R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'contributors()' for more information and
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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(38
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+ ,12)
+ ,dim=c(7
+ ,126)
+ ,dimnames=list(c('CM+D'
+ ,'PE+PC'
+ ,'happiness'
+ ,'depression'
+ ,'connected'
+ ,'separated'
+ ,'populariteit')
+ ,1:126))
> y <- array(NA,dim=c(7,126),dimnames=list(c('CM+D','PE+PC','happiness','depression','connected','separated','populariteit'),1:126))
> 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 = 'Do not include Seasonal 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)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
CM+D PE+PC happiness depression connected separated populariteit t
1 38 23 10 11 35 37 12 1
2 36 15 10 11 35 37 12 2
3 23 25 10 11 35 37 12 3
4 30 18 10 11 35 37 12 4
5 26 21 10 11 35 37 12 5
6 26 19 10 11 35 37 12 6
7 30 15 13 12 38 34 12 7
8 27 22 10 11 35 37 12 8
9 34 19 10 11 35 37 14 9
10 28 20 13 9 34 32 12 10
11 36 26 10 11 35 37 12 11
12 42 26 10 11 35 37 12 12
13 31 21 10 11 35 37 14 13
14 26 19 10 11 35 37 12 14
15 16 19 13 12 38 34 12 15
16 23 19 10 11 35 37 14 16
17 45 28 10 11 35 37 12 17
18 30 27 10 11 35 37 15 18
19 45 18 10 11 35 37 12 19
20 30 19 10 11 35 37 15 20
21 24 24 10 11 35 37 12 21
22 29 21 13 12 38 34 12 22
23 30 22 13 9 34 32 12 23
24 31 25 10 11 35 37 14 24
25 34 15 10 11 35 37 14 25
26 41 34 10 11 35 37 12 26
27 37 23 10 11 35 37 12 27
28 33 19 10 11 35 37 12 28
29 48 15 10 11 35 37 14 29
30 44 15 10 11 35 37 15 30
31 29 17 10 11 35 37 14 31
32 44 30 13 9 34 32 12 32
33 43 28 10 11 35 37 14 33
34 31 23 10 11 35 37 14 34
35 28 23 10 11 35 37 12 35
36 26 21 10 11 35 37 14 36
37 30 18 10 11 35 37 12 37
38 27 19 15 11 33 36 12 38
39 34 24 10 11 35 37 12 39
40 47 15 10 11 35 37 12 40
41 37 24 13 16 34 36 12 41
42 27 20 10 11 35 37 12 42
43 30 20 10 11 35 37 12 43
44 36 44 10 11 35 37 14 44
45 39 20 10 11 35 37 12 45
46 32 20 10 11 35 37 12 46
47 25 20 10 11 35 37 12 47
48 19 11 10 11 35 37 12 48
49 29 21 10 11 35 37 12 49
50 26 21 13 9 34 32 12 50
51 31 19 13 12 38 34 12 51
52 31 21 10 11 35 37 12 52
53 31 17 10 11 35 37 15 53
54 39 19 10 11 35 37 12 54
55 28 21 10 11 35 37 12 55
56 22 16 10 11 35 37 12 56
57 31 19 10 11 35 37 12 57
58 36 19 10 11 35 37 14 58
59 28 16 10 11 35 37 12 59
60 39 24 10 11 35 37 12 60
61 35 21 10 11 35 37 12 61
62 33 20 10 11 35 37 12 62
63 27 19 10 11 35 37 12 63
64 33 23 10 11 35 37 12 64
65 31 18 10 11 35 37 12 65
66 39 19 10 11 35 37 14 66
67 37 23 10 11 35 37 14 67
68 24 19 10 11 35 37 15 68
69 28 26 13 12 38 34 12 69
70 37 13 13 12 38 34 12 70
71 32 23 10 11 35 37 14 71
72 31 16 13 12 38 34 12 72
73 29 17 13 12 38 34 12 73
74 40 30 10 11 35 37 12 74
75 40 22 10 11 35 37 14 75
76 15 14 10 11 35 37 12 76
77 27 14 13 9 34 32 12 77
78 32 21 13 9 34 32 12 78
79 28 21 10 11 35 37 12 79
80 41 33 10 11 35 37 14 80
81 47 23 10 11 35 37 12 81
