R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(7
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+ ,dim=c(9
+ ,162)
+ ,dimnames=list(c('Age'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(9,162),dimnames=list(c('Age','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:162))
> 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 = '4'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
Learning Age Connected Separate Software Happiness Depression Belonging
1 13 7 41 38 12 14 12 53
2 16 5 39 32 11 18 11 86
3 19 5 30 35 15 11 14 66
4 15 5 31 33 6 12 12 67
5 14 8 34 37 13 16 21 76
6 13 6 35 29 10 18 12 78
7 19 5 39 31 12 14 22 53
8 15 6 34 36 14 14 11 80
9 14 5 36 35 12 15 10 74
10 15 4 37 38 6 15 13 76
11 16 6 38 31 10 17 10 79
12 16 5 36 34 12 19 8 54
13 16 5 38 35 12 10 15 67
14 16 6 39 38 11 16 14 54
15 17 7 33 37 15 18 10 87
16 15 6 32 33 12 14 14 58
17 15 7 36 32 10 14 14 75
18 20 6 38 38 12 17 11 88
19 18 8 39 38 11 14 10 64
20 16 7 32 32 12 16 13 57
21 16 5 32 33 11 18 7 66
22 16 5 31 31 12 11 14 68
23 19 7 39 38 13 14 12 54
24 16 7 37 39 11 12 14 56
25 17 5 39 32 9 17 11 86
26 17 4 41 32 13 9 9 80
27 16 10 36 35 10 16 11 76
28 15 6 33 37 14 14 15 69
29 16 5 33 33 12 15 14 78
30 14 5 34 33 10 11 13 67
31 15 5 31 28 12 16 9 80
32 12 5 27 32 8 13 15 54
33 14 6 37 31 10 17 10 71
34 16 5 34 37 12 15 11 84
35 14 5 34 30 12 14 13 74
36 7 5 32 33 7 16 8 71
37 10 5 29 31 6 9 20 63
38 14 5 36 33 12 15 12 71
39 16 5 29 31 10 17 10 76
40 16 5 35 33 10 13 10 69
41 16 5 37 32 10 15 9 74
42 14 7 34 33 12 16 14 75
43 20 5 38 32 15 16 8 54
44 14 6 35 33 10 12 14 52
45 14 7 38 28 10 12 11 69
46 11 7 37 35 12 11 13 68
47 14 5 38 39 13 15 9 65
48 15 5 33 34 11 15 11 75
49 16 4 36 38 11 17 15 74
50 14 5 38 32 12 13 11 75
51 16 4 32 38 14 16 10 72
52 14 5 32 30 10 14 14 67
53 12 5 32 33 12 11 18 63
54 16 7 34 38 13 12 14 62
55 9 5 32 32 5 12 11 63
56 14 5 37 32 6 15 12 76
57 16 6 39 34 12 16 13 74
58 16 4 29 34 12 15 9 67
59 15 6 37 36 11 12 10 73
60 16 6 35 34 10 12 15 70
61 12 5 30 28 7 8 20 53
62 16 7 38 34 12 13 12 77
63 16 6 34 35 14 11 12 77
64 14 8 31 35 11 14 14 52
65 16 7 34 31 12 15 13 54
66 17 5 35 37 13 10 11 80
67 18 6 36 35 14 11 17 66
68 18 6 30 27 11 12 12 73
69 12 5 39 40 12 15 13 63
70 16 5 35 37 12 15 14 69
71 10 5 38 36 8 14 13 67
72 14 5 31 38 11 16 15 54
73 18 4 34 39 14 15 13 81
74 18 6 38 41 14 15 10 69
75 16 6 34 27 12 13 11 84
76 17 6 39 30 9 12 19 80
77 16 6 37 37 13 17 13 70
78 16 7 34 31 11 13 17 69
79 13 5 28 31 12 15 13 77
80 16 7 37 27 12 13 9 54
81 16 6 33 36 12 15 11 79
82 20 5 37 38 12 16 10 30
83 16 5 35 37 12 15 9 71
84 15 4 37 33 12 16 12 73
85 15 8 32 34 11 15 12 72
86 16 8 33 31 10 14 13 77
87 14 5 38 39 9 15 13 75
88 16 5 33 34 12 14 12 69
89 16 6 29 32 12 13 15 54
90 15 4 33 33 12 7 22 70
91 12 5 31 36 9 17 13 73
92 17 5 36 32 15 13 15 54
93 16 5 35 41 12 15 13 77
94 15 5 32 28 12 14 15 82
95 13 6 29 30 12 13 10 80
96 16 6 39 36 10 16 11 80
97 16 5 37 35 13 12 16 69
98 16 6 35 31 9 14 11 78
99 16 5 37 34 12 17 11 81
100 14 7 32 36 10 15 10 76
