R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(13
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+ ,-13
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+ ,2
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+ ,-2
+ ,3
+ ,16
+ ,0
+ ,6)
+ ,dim=c(5
+ ,120)
+ ,dimnames=list(c('IndicatorConsumerConfidence'
+ ,'EconomicSituation'
+ ,'UnemploymentBelgium'
+ ,'FinancialSituationFam'
+ ,'SavingsFam')
+ ,1:120))
> y <- array(NA,dim=c(5,120),dimnames=list(c('IndicatorConsumerConfidence','EconomicSituation','UnemploymentBelgium','FinancialSituationFam','SavingsFam'),1:120))
> 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
> 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
IndicatorConsumerConfidence EconomicSituation UnemploymentBelgium
1 13 15 -13
2 8 3 -2
3 7 2 -1
4 3 -2 5
5 3 1 8
6 4 1 6
7 4 -1 7
8 0 -6 15
9 -4 -13 23
10 -14 -25 43
11 -18 -26 60
12 -8 -9 36
13 -1 1 28
14 1 3 23
15 2 6 23
16 0 2 22
17 1 5 22
18 0 5 24
19 -1 0 32
20 -3 -5 27
21 -3 -4 27
22 -3 -2 27
23 -4 -1 29
24 -8 -8 38
25 -9 -16 40
26 -13 -19 45
27 -18 -28 50
28 -11 -11 43
29 -9 -4 44
30 -10 -9 44
31 -13 -12 49
32 -11 -10 42
33 -5 -2 36
34 -15 -13 57
35 -6 0 42
36 -6 0 39
37 -3 4 33
38 -1 7 32
39 -3 5 34
40 -4 2 37
41 -6 -2 38
42 0 6 28
43 -4 -3 31
44 -2 1 28
45 -2 0 30
46 -6 -7 39
47 -7 -6 38
48 -6 -4 39
49 -6 -4 38
50 -3 -2 37
51 -2 2 32
52 -5 -5 32
53 -11 -15 44
54 -11 -16 43
55 -11 -18 42
56 -10 -13 38
57 -14 -23 37
58 -8 -10 35
59 -9 -10 37
60 -5 -6 33
61 -1 -3 24
62 -2 -4 24
63 -5 -7 31
64 -4 -7 25
65 -6 -7 28
66 -2 -3 24
67 -2 0 25
68 -2 -5 16
69 -2 -3 17
70 2 3 11
71 1 2 12
72 -8 -7 39
73 -1 -1 19
74 1 0 14
75 -1 -3 15
76 2 4 7
77 2 2 12
78 1 3 12
79 -1 0 14
80 -2 -10 9
81 -2 -10 8
82 -1 -9 4
83 -8 -22 7
84 -4 -16 3
85 -6 -18 5
86 -3 -14 0
87 -3 -12 -2
88 -7 -17 6
89 -9 -23 11
90 -11 -28 9
91 -13 -31 17
92 -11 -21 21
93 -9 -19 21
94 -17 -22 41
95 -22 -22 57
96 -25 -25 65
97 -20 -16 68
98 -24 -22 73
99 -24 -21 71
100 -22 -10 71
101 -19 -7 70
102 -18 -5 69
103 -17 -4 65
104 -11 7 57
105 -11 6 57
106 -12 3 57
107 -10 10 55
108 -15 0 65
109 -15 -2 65
110 -15 -1 64
111 -13 2 60
112 -8 8 43
113 -13 -6 47
114 -9 -4 40
115 -7 4 31
116 -4 7 27
117 -4 3 24
118 -2 3 23
119 0 8 17
120 -2 3 16
FinancialSituationFam SavingsFam t
1 11 13 1
2 11 17 2
3 9 17 3
4 8 13 4
5 6 14 5
6 7 13 6
7 8 17 7
8 6 17 8
9 5 15 9
10 2 9 10
11 3 10 11
12 3 9 12
13 7 14 13
14 8 18 14
15 7 18 15
16 7 12 16
17 6 16 17
18 6 12 18
19 7 19 19
20 5 13 20
21 5 12 21
22 5 13 22
23 4 11 23
24 4 10 24
25 4 16 25
26 1 12 26
27 -1 6 27
28 3 8 28
29 4 6 29
30 3 8 30
31 2 8 31
32 1 9 32
33 4 13 33
34 3 8 34
35 5 11 35
36 6 8 36
37 6 10 37
38 6 15 38
39 6 12 39
40 6 13 40
41 5 12 41
42 6 15 42
43 5 13 43
44 6 13 44
45 5 16 45
46 7 14 46
47 4 12 47
48 5 15 48
49 6 14 49
50 6 19 50
51 5 16 51
52 3 16 52
53 2 11 53
54 3 13 54
55 3 12 55
56 2 11 56
57 0 6 57
58 4 9 58
59 4 6 59
60 5 15 60
61 6 17 61
62 6 13 62
63 5 12 63
64 5 13 64
65 3 10 65
66 5 14 66
67 5 13 67
68 5 10 68
69 3 11 69
70 6 12 70
71 6 7 71
72 4 11 72
73 6 9 73
74 5 13 74
75 4 12 75
76 5 5 76
77 5 13 77
78 4 11 78
79 3 8 79
80 2 8 80
81 3 8 81
82 2 8 82
83 -1 0 83
84 0 3 84
85 -2 0 85
86 1 -1 86
87 -2 -1 87
88 -2 -4 88
89 -2 1 89
90 -6 -1 90
91 -4 0 91
92 -2 -1 92
93 0 6 93
94 -5 0 94
