R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'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(27951
+ ,6.4
+ ,91.18
+ ,29781
+ ,7.7
+ ,91.53
+ ,32914
+ ,9.2
+ ,91.88
+ ,33488
+ ,8.6
+ ,92.05
+ ,35652
+ ,7.4
+ ,92.31
+ ,36488
+ ,8.6
+ ,92.66
+ ,35387
+ ,6.2
+ ,92.85
+ ,35676
+ ,6
+ ,92.82
+ ,34844
+ ,6.6
+ ,93.45
+ ,32447
+ ,5.1
+ ,93.23
+ ,31068
+ ,4.7
+ ,93.53
+ ,29010
+ ,5
+ ,93.29
+ ,29812
+ ,3.6
+ ,93.19
+ ,30951
+ ,1.9
+ ,93.59
+ ,32974
+ ,-0.1
+ ,93.8
+ ,32936
+ ,-5.7
+ ,94.62
+ ,34012
+ ,-5.6
+ ,95.21
+ ,32946
+ ,-6.4
+ ,95.38
+ ,31948
+ ,-7.7
+ ,95.31
+ ,30599
+ ,-8
+ ,95.3
+ ,27691
+ ,-11.9
+ ,95.57
+ ,25073
+ ,-15.4
+ ,95.42
+ ,23406
+ ,-15.5
+ ,95.52
+ ,22248
+ ,-13.4
+ ,95.32
+ ,22896
+ ,-10.9
+ ,95.9
+ ,25317
+ ,-10.8
+ ,96.06
+ ,26558
+ ,-7.3
+ ,96.31
+ ,26471
+ ,-6.5
+ ,96.33
+ ,27543
+ ,-5.1
+ ,96.48
+ ,26198
+ ,-5.3
+ ,96.21
+ ,24725
+ ,-6.8
+ ,96.53
+ ,25005
+ ,-8.4
+ ,96.5
+ ,23462
+ ,-8.4
+ ,96.77
+ ,20780
+ ,-9.7
+ ,96.66
+ ,19815
+ ,-8.8
+ ,96.58
+ ,19761
+ ,-9.6
+ ,96.63
+ ,21454
+ ,-11.5
+ ,97.06
+ ,23899
+ ,-11
+ ,97.73
+ ,24939
+ ,-14.9
+ ,98
+ ,23580
+ ,-16.2
+ ,97.76
+ ,24562
+ ,-14.4
+ ,97.48
+ ,24696
+ ,-17.3
+ ,97.77
+ ,23785
+ ,-15.7
+ ,97.96
+ ,23812
+ ,-12.6
+ ,98.22
+ ,21917
+ ,-9.4
+ ,98.51
+ ,19713
+ ,-8.1
+ ,98.19
+ ,19282
+ ,-5.4
+ ,98.37
+ ,18788
+ ,-4.6
+ ,98.31
+ ,21453
+ ,-4.9
+ ,98.6
+ ,24482
+ ,-4
+ ,98.96
+ ,27474
+ ,-3.1
+ ,99.11
+ ,27264
+ ,-1.3
+ ,99.64
+ ,27349
+ ,0
+ ,100.02
+ ,30632
+ ,-0.4
+ ,99.98
+ ,29429
+ ,3
+ ,100.32
+ ,30084
+ ,0.4
+ ,100.44
+ ,26290
+ ,1.2
+ ,100.51
+ ,24379
+ ,0.6
+ ,101
+ ,23335
+ ,-1.3
+ ,100.88
+ ,21346
+ ,-3.2
+ ,100.55
+ ,21106
+ ,-1.8
+ ,100.82
+ ,24514
+ ,-3.6
+ ,101.5
+ ,28353
+ ,-4.2
+ ,102.15
+ ,30805
+ ,-6.9
+ ,102.39
+ ,31348
+ ,-8
+ ,102.54
+ ,34556
+ ,-7.5
+ ,102.85
+ ,33855
+ ,-8.2
+ ,103.47
+ ,34787
+ ,-7.6
+ ,103.56
+ ,32529
+ ,-3.7
+ ,103.69
+ ,29998
+ ,-1.7
+ ,103.49
+ ,29257
+ ,-0.7
+ ,103.47
+ ,28155
+ ,0.2
+ ,103.45
+ ,30466
+ ,0.6
+ ,103.48
+ ,35704
+ ,2.2
+ ,103.93
+ ,39327
+ ,3.3
+ ,103.89
+ ,39351
+ ,5.3
+ ,104.4
+ ,42234
+ ,5.5
+ ,104.79
+ ,43630
+ ,6.3
+ ,104.77
+ ,43722
+ ,7.7
+ ,105.13
+ ,43121
+ ,6.5
+ ,105.26
+ ,37985
+ ,5.5
+ ,104.96
+ ,37135
+ ,6.9
+ ,104.75
+ ,34646
+ ,5.7
+ ,105.01
+ ,33026
+ ,6.9
+ ,105.15
+ ,35087
+ ,6.1
+ ,105.2
+ ,38846
+ ,4.8
+ ,105.77
+ ,42013
+ ,3.7
+ ,105.78
+ ,43908
+ ,5.8
+ ,106.26
+ ,42868
+ ,6.8
+ ,106.13
+ ,44423
+ ,8.5
+ ,106.12
+ ,44167
+ ,7.2
+ ,106.57
+ ,43636
+ ,5
+ ,106.44
+ ,44382
+ ,4.7
+ ,106.54
+ ,42142
+ ,2.3
+ ,107.1
+ ,43452
+ ,2.4
+ ,108.1
+ ,36912
+ ,0.1
+ ,108.4
+ ,42413
+ ,1.9
+ ,108.84
+ ,45344
+ ,1.7
+ ,109.62
+ ,44873
+ ,2
+ ,110.42
+ ,47510
+ ,-1.9
+ ,110.67
+ ,49554
+ ,0.5
+ ,111.66
+ ,47369
+ ,-1.3
+ ,112.28
+ ,45998
+ ,-3.3
+ ,112.87
+ ,48140
+ ,-2.8
+ ,112.18
+ ,48441
+ ,-8
+ ,112.36
+ ,44928
+ ,-13.9
+ ,112.16
+ ,40454
+ ,-21.9
+ ,111.49
+ ,38661
+ ,-28.8
+ ,111.25
+ ,37246
+ ,-27.6
+ ,111.36
+ ,36843
+ ,-31.4
+ ,111.74
+ ,36424
+ ,-31.8
+ ,111.1
+ ,37594
+ ,-29.4
+ ,111.33
+ ,38144
+ ,-27.6
+ ,111.25
+ ,38737
+ ,-23.6
+ ,111.04
+ ,34560
+ ,-22.8
+ ,110.97
+ ,36080
+ ,-18.2
+ ,111.31