82 42 30 10 11 35 37 12 82
83 32 21 11 17 36 35 12 83
84 33 25 10 11 35 37 15 84
85 29 29 10 11 35 37 12 85
86 37 21 10 11 35 37 14 86
87 39 16 10 11 35 37 15 87
88 29 17 10 11 35 37 12 88
89 33 23 10 11 35 37 12 89
90 31 18 13 9 34 32 12 90
91 21 19 10 11 35 37 15 91
92 36 28 10 11 35 37 14 92
93 32 29 10 11 35 37 14 93
94 15 19 10 11 35 37 12 94
95 25 25 13 9 34 32 12 95
96 28 15 10 11 35 37 12 96
97 39 24 10 11 35 37 12 97
98 31 12 13 9 34 32 12 98
99 40 11 10 11 35 37 12 99
100 25 19 10 11 35 37 12 100
101 36 25 10 11 35 37 14 101
102 23 12 10 11 35 37 14 102
103 39 15 10 11 35 37 12 103
104 31 25 10 11 35 37 14 104
105 23 14 10 11 35 37 12 105
106 31 19 10 11 35 37 14 106
107 28 23 13 9 34 32 12 107
108 47 19 13 9 34 32 12 108
109 25 20 10 11 35 37 15 109
110 26 16 13 9 34 32 12 110
111 24 13 12 18 32 35 12 111
112 30 22 10 11 35 37 15 112
113 25 21 13 16 34 36 12 113
114 44 18 15 13 34 31 12 114
115 38 44 10 11 35 37 15 115
116 36 12 10 11 35 37 12 116
117 34 28 13 12 38 34 12 117
118 45 17 13 16 34 36 12 118
119 29 18 10 11 35 37 14 119
120 25 21 10 11 35 37 12 120
121 30 24 10 11 35 37 12 121
122 27 20 10 11 35 37 16 122
123 44 24 10 11 35 37 14 123
124 31 33 10 11 35 37 12 124
125 35 25 10 11 35 37 12 125
126 47 35 10 11 35 37 12 126
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `PE+PC` happiness depression connected
44.174476 0.355225 -0.223616 0.253261 -0.457391
separated populariteit t
-0.134164 0.073103 0.001736
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-16.541 -4.570 -1.049 4.335 17.846
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.174476 50.901410 0.868 0.38724
`PE+PC` 0.355225 0.112263 3.164 0.00198 **
happiness -0.223616 1.298405 -0.172 0.86356
depression 0.253261 0.599177 0.423 0.67330
connected -0.457391 0.676092 -0.677 0.50003
separated -0.134164 1.036453 -0.129 0.89723
populariteit 0.073103 0.598537 0.122 0.90300
t 0.001736 0.017724 0.098 0.92216
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.949 on 118 degrees of freedom
Multiple R-squared: 0.08641, Adjusted R-squared: 0.03221
F-statistic: 1.594 on 7 and 118 DF, p-value: 0.1436
> 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,] 0.77417955 0.4516409 0.2258204
[2,] 0.81491455 0.3701709 0.1850854
[3,] 0.72975509 0.5404898 0.2702449
[4,] 0.67958002 0.6408400 0.3204200
[5,] 0.76036371 0.4792726 0.2396363
[6,] 0.73521251 0.5295750 0.2647875
[7,] 0.83539785 0.3292043 0.1646022
[8,] 0.78085791 0.4382842 0.2191421
[9,] 0.85789324 0.2842135 0.1421068
[10,] 0.80528885 0.3894223 0.1947111
[11,] 0.87660437 0.2467913 0.1233956
[12,] 0.84092862 0.3181428 0.1590714
[13,] 0.79199725 0.4160055 0.2080027
[14,] 0.73803874 0.5239225 0.2619613
[15,] 0.68235590 0.6352882 0.3176441
[16,] 0.62839773 0.7432045 0.3716023
[17,] 0.56068953 0.8786209 0.4393105
[18,] 0.49937254 0.9987451 0.5006275
[19,] 0.71249463 0.5750107 0.2875054
[20,] 0.75895650 0.4820870 0.2410435
[21,] 0.76280838 0.4743832 0.2371916