101 16 5 38 36 14 17 10 76
102 16 6 37 35 11 12 16 73
103 20 6 36 37 15 16 12 85
104 15 6 32 28 11 11 11 66
105 16 4 33 39 11 15 16 79
106 13 5 40 32 12 9 19 68
107 17 5 38 35 12 16 11 76
108 16 7 41 39 12 15 16 71
109 16 6 36 35 11 10 15 54
110 12 9 43 42 7 10 24 46
111 16 6 30 34 12 15 14 82
112 16 6 31 33 14 11 15 74
113 17 5 32 41 11 13 11 88
114 13 6 32 33 11 14 15 38
115 12 5 37 34 10 18 12 76
116 18 8 37 32 13 16 10 86
117 14 7 33 40 13 14 14 54
118 14 5 34 40 8 14 13 70
119 13 7 33 35 11 14 9 69
120 16 6 38 36 12 14 15 90
121 13 6 33 37 11 12 15 54
122 16 9 31 27 13 14 14 76
123 13 7 38 39 12 15 11 89
124 16 6 37 38 14 15 8 76
125 15 5 33 31 13 15 11 73
126 16 5 31 33 15 13 11 79
127 15 6 39 32 10 17 8 90
128 17 6 44 39 11 17 10 74
129 15 7 33 36 9 19 11 81
130 12 5 35 33 11 15 13 72
131 16 5 32 33 10 13 11 71
132 10 5 28 32 11 9 20 66
133 16 6 40 37 8 15 10 77
134 12 4 27 30 11 15 15 65
135 14 5 37 38 12 15 12 74
136 15 7 32 29 12 16 14 82
137 13 5 28 22 9 11 23 54
138 15 7 34 35 11 14 14 63
139 11 7 30 35 10 11 16 54
140 12 6 35 34 8 15 11 64
141 8 5 31 35 9 13 12 69
142 16 8 32 34 8 15 10 54
143 15 5 30 34 9 16 14 84
144 17 5 30 35 15 14 12 86
145 16 5 31 23 11 15 12 77
146 10 6 40 31 8 16 11 89
147 18 4 32 27 13 16 12 76
148 13 5 36 36 12 11 13 60
149 16 5 32 31 12 12 11 75
150 13 7 35 32 9 9 19 73
151 10 6 38 39 7 16 12 85
152 15 7 42 37 13 13 17 79
153 16 10 34 38 9 16 9 71
154 16 6 35 39 6 12 12 72
155 14 8 35 34 8 9 19 69
156 10 4 33 31 8 13 18 78
157 17 5 36 32 15 13 15 54
158 13 6 32 37 6 14 14 69
159 15 7 33 36 9 19 11 81
160 16 7 34 32 11 13 9 84
161 12 6 32 35 8 12 18 84
162 13 6 34 36 8 13 16 69
Belonging_Final
1 32
2 51
3 42
4 41
5 46
6 47
7 37
8 49
9 45
10 47
11 49
12 33
13 42
14 33
15 53
16 36
17 45
18 54
19 41
20 36
21 41
22 44
23 33
24 37
25 52
26 47
27 43
28 44
29 45
30 44
31 49
32 33
33 43
34 54
35 42
36 44
37 37
38 43
39 46
40 42
41 45
42 44
43 33
44 31
45 42
46 40
47 43
48 46
49 42
50 45
51 44
52 40
53 37
54 46
55 36
56 47
57 45
58 42
59 43
60 43
61 32
62 45
63 45
64 31
65 33
66 49
67 42
68 41
69 38
70 42
71 44
72 33
73 48
74 40
75 50
76 49
77 43
78 44
79 47
80 33
81 46
82 0
83 45
84 43
85 44
86 47
87 45
88 42
89 33
90 43
91 46
92 33
93 46
94 48
95 47
96 47
97 43
98 46
99 48
100 46
101 45
102 45
103 52
104 42
105 47
106 41
107 47
108 43
109 33
110 30
111 49
112 44
113 55
114 11
115 47
116 53
117 33
118 44
119 42
120 55
121 33
122 46
123 54
124 47
125 45
126 47
127 55
128 44
129 53
130 44
131 42
132 40
133 46
134 40
135 46
136 53
137 33
138 42
139 35
140 40
141 41
142 33
143 51
144 53
145 46
146 55
147 47
148 38
149 46
150 46
151 53
152 47
153 41
154 44
155 43
156 51
157 33
158 43
159 53
160 51
161 50
162 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Age Connected Separate
4.95375 0.13347 0.10767 -0.02286
Software Happiness Depression Belonging
0.54718 0.05695 -0.07286 0.03936
Belonging_Final
-0.05607
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.7979 -1.1121 0.1765 1.1170 4.1166
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.95375 2.65060 1.869 0.0635 .
