R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(12
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+ ,2)
+ ,dim=c(5
+ ,112)
+ ,dimnames=list(c('Score_op_20'
+ ,'Blogs'
+ ,'Reviews'
+ ,'Compendium_Writing'
+ ,'Gedeelde_compendia')
+ ,1:112))
> y <- array(NA,dim=c(5,112),dimnames=list(c('Score_op_20','Blogs','Reviews','Compendium_Writing','Gedeelde_compendia'),1:112))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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
Score_op_20 Blogs Reviews Compendium_Writing Gedeelde_compendia
1 12 65 22 114468 2
2 13 54 20 88594 4
3 11 58 24 74151 9
4 12 77 21 77921 2
5 8 41 15 53212 1
6 7 0 16 34956 2
7 18 111 20 149703 0
8 0 1 18 6853 0
9 9 36 19 58907 5
10 11 60 20 67067 0
11 13 63 25 110563 0
12 13 71 37 58126 7
13 9 38 23 57113 6
14 12 76 28 77993 3
15 11 61 25 68091 4
16 17 125 35 124676 0
17 14 84 20 109522 4
18 15 69 22 75865 3
19 13 77 19 79746 0
20 15 100 26 77844 5
21 13 78 27 98681 0
22 13 76 22 105531 1
23 8 40 15 51428 3
24 16 81 26 65703 5
25 14 102 24 72562 0
26 14 70 22 81728 0
27 14 75 21 95580 4
28 14 93 23 98278 0
29 12 42 21 46629 0
30 14 95 25 115189 0
31 2 8 4 15049 0
32 12 87 30 109011 5
33 13 87 20 134245 5
34 16 112 26 136692 0
35 15 96 27 149510 6
36 16 93 18 147888 6
37 15 98 20 79169 2
38 16 99 17 65469 5
39 14 94 22 56756 0
40 17 98 25 81399 3
41 18 109 30 104953 0
42 16 108 26 59633 1
43 10 42 20 63249 1
44 15 108 25 82928 2
45 10 27 21 50000 4
46 16 115 23 139357 0
47 17 92 33 110044 7
48 17 106 19 155118 7
49 13 73 31 83061 6
50 14 105 25 127122 0
51 12 30 20 45653 0
52 7 13 19 19630 4
53 14 69 15 67229 4
54 12 72 21 86060 0
55 16 80 22 88003 0
56 14 106 24 95815 0
57 8 28 19 85499 0
58 14 70 20 27220 0
59 15 51 23 109882 4
60 16 90 27 72579 0
61 0 12 1 5841 0
62 12 84 20 68369 0
63 8 23 11 24610 4
64 12 57 27 30995 0
65 15 84 22 150662 1
66 0 4 0 6622 0
67 11 56 17 93694 5
68 15 18 8 13155 0
69 17 86 23 111908 1
70 13 39 26 57550 7
71 8 16 20 16356 5
72 15 18 16 40174 2
73 12 16 8 13983 0
74 10 42 22 52316 1
75 13 77 33 99585 0
76 17 30 28 86271 0
77 17 104 26 131012 2
78 16 121 27 130274 0
79 18 109 35 159051 2
80 14 57 21 76506 0
81 9 28 20 49145 0
82 10 56 24 66398 4
83 15 81 26 127546 4
84 2 2 20 6802 8
85 11 88 22 99509 0
86 15 41 24 43106 4
87 14 83 23 108303 0
88 13 55 22 64167 1
89 4 3 12 8579 0
90 12 54 21 97811 9
91 11 89 24 84365 0
92 9 41 21 10901 3
93 15 94 25 91346 7
94 16 101 32 33660 5
95 14 70 24 93634 2
96 16 111 29 109348 1
97 0 0 0 0 9
98 0 4 0 7953 0
99 0 0 0 0 0
100 0 0 0 0 0
101 0 0 0 0 1
102 0 0 0 0 0
103 10 42 20 63538 2
104 12 97 27 108281 1
105 0 0 0 0 0
106 0 0 0 0 0
107 2 7 0 4245 0
108 4 12 5 21509 0
109 0 0 1 7670 0
110 5 37 23 10641 0
111 0 0 0 0 0
112 3 39 16 41243 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Blogs Reviews Compendium_Writing
1.580e+00 5.884e-02 2.184e-01 2.378e-05
Gedeelde_compendia
1.710e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.733 -1.580 -0.121 1.065 10.301