[22,] 0.77789457 0.4442109 0.2221054
[23,] 0.76403185 0.4719363 0.2359681
[24,] 0.75439700 0.4912060 0.2456030
[25,] 0.79051804 0.4189639 0.2094820
[26,] 0.81555443 0.3688911 0.1844456
[27,] 0.78963738 0.4207252 0.2103626
[28,] 0.76038122 0.4792376 0.2396188
[29,] 0.71470457 0.5705909 0.2852954
[30,] 0.83459375 0.3308125 0.1654063
[31,] 0.79565379 0.4086924 0.2043462
[32,] 0.81078285 0.3784343 0.1892172
[33,] 0.78789115 0.4242177 0.2121088
[34,] 0.75337097 0.4932581 0.2466290
[35,] 0.73564580 0.5287084 0.2643542
[36,] 0.69757175 0.6048565 0.3024282
[37,] 0.72256613 0.5548677 0.2774339
[38,] 0.79191494 0.4161701 0.2080851
[39,] 0.76026130 0.4794774 0.2397387
[40,] 0.75302264 0.4939547 0.2469774
[41,] 0.72055859 0.5588828 0.2794414
[42,] 0.67454694 0.6509061 0.3254531
[43,] 0.62588503 0.7482299 0.3741150
[44,] 0.62589878 0.7482024 0.3741012
[45,] 0.59468530 0.8106294 0.4053147
[46,] 0.62100382 0.7579924 0.3789962
[47,] 0.56857017 0.8628597 0.4314298
[48,] 0.53180428 0.9363914 0.4681957
[49,] 0.48465310 0.9693062 0.5153469
[50,] 0.46527391 0.9305478 0.5347261
[51,] 0.41921617 0.8384323 0.5807838
[52,] 0.36816454 0.7363291 0.6318355
[53,] 0.33896894 0.6779379 0.6610311
[54,] 0.29077819 0.5815564 0.7092218
[55,] 0.24611491 0.4922298 0.7538851
[56,] 0.24656379 0.4931276 0.7534362
[57,] 0.21855016 0.4371003 0.7814498
[58,] 0.23019168 0.4603834 0.7698083
[59,] 0.21651685 0.4330337 0.7834832
[60,] 0.23764251 0.4752850 0.7623575
[61,] 0.19905642 0.3981128 0.8009436
[62,] 0.16526376 0.3305275 0.8347362
[63,] 0.13785321 0.2757064 0.8621468
[64,] 0.12025931 0.2405186 0.8797407
[65,] 0.12198667 0.2439733 0.8780133
[66,] 0.23859220 0.4771844 0.7614078
[67,] 0.20466210 0.4093242 0.7953379
[68,] 0.16846483 0.3369297 0.8315352
[69,] 0.14940332 0.2988066 0.8505967
[70,] 0.12930222 0.2586044 0.8706978
[71,] 0.22958576 0.4591715 0.7704142
[72,] 0.23629295 0.4725859 0.7637071
[73,] 0.21256913 0.4251383 0.7874309
[74,] 0.17945198 0.3589040 0.8205480
[75,] 0.15969257 0.3193851 0.8403074
[76,] 0.15234261 0.3046852 0.8476574
[77,] 0.20346518 0.4069304 0.7965348
[78,] 0.16632674 0.3326535 0.8336733
[79,] 0.13805570 0.2761114 0.8619443
[80,] 0.10759845 0.2151969 0.8924015
[81,] 0.11863634 0.2372727 0.8813637
[82,] 0.10671068 0.2134214 0.8932893
[83,] 0.08751513 0.1750303 0.9124849
[84,] 0.18817050 0.3763410 0.8118295
[85,] 0.21023347 0.4204669 0.7897665
[86,] 0.16957014 0.3391403 0.8304299
[87,] 0.15961028 0.3192206 0.8403897
[88,] 0.12591847 0.2518369 0.8740815
[89,] 0.20045629 0.4009126 0.7995437
[90,] 0.17119469 0.3423894 0.8288053
[91,] 0.15564204 0.3112841 0.8443580
[92,] 0.12568722 0.2513744 0.8743128
[93,] 0.20451957 0.4090391 0.7954804
[94,] 0.17133602 0.3426720 0.8286640
[95,] 0.13076754 0.2615351 0.8692325
[96,] 0.12134003 0.2426801 0.8786600
[97,] 0.11903599 0.2380720 0.8809640
[98,] 0.24848143 0.4969629 0.7515186
[99,] 0.18889527 0.3777905 0.8111047
[100,] 0.19122816 0.3824563 0.8087718
[101,] 0.13169339 0.2633868 0.8683066
