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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Type 'q()' to quit R.
> x <- array(list(36
+ ,27
+ ,71
+ ,8.1
+ ,3.34
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+ ,3.14
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+ ,26
+ ,26
+ ,146
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+ ,970
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+ ,4259
+ ,13.1
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+ ,23
+ ,9
+ ,15
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+ ,35
+ ,46
+ ,85
+ ,7.1
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+ ,79.9
+ ,1441
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+ ,1
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+ ,7.5
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+ ,11.4
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+ ,6
+ ,4
+ ,16
+ ,58
+ ,936
+ ,15
+ ,30
+ ,73
+ ,8.2
+ ,3.15
+ ,12.2
+ ,84.2
+ ,4824
+ ,4.7
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+ ,17
+ ,8
+ ,28
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+ ,871
+ ,31
+ ,27
+ ,74
+ ,7.2
+ ,3.44
+ ,10.8
+ ,87.0
+ ,4834
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+ ,13.6
+ ,52
+ ,35
+ ,124
+ ,59
+ ,959
+ ,30
+ ,24
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+ ,4
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+ ,61
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+ ,31
+ ,45
+ ,85
+ ,7.3
+ ,3.22
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+ ,1844
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+ ,48.1
+ ,18.5
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+ ,1
+ ,1
+ ,53
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+ ,5
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+ ,950
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+ ,7
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+ ,972
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+ ,2
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+ ,65
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+ ,102
+ ,52
+ ,967
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+ ,32
+ ,81
+ ,7.0
+ ,3.27
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+ ,81.0
+ ,3665
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+ ,4
+ ,2
+ ,1
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+ ,823
+ ,45
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+ ,82.2
+ ,3152
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+ ,47.3
+ ,10.9
+ ,14
+ ,11
+ ,42
+ ,56
+ ,100
+ ,45
+ ,24
+ ,70
+ ,11.8
+ ,3.25
+ ,11.1
+ ,79.8
+ ,3678
+ ,1.0
+ ,44.8
+ ,14.0
+ ,7
+ ,3
+ ,8
+ ,56
+ ,895
+ ,42
+ ,83
+ ,76
+ ,9.7
+ ,3.22
+ ,9.0
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+ ,9699
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+ ,14.5