Age 0.13347 0.12823 1.041 0.2995
Connected 0.10767 0.04728 2.277 0.0241 *
Separate -0.02286 0.04485 -0.510 0.6109
Software 0.54718 0.06908 7.921 4.49e-13 ***
Happiness 0.05695 0.07641 0.745 0.4572
Depression -0.07286 0.05635 -1.293 0.1980
Belonging 0.03936 0.04465 0.881 0.3795
Belonging_Final -0.05607 0.06405 -0.875 0.3827
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.85 on 153 degrees of freedom
Multiple R-squared: 0.3612, Adjusted R-squared: 0.3278
F-statistic: 10.82 on 8 and 153 DF, p-value: 5.229e-12
> 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,] 0.59747720 0.80504560 0.40252280
[2,] 0.43552917 0.87105834 0.56447083
[3,] 0.38855994 0.77711988 0.61144006
[4,] 0.33609160 0.67218321 0.66390840
[5,] 0.24239452 0.48478904 0.75760548
[6,] 0.24805692 0.49611384 0.75194308
[7,] 0.50086812 0.99826375 0.49913188
[8,] 0.42416264 0.84832529 0.57583736
[9,] 0.33507672 0.67015344 0.66492328
[10,] 0.26815296 0.53630593 0.73184704
[11,] 0.24673240 0.49346480 0.75326760
[12,] 0.45381649 0.90763298 0.54618351
[13,] 0.47377867 0.94755734 0.52622133
[14,] 0.45977956 0.91955911 0.54022044
[15,] 0.40959600 0.81919199 0.59040400
[16,] 0.49200531 0.98401062 0.50799469
[17,] 0.49501785 0.99003570 0.50498215
[18,] 0.48195394 0.96390787 0.51804606
[19,] 0.52372881 0.95254238 0.47627119
[20,] 0.46306569 0.92613138 0.53693431
[21,] 0.41394222 0.82788443 0.58605778
[22,] 0.38794103 0.77588207 0.61205897
[23,] 0.35240573 0.70481146 0.64759427
[24,] 0.30553806 0.61107612 0.69446194
[25,] 0.86504279 0.26991443 0.13495721
[26,] 0.84116623 0.31766754 0.15883377
[27,] 0.83331391 0.33337218 0.16668609
[28,] 0.85300203 0.29399594 0.14699797
[29,] 0.83845781 0.32308439 0.16154219
[30,] 0.81089683 0.37820634 0.18910317
[31,] 0.79258627 0.41482746 0.20741373
[32,] 0.81863568 0.36272864 0.18136432
[33,] 0.78103189 0.43793623 0.21896811
[34,] 0.75197176 0.49605648 0.24802824
[35,] 0.89762521 0.20474958 0.10237479
[36,] 0.93631280 0.12737441 0.06368720
[37,] 0.91848486 0.16303028 0.08151514
[38,] 0.90352788 0.19294424 0.09647212
[39,] 0.90190543 0.19618913 0.09809457
[40,] 0.87862615 0.24274769 0.12137385
[41,] 0.85095959 0.29808083 0.14904041
[42,] 0.86616390 0.26767221 0.13383610
[43,] 0.85061197 0.29877605 0.14938803
[44,] 0.85700948 0.28598103 0.14299052
[45,] 0.83827733 0.32344535 0.16172267
[46,] 0.80769060 0.38461880 0.19230940
[47,] 0.78886696 0.42226608 0.21113304
[48,] 0.75458242 0.49083515 0.24541758
[49,] 0.75300601 0.49398799 0.24699399
[50,] 0.71954757 0.56090485 0.28045243
[51,] 0.68259454 0.63481092 0.31740546
[52,] 0.64706714 0.70586572 0.35293286
[53,] 0.60620993 0.78758014 0.39379007
[54,] 0.56693379 0.86613242 0.43306621
[55,] 0.53952540 0.92094920 0.46047460
[56,] 0.53409515 0.93180969 0.46590485
[57,] 0.68310013 0.63379974 0.31689987
[58,] 0.78178709 0.43642581 0.21821291
[59,] 0.75193391 0.49613219 0.24806609
[60,] 0.83626249 0.32747502 0.16373751
[61,] 0.80521510 0.38956980 0.19478490
[62,] 0.80178373 0.39643253 0.19821627
[63,] 0.78356125 0.43287749 0.21643875
[64,] 0.74724995 0.50550010 0.25275005
[65,] 0.79473489 0.41053022 0.20526511
[66,] 0.76093133 0.47813735 0.23906867
[67,] 0.74250077 0.51499846 0.25749923
[68,] 0.74620322 0.50759357 0.25379678
[69,] 0.70679302 0.58641397 0.29320698
[70,] 0.66891705 0.66216591 0.33108295
[71,] 0.79049325 0.41901349 0.20950675
[72,] 0.75759652 0.48480696 0.24240348
[73,] 0.72904793 0.54190413 0.27095207
[74,] 0.68981947 0.62036106 0.31018053
[75,] 0.67223300 0.65553400 0.32776700
[76,] 0.62916130 0.74167741 0.37083870
[77,] 0.59287566 0.81424868 0.40712434
[78,] 0.57418909 0.85162182 0.42581091
[79,] 0.54978843 0.90042314 0.45021157
[80,] 0.52511646 0.94976708 0.47488354
[81,] 0.48967580 0.97935160 0.51032420
[82,] 0.45000992 0.90001985 0.54999008
[83,] 0.40633579 0.81267158 0.59366421
[84,] 0.43067108 0.86134216 0.56932892
[85,] 0.39521481 0.79042963 0.60478519
[86,] 0.35947961 0.71895923 0.64052039
[87,] 0.35831775 0.71663550 0.64168225
[88,] 0.31625669 0.63251338 0.68374331
