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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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(478
+ ,184
+ ,40
+ ,74
+ ,11
+ ,31
+ ,20
+ ,494
+ ,213
+ ,32
+ ,72
+ ,11
+ ,43
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+ ,70
+ ,18
+ ,16
+ ,16
+ ,341
+ ,565
+ ,31
+ ,71
+ ,11
+ ,25
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+ ,773
+ ,327
+ ,67
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+ ,9
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+ ,260
+ ,25
+ ,68
+ ,8
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+ ,15
+ ,484
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+ ,34
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+ ,24
+ ,14
+ ,546
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+ ,33
+ ,62
+ ,13
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+ ,11
+ ,424
+ ,38
+ ,36
+ ,69
+ ,7
+ ,25
+ ,12
+ ,548
+ ,226
+ ,31
+ ,66
+ ,9
+ ,58
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+ ,506
+ ,137
+ ,35
+ ,60
+ ,13
+ ,21
+ ,9
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+ ,369
+ ,30
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+ ,4
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+ ,541
+ ,109
+ ,44
+ ,66
+ ,9
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+ ,12
+ ,491
+ ,809
+ ,32
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+ ,16
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+ ,36
+ ,62
+ ,7
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+ ,27
+ ,570
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+ ,30
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+ ,33
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+ ,799
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+ ,630
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+ ,9
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+ ,631
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+ ,14
+ ,21
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+ ,821
+ ,1139
+ ,30
+ ,54
+ ,13
+ ,27
+ ,12
+ ,1740
+ ,3545
+ ,86
+ ,62
+ ,22
+ ,18
+ ,15
+ ,815
+ ,706
+ ,30
+ ,47
+ ,17
+ ,39
+ ,11
+ ,760
+ ,451
+ ,32
+ ,45
+ ,34
+ ,15
+ ,10
+ ,936
+ ,433
+ ,43
+ ,48
+ ,26
+ ,23
+ ,12
+ ,863
+ ,601
+ ,20
+ ,69
+ ,23
+ ,7
+ ,12
+ ,783
+ ,1024
+ ,55
+ ,42
+ ,23
+ ,23
+ ,11
+ ,715
+ ,457
+ ,44
+ ,49
+ ,18
+ ,30
+ ,12
+ ,1504
+ ,1441
+ ,37
+ ,57
+ ,15
+ ,35
+ ,13
+ ,1324
+ ,1022
+ ,82
+ ,72
+ ,22
+ ,15
+ ,16
+ ,940
+ ,1244
+ ,66
+ ,67
+ ,26
+ ,18
+ ,16
+ ,478
+ ,184
+ ,40
+ ,74
+ ,11
+ ,31
+ ,20
+ ,494
+ ,213
+ ,32
+ ,72
+ ,11
+ ,43
+ ,18
+ ,643
+ ,347
+ ,57
+ ,70
+ ,18
+ ,16
+ ,16
+ ,341
+ ,565
+ ,31
+ ,71
+ ,11
+ ,25
+ ,19
+ ,773
+ ,327
+ ,67
+ ,72
+ ,9
+ ,29
+ ,24
+ ,603
+ ,260
+ ,25
+ ,68
+ ,8
+ ,32
+ ,15
+ ,484
+ ,325
+ ,34
+ ,68
+ ,12
+ ,24
+ ,14
+ ,546
+ ,102
+ ,33
+ ,62
+ ,13
+ ,28
+ ,11
+ ,424
+ ,38
+ ,36
+ ,69
+ ,7
+ ,25
+ ,12
+ ,548
+ ,226
+ ,31
+ ,66
+ ,9
+ ,58
+ ,15
+ ,506
+ ,137
+ ,35
+ ,60
+ ,13
+ ,21
+ ,9
+ ,819
+ ,369
+ ,30
+ ,81
+ ,4
+ ,77
+ ,36
+ ,541
+ ,109
+ ,44
+ ,66
+ ,9
+ ,37
+ ,12
+ ,491
+ ,809
+ ,32
+ ,67
+ ,11
+ ,37
+ ,16
+ ,514
+ ,29
+ ,30
+ ,65
+ ,12