+ ,33508
+ ,-17.8
+ ,111.02
+ ,35462
+ ,-14.2
+ ,111.07
+ ,33374
+ ,-8.8
+ ,111.36
+ ,32110
+ ,-7.9
+ ,111.54
+ ,35533
+ ,-7
+ ,112.05
+ ,35532
+ ,-7
+ ,112.52
+ ,37903
+ ,-3.6
+ ,112.94
+ ,36763
+ ,-2.4
+ ,113.33
+ ,40399
+ ,-4.9
+ ,113.78
+ ,44164
+ ,-7.7
+ ,113.77
+ ,44496
+ ,-6.5
+ ,113.82
+ ,43110
+ ,-5.1
+ ,113.89
+ ,43880
+ ,-3.4
+ ,114.25)
+ ,dim=c(3
+ ,129)
+ ,dimnames=list(c('Vacatures'
+ ,'Ondernemersvertrouwen'
+ ,'CPI')
+ ,1:129))
> y <- array(NA,dim=c(3,129),dimnames=list(c('Vacatures','Ondernemersvertrouwen','CPI'),1:129))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> 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
Vacatures Ondernemersvertrouwen CPI M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 27951 6.4 91.18 1 0 0 0 0 0 0 0 0 0 0
2 29781 7.7 91.53 0 1 0 0 0 0 0 0 0 0 0
3 32914 9.2 91.88 0 0 1 0 0 0 0 0 0 0 0
4 33488 8.6 92.05 0 0 0 1 0 0 0 0 0 0 0
5 35652 7.4 92.31 0 0 0 0 1 0 0 0 0 0 0
6 36488 8.6 92.66 0 0 0 0 0 1 0 0 0 0 0
7 35387 6.2 92.85 0 0 0 0 0 0 1 0 0 0 0
8 35676 6.0 92.82 0 0 0 0 0 0 0 1 0 0 0
9 34844 6.6 93.45 0 0 0 0 0 0 0 0 1 0 0
10 32447 5.1 93.23 0 0 0 0 0 0 0 0 0 1 0
11 31068 4.7 93.53 0 0 0 0 0 0 0 0 0 0 1
12 29010 5.0 93.29 0 0 0 0 0 0 0 0 0 0 0
13 29812 3.6 93.19 1 0 0 0 0 0 0 0 0 0 0
14 30951 1.9 93.59 0 1 0 0 0 0 0 0 0 0 0
15 32974 -0.1 93.80 0 0 1 0 0 0 0 0 0 0 0
16 32936 -5.7 94.62 0 0 0 1 0 0 0 0 0 0 0
17 34012 -5.6 95.21 0 0 0 0 1 0 0 0 0 0 0
18 32946 -6.4 95.38 0 0 0 0 0 1 0 0 0 0 0
19 31948 -7.7 95.31 0 0 0 0 0 0 1 0 0 0 0
20 30599 -8.0 95.30 0 0 0 0 0 0 0 1 0 0 0
21 27691 -11.9 95.57 0 0 0 0 0 0 0 0 1 0 0
22 25073 -15.4 95.42 0 0 0 0 0 0 0 0 0 1 0
23 23406 -15.5 95.52 0 0 0 0 0 0 0 0 0 0 1
24 22248 -13.4 95.32 0 0 0 0 0 0 0 0 0 0 0
25 22896 -10.9 95.90 1 0 0 0 0 0 0 0 0 0 0
26 25317 -10.8 96.06 0 1 0 0 0 0 0 0 0 0 0
27 26558 -7.3 96.31 0 0 1 0 0 0 0 0 0 0 0
28 26471 -6.5 96.33 0 0 0 1 0 0 0 0 0 0 0
29 27543 -5.1 96.48 0 0 0 0 1 0 0 0 0 0 0
30 26198 -5.3 96.21 0 0 0 0 0 1 0 0 0 0 0
31 24725 -6.8 96.53 0 0 0 0 0 0 1 0 0 0 0
32 25005 -8.4 96.50 0 0 0 0 0 0 0 1 0 0 0
33 23462 -8.4 96.77 0 0 0 0 0 0 0 0 1 0 0
34 20780 -9.7 96.66 0 0 0 0 0 0 0 0 0 1 0
35 19815 -8.8 96.58 0 0 0 0 0 0 0 0 0 0 1
36 19761 -9.6 96.63 0 0 0 0 0 0 0 0 0 0 0
37 21454 -11.5 97.06 1 0 0 0 0 0 0 0 0 0 0
38 23899 -11.0 97.73 0 1 0 0 0 0 0 0 0 0 0
39 24939 -14.9 98.00 0 0 1 0 0 0 0 0 0 0 0
40 23580 -16.2 97.76 0 0 0 1 0 0 0 0 0 0 0
41 24562 -14.4 97.48 0 0 0 0 1 0 0 0 0 0 0
42 24696 -17.3 97.77 0 0 0 0 0 1 0 0 0 0 0
43 23785 -15.7 97.96 0 0 0 0 0 0 1 0 0 0 0
44 23812 -12.6 98.22 0 0 0 0 0 0 0 1 0 0 0
45 21917 -9.4 98.51 0 0 0 0 0 0 0 0 1 0 0
46 19713 -8.1 98.19 0 0 0 0 0 0 0 0 0 1 0
47 19282 -5.4 98.37 0 0 0 0 0 0 0 0 0 0 1
48 18788 -4.6 98.31 0 0 0 0 0 0 0 0 0 0 0
49 21453 -4.9 98.60 1 0 0 0 0 0 0 0 0 0 0
50 24482 -4.0 98.96 0 1 0 0 0 0 0 0 0 0 0
51 27474 -3.1 99.11 0 0 1 0 0 0 0 0 0 0 0
52 27264 -1.3 99.64 0 0 0 1 0 0 0 0 0 0 0
53 27349 0.0 100.02 0 0 0 0 1 0 0 0 0 0 0
54 30632 -0.4 99.98 0 0 0 0 0 1 0 0 0 0 0
55 29429 3.0 100.32 0 0 0 0 0 0 1 0 0 0 0
56 30084 0.4 100.44 0 0 0 0 0 0 0 1 0 0 0
57 26290 1.2 100.51 0 0 0 0 0 0 0 0 1 0 0