[89,] 0.28031891 0.56063781 0.71968109
[90,] 0.25176250 0.50352499 0.74823750
[91,] 0.23630585 0.47261170 0.76369415
[92,] 0.28238968 0.56477937 0.71761032
[93,] 0.24361473 0.48722945 0.75638527
[94,] 0.26111561 0.52223122 0.73888439
[95,] 0.26730959 0.53461917 0.73269041
[96,] 0.25686354 0.51372707 0.74313646
[97,] 0.23068848 0.46137696 0.76931152
[98,] 0.22933043 0.45866086 0.77066957
[99,] 0.20406306 0.40812611 0.79593694
[100,] 0.17994412 0.35988825 0.82005588
[101,] 0.15082664 0.30165328 0.84917336
[102,] 0.19156503 0.38313007 0.80843497
[103,] 0.17768659 0.35537317 0.82231341
[104,] 0.20268285 0.40536570 0.79731715
[105,] 0.17802647 0.35605294 0.82197353
[106,] 0.15921652 0.31843303 0.84078348
[107,] 0.15064492 0.30128984 0.84935508
[108,] 0.16617961 0.33235921 0.83382039
[109,] 0.15613415 0.31226830 0.84386585
[110,] 0.13363125 0.26726249 0.86636875
[111,] 0.11286550 0.22573099 0.88713450
[112,] 0.13905473 0.27810945 0.86094527
[113,] 0.11430239 0.22860478 0.88569761
[114,] 0.09299567 0.18599134 0.90700433
[115,] 0.07292153 0.14584305 0.92707847
[116,] 0.05651305 0.11302611 0.94348695
[117,] 0.04993889 0.09987779 0.95006111
[118,] 0.03841592 0.07683183 0.96158408
[119,] 0.04514920 0.09029840 0.95485080
[120,] 0.04269555 0.08539109 0.95730445
[121,] 0.06052436 0.12104872 0.93947564
[122,] 0.07918189 0.15836377 0.92081811
[123,] 0.08097933 0.16195866 0.91902067
[124,] 0.06163016 0.12326032 0.93836984
[125,] 0.04901628 0.09803257 0.95098372
[126,] 0.03403094 0.06806188 0.96596906
[127,] 0.02269194 0.04538387 0.97730806
[128,] 0.06297913 0.12595826 0.93702087
[129,] 0.04864498 0.09728995 0.95135502
[130,] 0.62292272 0.75415456 0.37707728
[131,] 0.56712250 0.86575501 0.43287750
[132,] 0.48808101 0.97616202 0.51191899
[133,] 0.39824771 0.79649542 0.60175229
[134,] 0.30614610 0.61229220 0.69385390
[135,] 0.34459163 0.68918326 0.65540837
[136,] 0.35890614 0.71781229 0.64109386
[137,] 0.72676623 0.54646755 0.27323377
[138,] 0.62495548 0.75008904 0.37504452
[139,] 0.45563009 0.91126018 0.54436991
> postscript(file="/var/fisher/rcomp/tmp/1jn9x1352161039.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/fisher/rcomp/tmp/2xy481352161039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3pagk1352161039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4nja61352161039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5q8hr1352161039.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5 6
-3.21453326 0.14358333 2.89222349 3.36535446 -1.74282681 -1.91718876
7 8 9 10 11 12
4.11660932 -1.64982719 -1.77812746 2.85134340 0.78954706 -0.06017030
13 14 15 16 17 18
0.76286865 0.72998346 -0.55183402 -0.05317165 0.28971218 3.85422087
19 20 21 22 23 24
2.34046839 0.64308958 0.85516087 1.36805557 2.47033306 1.20806000
25 26 27 28 29 30
2.35095846 0.34606490 0.47392090 -1.07527324 0.63315118 0.15163907
31 32 33 34 35 36
-0.54152067 -0.09646957 -1.12433113 0.66682815 -1.56979218 -5.79794807
37 38 39 40 41 42
-0.77816908 -1.67220451 1.84190367 1.52065269 1.06711213 -1.73641166
43 44 45 46 47 48
2.20807366 -0.21211075 -1.05381645 -4.75056704 -2.27996510 0.15875908
49 50 51 52 53 54
1.05331043 -1.91465771 -0.27404881 -0.02387568 -2.59813531 0.68229964
55 56 57 58 59 60
-2.41376262 1.50782339 -0.09584682 1.12059766 -0.35115432 1.84799052
61 62 63 64 65 66
0.66857263 -0.14173316 -0.53518259 -0.66373675 0.41170850 1.17382084
67 68 69 70 71 72
1.87849371 3.23032198 -3.72777787 0.69528417 -3.28692778 -0.20234043
73 74 75 76 77 78
1.67905844 0.83234106 0.19433940 3.10730313 -0.37075229 1.39059991
79 80 81 82 83 84
-1.79560587 -0.18028612 0.36640745 3.33454557 0.42048223 -0.78206073
85 86 87 88 89 90
-0.05520190 1.41693918 -0.08124977 0.75326019 1.36600161 1.00780213
91 92 93 94 95 96
-1.37528636 0.10426143 0.62329794 -0.23292392 -2.28235752 0.77451929
97 98 99 100 101 102
0.25966932 1.77459307 -0.05699806 -0.51982747 -1.25757002 1.17526044
103 104 105 106 107 108
2.54089731 0.35350583 1.66948502 -2.26810450 1.05586479 -0.04887488
