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 '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(13
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+ ,2)
+ ,dim=c(5
+ ,131)
+ ,dimnames=list(c('Pop*geslacht'
+ ,'Popularity'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity')
+ ,1:131))
> y <- array(NA,dim=c(5,131),dimnames=list(c('Pop*geslacht','Popularity','KnowingPeople','Liked','Celebrity'),1:131))
> 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
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
Popularity Pop*geslacht KnowingPeople Liked Celebrity
1 13 13 14 13 3
2 12 0 8 13 5
3 15 0 12 16 6
4 12 12 7 12 6
5 10 10 10 11 5
6 12 12 7 12 3
7 15 0 16 18 8
8 9 0 11 11 4
9 12 12 14 14 4
10 11 0 6 9 4
11 11 0 16 14 6
12 11 0 11 12 6
13 15 15 16 11 5
14 7 7 12 12 4
15 11 11 7 13 6
16 11 0 13 11 4
17 10 10 11 12 6
18 14 0 15 16 6
19 10 10 7 9 4
20 6 6 9 11 4
21 11 11 7 13 2
22 15 15 14 15 7
23 11 11 15 10 5
24 14 14 15 13 6
25 9 0 15 15 7
26 13 13 14 14 5
27 16 16 8 14 4
28 13 13 8 8 4
29 12 0 14 13 7
30 14 0 14 15 7
31 11 11 8 13 4
32 9 9 11 11 4
33 16 16 16 15 6
34 12 12 10 15 6
35 10 0 8 9 5
36 13 13 14 13 6
37 16 16 16 16 7
38 14 14 13 13 6
39 15 15 5 11 3
40 5 0 8 12 3
41 8 8 10 12 4
42 11 11 8 12 6
43 16 16 13 14 7
44 17 17 15 14 5
45 9 9 6 8 4
46 9 9 12 13 5
47 13 13 16 16 6
48 12 12 12 14 5
49 8 8 8 13 4
50 14 0 13 13 5
51 12 12 14 13 5
52 11 11 12 12 4
53 16 16 16 16 6
54 8 8 10 15 2
55 15 15 15 15 8
56 7 7 8 12 3
57 16 0 16 14 6
58 14 14 19 12 6
59 9 9 6 12 5
60 14 14 13 13 5
61 11 11 15 12 6
62 15 0 13 13 6
63 15 15 14 13 5
64 13 13 13 13 5
65 11 11 11 14 5
66 11 0 14 17 6
67 12 12 12 13 6
68 12 12 15 13 6
69 12 12 14 12 5
70 12 12 13 13 5
71 14 14 8 14 4
72 6 6 6 11 2
73 7 7 7 12 4
74 14 14 13 16 6
75 10 10 11 12 5
76 13 0 5 12 3
77 12 12 12 12 6
78 9 9 8 10 4
79 12 0 11 15 5
80 16 16 14 15 8
81 10 10 9 12 4
82 10 10 13 15 6
83 16 0 16 16 7
84 15 15 16 13 6
85 10 0 8 11 4
86 8 8 4 13 6
87 8 8 7 10 3
88 11 11 14 15 5
89 13 13 11 13 6
90 16 16 17 16 7
91 14 14 17 18 6
92 9 9 11 13 3
93 8 8 10 14 3
94 8 8 9 15 4
95 11 11 12 14 5
96 12 12 15 13 7
97 14 14 13 15 6
98 15 15 12 16 7
99 16 16 14 14 6
100 16 16 14 14 6
101 11 11 8 16 6
102 14 14 15 14 6
103 14 14 12 12 4
104 12 12 12 13 4
105 13 13 15 14 6
106 12 0 6 14 5
107 16 16 14 16 8
108 12 12 15 13 6
109 11 11 10 14 5
110 4 4 6 4 4
111 16 16 14 16 8
112 10 10 8 16 4
113 13 13 11 15 6
114 14 14 15 14 6
115 7 7 13 12 3
116 12 12 14 14 5
117 12 0 16 13 4
118 13 13 14 14 6
119 15 15 14 16 4
120 12 12 10 13 4
121 10 10 4 13 6
122 8 8 8 14 5
123 10 10 15 15 6
124 15 15 16 14 6
125 16 16 12 15 8
126 13 13 12 13 7
127 16 16 15 16 7
128 9 9 9 12 4
129 14 14 12 15 6
130 14 14 14 12 6
131 12 12 11 14 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Pop*geslacht` KnowingPeople Liked Celebrity
1.5906 0.1427 0.2135 0.2587 0.5916
