R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2
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+ ,0)
+ ,dim=c(8
+ ,85)
+ ,dimnames=list(c('estscore'
+ ,'time_in_rfc'
+ ,'blogged_computations'
+ ,'feedback_messages_p120'
+ ,'totsize'
+ ,'totseconds'
+ ,'compendiums_reviewed'
+ ,'difference_hyperlinks-blogs')
+ ,1:85))
> y <- array(NA,dim=c(8,85),dimnames=list(c('estscore','time_in_rfc','blogged_computations','feedback_messages_p120','totsize','totseconds','compendiums_reviewed','difference_hyperlinks-blogs'),1:85))
> 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 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
time_in_rfc estscore blogged_computations feedback_messages_p120 totsize
1 210907 2 79 94 112285
2 179321 4 108 103 101193
3 149061 0 43 93 116174
4 237213 0 78 123 66198
5 173326 -4 86 148 71701
6 133131 4 44 90 57793
7 258873 4 104 124 80444
8 324799 0 158 168 97668
9 230964 -1 102 115 133824
10 236785 0 77 71 101481
11 344297 1 80 108 67654
12 174724 0 123 120 69112
13 174415 3 73 114 82753
14 223632 -1 105 120 72654
15 294424 4 107 124 101494
16 325107 3 84 126 79215
17 106408 1 33 37 31081
18 96560 0 42 38 22996
19 265769 -2 96 120 83122
20 269651 -3 106 93 70106
21 149112 -4 56 95 60578
22 152871 2 59 90 79892
23 362301 2 76 110 100708
24 183167 -4 91 138 82875
25 277965 3 115 133 139077
26 218946 2 76 96 80670
27 244052 2 101 164 143558
28 341570 0 94 78 117105
29 233328 5 92 102 120733
30 206161 -2 75 99 73107
31 311473 0 128 129 132068
32 207176 -2 56 114 87011
33 196553 -3 41 99 95260
34 143246 2 67 104 106671
35 182192 2 77 138 70054
36 194979 2 66 151 74011
37 167488 0 69 72 83737
38 143756 4 105 120 69094
39 275541 4 116 115 93133
40 152299 2 62 98 61370
41 193339 2 100 71 84651
42 130585 -4 67 107 95364
43 112611 3 46 73 26706
44 148446 3 135 129 126846
45 182079 2 124 118 102860
46 243060 -1 58 104 111813
47 162765 -3 68 107 120293
48 85574 0 37 36 24266
49 225060 1 93 139 109825
50 133328 -3 56 56 40909
51 100750 3 83 93 140867
52 101523 0 59 87 61056
53 243511 0 133 110 101338
54 152474 0 106 83 65567
55 132487 3 71 98 40735
56 317394 -3 116 82 91413
57 244749 0 98 115 76643
58 184510 -4 64 140 110681
59 128423 2 32 120 92696
60 97839 -1 25 66 94785
61 172494 3 46 139 86687
62 229242 2 63 119 91721
63 351619 5 95 141 115168
64 324598 2 113 133 135777
65 195838 -2 111 98 102372
66 254488 0 120 117 103772
67 199476 3 87 105 135400
68 92499 -2 25 55 21399
69 224330 0 131 132 130115
70 181633 6 47 73 64466
71 271856 -3 109 86 54990
72 95227 3 37 48 34777
73 98146 0 15 48 27114
74 118612 -2 54 43 30080
75 65475 1 16 46 69008
76 108446 0 22 65 46300
77 121848 2 37 52 30594
78 76302 2 29 68 30976
79 98104 -3 55 47 25568
80 30989 -2 5 41 4154
81 31774 1 0 47 4143
82 150580 -4 27 71 45588
83 54157 0 37 30 18625
84 59382 1 29 24 26263
85 84105 0 17 63 20055
totseconds compendiums_reviewed difference_hyperlinks-blogs
1 146283 30 -1
2 96933 30 3
3 95757 26 0
4 143983 38 3
5 75851 44 4
6 59238 30 0
7 93163 40 0
8 151511 47 7
