-01
[80,] 0.5793919 8.412162e-01 4.206081e-01
[81,] 0.7695349 4.609301e-01 2.304651e-01
[82,] 0.7598842 4.802316e-01 2.401158e-01
[83,] 0.8766055 2.467890e-01 1.233945e-01
[84,] 0.8618864 2.762272e-01 1.381136e-01
[85,] 0.8720364 2.559272e-01 1.279636e-01
[86,] 0.8515631 2.968737e-01 1.484369e-01
[87,] 0.8250423 3.499154e-01 1.749577e-01
[88,] 0.8440496 3.119008e-01 1.559504e-01
[89,] 0.8184020 3.631960e-01 1.815980e-01
[90,] 0.7990118 4.019764e-01 2.009882e-01
[91,] 0.7846279 4.307442e-01 2.153721e-01
[92,] 0.7586368 4.827264e-01 2.413632e-01
[93,] 0.7626550 4.746900e-01 2.373450e-01
[94,] 0.7381579 5.236841e-01 2.618421e-01
[95,] 0.7143491 5.713017e-01 2.856509e-01
[96,] 0.6843911 6.312178e-01 3.156089e-01
[97,] 0.6705548 6.588905e-01 3.294452e-01
[98,] 0.6290521 7.418958e-01 3.709479e-01
[99,] 0.5907559 8.184882e-01 4.092441e-01
[100,] 0.5475788 9.048424e-01 4.524212e-01
[101,] 0.5021055 9.957889e-01 4.978945e-01
[102,] 0.6216493 7.567013e-01 3.783507e-01
[103,] 0.6869111 6.261779e-01 3.130889e-01
[104,] 0.6660675 6.678651e-01 3.339325e-01
[105,] 0.6470688 7.058624e-01 3.529312e-01
[106,] 0.5994911 8.010179e-01 4.005089e-01
[107,] 0.6008219 7.983562e-01 3.991781e-01
[108,] 0.5511920 8.976159e-01 4.488080e-01
[109,] 0.5371632 9.256735e-01 4.628368e-01
[110,] 0.6885659 6.228683e-01 3.114341e-01
[111,] 0.6431297 7.137405e-01 3.568703e-01
[112,] 0.6312800 7.374400e-01 3.687200e-01
[113,] 0.5870043 8.259913e-01 4.129957e-01
[114,] 0.5488615 9.022769e-01 4.511385e-01
[115,] 0.5036459 9.927082e-01 4.963541e-01
[116,] 0.4693754 9.387507e-01 5.306246e-01
[117,] 0.4320790 8.641580e-01 5.679210e-01
[118,] 0.3797926 7.595852e-01 6.202074e-01
[119,] 0.3675214 7.350427e-01 6.324786e-01
[120,] 0.3194928 6.389856e-01 6.805072e-01
[121,] 0.2781918 5.563835e-01 7.218082e-01
[122,] 0.2820985 5.641969e-01 7.179015e-01
[123,] 0.2487382 4.974764e-01 7.512618e-01
[124,] 0.2156058 4.312117e-01 7.843942e-01
[125,] 0.1760741 3.521481e-01 8.239259e-01
[126,] 0.1474826 2.949652e-01 8.525174e-01
[127,] 0.1556263 3.112526e-01 8.443737e-01
[128,] 0.3578398 7.156796e-01 6.421602e-01
[129,] 0.3640795 7.281591e-01 6.359205e-01
[130,] 0.4640843 9.281686e-01 5.359157e-01
[131,] 0.4174154 8.348308e-01 5.825846e-01
[132,] 0.3685803 7.371607e-01 6.314197e-01
[133,] 0.3063340 6.126680e-01 6.936660e-01
[134,] 0.2601467 5.202933e-01 7.398533e-01
[135,] 0.7120456 5.759088e-01 2.879544e-01
[136,] 0.6528982 6.942036e-01 3.471018e-01
[137,] 0.9351289 1.297422e-01 6.487112e-02
[138,] 0.9916430 1.671397e-02 8.356986e-03
[139,] 0.9997984 4.032060e-04 2.016030e-04
[140,] 0.9994922 1.015657e-03 5.078284e-04
[141,] 1.0000000 5.326572e-10 2.663286e-10
[142,] 1.0000000 5.010605e-09 2.505302e-09
[143,] 1.0000000 2.360642e-08 1.180321e-08
[144,] 0.9999999 2.329535e-07 1.164768e-07
[145,] 0.9999989 2.243442e-06 1.121721e-06
[146,] 0.9999903 1.933939e-05 9.669695e-06
[147,] 0.9999228 1.543307e-04 7.716537e-05
[148,] 0.9999910 1.799341e-05 8.996706e-06