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.580e+00 5.746e-01 2.751 0.00699 **
Blogs 5.884e-02 1.340e-02 4.389 2.68e-05 ***
Reviews 2.184e-01 4.207e-02 5.191 1.00e-06 ***
Compendium_Writing 2.378e-05 1.003e-05 2.371 0.01953 *
Gedeelde_compendia 1.710e-02 9.685e-02 0.177 0.86020
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.476 on 107 degrees of freedom
Multiple R-squared: 0.7924, Adjusted R-squared: 0.7846
F-statistic: 102.1 on 4 and 107 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,] 2.750515e-01 5.501029e-01 0.724948533
[2,] 1.505424e-01 3.010849e-01 0.849457564
[3,] 1.832062e-01 3.664123e-01 0.816793833
[4,] 1.327488e-01 2.654975e-01 0.867251227
[5,] 1.373370e-01 2.746740e-01 0.862663004
[6,] 8.249047e-02 1.649809e-01 0.917509528
[7,] 4.844871e-02 9.689741e-02 0.951551295
[8,] 2.629457e-02 5.258913e-02 0.973705433
[9,] 1.496155e-02 2.992311e-02 0.985038447
[10,] 8.194167e-03 1.638833e-02 0.991805833
[11,] 2.896434e-02 5.792868e-02 0.971035660
[12,] 1.882079e-02 3.764158e-02 0.981179209
[13,] 1.048128e-02 2.096257e-02 0.989518716
[14,] 5.773511e-03 1.154702e-02 0.994226489
[15,] 3.148477e-03 6.296955e-03 0.996851523
[16,] 1.750676e-03 3.501351e-03 0.998249324
[17,] 4.115161e-03 8.230322e-03 0.995884839
[18,] 2.224434e-03 4.448869e-03 0.997775566
[19,] 2.575023e-03 5.150047e-03 0.997424977
[20,] 1.412953e-03 2.825907e-03 0.998587047
[21,] 7.718586e-04 1.543717e-03 0.999228141
[22,] 2.945996e-03 5.891992e-03 0.997054004
[23,] 2.094064e-03 4.188127e-03 0.997905936
[24,] 2.138406e-03 4.276813e-03 0.997861594
[25,] 5.398916e-03 1.079783e-02 0.994601084
[26,] 5.648477e-03 1.129695e-02 0.994351523
[27,] 3.864183e-03 7.728366e-03 0.996135817
[28,] 2.945909e-03 5.891818e-03 0.997054091
[29,] 1.857550e-03 3.715100e-03 0.998142450
[30,] 1.152818e-03 2.305637e-03 0.998847182
[31,] 1.003394e-03 2.006788e-03 0.998996606
[32,] 5.864806e-04 1.172961e-03 0.999413519
[33,] 5.511779e-04 1.102356e-03 0.999448822
[34,] 4.190697e-04 8.381395e-04 0.999580930
[35,] 2.488328e-04 4.976655e-04 0.999751167
[36,] 1.542052e-04 3.084103e-04 0.999845795
[37,] 9.984840e-05 1.996968e-04 0.999900152
[38,] 9.462706e-05 1.892541e-04 0.999905373
[39,] 5.895150e-05 1.179030e-04 0.999941048
[40,] 3.734708e-05 7.469417e-05 0.999962653
[41,] 2.250734e-05 4.501468e-05 0.999977493
[42,] 1.373737e-05 2.747475e-05 0.999986263
[43,] 1.278955e-05 2.557910e-05 0.999987210
[44,] 5.489195e-05 1.097839e-04 0.999945108
[45,] 3.075484e-05 6.150969e-05 0.999969245
[46,] 4.185524e-05 8.371048e-05 0.999958145
[47,] 2.291631e-05 4.583262e-05 0.999977084
[48,] 3.699526e-05 7.399051e-05 0.999963005
[49,] 2.718908e-05 5.437816e-05 0.999972811
[50,] 1.877836e-05 3.755672e-05 0.999981222
[51,] 2.986167e-05 5.972334e-05 0.999970138
[52,] 6.510520e-05 1.302104e-04 0.999934895
[53,] 5.451248e-05 1.090250e-04 0.999945488
[54,] 1.889112e-04 3.778224e-04 0.999811089
[55,] 1.202396e-04 2.404792e-04 0.999879760
[56,] 9.157655e-05 1.831531e-04 0.999908423