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.81612 -1.28579 -0.08511 1.24203 5.58000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.59055 1.11561 1.426 0.156421
`Pop*geslacht` 0.14271 0.03302 4.321 3.12e-05 ***
KnowingPeople 0.21353 0.06396 3.339 0.001108 **
Liked 0.25874 0.09964 2.597 0.010528 *
Celebrity 0.59165 0.15395 3.843 0.000192 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.928 on 126 degrees of freedom
Multiple R-squared: 0.5336, Adjusted R-squared: 0.5188
F-statistic: 36.04 on 4 and 126 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,] 0.04595354 9.190709e-02 9.540465e-01
[2,] 0.04589349 9.178697e-02 9.541065e-01
[3,] 0.12774328 2.554866e-01 8.722567e-01
[4,] 0.06873605 1.374721e-01 9.312639e-01
[5,] 0.03404281 6.808562e-02 9.659572e-01
[6,] 0.33122973 6.624595e-01 6.687703e-01
[7,] 0.72521588 5.495682e-01 2.747841e-01
[8,] 0.70341595 5.931681e-01 2.965840e-01
[9,] 0.63602468 7.279506e-01 3.639753e-01
[10,] 0.62710608 7.457878e-01 3.728939e-01
[11,] 0.56181929 8.763614e-01 4.381807e-01
[12,] 0.48259693 9.651939e-01 5.174031e-01
[13,] 0.74841031 5.031794e-01 2.515897e-01
[14,] 0.69304536 6.139093e-01 3.069546e-01
[15,] 0.63349260 7.330148e-01 3.665074e-01
[16,] 0.56530838 8.693832e-01 4.346916e-01
[17,] 0.50571512 9.885698e-01 4.942849e-01
[18,] 0.68946646 6.210671e-01 3.105335e-01
[19,] 0.62667763 7.466447e-01 3.733224e-01
[20,] 0.73364248 5.327150e-01 2.663575e-01
[21,] 0.81912227 3.617555e-01 1.808777e-01
[22,] 0.77917308 4.416538e-01 2.208269e-01
[23,] 0.75890151 4.821970e-01 2.410985e-01
[24,] 0.72346073 5.530785e-01 2.765393e-01
[25,] 0.71363670 5.727266e-01 2.863633e-01
[26,] 0.68259589 6.348082e-01 3.174041e-01
[27,] 0.66515398 6.696920e-01 3.348460e-01
[28,] 0.63293398 7.341320e-01 3.670660e-01
[29,] 0.57659810 8.468038e-01 4.234019e-01
[30,] 0.52294669 9.541066e-01 4.770533e-01
[31,] 0.47195696 9.439139e-01 5.280430e-01
[32,] 0.74370593 5.125881e-01 2.562941e-01
[33,] 0.86607395 2.678521e-01 1.339260e-01
[34,] 0.89236971 2.152606e-01 1.076303e-01
[35,] 0.87575312 2.484938e-01 1.242469e-01
[36,] 0.86227554 2.754489e-01 1.377245e-01
[37,] 0.90302089 1.939582e-01 9.697911e-02
[38,] 0.88372418 2.325516e-01 1.162758e-01
[39,] 0.91672564 1.665487e-01 8.327436e-02
[40,] 0.91414767 1.717047e-01 8.585233e-02
[41,] 0.89562956 2.087409e-01 1.043704e-01
[42,] 0.91234704 1.753059e-01 8.765296e-02
[43,] 0.94476841 1.104632e-01 5.523159e-02
[44,] 0.93053585 1.389283e-01 6.946415e-02
[45,] 0.91229960 1.754008e-01 8.770040e-02
[46,] 0.89492728 2.101454e-01 1.050727e-01
[47,] 0.89911155 2.017769e-01 1.008884e-01
[48,] 0.87817887 2.436423e-01 1.218211e-01
[49,] 0.88613223 2.277355e-01 1.138678e-01
[50,] 0.94007914 1.198417e-01 5.992086e-02
[51,] 0.92332572 1.533486e-01 7.667428e-02
[52,] 0.91333204 1.733359e-01 8.666796e-02
[53,] 0.90269140 1.946172e-01 9.730860e-02
[54,] 0.90438213 1.912357e-01 9.561787e-02