+ ,35
+ ,11
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+ ,245
+ ,16
+ ,64
+ ,10
+ ,42
+ ,14
+ ,457
+ ,118
+ ,29
+ ,64
+ ,12
+ ,21
+ ,10
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+ ,36
+ ,62
+ ,7
+ ,81
+ ,27
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+ ,30
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+ ,15
+ ,31
+ ,16
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+ ,23
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+ ,15
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+ ,15
+ ,619
+ ,608
+ ,33
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+ ,22
+ ,24
+ ,8
+ ,357
+ ,218
+ ,35
+ ,54
+ ,14
+ ,27
+ ,13
+ ,623
+ ,254
+ ,38
+ ,54
+ ,20
+ ,22
+ ,11
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+ ,697
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+ ,45
+ ,26
+ ,18
+ ,8
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+ ,827
+ ,28
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+ ,12
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+ ,11
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+ ,31
+ ,61
+ ,19
+ ,14
+ ,12
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+ ,942
+ ,39
+ ,52
+ ,17
+ ,31
+ ,10
+ ,912
+ ,1017
+ ,27
+ ,44
+ ,21
+ ,24
+ ,9
+ ,462
+ ,216
+ ,36
+ ,43
+ ,18
+ ,23
+ ,8
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+ ,673
+ ,38
+ ,48
+ ,19
+ ,22
+ ,10
+ ,805
+ ,989
+ ,46
+ ,57
+ ,14
+ ,25
+ ,12
+ ,652
+ ,630
+ ,29
+ ,47
+ ,19
+ ,25
+ ,9
+ ,776
+ ,404
+ ,32
+ ,50
+ ,19
+ ,21
+ ,9
+ ,919
+ ,692
+ ,39
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+ ,16
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+ ,11
+ ,732
+ ,1517
+ ,44
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+ ,13
+ ,31
+ ,14
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+ ,879
+ ,33
+ ,72
+ ,13
+ ,13
+ ,22
+ ,1419
+ ,631
+ ,43
+ ,59
+ ,14
+ ,21
+ ,13
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+ ,1375
+ ,22
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+ ,13
+ ,821
+ ,1139
+ ,30
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+ ,13
+ ,27
+ ,12
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+ ,3545
+ ,86
+ ,62
+ ,22
+ ,18
+ ,15
+ ,815
+ ,706
+ ,30
+ ,47
+ ,17
+ ,39
+ ,11
+ ,760
+ ,451
+ ,32
+ ,45
+ ,34
+ ,15
+ ,10
+ ,936
+ ,433
+ ,43
+ ,48
+ ,26
+ ,23
+ ,12
+ ,863
+ ,601
+ ,20
+ ,69
+ ,23
+ ,7
+ ,12
+ ,783
+ ,1024
+ ,55
+ ,42
+ ,23
+ ,23
+ ,11
+ ,715
+ ,457
+ ,44
+ ,49
+ ,18
+ ,30
+ ,12
+ ,1504
+ ,1441
+ ,37
+ ,57
+ ,15
+ ,35
+ ,13
+ ,1324
+ ,1022
+ ,82
+ ,72
+ ,22
+ ,15
+ ,16
+ ,940
+ ,1244
+ ,66
+ ,67
+ ,26
+ ,18
+ ,16)
+ ,dim=c(7
+ ,100)
+ ,dimnames=list(c('Y1_Total_overal_crime'
+ ,'X1_violent_crime'
+ ,'X2_police_fund'
+ ,'X3_%_25+_4yrs_HS'
+ ,'X4_%_16-19_unschooled'
+ ,'X5_%_18-24_in_college'
+ ,'X6_%_25+_with_4_yrs_or_more_college')
+ ,1:100))
> y <- array(NA,dim=c(7,100),dimnames=list(c('Y1_Total_overal_crime','X1_violent_crime','X2_police_fund','X3_%_25+_4yrs_HS','X4_%_16-19_unschooled','X5_%_18-24_in_college','X6_%_25+_with_4_yrs_or_more_college'),1:100))
> 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
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