109 110 111 112 113 114
1.39894715 -0.60400855 0.91238500 0.02266864 2.53334073 -2.04766615
115 116 117 118 119 120
-2.80601364 1.01731086 -1.69221435 1.11713261 -2.16221766 0.24812355
121 122 123 124 125 126
-1.34621826 -0.17804889 -3.18184705 -1.15733072 -0.98154723 -0.82494946
127 128 129 130 131 132
-0.53749560 0.69572698 0.96042744 -2.92778932 1.83780094 -3.33340459
133 134 135 136 137 138
1.83017022 -1.80461905 -1.61541953 -0.38343145 0.71679664 0.33054302
139 140 141 142 143 144
-2.41329820 -1.45198871 -5.36611775 2.53230917 1.66386880 0.40525905
145 146 147 148 149 150
1.11672833 -4.25878997 2.17810823 -2.15036505 0.82150154 -0.27162515
151 152 153 154 155 156
-3.19520608 -1.65328249 1.24396525 3.90981221 1.17703612 -2.34865605
157 158 159 160 161 162
0.10426143 1.28090711 0.96042744 0.63271892 -0.65173605 0.31921193
> postscript(file="/var/fisher/rcomp/tmp/66ny61352161039.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.21453326 NA
1 0.14358333 -3.21453326
2 2.89222349 0.14358333
3 3.36535446 2.89222349
4 -1.74282681 3.36535446
5 -1.91718876 -1.74282681
6 4.11660932 -1.91718876
7 -1.64982719 4.11660932
8 -1.77812746 -1.64982719
9 2.85134340 -1.77812746
10 0.78954706 2.85134340
11 -0.06017030 0.78954706
12 0.76286865 -0.06017030
13 0.72998346 0.76286865
14 -0.55183402 0.72998346
15 -0.05317165 -0.55183402
16 0.28971218 -0.05317165
17 3.85422087 0.28971218
18 2.34046839 3.85422087
19 0.64308958 2.34046839
20 0.85516087 0.64308958
21 1.36805557 0.85516087
22 2.47033306 1.36805557
23 1.20806000 2.47033306
24 2.35095846 1.20806000
25 0.34606490 2.35095846
26 0.47392090 0.34606490
27 -1.07527324 0.47392090
28 0.63315118 -1.07527324
29 0.15163907 0.63315118
30 -0.54152067 0.15163907
31 -0.09646957 -0.54152067
32 -1.12433113 -0.09646957
33 0.66682815 -1.12433113
34 -1.56979218 0.66682815
35 -5.79794807 -1.56979218
36 -0.77816908 -5.79794807
37 -1.67220451 -0.77816908
38 1.84190367 -1.67220451
39 1.52065269 1.84190367
40 1.06711213 1.52065269
41 -1.73641166 1.06711213
42 2.20807366 -1.73641166
43 -0.21211075 2.20807366
44 -1.05381645 -0.21211075
45 -4.75056704 -1.05381645
46 -2.27996510 -4.75056704
47 0.15875908 -2.27996510
48 1.05331043 0.15875908
49 -1.91465771 1.05331043
50 -0.27404881 -1.91465771
51 -0.02387568 -0.27404881
52 -2.59813531 -0.02387568
53 0.68229964 -2.59813531
54 -2.41376262 0.68229964
55 1.50782339 -2.41376262
56 -0.09584682 1.50782339
57 1.12059766 -0.09584682
58 -0.35115432 1.12059766
59 1.84799052 -0.35115432
60 0.66857263 1.84799052
61 -0.14173316 0.66857263
62 -0.53518259 -0.14173316
63 -0.66373675 -0.53518259
64 0.41170850 -0.66373675
65 1.17382084 0.41170850
66 1.87849371 1.17382084
67 3.23032198 1.87849371
68 -3.72777787 3.23032198
69 0.69528417 -3.72777787
70 -3.28692778 0.69528417
71 -0.20234043 -3.28692778
72 1.67905844 -0.20234043
73 0.83234106 1.67905844
74 0.19433940 0.83234106
75 3.10730313 0.19433940
76 -0.37075229 3.10730313
77 1.39059991 -0.37075229
78 -1.79560587 1.39059991
79 -0.18028612 -1.79560587
80 0.36640745 -0.18028612
81 3.33454557 0.36640745
82 0.42048223 3.33454557
83 -0.78206073 0.42048223
84 -0.05520190 -0.78206073
85 1.41693918 -0.05520190
86 -0.08124977 1.41693918
87 0.75326019 -0.08124977
88 1.36600161 0.75326019
89 1.00780213 1.36600161
90 -1.37528636 1.00780213
91 0.10426143 -1.37528636
92 0.62329794 0.10426143
93 -0.23292392 0.62329794
94 -2.28235752 -0.23292392
95 0.77451929 -2.28235752
96 0.25966932 0.77451929
97 1.77459307 0.25966932
98 -0.05699806 1.77459307
99 -0.51982747 -0.05699806
100 -1.25757002 -0.51982747
101 1.17526044 -1.25757002
102 2.54089731 1.17526044
103 0.35350583 2.54089731
104 1.66948502 0.35350583
105 -2.26810450 1.66948502
106 1.05586479 -2.26810450
107 -0.04887488 1.05586479
108 1.39894715 -0.04887488
109 -0.60400855 1.39894715
110 0.91238500 -0.60400855
111 0.02266864 0.91238500
112 2.53334073 0.02266864
113 -2.04766615 2.53334073
114 -2.80601364 -2.04766615
115 1.01731086 -2.80601364
116 -1.69221435 1.01731086
117 1.11713261 -1.69221435