95 -4 -3 95
96 -5 -3 96
97 -1 4 97
98 -2 1 98
99 -4 0 99
100 -1 -4 100
101 1 -2 101
102 1 3 102
103 -2 2 103
104 1 5 104
105 1 6 105
106 3 6 106
107 3 3 107
108 1 4 108
109 1 7 109
110 0 5 110
111 2 6 111
112 2 1 112
113 -1 3 113
114 1 6 114
115 0 0 115
116 1 3 116
117 1 4 117
118 3 7 118
119 2 6 119
120 0 6 120
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) EconomicSituation UnemploymentBelgium
0.191400 0.250867 -0.249339
FinancialSituationFam SavingsFam t
0.270905 0.229549 -0.002174
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6912 -0.2647 0.0491 0.2288 0.6577
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.191400 0.204307 0.937 0.351
EconomicSituation 0.250867 0.005537 45.305 <2e-16 ***
UnemploymentBelgium -0.249339 0.001682 -148.276 <2e-16 ***
FinancialSituationFam 0.270905 0.026761 10.123 <2e-16 ***
SavingsFam 0.229549 0.010666 21.522 <2e-16 ***
t -0.002174 0.001574 -1.382 0.170
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3225 on 114 degrees of freedom
Multiple R-squared: 0.998, Adjusted R-squared: 0.9979
F-statistic: 1.133e+04 on 5 and 114 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.6212761 0.75744783 0.37872392
[2,] 0.4613061 0.92261216 0.53869392
[3,] 0.3465808 0.69316165 0.65341918
[4,] 0.2560106 0.51202119 0.74398940
[5,] 0.1832917 0.36658342 0.81670829
[6,] 0.3920442 0.78408833 0.60795583
[7,] 0.3092006 0.61840114 0.69079943
[8,] 0.2281012 0.45620232 0.77189884
[9,] 0.2013622 0.40272443 0.79863778
[10,] 0.1406907 0.28138135 0.85930933
[11,] 0.3162326 0.63246522 0.68376739
[12,] 0.2872685 0.57453702 0.71273149
[13,] 0.2356217 0.47124338 0.76437831
[14,] 0.4085439 0.81708780 0.59145610
[15,] 0.5173568 0.96528636 0.48264318
[16,] 0.5037889 0.99242222 0.49621111
[17,] 0.4321147 0.86422932 0.56788534
[18,] 0.3963490 0.79269794 0.60365103
[19,] 0.3508103 0.70162063 0.64918968
[20,] 0.4467076 0.89341523 0.55329239
[21,] 0.3996924 0.79938484 0.60030758
[22,] 0.3926092 0.78521834 0.60739083
[23,] 0.4634657 0.92693146 0.53653427
[24,] 0.5374399 0.92512010 0.46256005
[25,] 0.5219320 0.95613593 0.47806797
[26,] 0.6102812 0.77943769 0.38971884
[27,] 0.6278736 0.74425274 0.37212637
[28,] 0.5730196 0.85396084 0.42698042
[29,] 0.5232814 0.95343722 0.47671861
[30,] 0.4639068 0.92781355 0.53609323
[31,] 0.4618013 0.92360259 0.53819870
[32,] 0.4036868 0.80737364 0.59631318
[33,] 0.3718858 0.74377157 0.62811422
[34,] 0.3912231 0.78244618 0.60877691
[35,] 0.3455053 0.69101061 0.65449469
[36,] 0.2967312 0.59346245 0.70326877
[37,] 0.3224331 0.64486628 0.67756686
[38,] 0.2961448 0.59228960 0.70385520
[39,] 0.2515047 0.50300934 0.74849533
[40,] 0.2263433 0.45268656 0.77365672
[41,] 0.2765527 0.55310547 0.72344726
[42,] 0.4300904 0.86018089 0.56990956
[43,] 0.4593636 0.91872723 0.54063638
[44,] 0.4403348 0.88066966 0.55966517
[45,] 0.6215470 0.75690608 0.37845304
[46,] 0.5804707 0.83905850 0.41952925
[47,] 0.6204772 0.75904550 0.37952275
[48,] 0.6177531 0.76449379 0.38224689
[49,] 0.5995526 0.80089490 0.40044745
[50,] 0.5530495 0.89390091 0.44695045
[51,] 0.5455225 0.90895507 0.45447754
[52,] 0.4974359 0.99487189 0.50256405
[53,] 0.4769803 0.95396057 0.52301972
[54,] 0.5175073 0.96498543 0.48249272
[55,] 0.5708431 0.85831386 0.42915693
[56,] 0.5632237 0.87355256 0.43677628