[57,] 5.521723e-05 1.104345e-04 0.999944783
[58,] 3.144004e-05 6.288008e-05 0.999968560
[59,] 3.718576e-05 7.437152e-05 0.999962814
[60,] 2.034248e-05 4.068496e-05 0.999979658
[61,] 3.933362e-02 7.866724e-02 0.960666381
[62,] 4.577785e-02 9.155570e-02 0.954222149
[63,] 4.021497e-02 8.042994e-02 0.959785028
[64,] 2.933397e-02 5.866793e-02 0.970666034
[65,] 2.494520e-01 4.989040e-01 0.750547980
[66,] 7.593783e-01 4.812435e-01 0.240621732
[67,] 7.099865e-01 5.800269e-01 0.290013462
[68,] 7.359314e-01 5.281372e-01 0.264068623
[69,] 8.873146e-01 2.253708e-01 0.112685388
[70,] 8.628938e-01 2.742124e-01 0.137106176
[71,] 8.310242e-01 3.379517e-01 0.168975825
[72,] 8.027471e-01 3.945057e-01 0.197252851
[73,] 8.443631e-01 3.112737e-01 0.155636860
[74,] 8.113152e-01 3.773696e-01 0.188684805
[75,] 7.799442e-01 4.401115e-01 0.220055774
[76,] 7.272449e-01 5.455102e-01 0.272755112
[77,] 9.074078e-01 1.851844e-01 0.092592194
[78,] 8.954991e-01 2.090017e-01 0.104500866
[79,] 9.740003e-01 5.199948e-02 0.025999738
[80,] 9.645744e-01 7.085127e-02 0.035425634
[81,] 9.796505e-01 4.069905e-02 0.020349527
[82,] 9.734646e-01 5.307077e-02 0.026535383
[83,] 9.592929e-01 8.141421e-02 0.040707105
[84,] 9.558623e-01 8.827541e-02 0.044137706
[85,] 9.512126e-01 9.757481e-02 0.048787406
[86,] 9.264558e-01 1.470883e-01 0.073544174
[87,] 9.802178e-01 3.956439e-02 0.019782195
[88,] 9.803747e-01 3.925058e-02 0.019625290
[89,] 9.917337e-01 1.653268e-02 0.008266342
[90,] 9.937213e-01 1.255736e-02 0.006278681
[91,] 9.897713e-01 2.045733e-02 0.010228666
[92,] 9.788908e-01 4.221849e-02 0.021109247
[93,] 9.577230e-01 8.455405e-02 0.042277024
[94,] 9.353562e-01 1.292876e-01 0.064643821
[95,] 8.747365e-01 2.505270e-01 0.125263491
[96,] 9.497576e-01 1.004848e-01 0.050242390
[97,] 9.982763e-01 3.447330e-03 0.001723665
> postscript(file="/var/www/rcomp/tmp/146iz1321699728.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2roju1321699728.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3m4cn1321699728.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/47ns71321699728.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/584ug1321699728.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 112
Frequency = 1
1 2 3 4 5 6
-0.96524550 1.69983165 -1.15096067 -0.58376313 -0.55072634 1.06031014
7 8 9 10 11 12
1.96119181 -5.73270231 -0.33381233 -0.07284254 -0.37558891 -2.33922945
13 14 15 16 17 18
-1.29936181 -2.07226069 -1.31622595 -2.54274298 0.43700055 2.70037285
19 20 21 22 23 24
0.84375057 -0.07828407 -1.41228864 -0.38281607 -0.48365642 2.32836318
25 26 27 28 29 30
-0.54813250 1.55339268 1.07974650 -0.41181999 2.25392775 -1.36839688
31 32 33 34 35 36
-1.28243175 -3.92806310 -1.34457613 -1.09837944 -1.78276915 1.39756384
37 38 39 40 41 42
1.36933550 3.24010457 0.73518844 2.20740015 0.95951293 0.95250546
43 44 45 46 47 48
0.05993074 -0.40023779 0.98792685 -0.68318855 0.06390675 1.22527631
49 50 51 52 53 54
-1.72265159 -2.24056233 3.20154671 -0.02936469 3.41718372 -0.44894559
