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 = '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 t
1 12 65 22 114468 2 1
2 13 54 20 88594 4 2
3 11 58 24 74151 9 3
4 12 77 21 77921 2 4
5 8 41 15 53212 1 5
6 7 0 16 34956 2 6
7 18 111 20 149703 0 7
8 0 1 18 6853 0 8
9 9 36 19 58907 5 9
10 11 60 20 67067 0 10
11 13 63 25 110563 0 11
12 13 71 37 58126 7 12
13 9 38 23 57113 6 13
14 12 76 28 77993 3 14
15 11 61 25 68091 4 15
16 17 125 35 124676 0 16
17 14 84 20 109522 4 17
18 15 69 22 75865 3 18
19 13 77 19 79746 0 19
20 15 100 26 77844 5 20
21 13 78 27 98681 0 21
22 13 76 22 105531 1 22
23 8 40 15 51428 3 23
24 16 81 26 65703 5 24
25 14 102 24 72562 0 25
26 14 70 22 81728 0 26
27 14 75 21 95580 4 27
28 14 93 23 98278 0 28
29 12 42 21 46629 0 29
30 14 95 25 115189 0 30
31 2 8 4 15049 0 31
32 12 87 30 109011 5 32
33 13 87 20 134245 5 33
34 16 112 26 136692 0 34
35 15 96 27 149510 6 35
36 16 93 18 147888 6 36
37 15 98 20 79169 2 37
38 16 99 17 65469 5 38
39 14 94 22 56756 0 39
40 17 98 25 81399 3 40
41 18 109 30 104953 0 41
42 16 108 26 59633 1 42
43 10 42 20 63249 1 43
44 15 108 25 82928 2 44
45 10 27 21 50000 4 45
46 16 115 23 139357 0 46
47 17 92 33 110044 7 47
48 17 106 19 155118 7 48
49 13 73 31 83061 6 49
50 14 105 25 127122 0 50
51 12 30 20 45653 0 51
52 7 13 19 19630 4 52
53 14 69 15 67229 4 53
54 12 72 21 86060 0 54
55 16 80 22 88003 0 55
56 14 106 24 95815 0 56
57 8 28 19 85499 0 57
58 14 70 20 27220 0 58
59 15 51 23 109882 4 59
60 16 90 27 72579 0 60
61 0 12 1 5841 0 61
62 12 84 20 68369 0 62
63 8 23 11 24610 4 63
64 12 57 27 30995 0 64
65 15 84 22 150662 1 65
66 0 4 0 6622 0 66
67 11 56 17 93694 5 67
68 15 18 8 13155 0 68
69 17 86 23 111908 1 69
70 13 39 26 57550 7 70
71 8 16 20 16356 5 71
72 15 18 16 40174 2 72
73 12 16 8 13983 0 73
74 10 42 22 52316 1 74
75 13 77 33 99585 0 75
76 17 30 28 86271 0 76
77 17 104 26 131012 2 77
78 16 121 27 130274 0 78
79 18 109 35 159051 2 79
80 14 57 21 76506 0 80
81 9 28 20 49145 0 81
82 10 56 24 66398 4 82
83 15 81 26 127546 4 83
84 2 2 20 6802 8 84
85 11 88 22 99509 0 85
86 15 41 24 43106 4 86
87 14 83 23 108303 0 87
88 13 55 22 64167 1 88
89 4 3 12 8579 0 89
90 12 54 21 97811 9 90
91 11 89 24 84365 0 91
92 9 41 21 10901 3 92
93 15 94 25 91346 7 93
94 16 101 32 33660 5 94
95 14 70 24 93634 2 95
96 16 111 29 109348 1 96
97 0 0 0 0 9 97
98 0 4 0 7953 0 98
99 0 0 0 0 0 99
100 0 0 0 0 0 100
101 0 0 0 0 1 101
102 0 0 0 0 0 102
103 10 42 20 63538 2 103
104 12 97 27 108281 1 104
105 0 0 0 0 0 105
106 0 0 0 0 0 106
107 2 7 0 4245 0 107
108 4 12 5 21509 0 108
109 0 0 1 7670 0 109
110 5 37 23 10641 0 110
111 0 0 0 0 0 111
112 3 39 16 41243 2 112
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Blogs Reviews Compendium_Writing
2.183e+00 5.874e-02 2.129e-01 2.271e-05
Gedeelde_compendia t
1.024e-02 -7.080e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1728 -1.4762 -0.3431 0.9818 10.2391