9 136368 30 1
10 112642 31 0
11 127766 30 1
12 85646 34 4
13 98579 31 1
14 131741 33 5
15 171975 33 13
16 159676 36 4
17 58391 14 0
18 31580 17 0
19 136815 32 6
20 120642 30 0
21 69107 35 1
22 108016 28 3
23 79336 34 1
24 93176 39 0
25 161632 39 2
26 102996 29 3
27 160604 44 4
28 158051 21 12
29 162647 28 0
30 60622 28 3
31 179566 38 0
32 96144 32 4
33 129847 29 -1
34 71180 27 2
35 86767 40 1
36 93487 40 1
37 82981 28 0
38 73815 34 2
39 94552 33 0
40 67808 33 2
41 106175 35 4
42 76669 29 0
43 57283 20 0
44 72413 37 6
45 96971 33 13
46 120336 29 4
47 93913 28 -1
48 32036 21 3
49 102255 41 0
50 63506 20 2
51 68370 30 0
52 50517 22 1
53 103950 42 1
54 84396 32 0
55 55515 36 31
56 209056 31 2
57 142775 33 5
58 68847 40 1
59 20112 38 1
60 61023 24 2
61 112494 43 13
62 78876 31 5
63 170745 40 3
64 122037 37 1
65 112283 31 1
66 120691 39 4
67 122422 32 2
68 25899 18 0
69 139296 39 4
70 89455 30 0
71 147866 37 0
72 14336 32 0
73 30059 17 7
74 41907 12 3
75 35885 13 4
76 55764 17 1
77 35619 17 0
78 40557 20 2
79 44197 17 0
80 4103 17 0
81 4694 17 0
82 62991 22 2
83 24261 15 1
84 21425 12 0
85 27184 17 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) estscore
19762.3662 905.8339
blogged_computations feedback_messages_p120
323.2942 280.6691
totsize totseconds
-0.1882 1.2381
compendiums_reviewed `difference_hyperlinks-blogs`
599.3325 -566.5764
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-52140 -23271 -10012 16930 186197
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19762.3662 16255.0819 1.216 0.228
estscore 905.8339 1817.8234 0.498 0.620
blogged_computations 323.2942 211.3393 1.530 0.130
feedback_messages_p120 280.6691 321.2964 0.874 0.385
totsize -0.1882 0.2019 -0.932 0.354
totseconds 1.2381 0.1547 8.005 9.97e-12 ***
compendiums_reviewed 599.3325 1211.1296 0.495 0.622
`difference_hyperlinks-blogs` -566.5764 1048.3054 -0.540 0.590
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 39650 on 77 degrees of freedom
Multiple R-squared: 0.7741, Adjusted R-squared: 0.7535
F-statistic: 37.68 on 7 and 77 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,] 0.7728962 0.4542076780 2.271038e-01
[2,] 0.9213191 0.1573618512 7.868093e-02
[3,] 0.8762624 0.2474751239 1.237376e-01
[4,] 0.8212800 0.3574400621 1.787200e-01
[5,] 0.8307478 0.3385043294 1.692522e-01
[6,] 0.7785401 0.4429198613 2.214599e-01
[7,] 0.6959812 0.6080375770 3.040188e-01
[8,] 0.6125772 0.7748455322 3.874228e-01
[9,] 0.5432276 0.9135448152 4.567724e-01
[10,] 0.4736865 0.9473729792 5.263135e-01
[11,] 0.3842111 0.7684221995 6.157889e-01
[12,] 0.3805604 0.7611208501 6.194396e-01
[13,] 0.9995594 0.0008812115 4.406057e-04
[14,] 0.9993101 0.0013797533 6.898767e-04
[15,] 0.9989524 0.0020951356 1.047568e-03
[16,] 0.9982618 0.0034763249 1.738162e-03
[17,] 0.9985423 0.0029153988 1.457699e-03
[18,] 0.9998631 0.0002737252 1.368626e-04
[19,] 0.9998432 0.0003135412 1.567706e-04
[20,] 0.9999240 0.0001520267 7.601334e-05
[21,] 0.9998481 0.0003037525 1.518762e-04
[22,] 0.9997490 0.0005019113 2.509557e-04
