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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> x <- array(list(7.1,0,6.8,0,6.5,0,6.3,0,6.1,0,6.1,0,6.3,0,6.3,0,6.0,0,6.2,0,6.4,0,6.8,0,7.5,0,7.5,0,7.6,0,7.6,0,7.4,0,7.3,0,7.1,0,6.9,0,6.8,0,7.5,0,7.6,0,7.8,0,8.0,0,8.1,0,8.2,0,8.3,0,8.2,0,8.0,0,7.9,0,7.6,0,7.6,0,8.2,0,8.3,0,8.4,0,8.4,0,8.4,0,8.6,0,8.9,0,8.8,0,8.3,0,7.5,0,7.2,0,7.5,0,8.8,0,9.3,0,9.3,0,8.7,1,8.2,1,8.3,1,8.5,1,8.6,1,8.6,1,8.2,1,8.1,1,8.0,1,8.6,1,8.7,1,8.8,1,8.5,1,8.4,1,8.5,1,8.7,1,8.7,1,8.6,1,8.5,1,8.3,1,8.1,1,8.2,1,8.1,1,8.1,1,7.9,1,7.9,1,7.9,1,8.0,1,8.0,1,7.9,1,8.0,1,7.7,1,7.2,1,7.5,1,7.3,1,7.0,1,7.0,1,7.0,1,7.2,1,7.3,1,7.1,1,6.8,1,6.6,1,6.2,1,6.2,1,6.8,1,6.9,1,6.8,1),dim=c(2,96),dimnames=list(c('w','d'),1:96))
> y <- array(NA,dim=c(2,96),dimnames=list(c('w','d'),1:96))
> 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
w d t
1 7.1 0 1
2 6.8 0 2
3 6.5 0 3
4 6.3 0 4
5 6.1 0 5
6 6.1 0 6
7 6.3 0 7
8 6.3 0 8
9 6.0 0 9
10 6.2 0 10
11 6.4 0 11
12 6.8 0 12
13 7.5 0 13
14 7.5 0 14
15 7.6 0 15
16 7.6 0 16
17 7.4 0 17
18 7.3 0 18
19 7.1 0 19
20 6.9 0 20
21 6.8 0 21
22 7.5 0 22
23 7.6 0 23
24 7.8 0 24
25 8.0 0 25
26 8.1 0 26
27 8.2 0 27
28 8.3 0 28
29 8.2 0 29
30 8.0 0 30
31 7.9 0 31
32 7.6 0 32
33 7.6 0 33
34 8.2 0 34
35 8.3 0 35
36 8.4 0 36
37 8.4 0 37
38 8.4 0 38
39 8.6 0 39
40 8.9 0 40
41 8.8 0 41
42 8.3 0 42
43 7.5 0 43
44 7.2 0 44
45 7.5 0 45
46 8.8 0 46
47 9.3 0 47
48 9.3 0 48
49 8.7 1 49
50 8.2 1 50
51 8.3 1 51
52 8.5 1 52
53 8.6 1 53
54 8.6 1 54
55 8.2 1 55
56 8.1 1 56
57 8.0 1 57
58 8.6 1 58
59 8.7 1 59
60 8.8 1 60
61 8.5 1 61
62 8.4 1 62
63 8.5 1 63
64 8.7 1 64
65 8.7 1 65
66 8.6 1 66
67 8.5 1 67
68 8.3 1 68
69 8.1 1 69
70 8.2 1 70
71 8.1 1 71
72 8.1 1 72
73 7.9 1 73
74 7.9 1 74
75 7.9 1 75
76 8.0 1 76
77 8.0 1 77
78 7.9 1 78
79 8.0 1 79
80 7.7 1 80
81 7.2 1 81
82 7.5 1 82
83 7.3 1 83
84 7.0 1 84
85 7.0 1 85
86 7.0 1 86
87 7.2 1 87
88 7.3 1 88
89 7.1 1 89
90 6.8 1 90
91 6.6 1 91
92 6.2 1 92
93 6.2 1 93
94 6.8 1 94
95 6.9 1 95
96 6.8 1 96
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) d t
7.492221 0.118815 0.003124
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.70153 -0.66874 0.06093 0.67736 1.66097
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.492221 0.187138 40.036 <2e-16 ***
d 0.118815 0.330623 0.359 0.720
t 0.003124 0.005965 0.524 0.602
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8097 on 93 degrees of freedom
Multiple R-squared: 0.03042, Adjusted R-squared: 0.009571
F-statistic: 1.459 on 2 and 93 DF, p-value: 0.2377
> 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,] 0.005834185 1.166837e-02 9.941658e-01
[2,] 0.014826736 2.965347e-02 9.851733e-01
[3,] 0.011895684 2.379137e-02 9.881043e-01
[4,] 0.004688304 9.376608e-03 9.953117e-01
[5,] 0.003725382 7.450765e-03 9.962746e-01
[6,] 0.006072517 1.214503e-02 9.939275e-01
[7,] 0.025068897 5.013779e-02 9.749311e-01
[8,] 0.172465928 3.449319e-01 8.275341e-01
[9,] 0.248848814 4.976976e-01 7.511512e-01
[10,] 0.275863368 5.517267e-01 7.241366e-01