+ ,8
+ ,8
+ ,49
+ ,54
+ ,911
+ ,38
+ ,28
+ ,72
+ ,8.9
+ ,3.48
+ ,10.7
+ ,79.8
+ ,3451
+ ,11.7
+ ,37.5
+ ,13.0
+ ,14
+ ,13
+ ,39
+ ,58
+ ,954)
+ ,dim=c(16
+ ,60)
+ ,dimnames=list(c('Gem_jaarlijkse_neerslag'
+ ,'Gem_temp_januari'
+ ,'Gem_temp_juli'
+ ,'Omvang_bevolking_>65jaar'
+ ,'#leden_per_huishouden'
+ ,'#jaren_onderwijs_personen>22j'
+ ,'huishoudens_met_volledig_uitgeruste_keuken'
+ ,'bevolking_per_mijl²'
+ ,'omvang_niet-blanke_bevolking'
+ ,'#kantoormedewerkers'
+ ,'#gezinnen_inkomen<$3000'
+ ,'index_olievervuiling'
+ ,'index_stikstofoxidevervuiling'
+ ,'index_zwaveldioxidevervuiling'
+ ,'luchtvochtigheidgraad'
+ ,'sterftecijfer')
+ ,1:60))
> y <- array(NA,dim=c(16,60),dimnames=list(c('Gem_jaarlijkse_neerslag','Gem_temp_januari','Gem_temp_juli','Omvang_bevolking_>65jaar','#leden_per_huishouden','#jaren_onderwijs_personen>22j','huishoudens_met_volledig_uitgeruste_keuken','bevolking_per_mijl²','omvang_niet-blanke_bevolking','#kantoormedewerkers','#gezinnen_inkomen<$3000','index_olievervuiling','index_stikstofoxidevervuiling','index_zwaveldioxidevervuiling','luchtvochtigheidgraad','sterftecijfer'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '16'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '16'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
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
sterftecijfer Gem_jaarlijkse_neerslag Gem_temp_januari Gem_temp_juli
1 921 36 27 71
2 997 35 23 72
3 962 44 29 74
4 982 47 45 79
5 107 43 35 77
6 103 53 45 80
7 934 43 30 74
8 899 45 30 73
9 100 36 24 70
10 912 36 27 72
11 101 52 42 79
12 102 33 26 76
13 970 40 34 77
14 985 35 28 71
15 958 37 31 75
16 860 35 46 85
17 936 36 30 75
18 871 15 30 73
19 959 31 27 74
20 941 30 24 72
21 891 31 45 85
22 871 31 24 72
23 971 42 40 77
24 887 43 27 72
25 952 46 55 84
26 968 39 29 76
27 919 35 31 81
28 844 43 32 74
29 861 11 53 68
30 989 30 35 71
31 100 50 42 82
32 861 60 67 82
33 929 30 20 69
34 857 25 12 73
35 961 45 40 80
36 923 46 30 72
37 111 54 54 81
38 994 42 33 77
39 101 42 32 76
40 991 36 29 72
41 893 37 38 67
42 938 42 29 72
43 946 41 33 77
44 102 44 39 78
45 874 32 25 72
46 953 34 32 79
47 839 10 55 70
48 911 18 48 63
49 790 13 49 68
50 899 35 40 64
51 904 45 28 74
52 950 38 24 72
53 972 31 26 73
54 912 40 23 71
55 967 41 37 78
56 823 28 32 81
57 100 45 33 76
58 895 45 24 70
59 911 42 83 76
60 954 38 28 72
Omvang_bevolking_>65jaar #leden_per_huishouden #jaren_onderwijs_personen>22j
1 8.1 3.34 11.4
2 11.1 3.14 11.0
3 10.4 3.21 9.8
4 6.5 3.41 11.1
5 7.6 3.44 9.6
6 7.7 3.45 10.2
7 10.9 3.23 12.1
8 9.3 3.29 10.6
9 9.0 3.31 10.5
10 9.5 3.36 10.7
11 7.7 3.39 9.6
12 8.6 3.20 10.9
13 9.2 3.21 10.2
14 8.8 3.29 11.1
15 8.0 3.26 11.9
16 7.1 3.22 11.8
17 7.5 3.35 11.4
18 8.2 3.15 12.2
19 7.2 3.44 10.8
20 6.5 3.53 10.8
21 7.3 3.22 11.4