55 56 57 58 59 60
2.81578698 -1.33648917 -1.41004406 3.28643487 2.71498547 1.50242782
61 62 63 64 65 66
-2.64371191 -0.51590058 2.01071339 0.43301596 0.07317113 -1.97322722
67 68 69 70 71 72
0.09885270 10.30079651 2.65879172 1.95922856 0.63652845 7.87714437
73 74 75 76 77 78
7.39877931 -0.11677980 -2.68511489 5.48867506 0.47320660 -1.69364104
79 80 81 82 83 84
-1.45305551 2.66082814 0.23617370 -1.76341578 -0.12530267 -4.36382721
85 86 87 88 89 90
-2.92854900 4.67307797 -0.06186432 1.83649319 -0.58126050 0.17678334
91 92 93 94 95 96
-3.06394989 0.11116363 0.13779788 0.60350753 0.79932545 -1.06142102
97 98 99 100 101 102
-1.73426900 -2.00488135 -1.58039249 -1.58039249 -1.59748988 -1.58039249
103 104 105 106 107 108
0.03596030 -3.77560268 -1.58039249 -1.58039249 -0.09320861 0.11022551
109 110 111 112
-1.98116278 -4.03273335 -1.58039249 -5.38386071
> postscript(file="/var/www/rcomp/tmp/6103o1321699728.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 = 112
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.96524550 NA
1 1.69983165 -0.96524550
2 -1.15096067 1.69983165
3 -0.58376313 -1.15096067
4 -0.55072634 -0.58376313
5 1.06031014 -0.55072634
6 1.96119181 1.06031014
7 -5.73270231 1.96119181
8 -0.33381233 -5.73270231
9 -0.07284254 -0.33381233
10 -0.37558891 -0.07284254
11 -2.33922945 -0.37558891
12 -1.29936181 -2.33922945
13 -2.07226069 -1.29936181
14 -1.31622595 -2.07226069
15 -2.54274298 -1.31622595
16 0.43700055 -2.54274298
17 2.70037285 0.43700055
18 0.84375057 2.70037285
19 -0.07828407 0.84375057
20 -1.41228864 -0.07828407
21 -0.38281607 -1.41228864
22 -0.48365642 -0.38281607
23 2.32836318 -0.48365642
24 -0.54813250 2.32836318
25 1.55339268 -0.54813250
26 1.07974650 1.55339268
27 -0.41181999 1.07974650
28 2.25392775 -0.41181999
29 -1.36839688 2.25392775
30 -1.28243175 -1.36839688
31 -3.92806310 -1.28243175
32 -1.34457613 -3.92806310
33 -1.09837944 -1.34457613
34 -1.78276915 -1.09837944
35 1.39756384 -1.78276915
36 1.36933550 1.39756384
37 3.24010457 1.36933550
38 0.73518844 3.24010457
39 2.20740015 0.73518844
40 0.95951293 2.20740015
41 0.95250546 0.95951293
42 0.05993074 0.95250546
43 -0.40023779 0.05993074
44 0.98792685 -0.40023779
45 -0.68318855 0.98792685
46 0.06390675 -0.68318855
47 1.22527631 0.06390675
48 -1.72265159 1.22527631
49 -2.24056233 -1.72265159
50 3.20154671 -2.24056233
51 -0.02936469 3.20154671
52 3.41718372 -0.02936469
53 -0.44894559 3.41718372
54 2.81578698 -0.44894559
55 -1.33648917 2.81578698
56 -1.41004406 -1.33648917
57 3.28643487 -1.41004406
58 2.71498547 3.28643487
59 1.50242782 2.71498547
60 -2.64371191 1.50242782
61 -0.51590058 -2.64371191
62 2.01071339 -0.51590058
63 0.43301596 2.01071339
64 0.07317113 0.43301596
65 -1.97322722 0.07317113
66 0.09885270 -1.97322722
67 10.30079651 0.09885270
68 2.65879172 10.30079651
69 1.95922856 2.65879172
70 0.63652845 1.95922856
71 7.87714437 0.63652845
72 7.39877931 7.87714437
73 -0.11677980 7.39877931
74 -2.68511489 -0.11677980