[57,] 0.5447519 0.91049617 0.45524808
[58,] 0.5321221 0.93575581 0.46787790
[59,] 0.4813599 0.96271971 0.51864015
[60,] 0.4746310 0.94926194 0.52536903
[61,] 0.4414147 0.88282937 0.55858532
[62,] 0.4436718 0.88734361 0.55632819
[63,] 0.4554578 0.91091554 0.54454223
[64,] 0.4116663 0.82333258 0.58833371
[65,] 0.4257169 0.85143390 0.57428305
[66,] 0.4107553 0.82151053 0.58924473
[67,] 0.3876247 0.77524932 0.61237534
[68,] 0.3965828 0.79316553 0.60341724
[69,] 0.3794326 0.75886526 0.62056737
[70,] 0.3656373 0.73127459 0.63436271
[71,] 0.3228615 0.64572304 0.67713848
[72,] 0.3986724 0.79734471 0.60132765
[73,] 0.3485716 0.69714326 0.65142837
[74,] 0.3121668 0.62433359 0.68783321
[75,] 0.3836654 0.76733081 0.61633459
[76,] 0.3613127 0.72262546 0.63868727
[77,] 0.3633690 0.72673790 0.63663105
[78,] 0.4067617 0.81352343 0.59323828
[79,] 0.3860698 0.77213959 0.61393021
[80,] 0.3680473 0.73609455 0.63195273
[81,] 0.3232455 0.64649094 0.67675453
[82,] 0.2738645 0.54772909 0.72613545
[83,] 0.2249145 0.44982898 0.77508551
[84,] 0.2456101 0.49122020 0.75438990
[85,] 0.2695739 0.53914779 0.73042611
[86,] 0.2185283 0.43705667 0.78147166
[87,] 0.2762026 0.55240510 0.72379745
[88,] 0.3869089 0.77381782 0.61309109
[89,] 0.3390081 0.67801616 0.66099192
[90,] 0.2734525 0.54690509 0.72654746
[91,] 0.2134573 0.42691455 0.78654272
[92,] 0.3250555 0.65011110 0.67494445
[93,] 0.4874738 0.97494766 0.51252617
[94,] 0.4801269 0.96025388 0.51987306
[95,] 0.5069439 0.98611224 0.49305612
[96,] 0.4608082 0.92161646 0.53919177
[97,] 0.6425560 0.71488793 0.35744397
[98,] 0.8112976 0.37740480 0.18870240
[99,] 0.8290962 0.34180758 0.17090379
[100,] 0.7406137 0.51877254 0.25938627
[101,] 0.7616674 0.47666522 0.23833261
[102,] 0.9643730 0.07125402 0.03562701
[103,] 0.9704912 0.05901758 0.02950879
> postscript(file="/var/wessaorg/rcomp/tmp/1ewgp1322080773.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/227ik1322080773.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31o1n1322080773.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4zxzq1322080773.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5zszd1322080773.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 = 120
Frequency = 1
1 2 3 4 5 6
-0.157711401 -0.320605814 -0.276416709 -0.585644635 -0.275793288 0.186347594
7 8 9 10 11 12
-0.249504932 -0.456478368 0.026473925 0.215827398 0.207173028 0.190034280
13 14 15 16 17 18
0.457471119 -0.477880171 0.042598965 0.176192605 -0.221522491 0.197523530
19 20 21 22 23 24
0.570996141 0.499911010 0.480767318 -0.248340138 -0.268353200 -0.036515451
25 26 27 28 29 30
0.093978170 -0.173645734 0.252122681 -0.298522644 0.385116349 0.453431550
31 32 33 34 35 36
-0.274195950 -0.477769593 0.290531172 -0.293001228 0.477371279 0.149270581
37 38 39 40 41 42
0.192849098 0.045342067 -0.263427175 0.009814693 -0.234752384 0.307551521
43 44 45 46 47 48
0.045543631 0.025330669 0.359308113 0.278885056 0.052665271 -0.157105287
49 50 51 52 53 54
-0.445625709 0.657733907 0.369298592 -0.330651114 0.590901578 -0.135397871
55 56 57 58 59 60
0.348719684 -0.400340699 -0.449286368 0.020679817 0.210177280 -0.125311400
61 62 63 64 65 66
0.150212997 0.321448273 0.322046722 -0.401359612 -0.420714091 0.120635338
67 68 69 70 71 72