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.183e+00 8.899e-01 2.453 0.0158 *
Blogs 5.874e-02 1.342e-02 4.377 2.83e-05 ***
Reviews 2.129e-01 4.256e-02 5.002 2.26e-06 ***
Compendium_Writing 2.271e-05 1.011e-05 2.245 0.0268 *
Gedeelde_compendia 1.024e-02 9.725e-02 0.105 0.9163
t -7.080e-03 7.974e-03 -0.888 0.3766
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.478 on 106 degrees of freedom
Multiple R-squared: 0.7939, Adjusted R-squared: 0.7842
F-statistic: 81.68 on 5 and 106 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,] 0.2784944205 0.5569888410 0.721505580
[2,] 0.2992316192 0.5984632384 0.700768381
[3,] 0.1937923851 0.3875847702 0.806207615
[4,] 0.1918838690 0.3837677380 0.808116131
[5,] 0.1375731842 0.2751463684 0.862426816
[6,] 0.0895765944 0.1791531888 0.910423406
[7,] 0.0533934699 0.1067869397 0.946606530
[8,] 0.0329183174 0.0658366349 0.967081683
[9,] 0.0201129867 0.0402259733 0.979887013
[10,] 0.0454354093 0.0908708186 0.954564591
[11,] 0.0270313037 0.0540626073 0.972968696
[12,] 0.0153725413 0.0307450825 0.984627459
[13,] 0.0097490755 0.0194981510 0.990250925
[14,] 0.0066997584 0.0133995169 0.993300242
[15,] 0.0046828402 0.0093656803 0.995317160
[16,] 0.0080215037 0.0160430074 0.991978496
[17,] 0.0046452837 0.0092905674 0.995354716
[18,] 0.0037150270 0.0074300540 0.996284973
[19,] 0.0021765563 0.0043531126 0.997823444
[20,] 0.0014091154 0.0028182308 0.998590885
[21,] 0.0025774058 0.0051548116 0.997422594
[22,] 0.0029427692 0.0058855385 0.997057231
[23,] 0.0051697663 0.0103395326 0.994830234
[24,] 0.0194394417 0.0388788834 0.980560558
[25,] 0.0204372242 0.0408744484 0.979562776
[26,] 0.0153202993 0.0306405986 0.984679701
[27,] 0.0126348506 0.0252697013 0.987365149
[28,] 0.0085587047 0.0171174094 0.991441295
[29,] 0.0056525991 0.0113051982 0.994347401
[30,] 0.0045181277 0.0090362554 0.995481872
[31,] 0.0028188499 0.0056376998 0.997181150
[32,] 0.0026633893 0.0053267785 0.997336611
[33,] 0.0021608888 0.0043217775 0.997839111
[34,] 0.0012997895 0.0025995790 0.998700210
[35,] 0.0009653897 0.0019307793 0.999034610
[36,] 0.0006893073 0.0013786146 0.999310693
[37,] 0.0006531242 0.0013062485 0.999346876
[38,] 0.0004799401 0.0009598803 0.999520060
[39,] 0.0003223719 0.0006447438 0.999677628
[40,] 0.0001862859 0.0003725719 0.999813714
[41,] 0.0001704807 0.0003409613 0.999829519
[42,] 0.0002320022 0.0004640043 0.999767998
[43,] 0.0005516516 0.0011033032 0.999448348
[44,] 0.0004540975 0.0009081951 0.999545902
[45,] 0.0003672573 0.0007345146 0.999632743
[46,] 0.0002761864 0.0005523728 0.999723814
[47,] 0.0002718292 0.0005436584 0.999728171
[48,] 0.0003056911 0.0006113821 0.999694309
[49,] 0.0004054591 0.0008109182 0.999594541
[50,] 0.0003450613 0.0006901226 0.999654939
[51,] 0.0004225123 0.0008450247 0.999577488
[52,] 0.0002717225 0.0005434451 0.999728277
[53,] 0.0032783136 0.0065566273 0.996721686
[54,] 0.0030564636 0.0061129272 0.996943536
[55,] 0.0020939619 0.0041879238 0.997906038