[23,] 0.9995995 0.0008010681 4.005340e-04
[24,] 0.9993114 0.0013771585 6.885793e-04
[25,] 0.9989570 0.0020860266 1.043013e-03
[26,] 0.9985453 0.0029093492 1.454675e-03
[27,] 0.9976423 0.0047153495 2.357675e-03
[28,] 0.9984146 0.0031708275 1.585414e-03
[29,] 0.9993969 0.0012062131 6.031066e-04
[30,] 0.9989982 0.0020035518 1.001776e-03
[31,] 0.9986149 0.0027701018 1.385051e-03
[32,] 0.9984767 0.0030466825 1.523341e-03
[33,] 0.9979949 0.0040101324 2.005066e-03
[34,] 0.9980277 0.0039446031 1.972302e-03
[35,] 0.9973343 0.0053314244 2.665712e-03
[36,] 0.9973678 0.0052644550 2.632228e-03
[37,] 0.9958099 0.0083801085 4.190054e-03
[38,] 0.9933483 0.0133034339 6.651717e-03
[39,] 0.9896772 0.0206456595 1.032283e-02
[40,] 0.9840109 0.0319781792 1.598909e-02
[41,] 0.9890740 0.0218519020 1.092595e-02
[42,] 0.9887515 0.0224969985 1.124850e-02
[43,] 0.9823820 0.0352359310 1.761797e-02
[44,] 0.9852207 0.0295585684 1.477928e-02
[45,] 0.9823832 0.0352336239 1.761681e-02
[46,] 0.9751271 0.0497457528 2.487288e-02
[47,] 0.9650926 0.0698148947 3.490745e-02
[48,] 0.9482774 0.1034452990 5.172265e-02
[49,] 0.9271531 0.1456938781 7.284694e-02
[50,] 0.8959914 0.2080171504 1.040086e-01
[51,] 0.9303303 0.1393393902 6.966970e-02
[52,] 0.9263161 0.1473678515 7.368393e-02
[53,] 0.9158694 0.1682611068 8.413055e-02
[54,] 0.9979789 0.0040422230 2.021112e-03
[55,] 0.9956554 0.0086891924 4.344596e-03
[56,] 0.9948157 0.0103686681 5.184334e-03
[57,] 0.9888502 0.0222996485 1.114982e-02
[58,] 0.9809926 0.0380147113 1.900736e-02
[59,] 0.9839611 0.0320777314 1.603887e-02
[60,] 0.9684731 0.0630538972 3.152695e-02
[61,] 0.9430354 0.1139291726 5.696459e-02
[62,] 0.9077328 0.1845343903 9.226720e-02
[63,] 0.8622697 0.2754606184 1.377303e-01
[64,] 0.9506586 0.0986827479 4.934137e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1exqg1324320468.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2zqte1324320468.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/38ra41324320468.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4nefc1324320468.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/50uxz1324320468.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 = 85
Frequency = 1
1 2 3 4 5 6
-41122.3809 -25140.9624 -22983.7333 -29175.0854 -16679.1119 -10187.6656
7 8 9 10 11 12
42879.8894 13391.6008 -14214.7980 33255.2152 104580.8579 -29629.0338
13 14 15 16 17 18
-28154.0694 -29235.1235 -4596.2721 38004.6732 -10150.6662 7592.8301
19 20 21 22 23 24
13571.0671 38077.9334 -6367.9497 -46821.6103 186197.0982 -24265.2300
25 26 27 28 29 30
-15210.9882 17835.8891 -52140.1605 90089.8473 -44772.7038 59796.1233
31 32 33 34 35 36
-6123.2288 19548.7103 -22320.2142 -12281.8821 -20659.6012 -15540.6063
37 38 39 40 41 42
1446.7200 -44889.2652 63058.0235 -7875.0945 -14729.6578 -31605.8022
43 44 45 46 47 48
-23113.7696 -38445.1321 -25819.5898 33199.8241 -17280.8783 -2238.5352
49 50 51 52 53 54
4803.6825 677.9365 -50785.3427 -25406.6420 15639.5032 -36185.4217
55 56 57 58 59 60
-5533.0150 -19249.1115 -18268.3776 20568.2653 33159.3354 -18590.8410