[11,] 0.257179845 5.143597e-01 7.428202e-01
[12,] 0.215314048 4.306281e-01 7.846860e-01
[13,] 0.184661498 3.693230e-01 8.153385e-01
[14,] 0.186567501 3.731350e-01 8.134325e-01
[15,] 0.244363709 4.887274e-01 7.556363e-01
[16,] 0.368745039 7.374901e-01 6.312550e-01
[17,] 0.371256397 7.425128e-01 6.287436e-01
[18,] 0.373765486 7.475310e-01 6.262345e-01
[19,] 0.372651624 7.453032e-01 6.273484e-01
[20,] 0.369414619 7.388292e-01 6.305854e-01
[21,] 0.356260792 7.125216e-01 6.437392e-01
[22,] 0.334115053 6.682301e-01 6.658849e-01
[23,] 0.304690216 6.093804e-01 6.953098e-01
[24,] 0.263843813 5.276876e-01 7.361562e-01
[25,] 0.240844788 4.816896e-01 7.591552e-01
[26,] 0.240759546 4.815191e-01 7.592405e-01
[27,] 0.343686553 6.873731e-01 6.563134e-01
[28,] 0.474568202 9.491364e-01 5.254318e-01
[29,] 0.440545135 8.810903e-01 5.594549e-01
[30,] 0.397330697 7.946614e-01 6.026693e-01
[31,] 0.348037691 6.960754e-01 6.519623e-01
[32,] 0.299550587 5.991012e-01 7.004494e-01
[33,] 0.254224332 5.084487e-01 7.457757e-01
[34,] 0.208242104 4.164842e-01 7.917579e-01
[35,] 0.188942233 3.778845e-01 8.110578e-01
[36,] 0.159597732 3.191955e-01 8.404023e-01
[37,] 0.147738430 2.954769e-01 8.522616e-01
[38,] 0.426104534 8.522091e-01 5.738955e-01
[39,] 0.903693196 1.926136e-01 9.630680e-02
[40,] 0.996141014 7.717972e-03 3.858986e-03
[41,] 0.996062114 7.875772e-03 3.937886e-03
[42,] 0.995637800 8.724401e-03 4.362200e-03
[43,] 0.994615268 1.076946e-02 5.384732e-03
[44,] 0.992436742 1.512652e-02 7.563258e-03
[45,] 0.996030431 7.939139e-03 3.969569e-03
[46,] 0.997365821 5.268358e-03 2.634179e-03
[47,] 0.997209149 5.581702e-03 2.790851e-03
[48,] 0.996421912 7.156175e-03 3.578088e-03
[49,] 0.995286979 9.426043e-03 4.713021e-03
[50,] 0.997913006 4.173988e-03 2.086994e-03
[51,] 0.999612559 7.748830e-04 3.874415e-04
[52,] 0.999990301 1.939772e-05 9.698858e-06
[53,] 0.999989148 2.170374e-05 1.085187e-05
[54,] 0.999981519 3.696210e-05 1.848105e-05
[55,] 0.999962284 7.543147e-05 3.771573e-05
[56,] 0.999955189 8.962188e-05 4.481094e-05
[57,] 0.999962810 7.437942e-05 3.718971e-05
[58,] 0.999947793 1.044135e-04 5.220675e-05
[59,] 0.999896141 2.077182e-04 1.038591e-04
[60,] 0.999813834 3.723325e-04 1.861662e-04
[61,] 0.999679304 6.413919e-04 3.206960e-04
[62,] 0.999477087 1.045826e-03 5.229132e-04
[63,] 0.999254520 1.490961e-03 7.454804e-04
[64,] 0.999260731 1.478538e-03 7.392689e-04
[65,] 0.998982452 2.035096e-03 1.017548e-03
[66,] 0.998719748 2.560504e-03 1.280252e-03
[67,] 0.998271421 3.457159e-03 1.728579e-03
[68,] 0.998216258 3.567483e-03 1.783742e-03
[69,] 0.997898684 4.202632e-03 2.101316e-03
[70,] 0.997242602 5.514796e-03 2.757398e-03
[71,] 0.996024302 7.951395e-03 3.975698e-03
[72,] 0.994766651 1.046670e-02 5.233349e-03
[73,] 0.993352652 1.329470e-02 6.647348e-03
[74,] 0.995123769 9.752461e-03 4.876231e-03
[75,] 0.994820651 1.035870e-02 5.179349e-03
[76,] 0.993898485 1.220303e-02 6.101515e-03
[77,] 0.991970892 1.605822e-02 8.029108e-03
[78,] 0.987954543 2.409091e-02 1.204546e-02
[79,] 0.982661342 3.467732e-02 1.733866e-02
