R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'q()' to quit R.
> x <- array(list(0
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+ ,dim=c(8
+ ,120)
+ ,dimnames=list(c('Gender'
+ ,'Browser'
+ ,'CM'
+ ,'D'
+ ,'PE'
+ ,'PC'
+ ,'PS'
+ ,'O')
+ ,1:120))
> y <- array(NA,dim=c(8,120),dimnames=list(c('Gender','Browser','CM','D','PE','PC','PS','O'),1:120))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> #'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
PE Gender Browser CM D PC PS O
1 11 0 1 23 14 12 24 26
2 7 1 1 25 11 8 25 23
3 17 1 0 17 6 8 30 25
4 10 0 1 18 12 8 19 23
5 12 1 0 16 10 7 22 29
6 11 1 1 20 10 4 25 25
7 11 1 1 16 11 11 23 21
8 12 1 1 18 16 7 17 22
9 13 1 1 17 11 7 21 25
10 14 0 1 23 13 12 19 24
11 16 1 1 30 12 10 19 18
12 10 1 1 18 12 8 16 15
13 11 0 1 15 11 8 23 22
14 15 0 1 12 4 4 27 28
15 9 1 1 21 9 9 22 20
16 17 0 1 20 8 7 22 24
17 11 1 1 27 15 9 23 21
18 18 0 1 34 16 11 21 20
19 14 1 1 21 9 13 19 21
20 10 0 1 31 14 8 18 23
21 11 0 1 19 11 8 20 28
22 15 1 1 16 8 9 23 24
23 15 1 1 20 9 6 25 24
24 13 0 1 21 9 9 19 24
25 16 0 1 22 9 9 24 23
26 13 1 1 17 9 6 22 23
27 9 0 1 24 10 6 25 29
28 18 1 1 25 16 16 26 24
29 18 1 1 26 11 5 29 18
30 12 1 1 25 8 7 32 25
31 17 1 1 17 9 9 25 21
32 9 0 1 32 16 6 29 26
33 9 0 1 33 11 6 28 22
34 18 0 0 32 12 12 28 22
35 12 0 1 25 12 7 29 23
36 18 0 1 29 14 10 26 30
37 14 1 1 22 9 9 25 23
38 15 0 1 18 10 8 14 17
39 16 1 1 17 9 5 25 23
40 10 0 1 20 10 8 26 23
41 11 0 1 15 12 8 20 25
42 14 1 1 20 14 10 18 24
43 9 0 1 33 14 6 32 24
44 17 1 1 23 14 7 25 21
45 5 0 1 26 16 4 23 24
46 12 0 1 18 9 8 21 24
47 12 1 1 20 10 8 20 28
48 6 1 1 11 6 4 15 16
49 24 0 1 28 8 20 30 20
50 12 1 1 26 13 8 24 29
51 12 1 1 22 10 8 26 27
52 14 0 1 17 8 6 24 22
53 7 0 1 12 7 4 22 28
54 12 0 1 17 9 9 24 25
55 14 1 0 19 12 7 24 28
56 8 0 1 18 13 9 24 24
57 11 0 1 10 10 5 19 23
58 9 0 1 29 11 5 31 30
59 11 0 1 31 8 8 22 24
60 10 0 1 9 13 6 19 25
61 11 1 0 20 11 8 25 25
62 12 1 1 28 8 7 20 22
63 9 1 1 19 9 7 21 23
64 18 1 1 29 15 11 23 23
65 15 1 1 26 9 6 25 25
66 12 1 1 23 10 8 20 21
67 13 0 1 13 14 6 21 25
68 14 1 1 21 12 9 22 24
69 10 0 1 19 12 8 23 29
70 13 1 1 28 11 6 25 22
71 13 1 1 23 14 10 25 27
72 11 1 0 18 6 8 17 26
73 13 0 1 21 12 8 19 22
74 16 1 1 20 8 10 25 24
75 11 1 1 21 10 5 26 24
76 16 1 1 28 12 14 27 22
77 14 0 1 26 14 8 17 24
78 8 1 1 10 5 6 19 24
79 9 0 0 16 11 5 17 23
80 15 0 1 22 10 6 22 20
81 11 0 1 19 9 10 21 27
82 21 1 1 31 10 12 32 26
83 14 0 1 31 16 9 21 25
84 18 1 1 29 13 12 21 21
85 12 0 1 19 9 7 18 21
86 13 1 1 22 10 8 18 19
87 12 0 1 15 7 6 19 21
88 19 1 1 20 9 10 20 16
89 11 0 1 23 14 10 20 29
90 13 1 1 24 9 10 19 15
91 15 1 1 25 14 11 22 21
92 12 1 1 13 8 7 14 19
93 16 1 1 28 8 12 18 24
94 18 1 0 25 7 11 35 17
95 8 1 1 9 6 11 29 23
96 9 0 1 17 11 6 20 19
97 15 0 1 25 14 9 22 24
98 6 1 1 15 8 6 20 25
99 8 0 1 19 20 7 19 25
100 10 1 0 15 8 4 22 24
101 11 1 1 20 11 8 24 26
102 14 1 1 18 10 9 21 26
103 11 1 1 33 14 8 26 25
104 12 1 1 16 9 8 16 21
105 11 0 1 17 9 5 23 26
106 9 1 1 16 8 4 18 23
107 12 0 1 21 10 8 16 23
108 20 0 1 26 13 10 26 22
109 13 1 1 18 12 9 21 13