[102,] 0.08673177 0.1734635 0.9132682
[103,] 0.17305552 0.3461110 0.8269445
[104,] 0.11517405 0.2303481 0.8848260
[105,] 0.06585746 0.1317149 0.9341425
> postscript(file="/var/www/html/freestat/rcomp/tmp/1gyt01290517274.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2gyt01290517274.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3r7sl1290517274.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4r7sl1290517274.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5jyao1290517274.ps",horizontal=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 = 126
Frequency = 1
1 2 3 4 5 6
5.19941417 6.03948122 -10.51450777 -1.02966605 -6.09707769 -5.38836265
7 8 9 10 11 12
1.41807196 -5.45750992 2.46022519 -3.70136908 2.11638184 8.11464620
13 14 15 16 17 18
-1.25716801 -5.40224771 -14.01671444 -8.55192423 10.39551737 -4.47030081
19 20 21 22 23 24
13.94429946 -1.63196939 -9.19052382 -1.73931454 -2.43438298 -2.69716131
25 26 27 28 29 30
3.85335641 4.24854467 4.15428772 1.57345343 17.84641388 13.77157562
31 32 33 34 35 36
-1.86750805 8.70819365 8.22154200 -2.00406696 -4.85959734 -6.29708756
37 38 39 40 41 42
-1.08694193 -4.37476658 0.77823479 16.97352718 2.58775045 -4.80607076
43 44 45 46 47 48
-1.80780640 -4.48115533 7.18872234 0.18698671 -6.81474893 -9.61945654
49 50 51 52 53 54
-3.17344553 -6.12601972 0.92080279 -1.17865242 0.02120540 7.52832698
55 56 57 58 59 60
-4.18385932 -8.40946828 -0.47687992 4.37517920 -2.41467518 5.74178651
61 62 63 64 65 66
2.80572688 1.15921659 -4.48729371 0.09006931 -0.13553964 7.36129414
67 68 69 70 71 72
3.93865716 -7.71527976 -4.59701595 9.01917778 -1.06828537 1.95003051
73 74 75 76 77 78
-0.40693046 4.58613564 7.27999744 -14.73373026 -2.68630446 -0.17461743
79 80 81 82 83 84
-4.22551450 4.36384059 14.06056356 6.57225058 -1.33934652 -0.87440189
85 86 87 88 89 90
-6.07773098 4.61613081 8.31741923 -1.82023386 0.04667850 -0.12976902
91 92 93 94 95 96
-10.75519931 1.11913967 -3.23782130 -16.54109832 -8.62502453 -2.12366825
97 98 99 100 101 102
5.67756810 1.98769793 11.29202620 -6.55151212 2.16919498 -6.21461129
103 104 105 106 107 108
8.86418232 -2.83601191 -6.78406361 -0.70813117 -4.93540145 15.48376426
109 110 111 112 113 114
-7.14166603 -4.45403100 -8.40535057 -2.85732360 -8.47153909 12.12859875
115 116 117 118 119 120
-2.67748787 6.90729511 0.60922302 12.94068409 -2.37546906 -7.29667544
121 122 123 124 125 126
-3.36408708 -5.23733188 10.48623640 -5.56632199 1.27374505 9.71975607
> postscript(file="/var/www/html/freestat/rcomp/tmp/6jyao1290517274.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 126
Frequency = 1
lag(myerror, k = 1) myerror
0 5.19941417 NA
1 6.03948122 5.19941417
2 -10.51450777 6.03948122
3 -1.02966605 -10.51450777
4 -6.09707769 -1.02966605
5 -5.38836265 -6.09707769
6 1.41807196 -5.38836265
7 -5.45750992 1.41807196
8 2.46022519 -5.45750992
9 -3.70136908 2.46022519
10 2.11638184 -3.70136908
11 8.11464620 2.11638184
12 -1.25716801 8.11464620
13 -5.40224771 -1.25716801