[80,] 0.972871577 5.425685e-02 2.712842e-02
[81,] 0.954988777 9.002245e-02 4.501122e-02
[82,] 0.924157910 1.516842e-01 7.584209e-02
[83,] 0.914435527 1.711289e-01 8.556447e-02
[84,] 0.918313207 1.633736e-01 8.168679e-02
[85,] 0.907306357 1.853873e-01 9.269364e-02
> postscript(file="/var/www/html/rcomp/tmp/1vkea1227789088.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21ryu1227789088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3ge9q1227789088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fwbd1227789088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dzcs1227789088.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 = 96
Frequency = 1
1 2 3 4 5 6
-0.395344388 -0.698468031 -1.001591674 -1.204715317 -1.407838960 -1.410962603
7 8 9 10 11 12
-1.214086246 -1.217209889 -1.520333532 -1.323457175 -1.126580818 -0.729704462
13 14 15 16 17 18
-0.032828105 -0.035951748 0.060924609 0.057800966 -0.145322677 -0.248446320
19 20 21 22 23 24
-0.451569963 -0.654693606 -0.757817249 -0.060940892 0.035935465 0.232811822
25 26 27 28 29 30
0.429688178 0.526564535 0.623440892 0.720317249 0.617193606 0.414069963
31 32 33 34 35 36
0.310946320 0.007822677 0.004699034 0.601575391 0.698451748 0.795328105
37 38 39 40 41 42
0.792204462 0.789080818 0.985957175 1.282833532 1.179709889 0.676586246
43 44 45 46 47 48
-0.126537397 -0.429661040 -0.132784683 1.164091674 1.660968031 1.657844388
49 50 51 52 53 54
0.935905612 0.432781969 0.529658326 0.726534683 0.823411040 0.820287397
55 56 57 58 59 60
0.417163754 0.314040111 0.210916468 0.807792825 0.904669182 1.001545538
61 62 63 64 65 66
0.698421895 0.595298252 0.692174609 0.889050966 0.885927323 0.782803680
67 68 69 70 71 72
0.679680037 0.476556394 0.273432751 0.370309108 0.267185465 0.264061822
73 74 75 76 77 78
0.060938178 0.057814535 0.054690892 0.151567249 0.148443606 0.045319963
79 80 81 82 83 84
0.142196320 -0.160927323 -0.664050966 -0.367174609 -0.570298252 -0.873421895
85 86 87 88 89 90
-0.876545538 -0.879669182 -0.682792825 -0.585916468 -0.789040111 -1.092163754
91 92 93 94 95 96
-1.295287397 -1.698411040 -1.701534683 -1.104658326 -1.007781969 -1.110905612
> postscript(file="/var/www/html/rcomp/tmp/667nq1227789088.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 = 96
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.395344388 NA
1 -0.698468031 -0.395344388
2 -1.001591674 -0.698468031
3 -1.204715317 -1.001591674
4 -1.407838960 -1.204715317
5 -1.410962603 -1.407838960
6 -1.214086246 -1.410962603
7 -1.217209889 -1.214086246
8 -1.520333532 -1.217209889
9 -1.323457175 -1.520333532
10 -1.126580818 -1.323457175
11 -0.729704462 -1.126580818
12 -0.032828105 -0.729704462
13 -0.035951748 -0.032828105
14 0.060924609 -0.035951748
15 0.057800966 0.060924609
16 -0.145322677 0.057800966
17 -0.248446320 -0.145322677
18 -0.451569963 -0.248446320
19 -0.654693606 -0.451569963
20 -0.757817249 -0.654693606
21 -0.060940892 -0.757817249
22 0.035935465 -0.060940892
23 0.232811822 0.035935465
24 0.429688178 0.232811822
25 0.526564535 0.429688178