61 62 63 64 65 66
-45244.1659 57757.3832 45036.3187 82009.3309 -23271.4317 12082.8685
67 68 69 70 71 72
-24738.5132 12202.0669 -43919.0911 4147.3627 530.6338 16929.6774
73 74 75 76 77 78
21725.3269 19417.0310 -10244.7376 -6623.9497 25185.3966 -28971.8085
79 80 81 82 83 84
-10011.9841 -14572.5293 -17306.3819 24320.8290 -20943.8360 -6174.0578
85
1092.7890
> postscript(file="/var/wessaorg/rcomp/tmp/6wab11324320468.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 = 85
Frequency = 1
lag(myerror, k = 1) myerror
0 -41122.3809 NA
1 -25140.9624 -41122.3809
2 -22983.7333 -25140.9624
3 -29175.0854 -22983.7333
4 -16679.1119 -29175.0854
5 -10187.6656 -16679.1119
6 42879.8894 -10187.6656
7 13391.6008 42879.8894
8 -14214.7980 13391.6008
9 33255.2152 -14214.7980
10 104580.8579 33255.2152
11 -29629.0338 104580.8579
12 -28154.0694 -29629.0338
13 -29235.1235 -28154.0694
14 -4596.2721 -29235.1235
15 38004.6732 -4596.2721
16 -10150.6662 38004.6732
17 7592.8301 -10150.6662
18 13571.0671 7592.8301
19 38077.9334 13571.0671
20 -6367.9497 38077.9334
21 -46821.6103 -6367.9497
22 186197.0982 -46821.6103
23 -24265.2300 186197.0982
24 -15210.9882 -24265.2300
25 17835.8891 -15210.9882
26 -52140.1605 17835.8891
27 90089.8473 -52140.1605
28 -44772.7038 90089.8473
29 59796.1233 -44772.7038
30 -6123.2288 59796.1233
31 19548.7103 -6123.2288
32 -22320.2142 19548.7103
33 -12281.8821 -22320.2142
34 -20659.6012 -12281.8821
35 -15540.6063 -20659.6012
36 1446.7200 -15540.6063
37 -44889.2652 1446.7200
38 63058.0235 -44889.2652
39 -7875.0945 63058.0235
40 -14729.6578 -7875.0945
41 -31605.8022 -14729.6578
42 -23113.7696 -31605.8022
43 -38445.1321 -23113.7696
44 -25819.5898 -38445.1321
45 33199.8241 -25819.5898
46 -17280.8783 33199.8241
47 -2238.5352 -17280.8783
48 4803.6825 -2238.5352
49 677.9365 4803.6825
50 -50785.3427 677.9365
51 -25406.6420 -50785.3427
52 15639.5032 -25406.6420
53 -36185.4217 15639.5032
54 -5533.0150 -36185.4217
55 -19249.1115 -5533.0150
56 -18268.3776 -19249.1115
57 20568.2653 -18268.3776
58 33159.3354 20568.2653
59 -18590.8410 33159.3354
60 -45244.1659 -18590.8410
61 57757.3832 -45244.1659
62 45036.3187 57757.3832
63 82009.3309 45036.3187
64 -23271.4317 82009.3309
65 12082.8685 -23271.4317
66 -24738.5132 12082.8685
67 12202.0669 -24738.5132
68 -43919.0911 12202.0669
69 4147.3627 -43919.0911
70 530.6338 4147.3627
71 16929.6774 530.6338
72 21725.3269 16929.6774
73 19417.0310 21725.3269
74 -10244.7376 19417.0310
75 -6623.9497 -10244.7376
76 25185.3966 -6623.9497
77 -28971.8085 25185.3966
78 -10011.9841 -28971.8085
79 -14572.5293 -10011.9841
80 -17306.3819 -14572.5293
81 24320.8290 -17306.3819
82 -20943.8360 24320.8290
83 -6174.0578 -20943.8360
84 1092.7890 -6174.0578
85 NA 1092.7890
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -25140.9624 -41122.3809
[2,] -22983.7333 -25140.9624
[3,] -29175.0854 -22983.7333
[4,] -16679.1119 -29175.0854
[5,] -10187.6656 -16679.1119
[6,] 42879.8894 -10187.6656
[7,] 13391.6008 42879.8894
[8,] -14214.7980 13391.6008