14 -14.01671444 -5.40224771
15 -8.55192423 -14.01671444
16 10.39551737 -8.55192423
17 -4.47030081 10.39551737
18 13.94429946 -4.47030081
19 -1.63196939 13.94429946
20 -9.19052382 -1.63196939
21 -1.73931454 -9.19052382
22 -2.43438298 -1.73931454
23 -2.69716131 -2.43438298
24 3.85335641 -2.69716131
25 4.24854467 3.85335641
26 4.15428772 4.24854467
27 1.57345343 4.15428772
28 17.84641388 1.57345343
29 13.77157562 17.84641388
30 -1.86750805 13.77157562
31 8.70819365 -1.86750805
32 8.22154200 8.70819365
33 -2.00406696 8.22154200
34 -4.85959734 -2.00406696
35 -6.29708756 -4.85959734
36 -1.08694193 -6.29708756
37 -4.37476658 -1.08694193
38 0.77823479 -4.37476658
39 16.97352718 0.77823479
40 2.58775045 16.97352718
41 -4.80607076 2.58775045
42 -1.80780640 -4.80607076
43 -4.48115533 -1.80780640
44 7.18872234 -4.48115533
45 0.18698671 7.18872234
46 -6.81474893 0.18698671
47 -9.61945654 -6.81474893
48 -3.17344553 -9.61945654
49 -6.12601972 -3.17344553
50 0.92080279 -6.12601972
51 -1.17865242 0.92080279
52 0.02120540 -1.17865242
53 7.52832698 0.02120540
54 -4.18385932 7.52832698
55 -8.40946828 -4.18385932
56 -0.47687992 -8.40946828
57 4.37517920 -0.47687992
58 -2.41467518 4.37517920
59 5.74178651 -2.41467518
60 2.80572688 5.74178651
61 1.15921659 2.80572688
62 -4.48729371 1.15921659
63 0.09006931 -4.48729371
64 -0.13553964 0.09006931
65 7.36129414 -0.13553964
66 3.93865716 7.36129414
67 -7.71527976 3.93865716
68 -4.59701595 -7.71527976
69 9.01917778 -4.59701595
70 -1.06828537 9.01917778
71 1.95003051 -1.06828537
72 -0.40693046 1.95003051
73 4.58613564 -0.40693046
74 7.27999744 4.58613564
75 -14.73373026 7.27999744
76 -2.68630446 -14.73373026
77 -0.17461743 -2.68630446
78 -4.22551450 -0.17461743
79 4.36384059 -4.22551450
80 14.06056356 4.36384059
81 6.57225058 14.06056356
82 -1.33934652 6.57225058
83 -0.87440189 -1.33934652
84 -6.07773098 -0.87440189
85 4.61613081 -6.07773098
86 8.31741923 4.61613081
87 -1.82023386 8.31741923
88 0.04667850 -1.82023386
89 -0.12976902 0.04667850
90 -10.75519931 -0.12976902
91 1.11913967 -10.75519931
92 -3.23782130 1.11913967
93 -16.54109832 -3.23782130
94 -8.62502453 -16.54109832
95 -2.12366825 -8.62502453
96 5.67756810 -2.12366825
97 1.98769793 5.67756810
98 11.29202620 1.98769793
99 -6.55151212 11.29202620
100 2.16919498 -6.55151212
101 -6.21461129 2.16919498
102 8.86418232 -6.21461129
103 -2.83601191 8.86418232
104 -6.78406361 -2.83601191
105 -0.70813117 -6.78406361
106 -4.93540145 -0.70813117
107 15.48376426 -4.93540145
108 -7.14166603 15.48376426
109 -4.45403100 -7.14166603
110 -8.40535057 -4.45403100
111 -2.85732360 -8.40535057
112 -8.47153909 -2.85732360
113 12.12859875 -8.47153909
114 -2.67748787 12.12859875
115 6.90729511 -2.67748787
116 0.60922302 6.90729511
117 12.94068409 0.60922302
118 -2.37546906 12.94068409
119 -7.29667544 -2.37546906
120 -3.36408708 -7.29667544
121 -5.23733188 -3.36408708
122 10.48623640 -5.23733188
123 -5.56632199 10.48623640
124 1.27374505 -5.56632199
125 9.71975607 1.27374505
