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.
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(56
+ ,79
+ ,30
+ ,115
+ ,146283
+ ,9.5457
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+ ,0
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+ ,17
+ ,66
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+ ,4.2325)
+ ,dim=c(6
+ ,85)
+ ,dimnames=list(c('login'
+ ,'blog'
+ ,'review'
+ ,'fdb'
+ ,'sec'
+ ,'examen')
+ ,1:85))
> y <- array(NA,dim=c(6,85),dimnames=list(c('login','blog','review','fdb','sec','examen'),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 = '6'
> #'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
examen login blog review fdb sec
1 9.5457 56 79 30 115 146283
2 15.8949 89 108 30 116 96933
3 0.0000 44 43 26 100 95757
4 0.0000 84 78 38 140 143983
5 0.0000 88 86 44 166 75851
6 12.0989 55 44 30 99 59238
7 15.8949 60 104 40 139 93163
8 2.6529 154 158 47 181 151511
9 1.0579 53 102 30 116 136368
10 0.0000 119 77 31 116 112642
11 0.0000 75 80 30 108 127766
12 0.0000 92 123 34 129 85646
13 0.8401 100 73 31 118 98579
14 0.0000 73 105 33 125 131741
15 4.2325 77 107 33 127 171975
16 5.5091 99 84 36 136 159676
17 0.0000 30 33 14 46 58391
18 2.9596 76 42 17 54 31580
19 0.0000 146 96 32 124 136815
20 0.0000 67 106 30 115 120642
21 0.0000 56 56 35 128 69107
22 4.2325 58 59 28 97 108016
23 4.2325 119 76 34 125 79336
24 6.9999 66 91 39 149 93176
25 0.0000 89 115 39 149 161632
26 4.2325 41 76 29 108 102996
27 12.7203 68 101 44 166 160604
28 4.2325 168 94 21 80 158051
29 12.0989 132 92 28 107 162647
30 0.0000 71 75 28 107 60622
31 2.1093 112 128 38 146 179566
32 6.9999 70 56 32 123 96144
33 0.0000 57 41 29 111 129847
34 9.5457 103 67 27 105 71180
35 9.5531 52 77 40 155 86767
36 15.9023 62 66 40 155 93487
37 0.0000 45 69 28 104 82981
38 13.9969 46 105 34 132 73815
39 15.8949 63 116 33 127 94552
40 0.0000 53 62 33 122 67808
41 15.8949 78 100 35 87 106175
42 0.0000 46 67 29 109 76669
43 14.6220 41 46 20 78 57283
44 12.7203 91 135 37 141 72413
45 10.1745 63 124 33 124 96971
46 0.0000 63 58 29 112 120336
47 0.0000 32 68 28 108 93913
48 0.0000 34 37 21 78 32036
49 4.2325 93 93 41 158 102255
50 0.0000 55 56 20 78 63506
51 11.4474 72 83 30 119 68370
52 4.2325 42 59 22 88 50517
53 2.9596 71 133 42 155 103950
54 0.0000 65 106 32 123 84396
55 1.0579 41 71 36 136 55515
56 1.6867 86 116 31 117 209056
57 4.2325 95 98 33 124 142775
58 0.0000 49 64 40 151 68847
59 4.2325 64 32 38 145 20112
60 0.0000 38 25 24 87 61023
61 5.6162 52 46 43 165 112494
62 6.7857 247 63 31 120 78876
63 12.0989 139 95 40 150 170745
64 15.8949 110 113 37 136 122037
65 0.0000 67 111 31 116 112283
66 1.0579 83 120 39 150 120691
67 3.7145 70 87 32 118 122422
68 8.2765 32 25 18 71 25899
69 2.9596 83 131 39 144 139296
70 15.8949 70 47 30 110 89455
71 1.6867 103 109 37 147 147866
72 10.0674 34 37 32 111 14336
73 2.4416 40 15 17 68 30059
74 0.0000 46 54 12 48 41907
75 8.7908 18 16 13 51 35885
76 0.0000 60 22 17 68 55764
77 5.5091 39 37 17 64 35619
78 7.5179 31 29 20 76 40557
79 0.0000 54 55 17 66 44197
80 8.2728 14 5 17 68 4103
81 8.2728 23 0 17 66 4694
82 1.1687 77 27 22 83 62991
83 0.0000 19 37 15 55 24261
84 0.0000 49 29 12 41 21425
85 4.2325 20 17 17 66 27184
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) login blog review fdb sec
1.677e+00 8.083e-03 1.209e-02 7.367e-01 -1.600e-01 -2.555e-05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.337 -4.155 -2.061 4.081 11.271
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.677e+00 2.143e+00 0.783 0.4362
login 8.083e-03 1.918e-02 0.421 0.6747
blog 1.209e-02 2.799e-02 0.432 0.6670
review 7.367e-01 3.554e-01 2.073 0.0414 *
fdb -1.600e-01 9.252e-02 -1.730 0.0876 .