75 5.48867506 -2.68511489
76 0.47320660 5.48867506
77 -1.69364104 0.47320660
78 -1.45305551 -1.69364104
79 2.66082814 -1.45305551
80 0.23617370 2.66082814
81 -1.76341578 0.23617370
82 -0.12530267 -1.76341578
83 -4.36382721 -0.12530267
84 -2.92854900 -4.36382721
85 4.67307797 -2.92854900
86 -0.06186432 4.67307797
87 1.83649319 -0.06186432
88 -0.58126050 1.83649319
89 0.17678334 -0.58126050
90 -3.06394989 0.17678334
91 0.11116363 -3.06394989
92 0.13779788 0.11116363
93 0.60350753 0.13779788
94 0.79932545 0.60350753
95 -1.06142102 0.79932545
96 -1.73426900 -1.06142102
97 -2.00488135 -1.73426900
98 -1.58039249 -2.00488135
99 -1.58039249 -1.58039249
100 -1.59748988 -1.58039249
101 -1.58039249 -1.59748988
102 0.03596030 -1.58039249
103 -3.77560268 0.03596030
104 -1.58039249 -3.77560268
105 -1.58039249 -1.58039249
106 -0.09320861 -1.58039249
107 0.11022551 -0.09320861
108 -1.98116278 0.11022551
109 -4.03273335 -1.98116278
110 -1.58039249 -4.03273335
111 -5.38386071 -1.58039249
112 NA -5.38386071
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.69983165 -0.96524550
[2,] -1.15096067 1.69983165
[3,] -0.58376313 -1.15096067
[4,] -0.55072634 -0.58376313
[5,] 1.06031014 -0.55072634
[6,] 1.96119181 1.06031014
[7,] -5.73270231 1.96119181
[8,] -0.33381233 -5.73270231
[9,] -0.07284254 -0.33381233
[10,] -0.37558891 -0.07284254
[11,] -2.33922945 -0.37558891
[12,] -1.29936181 -2.33922945
[13,] -2.07226069 -1.29936181
[14,] -1.31622595 -2.07226069
[15,] -2.54274298 -1.31622595
[16,] 0.43700055 -2.54274298
[17,] 2.70037285 0.43700055
[18,] 0.84375057 2.70037285
[19,] -0.07828407 0.84375057
[20,] -1.41228864 -0.07828407
[21,] -0.38281607 -1.41228864
[22,] -0.48365642 -0.38281607
[23,] 2.32836318 -0.48365642
[24,] -0.54813250 2.32836318
[25,] 1.55339268 -0.54813250
[26,] 1.07974650 1.55339268
[27,] -0.41181999 1.07974650
[28,] 2.25392775 -0.41181999
[29,] -1.36839688 2.25392775
[30,] -1.28243175 -1.36839688
[31,] -3.92806310 -1.28243175
[32,] -1.34457613 -3.92806310
[33,] -1.09837944 -1.34457613
[34,] -1.78276915 -1.09837944
[35,] 1.39756384 -1.78276915
[36,] 1.36933550 1.39756384
[37,] 3.24010457 1.36933550
[38,] 0.73518844 3.24010457
[39,] 2.20740015 0.73518844
[40,] 0.95951293 2.20740015
[41,] 0.95250546 0.95951293
[42,] 0.05993074 0.95250546
[43,] -0.40023779 0.05993074
[44,] 0.98792685 -0.40023779
[45,] -0.68318855 0.98792685
[46,] 0.06390675 -0.68318855
[47,] 1.22527631 0.06390675
[48,] -1.72265159 1.22527631
[49,] -2.24056233 -1.72265159
[50,] 3.20154671 -2.24056233
[51,] -0.02936469 3.20154671
[52,] 3.41718372 -0.02936469
[53,] -0.44894559 3.41718372
[54,] 2.81578698 -0.44894559
[55,] -1.33648917 2.81578698
[56,] -1.41004406 -1.33648917
[57,] 3.28643487 -1.41004406
[58,] 2.71498547 3.28643487
[59,] 1.50242782 2.71498547
[60,] -2.64371191 1.50242782
[61,] -0.51590058 -2.64371191
[62,] 2.01071339 -0.51590058
[63,] 0.43301596 2.01071339
[64,] 0.07317113 0.43301596
[65,] -1.97322722 0.07317113
[66,] 0.09885270 -1.97322722