Y1_Total_overal_crime X1_violent_crime X2_police_fund X3_%_25+_4yrs_HS
1 478 184 40 74
2 494 213 32 72
3 643 347 57 70
4 341 565 31 71
5 773 327 67 72
6 603 260 25 68
7 484 325 34 68
8 546 102 33 62
9 424 38 36 69
10 548 226 31 66
11 506 137 35 60
12 819 369 30 81
13 541 109 44 66
14 491 809 32 67
15 514 29 30 65
16 371 245 16 64
17 457 118 29 64
18 437 148 36 62
19 570 387 30 59
20 432 98 23 56
21 619 608 33 46
22 357 218 35 54
23 623 254 38 54
24 547 697 44 45
25 792 827 28 57
26 799 693 35 57
27 439 448 31 61
28 867 942 39 52
29 912 1017 27 44
30 462 216 36 43
31 859 673 38 48
32 805 989 46 57
33 652 630 29 47
34 776 404 32 50
35 919 692 39 48
36 732 1517 44 49
37 657 879 33 72
38 1419 631 43 59
39 989 1375 22 49
40 821 1139 30 54
41 1740 3545 86 62
42 815 706 30 47
43 760 451 32 45
44 936 433 43 48
45 863 601 20 69
46 783 1024 55 42
47 715 457 44 49
48 1504 1441 37 57
49 1324 1022 82 72
50 940 1244 66 67
51 478 184 40 74
52 494 213 32 72
53 643 347 57 70
54 341 565 31 71
55 773 327 67 72
56 603 260 25 68
57 484 325 34 68
58 546 102 33 62
59 424 38 36 69
60 548 226 31 66
61 506 137 35 60
62 819 369 30 81
63 541 109 44 66
64 491 809 32 67
65 514 29 30 65
66 371 245 16 64
67 457 118 29 64
68 437 148 36 62
69 570 387 30 59
70 432 98 23 56
71 619 608 33 46
72 357 218 35 54
73 623 254 38 54
74 547 697 44 45
75 792 827 28 57
76 799 693 35 57
77 439 448 31 61
78 867 942 39 52
79 912 1017 27 44
80 462 216 36 43
81 859 673 38 48
82 805 989 46 57
83 652 630 29 47
84 776 404 32 50
85 919 692 39 48
86 732 1517 44 49
87 657 879 33 72
88 1419 631 43 59
89 989 1375 22 49
90 821 1139 30 54
91 1740 3545 86 62
92 815 706 30 47
93 760 451 32 45
94 936 433 43 48
95 863 601 20 69
96 783 1024 55 42
97 715 457 44 49
98 1504 1441 37 57
99 1324 1022 82 72
100 940 1244 66 67
X4_%_16-19_unschooled X5_%_18-24_in_college
1 11 31
2 11 43
3 18 16
4 11 25
5 9 29
6 8 32
7 12 24
8 13 28
9 7 25
10 9 58
11 13 21
12 4 77
13 9 37
14 11 37
15 12 35
16 10 42
17 12 21
18 7 81
19 15 31
20 15 50
21 22 24
22 14 27
23 20 22
24 26 18
25 12 23
26 9 60
27 19 14
28 17 31
29 21 24
30 18 23
31 19 22
32 14 25
33 19 25
34 19 21
35 16 32
36 13 31
37 13 13
38 14 21
39 9 46
40 13 27
41 22 18
42 17 39
43 34 15
44 26 23
45 23 7
46 23 23
47 18 30
48 15 35
49 22 15
50 26 18
51 11 31
52 11 43
53 18 16
54 11 25
55 9 29
56 8 32
57 12 24
58 13 28
59 7 25
60 9 58
61 13 21
62 4 77
63 9 37
64 11 37
65 12 35
66 10 42
67 12 21
68 7 81
69 15 31
70 15 50
71 22 24
72 14 27
73 20 22
74 26 18
75 12 23
76 9 60
77 19 14
78 17 31
79 21 24
80 18 23
81 19 22
82 14 25
83 19 25
84 19 21
85 16 32
86 13 31
87 13 13
88 14 21
89 9 46
90 13 27
91 22 18
92 17 39
93 34 15
94 26 23
95 23 7
96 23 23
97 18 30
98 15 35
99 22 15
100 26 18
X6_%_25+_with_4_yrs_or_more_college
1 20
2 18
3 16
4 19
5 24
6 15
7 14
8 11
9 12
10 15
11 9
12 36
13 12
14 16
15 11
16 14
17 10
18 27
19 16
20 15
21 8
22 13
23 11
24 8
25 11
26 18
27 12
28 10
29 9
30 8
31 10
32 12
33 9
34 9
35 11
36 14
37 22
38 13
39 13
40 12
41 15
42 11
43 10
44 12
45 12
46 11
47 12
48 13
49 16
50 16
51 20
52 18
53 16
54 19
55 24
56 15
57 14
58 11
59 12
60 15
61 9
62 36
63 12
64 16
65 11
66 14
67 10
68 27
69 16
70 15
71 8
72 13