58 24379 0.6 101.00 0 0 0 0 0 0 0 0 0 1 0
59 23335 -1.3 100.88 0 0 0 0 0 0 0 0 0 0 1
60 21346 -3.2 100.55 0 0 0 0 0 0 0 0 0 0 0
61 21106 -1.8 100.82 1 0 0 0 0 0 0 0 0 0 0
62 24514 -3.6 101.50 0 1 0 0 0 0 0 0 0 0 0
63 28353 -4.2 102.15 0 0 1 0 0 0 0 0 0 0 0
64 30805 -6.9 102.39 0 0 0 1 0 0 0 0 0 0 0
65 31348 -8.0 102.54 0 0 0 0 1 0 0 0 0 0 0
66 34556 -7.5 102.85 0 0 0 0 0 1 0 0 0 0 0
67 33855 -8.2 103.47 0 0 0 0 0 0 1 0 0 0 0
68 34787 -7.6 103.56 0 0 0 0 0 0 0 1 0 0 0
69 32529 -3.7 103.69 0 0 0 0 0 0 0 0 1 0 0
70 29998 -1.7 103.49 0 0 0 0 0 0 0 0 0 1 0
71 29257 -0.7 103.47 0 0 0 0 0 0 0 0 0 0 1
72 28155 0.2 103.45 0 0 0 0 0 0 0 0 0 0 0
73 30466 0.6 103.48 1 0 0 0 0 0 0 0 0 0 0
74 35704 2.2 103.93 0 1 0 0 0 0 0 0 0 0 0
75 39327 3.3 103.89 0 0 1 0 0 0 0 0 0 0 0
76 39351 5.3 104.40 0 0 0 1 0 0 0 0 0 0 0
77 42234 5.5 104.79 0 0 0 0 1 0 0 0 0 0 0
78 43630 6.3 104.77 0 0 0 0 0 1 0 0 0 0 0
79 43722 7.7 105.13 0 0 0 0 0 0 1 0 0 0 0
80 43121 6.5 105.26 0 0 0 0 0 0 0 1 0 0 0
81 37985 5.5 104.96 0 0 0 0 0 0 0 0 1 0 0
82 37135 6.9 104.75 0 0 0 0 0 0 0 0 0 1 0
83 34646 5.7 105.01 0 0 0 0 0 0 0 0 0 0 1
84 33026 6.9 105.15 0 0 0 0 0 0 0 0 0 0 0
85 35087 6.1 105.20 1 0 0 0 0 0 0 0 0 0 0
86 38846 4.8 105.77 0 1 0 0 0 0 0 0 0 0 0
87 42013 3.7 105.78 0 0 1 0 0 0 0 0 0 0 0
88 43908 5.8 106.26 0 0 0 1 0 0 0 0 0 0 0
89 42868 6.8 106.13 0 0 0 0 1 0 0 0 0 0 0
90 44423 8.5 106.12 0 0 0 0 0 1 0 0 0 0 0
91 44167 7.2 106.57 0 0 0 0 0 0 1 0 0 0 0
92 43636 5.0 106.44 0 0 0 0 0 0 0 1 0 0 0
93 44382 4.7 106.54 0 0 0 0 0 0 0 0 1 0 0
94 42142 2.3 107.10 0 0 0 0 0 0 0 0 0 1 0
95 43452 2.4 108.10 0 0 0 0 0 0 0 0 0 0 1
96 36912 0.1 108.40 0 0 0 0 0 0 0 0 0 0 0
97 42413 1.9 108.84 1 0 0 0 0 0 0 0 0 0 0
98 45344 1.7 109.62 0 1 0 0 0 0 0 0 0 0 0
99 44873 2.0 110.42 0 0 1 0 0 0 0 0 0 0 0
100 47510 -1.9 110.67 0 0 0 1 0 0 0 0 0 0 0
101 49554 0.5 111.66 0 0 0 0 1 0 0 0 0 0 0
102 47369 -1.3 112.28 0 0 0 0 0 1 0 0 0 0 0
103 45998 -3.3 112.87 0 0 0 0 0 0 1 0 0 0 0
104 48140 -2.8 112.18 0 0 0 0 0 0 0 1 0 0 0
105 48441 -8.0 112.36 0 0 0 0 0 0 0 0 1 0 0
106 44928 -13.9 112.16 0 0 0 0 0 0 0 0 0 1 0
107 40454 -21.9 111.49 0 0 0 0 0 0 0 0 0 0 1
108 38661 -28.8 111.25 0 0 0 0 0 0 0 0 0 0 0
109 37246 -27.6 111.36 1 0 0 0 0 0 0 0 0 0 0
110 36843 -31.4 111.74 0 1 0 0 0 0 0 0 0 0 0
111 36424 -31.8 111.10 0 0 1 0 0 0 0 0 0 0 0
112 37594 -29.4 111.33 0 0 0 1 0 0 0 0 0 0 0
113 38144 -27.6 111.25 0 0 0 0 1 0 0 0 0 0 0
114 38737 -23.6 111.04 0 0 0 0 0 1 0 0 0 0 0
115 34560 -22.8 110.97 0 0 0 0 0 0 1 0 0 0 0
116 36080 -18.2 111.31 0 0 0 0 0 0 0 1 0 0 0
117 33508 -17.8 111.02 0 0 0 0 0 0 0 0 1 0 0
118 35462 -14.2 111.07 0 0 0 0 0 0 0 0 0 1 0
119 33374 -8.8 111.36 0 0 0 0 0 0 0 0 0 0 1
120 32110 -7.9 111.54 0 0 0 0 0 0 0 0 0 0 0
121 35533 -7.0 112.05 1 0 0 0 0 0 0 0 0 0 0
122 35532 -7.0 112.52 0 1 0 0 0 0 0 0 0 0 0
123 37903 -3.6 112.94 0 0 1 0 0 0 0 0 0 0 0
124 36763 -2.4 113.33 0 0 0 1 0 0 0 0 0 0 0
125 40399 -4.9 113.78 0 0 0 0 1 0 0 0 0 0 0
126 44164 -7.7 113.77 0 0 0 0 0 1 0 0 0 0 0
127 44496 -6.5 113.82 0 0 0 0 0 0 1 0 0 0 0
128 43110 -5.1 113.89 0 0 0 0 0 0 0 1 0 0 0