[67,] 10.30079651 0.09885270
[68,] 2.65879172 10.30079651
[69,] 1.95922856 2.65879172
[70,] 0.63652845 1.95922856
[71,] 7.87714437 0.63652845
[72,] 7.39877931 7.87714437
[73,] -0.11677980 7.39877931
[74,] -2.68511489 -0.11677980
[75,] 5.48867506 -2.68511489
[76,] 0.47320660 5.48867506
[77,] -1.69364104 0.47320660
[78,] -1.45305551 -1.69364104
[79,] 2.66082814 -1.45305551
[80,] 0.23617370 2.66082814
[81,] -1.76341578 0.23617370
[82,] -0.12530267 -1.76341578
[83,] -4.36382721 -0.12530267
[84,] -2.92854900 -4.36382721
[85,] 4.67307797 -2.92854900
[86,] -0.06186432 4.67307797
[87,] 1.83649319 -0.06186432
[88,] -0.58126050 1.83649319
[89,] 0.17678334 -0.58126050
[90,] -3.06394989 0.17678334
[91,] 0.11116363 -3.06394989
[92,] 0.13779788 0.11116363
[93,] 0.60350753 0.13779788
[94,] 0.79932545 0.60350753
[95,] -1.06142102 0.79932545
[96,] -1.73426900 -1.06142102
[97,] -2.00488135 -1.73426900
[98,] -1.58039249 -2.00488135
[99,] -1.58039249 -1.58039249
[100,] -1.59748988 -1.58039249
[101,] -1.58039249 -1.59748988
[102,] 0.03596030 -1.58039249
[103,] -3.77560268 0.03596030
[104,] -1.58039249 -3.77560268
[105,] -1.58039249 -1.58039249
[106,] -0.09320861 -1.58039249
[107,] 0.11022551 -0.09320861
[108,] -1.98116278 0.11022551
[109,] -4.03273335 -1.98116278
[110,] -1.58039249 -4.03273335
[111,] -5.38386071 -1.58039249
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.69983165 -0.96524550
2 -1.15096067 1.69983165
3 -0.58376313 -1.15096067
4 -0.55072634 -0.58376313
5 1.06031014 -0.55072634
6 1.96119181 1.06031014
7 -5.73270231 1.96119181
8 -0.33381233 -5.73270231
9 -0.07284254 -0.33381233
10 -0.37558891 -0.07284254
11 -2.33922945 -0.37558891
12 -1.29936181 -2.33922945
13 -2.07226069 -1.29936181
14 -1.31622595 -2.07226069
15 -2.54274298 -1.31622595
16 0.43700055 -2.54274298
17 2.70037285 0.43700055
18 0.84375057 2.70037285
19 -0.07828407 0.84375057
20 -1.41228864 -0.07828407
21 -0.38281607 -1.41228864
22 -0.48365642 -0.38281607
23 2.32836318 -0.48365642
24 -0.54813250 2.32836318
25 1.55339268 -0.54813250
26 1.07974650 1.55339268
27 -0.41181999 1.07974650
28 2.25392775 -0.41181999
29 -1.36839688 2.25392775
30 -1.28243175 -1.36839688
31 -3.92806310 -1.28243175
32 -1.34457613 -3.92806310
33 -1.09837944 -1.34457613
34 -1.78276915 -1.09837944
35 1.39756384 -1.78276915
36 1.36933550 1.39756384
37 3.24010457 1.36933550
38 0.73518844 3.24010457
39 2.20740015 0.73518844
40 0.95951293 2.20740015
41 0.95250546 0.95951293
42 0.05993074 0.95250546
43 -0.40023779 0.05993074
44 0.98792685 -0.40023779
45 -0.68318855 0.98792685
46 0.06390675 -0.68318855
47 1.22527631 0.06390675
48 -1.72265159 1.22527631
49 -2.24056233 -1.72265159
50 3.20154671 -2.24056233
51 -0.02936469 3.20154671
52 3.41718372 -0.02936469
53 -0.44894559 3.41718372
54 2.81578698 -0.44894559
55 -1.33648917 2.81578698
56 -1.41004406 -1.33648917
57 3.28643487 -1.41004406
58 2.71498547 3.28643487
59 1.50242782 2.71498547
60 -2.64371191 1.50242782
61 -0.51590058 -2.64371191
62 2.01071339 -0.51590058