-0.150902954 -0.449797905 -0.387757302 -0.429077567 0.221044963 -0.163220899
73 74 75 76 77 78
0.264267453 0.121592175 -0.373841550 0.213491973 0.127704680 -0.390985773
79 80 81 82 83 84
-0.177983686 0.357068391 -0.161000589 -0.136142940 -0.475583528 0.064485846
85 86 87 88 89 90
0.297525967 0.466374808 0.280852564 0.220715438 -0.172959591 0.127586496
91 92 93 94 95 96
0.105712479 0.284314377 -0.363893791 0.109469218 -0.481196264 -0.460807678
97 98 99 100 101 102
0.341124344 0.054742461 0.078730741 -0.573147800 0.426181479 -0.470458904
103 104 105 106 107 108
0.325756667 0.072328656 0.095821143 -0.691213881 -0.255137734 0.061350900
109 110 111 112 113 114
-0.123387081 0.108583770 -0.410554479 -0.004595003 -0.139315827 0.385299368
115 116 117 118 119 120
-0.215311646 0.077357670 0.105434013 0.627814677 0.380076903 -0.070944794
> postscript(file="/var/wessaorg/rcomp/tmp/6m4731322080773.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.157711401 NA
1 -0.320605814 -0.157711401
2 -0.276416709 -0.320605814
3 -0.585644635 -0.276416709
4 -0.275793288 -0.585644635
5 0.186347594 -0.275793288
6 -0.249504932 0.186347594
7 -0.456478368 -0.249504932
8 0.026473925 -0.456478368
9 0.215827398 0.026473925
10 0.207173028 0.215827398
11 0.190034280 0.207173028
12 0.457471119 0.190034280
13 -0.477880171 0.457471119
14 0.042598965 -0.477880171
15 0.176192605 0.042598965
16 -0.221522491 0.176192605
17 0.197523530 -0.221522491
18 0.570996141 0.197523530
19 0.499911010 0.570996141
20 0.480767318 0.499911010
21 -0.248340138 0.480767318
22 -0.268353200 -0.248340138
23 -0.036515451 -0.268353200
24 0.093978170 -0.036515451
25 -0.173645734 0.093978170
26 0.252122681 -0.173645734
27 -0.298522644 0.252122681
28 0.385116349 -0.298522644
29 0.453431550 0.385116349
30 -0.274195950 0.453431550
31 -0.477769593 -0.274195950
32 0.290531172 -0.477769593
33 -0.293001228 0.290531172
34 0.477371279 -0.293001228
35 0.149270581 0.477371279
36 0.192849098 0.149270581
37 0.045342067 0.192849098
38 -0.263427175 0.045342067
39 0.009814693 -0.263427175
40 -0.234752384 0.009814693
41 0.307551521 -0.234752384
42 0.045543631 0.307551521
43 0.025330669 0.045543631
44 0.359308113 0.025330669
45 0.278885056 0.359308113
46 0.052665271 0.278885056
47 -0.157105287 0.052665271
48 -0.445625709 -0.157105287
49 0.657733907 -0.445625709
50 0.369298592 0.657733907
51 -0.330651114 0.369298592
52 0.590901578 -0.330651114
53 -0.135397871 0.590901578
54 0.348719684 -0.135397871
55 -0.400340699 0.348719684
56 -0.449286368 -0.400340699
57 0.020679817 -0.449286368
58 0.210177280 0.020679817
59 -0.125311400 0.210177280
60 0.150212997 -0.125311400
61 0.321448273 0.150212997
62 0.322046722 0.321448273
63 -0.401359612 0.322046722
64 -0.420714091 -0.401359612
65 0.120635338 -0.420714091
66 -0.150902954 0.120635338
67 -0.449797905 -0.150902954
68 -0.387757302 -0.449797905
69 -0.429077567 -0.387757302
70 0.221044963 -0.429077567
71 -0.163220899 0.221044963
72 0.264267453 -0.163220899
73 0.121592175 0.264267453
74 -0.373841550 0.121592175
75 0.213491973 -0.373841550
76 0.127704680 0.213491973
77 -0.390985773 0.127704680
78 -0.177983686 -0.390985773
79 0.357068391 -0.177983686
80 -0.161000589 0.357068391
81 -0.136142940 -0.161000589
82 -0.475583528 -0.136142940
83 0.064485846 -0.475583528