129 43880 -3.4 114.25 0 0 0 0 0 0 0 0 1 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
100 100
101 101
102 102
103 103
104 104
105 105
106 106
107 107
108 108
109 109
110 110
111 111
112 112
113 113
114 114
115 115
116 116
117 117
118 118
119 119
120 120
121 121
122 122
123 123
124 124
125 125
126 126
127 127
128 128
129 129
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Ondernemersvertrouwen CPI
-334812.3 363.5 3918.0
M1 M2 M3
1371.9 2535.9 4180.6
M4 M5 M6
4183.8 4812.5 5890.8
M7 M8 M9
4340.2 4958.4 3155.0
M10 M11 t
2162.4 884.9 -550.6
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7848.8 -2646.4 162.2 2544.1 8107.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -334812.33 33278.13 -10.061 < 2e-16 ***
Ondernemersvertrouwen 363.53 34.83 10.437 < 2e-16 ***
CPI 3918.01 367.41 10.664 < 2e-16 ***
M1 1371.88 1556.74 0.881 0.380036
M2 2535.85 1563.36 1.622 0.107556
M3 4180.65 1565.13 2.671 0.008666 **
M4 4183.76 1570.60 2.664 0.008846 **
M5 4812.49 1575.72 3.054 0.002810 **
M6 5890.79 1571.68 3.748 0.000281 ***
M7 4340.19 1576.74 2.753 0.006881 **
M8 4958.42 1568.29 3.162 0.002010 **
M9 3155.01 1567.45 2.013 0.046489 *
M10 2162.44 1595.19 1.356 0.177906
M11 884.88 1593.86 0.555 0.579857
t -550.55 66.38 -8.294 2.46e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3559 on 114 degrees of freedom
Multiple R-squared: 0.8208, Adjusted R-squared: 0.7988
F-statistic: 37.29 on 14 and 114 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1cwpo1291137410.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cwpo1291137410.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/35no91291137410.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45no91291137410.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55no91291137410.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 129
Frequency = 1
1 2 3 4 5 6
2371.68933 1744.38089 1866.54049 2540.03365 4043.40477 2544.12260
7 8 9 10 11 12
3672.31747 4083.88746 2919.38174 3472.76036 2891.87324 3100.56955
13 14 15 16 17 18
3981.97916 3558.35235 4391.37826 3723.80440 2373.64825 404.66165
19 20 21 22 23 24
2254.65790 986.22051 792.06958 1577.24242 1382.89819 1680.52515
25 26 27 28 29 30
-1674.06538 -529.72003 -2634.81449 -2543.55875 -2646.37801 -3388.55861
31 32 33 34 35 36
-3468.87901 -2557.37081 -2804.27820 -3039.58563 -2190.21620 -713.86141
37 38 39 40 41 42
-836.23102 -1811.47934 -1505.83189 -904.48742 442.02472 -33.71520
43 44 45 46 47 48
-169.62952 -2355.92119 -4196.47605 -4076.17333 -4365.83443 -3480.14274
49 50 51 52 53 54
-2663.63526 -1985.71284 -1002.83578 -3396.29008 -5350.89786 -2293.51424
55 56 57 58 59 60
-3963.47852 -2901.14382 -4906.27198 -6975.85176 -5030.88567 -3600.80880
61 62 63 64 65 66
-6228.93763 -5444.25322 -5028.08791 -1987.44781 -1710.44882 -426.54166
67 68 69 70 71 72
-1201.08552 -907.49802 -2738.64113 -3669.96817 -2868.03178 -2783.41303
73 74 75 76 77 78
-1556.69326 723.13957 3008.73219 854.93253 2059.02474 2714.81548
79 80 81 82 83 84
2988.54570 2246.76212 1003.64510 2010.61450 767.26943 -402.05087
85 86 87 88 89 90
932.54155 2317.44290 4750.89531 4549.28309 3576.91626 4025.35238
91 92 93 94 95 96