[149,] 0.9999476 1.048070e-04 5.240352e-05
> postscript(file="/var/wessaorg/rcomp/tmp/1fz5n1321901967.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/2jv2h1321901967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3kz4j1321901967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/46ae11321901967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5url81321901967.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
8604.7545 -39626.1028 -21448.2622 -36707.6345 -4566.1273 -24605.0979
7 8 9 10 11 12
113371.3485 -21042.2879 3220.1020 3833.4990 2213.3050 -58247.8526
13 14 15 16 17 18
-10448.5156 5951.0322 -3716.2540 14995.2375 -553.0577 47463.1479
19 20 21 22 23 24
-38675.1361 -42718.6018 -6878.0863 123660.8569 -2989.9026 -75961.8780
25 26 27 28 29 30
-48004.9137 -7407.6927 11771.0871 -21860.5067 -1757.9763 -2301.9444
31 32 33 34 35 36
-6828.7051 -15069.4395 16604.5814 -28757.7211 56495.9306 19808.3341
37 38 39 40 41 42
96327.9053 6396.1389 17742.1337 37837.0745 63979.7833 -8773.5994
43 44 45 46 47 48
7053.1268 -17332.8337 -3855.9406 141071.2421 -18935.8150 -23689.4522
49 50 51 52 53 54
19548.4164 -44361.8065 -10335.3864 -2074.6213 82781.2906 -17106.2680
55 56 57 58 59 60
18243.7599 27343.6089 -32876.4700 15445.1681 2937.8056 10429.9600
61 62 63 64 65 66
36511.2512 25152.7918 7571.3807 31124.5790 17173.5372 207.6612
67 68 69 70 71 72
-30172.5713 -11630.2901 -31422.1533 -75431.2313 -20939.1876 -15130.8816
73 74 75 76 77 78
-10461.7039 2939.7377 -74772.5913 4736.1359 43673.3108 -69385.3716
79 80 81 82 83 84
-59751.8641 -8803.9860 -9966.1520 67995.5776 31921.0488 -28648.5908
85 86 87 88 89 90
-10501.9164 22849.8437 -32187.6616 95954.1786 35419.3157 -90690.5781
91 92 93 94 95 96
23432.6708 -47715.3528 15475.6703 10260.7673 50338.7857 -14606.6410
97 98 99 100 101 102
-23336.5167 -25725.3661 -23853.8098 46716.3844 -25621.3490 -23415.9030
103 104 105 106 107 108
16001.5261 -36504.8188 9552.1816 -18500.1539 -11562.9822 -6153.1484
109 110 111 112 113 114
-55115.4403 -67030.5859 35276.5032 -32978.1345 1024.7048 -37998.7748
115 116 117 118 119 120
-1708.9279 -41447.6754 88071.5385 9045.9634 44272.5428 -15075.7514
121 122 123 124 125 126
20790.5136 -13291.7806 -21532.3887 -15211.9719 -6682.7543 -30888.5383
127 128 129 130 131 132
9329.4289 -3607.5420 36660.4209 -29484.1452 6977.9448 6831.2035
133 134 135 136 137 138
-16689.7577 50246.6598 94122.9543 -25845.4135 59050.1803 -13621.2821
139 140 141 142 143 144
-2929.4581 -6133.4657 27966.3380 104327.4356 -28206.6324 61685.7002
145 146 147 148 149 150
6535.9141 -144374.9446 -23890.5034 -37482.1224 -7842.4799 1252.4483
151 152 153 154 155 156
-7744.4799 -7387.4799 -7842.4799 -7842.4799 30436.0985 10028.3202
157 158 159 160 161 162
-7842.4799 -7639.4799 -7974.0159 13520.9345 6044.1953 -26894.6211
163 164
-6873.4799 9883.2194
> postscript(file="/var/wessaorg/rcomp/tmp/6v4kt1321901967.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 8604.7545 NA
1 -39626.1028 8604.7545
2 -21448.2622 -39626.1028