118 -2.16221766 1.11713261
119 0.24812355 -2.16221766
120 -1.34621826 0.24812355
121 -0.17804889 -1.34621826
122 -3.18184705 -0.17804889
123 -1.15733072 -3.18184705
124 -0.98154723 -1.15733072
125 -0.82494946 -0.98154723
126 -0.53749560 -0.82494946
127 0.69572698 -0.53749560
128 0.96042744 0.69572698
129 -2.92778932 0.96042744
130 1.83780094 -2.92778932
131 -3.33340459 1.83780094
132 1.83017022 -3.33340459
133 -1.80461905 1.83017022
134 -1.61541953 -1.80461905
135 -0.38343145 -1.61541953
136 0.71679664 -0.38343145
137 0.33054302 0.71679664
138 -2.41329820 0.33054302
139 -1.45198871 -2.41329820
140 -5.36611775 -1.45198871
141 2.53230917 -5.36611775
142 1.66386880 2.53230917
143 0.40525905 1.66386880
144 1.11672833 0.40525905
145 -4.25878997 1.11672833
146 2.17810823 -4.25878997
147 -2.15036505 2.17810823
148 0.82150154 -2.15036505
149 -0.27162515 0.82150154
150 -3.19520608 -0.27162515
151 -1.65328249 -3.19520608
152 1.24396525 -1.65328249
153 3.90981221 1.24396525
154 1.17703612 3.90981221
155 -2.34865605 1.17703612
156 0.10426143 -2.34865605
157 1.28090711 0.10426143
158 0.96042744 1.28090711
159 0.63271892 0.96042744
160 -0.65173605 0.63271892
161 0.31921193 -0.65173605
162 NA 0.31921193
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.14358333 -3.21453326
[2,] 2.89222349 0.14358333
[3,] 3.36535446 2.89222349
[4,] -1.74282681 3.36535446
[5,] -1.91718876 -1.74282681
[6,] 4.11660932 -1.91718876
[7,] -1.64982719 4.11660932
[8,] -1.77812746 -1.64982719
[9,] 2.85134340 -1.77812746
[10,] 0.78954706 2.85134340
[11,] -0.06017030 0.78954706
[12,] 0.76286865 -0.06017030
[13,] 0.72998346 0.76286865
[14,] -0.55183402 0.72998346
[15,] -0.05317165 -0.55183402
[16,] 0.28971218 -0.05317165
[17,] 3.85422087 0.28971218
[18,] 2.34046839 3.85422087
[19,] 0.64308958 2.34046839
[20,] 0.85516087 0.64308958
[21,] 1.36805557 0.85516087
[22,] 2.47033306 1.36805557
[23,] 1.20806000 2.47033306
[24,] 2.35095846 1.20806000
[25,] 0.34606490 2.35095846
[26,] 0.47392090 0.34606490
[27,] -1.07527324 0.47392090
[28,] 0.63315118 -1.07527324
[29,] 0.15163907 0.63315118
[30,] -0.54152067 0.15163907
[31,] -0.09646957 -0.54152067
[32,] -1.12433113 -0.09646957
[33,] 0.66682815 -1.12433113
[34,] -1.56979218 0.66682815
[35,] -5.79794807 -1.56979218
[36,] -0.77816908 -5.79794807
[37,] -1.67220451 -0.77816908
[38,] 1.84190367 -1.67220451
[39,] 1.52065269 1.84190367
[40,] 1.06711213 1.52065269
[41,] -1.73641166 1.06711213
[42,] 2.20807366 -1.73641166
[43,] -0.21211075 2.20807366
[44,] -1.05381645 -0.21211075
[45,] -4.75056704 -1.05381645
[46,] -2.27996510 -4.75056704
[47,] 0.15875908 -2.27996510
[48,] 1.05331043 0.15875908
[49,] -1.91465771 1.05331043
[50,] -0.27404881 -1.91465771
[51,] -0.02387568 -0.27404881
[52,] -2.59813531 -0.02387568
[53,] 0.68229964 -2.59813531
[54,] -2.41376262 0.68229964
[55,] 1.50782339 -2.41376262
[56,] -0.09584682 1.50782339
[57,] 1.12059766 -0.09584682
[58,] -0.35115432 1.12059766
[59,] 1.84799052 -0.35115432
[60,] 0.66857263 1.84799052
[61,] -0.14173316 0.66857263
[62,] -0.53518259 -0.14173316
[63,] -0.66373675 -0.53518259
[64,] 0.41170850 -0.66373675
[65,] 1.17382084 0.41170850
[66,] 1.87849371 1.17382084
[67,] 3.23032198 1.87849371
[68,] -3.72777787 3.23032198
[69,] 0.69528417 -3.72777787
[70,] -3.28692778 0.69528417
[71,] -0.20234043 -3.28692778
[72,] 1.67905844 -0.20234043
[73,] 0.83234106 1.67905844
[74,] 0.19433940 0.83234106
[75,] 3.10730313 0.19433940
[76,] -0.37075229 3.10730313
[77,] 1.39059991 -0.37075229
[78,] -1.79560587 1.39059991
[79,] -0.18028612 -1.79560587
[80,] 0.36640745 -0.18028612
[81,] 3.33454557 0.36640745
[82,] 0.42048223 3.33454557
[83,] -0.78206073 0.42048223
[84,] -0.05520190 -0.78206073
[85,] 1.41693918 -0.05520190
[86,] -0.08124977 1.41693918
[87,] 0.75326019 -0.08124977
[88,] 1.36600161 0.75326019
[89,] 1.00780213 1.36600161
[90,] -1.37528636 1.00780213
[91,] 0.10426143 -1.37528636
[92,] 0.62329794 0.10426143
[93,] -0.23292392 0.62329794
[94,] -2.28235752 -0.23292392
[95,] 0.77451929 -2.28235752