110 12 1 1 22 13 13 22 15
111 9 1 1 30 14 9 23 14
112 24 1 1 24 14 20 21 10
113 11 1 1 29 16 6 27 24
114 17 1 1 31 9 9 25 19
115 11 1 0 20 9 7 21 20
116 11 1 1 20 7 9 26 22
117 16 1 1 28 16 8 24 24
118 13 1 1 17 9 6 19 21
119 11 0 1 28 14 8 24 24
120 19 1 1 31 16 16 17 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Browser CM D PC
7.08589 0.18344 -0.54504 0.09889 -0.16149 0.67943
PS O
0.10350 -0.09753
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.87750 -1.79476 0.08038 1.84293 5.83780
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.08589 2.64374 2.680 0.00847 **
Gender 0.18344 0.54162 0.339 0.73549
Browser -0.54504 0.93364 -0.584 0.56054
CM 0.09889 0.05714 1.731 0.08628 .
D -0.16149 0.10606 -1.523 0.13067
PC 0.67943 0.09968 6.816 4.95e-10 ***
PS 0.10350 0.07257 1.426 0.15655
O -0.09753 0.07961 -1.225 0.22312
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.73 on 112 degrees of freedom
Multiple R-squared: 0.4366, Adjusted R-squared: 0.4014
F-statistic: 12.4 on 7 and 112 DF, p-value: 1.128e-11
> 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,] 0.87247376 0.25505248 0.12752624
[2,] 0.86490018 0.27019963 0.13509982
[3,] 0.78534184 0.42931631 0.21465816
[4,] 0.74959261 0.50081477 0.25040739
[5,] 0.80889456 0.38221087 0.19110544
[6,] 0.81499678 0.37000644 0.18500322
[7,] 0.76178439 0.47643123 0.23821561
[8,] 0.77245401 0.45509198 0.22754599
[9,] 0.72437659 0.55124682 0.27562341
[10,] 0.84102073 0.31795854 0.15897927
[11,] 0.79598635 0.40802731 0.20401365
[12,] 0.79581056 0.40837889 0.20418944
[13,] 0.80289826 0.39420348 0.19710174
[14,] 0.75202719 0.49594562 0.24797281
[15,] 0.71632495 0.56735010 0.28367505
[16,] 0.66590401 0.66819198 0.33409599
[17,] 0.70512609 0.58974783 0.29487391
[18,] 0.76303788 0.47392423 0.23696212
[19,] 0.83428347 0.33143306 0.16571653
[20,] 0.83183378 0.33633244 0.16816622
[21,] 0.84155651 0.31688698 0.15844349
[22,] 0.84241761 0.31516479 0.15758239
[23,] 0.88322700 0.23354600 0.11677300
[24,] 0.85096950 0.29806101 0.14903050
[25,] 0.81569021 0.36861959 0.18430979
[26,] 0.89887658 0.20224684 0.10112342
[27,] 0.86932394 0.26135212 0.13067606
[28,] 0.86293016 0.27413968 0.13706984
[29,] 0.91887544 0.16224912 0.08112456
[30,] 0.92484959 0.15030082 0.07515041
[31,] 0.90257386 0.19485229 0.09742614
[32,] 0.88275860 0.23448281 0.11724140
[33,] 0.89150082 0.21699837 0.10849918
[34,] 0.94104645 0.11790709 0.05895355
[35,] 0.96354311 0.07291378 0.03645689
[36,] 0.95114391 0.09771218 0.04885609
[37,] 0.93567529 0.12864942 0.06432471
[38,] 0.95755666 0.08488667 0.04244334
[39,] 0.94678168 0.10643665 0.05321832
[40,] 0.92974265 0.14051470 0.07025735
[41,] 0.91152148 0.17695704 0.08847852
[42,] 0.90924955 0.18150091 0.09075045
[43,] 0.89996367 0.20007266 0.10003633
[44,] 0.87695637 0.24608726 0.12304363
[45,] 0.86930952 0.26138096 0.13069048
[46,] 0.90973038 0.18053924 0.09026962
[47,] 0.89980959 0.20038083 0.10019041
[48,] 0.89352922 0.21294156 0.10647078
[49,] 0.90711187 0.18577626 0.09288813
[50,] 0.89018170 0.21963660 0.10981830
[51,] 0.88223082 0.23553836 0.11776918
[52,] 0.86028544 0.27942912 0.13971456