sec -2.555e-05 1.944e-05 -1.314 0.1925
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.249 on 79 degrees of freedom
Multiple R-squared: 0.08713, Adjusted R-squared: 0.02936
F-statistic: 1.508 on 5 and 79 DF, p-value: 0.1968
> 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,] 0.7817038 0.43659232 0.218296159
[2,] 0.7259355 0.54812895 0.274064475
[3,] 0.7760626 0.44787475 0.223937375
[4,] 0.9139539 0.17209226 0.086046132
[5,] 0.8663043 0.26739135 0.133695673
[6,] 0.8506365 0.29872703 0.149363513
[7,] 0.7920957 0.41580852 0.207904261
[8,] 0.7706854 0.45862922 0.229314612
[9,] 0.7736728 0.45265448 0.226327240
[10,] 0.7065756 0.58684878 0.293424388
[11,] 0.6460755 0.70784897 0.353924483
[12,] 0.6125179 0.77496420 0.387482102
[13,] 0.5825058 0.83498850 0.417494249
[14,] 0.5081790 0.98364200 0.491821000
[15,] 0.4477296 0.89545917 0.552270416
[16,] 0.4055615 0.81112303 0.594438483
[17,] 0.3827077 0.76541534 0.617292332
[18,] 0.3119024 0.62380480 0.688097598
[19,] 0.3746923 0.74938460 0.625307700
[20,] 0.3517736 0.70354718 0.648226409
[21,] 0.4872961 0.97459220 0.512703898
[22,] 0.4453220 0.89064409 0.554677956
[23,] 0.4012120 0.80242392 0.598788038
[24,] 0.3971559 0.79431187 0.602844065
[25,] 0.3422996 0.68459930 0.657700351
[26,] 0.4016942 0.80338844 0.598305780
[27,] 0.4026516 0.80530321 0.597348396
[28,] 0.6078199 0.78436027 0.392180134
[29,] 0.5853495 0.82930105 0.414650526
[30,] 0.6864198 0.62716035 0.313580173
[31,] 0.8303921 0.33921589 0.169607943
[32,] 0.8360778 0.32784448 0.163922240
[33,] 0.8046003 0.39079946 0.195399730
[34,] 0.7924463 0.41510741 0.207553704
[35,] 0.9212852 0.15742967 0.078714835
[36,] 0.9350158 0.12996837 0.064984184
[37,] 0.9444409 0.11111819 0.055559093
[38,] 0.9360338 0.12793245 0.063966226
[39,] 0.9252174 0.14956520 0.074782598
[40,] 0.9176500 0.16469996 0.082349978
[41,] 0.8891369 0.22172611 0.110863055
[42,] 0.8669614 0.26607730 0.133038650
[43,] 0.9289322 0.14213557 0.071067787
[44,] 0.9113663 0.17726734 0.088633668
[45,] 0.8962994 0.20740122 0.103700612
[46,] 0.8773058 0.24538835 0.122694173
[47,] 0.8565681 0.28686370 0.143431852
[48,] 0.8241527 0.35169453 0.175847267
[49,] 0.7745588 0.45088239 0.225441195
[50,] 0.7899166 0.42016687 0.210083436
[51,] 0.7646765 0.47064706 0.235323531
[52,] 0.8301669 0.33966620 0.169833098
[53,] 0.8704634 0.25907311 0.129536553
[54,] 0.8267499 0.34650025 0.173250127
[55,] 0.8011716 0.39765683 0.198828417
[56,] 0.9891979 0.02160428 0.010802141
[57,] 0.9832398 0.03352045 0.016760227
[58,] 0.9730832 0.05383365 0.026916825
[59,] 0.9669483 0.06610334 0.033051669
[60,] 0.9663545 0.06729093 0.033645467
[61,] 0.9553691 0.08926188 0.044630938
[62,] 0.9933503 0.01329947 0.006649737
[63,] 0.9847661 0.03046776 0.015233881
[64,] 0.9684755 0.06304901 0.031524507
[65,] 0.9461671 0.10766580 0.053832898
[66,] 0.8930587 0.21388265 0.106941327
[67,] 0.9319278 0.13614434 0.068072171
[68,] 0.8441811 0.31163779 0.155818895
> postscript(file="/var/wessaorg/rcomp/tmp/1iozp1324322917.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2yv7t1324322917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mlr11324322917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hq6d1324322917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/52tay1324322917.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 7