84 0.297525967 0.064485846
85 0.466374808 0.297525967
86 0.280852564 0.466374808
87 0.220715438 0.280852564
88 -0.172959591 0.220715438
89 0.127586496 -0.172959591
90 0.105712479 0.127586496
91 0.284314377 0.105712479
92 -0.363893791 0.284314377
93 0.109469218 -0.363893791
94 -0.481196264 0.109469218
95 -0.460807678 -0.481196264
96 0.341124344 -0.460807678
97 0.054742461 0.341124344
98 0.078730741 0.054742461
99 -0.573147800 0.078730741
100 0.426181479 -0.573147800
101 -0.470458904 0.426181479
102 0.325756667 -0.470458904
103 0.072328656 0.325756667
104 0.095821143 0.072328656
105 -0.691213881 0.095821143
106 -0.255137734 -0.691213881
107 0.061350900 -0.255137734
108 -0.123387081 0.061350900
109 0.108583770 -0.123387081
110 -0.410554479 0.108583770
111 -0.004595003 -0.410554479
112 -0.139315827 -0.004595003
113 0.385299368 -0.139315827
114 -0.215311646 0.385299368
115 0.077357670 -0.215311646
116 0.105434013 0.077357670
117 0.627814677 0.105434013
118 0.380076903 0.627814677
119 -0.070944794 0.380076903
120 NA -0.070944794
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.320605814 -0.157711401
[2,] -0.276416709 -0.320605814
[3,] -0.585644635 -0.276416709
[4,] -0.275793288 -0.585644635
[5,] 0.186347594 -0.275793288
[6,] -0.249504932 0.186347594
[7,] -0.456478368 -0.249504932
[8,] 0.026473925 -0.456478368
[9,] 0.215827398 0.026473925
[10,] 0.207173028 0.215827398
[11,] 0.190034280 0.207173028
[12,] 0.457471119 0.190034280
[13,] -0.477880171 0.457471119
[14,] 0.042598965 -0.477880171
[15,] 0.176192605 0.042598965
[16,] -0.221522491 0.176192605
[17,] 0.197523530 -0.221522491
[18,] 0.570996141 0.197523530
[19,] 0.499911010 0.570996141
[20,] 0.480767318 0.499911010
[21,] -0.248340138 0.480767318
[22,] -0.268353200 -0.248340138
[23,] -0.036515451 -0.268353200
[24,] 0.093978170 -0.036515451
[25,] -0.173645734 0.093978170
[26,] 0.252122681 -0.173645734
[27,] -0.298522644 0.252122681
[28,] 0.385116349 -0.298522644
[29,] 0.453431550 0.385116349
[30,] -0.274195950 0.453431550
[31,] -0.477769593 -0.274195950
[32,] 0.290531172 -0.477769593
[33,] -0.293001228 0.290531172
[34,] 0.477371279 -0.293001228
[35,] 0.149270581 0.477371279
[36,] 0.192849098 0.149270581
[37,] 0.045342067 0.192849098
[38,] -0.263427175 0.045342067
[39,] 0.009814693 -0.263427175
[40,] -0.234752384 0.009814693
[41,] 0.307551521 -0.234752384
[42,] 0.045543631 0.307551521
[43,] 0.025330669 0.045543631
[44,] 0.359308113 0.025330669
[45,] 0.278885056 0.359308113
[46,] 0.052665271 0.278885056
[47,] -0.157105287 0.052665271
[48,] -0.445625709 -0.157105287
[49,] 0.657733907 -0.445625709
[50,] 0.369298592 0.657733907
[51,] -0.330651114 0.369298592
[52,] 0.590901578 -0.330651114
[53,] -0.135397871 0.590901578
[54,] 0.348719684 -0.135397871
[55,] -0.400340699 0.348719684
[56,] -0.449286368 -0.400340699
[57,] 0.020679817 -0.449286368
[58,] 0.210177280 0.020679817
[59,] -0.125311400 0.210177280
[60,] 0.150212997 -0.125311400
[61,] 0.321448273 0.150212997
[62,] 0.322046722 0.321448273
[63,] -0.401359612 0.322046722
[64,] -0.420714091 -0.401359612
[65,] 0.120635338 -0.420714091
[66,] -0.150902954 0.120635338
[67,] -0.449797905 -0.150902954
[68,] -0.387757302 -0.449797905
[69,] -0.429077567 -0.387757302
[70,] 0.221044963 -0.429077567
[71,] -0.163220899 0.221044963