3 -36707.6345 -21448.2622
4 -4566.1273 -36707.6345
5 -24605.0979 -4566.1273
6 113371.3485 -24605.0979
7 -21042.2879 113371.3485
8 3220.1020 -21042.2879
9 3833.4990 3220.1020
10 2213.3050 3833.4990
11 -58247.8526 2213.3050
12 -10448.5156 -58247.8526
13 5951.0322 -10448.5156
14 -3716.2540 5951.0322
15 14995.2375 -3716.2540
16 -553.0577 14995.2375
17 47463.1479 -553.0577
18 -38675.1361 47463.1479
19 -42718.6018 -38675.1361
20 -6878.0863 -42718.6018
21 123660.8569 -6878.0863
22 -2989.9026 123660.8569
23 -75961.8780 -2989.9026
24 -48004.9137 -75961.8780
25 -7407.6927 -48004.9137
26 11771.0871 -7407.6927
27 -21860.5067 11771.0871
28 -1757.9763 -21860.5067
29 -2301.9444 -1757.9763
30 -6828.7051 -2301.9444
31 -15069.4395 -6828.7051
32 16604.5814 -15069.4395
33 -28757.7211 16604.5814
34 56495.9306 -28757.7211
35 19808.3341 56495.9306
36 96327.9053 19808.3341
37 6396.1389 96327.9053
38 17742.1337 6396.1389
39 37837.0745 17742.1337
40 63979.7833 37837.0745
41 -8773.5994 63979.7833
42 7053.1268 -8773.5994
43 -17332.8337 7053.1268
44 -3855.9406 -17332.8337
45 141071.2421 -3855.9406
46 -18935.8150 141071.2421
47 -23689.4522 -18935.8150
48 19548.4164 -23689.4522
49 -44361.8065 19548.4164
50 -10335.3864 -44361.8065
51 -2074.6213 -10335.3864
52 82781.2906 -2074.6213
53 -17106.2680 82781.2906
54 18243.7599 -17106.2680
55 27343.6089 18243.7599
56 -32876.4700 27343.6089
57 15445.1681 -32876.4700
58 2937.8056 15445.1681
59 10429.9600 2937.8056
60 36511.2512 10429.9600
61 25152.7918 36511.2512
62 7571.3807 25152.7918
63 31124.5790 7571.3807
64 17173.5372 31124.5790
65 207.6612 17173.5372
66 -30172.5713 207.6612
67 -11630.2901 -30172.5713
68 -31422.1533 -11630.2901
69 -75431.2313 -31422.1533
70 -20939.1876 -75431.2313
71 -15130.8816 -20939.1876
72 -10461.7039 -15130.8816
73 2939.7377 -10461.7039
74 -74772.5913 2939.7377
75 4736.1359 -74772.5913
76 43673.3108 4736.1359
77 -69385.3716 43673.3108
78 -59751.8641 -69385.3716
79 -8803.9860 -59751.8641
80 -9966.1520 -8803.9860
81 67995.5776 -9966.1520
82 31921.0488 67995.5776
83 -28648.5908 31921.0488
84 -10501.9164 -28648.5908
85 22849.8437 -10501.9164
86 -32187.6616 22849.8437
87 95954.1786 -32187.6616
88 35419.3157 95954.1786
89 -90690.5781 35419.3157
90 23432.6708 -90690.5781
91 -47715.3528 23432.6708
92 15475.6703 -47715.3528
93 10260.7673 15475.6703
94 50338.7857 10260.7673
95 -14606.6410 50338.7857
96 -23336.5167 -14606.6410
97 -25725.3661 -23336.5167
98 -23853.8098 -25725.3661
99 46716.3844 -23853.8098
100 -25621.3490 46716.3844
101 -23415.9030 -25621.3490
102 16001.5261 -23415.9030
103 -36504.8188 16001.5261
104 9552.1816 -36504.8188
105 -18500.1539 9552.1816
106 -11562.9822 -18500.1539
107 -6153.1484 -11562.9822
108 -55115.4403 -6153.1484
109 -67030.5859 -55115.4403
110 35276.5032 -67030.5859
111 -32978.1345 35276.5032
112 1024.7048 -32978.1345
113 -37998.7748 1024.7048
114 -1708.9279 -37998.7748
115 -41447.6754 -1708.9279
116 88071.5385 -41447.6754
117 9045.9634 88071.5385