[55,] 0.95030858 9.938283e-02 4.969142e-02
[56,] 0.95167601 9.664797e-02 4.832399e-02
[57,] 0.93884168 1.223166e-01 6.115832e-02
[58,] 0.92776054 1.444789e-01 7.223946e-02
[59,] 0.92441088 1.511782e-01 7.558912e-02
[60,] 0.90782409 1.843518e-01 9.217591e-02
[61,] 0.89672672 2.065466e-01 1.032733e-01
[62,] 0.87226763 2.554647e-01 1.277324e-01
[63,] 0.84425191 3.114962e-01 1.557481e-01
[64,] 0.88365317 2.326937e-01 1.163468e-01
[65,] 0.87430222 2.513956e-01 1.256978e-01
[66,] 0.88931341 2.213732e-01 1.106866e-01
[67,] 0.86276300 2.744740e-01 1.372370e-01
[68,] 0.84681273 3.063745e-01 1.531873e-01
[69,] 0.98342735 3.314530e-02 1.657265e-02
[70,] 0.97738016 4.523968e-02 2.261984e-02
[71,] 0.97000653 5.998695e-02 2.999347e-02
[72,] 0.96588466 6.823068e-02 3.411534e-02
[73,] 0.95499407 9.001186e-02 4.500593e-02
[74,] 0.94166634 1.166673e-01 5.833366e-02
[75,] 0.96690308 6.619384e-02 3.309692e-02
[76,] 0.97987085 4.025831e-02 2.012915e-02
[77,] 0.97412299 5.175402e-02 2.587701e-02
[78,] 0.98501470 2.997060e-02 1.498530e-02
[79,] 0.98531246 2.937509e-02 1.468754e-02
[80,] 0.97959307 4.081386e-02 2.040693e-02
[81,] 0.97911241 4.177518e-02 2.088759e-02
[82,] 0.97143683 5.712634e-02 2.856317e-02
[83,] 0.96046347 7.907306e-02 3.953653e-02
[84,] 0.95866790 8.266419e-02 4.133210e-02
[85,] 0.94865311 1.026938e-01 5.134689e-02
[86,] 0.95057119 9.885762e-02 4.942881e-02
[87,] 0.96645114 6.709773e-02 3.354886e-02
[88,] 0.95955056 8.089887e-02 4.044944e-02
[89,] 0.95759520 8.480960e-02 4.240480e-02
[90,] 0.94127685 1.174463e-01 5.872315e-02
[91,] 0.92071516 1.585697e-01 7.928484e-02
[92,] 0.92057547 1.588491e-01 7.942453e-02
[93,] 0.92338832 1.532234e-01 7.661168e-02
[94,] 0.91606996 1.678601e-01 8.393004e-02
[95,] 0.88693019 2.261396e-01 1.130698e-01
[96,] 0.92493690 1.501262e-01 7.506310e-02
[97,] 0.90300503 1.939899e-01 9.699497e-02
[98,] 0.87281498 2.543700e-01 1.271850e-01
[99,] 0.96953055 6.093891e-02 3.046945e-02
[100,] 0.95392545 9.214909e-02 4.607455e-02
[101,] 0.94384248 1.123150e-01 5.615752e-02
[102,] 0.91866874 1.626625e-01 8.133126e-02
[103,] 0.89565345 2.086931e-01 1.043466e-01
[104,] 0.85334338 2.933132e-01 1.466566e-01
[105,] 0.80707899 3.858420e-01 1.929210e-01
[106,] 0.74180215 5.163957e-01 2.581979e-01
[107,] 0.66503384 6.699323e-01 3.349662e-01
[108,] 0.81040591 3.791882e-01 1.895941e-01
[109,] 0.77294760 4.541048e-01 2.270524e-01
[110,] 1.00000000 1.039526e-141 5.197631e-142
[111,] 1.00000000 1.288881e-122 6.444405e-123
[112,] 1.00000000 1.519173e-108 7.595864e-109
[113,] 1.00000000 2.976169e-92 1.488085e-92
[114,] 1.00000000 3.951637e-78 1.975819e-78
[115,] 1.00000000 4.505952e-62 2.252976e-62
[116,] 1.00000000 5.421761e-48 2.710881e-48