[96,] 0.25966932 0.77451929
[97,] 1.77459307 0.25966932
[98,] -0.05699806 1.77459307
[99,] -0.51982747 -0.05699806
[100,] -1.25757002 -0.51982747
[101,] 1.17526044 -1.25757002
[102,] 2.54089731 1.17526044
[103,] 0.35350583 2.54089731
[104,] 1.66948502 0.35350583
[105,] -2.26810450 1.66948502
[106,] 1.05586479 -2.26810450
[107,] -0.04887488 1.05586479
[108,] 1.39894715 -0.04887488
[109,] -0.60400855 1.39894715
[110,] 0.91238500 -0.60400855
[111,] 0.02266864 0.91238500
[112,] 2.53334073 0.02266864
[113,] -2.04766615 2.53334073
[114,] -2.80601364 -2.04766615
[115,] 1.01731086 -2.80601364
[116,] -1.69221435 1.01731086
[117,] 1.11713261 -1.69221435
[118,] -2.16221766 1.11713261
[119,] 0.24812355 -2.16221766
[120,] -1.34621826 0.24812355
[121,] -0.17804889 -1.34621826
[122,] -3.18184705 -0.17804889
[123,] -1.15733072 -3.18184705
[124,] -0.98154723 -1.15733072
[125,] -0.82494946 -0.98154723
[126,] -0.53749560 -0.82494946
[127,] 0.69572698 -0.53749560
[128,] 0.96042744 0.69572698
[129,] -2.92778932 0.96042744
[130,] 1.83780094 -2.92778932
[131,] -3.33340459 1.83780094
[132,] 1.83017022 -3.33340459
[133,] -1.80461905 1.83017022
[134,] -1.61541953 -1.80461905
[135,] -0.38343145 -1.61541953
[136,] 0.71679664 -0.38343145
[137,] 0.33054302 0.71679664
[138,] -2.41329820 0.33054302
[139,] -1.45198871 -2.41329820
[140,] -5.36611775 -1.45198871
[141,] 2.53230917 -5.36611775
[142,] 1.66386880 2.53230917
[143,] 0.40525905 1.66386880
[144,] 1.11672833 0.40525905
[145,] -4.25878997 1.11672833
[146,] 2.17810823 -4.25878997
[147,] -2.15036505 2.17810823
[148,] 0.82150154 -2.15036505
[149,] -0.27162515 0.82150154
[150,] -3.19520608 -0.27162515
[151,] -1.65328249 -3.19520608
[152,] 1.24396525 -1.65328249
[153,] 3.90981221 1.24396525
[154,] 1.17703612 3.90981221
[155,] -2.34865605 1.17703612
[156,] 0.10426143 -2.34865605
[157,] 1.28090711 0.10426143
[158,] 0.96042744 1.28090711
[159,] 0.63271892 0.96042744
[160,] -0.65173605 0.63271892
[161,] 0.31921193 -0.65173605
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.14358333 -3.21453326
2 2.89222349 0.14358333
3 3.36535446 2.89222349
4 -1.74282681 3.36535446
5 -1.91718876 -1.74282681
6 4.11660932 -1.91718876
7 -1.64982719 4.11660932
8 -1.77812746 -1.64982719
9 2.85134340 -1.77812746
10 0.78954706 2.85134340
11 -0.06017030 0.78954706
12 0.76286865 -0.06017030
13 0.72998346 0.76286865
14 -0.55183402 0.72998346
15 -0.05317165 -0.55183402
16 0.28971218 -0.05317165
17 3.85422087 0.28971218
18 2.34046839 3.85422087
19 0.64308958 2.34046839
20 0.85516087 0.64308958
21 1.36805557 0.85516087
22 2.47033306 1.36805557
23 1.20806000 2.47033306
24 2.35095846 1.20806000
25 0.34606490 2.35095846
26 0.47392090 0.34606490
27 -1.07527324 0.47392090
28 0.63315118 -1.07527324
29 0.15163907 0.63315118
30 -0.54152067 0.15163907
31 -0.09646957 -0.54152067
32 -1.12433113 -0.09646957
33 0.66682815 -1.12433113
34 -1.56979218 0.66682815
35 -5.79794807 -1.56979218
36 -0.77816908 -5.79794807
37 -1.67220451 -0.77816908
38 1.84190367 -1.67220451
39 1.52065269 1.84190367
40 1.06711213 1.52065269
41 -1.73641166 1.06711213
42 2.20807366 -1.73641166
43 -0.21211075 2.20807366
44 -1.05381645 -0.21211075
45 -4.75056704 -1.05381645
46 -2.27996510 -4.75056704
47 0.15875908 -2.27996510
48 1.05331043 0.15875908
49 -1.91465771 1.05331043
50 -0.27404881 -1.91465771
51 -0.02387568 -0.27404881
52 -2.59813531 -0.02387568
53 0.68229964 -2.59813531
54 -2.41376262 0.68229964
55 1.50782339 -2.41376262
56 -0.09584682 1.50782339
57 1.12059766 -0.09584682
58 -0.35115432 1.12059766
59 1.84799052 -0.35115432
60 0.66857263 1.84799052
61 -0.14173316 0.66857263
62 -0.53518259 -0.14173316
63 -0.66373675 -0.53518259
64 0.41170850 -0.66373675
65 1.17382084 0.41170850
66 1.87849371 1.17382084
67 3.23032198 1.87849371
68 -3.72777787 3.23032198
69 0.69528417 -3.72777787
70 -3.28692778 0.69528417
71 -0.20234043 -3.28692778
72 1.67905844 -0.20234043
73 0.83234106 1.67905844
74 0.19433940 0.83234106
75 3.10730313 0.19433940
76 -0.37075229 3.10730313