118 44272.5428 9045.9634
119 -15075.7514 44272.5428
120 20790.5136 -15075.7514
121 -13291.7806 20790.5136
122 -21532.3887 -13291.7806
123 -15211.9719 -21532.3887
124 -6682.7543 -15211.9719
125 -30888.5383 -6682.7543
126 9329.4289 -30888.5383
127 -3607.5420 9329.4289
128 36660.4209 -3607.5420
129 -29484.1452 36660.4209
130 6977.9448 -29484.1452
131 6831.2035 6977.9448
132 -16689.7577 6831.2035
133 50246.6598 -16689.7577
134 94122.9543 50246.6598
135 -25845.4135 94122.9543
136 59050.1803 -25845.4135
137 -13621.2821 59050.1803
138 -2929.4581 -13621.2821
139 -6133.4657 -2929.4581
140 27966.3380 -6133.4657
141 104327.4356 27966.3380
142 -28206.6324 104327.4356
143 61685.7002 -28206.6324
144 6535.9141 61685.7002
145 -144374.9446 6535.9141
146 -23890.5034 -144374.9446
147 -37482.1224 -23890.5034
148 -7842.4799 -37482.1224
149 1252.4483 -7842.4799
150 -7744.4799 1252.4483
151 -7387.4799 -7744.4799
152 -7842.4799 -7387.4799
153 -7842.4799 -7842.4799
154 30436.0985 -7842.4799
155 10028.3202 30436.0985
156 -7842.4799 10028.3202
157 -7639.4799 -7842.4799
158 -7974.0159 -7639.4799
159 13520.9345 -7974.0159
160 6044.1953 13520.9345
161 -26894.6211 6044.1953
162 -6873.4799 -26894.6211
163 9883.2194 -6873.4799
164 NA 9883.2194
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -39626.1028 8604.7545
[2,] -21448.2622 -39626.1028
[3,] -36707.6345 -21448.2622
[4,] -4566.1273 -36707.6345
[5,] -24605.0979 -4566.1273
[6,] 113371.3485 -24605.0979
[7,] -21042.2879 113371.3485
[8,] 3220.1020 -21042.2879
[9,] 3833.4990 3220.1020
[10,] 2213.3050 3833.4990
[11,] -58247.8526 2213.3050
[12,] -10448.5156 -58247.8526
[13,] 5951.0322 -10448.5156
[14,] -3716.2540 5951.0322
[15,] 14995.2375 -3716.2540
[16,] -553.0577 14995.2375
[17,] 47463.1479 -553.0577
[18,] -38675.1361 47463.1479
[19,] -42718.6018 -38675.1361
[20,] -6878.0863 -42718.6018
[21,] 123660.8569 -6878.0863
[22,] -2989.9026 123660.8569
[23,] -75961.8780 -2989.9026
[24,] -48004.9137 -75961.8780
[25,] -7407.6927 -48004.9137
[26,] 11771.0871 -7407.6927
[27,] -21860.5067 11771.0871
[28,] -1757.9763 -21860.5067
[29,] -2301.9444 -1757.9763
[30,] -6828.7051 -2301.9444
[31,] -15069.4395 -6828.7051
[32,] 16604.5814 -15069.4395
[33,] -28757.7211 16604.5814
[34,] 56495.9306 -28757.7211
[35,] 19808.3341 56495.9306
[36,] 96327.9053 19808.3341
[37,] 6396.1389 96327.9053
[38,] 17742.1337 6396.1389
[39,] 37837.0745 17742.1337
[40,] 63979.7833 37837.0745
[41,] -8773.5994 63979.7833
[42,] 7053.1268 -8773.5994
[43,] -17332.8337 7053.1268
[44,] -3855.9406 -17332.8337
[45,] 141071.2421 -3855.9406
[46,] -18935.8150 141071.2421
[47,] -23689.4522 -18935.8150
[48,] 19548.4164 -23689.4522
[49,] -44361.8065 19548.4164
[50,] -10335.3864 -44361.8065
[51,] -2074.6213 -10335.3864
[52,] 82781.2906 -2074.6213
[53,] -17106.2680 82781.2906
[54,] 18243.7599 -17106.2680
[55,] 27343.6089 18243.7599
[56,] -32876.4700 27343.6089
[57,] 15445.1681 -32876.4700
[58,] 2937.8056 15445.1681
[59,] 10429.9600 2937.8056
[60,] 36511.2512 10429.9600