4579.98582 5290.41094 8107.62266 6089.13164 5272.87708 -170.97853
97 98 99 100 101 102
2130.42080 1464.66099 -3344.04908 278.64373 -2506.82736 -6994.38909
103 104 105 106 107 108
-7848.80730 -3252.82286 587.23219 1545.77082 4433.15641 7524.24928
109 110 111 112 113 114
4420.70678 3296.84740 4436.53422 4380.36515 4511.27621 3945.20062
115 116 117 118 119 120
1852.78954 300.76649 1073.53120 3066.05914 -293.10627 -1154.08860
121 122 123 124 125 126
-877.77507 -3333.65867 -4938.46132 -7495.27849 -4791.74290 -497.43393
127 128 129
1303.58342 -933.29081 162.18490
> postscript(file="/var/www/html/rcomp/tmp/6gfnu1291137410.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 = 129
Frequency = 1
lag(myerror, k = 1) myerror
0 2371.68933 NA
1 1744.38089 2371.68933
2 1866.54049 1744.38089
3 2540.03365 1866.54049
4 4043.40477 2540.03365
5 2544.12260 4043.40477
6 3672.31747 2544.12260
7 4083.88746 3672.31747
8 2919.38174 4083.88746
9 3472.76036 2919.38174
10 2891.87324 3472.76036
11 3100.56955 2891.87324
12 3981.97916 3100.56955
13 3558.35235 3981.97916
14 4391.37826 3558.35235
15 3723.80440 4391.37826
16 2373.64825 3723.80440
17 404.66165 2373.64825
18 2254.65790 404.66165
19 986.22051 2254.65790
20 792.06958 986.22051
21 1577.24242 792.06958
22 1382.89819 1577.24242
23 1680.52515 1382.89819
24 -1674.06538 1680.52515
25 -529.72003 -1674.06538
26 -2634.81449 -529.72003
27 -2543.55875 -2634.81449
28 -2646.37801 -2543.55875
29 -3388.55861 -2646.37801
30 -3468.87901 -3388.55861
31 -2557.37081 -3468.87901
32 -2804.27820 -2557.37081
33 -3039.58563 -2804.27820
34 -2190.21620 -3039.58563
35 -713.86141 -2190.21620
36 -836.23102 -713.86141
37 -1811.47934 -836.23102
38 -1505.83189 -1811.47934
39 -904.48742 -1505.83189
40 442.02472 -904.48742
41 -33.71520 442.02472
42 -169.62952 -33.71520
43 -2355.92119 -169.62952
44 -4196.47605 -2355.92119
45 -4076.17333 -4196.47605
46 -4365.83443 -4076.17333
47 -3480.14274 -4365.83443
48 -2663.63526 -3480.14274
49 -1985.71284 -2663.63526
50 -1002.83578 -1985.71284
51 -3396.29008 -1002.83578
52 -5350.89786 -3396.29008
53 -2293.51424 -5350.89786
54 -3963.47852 -2293.51424
55 -2901.14382 -3963.47852
56 -4906.27198 -2901.14382
57 -6975.85176 -4906.27198
58 -5030.88567 -6975.85176
59 -3600.80880 -5030.88567
60 -6228.93763 -3600.80880
61 -5444.25322 -6228.93763
62 -5028.08791 -5444.25322
63 -1987.44781 -5028.08791
64 -1710.44882 -1987.44781
65 -426.54166 -1710.44882
66 -1201.08552 -426.54166
67 -907.49802 -1201.08552
68 -2738.64113 -907.49802
69 -3669.96817 -2738.64113
70 -2868.03178 -3669.96817
71 -2783.41303 -2868.03178
72 -1556.69326 -2783.41303
73 723.13957 -1556.69326
74 3008.73219 723.13957
75 854.93253 3008.73219
76 2059.02474 854.93253
77 2714.81548 2059.02474
78 2988.54570 2714.81548
79 2246.76212 2988.54570
80 1003.64510 2246.76212
81 2010.61450 1003.64510
82 767.26943 2010.61450
83 -402.05087 767.26943
84 932.54155 -402.05087
85 2317.44290 932.54155
86 4750.89531 2317.44290
87 4549.28309 4750.89531
88 3576.91626 4549.28309
89 4025.35238 3576.91626
90 4579.98582 4025.35238
91 5290.41094 4579.98582
92 8107.62266 5290.41094
93 6089.13164 8107.62266
94 5272.87708 6089.13164