[56,] 0.0018461298 0.0036922596 0.998153870
[57,] 0.0013221743 0.0026443485 0.998677826
[58,] 0.0089886471 0.0179772941 0.991011353
[59,] 0.0087788044 0.0175576087 0.991221196
[60,] 0.2205898036 0.4411796072 0.779410196
[61,] 0.1922300394 0.3844600789 0.807769961
[62,] 0.1588334115 0.3176668230 0.841166588
[63,] 0.1469015831 0.2938031662 0.853098417
[64,] 0.3961916814 0.7923833628 0.603808319
[65,] 0.6854001403 0.6291997195 0.314599860
[66,] 0.6481858047 0.7036283906 0.351814195
[67,] 0.7399816578 0.5200366844 0.260018342
[68,] 0.8643385004 0.2713229993 0.135661500
[69,] 0.8291625591 0.3416748817 0.170837441
[70,] 0.8304600705 0.3390798590 0.169539930
[71,] 0.8137513737 0.3724972525 0.186248626
[72,] 0.8097662779 0.3804674442 0.190233722
[73,] 0.7670100879 0.4659798242 0.232989912
[74,] 0.7701152815 0.4597694371 0.229884719
[75,] 0.7176991858 0.5646016284 0.282300814
[76,] 0.9641744827 0.0716510346 0.035825517
[77,] 0.9794969139 0.0410061722 0.020503086
[78,] 0.9931627820 0.0136744361 0.006837218
[79,] 0.9884741294 0.0230517413 0.011525871
[80,] 0.9877277894 0.0245444211 0.012272211
[81,] 0.9818657863 0.0362684275 0.018134214
[82,] 0.9701637423 0.0596725153 0.029836258
[83,] 0.9857488735 0.0285022530 0.014251127
[84,] 0.9753360806 0.0493278389 0.024663919
[85,] 0.9605539123 0.0788921755 0.039446088
[86,] 0.9859609697 0.0280780605 0.014039030
[87,] 0.9780456784 0.0439086432 0.021954322
[88,] 0.9854828792 0.0290342417 0.014517121
[89,] 0.9879125817 0.0241748365 0.012087418
[90,] 0.9845355169 0.0309289663 0.015464483
[91,] 0.9729748164 0.0540503672 0.027025184
[92,] 0.9571362923 0.0857274154 0.042863708
[93,] 0.9108201088 0.1783597824 0.089179891
[94,] 0.8699010805 0.2601978389 0.130098919
[95,] 0.8732416891 0.2535166218 0.126758311
> postscript(file="/var/www/rcomp/tmp/1bx9z1321705340.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/2twsa1321705340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3cyk01321705340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/48ygy1321705340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/51t6a1321705340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-1.29721905 1.34878411 -1.45380482 -0.93799974 -0.96783918 0.63884653
7 8 9 10 11 12
1.68952212 -6.17282227 -0.66763510 -0.41718347 -0.63842346 -2.53665460
13 14 15 16 17 18
-1.57779375 -2.31047740 -1.56911716 -2.69387360 0.21766740 2.45456990
19 20 21 22 23 24
0.57298315 -0.26899674 -1.60458941 -0.58145229 -0.76164078 2.15100046
25 26 27 28 29 30
-0.75416914 1.35005142 0.92080893 -0.57540881 2.02580814 -1.48849778
31 32 33 34 35 36
-1.62699481 -3.97974390 -1.41693110 -1.15986161 -1.77841129 1.35759338
37 38 39 40 41 42
1.24671546 3.11407433 0.59951228 2.14269279 0.93514451 0.87137651
43 44 45 46 47 48
-0.04985921 -0.44082498 0.90260080 -0.67300359 0.15020447 1.29168689
49 50 51 52 53 54
-1.67092399 -2.20524346 3.12142339 -0.11013387 3.37835747 -0.45469007
55 56 57 58 59 60