[61,] 25152.7918 36511.2512
[62,] 7571.3807 25152.7918
[63,] 31124.5790 7571.3807
[64,] 17173.5372 31124.5790
[65,] 207.6612 17173.5372
[66,] -30172.5713 207.6612
[67,] -11630.2901 -30172.5713
[68,] -31422.1533 -11630.2901
[69,] -75431.2313 -31422.1533
[70,] -20939.1876 -75431.2313
[71,] -15130.8816 -20939.1876
[72,] -10461.7039 -15130.8816
[73,] 2939.7377 -10461.7039
[74,] -74772.5913 2939.7377
[75,] 4736.1359 -74772.5913
[76,] 43673.3108 4736.1359
[77,] -69385.3716 43673.3108
[78,] -59751.8641 -69385.3716
[79,] -8803.9860 -59751.8641
[80,] -9966.1520 -8803.9860
[81,] 67995.5776 -9966.1520
[82,] 31921.0488 67995.5776
[83,] -28648.5908 31921.0488
[84,] -10501.9164 -28648.5908
[85,] 22849.8437 -10501.9164
[86,] -32187.6616 22849.8437
[87,] 95954.1786 -32187.6616
[88,] 35419.3157 95954.1786
[89,] -90690.5781 35419.3157
[90,] 23432.6708 -90690.5781
[91,] -47715.3528 23432.6708
[92,] 15475.6703 -47715.3528
[93,] 10260.7673 15475.6703
[94,] 50338.7857 10260.7673
[95,] -14606.6410 50338.7857
[96,] -23336.5167 -14606.6410
[97,] -25725.3661 -23336.5167
[98,] -23853.8098 -25725.3661
[99,] 46716.3844 -23853.8098
[100,] -25621.3490 46716.3844
[101,] -23415.9030 -25621.3490
[102,] 16001.5261 -23415.9030
[103,] -36504.8188 16001.5261
[104,] 9552.1816 -36504.8188
[105,] -18500.1539 9552.1816
[106,] -11562.9822 -18500.1539
[107,] -6153.1484 -11562.9822
[108,] -55115.4403 -6153.1484
[109,] -67030.5859 -55115.4403
[110,] 35276.5032 -67030.5859
[111,] -32978.1345 35276.5032
[112,] 1024.7048 -32978.1345
[113,] -37998.7748 1024.7048
[114,] -1708.9279 -37998.7748
[115,] -41447.6754 -1708.9279
[116,] 88071.5385 -41447.6754
[117,] 9045.9634 88071.5385
[118,] 44272.5428 9045.9634
[119,] -15075.7514 44272.5428
[120,] 20790.5136 -15075.7514
[121,] -13291.7806 20790.5136
[122,] -21532.3887 -13291.7806
[123,] -15211.9719 -21532.3887
[124,] -6682.7543 -15211.9719
[125,] -30888.5383 -6682.7543
[126,] 9329.4289 -30888.5383
[127,] -3607.5420 9329.4289
[128,] 36660.4209 -3607.5420
[129,] -29484.1452 36660.4209
[130,] 6977.9448 -29484.1452
[131,] 6831.2035 6977.9448
[132,] -16689.7577 6831.2035
[133,] 50246.6598 -16689.7577
[134,] 94122.9543 50246.6598
[135,] -25845.4135 94122.9543
[136,] 59050.1803 -25845.4135
[137,] -13621.2821 59050.1803
[138,] -2929.4581 -13621.2821
[139,] -6133.4657 -2929.4581
[140,] 27966.3380 -6133.4657
[141,] 104327.4356 27966.3380
[142,] -28206.6324 104327.4356
[143,] 61685.7002 -28206.6324
[144,] 6535.9141 61685.7002
[145,] -144374.9446 6535.9141
[146,] -23890.5034 -144374.9446
[147,] -37482.1224 -23890.5034
[148,] -7842.4799 -37482.1224
[149,] 1252.4483 -7842.4799
[150,] -7744.4799 1252.4483
[151,] -7387.4799 -7744.4799
[152,] -7842.4799 -7387.4799
[153,] -7842.4799 -7842.4799
[154,] 30436.0985 -7842.4799
[155,] 10028.3202 30436.0985
[156,] -7842.4799 10028.3202
[157,] -7639.4799 -7842.4799
[158,] -7974.0159 -7639.4799
[159,] 13520.9345 -7974.0159
[160,] 6044.1953 13520.9345
[161,] -26894.6211 6044.1953
[162,] -6873.4799 -26894.6211