6.5026582 11.1338016 -3.2556089 -5.2083422 -7.3369867 4.7026638 7.6345404
8 9 10 11 12 13 14
-3.9644100 -2.3322125 -4.9641372 -4.8020228 -6.1211808 -3.9613194 -4.4758734
15 16 17 18 19 20 21
1.0480832 1.3410686 -3.7783053 -2.9139368 -4.2508525 -4.1134262 -6.3394692
22 23 24 25 26 27 28
-0.9699126 -2.3400777 1.1855498 -4.5414817 -0.1425271 7.5288865 1.4321315
29 30 31 32 33 34 35
8.8955074 -5.1113629 -2.0605184 2.6477900 -2.9151380 4.9587221 4.0809803
36 37 38 39 40 41 42
10.6540155 -4.7375664 8.6429714 10.7368462 -5.9078539 3.2312197 -4.8192008
43 44 45 46 47 48 49
11.2705640 5.8345346 4.5014256 -3.2520962 -3.7009701 -4.5677165 -1.6252882
50 51 52 53 54 55 56
-3.4264977 6.8763160 0.6700238 -4.3773909 -5.2162847 -5.1452757 -0.8589775
57 58 59 60 61 62 63
-0.2147077 -6.3886772 -2.6225531 -4.4840555 0.5665132 0.7337376 7.0512717
64 65 66 67 68 69 70
9.5891301 -4.9640426 -4.3814500 -1.1412044 4.8029350 -3.0976288 10.8735364
71 72 73 74 75 76 77
-2.0938617 2.2254999 -0.6129119 -2.7892482 6.2768529 -2.6440787 1.6986132
78 79 80 81 82 83 84
3.7054172 -3.6101057 4.8862047 4.5689331 -2.7713142 -3.9060284 -4.1547951
85
0.9219386
> postscript(file="/var/wessaorg/rcomp/tmp/6sxat1324322917.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 6.5026582 NA
1 11.1338016 6.5026582
2 -3.2556089 11.1338016
3 -5.2083422 -3.2556089
4 -7.3369867 -5.2083422
5 4.7026638 -7.3369867
6 7.6345404 4.7026638
7 -3.9644100 7.6345404
8 -2.3322125 -3.9644100
9 -4.9641372 -2.3322125
10 -4.8020228 -4.9641372
11 -6.1211808 -4.8020228
12 -3.9613194 -6.1211808
13 -4.4758734 -3.9613194
14 1.0480832 -4.4758734
15 1.3410686 1.0480832
16 -3.7783053 1.3410686
17 -2.9139368 -3.7783053
18 -4.2508525 -2.9139368
19 -4.1134262 -4.2508525
20 -6.3394692 -4.1134262
21 -0.9699126 -6.3394692
22 -2.3400777 -0.9699126
23 1.1855498 -2.3400777
24 -4.5414817 1.1855498
25 -0.1425271 -4.5414817
26 7.5288865 -0.1425271
27 1.4321315 7.5288865
28 8.8955074 1.4321315
29 -5.1113629 8.8955074
30 -2.0605184 -5.1113629
31 2.6477900 -2.0605184
32 -2.9151380 2.6477900
33 4.9587221 -2.9151380
34 4.0809803 4.9587221
35 10.6540155 4.0809803
36 -4.7375664 10.6540155
37 8.6429714 -4.7375664
38 10.7368462 8.6429714
39 -5.9078539 10.7368462
40 3.2312197 -5.9078539
41 -4.8192008 3.2312197
42 11.2705640 -4.8192008
43 5.8345346 11.2705640
44 4.5014256 5.8345346
45 -3.2520962 4.5014256
46 -3.7009701 -3.2520962
47 -4.5677165 -3.7009701
48 -1.6252882 -4.5677165
49 -3.4264977 -1.6252882
50 6.8763160 -3.4264977
51 0.6700238 6.8763160
52 -4.3773909 0.6700238
53 -5.2162847 -4.3773909
54 -5.1452757 -5.2162847
55 -0.8589775 -5.1452757
56 -0.2147077 -0.8589775
57 -6.3886772 -0.2147077
58 -2.6225531 -6.3886772
59 -4.4840555 -2.6225531
60 0.5665132 -4.4840555
61 0.7337376 0.5665132
62 7.0512717 0.7337376
63 9.5891301 7.0512717
64 -4.9640426 9.5891301
65 -4.3814500 -4.9640426
66 -1.1412044 -4.3814500
67 4.8029350 -1.1412044
68 -3.0976288 4.8029350
69 10.8735364 -3.0976288
70 -2.0938617 10.8735364
71 2.2254999 -2.0938617
72 -0.6129119 2.2254999