63 0.43301596 2.01071339
64 0.07317113 0.43301596
65 -1.97322722 0.07317113
66 0.09885270 -1.97322722
67 10.30079651 0.09885270
68 2.65879172 10.30079651
69 1.95922856 2.65879172
70 0.63652845 1.95922856
71 7.87714437 0.63652845
72 7.39877931 7.87714437
73 -0.11677980 7.39877931
74 -2.68511489 -0.11677980
75 5.48867506 -2.68511489
76 0.47320660 5.48867506
77 -1.69364104 0.47320660
78 -1.45305551 -1.69364104
79 2.66082814 -1.45305551
80 0.23617370 2.66082814
81 -1.76341578 0.23617370
82 -0.12530267 -1.76341578
83 -4.36382721 -0.12530267
84 -2.92854900 -4.36382721
85 4.67307797 -2.92854900
86 -0.06186432 4.67307797
87 1.83649319 -0.06186432
88 -0.58126050 1.83649319
89 0.17678334 -0.58126050
90 -3.06394989 0.17678334
91 0.11116363 -3.06394989
92 0.13779788 0.11116363
93 0.60350753 0.13779788
94 0.79932545 0.60350753
95 -1.06142102 0.79932545
96 -1.73426900 -1.06142102
97 -2.00488135 -1.73426900
98 -1.58039249 -2.00488135
99 -1.58039249 -1.58039249
100 -1.59748988 -1.58039249
101 -1.58039249 -1.59748988
102 0.03596030 -1.58039249
103 -3.77560268 0.03596030
104 -1.58039249 -3.77560268
105 -1.58039249 -1.58039249
106 -0.09320861 -1.58039249
107 0.11022551 -0.09320861
108 -1.98116278 0.11022551
109 -4.03273335 -1.98116278
110 -1.58039249 -4.03273335
111 -5.38386071 -1.58039249
> 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/rcomp/tmp/7u9vr1321699728.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8dl5i1321699728.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9rohj1321699728.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/www/rcomp/tmp/10d0ez1321699728.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/114qmc1321699728.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12dfjm1321699728.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13je2l1321699728.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14e5991321699728.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/rcomp/tmp/15f50t1321699728.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/rcomp/tmp/16r6bi1321699728.tab")
+ }
>
> try(system("convert tmp/146iz1321699728.ps tmp/146iz1321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/2roju1321699728.ps tmp/2roju1321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m4cn1321699728.ps tmp/3m4cn1321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/47ns71321699728.ps tmp/47ns71321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/584ug1321699728.ps tmp/584ug1321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/6103o1321699728.ps tmp/6103o1321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u9vr1321699728.ps tmp/7u9vr1321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dl5i1321699728.ps tmp/8dl5i1321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rohj1321699728.ps tmp/9rohj1321699728.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d0ez1321699728.ps tmp/10d0ez1321699728.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.900 0.230 4.126