2.82550666 -1.29768816 -1.41059828 3.24014790 2.80647522 1.55942580
61 62 63 64 65 66
-2.80182176 -0.48826604 1.97030662 0.47032846 0.22822287 -2.10139946
67 68 69 70 71 72
0.20405704 10.23909845 2.80624210 2.10821160 0.69940214 7.93036354
73 74 75 76 77 78
7.37316438 -0.00787314 -2.46134467 5.67302546 0.72294611 -1.44411408
79 80 81 82 83 84
-1.10918344 2.82736780 0.37198197 -1.54978932 0.17456664 -4.20003054
85 86 87 88 89 90
-2.69327617 4.88848929 0.20198494 2.05855963 -0.47878925 0.49839990
91 92 93 94 95 96
-2.79138806 0.31116506 0.48600617 0.92225203 1.12193772 -0.69012063
97 98 99 100 101 102
-1.58877925 -1.90507956 -1.48245676 -1.47537723 -1.47853808 -1.46121817
103 104 105 106 107 108
0.35810955 -3.36120522 -1.43997957 -1.43290004 0.06663318 0.32361364
109 110 111 112
-1.79871239 -3.71558835 -1.39750238 -5.04417586
> postscript(file="/var/www/rcomp/tmp/67m0e1321705340.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 -1.29721905 NA
1 1.34878411 -1.29721905
2 -1.45380482 1.34878411
3 -0.93799974 -1.45380482
4 -0.96783918 -0.93799974
5 0.63884653 -0.96783918
6 1.68952212 0.63884653
7 -6.17282227 1.68952212
8 -0.66763510 -6.17282227
9 -0.41718347 -0.66763510
10 -0.63842346 -0.41718347
11 -2.53665460 -0.63842346
12 -1.57779375 -2.53665460
13 -2.31047740 -1.57779375
14 -1.56911716 -2.31047740
15 -2.69387360 -1.56911716
16 0.21766740 -2.69387360
17 2.45456990 0.21766740
18 0.57298315 2.45456990
19 -0.26899674 0.57298315
20 -1.60458941 -0.26899674
21 -0.58145229 -1.60458941
22 -0.76164078 -0.58145229
23 2.15100046 -0.76164078
24 -0.75416914 2.15100046
25 1.35005142 -0.75416914
26 0.92080893 1.35005142
27 -0.57540881 0.92080893
28 2.02580814 -0.57540881
29 -1.48849778 2.02580814
30 -1.62699481 -1.48849778
31 -3.97974390 -1.62699481
32 -1.41693110 -3.97974390
33 -1.15986161 -1.41693110
34 -1.77841129 -1.15986161
35 1.35759338 -1.77841129
36 1.24671546 1.35759338
37 3.11407433 1.24671546
38 0.59951228 3.11407433
39 2.14269279 0.59951228
40 0.93514451 2.14269279
41 0.87137651 0.93514451
42 -0.04985921 0.87137651
43 -0.44082498 -0.04985921
44 0.90260080 -0.44082498
45 -0.67300359 0.90260080
46 0.15020447 -0.67300359
47 1.29168689 0.15020447
48 -1.67092399 1.29168689
49 -2.20524346 -1.67092399
50 3.12142339 -2.20524346
51 -0.11013387 3.12142339
52 3.37835747 -0.11013387
53 -0.45469007 3.37835747
54 2.82550666 -0.45469007
55 -1.29768816 2.82550666
56 -1.41059828 -1.29768816
57 3.24014790 -1.41059828
58 2.80647522 3.24014790
59 1.55942580 2.80647522
60 -2.80182176 1.55942580
61 -0.48826604 -2.80182176
62 1.97030662 -0.48826604
63 0.47032846 1.97030662
64 0.22822287 0.47032846
65 -2.10139946 0.22822287
66 0.20405704 -2.10139946
67 10.23909845 0.20405704
68 2.80624210 10.23909845
69 2.10821160 2.80624210
70 0.69940214 2.10821160
71 7.93036354 0.69940214
72 7.37316438 7.93036354
73 -0.00787314 7.37316438
74 -2.46134467 -0.00787314
75 5.67302546 -2.46134467
76 0.72294611 5.67302546
77 -1.44411408 0.72294611