73 -2.7892482 -0.6129119
74 6.2768529 -2.7892482
75 -2.6440787 6.2768529
76 1.6986132 -2.6440787
77 3.7054172 1.6986132
78 -3.6101057 3.7054172
79 4.8862047 -3.6101057
80 4.5689331 4.8862047
81 -2.7713142 4.5689331
82 -3.9060284 -2.7713142
83 -4.1547951 -3.9060284
84 0.9219386 -4.1547951
85 NA 0.9219386
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 11.1338016 6.5026582
[2,] -3.2556089 11.1338016
[3,] -5.2083422 -3.2556089
[4,] -7.3369867 -5.2083422
[5,] 4.7026638 -7.3369867
[6,] 7.6345404 4.7026638
[7,] -3.9644100 7.6345404
[8,] -2.3322125 -3.9644100
[9,] -4.9641372 -2.3322125
[10,] -4.8020228 -4.9641372
[11,] -6.1211808 -4.8020228
[12,] -3.9613194 -6.1211808
[13,] -4.4758734 -3.9613194
[14,] 1.0480832 -4.4758734
[15,] 1.3410686 1.0480832
[16,] -3.7783053 1.3410686
[17,] -2.9139368 -3.7783053
[18,] -4.2508525 -2.9139368
[19,] -4.1134262 -4.2508525
[20,] -6.3394692 -4.1134262
[21,] -0.9699126 -6.3394692
[22,] -2.3400777 -0.9699126
[23,] 1.1855498 -2.3400777
[24,] -4.5414817 1.1855498
[25,] -0.1425271 -4.5414817
[26,] 7.5288865 -0.1425271
[27,] 1.4321315 7.5288865
[28,] 8.8955074 1.4321315
[29,] -5.1113629 8.8955074
[30,] -2.0605184 -5.1113629
[31,] 2.6477900 -2.0605184
[32,] -2.9151380 2.6477900
[33,] 4.9587221 -2.9151380
[34,] 4.0809803 4.9587221
[35,] 10.6540155 4.0809803
[36,] -4.7375664 10.6540155
[37,] 8.6429714 -4.7375664
[38,] 10.7368462 8.6429714
[39,] -5.9078539 10.7368462
[40,] 3.2312197 -5.9078539
[41,] -4.8192008 3.2312197
[42,] 11.2705640 -4.8192008
[43,] 5.8345346 11.2705640
[44,] 4.5014256 5.8345346
[45,] -3.2520962 4.5014256
[46,] -3.7009701 -3.2520962
[47,] -4.5677165 -3.7009701
[48,] -1.6252882 -4.5677165
[49,] -3.4264977 -1.6252882
[50,] 6.8763160 -3.4264977
[51,] 0.6700238 6.8763160
[52,] -4.3773909 0.6700238
[53,] -5.2162847 -4.3773909
[54,] -5.1452757 -5.2162847
[55,] -0.8589775 -5.1452757
[56,] -0.2147077 -0.8589775
[57,] -6.3886772 -0.2147077
[58,] -2.6225531 -6.3886772
[59,] -4.4840555 -2.6225531
[60,] 0.5665132 -4.4840555
[61,] 0.7337376 0.5665132
[62,] 7.0512717 0.7337376
[63,] 9.5891301 7.0512717
[64,] -4.9640426 9.5891301
[65,] -4.3814500 -4.9640426
[66,] -1.1412044 -4.3814500
[67,] 4.8029350 -1.1412044
[68,] -3.0976288 4.8029350
[69,] 10.8735364 -3.0976288
[70,] -2.0938617 10.8735364
[71,] 2.2254999 -2.0938617
[72,] -0.6129119 2.2254999
[73,] -2.7892482 -0.6129119
[74,] 6.2768529 -2.7892482
[75,] -2.6440787 6.2768529
[76,] 1.6986132 -2.6440787
[77,] 3.7054172 1.6986132
[78,] -3.6101057 3.7054172
[79,] 4.8862047 -3.6101057
[80,] 4.5689331 4.8862047
[81,] -2.7713142 4.5689331
[82,] -3.9060284 -2.7713142
[83,] -4.1547951 -3.9060284
[84,] 0.9219386 -4.1547951
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 11.1338016 6.5026582
2 -3.2556089 11.1338016
3 -5.2083422 -3.2556089
4 -7.3369867 -5.2083422
5 4.7026638 -7.3369867
6 7.6345404 4.7026638
7 -3.9644100 7.6345404
8 -2.3322125 -3.9644100
9 -4.9641372 -2.3322125
10 -4.8020228 -4.9641372
11 -6.1211808 -4.8020228
12 -3.9613194 -6.1211808
13 -4.4758734 -3.9613194
14 1.0480832 -4.4758734
15 1.3410686 1.0480832
16 -3.7783053 1.3410686
17 -2.9139368 -3.7783053
18 -4.2508525 -2.9139368
19 -4.1134262 -4.2508525
20 -6.3394692 -4.1134262