[53,] 0.86050592 0.27898817 0.13949408
[54,] 0.87556588 0.24886824 0.12443412
[55,] 0.87765260 0.24469480 0.12234740
[56,] 0.84993122 0.30013755 0.15006878
[57,] 0.89306682 0.21386635 0.10693318
[58,] 0.87537349 0.24925302 0.12462651
[59,] 0.85054323 0.29891354 0.14945677
[60,] 0.81648792 0.36702417 0.18351208
[61,] 0.77789586 0.44420827 0.22210414
[62,] 0.74862382 0.50275235 0.25137618
[63,] 0.70672113 0.58655774 0.29327887
[64,] 0.68434209 0.63131581 0.31565791
[65,] 0.63805685 0.72388631 0.36194315
[66,] 0.60474702 0.79050597 0.39525298
[67,] 0.57974579 0.84050841 0.42025421
[68,] 0.55445176 0.89109648 0.44554824
[69,] 0.50056865 0.99886271 0.49943135
[70,] 0.51920421 0.96159158 0.48079579
[71,] 0.51277883 0.97444233 0.48722117
[72,] 0.58159355 0.83681290 0.41840645
[73,] 0.53043264 0.93913472 0.46956736
[74,] 0.49938208 0.99876415 0.50061792
[75,] 0.43828548 0.87657096 0.56171452
[76,] 0.37669807 0.75339614 0.62330193
[77,] 0.32347999 0.64695997 0.67652001
[78,] 0.45116690 0.90233381 0.54883310
[79,] 0.44694230 0.89388461 0.55305770
[80,] 0.40772823 0.81545647 0.59227177
[81,] 0.34656503 0.69313006 0.65343497
[82,] 0.31520812 0.63041624 0.68479188
[83,] 0.26960499 0.53920998 0.73039501
[84,] 0.22967467 0.45934934 0.77032533
[85,] 0.39048362 0.78096724 0.60951638
[86,] 0.33555042 0.67110084 0.66444958
[87,] 0.28096006 0.56192013 0.71903994
[88,] 0.37520208 0.75040417 0.62479792
[89,] 0.34900532 0.69801063 0.65099468
[90,] 0.28040512 0.56081024 0.71959488
[91,] 0.24350052 0.48700104 0.75649948
[92,] 0.18088382 0.36176764 0.81911618
[93,] 0.16338500 0.32676999 0.83661500
[94,] 0.11012849 0.22025699 0.88987151
[95,] 0.07057871 0.14115743 0.92942129
[96,] 0.04128577 0.08257154 0.95871423
[97,] 0.02464682 0.04929365 0.97535318
[98,] 0.06842256 0.13684511 0.93157744
[99,] 0.04156174 0.08312348 0.95843826
> postscript(file="/var/www/html/rcomp/tmp/173ib1292234129.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hczw1292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3hczw1292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hczw1292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hczw1292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 120
Frequency = 1
1 2 3 4 5 6
-3.65590468 -6.19994613 2.91621536 -1.54178355 0.55864775 1.04580218
7 8 9 10 11 12
-3.33630928 1.70965190 1.87966817 -0.49493966 1.24160136 -2.19495454
13 14 15 16 17 18
-0.91815318 5.13698740 -4.78888992 5.08093420 -2.41925069 2.98406517
19 20 21 22 23 24
-2.09858526 -2.40084636 -0.41801157 1.83068047 3.42791326 0.09517812
25 26 27 28 29 30
2.38124256 1.93755708 -2.13506055 0.16605996 5.83780076 -1.53444443
31 32 33 34 35 36
3.39368359 -2.66383061 -3.85678920 0.78194701 -0.58960013 4.29275048
37 38 39 40 41 42
0.09430410 3.06756839 5.30648111 -2.78706354 -0.15356084 1.24215129
43 44 45 46 47 48
-3.59127056 4.96667328 -4.28567494 -0.13572995 0.13817495 -3.55290806
49 50 51 52 53 54
1.23903695 -0.28716629 -0.77815213 2.65496466 -1.86102548 -0.92925503
55 56 57 58 59 60
2.28042611 -4.47971331 1.96464369 -2.31206656 -2.68626990 1.06362981