126 NA 9.71975607
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.03948122 5.19941417
[2,] -10.51450777 6.03948122
[3,] -1.02966605 -10.51450777
[4,] -6.09707769 -1.02966605
[5,] -5.38836265 -6.09707769
[6,] 1.41807196 -5.38836265
[7,] -5.45750992 1.41807196
[8,] 2.46022519 -5.45750992
[9,] -3.70136908 2.46022519
[10,] 2.11638184 -3.70136908
[11,] 8.11464620 2.11638184
[12,] -1.25716801 8.11464620
[13,] -5.40224771 -1.25716801
[14,] -14.01671444 -5.40224771
[15,] -8.55192423 -14.01671444
[16,] 10.39551737 -8.55192423
[17,] -4.47030081 10.39551737
[18,] 13.94429946 -4.47030081
[19,] -1.63196939 13.94429946
[20,] -9.19052382 -1.63196939
[21,] -1.73931454 -9.19052382
[22,] -2.43438298 -1.73931454
[23,] -2.69716131 -2.43438298
[24,] 3.85335641 -2.69716131
[25,] 4.24854467 3.85335641
[26,] 4.15428772 4.24854467
[27,] 1.57345343 4.15428772
[28,] 17.84641388 1.57345343
[29,] 13.77157562 17.84641388
[30,] -1.86750805 13.77157562
[31,] 8.70819365 -1.86750805
[32,] 8.22154200 8.70819365
[33,] -2.00406696 8.22154200
[34,] -4.85959734 -2.00406696
[35,] -6.29708756 -4.85959734
[36,] -1.08694193 -6.29708756
[37,] -4.37476658 -1.08694193
[38,] 0.77823479 -4.37476658
[39,] 16.97352718 0.77823479
[40,] 2.58775045 16.97352718
[41,] -4.80607076 2.58775045
[42,] -1.80780640 -4.80607076
[43,] -4.48115533 -1.80780640
[44,] 7.18872234 -4.48115533
[45,] 0.18698671 7.18872234
[46,] -6.81474893 0.18698671
[47,] -9.61945654 -6.81474893
[48,] -3.17344553 -9.61945654
[49,] -6.12601972 -3.17344553
[50,] 0.92080279 -6.12601972
[51,] -1.17865242 0.92080279
[52,] 0.02120540 -1.17865242
[53,] 7.52832698 0.02120540
[54,] -4.18385932 7.52832698
[55,] -8.40946828 -4.18385932
[56,] -0.47687992 -8.40946828
[57,] 4.37517920 -0.47687992
[58,] -2.41467518 4.37517920
[59,] 5.74178651 -2.41467518
[60,] 2.80572688 5.74178651
[61,] 1.15921659 2.80572688
[62,] -4.48729371 1.15921659
[63,] 0.09006931 -4.48729371
[64,] -0.13553964 0.09006931
[65,] 7.36129414 -0.13553964
[66,] 3.93865716 7.36129414
[67,] -7.71527976 3.93865716
[68,] -4.59701595 -7.71527976
[69,] 9.01917778 -4.59701595
[70,] -1.06828537 9.01917778
[71,] 1.95003051 -1.06828537
[72,] -0.40693046 1.95003051
[73,] 4.58613564 -0.40693046
[74,] 7.27999744 4.58613564
[75,] -14.73373026 7.27999744
[76,] -2.68630446 -14.73373026
[77,] -0.17461743 -2.68630446
[78,] -4.22551450 -0.17461743
[79,] 4.36384059 -4.22551450
[80,] 14.06056356 4.36384059
[81,] 6.57225058 14.06056356
[82,] -1.33934652 6.57225058
[83,] -0.87440189 -1.33934652
[84,] -6.07773098 -0.87440189
[85,] 4.61613081 -6.07773098
[86,] 8.31741923 4.61613081
[87,] -1.82023386 8.31741923
[88,] 0.04667850 -1.82023386
[89,] -0.12976902 0.04667850
[90,] -10.75519931 -0.12976902
[91,] 1.11913967 -10.75519931
[92,] -3.23782130 1.11913967
[93,] -16.54109832 -3.23782130
[94,] -8.62502453 -16.54109832
[95,] -2.12366825 -8.62502453
[96,] 5.67756810 -2.12366825
[97,] 1.98769793 5.67756810
[98,] 11.29202620 1.98769793
[99,] -6.55151212 11.29202620