21 -0.9699126 -6.3394692
22 -2.3400777 -0.9699126
23 1.1855498 -2.3400777
24 -4.5414817 1.1855498
25 -0.1425271 -4.5414817
26 7.5288865 -0.1425271
27 1.4321315 7.5288865
28 8.8955074 1.4321315
29 -5.1113629 8.8955074
30 -2.0605184 -5.1113629
31 2.6477900 -2.0605184
32 -2.9151380 2.6477900
33 4.9587221 -2.9151380
34 4.0809803 4.9587221
35 10.6540155 4.0809803
36 -4.7375664 10.6540155
37 8.6429714 -4.7375664
38 10.7368462 8.6429714
39 -5.9078539 10.7368462
40 3.2312197 -5.9078539
41 -4.8192008 3.2312197
42 11.2705640 -4.8192008
43 5.8345346 11.2705640
44 4.5014256 5.8345346
45 -3.2520962 4.5014256
46 -3.7009701 -3.2520962
47 -4.5677165 -3.7009701
48 -1.6252882 -4.5677165
49 -3.4264977 -1.6252882
50 6.8763160 -3.4264977
51 0.6700238 6.8763160
52 -4.3773909 0.6700238
53 -5.2162847 -4.3773909
54 -5.1452757 -5.2162847
55 -0.8589775 -5.1452757
56 -0.2147077 -0.8589775
57 -6.3886772 -0.2147077
58 -2.6225531 -6.3886772
59 -4.4840555 -2.6225531
60 0.5665132 -4.4840555
61 0.7337376 0.5665132
62 7.0512717 0.7337376
63 9.5891301 7.0512717
64 -4.9640426 9.5891301
65 -4.3814500 -4.9640426
66 -1.1412044 -4.3814500
67 4.8029350 -1.1412044
68 -3.0976288 4.8029350
69 10.8735364 -3.0976288
70 -2.0938617 10.8735364
71 2.2254999 -2.0938617
72 -0.6129119 2.2254999
73 -2.7892482 -0.6129119
74 6.2768529 -2.7892482
75 -2.6440787 6.2768529
76 1.6986132 -2.6440787
77 3.7054172 1.6986132
78 -3.6101057 3.7054172
79 4.8862047 -3.6101057
80 4.5689331 4.8862047
81 -2.7713142 4.5689331
82 -3.9060284 -2.7713142
83 -4.1547951 -3.9060284
84 0.9219386 -4.1547951
> 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/7vbrw1324322917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8igl11324322917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/94kq31324322917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10fxw21324322917.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11io401324322917.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/12fbdh1324322917.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/134ofx1324322917.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/14mprh1324322917.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/151iej1324322918.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/16yzl31324322918.tab")
+ }
>
> try(system("convert tmp/1iozp1324322917.ps tmp/1iozp1324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yv7t1324322917.ps tmp/2yv7t1324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mlr11324322917.ps tmp/3mlr11324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hq6d1324322917.ps tmp/4hq6d1324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/52tay1324322917.ps tmp/52tay1324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sxat1324322917.ps tmp/6sxat1324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vbrw1324322917.ps tmp/7vbrw1324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/8igl11324322917.ps tmp/8igl11324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/94kq31324322917.ps tmp/94kq31324322917.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fxw21324322917.ps tmp/10fxw21324322917.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.704 0.693 4.409