61 62 63 64 65 66
-2.05548177 -0.88166126 -2.83614999 3.21916636 2.93211485 -0.84120474
67 68 69 70 71 72
3.62256056 1.08570349 -1.46950066 0.76472660 -0.48644438 -1.73959884
73 74 75 76 77 78
1.06402116 1.54868704 0.06644591 -1.71626201 2.29462865 -2.60814552
79 80 81 82 83 84
-0.80522668 3.49544923 -2.30089398 3.89556741 1.12725142 2.22869722
85 86 87 88 89 90
0.46273367 0.26962865 1.11123679 4.44744799 -1.59043114 -1.94213217
91 92 93 94 95 96
0.36167083 0.93008936 0.12323678 -0.04946453 -6.87750125 -1.73914374
97 98 99 100 101 102
2.19656618 -4.62408859 -1.47425498 -0.11479599 -1.30940966 1.35795252
103 104 105 106 107 108
-2.41502303 0.10353574 0.98951509 -0.35216355 0.14908155 5.64767905
109 110 111 112 113 114
-0.58696666 -4.44720718 -5.56012053 2.37631817 -0.53865598 1.81418747
115 116 117 118 119 120
-1.77266842 -3.23193390 3.51187416 2.05300560 -1.62767103 1.11413784
> postscript(file="/var/www/html/rcomp/tmp/6s3gz1292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.65590468 NA
1 -6.19994613 -3.65590468
2 2.91621536 -6.19994613
3 -1.54178355 2.91621536
4 0.55864775 -1.54178355
5 1.04580218 0.55864775
6 -3.33630928 1.04580218
7 1.70965190 -3.33630928
8 1.87966817 1.70965190
9 -0.49493966 1.87966817
10 1.24160136 -0.49493966
11 -2.19495454 1.24160136
12 -0.91815318 -2.19495454
13 5.13698740 -0.91815318
14 -4.78888992 5.13698740
15 5.08093420 -4.78888992
16 -2.41925069 5.08093420
17 2.98406517 -2.41925069
18 -2.09858526 2.98406517
19 -2.40084636 -2.09858526
20 -0.41801157 -2.40084636
21 1.83068047 -0.41801157
22 3.42791326 1.83068047
23 0.09517812 3.42791326
24 2.38124256 0.09517812
25 1.93755708 2.38124256
26 -2.13506055 1.93755708
27 0.16605996 -2.13506055
28 5.83780076 0.16605996
29 -1.53444443 5.83780076
30 3.39368359 -1.53444443
31 -2.66383061 3.39368359
32 -3.85678920 -2.66383061
33 0.78194701 -3.85678920
34 -0.58960013 0.78194701
35 4.29275048 -0.58960013
36 0.09430410 4.29275048
37 3.06756839 0.09430410
38 5.30648111 3.06756839
39 -2.78706354 5.30648111
40 -0.15356084 -2.78706354
41 1.24215129 -0.15356084
42 -3.59127056 1.24215129
43 4.96667328 -3.59127056
44 -4.28567494 4.96667328
45 -0.13572995 -4.28567494
46 0.13817495 -0.13572995
47 -3.55290806 0.13817495
48 1.23903695 -3.55290806
49 -0.28716629 1.23903695
50 -0.77815213 -0.28716629
51 2.65496466 -0.77815213
52 -1.86102548 2.65496466
53 -0.92925503 -1.86102548
54 2.28042611 -0.92925503
55 -4.47971331 2.28042611
56 1.96464369 -4.47971331
57 -2.31206656 1.96464369
58 -2.68626990 -2.31206656
59 1.06362981 -2.68626990
60 -2.05548177 1.06362981
61 -0.88166126 -2.05548177
62 -2.83614999 -0.88166126
63 3.21916636 -2.83614999
64 2.93211485 3.21916636
65 -0.84120474 2.93211485
66 3.62256056 -0.84120474
67 1.08570349 3.62256056
68 -1.46950066 1.08570349
69 0.76472660 -1.46950066
70 -0.48644438 0.76472660
71 -1.73959884 -0.48644438
72 1.06402116 -1.73959884
73 1.54868704 1.06402116
74 0.06644591 1.54868704
75 -1.71626201 0.06644591
76 2.29462865 -1.71626201
77 -2.60814552 2.29462865
78 -0.80522668 -2.60814552
79 3.49544923 -0.80522668
80 -2.30089398 3.49544923
81 3.89556741 -2.30089398