22 9.0 3.37 10.9
23 6.1 3.45 10.4
24 9.0 3.25 11.5
25 5.6 3.35 11.4
26 8.7 3.23 11.4
27 9.2 3.10 12.0
28 10.1 3.38 9.5
29 9.2 2.99 12.1
30 8.3 3.37 9.9
31 7.3 3.49 10.4
32 10.0 2.98 11.5
33 8.8 3.26 11.1
34 9.2 3.28 12.1
35 8.3 3.32 10.1
36 10.2 3.16 11.3
37 7.4 3.36 9.7
38 9.7 3.03 10.7
39 9.1 3.32 10.5
40 9.5 3.32 10.6
41 11.3 2.99 12.0
42 10.7 3.19 10.1
43 11.2 3.08 9.6
44 8.2 3.32 11.0
45 10.9 3.21 11.1
46 9.3 3.23 9.7
47 7.3 3.11 12.1
48 9.2 2.92 12.2
49 7.0 3.36 12.2
50 9.6 3.02 12.2
51 10.6 3.21 11.1
52 9.8 3.34 11.4
53 9.3 3.22 10.7
54 11.3 3.28 10.3
55 6.2 3.25 12.3
56 7.0 3.27 12.1
57 7.7 3.39 11.3
58 11.8 3.25 11.1
59 9.7 3.22 9.0
60 8.9 3.48 10.7
huishoudens_met_volledig_uitgeruste_keuken bevolking_per_mijl\302\262
1 81.5 3243
2 78.8 4281
3 81.6 4260
4 77.5 3125
5 84.6 6441
6 66.8 3325
7 83.9 4679
8 86.0 2140
9 83.2 6582
10 79.3 4213
11 69.2 2302
12 83.4 6122
13 77.0 4101
14 86.3 3042
15 78.4 4259
16 79.9 1441
17 81.9 4029
18 84.2 4824
19 87.0 4834
20 79.5 3694
21 80.7 1844
22 82.8 3226
23 71.8 2269
24 87.1 2909
25 79.7 2647
26 78.6 4412
27 78.3 3262
28 79.2 3214
29 90.6 4700
30 77.4 4474
31 72.5 3497
32 88.6 4657
33 85.4 2934
34 83.1 2095
35 70.3 2682
36 83.2 3327
37 72.8 3172
38 83.5 7462
39 87.5 6092
40 77.6 3437
41 81.5 3387
42 79.5 3508
43 79.9 4843
44 79.9 3768
45 82.5 4355
46 76.8 5160
47 88.9 3033
48 87.7 4253
49 90.7 2702
50 82.5 3626
51 82.6 1883
52 78.0 4923
53 81.3 3249
54 73.8 1671
55 89.5 5308
56 81.0 3665
57 82.2 3152
58 79.8 3678
59 76.2 9699
60 79.8 3451
omvang_niet-blanke_bevolking #kantoormedewerkers #gezinnen_inkomen<$3000
1 8.8 42.6 11.7
2 3.6 50.7 14.4
3 0.8 39.4 12.4
4 27.1 50.2 20.6
5 24.4 43.7 14.3
6 38.5 43.1 25.5
7 3.5 49.2 11.3
8 5.3 40.4 10.5
9 8.1 42.5 12.6
10 6.7 41.0 13.2
11 22.2 41.3 24.2
12 16.3 44.9 10.7
13 13.0 45.7 15.1
14 14.7 44.6 11.4
15 13.1 49.6 13.9
16 14.8 51.2 16.1
17 12.4 44.0 12.0
18 4.7 53.1 12.7
19 15.8 43.5 13.6
20 13.1 33.8 12.4
21 11.5 48.1 18.5
22 5.1 45.2 12.3
23 22.7 41.4 19.5
24 7.2 51.6 9.5
25 21.0 46.9 17.9
26 15.6 46.6 13.2
27 12.6 48.6 13.9
28 2.9 43.7 12.0
29 7.8 48.9 12.3
30 13.1 42.6 17.7
31 36.7 43.3 26.4
32 13.6 47.3 22.4
33 5.8 44.0 9.4
34 2.0 51.9 9.8
35 21.0 46.1 24.1
36 8.8 45.3 12.2
37 31.4 45.5 24.2
38 11.3 48.7 12.4
39 17.5 45.3 13.2
40 8.1 45.5 13.8
41 3.6 50.3 13.5
42 2.2 38.3 15.7
43 2.7 38.6 14.1
44 28.6 49.5 17.5
45 5.0 46.4 10.8
46 17.2 45.1 15.3
47 5.9 51.0 14.0
48 13.7 51.2 12.0
49 3.0 51.9 9.7
50 5.7 54.3 10.1
51 3.4 41.9 12.3
52 3.8 50.5 11.1
53 9.5 43.9 13.6
54 2.5 47.4 13.5
55 25.9 59.7 10.3
56 7.5 51.6 13.2
57 12.1 47.3 10.9
58 1.0 44.8 14.0
59 4.8 42.2 14.5
60 11.7 37.5 13.0
index_olievervuiling index_stikstofoxidevervuiling
1 21 15
2 8 10
3 6 6
4 18 8
5 43 38
6 30 32
7 21 32
8 6 4
9 18 12
10 12 7
11 18 8
12 88 63
13 26 26
14 31 21
15 23 9
16 1 1
17 6 4