77 1.39059991 -0.37075229
78 -1.79560587 1.39059991
79 -0.18028612 -1.79560587
80 0.36640745 -0.18028612
81 3.33454557 0.36640745
82 0.42048223 3.33454557
83 -0.78206073 0.42048223
84 -0.05520190 -0.78206073
85 1.41693918 -0.05520190
86 -0.08124977 1.41693918
87 0.75326019 -0.08124977
88 1.36600161 0.75326019
89 1.00780213 1.36600161
90 -1.37528636 1.00780213
91 0.10426143 -1.37528636
92 0.62329794 0.10426143
93 -0.23292392 0.62329794
94 -2.28235752 -0.23292392
95 0.77451929 -2.28235752
96 0.25966932 0.77451929
97 1.77459307 0.25966932
98 -0.05699806 1.77459307
99 -0.51982747 -0.05699806
100 -1.25757002 -0.51982747
101 1.17526044 -1.25757002
102 2.54089731 1.17526044
103 0.35350583 2.54089731
104 1.66948502 0.35350583
105 -2.26810450 1.66948502
106 1.05586479 -2.26810450
107 -0.04887488 1.05586479
108 1.39894715 -0.04887488
109 -0.60400855 1.39894715
110 0.91238500 -0.60400855
111 0.02266864 0.91238500
112 2.53334073 0.02266864
113 -2.04766615 2.53334073
114 -2.80601364 -2.04766615
115 1.01731086 -2.80601364
116 -1.69221435 1.01731086
117 1.11713261 -1.69221435
118 -2.16221766 1.11713261
119 0.24812355 -2.16221766
120 -1.34621826 0.24812355
121 -0.17804889 -1.34621826
122 -3.18184705 -0.17804889
123 -1.15733072 -3.18184705
124 -0.98154723 -1.15733072
125 -0.82494946 -0.98154723
126 -0.53749560 -0.82494946
127 0.69572698 -0.53749560
128 0.96042744 0.69572698
129 -2.92778932 0.96042744
130 1.83780094 -2.92778932
131 -3.33340459 1.83780094
132 1.83017022 -3.33340459
133 -1.80461905 1.83017022
134 -1.61541953 -1.80461905
135 -0.38343145 -1.61541953
136 0.71679664 -0.38343145
137 0.33054302 0.71679664
138 -2.41329820 0.33054302
139 -1.45198871 -2.41329820
140 -5.36611775 -1.45198871
141 2.53230917 -5.36611775
142 1.66386880 2.53230917
143 0.40525905 1.66386880
144 1.11672833 0.40525905
145 -4.25878997 1.11672833
146 2.17810823 -4.25878997
147 -2.15036505 2.17810823
148 0.82150154 -2.15036505
149 -0.27162515 0.82150154
150 -3.19520608 -0.27162515
151 -1.65328249 -3.19520608
152 1.24396525 -1.65328249
153 3.90981221 1.24396525
154 1.17703612 3.90981221
155 -2.34865605 1.17703612
156 0.10426143 -2.34865605
157 1.28090711 0.10426143
158 0.96042744 1.28090711
159 0.63271892 0.96042744
160 -0.65173605 0.63271892
161 0.31921193 -0.65173605
> 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/fisher/rcomp/tmp/7u65m1352161039.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/fisher/rcomp/tmp/85w9o1352161039.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/fisher/rcomp/tmp/9n4z71352161039.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/fisher/rcomp/tmp/10rnck1352161039.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/116p621352161039.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12gslj1352161040.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/132u8x1352161040.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14w7la1352161040.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/fisher/rcomp/tmp/15fqns1352161040.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/fisher/rcomp/tmp/16wylx1352161040.tab")
+ }
>
> try(system("convert tmp/1jn9x1352161039.ps tmp/1jn9x1352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xy481352161039.ps tmp/2xy481352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pagk1352161039.ps tmp/3pagk1352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nja61352161039.ps tmp/4nja61352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q8hr1352161039.ps tmp/5q8hr1352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/66ny61352161039.ps tmp/66ny61352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u65m1352161039.ps tmp/7u65m1352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/85w9o1352161039.ps tmp/85w9o1352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/9n4z71352161039.ps tmp/9n4z71352161039.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rnck1352161039.ps tmp/10rnck1352161039.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.076 1.106 9.179