73 11
74 8
75 11
76 18
77 12
78 10
79 9
80 8
81 10
82 12
83 9
84 9
85 11
86 14
87 22
88 13
89 13
90 12
91 15
92 11
93 10
94 12
95 12
96 11
97 12
98 13
99 16
100 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1_violent_crime
100.3936 0.3323
X2_police_fund `X3_%_25+_4yrs_HS`
3.9982 1.8579
`X4_%_16-19_unschooled` `X5_%_18-24_in_college`
7.8389 2.5588
`X6_%_25+_with_4_yrs_or_more_college`
-3.2312
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-291.82 -108.10 -26.78 86.98 705.89
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.39361 252.06198 0.398 0.6913
X1_violent_crime 0.33234 0.04054 8.198 1.31e-12 ***
X2_police_fund 3.99817 1.82402 2.192 0.0309 *
`X3_%_25+_4yrs_HS` 1.85791 3.56366 0.521 0.6034
`X4_%_16-19_unschooled` 7.83886 5.27652 1.486 0.1408
`X5_%_18-24_in_college` 2.55877 2.33024 1.098 0.2750
`X6_%_25+_with_4_yrs_or_more_college` -3.23116 7.28618 -0.443 0.6585
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 187.7 on 93 degrees of freedom
Multiple R-squared: 0.6132, Adjusted R-squared: 0.5882
F-statistic: 24.57 on 6 and 93 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,] 0.214035908 0.428071815 0.7859641
[2,] 0.116195106 0.232390213 0.8838049
[3,] 0.067006308 0.134012615 0.9329937
[4,] 0.029054939 0.058109878 0.9709451
[5,] 0.014154047 0.028308094 0.9858460
[6,] 0.005608341 0.011216681 0.9943917
[7,] 0.004576302 0.009152604 0.9954237
[8,] 0.001880867 0.003761734 0.9981191
[9,] 0.015644581 0.031289162 0.9843554
[10,] 0.012981905 0.025963809 0.9870181
[11,] 0.006731045 0.013462091 0.9932690
[12,] 0.006672548 0.013345096 0.9933275
[13,] 0.006131808 0.012263616 0.9938682
[14,] 0.003919024 0.007838048 0.9960810
[15,] 0.002490549 0.004981098 0.9975095
[16,] 0.008558524 0.017117047 0.9914415
[17,] 0.006496737 0.012993474 0.9935033
[18,] 0.004275124 0.008550248 0.9957249
[19,] 0.003447295 0.006894591 0.9965527
[20,] 0.004591563 0.009183125 0.9954084
[21,] 0.002863034 0.005726068 0.9971370
[22,] 0.003140392 0.006280785 0.9968596
[23,] 0.001895601 0.003791203 0.9981044
[24,] 0.001062665 0.002125330 0.9989373
[25,] 0.001598189 0.003196378 0.9984018
[26,] 0.001589408 0.003178817 0.9984106
[27,] 0.004766246 0.009532491 0.9952338
[28,] 0.002954170 0.005908340 0.9970458
[29,] 0.369391370 0.738782739 0.6306086
[30,] 0.323771383 0.647542765 0.6762286
[31,] 0.268696317 0.537392635 0.7313037
[32,] 0.236204728 0.472409457 0.7637953
[33,] 0.199458770 0.398917540 0.8005412
[34,] 0.177411254 0.354822508 0.8225887
[35,] 0.189430974 0.378861948 0.8105690
[36,] 0.214653977 0.429307953 0.7853460
[37,] 0.199289055 0.398578111 0.8007109
[38,] 0.159130876 0.318261753 0.8408691
[39,] 0.466387960 0.932775921 0.5336120
[40,] 0.518493828 0.963012345 0.4815062
[41,] 0.500000000 1.000000000 0.5000000
[42,] 0.449426204 0.898852408 0.5505738
[43,] 0.398691977 0.797383953 0.6013080
[44,] 0.346708077 0.693416154 0.6532919
[45,] 0.424727783 0.849455565 0.5752722
[46,] 0.376278012 0.752556025 0.6237220
[47,] 0.330685179 0.661370357 0.6693148
[48,] 0.292510261 0.585020522 0.7074897
[49,] 0.242065058 0.484130116 0.7579349
[50,] 0.200189651 0.400379302 0.7998103
[51,] 0.161936233 0.323872465 0.8380638