26 0.623440892 0.526564535
27 0.720317249 0.623440892
28 0.617193606 0.720317249
29 0.414069963 0.617193606
30 0.310946320 0.414069963
31 0.007822677 0.310946320
32 0.004699034 0.007822677
33 0.601575391 0.004699034
34 0.698451748 0.601575391
35 0.795328105 0.698451748
36 0.792204462 0.795328105
37 0.789080818 0.792204462
38 0.985957175 0.789080818
39 1.282833532 0.985957175
40 1.179709889 1.282833532
41 0.676586246 1.179709889
42 -0.126537397 0.676586246
43 -0.429661040 -0.126537397
44 -0.132784683 -0.429661040
45 1.164091674 -0.132784683
46 1.660968031 1.164091674
47 1.657844388 1.660968031
48 0.935905612 1.657844388
49 0.432781969 0.935905612
50 0.529658326 0.432781969
51 0.726534683 0.529658326
52 0.823411040 0.726534683
53 0.820287397 0.823411040
54 0.417163754 0.820287397
55 0.314040111 0.417163754
56 0.210916468 0.314040111
57 0.807792825 0.210916468
58 0.904669182 0.807792825
59 1.001545538 0.904669182
60 0.698421895 1.001545538
61 0.595298252 0.698421895
62 0.692174609 0.595298252
63 0.889050966 0.692174609
64 0.885927323 0.889050966
65 0.782803680 0.885927323
66 0.679680037 0.782803680
67 0.476556394 0.679680037
68 0.273432751 0.476556394
69 0.370309108 0.273432751
70 0.267185465 0.370309108
71 0.264061822 0.267185465
72 0.060938178 0.264061822
73 0.057814535 0.060938178
74 0.054690892 0.057814535
75 0.151567249 0.054690892
76 0.148443606 0.151567249
77 0.045319963 0.148443606
78 0.142196320 0.045319963
79 -0.160927323 0.142196320
80 -0.664050966 -0.160927323
81 -0.367174609 -0.664050966
82 -0.570298252 -0.367174609
83 -0.873421895 -0.570298252
84 -0.876545538 -0.873421895
85 -0.879669182 -0.876545538
86 -0.682792825 -0.879669182
87 -0.585916468 -0.682792825
88 -0.789040111 -0.585916468
89 -1.092163754 -0.789040111
90 -1.295287397 -1.092163754
91 -1.698411040 -1.295287397
92 -1.701534683 -1.698411040
93 -1.104658326 -1.701534683
94 -1.007781969 -1.104658326
95 -1.110905612 -1.007781969
96 NA -1.110905612
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.698468031 -0.395344388
[2,] -1.001591674 -0.698468031
[3,] -1.204715317 -1.001591674
[4,] -1.407838960 -1.204715317
[5,] -1.410962603 -1.407838960
[6,] -1.214086246 -1.410962603
[7,] -1.217209889 -1.214086246
[8,] -1.520333532 -1.217209889
[9,] -1.323457175 -1.520333532
[10,] -1.126580818 -1.323457175
[11,] -0.729704462 -1.126580818
[12,] -0.032828105 -0.729704462
[13,] -0.035951748 -0.032828105
[14,] 0.060924609 -0.035951748
[15,] 0.057800966 0.060924609
[16,] -0.145322677 0.057800966
[17,] -0.248446320 -0.145322677
[18,] -0.451569963 -0.248446320
[19,] -0.654693606 -0.451569963
[20,] -0.757817249 -0.654693606
[21,] -0.060940892 -0.757817249
[22,] 0.035935465 -0.060940892
[23,] 0.232811822 0.035935465
[24,] 0.429688178 0.232811822
[25,] 0.526564535 0.429688178
[26,] 0.623440892 0.526564535
[27,] 0.720317249 0.623440892
[28,] 0.617193606 0.720317249
[29,] 0.414069963 0.617193606
[30,] 0.310946320 0.414069963
[31,] 0.007822677 0.310946320
[32,] 0.004699034 0.007822677
[33,] 0.601575391 0.004699034