[9,] 33255.2152 -14214.7980
[10,] 104580.8579 33255.2152
[11,] -29629.0338 104580.8579
[12,] -28154.0694 -29629.0338
[13,] -29235.1235 -28154.0694
[14,] -4596.2721 -29235.1235
[15,] 38004.6732 -4596.2721
[16,] -10150.6662 38004.6732
[17,] 7592.8301 -10150.6662
[18,] 13571.0671 7592.8301
[19,] 38077.9334 13571.0671
[20,] -6367.9497 38077.9334
[21,] -46821.6103 -6367.9497
[22,] 186197.0982 -46821.6103
[23,] -24265.2300 186197.0982
[24,] -15210.9882 -24265.2300
[25,] 17835.8891 -15210.9882
[26,] -52140.1605 17835.8891
[27,] 90089.8473 -52140.1605
[28,] -44772.7038 90089.8473
[29,] 59796.1233 -44772.7038
[30,] -6123.2288 59796.1233
[31,] 19548.7103 -6123.2288
[32,] -22320.2142 19548.7103
[33,] -12281.8821 -22320.2142
[34,] -20659.6012 -12281.8821
[35,] -15540.6063 -20659.6012
[36,] 1446.7200 -15540.6063
[37,] -44889.2652 1446.7200
[38,] 63058.0235 -44889.2652
[39,] -7875.0945 63058.0235
[40,] -14729.6578 -7875.0945
[41,] -31605.8022 -14729.6578
[42,] -23113.7696 -31605.8022
[43,] -38445.1321 -23113.7696
[44,] -25819.5898 -38445.1321
[45,] 33199.8241 -25819.5898
[46,] -17280.8783 33199.8241
[47,] -2238.5352 -17280.8783
[48,] 4803.6825 -2238.5352
[49,] 677.9365 4803.6825
[50,] -50785.3427 677.9365
[51,] -25406.6420 -50785.3427
[52,] 15639.5032 -25406.6420
[53,] -36185.4217 15639.5032
[54,] -5533.0150 -36185.4217
[55,] -19249.1115 -5533.0150
[56,] -18268.3776 -19249.1115
[57,] 20568.2653 -18268.3776
[58,] 33159.3354 20568.2653
[59,] -18590.8410 33159.3354
[60,] -45244.1659 -18590.8410
[61,] 57757.3832 -45244.1659
[62,] 45036.3187 57757.3832
[63,] 82009.3309 45036.3187
[64,] -23271.4317 82009.3309
[65,] 12082.8685 -23271.4317
[66,] -24738.5132 12082.8685
[67,] 12202.0669 -24738.5132
[68,] -43919.0911 12202.0669
[69,] 4147.3627 -43919.0911
[70,] 530.6338 4147.3627
[71,] 16929.6774 530.6338
[72,] 21725.3269 16929.6774
[73,] 19417.0310 21725.3269
[74,] -10244.7376 19417.0310
[75,] -6623.9497 -10244.7376
[76,] 25185.3966 -6623.9497
[77,] -28971.8085 25185.3966
[78,] -10011.9841 -28971.8085
[79,] -14572.5293 -10011.9841
[80,] -17306.3819 -14572.5293
[81,] 24320.8290 -17306.3819
[82,] -20943.8360 24320.8290
[83,] -6174.0578 -20943.8360
[84,] 1092.7890 -6174.0578
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -25140.9624 -41122.3809
2 -22983.7333 -25140.9624
3 -29175.0854 -22983.7333
4 -16679.1119 -29175.0854
5 -10187.6656 -16679.1119
6 42879.8894 -10187.6656
7 13391.6008 42879.8894
8 -14214.7980 13391.6008
9 33255.2152 -14214.7980
10 104580.8579 33255.2152
11 -29629.0338 104580.8579
12 -28154.0694 -29629.0338
13 -29235.1235 -28154.0694
14 -4596.2721 -29235.1235
15 38004.6732 -4596.2721
16 -10150.6662 38004.6732
17 7592.8301 -10150.6662
18 13571.0671 7592.8301
19 38077.9334 13571.0671
20 -6367.9497 38077.9334
21 -46821.6103 -6367.9497
22 186197.0982 -46821.6103
23 -24265.2300 186197.0982
24 -15210.9882 -24265.2300
25 17835.8891 -15210.9882
26 -52140.1605 17835.8891
27 90089.8473 -52140.1605
28 -44772.7038 90089.8473
29 59796.1233 -44772.7038
30 -6123.2288 59796.1233