[52,] 0.127600142 0.255200284 0.8723999
[53,] 0.138257111 0.276514222 0.8617429
[54,] 0.109363481 0.218726961 0.8906365
[55,] 0.140133616 0.280267232 0.8598664
[56,] 0.113309740 0.226619480 0.8866903
[57,] 0.117042161 0.234084323 0.8829578
[58,] 0.119240978 0.238481955 0.8807590
[59,] 0.100671785 0.201343570 0.8993282
[60,] 0.076259610 0.152519220 0.9237404
[61,] 0.079603987 0.159207974 0.9203960
[62,] 0.067087434 0.134174867 0.9329126
[63,] 0.079136719 0.158273437 0.9208633
[64,] 0.060284230 0.120568459 0.9397158
[65,] 0.080333465 0.160666929 0.9196665
[66,] 0.059810069 0.119620138 0.9401899
[67,] 0.054213423 0.108426845 0.9457866
[68,] 0.090258019 0.180516037 0.9097420
[69,] 0.078751239 0.157502478 0.9212488
[70,] 0.061297674 0.122595348 0.9387023
[71,] 0.057701898 0.115403796 0.9422981
[72,] 0.040506733 0.081013465 0.9594933
[73,] 0.045405777 0.090811553 0.9545942
[74,] 0.042450731 0.084901463 0.9575493
[75,] 0.033841086 0.067682172 0.9661589
[76,] 0.021466826 0.042933651 0.9785332
[77,] 0.028014019 0.056028038 0.9719860
[78,] 0.014962531 0.029925061 0.9850375
[79,] 0.255340278 0.510680557 0.7446597
[80,] 0.160532498 0.321064995 0.8394675
[81,] 0.091683720 0.183367440 0.9083163
> postscript(file="/var/fisher/rcomp/tmp/1nhap1353448197.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/2j1z81353448197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3ryig1353448197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4za5t1353448197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5a7d51353448197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 100
Frequency = 1
1 2 3 4 5 6
-81.882079 -76.986177 -61.005364 -291.823520 95.078842 93.782435
7 8 9 10 11 12
-96.919451 26.569808 -41.219933 -44.558207 -17.893430 213.479251
13 14 15 16 17 18
-20.610430 -259.878862 15.178630 -134.313880 -32.951621 -146.595211
19 20 21 22 23 24
-33.776048 -94.017631 -108.874004 -192.932039 8.407685 -268.576777
25 26 27 28 29 30
81.539001 56.545123 -193.543584 20.734336 86.975268 -108.104677
31 32 33 34 35 36
120.913875 -48.830736 -44.861649 146.913213 171.761359 -275.496179
37 38 39 40 41 42
-65.310438 705.890659 106.398167 -10.415366 -167.605302 75.199880
43 44 45 46 47 48
25.583830 206.715290 195.281491 -159.240760 16.682628 505.741350
49 50 51 52 53 54
263.200669 -160.349435 -81.882079 -76.986177 -61.005364 -291.823520
55 56 57 58 59 60
95.078842 93.782435 -96.919451 26.569808 -41.219933 -44.558207
61 62 63 64 65 66
-17.893430 213.479251 -20.610430 -259.878862 15.178630 -134.313880
67 68 69 70 71 72
-32.951621 -146.595211 -33.776048 -94.017631 -108.874004 -192.932039
73 74 75 76 77 78
8.407685 -268.576777 81.539001 56.545123 -193.543584 20.734336
79 80 81 82 83 84
86.975268 -108.104677 120.913875 -48.830736 -44.861649 146.913213
85 86 87 88 89 90
171.761359 -275.496179 -65.310438 705.890659 106.398167 -10.415366
91 92 93 94 95 96
-167.605302 75.199880 25.583830 206.715290 195.281491 -159.240760
97 98 99 100
16.682628 505.741350 263.200669 -160.349435
> postscript(file="/var/fisher/rcomp/tmp/6ht9i1353448197.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 = 100
Frequency = 1
lag(myerror, k = 1) myerror