78 -1.10918344 -1.44411408
79 2.82736780 -1.10918344
80 0.37198197 2.82736780
81 -1.54978932 0.37198197
82 0.17456664 -1.54978932
83 -4.20003054 0.17456664
84 -2.69327617 -4.20003054
85 4.88848929 -2.69327617
86 0.20198494 4.88848929
87 2.05855963 0.20198494
88 -0.47878925 2.05855963
89 0.49839990 -0.47878925
90 -2.79138806 0.49839990
91 0.31116506 -2.79138806
92 0.48600617 0.31116506
93 0.92225203 0.48600617
94 1.12193772 0.92225203
95 -0.69012063 1.12193772
96 -1.58877925 -0.69012063
97 -1.90507956 -1.58877925
98 -1.48245676 -1.90507956
99 -1.47537723 -1.48245676
100 -1.47853808 -1.47537723
101 -1.46121817 -1.47853808
102 0.35810955 -1.46121817
103 -3.36120522 0.35810955
104 -1.43997957 -3.36120522
105 -1.43290004 -1.43997957
106 0.06663318 -1.43290004
107 0.32361364 0.06663318
108 -1.79871239 0.32361364
109 -3.71558835 -1.79871239
110 -1.39750238 -3.71558835
111 -5.04417586 -1.39750238
112 NA -5.04417586
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.34878411 -1.29721905
[2,] -1.45380482 1.34878411
[3,] -0.93799974 -1.45380482
[4,] -0.96783918 -0.93799974
[5,] 0.63884653 -0.96783918
[6,] 1.68952212 0.63884653
[7,] -6.17282227 1.68952212
[8,] -0.66763510 -6.17282227
[9,] -0.41718347 -0.66763510
[10,] -0.63842346 -0.41718347
[11,] -2.53665460 -0.63842346
[12,] -1.57779375 -2.53665460
[13,] -2.31047740 -1.57779375
[14,] -1.56911716 -2.31047740
[15,] -2.69387360 -1.56911716
[16,] 0.21766740 -2.69387360
[17,] 2.45456990 0.21766740
[18,] 0.57298315 2.45456990
[19,] -0.26899674 0.57298315
[20,] -1.60458941 -0.26899674
[21,] -0.58145229 -1.60458941
[22,] -0.76164078 -0.58145229
[23,] 2.15100046 -0.76164078
[24,] -0.75416914 2.15100046
[25,] 1.35005142 -0.75416914
[26,] 0.92080893 1.35005142
[27,] -0.57540881 0.92080893
[28,] 2.02580814 -0.57540881
[29,] -1.48849778 2.02580814
[30,] -1.62699481 -1.48849778
[31,] -3.97974390 -1.62699481
[32,] -1.41693110 -3.97974390
[33,] -1.15986161 -1.41693110
[34,] -1.77841129 -1.15986161
[35,] 1.35759338 -1.77841129
[36,] 1.24671546 1.35759338
[37,] 3.11407433 1.24671546
[38,] 0.59951228 3.11407433
[39,] 2.14269279 0.59951228
[40,] 0.93514451 2.14269279
[41,] 0.87137651 0.93514451
[42,] -0.04985921 0.87137651
[43,] -0.44082498 -0.04985921
[44,] 0.90260080 -0.44082498
[45,] -0.67300359 0.90260080
[46,] 0.15020447 -0.67300359
[47,] 1.29168689 0.15020447
[48,] -1.67092399 1.29168689
[49,] -2.20524346 -1.67092399
[50,] 3.12142339 -2.20524346
[51,] -0.11013387 3.12142339
[52,] 3.37835747 -0.11013387
[53,] -0.45469007 3.37835747
[54,] 2.82550666 -0.45469007
[55,] -1.29768816 2.82550666
[56,] -1.41059828 -1.29768816
[57,] 3.24014790 -1.41059828
[58,] 2.80647522 3.24014790
[59,] 1.55942580 2.80647522
[60,] -2.80182176 1.55942580
[61,] -0.48826604 -2.80182176
[62,] 1.97030662 -0.48826604
[63,] 0.47032846 1.97030662
[64,] 0.22822287 0.47032846
[65,] -2.10139946 0.22822287
[66,] 0.20405704 -2.10139946
[67,] 10.23909845 0.20405704