[34,] 0.698451748 0.601575391
[35,] 0.795328105 0.698451748
[36,] 0.792204462 0.795328105
[37,] 0.789080818 0.792204462
[38,] 0.985957175 0.789080818
[39,] 1.282833532 0.985957175
[40,] 1.179709889 1.282833532
[41,] 0.676586246 1.179709889
[42,] -0.126537397 0.676586246
[43,] -0.429661040 -0.126537397
[44,] -0.132784683 -0.429661040
[45,] 1.164091674 -0.132784683
[46,] 1.660968031 1.164091674
[47,] 1.657844388 1.660968031
[48,] 0.935905612 1.657844388
[49,] 0.432781969 0.935905612
[50,] 0.529658326 0.432781969
[51,] 0.726534683 0.529658326
[52,] 0.823411040 0.726534683
[53,] 0.820287397 0.823411040
[54,] 0.417163754 0.820287397
[55,] 0.314040111 0.417163754
[56,] 0.210916468 0.314040111
[57,] 0.807792825 0.210916468
[58,] 0.904669182 0.807792825
[59,] 1.001545538 0.904669182
[60,] 0.698421895 1.001545538
[61,] 0.595298252 0.698421895
[62,] 0.692174609 0.595298252
[63,] 0.889050966 0.692174609
[64,] 0.885927323 0.889050966
[65,] 0.782803680 0.885927323
[66,] 0.679680037 0.782803680
[67,] 0.476556394 0.679680037
[68,] 0.273432751 0.476556394
[69,] 0.370309108 0.273432751
[70,] 0.267185465 0.370309108
[71,] 0.264061822 0.267185465
[72,] 0.060938178 0.264061822
[73,] 0.057814535 0.060938178
[74,] 0.054690892 0.057814535
[75,] 0.151567249 0.054690892
[76,] 0.148443606 0.151567249
[77,] 0.045319963 0.148443606
[78,] 0.142196320 0.045319963
[79,] -0.160927323 0.142196320
[80,] -0.664050966 -0.160927323
[81,] -0.367174609 -0.664050966
[82,] -0.570298252 -0.367174609
[83,] -0.873421895 -0.570298252
[84,] -0.876545538 -0.873421895
[85,] -0.879669182 -0.876545538
[86,] -0.682792825 -0.879669182
[87,] -0.585916468 -0.682792825
[88,] -0.789040111 -0.585916468
[89,] -1.092163754 -0.789040111
[90,] -1.295287397 -1.092163754
[91,] -1.698411040 -1.295287397
[92,] -1.701534683 -1.698411040
[93,] -1.104658326 -1.701534683
[94,] -1.007781969 -1.104658326
[95,] -1.110905612 -1.007781969
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.698468031 -0.395344388
2 -1.001591674 -0.698468031
3 -1.204715317 -1.001591674
4 -1.407838960 -1.204715317
5 -1.410962603 -1.407838960
6 -1.214086246 -1.410962603
7 -1.217209889 -1.214086246
8 -1.520333532 -1.217209889
9 -1.323457175 -1.520333532
10 -1.126580818 -1.323457175
11 -0.729704462 -1.126580818
12 -0.032828105 -0.729704462
13 -0.035951748 -0.032828105
14 0.060924609 -0.035951748
15 0.057800966 0.060924609
16 -0.145322677 0.057800966
17 -0.248446320 -0.145322677
18 -0.451569963 -0.248446320
19 -0.654693606 -0.451569963
20 -0.757817249 -0.654693606
21 -0.060940892 -0.757817249
22 0.035935465 -0.060940892
23 0.232811822 0.035935465
24 0.429688178 0.232811822
25 0.526564535 0.429688178
26 0.623440892 0.526564535
27 0.720317249 0.623440892
28 0.617193606 0.720317249
29 0.414069963 0.617193606
30 0.310946320 0.414069963
31 0.007822677 0.310946320
32 0.004699034 0.007822677
33 0.601575391 0.004699034
34 0.698451748 0.601575391
35 0.795328105 0.698451748
36 0.792204462 0.795328105
37 0.789080818 0.792204462
38 0.985957175 0.789080818