0 -81.882079 NA
1 -76.986177 -81.882079
2 -61.005364 -76.986177
3 -291.823520 -61.005364
4 95.078842 -291.823520
5 93.782435 95.078842
6 -96.919451 93.782435
7 26.569808 -96.919451
8 -41.219933 26.569808
9 -44.558207 -41.219933
10 -17.893430 -44.558207
11 213.479251 -17.893430
12 -20.610430 213.479251
13 -259.878862 -20.610430
14 15.178630 -259.878862
15 -134.313880 15.178630
16 -32.951621 -134.313880
17 -146.595211 -32.951621
18 -33.776048 -146.595211
19 -94.017631 -33.776048
20 -108.874004 -94.017631
21 -192.932039 -108.874004
22 8.407685 -192.932039
23 -268.576777 8.407685
24 81.539001 -268.576777
25 56.545123 81.539001
26 -193.543584 56.545123
27 20.734336 -193.543584
28 86.975268 20.734336
29 -108.104677 86.975268
30 120.913875 -108.104677
31 -48.830736 120.913875
32 -44.861649 -48.830736
33 146.913213 -44.861649
34 171.761359 146.913213
35 -275.496179 171.761359
36 -65.310438 -275.496179
37 705.890659 -65.310438
38 106.398167 705.890659
39 -10.415366 106.398167
40 -167.605302 -10.415366
41 75.199880 -167.605302
42 25.583830 75.199880
43 206.715290 25.583830
44 195.281491 206.715290
45 -159.240760 195.281491
46 16.682628 -159.240760
47 505.741350 16.682628
48 263.200669 505.741350
49 -160.349435 263.200669
50 -81.882079 -160.349435
51 -76.986177 -81.882079
52 -61.005364 -76.986177
53 -291.823520 -61.005364
54 95.078842 -291.823520
55 93.782435 95.078842
56 -96.919451 93.782435
57 26.569808 -96.919451
58 -41.219933 26.569808
59 -44.558207 -41.219933
60 -17.893430 -44.558207
61 213.479251 -17.893430
62 -20.610430 213.479251
63 -259.878862 -20.610430
64 15.178630 -259.878862
65 -134.313880 15.178630
66 -32.951621 -134.313880
67 -146.595211 -32.951621
68 -33.776048 -146.595211
69 -94.017631 -33.776048
70 -108.874004 -94.017631
71 -192.932039 -108.874004
72 8.407685 -192.932039
73 -268.576777 8.407685
74 81.539001 -268.576777
75 56.545123 81.539001
76 -193.543584 56.545123
77 20.734336 -193.543584
78 86.975268 20.734336
79 -108.104677 86.975268
80 120.913875 -108.104677
81 -48.830736 120.913875
82 -44.861649 -48.830736
83 146.913213 -44.861649
84 171.761359 146.913213
85 -275.496179 171.761359
86 -65.310438 -275.496179
87 705.890659 -65.310438
88 106.398167 705.890659
89 -10.415366 106.398167
90 -167.605302 -10.415366
91 75.199880 -167.605302
92 25.583830 75.199880
93 206.715290 25.583830
94 195.281491 206.715290
95 -159.240760 195.281491
96 16.682628 -159.240760
97 505.741350 16.682628
98 263.200669 505.741350
99 -160.349435 263.200669
100 NA -160.349435
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -76.986177 -81.882079
[2,] -61.005364 -76.986177
[3,] -291.823520 -61.005364
[4,] 95.078842 -291.823520
[5,] 93.782435 95.078842
[6,] -96.919451 93.782435
[7,] 26.569808 -96.919451
[8,] -41.219933 26.569808
[9,] -44.558207 -41.219933
[10,] -17.893430 -44.558207
[11,] 213.479251 -17.893430
[12,] -20.610430 213.479251
[13,] -259.878862 -20.610430
[14,] 15.178630 -259.878862
[15,] -134.313880 15.178630
[16,] -32.951621 -134.313880
[17,] -146.595211 -32.951621
[18,] -33.776048 -146.595211
[19,] -94.017631 -33.776048
[20,] -108.874004 -94.017631
[21,] -192.932039 -108.874004
[22,] 8.407685 -192.932039