18 17 8
19 52 35
20 11 4
21 1 1
22 5 3
23 8 3
24 7 3
25 6 5
26 13 7
27 7 4
28 11 7
29 648 319
30 38 37
31 15 10
32 3 1
33 33 23
34 20 11
35 17 14
36 4 3
37 20 17
38 41 26
39 29 32
40 45 59
41 56 21
42 6 4
43 11 11
44 12 9
45 7 4
46 31 15
47 144 66
48 311 171
49 105 32
50 20 7
51 5 4
52 8 5
53 11 7
54 5 2
55 65 28
56 4 2
57 14 11
58 7 3
59 8 8
60 14 13
index_zwaveldioxidevervuiling luchtvochtigheidgraad
1 59 59
2 39 57
3 33 54
4 24 56
5 206 55
6 72 54
7 62 56
8 4 56
9 37 61
10 20 59
11 27 56
12 278 58
13 146 57
14 64 60
15 15 58
16 1 54
17 16 58
18 28 38
19 124 59
20 11 61
21 1 53
22 10 61
23 5 53
24 10 56
25 1 59
26 33 60
27 4 55
28 32 54
29 130 47
30 193 57
31 34 59
32 1 60
33 125 64
34 26 50
35 78 56
36 8 58
37 1 62
38 108 58
39 161 54
40 263 56
41 44 73
42 18 56
43 89 54
44 48 53
45 18 60
46 68 57
47 20 61
48 86 71
49 3 71
50 20 72
51 20 56
52 25 61
53 25 59
54 11 60
55 102 52
56 1 54
57 42 56
58 8 56
59 49 54
60 39 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept)
5573.72292
Gem_jaarlijkse_neerslag
0.18516
Gem_temp_januari
1.02574
Gem_temp_juli
-1.75174
`Omvang_bevolking_>65jaar`
-66.07797
`#leden_per_huishouden`
-757.87310
`#jaren_onderwijs_personen>22j`
43.01987
huishoudens_met_volledig_uitgeruste_keuken
-15.04110
`bevolking_per_mijl\\302\\262`
-0.02345
`omvang_niet-blanke_bevolking`
-20.75219
`#kantoormedewerkers`
-6.31414
`#gezinnen_inkomen<$3000`
-9.42415
index_olievervuiling
-0.07800
index_stikstofoxidevervuiling
-0.35712
index_zwaveldioxidevervuiling
0.01033
luchtvochtigheidgraad
-2.20669
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-699.98 -123.53 26.57 129.13 468.17
Coefficients:
Estimate Std. Error t value
(Intercept) 5573.72292 3574.61474 1.559
Gem_jaarlijkse_neerslag 0.18516 7.32414 0.025
Gem_temp_januari 1.02574 5.87478 0.175
Gem_temp_juli -1.75174 15.40823 -0.114
`Omvang_bevolking_>65jaar` -66.07797 67.40168 -0.980
`#leden_per_huishouden` -757.87310 539.49343 -1.405
`#jaren_onderwijs_personen>22j` 43.01987 97.55480 0.441
huishoudens_met_volledig_uitgeruste_keuken -15.04110 12.76014 -1.179
`bevolking_per_mijl\\302\\262` -0.02345 0.03588 -0.654
`omvang_niet-blanke_bevolking` -20.75219 11.15857 -1.860
`#kantoormedewerkers` -6.31414 13.49535 -0.468
`#gezinnen_inkomen<$3000` -9.42415 22.31875 -0.422
index_olievervuiling -0.07800 3.93637 -0.020
index_stikstofoxidevervuiling -0.35712 8.13100 -0.044
index_zwaveldioxidevervuiling 0.01033 1.23796 0.008
luchtvochtigheidgraad -2.20669 9.15047 -0.241
Pr(>|t|)
(Intercept) 0.1261
Gem_jaarlijkse_neerslag 0.9799
Gem_temp_januari 0.8622
Gem_temp_juli 0.9100
`Omvang_bevolking_>65jaar` 0.3323
`#leden_per_huishouden` 0.1671
`#jaren_onderwijs_personen>22j` 0.6614
huishoudens_met_volledig_uitgeruste_keuken 0.2448
`bevolking_per_mijl\\302\\262` 0.5168
`omvang_niet-blanke_bevolking` 0.0696 .