[100,] 2.16919498 -6.55151212
[101,] -6.21461129 2.16919498
[102,] 8.86418232 -6.21461129
[103,] -2.83601191 8.86418232
[104,] -6.78406361 -2.83601191
[105,] -0.70813117 -6.78406361
[106,] -4.93540145 -0.70813117
[107,] 15.48376426 -4.93540145
[108,] -7.14166603 15.48376426
[109,] -4.45403100 -7.14166603
[110,] -8.40535057 -4.45403100
[111,] -2.85732360 -8.40535057
[112,] -8.47153909 -2.85732360
[113,] 12.12859875 -8.47153909
[114,] -2.67748787 12.12859875
[115,] 6.90729511 -2.67748787
[116,] 0.60922302 6.90729511
[117,] 12.94068409 0.60922302
[118,] -2.37546906 12.94068409
[119,] -7.29667544 -2.37546906
[120,] -3.36408708 -7.29667544
[121,] -5.23733188 -3.36408708
[122,] 10.48623640 -5.23733188
[123,] -5.56632199 10.48623640
[124,] 1.27374505 -5.56632199
[125,] 9.71975607 1.27374505
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.03948122 5.19941417
2 -10.51450777 6.03948122
3 -1.02966605 -10.51450777
4 -6.09707769 -1.02966605
5 -5.38836265 -6.09707769
6 1.41807196 -5.38836265
7 -5.45750992 1.41807196
8 2.46022519 -5.45750992
9 -3.70136908 2.46022519
10 2.11638184 -3.70136908
11 8.11464620 2.11638184
12 -1.25716801 8.11464620
13 -5.40224771 -1.25716801
14 -14.01671444 -5.40224771
15 -8.55192423 -14.01671444
16 10.39551737 -8.55192423
17 -4.47030081 10.39551737
18 13.94429946 -4.47030081
19 -1.63196939 13.94429946
20 -9.19052382 -1.63196939
21 -1.73931454 -9.19052382
22 -2.43438298 -1.73931454
23 -2.69716131 -2.43438298
24 3.85335641 -2.69716131
25 4.24854467 3.85335641
26 4.15428772 4.24854467
27 1.57345343 4.15428772
28 17.84641388 1.57345343
29 13.77157562 17.84641388
30 -1.86750805 13.77157562
31 8.70819365 -1.86750805
32 8.22154200 8.70819365
33 -2.00406696 8.22154200
34 -4.85959734 -2.00406696
35 -6.29708756 -4.85959734
36 -1.08694193 -6.29708756
37 -4.37476658 -1.08694193
38 0.77823479 -4.37476658
39 16.97352718 0.77823479
40 2.58775045 16.97352718
41 -4.80607076 2.58775045
42 -1.80780640 -4.80607076
43 -4.48115533 -1.80780640
44 7.18872234 -4.48115533
45 0.18698671 7.18872234
46 -6.81474893 0.18698671
47 -9.61945654 -6.81474893
48 -3.17344553 -9.61945654
49 -6.12601972 -3.17344553
50 0.92080279 -6.12601972
51 -1.17865242 0.92080279
52 0.02120540 -1.17865242
53 7.52832698 0.02120540
54 -4.18385932 7.52832698
55 -8.40946828 -4.18385932
56 -0.47687992 -8.40946828
57 4.37517920 -0.47687992
58 -2.41467518 4.37517920
59 5.74178651 -2.41467518
60 2.80572688 5.74178651
61 1.15921659 2.80572688
62 -4.48729371 1.15921659
63 0.09006931 -4.48729371
64 -0.13553964 0.09006931
65 7.36129414 -0.13553964
66 3.93865716 7.36129414
67 -7.71527976 3.93865716
68 -4.59701595 -7.71527976
69 9.01917778 -4.59701595
70 -1.06828537 9.01917778
71 1.95003051 -1.06828537
72 -0.40693046 1.95003051
73 4.58613564 -0.40693046
74 7.27999744 4.58613564
75 -14.73373026 7.27999744
76 -2.68630446 -14.73373026
77 -0.17461743 -2.68630446
78 -4.22551450 -0.17461743
79 4.36384059 -4.22551450
80 14.06056356 4.36384059