95 -170.97853 5272.87708
96 2130.42080 -170.97853
97 1464.66099 2130.42080
98 -3344.04908 1464.66099
99 278.64373 -3344.04908
100 -2506.82736 278.64373
101 -6994.38909 -2506.82736
102 -7848.80730 -6994.38909
103 -3252.82286 -7848.80730
104 587.23219 -3252.82286
105 1545.77082 587.23219
106 4433.15641 1545.77082
107 7524.24928 4433.15641
108 4420.70678 7524.24928
109 3296.84740 4420.70678
110 4436.53422 3296.84740
111 4380.36515 4436.53422
112 4511.27621 4380.36515
113 3945.20062 4511.27621
114 1852.78954 3945.20062
115 300.76649 1852.78954
116 1073.53120 300.76649
117 3066.05914 1073.53120
118 -293.10627 3066.05914
119 -1154.08860 -293.10627
120 -877.77507 -1154.08860
121 -3333.65867 -877.77507
122 -4938.46132 -3333.65867
123 -7495.27849 -4938.46132
124 -4791.74290 -7495.27849
125 -497.43393 -4791.74290
126 1303.58342 -497.43393
127 -933.29081 1303.58342
128 162.18490 -933.29081
129 NA 162.18490
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1744.38089 2371.68933
[2,] 1866.54049 1744.38089
[3,] 2540.03365 1866.54049
[4,] 4043.40477 2540.03365
[5,] 2544.12260 4043.40477
[6,] 3672.31747 2544.12260
[7,] 4083.88746 3672.31747
[8,] 2919.38174 4083.88746
[9,] 3472.76036 2919.38174
[10,] 2891.87324 3472.76036
[11,] 3100.56955 2891.87324
[12,] 3981.97916 3100.56955
[13,] 3558.35235 3981.97916
[14,] 4391.37826 3558.35235
[15,] 3723.80440 4391.37826
[16,] 2373.64825 3723.80440
[17,] 404.66165 2373.64825
[18,] 2254.65790 404.66165
[19,] 986.22051 2254.65790
[20,] 792.06958 986.22051
[21,] 1577.24242 792.06958
[22,] 1382.89819 1577.24242
[23,] 1680.52515 1382.89819
[24,] -1674.06538 1680.52515
[25,] -529.72003 -1674.06538
[26,] -2634.81449 -529.72003
[27,] -2543.55875 -2634.81449
[28,] -2646.37801 -2543.55875
[29,] -3388.55861 -2646.37801
[30,] -3468.87901 -3388.55861
[31,] -2557.37081 -3468.87901
[32,] -2804.27820 -2557.37081
[33,] -3039.58563 -2804.27820
[34,] -2190.21620 -3039.58563
[35,] -713.86141 -2190.21620
[36,] -836.23102 -713.86141
[37,] -1811.47934 -836.23102
[38,] -1505.83189 -1811.47934
[39,] -904.48742 -1505.83189
[40,] 442.02472 -904.48742
[41,] -33.71520 442.02472
[42,] -169.62952 -33.71520
[43,] -2355.92119 -169.62952
[44,] -4196.47605 -2355.92119
[45,] -4076.17333 -4196.47605
[46,] -4365.83443 -4076.17333
[47,] -3480.14274 -4365.83443
[48,] -2663.63526 -3480.14274
[49,] -1985.71284 -2663.63526
[50,] -1002.83578 -1985.71284
[51,] -3396.29008 -1002.83578
[52,] -5350.89786 -3396.29008
[53,] -2293.51424 -5350.89786
[54,] -3963.47852 -2293.51424
[55,] -2901.14382 -3963.47852
[56,] -4906.27198 -2901.14382
[57,] -6975.85176 -4906.27198
[58,] -5030.88567 -6975.85176
[59,] -3600.80880 -5030.88567
[60,] -6228.93763 -3600.80880
[61,] -5444.25322 -6228.93763
[62,] -5028.08791 -5444.25322
[63,] -1987.44781 -5028.08791
[64,] -1710.44882 -1987.44781
[65,] -426.54166 -1710.44882
[66,] -1201.08552 -426.54166
[67,] -907.49802 -1201.08552
[68,] -2738.64113 -907.49802
[69,] -3669.96817 -2738.64113
[70,] -2868.03178 -3669.96817
[71,] -2783.41303 -2868.03178
[72,] -1556.69326 -2783.41303
[73,] 723.13957 -1556.69326
[74,] 3008.73219 723.13957
[75,] 854.93253 3008.73219