[72,] 0.264267453 -0.163220899
[73,] 0.121592175 0.264267453
[74,] -0.373841550 0.121592175
[75,] 0.213491973 -0.373841550
[76,] 0.127704680 0.213491973
[77,] -0.390985773 0.127704680
[78,] -0.177983686 -0.390985773
[79,] 0.357068391 -0.177983686
[80,] -0.161000589 0.357068391
[81,] -0.136142940 -0.161000589
[82,] -0.475583528 -0.136142940
[83,] 0.064485846 -0.475583528
[84,] 0.297525967 0.064485846
[85,] 0.466374808 0.297525967
[86,] 0.280852564 0.466374808
[87,] 0.220715438 0.280852564
[88,] -0.172959591 0.220715438
[89,] 0.127586496 -0.172959591
[90,] 0.105712479 0.127586496
[91,] 0.284314377 0.105712479
[92,] -0.363893791 0.284314377
[93,] 0.109469218 -0.363893791
[94,] -0.481196264 0.109469218
[95,] -0.460807678 -0.481196264
[96,] 0.341124344 -0.460807678
[97,] 0.054742461 0.341124344
[98,] 0.078730741 0.054742461
[99,] -0.573147800 0.078730741
[100,] 0.426181479 -0.573147800
[101,] -0.470458904 0.426181479
[102,] 0.325756667 -0.470458904
[103,] 0.072328656 0.325756667
[104,] 0.095821143 0.072328656
[105,] -0.691213881 0.095821143
[106,] -0.255137734 -0.691213881
[107,] 0.061350900 -0.255137734
[108,] -0.123387081 0.061350900
[109,] 0.108583770 -0.123387081
[110,] -0.410554479 0.108583770
[111,] -0.004595003 -0.410554479
[112,] -0.139315827 -0.004595003
[113,] 0.385299368 -0.139315827
[114,] -0.215311646 0.385299368
[115,] 0.077357670 -0.215311646
[116,] 0.105434013 0.077357670
[117,] 0.627814677 0.105434013
[118,] 0.380076903 0.627814677
[119,] -0.070944794 0.380076903
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.320605814 -0.157711401
2 -0.276416709 -0.320605814
3 -0.585644635 -0.276416709
4 -0.275793288 -0.585644635
5 0.186347594 -0.275793288
6 -0.249504932 0.186347594
7 -0.456478368 -0.249504932
8 0.026473925 -0.456478368
9 0.215827398 0.026473925
10 0.207173028 0.215827398
11 0.190034280 0.207173028
12 0.457471119 0.190034280
13 -0.477880171 0.457471119
14 0.042598965 -0.477880171
15 0.176192605 0.042598965
16 -0.221522491 0.176192605
17 0.197523530 -0.221522491
18 0.570996141 0.197523530
19 0.499911010 0.570996141
20 0.480767318 0.499911010
21 -0.248340138 0.480767318
22 -0.268353200 -0.248340138
23 -0.036515451 -0.268353200
24 0.093978170 -0.036515451
25 -0.173645734 0.093978170
26 0.252122681 -0.173645734
27 -0.298522644 0.252122681
28 0.385116349 -0.298522644
29 0.453431550 0.385116349
30 -0.274195950 0.453431550
31 -0.477769593 -0.274195950
32 0.290531172 -0.477769593
33 -0.293001228 0.290531172
34 0.477371279 -0.293001228
35 0.149270581 0.477371279
36 0.192849098 0.149270581
37 0.045342067 0.192849098
38 -0.263427175 0.045342067
39 0.009814693 -0.263427175
40 -0.234752384 0.009814693
41 0.307551521 -0.234752384
42 0.045543631 0.307551521
43 0.025330669 0.045543631
44 0.359308113 0.025330669
45 0.278885056 0.359308113
46 0.052665271 0.278885056
47 -0.157105287 0.052665271
48 -0.445625709 -0.157105287
49 0.657733907 -0.445625709
50 0.369298592 0.657733907
51 -0.330651114 0.369298592
52 0.590901578 -0.330651114
53 -0.135397871 0.590901578
54 0.348719684 -0.135397871
55 -0.400340699 0.348719684
56 -0.449286368 -0.400340699
57 0.020679817 -0.449286368
58 0.210177280 0.020679817
59 -0.125311400 0.210177280
60 0.150212997 -0.125311400
61 0.321448273 0.150212997