82 1.12725142 3.89556741
83 2.22869722 1.12725142
84 0.46273367 2.22869722
85 0.26962865 0.46273367
86 1.11123679 0.26962865
87 4.44744799 1.11123679
88 -1.59043114 4.44744799
89 -1.94213217 -1.59043114
90 0.36167083 -1.94213217
91 0.93008936 0.36167083
92 0.12323678 0.93008936
93 -0.04946453 0.12323678
94 -6.87750125 -0.04946453
95 -1.73914374 -6.87750125
96 2.19656618 -1.73914374
97 -4.62408859 2.19656618
98 -1.47425498 -4.62408859
99 -0.11479599 -1.47425498
100 -1.30940966 -0.11479599
101 1.35795252 -1.30940966
102 -2.41502303 1.35795252
103 0.10353574 -2.41502303
104 0.98951509 0.10353574
105 -0.35216355 0.98951509
106 0.14908155 -0.35216355
107 5.64767905 0.14908155
108 -0.58696666 5.64767905
109 -4.44720718 -0.58696666
110 -5.56012053 -4.44720718
111 2.37631817 -5.56012053
112 -0.53865598 2.37631817
113 1.81418747 -0.53865598
114 -1.77266842 1.81418747
115 -3.23193390 -1.77266842
116 3.51187416 -3.23193390
117 2.05300560 3.51187416
118 -1.62767103 2.05300560
119 1.11413784 -1.62767103
120 NA 1.11413784
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.19994613 -3.65590468
[2,] 2.91621536 -6.19994613
[3,] -1.54178355 2.91621536
[4,] 0.55864775 -1.54178355
[5,] 1.04580218 0.55864775
[6,] -3.33630928 1.04580218
[7,] 1.70965190 -3.33630928
[8,] 1.87966817 1.70965190
[9,] -0.49493966 1.87966817
[10,] 1.24160136 -0.49493966
[11,] -2.19495454 1.24160136
[12,] -0.91815318 -2.19495454
[13,] 5.13698740 -0.91815318
[14,] -4.78888992 5.13698740
[15,] 5.08093420 -4.78888992
[16,] -2.41925069 5.08093420
[17,] 2.98406517 -2.41925069
[18,] -2.09858526 2.98406517
[19,] -2.40084636 -2.09858526
[20,] -0.41801157 -2.40084636
[21,] 1.83068047 -0.41801157
[22,] 3.42791326 1.83068047
[23,] 0.09517812 3.42791326
[24,] 2.38124256 0.09517812
[25,] 1.93755708 2.38124256
[26,] -2.13506055 1.93755708
[27,] 0.16605996 -2.13506055
[28,] 5.83780076 0.16605996
[29,] -1.53444443 5.83780076
[30,] 3.39368359 -1.53444443
[31,] -2.66383061 3.39368359
[32,] -3.85678920 -2.66383061
[33,] 0.78194701 -3.85678920
[34,] -0.58960013 0.78194701
[35,] 4.29275048 -0.58960013
[36,] 0.09430410 4.29275048
[37,] 3.06756839 0.09430410
[38,] 5.30648111 3.06756839
[39,] -2.78706354 5.30648111
[40,] -0.15356084 -2.78706354
[41,] 1.24215129 -0.15356084
[42,] -3.59127056 1.24215129
[43,] 4.96667328 -3.59127056
[44,] -4.28567494 4.96667328
[45,] -0.13572995 -4.28567494
[46,] 0.13817495 -0.13572995
[47,] -3.55290806 0.13817495
[48,] 1.23903695 -3.55290806
[49,] -0.28716629 1.23903695
[50,] -0.77815213 -0.28716629
[51,] 2.65496466 -0.77815213
[52,] -1.86102548 2.65496466
[53,] -0.92925503 -1.86102548
[54,] 2.28042611 -0.92925503
[55,] -4.47971331 2.28042611
[56,] 1.96464369 -4.47971331
[57,] -2.31206656 1.96464369
[58,] -2.68626990 -2.31206656
[59,] 1.06362981 -2.68626990
[60,] -2.05548177 1.06362981
[61,] -0.88166126 -2.05548177
[62,] -2.83614999 -0.88166126
[63,] 3.21916636 -2.83614999
[64,] 2.93211485 3.21916636
[65,] -0.84120474 2.93211485
[66,] 3.62256056 -0.84120474
[67,] 1.08570349 3.62256056
[68,] -1.46950066 1.08570349
[69,] 0.76472660 -1.46950066
[70,] -0.48644438 0.76472660