[68,] 2.80624210 10.23909845
[69,] 2.10821160 2.80624210
[70,] 0.69940214 2.10821160
[71,] 7.93036354 0.69940214
[72,] 7.37316438 7.93036354
[73,] -0.00787314 7.37316438
[74,] -2.46134467 -0.00787314
[75,] 5.67302546 -2.46134467
[76,] 0.72294611 5.67302546
[77,] -1.44411408 0.72294611
[78,] -1.10918344 -1.44411408
[79,] 2.82736780 -1.10918344
[80,] 0.37198197 2.82736780
[81,] -1.54978932 0.37198197
[82,] 0.17456664 -1.54978932
[83,] -4.20003054 0.17456664
[84,] -2.69327617 -4.20003054
[85,] 4.88848929 -2.69327617
[86,] 0.20198494 4.88848929
[87,] 2.05855963 0.20198494
[88,] -0.47878925 2.05855963
[89,] 0.49839990 -0.47878925
[90,] -2.79138806 0.49839990
[91,] 0.31116506 -2.79138806
[92,] 0.48600617 0.31116506
[93,] 0.92225203 0.48600617
[94,] 1.12193772 0.92225203
[95,] -0.69012063 1.12193772
[96,] -1.58877925 -0.69012063
[97,] -1.90507956 -1.58877925
[98,] -1.48245676 -1.90507956
[99,] -1.47537723 -1.48245676
[100,] -1.47853808 -1.47537723
[101,] -1.46121817 -1.47853808
[102,] 0.35810955 -1.46121817
[103,] -3.36120522 0.35810955
[104,] -1.43997957 -3.36120522
[105,] -1.43290004 -1.43997957
[106,] 0.06663318 -1.43290004
[107,] 0.32361364 0.06663318
[108,] -1.79871239 0.32361364
[109,] -3.71558835 -1.79871239
[110,] -1.39750238 -3.71558835
[111,] -5.04417586 -1.39750238
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.34878411 -1.29721905
2 -1.45380482 1.34878411
3 -0.93799974 -1.45380482
4 -0.96783918 -0.93799974
5 0.63884653 -0.96783918
6 1.68952212 0.63884653
7 -6.17282227 1.68952212
8 -0.66763510 -6.17282227
9 -0.41718347 -0.66763510
10 -0.63842346 -0.41718347
11 -2.53665460 -0.63842346
12 -1.57779375 -2.53665460
13 -2.31047740 -1.57779375
14 -1.56911716 -2.31047740
15 -2.69387360 -1.56911716
16 0.21766740 -2.69387360
17 2.45456990 0.21766740
18 0.57298315 2.45456990
19 -0.26899674 0.57298315
20 -1.60458941 -0.26899674
21 -0.58145229 -1.60458941
22 -0.76164078 -0.58145229
23 2.15100046 -0.76164078
24 -0.75416914 2.15100046
25 1.35005142 -0.75416914
26 0.92080893 1.35005142
27 -0.57540881 0.92080893
28 2.02580814 -0.57540881
29 -1.48849778 2.02580814
30 -1.62699481 -1.48849778
31 -3.97974390 -1.62699481
32 -1.41693110 -3.97974390
33 -1.15986161 -1.41693110
34 -1.77841129 -1.15986161
35 1.35759338 -1.77841129
36 1.24671546 1.35759338
37 3.11407433 1.24671546
38 0.59951228 3.11407433
39 2.14269279 0.59951228
40 0.93514451 2.14269279
41 0.87137651 0.93514451
42 -0.04985921 0.87137651
43 -0.44082498 -0.04985921
44 0.90260080 -0.44082498
45 -0.67300359 0.90260080
46 0.15020447 -0.67300359
47 1.29168689 0.15020447
48 -1.67092399 1.29168689
49 -2.20524346 -1.67092399
50 3.12142339 -2.20524346
51 -0.11013387 3.12142339
52 3.37835747 -0.11013387
53 -0.45469007 3.37835747
54 2.82550666 -0.45469007
55 -1.29768816 2.82550666
56 -1.41059828 -1.29768816
57 3.24014790 -1.41059828
58 2.80647522 3.24014790
59 1.55942580 2.80647522
60 -2.80182176 1.55942580
61 -0.48826604 -2.80182176