39 1.282833532 0.985957175
40 1.179709889 1.282833532
41 0.676586246 1.179709889
42 -0.126537397 0.676586246
43 -0.429661040 -0.126537397
44 -0.132784683 -0.429661040
45 1.164091674 -0.132784683
46 1.660968031 1.164091674
47 1.657844388 1.660968031
48 0.935905612 1.657844388
49 0.432781969 0.935905612
50 0.529658326 0.432781969
51 0.726534683 0.529658326
52 0.823411040 0.726534683
53 0.820287397 0.823411040
54 0.417163754 0.820287397
55 0.314040111 0.417163754
56 0.210916468 0.314040111
57 0.807792825 0.210916468
58 0.904669182 0.807792825
59 1.001545538 0.904669182
60 0.698421895 1.001545538
61 0.595298252 0.698421895
62 0.692174609 0.595298252
63 0.889050966 0.692174609
64 0.885927323 0.889050966
65 0.782803680 0.885927323
66 0.679680037 0.782803680
67 0.476556394 0.679680037
68 0.273432751 0.476556394
69 0.370309108 0.273432751
70 0.267185465 0.370309108
71 0.264061822 0.267185465
72 0.060938178 0.264061822
73 0.057814535 0.060938178
74 0.054690892 0.057814535
75 0.151567249 0.054690892
76 0.148443606 0.151567249
77 0.045319963 0.148443606
78 0.142196320 0.045319963
79 -0.160927323 0.142196320
80 -0.664050966 -0.160927323
81 -0.367174609 -0.664050966
82 -0.570298252 -0.367174609
83 -0.873421895 -0.570298252
84 -0.876545538 -0.873421895
85 -0.879669182 -0.876545538
86 -0.682792825 -0.879669182
87 -0.585916468 -0.682792825
88 -0.789040111 -0.585916468
89 -1.092163754 -0.789040111
90 -1.295287397 -1.092163754
91 -1.698411040 -1.295287397
92 -1.701534683 -1.698411040
93 -1.104658326 -1.701534683
94 -1.007781969 -1.104658326
95 -1.110905612 -1.007781969
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7n1sb1227789088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8twf61227789088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9o35n1227789088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10el311227789088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11dx731227789088.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12ybta1227789088.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/134osr1227789088.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14c3si1227789088.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/rcomp/tmp/15ll1r1227789088.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/rcomp/tmp/167w5u1227789088.tab")
+ }
>
> system("convert tmp/1vkea1227789088.ps tmp/1vkea1227789088.png")
> system("convert tmp/21ryu1227789088.ps tmp/21ryu1227789088.png")
> system("convert tmp/3ge9q1227789088.ps tmp/3ge9q1227789088.png")
> system("convert tmp/4fwbd1227789088.ps tmp/4fwbd1227789088.png")
> system("convert tmp/5dzcs1227789088.ps tmp/5dzcs1227789088.png")
> system("convert tmp/667nq1227789088.ps tmp/667nq1227789088.png")
> system("convert tmp/7n1sb1227789088.ps tmp/7n1sb1227789088.png")
> system("convert tmp/8twf61227789088.ps tmp/8twf61227789088.png")
> system("convert tmp/9o35n1227789088.ps tmp/9o35n1227789088.png")
> system("convert tmp/10el311227789088.ps tmp/10el311227789088.png")
>
>
> proc.time()
user system elapsed
2.842 1.601 3.244