[23,] -268.576777 8.407685
[24,] 81.539001 -268.576777
[25,] 56.545123 81.539001
[26,] -193.543584 56.545123
[27,] 20.734336 -193.543584
[28,] 86.975268 20.734336
[29,] -108.104677 86.975268
[30,] 120.913875 -108.104677
[31,] -48.830736 120.913875
[32,] -44.861649 -48.830736
[33,] 146.913213 -44.861649
[34,] 171.761359 146.913213
[35,] -275.496179 171.761359
[36,] -65.310438 -275.496179
[37,] 705.890659 -65.310438
[38,] 106.398167 705.890659
[39,] -10.415366 106.398167
[40,] -167.605302 -10.415366
[41,] 75.199880 -167.605302
[42,] 25.583830 75.199880
[43,] 206.715290 25.583830
[44,] 195.281491 206.715290
[45,] -159.240760 195.281491
[46,] 16.682628 -159.240760
[47,] 505.741350 16.682628
[48,] 263.200669 505.741350
[49,] -160.349435 263.200669
[50,] -81.882079 -160.349435
[51,] -76.986177 -81.882079
[52,] -61.005364 -76.986177
[53,] -291.823520 -61.005364
[54,] 95.078842 -291.823520
[55,] 93.782435 95.078842
[56,] -96.919451 93.782435
[57,] 26.569808 -96.919451
[58,] -41.219933 26.569808
[59,] -44.558207 -41.219933
[60,] -17.893430 -44.558207
[61,] 213.479251 -17.893430
[62,] -20.610430 213.479251
[63,] -259.878862 -20.610430
[64,] 15.178630 -259.878862
[65,] -134.313880 15.178630
[66,] -32.951621 -134.313880
[67,] -146.595211 -32.951621
[68,] -33.776048 -146.595211
[69,] -94.017631 -33.776048
[70,] -108.874004 -94.017631
[71,] -192.932039 -108.874004
[72,] 8.407685 -192.932039
[73,] -268.576777 8.407685
[74,] 81.539001 -268.576777
[75,] 56.545123 81.539001
[76,] -193.543584 56.545123
[77,] 20.734336 -193.543584
[78,] 86.975268 20.734336
[79,] -108.104677 86.975268
[80,] 120.913875 -108.104677
[81,] -48.830736 120.913875
[82,] -44.861649 -48.830736
[83,] 146.913213 -44.861649
[84,] 171.761359 146.913213
[85,] -275.496179 171.761359
[86,] -65.310438 -275.496179
[87,] 705.890659 -65.310438
[88,] 106.398167 705.890659
[89,] -10.415366 106.398167
[90,] -167.605302 -10.415366
[91,] 75.199880 -167.605302
[92,] 25.583830 75.199880
[93,] 206.715290 25.583830
[94,] 195.281491 206.715290
[95,] -159.240760 195.281491
[96,] 16.682628 -159.240760
[97,] 505.741350 16.682628
[98,] 263.200669 505.741350
[99,] -160.349435 263.200669
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -76.986177 -81.882079
2 -61.005364 -76.986177
3 -291.823520 -61.005364
4 95.078842 -291.823520
5 93.782435 95.078842
6 -96.919451 93.782435
7 26.569808 -96.919451
8 -41.219933 26.569808
9 -44.558207 -41.219933
10 -17.893430 -44.558207
11 213.479251 -17.893430
12 -20.610430 213.479251
13 -259.878862 -20.610430
14 15.178630 -259.878862
15 -134.313880 15.178630
16 -32.951621 -134.313880
17 -146.595211 -32.951621
18 -33.776048 -146.595211
19 -94.017631 -33.776048
20 -108.874004 -94.017631
21 -192.932039 -108.874004
22 8.407685 -192.932039
23 -268.576777 8.407685
24 81.539001 -268.576777
25 56.545123 81.539001
26 -193.543584 56.545123
27 20.734336 -193.543584
28 86.975268 20.734336
29 -108.104677 86.975268
30 120.913875 -108.104677
31 -48.830736 120.913875
32 -44.861649 -48.830736
33 146.913213 -44.861649
34 171.761359 146.913213
35 -275.496179 171.761359
36 -65.310438 -275.496179
37 705.890659 -65.310438
38 106.398167 705.890659