`#kantoormedewerkers` 0.6422
`#gezinnen_inkomen<$3000` 0.6749
index_olievervuiling 0.9843
index_stikstofoxidevervuiling 0.9652
index_zwaveldioxidevervuiling 0.9934
luchtvochtigheidgraad 0.8106
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 281.3 on 44 degrees of freedom
Multiple R-squared: 0.39, Adjusted R-squared: 0.182
F-statistic: 1.875 on 15 and 44 DF, p-value: 0.05356
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$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,] 0.9816997 0.03660059 0.01830030
[2,] 0.9622344 0.07553120 0.03776560
[3,] 0.9372779 0.12544425 0.06272213
[4,] 0.9409840 0.11803196 0.05901598
[5,] 0.9135296 0.17294071 0.08647035
[6,] 0.8913640 0.21727198 0.10863599
[7,] 0.8668088 0.26638231 0.13319115
[8,] 0.8388511 0.32229777 0.16114888
[9,] 0.7642448 0.47151050 0.23575525
[10,] 0.6837686 0.63246271 0.31623136
[11,] 0.6554647 0.68907059 0.34453530
[12,] 0.5754830 0.84903402 0.42451701
[13,] 0.5057693 0.98846138 0.49423069
[14,] 0.6224003 0.75519937 0.37759968
[15,] 0.5382801 0.92343971 0.46171986
[16,] 0.4589204 0.91784081 0.54107960
[17,] 0.4415896 0.88317926 0.55841037
[18,] 0.3951921 0.79038423 0.60480788
[19,] 0.7396910 0.52061798 0.26030899
[20,] 0.7151079 0.56978421 0.28489210
[21,] 0.5939247 0.81215060 0.40607530
[22,] 0.6351024 0.72979520 0.36489760
[23,] 0.5036914 0.99261715 0.49630858
> postscript(file="/var/wessaorg/rcomp/tmp/1v06h1353343855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/20fxk1353343855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3zzin1353343855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/41e5i1353343855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/581u41353343855.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 = 60
Frequency = 1
1 2 3 4 5 6
13.7230910 105.0291290 9.3079846 468.1734221 -202.7068682 -175.5892391
7 8 9 10 11 12
96.2487073 -0.8612076 -629.9814912 91.2063553 -550.1327905 -571.7702018
13 14 15 16 17 18
181.3127065 302.9505324 92.3167014 -79.3170160 104.5446592 -184.2791783
19 20 21 22 23 24
397.8060162 124.7062319 -62.2899459 37.0232772 221.6872545 34.9365955
25 26 27 28 29 30
172.8741840 186.4478905 -33.1815235 17.4310458 29.7161921 291.4686035
31 32 33 34 35 36
-111.6161458 142.4038922 15.5088704 -156.4273482 311.5080139 61.2030172
37 38 39 40 41 42
-287.3050494 219.6398180 -350.7363123 173.5123114 -115.4017197 -18.2222817
43 44 45 46 47 48
-1.9457570 -316.7527890 46.0453623 318.5988469 -197.6862234 71.7464258
49 50 51 52 53 54
-147.8987047 -166.8658358 -58.6997294 86.9069425 123.1283712 -17.9459715
55 56 57 58 59 60
438.8981162 -196.3007939 -699.9790140 23.4264890 70.0213873 252.4346937
> postscript(file="/var/wessaorg/rcomp/tmp/6xnte1353343855.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 13.7230910 NA
1 105.0291290 13.7230910
2 9.3079846 105.0291290
3 468.1734221 9.3079846
4 -202.7068682 468.1734221
5 -175.5892391 -202.7068682
6 96.2487073 -175.5892391
7 -0.8612076 96.2487073
8 -629.9814912 -0.8612076
9 91.2063553 -629.9814912
10 -550.1327905 91.2063553
11 -571.7702018 -550.1327905
12 181.3127065 -571.7702018
13 302.9505324 181.3127065
14 92.3167014 302.9505324