62 1.97030662 -0.48826604
63 0.47032846 1.97030662
64 0.22822287 0.47032846
65 -2.10139946 0.22822287
66 0.20405704 -2.10139946
67 10.23909845 0.20405704
68 2.80624210 10.23909845
69 2.10821160 2.80624210
70 0.69940214 2.10821160
71 7.93036354 0.69940214
72 7.37316438 7.93036354
73 -0.00787314 7.37316438
74 -2.46134467 -0.00787314
75 5.67302546 -2.46134467
76 0.72294611 5.67302546
77 -1.44411408 0.72294611
78 -1.10918344 -1.44411408
79 2.82736780 -1.10918344
80 0.37198197 2.82736780
81 -1.54978932 0.37198197
82 0.17456664 -1.54978932
83 -4.20003054 0.17456664
84 -2.69327617 -4.20003054
85 4.88848929 -2.69327617
86 0.20198494 4.88848929
87 2.05855963 0.20198494
88 -0.47878925 2.05855963
89 0.49839990 -0.47878925
90 -2.79138806 0.49839990
91 0.31116506 -2.79138806
92 0.48600617 0.31116506
93 0.92225203 0.48600617
94 1.12193772 0.92225203
95 -0.69012063 1.12193772
96 -1.58877925 -0.69012063
97 -1.90507956 -1.58877925
98 -1.48245676 -1.90507956
99 -1.47537723 -1.48245676
100 -1.47853808 -1.47537723
101 -1.46121817 -1.47853808
102 0.35810955 -1.46121817
103 -3.36120522 0.35810955
104 -1.43997957 -3.36120522
105 -1.43290004 -1.43997957
106 0.06663318 -1.43290004
107 0.32361364 0.06663318
108 -1.79871239 0.32361364
109 -3.71558835 -1.79871239
110 -1.39750238 -3.71558835
111 -5.04417586 -1.39750238
> 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/7lkf41321705340.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/8q4o21321705340.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/91y1u1321705340.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/10ut571321705340.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11sq711321705340.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12oozp1321705340.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13i60p1321705340.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14t13e1321705340.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/15liib1321705340.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/16m7eo1321705340.tab")
+ }
>
> try(system("convert tmp/1bx9z1321705340.ps tmp/1bx9z1321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/2twsa1321705340.ps tmp/2twsa1321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cyk01321705340.ps tmp/3cyk01321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/48ygy1321705340.ps tmp/48ygy1321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/51t6a1321705340.ps tmp/51t6a1321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/67m0e1321705340.ps tmp/67m0e1321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lkf41321705340.ps tmp/7lkf41321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/8q4o21321705340.ps tmp/8q4o21321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/91y1u1321705340.ps tmp/91y1u1321705340.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ut571321705340.ps tmp/10ut571321705340.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.610 0.210 3.806