[163,] 9883.2194 -6873.4799
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -39626.1028 8604.7545
2 -21448.2622 -39626.1028
3 -36707.6345 -21448.2622
4 -4566.1273 -36707.6345
5 -24605.0979 -4566.1273
6 113371.3485 -24605.0979
7 -21042.2879 113371.3485
8 3220.1020 -21042.2879
9 3833.4990 3220.1020
10 2213.3050 3833.4990
11 -58247.8526 2213.3050
12 -10448.5156 -58247.8526
13 5951.0322 -10448.5156
14 -3716.2540 5951.0322
15 14995.2375 -3716.2540
16 -553.0577 14995.2375
17 47463.1479 -553.0577
18 -38675.1361 47463.1479
19 -42718.6018 -38675.1361
20 -6878.0863 -42718.6018
21 123660.8569 -6878.0863
22 -2989.9026 123660.8569
23 -75961.8780 -2989.9026
24 -48004.9137 -75961.8780
25 -7407.6927 -48004.9137
26 11771.0871 -7407.6927
27 -21860.5067 11771.0871
28 -1757.9763 -21860.5067
29 -2301.9444 -1757.9763
30 -6828.7051 -2301.9444
31 -15069.4395 -6828.7051
32 16604.5814 -15069.4395
33 -28757.7211 16604.5814
34 56495.9306 -28757.7211
35 19808.3341 56495.9306
36 96327.9053 19808.3341
37 6396.1389 96327.9053
38 17742.1337 6396.1389
39 37837.0745 17742.1337
40 63979.7833 37837.0745
41 -8773.5994 63979.7833
42 7053.1268 -8773.5994
43 -17332.8337 7053.1268
44 -3855.9406 -17332.8337
45 141071.2421 -3855.9406
46 -18935.8150 141071.2421
47 -23689.4522 -18935.8150
48 19548.4164 -23689.4522
49 -44361.8065 19548.4164
50 -10335.3864 -44361.8065
51 -2074.6213 -10335.3864
52 82781.2906 -2074.6213
53 -17106.2680 82781.2906
54 18243.7599 -17106.2680
55 27343.6089 18243.7599
56 -32876.4700 27343.6089
57 15445.1681 -32876.4700
58 2937.8056 15445.1681
59 10429.9600 2937.8056
60 36511.2512 10429.9600
61 25152.7918 36511.2512
62 7571.3807 25152.7918
63 31124.5790 7571.3807
64 17173.5372 31124.5790
65 207.6612 17173.5372
66 -30172.5713 207.6612
67 -11630.2901 -30172.5713
68 -31422.1533 -11630.2901
69 -75431.2313 -31422.1533
70 -20939.1876 -75431.2313
71 -15130.8816 -20939.1876
72 -10461.7039 -15130.8816
73 2939.7377 -10461.7039
74 -74772.5913 2939.7377
75 4736.1359 -74772.5913
76 43673.3108 4736.1359
77 -69385.3716 43673.3108
78 -59751.8641 -69385.3716
79 -8803.9860 -59751.8641
80 -9966.1520 -8803.9860
81 67995.5776 -9966.1520
82 31921.0488 67995.5776
83 -28648.5908 31921.0488
84 -10501.9164 -28648.5908
85 22849.8437 -10501.9164
86 -32187.6616 22849.8437
87 95954.1786 -32187.6616
88 35419.3157 95954.1786
89 -90690.5781 35419.3157
90 23432.6708 -90690.5781
91 -47715.3528 23432.6708
92 15475.6703 -47715.3528
93 10260.7673 15475.6703
94 50338.7857 10260.7673
95 -14606.6410 50338.7857
96 -23336.5167 -14606.6410
97 -25725.3661 -23336.5167
98 -23853.8098 -25725.3661
99 46716.3844 -23853.8098
100 -25621.3490 46716.3844
101 -23415.9030 -25621.3490
102 16001.5261 -23415.9030
103 -36504.8188 16001.5261
104 9552.1816 -36504.8188
105 -18500.1539 9552.1816
106 -11562.9822 -18500.1539
107 -6153.1484 -11562.9822
108 -55115.4403 -6153.1484
109 -67030.5859 -55115.4403
110 35276.5032 -67030.5859
111 -32978.1345 35276.5032
112 1024.7048 -32978.1345