31 19548.7103 -6123.2288
32 -22320.2142 19548.7103
33 -12281.8821 -22320.2142
34 -20659.6012 -12281.8821
35 -15540.6063 -20659.6012
36 1446.7200 -15540.6063
37 -44889.2652 1446.7200
38 63058.0235 -44889.2652
39 -7875.0945 63058.0235
40 -14729.6578 -7875.0945
41 -31605.8022 -14729.6578
42 -23113.7696 -31605.8022
43 -38445.1321 -23113.7696
44 -25819.5898 -38445.1321
45 33199.8241 -25819.5898
46 -17280.8783 33199.8241
47 -2238.5352 -17280.8783
48 4803.6825 -2238.5352
49 677.9365 4803.6825
50 -50785.3427 677.9365
51 -25406.6420 -50785.3427
52 15639.5032 -25406.6420
53 -36185.4217 15639.5032
54 -5533.0150 -36185.4217
55 -19249.1115 -5533.0150
56 -18268.3776 -19249.1115
57 20568.2653 -18268.3776
58 33159.3354 20568.2653
59 -18590.8410 33159.3354
60 -45244.1659 -18590.8410
61 57757.3832 -45244.1659
62 45036.3187 57757.3832
63 82009.3309 45036.3187
64 -23271.4317 82009.3309
65 12082.8685 -23271.4317
66 -24738.5132 12082.8685
67 12202.0669 -24738.5132
68 -43919.0911 12202.0669
69 4147.3627 -43919.0911
70 530.6338 4147.3627
71 16929.6774 530.6338
72 21725.3269 16929.6774
73 19417.0310 21725.3269
74 -10244.7376 19417.0310
75 -6623.9497 -10244.7376
76 25185.3966 -6623.9497
77 -28971.8085 25185.3966
78 -10011.9841 -28971.8085
79 -14572.5293 -10011.9841
80 -17306.3819 -14572.5293
81 24320.8290 -17306.3819
82 -20943.8360 24320.8290
83 -6174.0578 -20943.8360
84 1092.7890 -6174.0578
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7dzvp1324320468.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8tsb81324320468.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9q1op1324320468.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10ri301324320468.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/119d5x1324320468.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12rkas1324320468.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13e7ra1324320468.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14i2bw1324320468.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/1527lw1324320468.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16zw4p1324320468.tab")
+ }
>
> try(system("convert tmp/1exqg1324320468.ps tmp/1exqg1324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zqte1324320468.ps tmp/2zqte1324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/38ra41324320468.ps tmp/38ra41324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nefc1324320468.ps tmp/4nefc1324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/50uxz1324320468.ps tmp/50uxz1324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wab11324320468.ps tmp/6wab11324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dzvp1324320468.ps tmp/7dzvp1324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tsb81324320468.ps tmp/8tsb81324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/9q1op1324320468.ps tmp/9q1op1324320468.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ri301324320468.ps tmp/10ri301324320468.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.666 0.668 4.350