39 -10.415366 106.398167
40 -167.605302 -10.415366
41 75.199880 -167.605302
42 25.583830 75.199880
43 206.715290 25.583830
44 195.281491 206.715290
45 -159.240760 195.281491
46 16.682628 -159.240760
47 505.741350 16.682628
48 263.200669 505.741350
49 -160.349435 263.200669
50 -81.882079 -160.349435
51 -76.986177 -81.882079
52 -61.005364 -76.986177
53 -291.823520 -61.005364
54 95.078842 -291.823520
55 93.782435 95.078842
56 -96.919451 93.782435
57 26.569808 -96.919451
58 -41.219933 26.569808
59 -44.558207 -41.219933
60 -17.893430 -44.558207
61 213.479251 -17.893430
62 -20.610430 213.479251
63 -259.878862 -20.610430
64 15.178630 -259.878862
65 -134.313880 15.178630
66 -32.951621 -134.313880
67 -146.595211 -32.951621
68 -33.776048 -146.595211
69 -94.017631 -33.776048
70 -108.874004 -94.017631
71 -192.932039 -108.874004
72 8.407685 -192.932039
73 -268.576777 8.407685
74 81.539001 -268.576777
75 56.545123 81.539001
76 -193.543584 56.545123
77 20.734336 -193.543584
78 86.975268 20.734336
79 -108.104677 86.975268
80 120.913875 -108.104677
81 -48.830736 120.913875
82 -44.861649 -48.830736
83 146.913213 -44.861649
84 171.761359 146.913213
85 -275.496179 171.761359
86 -65.310438 -275.496179
87 705.890659 -65.310438
88 106.398167 705.890659
89 -10.415366 106.398167
90 -167.605302 -10.415366
91 75.199880 -167.605302
92 25.583830 75.199880
93 206.715290 25.583830
94 195.281491 206.715290
95 -159.240760 195.281491
96 16.682628 -159.240760
97 505.741350 16.682628
98 263.200669 505.741350
99 -160.349435 263.200669
> 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/7kxai1353448197.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/8wcqe1353448197.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/9t9v31353448197.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/10tyqy1353448197.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11c5n71353448197.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/129w5v1353448197.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/130rrh1353448197.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14hmiq1353448198.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/15lhkc1353448198.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/167ig21353448198.tab")
+ }
>
> try(system("convert tmp/1nhap1353448197.ps tmp/1nhap1353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/2j1z81353448197.ps tmp/2j1z81353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ryig1353448197.ps tmp/3ryig1353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/4za5t1353448197.ps tmp/4za5t1353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/5a7d51353448197.ps tmp/5a7d51353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ht9i1353448197.ps tmp/6ht9i1353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kxai1353448197.ps tmp/7kxai1353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wcqe1353448197.ps tmp/8wcqe1353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/9t9v31353448197.ps tmp/9t9v31353448197.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tyqy1353448197.ps tmp/10tyqy1353448197.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.695 1.592 9.305