113 -37998.7748 1024.7048
114 -1708.9279 -37998.7748
115 -41447.6754 -1708.9279
116 88071.5385 -41447.6754
117 9045.9634 88071.5385
118 44272.5428 9045.9634
119 -15075.7514 44272.5428
120 20790.5136 -15075.7514
121 -13291.7806 20790.5136
122 -21532.3887 -13291.7806
123 -15211.9719 -21532.3887
124 -6682.7543 -15211.9719
125 -30888.5383 -6682.7543
126 9329.4289 -30888.5383
127 -3607.5420 9329.4289
128 36660.4209 -3607.5420
129 -29484.1452 36660.4209
130 6977.9448 -29484.1452
131 6831.2035 6977.9448
132 -16689.7577 6831.2035
133 50246.6598 -16689.7577
134 94122.9543 50246.6598
135 -25845.4135 94122.9543
136 59050.1803 -25845.4135
137 -13621.2821 59050.1803
138 -2929.4581 -13621.2821
139 -6133.4657 -2929.4581
140 27966.3380 -6133.4657
141 104327.4356 27966.3380
142 -28206.6324 104327.4356
143 61685.7002 -28206.6324
144 6535.9141 61685.7002
145 -144374.9446 6535.9141
146 -23890.5034 -144374.9446
147 -37482.1224 -23890.5034
148 -7842.4799 -37482.1224
149 1252.4483 -7842.4799
150 -7744.4799 1252.4483
151 -7387.4799 -7744.4799
152 -7842.4799 -7387.4799
153 -7842.4799 -7842.4799
154 30436.0985 -7842.4799
155 10028.3202 30436.0985
156 -7842.4799 10028.3202
157 -7639.4799 -7842.4799
158 -7974.0159 -7639.4799
159 13520.9345 -7974.0159
160 6044.1953 13520.9345
161 -26894.6211 6044.1953
162 -6873.4799 -26894.6211
163 9883.2194 -6873.4799
> 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/7d8i01321901967.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/88r7o1321901967.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/9rdca1321901967.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/109eru1321901967.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11evdk1321901967.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/126rl11321901967.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13j79e1321901967.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14bt8p1321901967.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/152qpb1321901967.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/16ix091321901967.tab")
+ }
>
> try(system("convert tmp/1fz5n1321901967.ps tmp/1fz5n1321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jv2h1321901967.ps tmp/2jv2h1321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kz4j1321901967.ps tmp/3kz4j1321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/46ae11321901967.ps tmp/46ae11321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/5url81321901967.ps tmp/5url81321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v4kt1321901967.ps tmp/6v4kt1321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/7d8i01321901967.ps tmp/7d8i01321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/88r7o1321901967.ps tmp/88r7o1321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rdca1321901967.ps tmp/9rdca1321901967.png",intern=TRUE))
character(0)
> try(system("convert tmp/109eru1321901967.ps tmp/109eru1321901967.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.911 0.537 5.480