[76,] 2059.02474 854.93253
[77,] 2714.81548 2059.02474
[78,] 2988.54570 2714.81548
[79,] 2246.76212 2988.54570
[80,] 1003.64510 2246.76212
[81,] 2010.61450 1003.64510
[82,] 767.26943 2010.61450
[83,] -402.05087 767.26943
[84,] 932.54155 -402.05087
[85,] 2317.44290 932.54155
[86,] 4750.89531 2317.44290
[87,] 4549.28309 4750.89531
[88,] 3576.91626 4549.28309
[89,] 4025.35238 3576.91626
[90,] 4579.98582 4025.35238
[91,] 5290.41094 4579.98582
[92,] 8107.62266 5290.41094
[93,] 6089.13164 8107.62266
[94,] 5272.87708 6089.13164
[95,] -170.97853 5272.87708
[96,] 2130.42080 -170.97853
[97,] 1464.66099 2130.42080
[98,] -3344.04908 1464.66099
[99,] 278.64373 -3344.04908
[100,] -2506.82736 278.64373
[101,] -6994.38909 -2506.82736
[102,] -7848.80730 -6994.38909
[103,] -3252.82286 -7848.80730
[104,] 587.23219 -3252.82286
[105,] 1545.77082 587.23219
[106,] 4433.15641 1545.77082
[107,] 7524.24928 4433.15641
[108,] 4420.70678 7524.24928
[109,] 3296.84740 4420.70678
[110,] 4436.53422 3296.84740
[111,] 4380.36515 4436.53422
[112,] 4511.27621 4380.36515
[113,] 3945.20062 4511.27621
[114,] 1852.78954 3945.20062
[115,] 300.76649 1852.78954
[116,] 1073.53120 300.76649
[117,] 3066.05914 1073.53120
[118,] -293.10627 3066.05914
[119,] -1154.08860 -293.10627
[120,] -877.77507 -1154.08860
[121,] -3333.65867 -877.77507
[122,] -4938.46132 -3333.65867
[123,] -7495.27849 -4938.46132
[124,] -4791.74290 -7495.27849
[125,] -497.43393 -4791.74290
[126,] 1303.58342 -497.43393
[127,] -933.29081 1303.58342
[128,] 162.18490 -933.29081
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1744.38089 2371.68933
2 1866.54049 1744.38089
3 2540.03365 1866.54049
4 4043.40477 2540.03365
5 2544.12260 4043.40477
6 3672.31747 2544.12260
7 4083.88746 3672.31747
8 2919.38174 4083.88746
9 3472.76036 2919.38174
10 2891.87324 3472.76036
11 3100.56955 2891.87324
12 3981.97916 3100.56955
13 3558.35235 3981.97916
14 4391.37826 3558.35235
15 3723.80440 4391.37826
16 2373.64825 3723.80440
17 404.66165 2373.64825
18 2254.65790 404.66165
19 986.22051 2254.65790
20 792.06958 986.22051
21 1577.24242 792.06958
22 1382.89819 1577.24242
23 1680.52515 1382.89819
24 -1674.06538 1680.52515
25 -529.72003 -1674.06538
26 -2634.81449 -529.72003
27 -2543.55875 -2634.81449
28 -2646.37801 -2543.55875
29 -3388.55861 -2646.37801
30 -3468.87901 -3388.55861
31 -2557.37081 -3468.87901
32 -2804.27820 -2557.37081
33 -3039.58563 -2804.27820
34 -2190.21620 -3039.58563
35 -713.86141 -2190.21620
36 -836.23102 -713.86141
37 -1811.47934 -836.23102
38 -1505.83189 -1811.47934
39 -904.48742 -1505.83189
40 442.02472 -904.48742
41 -33.71520 442.02472
42 -169.62952 -33.71520
43 -2355.92119 -169.62952
44 -4196.47605 -2355.92119
45 -4076.17333 -4196.47605
46 -4365.83443 -4076.17333
47 -3480.14274 -4365.83443
48 -2663.63526 -3480.14274
49 -1985.71284 -2663.63526
50 -1002.83578 -1985.71284
51 -3396.29008 -1002.83578
52 -5350.89786 -3396.29008
53 -2293.51424 -5350.89786
54 -3963.47852 -2293.51424
55 -2901.14382 -3963.47852
56 -4906.27198 -2901.14382
57 -6975.85176 -4906.27198
58 -5030.88567 -6975.85176
59 -3600.80880 -5030.88567
60 -6228.93763 -3600.80880