81 6.57225058 14.06056356
82 -1.33934652 6.57225058
83 -0.87440189 -1.33934652
84 -6.07773098 -0.87440189
85 4.61613081 -6.07773098
86 8.31741923 4.61613081
87 -1.82023386 8.31741923
88 0.04667850 -1.82023386
89 -0.12976902 0.04667850
90 -10.75519931 -0.12976902
91 1.11913967 -10.75519931
92 -3.23782130 1.11913967
93 -16.54109832 -3.23782130
94 -8.62502453 -16.54109832
95 -2.12366825 -8.62502453
96 5.67756810 -2.12366825
97 1.98769793 5.67756810
98 11.29202620 1.98769793
99 -6.55151212 11.29202620
100 2.16919498 -6.55151212
101 -6.21461129 2.16919498
102 8.86418232 -6.21461129
103 -2.83601191 8.86418232
104 -6.78406361 -2.83601191
105 -0.70813117 -6.78406361
106 -4.93540145 -0.70813117
107 15.48376426 -4.93540145
108 -7.14166603 15.48376426
109 -4.45403100 -7.14166603
110 -8.40535057 -4.45403100
111 -2.85732360 -8.40535057
112 -8.47153909 -2.85732360
113 12.12859875 -8.47153909
114 -2.67748787 12.12859875
115 6.90729511 -2.67748787
116 0.60922302 6.90729511
117 12.94068409 0.60922302
118 -2.37546906 12.94068409
119 -7.29667544 -2.37546906
120 -3.36408708 -7.29667544
121 -5.23733188 -3.36408708
122 10.48623640 -5.23733188
123 -5.56632199 10.48623640
124 1.27374505 -5.56632199
125 9.71975607 1.27374505
> 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/freestat/rcomp/tmp/7u7r81290517274.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8u7r81290517274.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9nz8t1290517274.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10nz8t1290517274.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11qz7h1290517274.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/freestat/rcomp/tmp/12ti5n1290517274.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/freestat/rcomp/tmp/13012h1290517274.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/freestat/rcomp/tmp/14m21n1290517274.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/www/html/freestat/rcomp/tmp/15pkhs1290517274.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/www/html/freestat/rcomp/tmp/16lu0t1290517275.tab")
+ }
>
> try(system("convert tmp/1gyt01290517274.ps tmp/1gyt01290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gyt01290517274.ps tmp/2gyt01290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/3r7sl1290517274.ps tmp/3r7sl1290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/4r7sl1290517274.ps tmp/4r7sl1290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jyao1290517274.ps tmp/5jyao1290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jyao1290517274.ps tmp/6jyao1290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u7r81290517274.ps tmp/7u7r81290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u7r81290517274.ps tmp/8u7r81290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nz8t1290517274.ps tmp/9nz8t1290517274.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nz8t1290517274.ps tmp/10nz8t1290517274.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.249 2.691 10.894