[71,] -1.73959884 -0.48644438
[72,] 1.06402116 -1.73959884
[73,] 1.54868704 1.06402116
[74,] 0.06644591 1.54868704
[75,] -1.71626201 0.06644591
[76,] 2.29462865 -1.71626201
[77,] -2.60814552 2.29462865
[78,] -0.80522668 -2.60814552
[79,] 3.49544923 -0.80522668
[80,] -2.30089398 3.49544923
[81,] 3.89556741 -2.30089398
[82,] 1.12725142 3.89556741
[83,] 2.22869722 1.12725142
[84,] 0.46273367 2.22869722
[85,] 0.26962865 0.46273367
[86,] 1.11123679 0.26962865
[87,] 4.44744799 1.11123679
[88,] -1.59043114 4.44744799
[89,] -1.94213217 -1.59043114
[90,] 0.36167083 -1.94213217
[91,] 0.93008936 0.36167083
[92,] 0.12323678 0.93008936
[93,] -0.04946453 0.12323678
[94,] -6.87750125 -0.04946453
[95,] -1.73914374 -6.87750125
[96,] 2.19656618 -1.73914374
[97,] -4.62408859 2.19656618
[98,] -1.47425498 -4.62408859
[99,] -0.11479599 -1.47425498
[100,] -1.30940966 -0.11479599
[101,] 1.35795252 -1.30940966
[102,] -2.41502303 1.35795252
[103,] 0.10353574 -2.41502303
[104,] 0.98951509 0.10353574
[105,] -0.35216355 0.98951509
[106,] 0.14908155 -0.35216355
[107,] 5.64767905 0.14908155
[108,] -0.58696666 5.64767905
[109,] -4.44720718 -0.58696666
[110,] -5.56012053 -4.44720718
[111,] 2.37631817 -5.56012053
[112,] -0.53865598 2.37631817
[113,] 1.81418747 -0.53865598
[114,] -1.77266842 1.81418747
[115,] -3.23193390 -1.77266842
[116,] 3.51187416 -3.23193390
[117,] 2.05300560 3.51187416
[118,] -1.62767103 2.05300560
[119,] 1.11413784 -1.62767103
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.19994613 -3.65590468
2 2.91621536 -6.19994613
3 -1.54178355 2.91621536
4 0.55864775 -1.54178355
5 1.04580218 0.55864775
6 -3.33630928 1.04580218
7 1.70965190 -3.33630928
8 1.87966817 1.70965190
9 -0.49493966 1.87966817
10 1.24160136 -0.49493966
11 -2.19495454 1.24160136
12 -0.91815318 -2.19495454
13 5.13698740 -0.91815318
14 -4.78888992 5.13698740
15 5.08093420 -4.78888992
16 -2.41925069 5.08093420
17 2.98406517 -2.41925069
18 -2.09858526 2.98406517
19 -2.40084636 -2.09858526
20 -0.41801157 -2.40084636
21 1.83068047 -0.41801157
22 3.42791326 1.83068047
23 0.09517812 3.42791326
24 2.38124256 0.09517812
25 1.93755708 2.38124256
26 -2.13506055 1.93755708
27 0.16605996 -2.13506055
28 5.83780076 0.16605996
29 -1.53444443 5.83780076
30 3.39368359 -1.53444443
31 -2.66383061 3.39368359
32 -3.85678920 -2.66383061
33 0.78194701 -3.85678920
34 -0.58960013 0.78194701
35 4.29275048 -0.58960013
36 0.09430410 4.29275048
37 3.06756839 0.09430410
38 5.30648111 3.06756839
39 -2.78706354 5.30648111
40 -0.15356084 -2.78706354
41 1.24215129 -0.15356084
42 -3.59127056 1.24215129
43 4.96667328 -3.59127056
44 -4.28567494 4.96667328
45 -0.13572995 -4.28567494
46 0.13817495 -0.13572995
47 -3.55290806 0.13817495
48 1.23903695 -3.55290806
49 -0.28716629 1.23903695
50 -0.77815213 -0.28716629
51 2.65496466 -0.77815213
52 -1.86102548 2.65496466
53 -0.92925503 -1.86102548
54 2.28042611 -0.92925503
55 -4.47971331 2.28042611
56 1.96464369 -4.47971331
57 -2.31206656 1.96464369
58 -2.68626990 -2.31206656
59 1.06362981 -2.68626990
60 -2.05548177 1.06362981
61 -0.88166126 -2.05548177