61 -5444.25322 -6228.93763
62 -5028.08791 -5444.25322
63 -1987.44781 -5028.08791
64 -1710.44882 -1987.44781
65 -426.54166 -1710.44882
66 -1201.08552 -426.54166
67 -907.49802 -1201.08552
68 -2738.64113 -907.49802
69 -3669.96817 -2738.64113
70 -2868.03178 -3669.96817
71 -2783.41303 -2868.03178
72 -1556.69326 -2783.41303
73 723.13957 -1556.69326
74 3008.73219 723.13957
75 854.93253 3008.73219
76 2059.02474 854.93253
77 2714.81548 2059.02474
78 2988.54570 2714.81548
79 2246.76212 2988.54570
80 1003.64510 2246.76212
81 2010.61450 1003.64510
82 767.26943 2010.61450
83 -402.05087 767.26943
84 932.54155 -402.05087
85 2317.44290 932.54155
86 4750.89531 2317.44290
87 4549.28309 4750.89531
88 3576.91626 4549.28309
89 4025.35238 3576.91626
90 4579.98582 4025.35238
91 5290.41094 4579.98582
92 8107.62266 5290.41094
93 6089.13164 8107.62266
94 5272.87708 6089.13164
95 -170.97853 5272.87708
96 2130.42080 -170.97853
97 1464.66099 2130.42080
98 -3344.04908 1464.66099
99 278.64373 -3344.04908
100 -2506.82736 278.64373
101 -6994.38909 -2506.82736
102 -7848.80730 -6994.38909
103 -3252.82286 -7848.80730
104 587.23219 -3252.82286
105 1545.77082 587.23219
106 4433.15641 1545.77082
107 7524.24928 4433.15641
108 4420.70678 7524.24928
109 3296.84740 4420.70678
110 4436.53422 3296.84740
111 4380.36515 4436.53422
112 4511.27621 4380.36515
113 3945.20062 4511.27621
114 1852.78954 3945.20062
115 300.76649 1852.78954
116 1073.53120 300.76649
117 3066.05914 1073.53120
118 -293.10627 3066.05914
119 -1154.08860 -293.10627
120 -877.77507 -1154.08860
121 -3333.65867 -877.77507
122 -4938.46132 -3333.65867
123 -7495.27849 -4938.46132
124 -4791.74290 -7495.27849
125 -497.43393 -4791.74290
126 1303.58342 -497.43393
127 -933.29081 1303.58342
128 162.18490 -933.29081
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7qo5f1291137410.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qo5f1291137410.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qo5f1291137410.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/10upl31291137410.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11f7281291137410.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12m8hk1291137410.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13fhyn1291137410.tab")
>
> try(system("convert tmp/1cwpo1291137410.ps tmp/1cwpo1291137410.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cwpo1291137410.ps tmp/2cwpo1291137410.png",intern=TRUE))
character(0)
> try(system("convert tmp/35no91291137410.ps tmp/35no91291137410.png",intern=TRUE))
character(0)
> try(system("convert tmp/45no91291137410.ps tmp/45no91291137410.png",intern=TRUE))
character(0)
> try(system("convert tmp/55no91291137410.ps tmp/55no91291137410.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gfnu1291137410.ps tmp/6gfnu1291137410.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qo5f1291137410.ps tmp/7qo5f1291137410.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qo5f1291137410.ps tmp/8qo5f1291137410.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qo5f1291137410.ps tmp/9qo5f1291137410.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.324 1.572 7.676