15 -79.3170160 92.3167014
16 104.5446592 -79.3170160
17 -184.2791783 104.5446592
18 397.8060162 -184.2791783
19 124.7062319 397.8060162
20 -62.2899459 124.7062319
21 37.0232772 -62.2899459
22 221.6872545 37.0232772
23 34.9365955 221.6872545
24 172.8741840 34.9365955
25 186.4478905 172.8741840
26 -33.1815235 186.4478905
27 17.4310458 -33.1815235
28 29.7161921 17.4310458
29 291.4686035 29.7161921
30 -111.6161458 291.4686035
31 142.4038922 -111.6161458
32 15.5088704 142.4038922
33 -156.4273482 15.5088704
34 311.5080139 -156.4273482
35 61.2030172 311.5080139
36 -287.3050494 61.2030172
37 219.6398180 -287.3050494
38 -350.7363123 219.6398180
39 173.5123114 -350.7363123
40 -115.4017197 173.5123114
41 -18.2222817 -115.4017197
42 -1.9457570 -18.2222817
43 -316.7527890 -1.9457570
44 46.0453623 -316.7527890
45 318.5988469 46.0453623
46 -197.6862234 318.5988469
47 71.7464258 -197.6862234
48 -147.8987047 71.7464258
49 -166.8658358 -147.8987047
50 -58.6997294 -166.8658358
51 86.9069425 -58.6997294
52 123.1283712 86.9069425
53 -17.9459715 123.1283712
54 438.8981162 -17.9459715
55 -196.3007939 438.8981162
56 -699.9790140 -196.3007939
57 23.4264890 -699.9790140
58 70.0213873 23.4264890
59 252.4346937 70.0213873
60 NA 252.4346937
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 105.0291290 13.7230910
[2,] 9.3079846 105.0291290
[3,] 468.1734221 9.3079846
[4,] -202.7068682 468.1734221
[5,] -175.5892391 -202.7068682
[6,] 96.2487073 -175.5892391
[7,] -0.8612076 96.2487073
[8,] -629.9814912 -0.8612076
[9,] 91.2063553 -629.9814912
[10,] -550.1327905 91.2063553
[11,] -571.7702018 -550.1327905
[12,] 181.3127065 -571.7702018
[13,] 302.9505324 181.3127065
[14,] 92.3167014 302.9505324
[15,] -79.3170160 92.3167014
[16,] 104.5446592 -79.3170160
[17,] -184.2791783 104.5446592
[18,] 397.8060162 -184.2791783
[19,] 124.7062319 397.8060162
[20,] -62.2899459 124.7062319
[21,] 37.0232772 -62.2899459
[22,] 221.6872545 37.0232772
[23,] 34.9365955 221.6872545
[24,] 172.8741840 34.9365955
[25,] 186.4478905 172.8741840
[26,] -33.1815235 186.4478905
[27,] 17.4310458 -33.1815235
[28,] 29.7161921 17.4310458
[29,] 291.4686035 29.7161921
[30,] -111.6161458 291.4686035
[31,] 142.4038922 -111.6161458
[32,] 15.5088704 142.4038922
[33,] -156.4273482 15.5088704
[34,] 311.5080139 -156.4273482
[35,] 61.2030172 311.5080139
[36,] -287.3050494 61.2030172
[37,] 219.6398180 -287.3050494
[38,] -350.7363123 219.6398180
[39,] 173.5123114 -350.7363123
[40,] -115.4017197 173.5123114
[41,] -18.2222817 -115.4017197
[42,] -1.9457570 -18.2222817
[43,] -316.7527890 -1.9457570
[44,] 46.0453623 -316.7527890
[45,] 318.5988469 46.0453623
[46,] -197.6862234 318.5988469
[47,] 71.7464258 -197.6862234
[48,] -147.8987047 71.7464258
[49,] -166.8658358 -147.8987047
[50,] -58.6997294 -166.8658358
[51,] 86.9069425 -58.6997294
[52,] 123.1283712 86.9069425
[53,] -17.9459715 123.1283712
[54,] 438.8981162 -17.9459715
[55,] -196.3007939 438.8981162
[56,] -699.9790140 -196.3007939
[57,] 23.4264890 -699.9790140
[58,] 70.0213873 23.4264890
[59,] 252.4346937 70.0213873
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 105.0291290 13.7230910
2 9.3079846 105.0291290