62 0.322046722 0.321448273
63 -0.401359612 0.322046722
64 -0.420714091 -0.401359612
65 0.120635338 -0.420714091
66 -0.150902954 0.120635338
67 -0.449797905 -0.150902954
68 -0.387757302 -0.449797905
69 -0.429077567 -0.387757302
70 0.221044963 -0.429077567
71 -0.163220899 0.221044963
72 0.264267453 -0.163220899
73 0.121592175 0.264267453
74 -0.373841550 0.121592175
75 0.213491973 -0.373841550
76 0.127704680 0.213491973
77 -0.390985773 0.127704680
78 -0.177983686 -0.390985773
79 0.357068391 -0.177983686
80 -0.161000589 0.357068391
81 -0.136142940 -0.161000589
82 -0.475583528 -0.136142940
83 0.064485846 -0.475583528
84 0.297525967 0.064485846
85 0.466374808 0.297525967
86 0.280852564 0.466374808
87 0.220715438 0.280852564
88 -0.172959591 0.220715438
89 0.127586496 -0.172959591
90 0.105712479 0.127586496
91 0.284314377 0.105712479
92 -0.363893791 0.284314377
93 0.109469218 -0.363893791
94 -0.481196264 0.109469218
95 -0.460807678 -0.481196264
96 0.341124344 -0.460807678
97 0.054742461 0.341124344
98 0.078730741 0.054742461
99 -0.573147800 0.078730741
100 0.426181479 -0.573147800
101 -0.470458904 0.426181479
102 0.325756667 -0.470458904
103 0.072328656 0.325756667
104 0.095821143 0.072328656
105 -0.691213881 0.095821143
106 -0.255137734 -0.691213881
107 0.061350900 -0.255137734
108 -0.123387081 0.061350900
109 0.108583770 -0.123387081
110 -0.410554479 0.108583770
111 -0.004595003 -0.410554479
112 -0.139315827 -0.004595003
113 0.385299368 -0.139315827
114 -0.215311646 0.385299368
115 0.077357670 -0.215311646
116 0.105434013 0.077357670
117 0.627814677 0.105434013
118 0.380076903 0.627814677
119 -0.070944794 0.380076903
> 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/7u3in1322080773.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ap0p1322080773.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9mhfo1322080773.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10ox8q1322080773.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11m9dl1322080773.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/126dc61322080773.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/135cks1322080773.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/14grva1322080773.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/15uey51322080773.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/163yvq1322080773.tab")
+ }
>
> try(system("convert tmp/1ewgp1322080773.ps tmp/1ewgp1322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/227ik1322080773.ps tmp/227ik1322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/31o1n1322080773.ps tmp/31o1n1322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zxzq1322080773.ps tmp/4zxzq1322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zszd1322080773.ps tmp/5zszd1322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/6m4731322080773.ps tmp/6m4731322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u3in1322080773.ps tmp/7u3in1322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ap0p1322080773.ps tmp/8ap0p1322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mhfo1322080773.ps tmp/9mhfo1322080773.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ox8q1322080773.ps tmp/10ox8q1322080773.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.082 0.506 4.605