62 -2.83614999 -0.88166126
63 3.21916636 -2.83614999
64 2.93211485 3.21916636
65 -0.84120474 2.93211485
66 3.62256056 -0.84120474
67 1.08570349 3.62256056
68 -1.46950066 1.08570349
69 0.76472660 -1.46950066
70 -0.48644438 0.76472660
71 -1.73959884 -0.48644438
72 1.06402116 -1.73959884
73 1.54868704 1.06402116
74 0.06644591 1.54868704
75 -1.71626201 0.06644591
76 2.29462865 -1.71626201
77 -2.60814552 2.29462865
78 -0.80522668 -2.60814552
79 3.49544923 -0.80522668
80 -2.30089398 3.49544923
81 3.89556741 -2.30089398
82 1.12725142 3.89556741
83 2.22869722 1.12725142
84 0.46273367 2.22869722
85 0.26962865 0.46273367
86 1.11123679 0.26962865
87 4.44744799 1.11123679
88 -1.59043114 4.44744799
89 -1.94213217 -1.59043114
90 0.36167083 -1.94213217
91 0.93008936 0.36167083
92 0.12323678 0.93008936
93 -0.04946453 0.12323678
94 -6.87750125 -0.04946453
95 -1.73914374 -6.87750125
96 2.19656618 -1.73914374
97 -4.62408859 2.19656618
98 -1.47425498 -4.62408859
99 -0.11479599 -1.47425498
100 -1.30940966 -0.11479599
101 1.35795252 -1.30940966
102 -2.41502303 1.35795252
103 0.10353574 -2.41502303
104 0.98951509 0.10353574
105 -0.35216355 0.98951509
106 0.14908155 -0.35216355
107 5.64767905 0.14908155
108 -0.58696666 5.64767905
109 -4.44720718 -0.58696666
110 -5.56012053 -4.44720718
111 2.37631817 -5.56012053
112 -0.53865598 2.37631817
113 1.81418747 -0.53865598
114 -1.77266842 1.81418747
115 -3.23193390 -1.77266842
116 3.51187416 -3.23193390
117 2.05300560 3.51187416
118 -1.62767103 2.05300560
119 1.11413784 -1.62767103
> 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/7ldyk1292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ldyk1292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ldyk1292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10v4f51292234129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z4vt1292234129.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/12k5cz1292234129.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/1330zw1292234129.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/1470xj1292234129.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/15ngp11292234129.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/16qy571292234129.tab")
+ }
> try(system("convert tmp/173ib1292234129.ps tmp/173ib1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hczw1292234129.ps tmp/2hczw1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hczw1292234129.ps tmp/3hczw1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hczw1292234129.ps tmp/4hczw1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hczw1292234129.ps tmp/5hczw1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/6s3gz1292234129.ps tmp/6s3gz1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ldyk1292234129.ps tmp/7ldyk1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ldyk1292234129.ps tmp/8ldyk1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ldyk1292234129.ps tmp/9ldyk1292234129.png",intern=TRUE))
character(0)
> try(system("convert tmp/10v4f51292234129.ps tmp/10v4f51292234129.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.510 1.817 8.186