3 468.1734221 9.3079846
4 -202.7068682 468.1734221
5 -175.5892391 -202.7068682
6 96.2487073 -175.5892391
7 -0.8612076 96.2487073
8 -629.9814912 -0.8612076
9 91.2063553 -629.9814912
10 -550.1327905 91.2063553
11 -571.7702018 -550.1327905
12 181.3127065 -571.7702018
13 302.9505324 181.3127065
14 92.3167014 302.9505324
15 -79.3170160 92.3167014
16 104.5446592 -79.3170160
17 -184.2791783 104.5446592
18 397.8060162 -184.2791783
19 124.7062319 397.8060162
20 -62.2899459 124.7062319
21 37.0232772 -62.2899459
22 221.6872545 37.0232772
23 34.9365955 221.6872545
24 172.8741840 34.9365955
25 186.4478905 172.8741840
26 -33.1815235 186.4478905
27 17.4310458 -33.1815235
28 29.7161921 17.4310458
29 291.4686035 29.7161921
30 -111.6161458 291.4686035
31 142.4038922 -111.6161458
32 15.5088704 142.4038922
33 -156.4273482 15.5088704
34 311.5080139 -156.4273482
35 61.2030172 311.5080139
36 -287.3050494 61.2030172
37 219.6398180 -287.3050494
38 -350.7363123 219.6398180
39 173.5123114 -350.7363123
40 -115.4017197 173.5123114
41 -18.2222817 -115.4017197
42 -1.9457570 -18.2222817
43 -316.7527890 -1.9457570
44 46.0453623 -316.7527890
45 318.5988469 46.0453623
46 -197.6862234 318.5988469
47 71.7464258 -197.6862234
48 -147.8987047 71.7464258
49 -166.8658358 -147.8987047
50 -58.6997294 -166.8658358
51 86.9069425 -58.6997294
52 123.1283712 86.9069425
53 -17.9459715 123.1283712
54 438.8981162 -17.9459715
55 -196.3007939 438.8981162
56 -699.9790140 -196.3007939
57 23.4264890 -699.9790140
58 70.0213873 23.4264890
59 252.4346937 70.0213873
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7i7f61353343855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8j0ku1353343855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9cxtz1353343855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10357x1353343855.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11142z1353343855.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12rzb31353343855.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13qfk91353343855.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/148u0j1353343855.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15ceu51353343855.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/161kj51353343855.tab")
+ }
>
> try(system("convert tmp/1v06h1353343855.ps tmp/1v06h1353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/20fxk1353343855.ps tmp/20fxk1353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zzin1353343855.ps tmp/3zzin1353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/41e5i1353343855.ps tmp/41e5i1353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/581u41353343855.ps tmp/581u41353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xnte1353343855.ps tmp/6xnte1353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/7i7f61353343855.ps tmp/7i7f61353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/8j0ku1353343855.ps tmp/8j0ku1353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cxtz1353343855.ps tmp/9cxtz1353343855.png",intern=TRUE))
character(0)
> try(system("convert tmp/10357x1353343855.ps tmp/10357x1353343855.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.420 0.918 7.382