> postscript(file="/var/www/html/rcomp/tmp/1xopl1291553896.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xopl1291553896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xopl1291553896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48f7o1291553896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58f7o1291553896.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 = 131
Frequency = 1
1 2 3 4 5 6
1.42617211 2.37936389 3.15737602 0.54740919 -0.95737008 2.32234500
7 8 9 10 11 12
0.60248209 -0.15210614 -0.28149739 3.43302849 -1.17927053 0.40586410
13 14 15 16 17 18
2.04786752 -3.62338153 -0.56861503 1.42083137 -2.02128580 1.51678229
19 20 21 22 23 24
0.79234734 -3.58133360 1.79796605 0.25668262 -0.90900207 0.29499006
25 26 27 28 29 30
-3.81612377 -0.01585765 4.42883010 3.40941034 -0.08511410 1.39740747
31 32 33 34 35 36
0.40114427 -1.43654106 1.27855042 -0.86940217 1.41432073 -0.34876371
37 38 39 40 41 42
0.42816593 0.72205254 5.58000172 -3.17860636 -2.33903403 -0.52340707
43 44 45 46 47 48
1.58623808 3.19975115 0.40733279 -2.75919599 -1.55204382 -0.44608017
49 50 51 52 53 54
-2.17071076 3.31170768 -0.61440345 -0.19424149 1.01981120 -1.93196112
55 56 57 58 59 60
-0.54849390 -2.17761129 3.82072947 -0.30039570 -1.21926933 1.31369781
61 62 63 64 65 66
-2.01812576 3.72006241 1.95745158 0.45641281 -1.08983394 -1.52842568
67 68 69 70 71 72
-0.77898623 -1.41957996 -0.35566424 -0.40087220 2.71426009 -1.75744933
73 74 75 76 77 78
-2.55572532 -0.05416509 -1.42964053 5.46198737 -0.52024702 -0.53720812
79 80 81 82 83 84
1.22129174 0.52232236 -0.41093277 -3.22456592 2.71160578 0.93874383
85 86 87 88 89 90
1.48848758 -2.49987634 -0.58931662 -1.98916688 0.29183002 0.21463469
91 92 93 94 95 96
-1.42576848 -1.36237421 -2.26486718 -2.90172042 -1.30336518 -2.01122523
97 98 99 100 101 102
0.20457412 0.42500589 1.96435211 1.96435211 -1.55836391 0.03625085
103 104 105 106 107 108
2.37761354 0.40430431 -0.82103416 2.54768716 0.26358314 -1.41957996
109 110 111 112 113 114
-0.87630270 -2.84413542 0.26358314 -1.23235837 -0.22564840 0.03625085
115 116 117 118 119 120
-3.24526750 -0.87314266 1.26275923 -0.60750292 1.77287922 0.83136679
121 122 123 124 125 126
-0.78530632 -3.02109524 -3.65162840 0.68000462 0.94938484 -0.51334650
127 128 129 130 131
0.64169717 -1.26821778 0.41810536 0.76726051 1.54238688
> postscript(file="/var/www/html/rcomp/tmp/68f7o1291553896.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 = 131
Frequency = 1
lag(myerror, k = 1) myerror
0 1.42617211 NA
1 2.37936389 1.42617211
2 3.15737602 2.37936389
3 0.54740919 3.15737602
4 -0.95737008 0.54740919
5 2.32234500 -0.95737008
6 0.60248209 2.32234500
7 -0.15210614 0.60248209
8 -0.28149739 -0.15210614
9 3.43302849 -0.28149739
10 -1.17927053 3.43302849
11 0.40586410 -1.17927053
12 2.04786752 0.40586410
13 -3.62338153 2.04786752
14 -0.56861503 -3.62338153
15 1.42083137 -0.56861503
16 -2.02128580 1.42083137
17 1.51678229 -2.02128580
18 0.79234734 1.51678229
19 -3.58133360 0.79234734
20 1.79796605 -3.58133360
21 0.25668262 1.79796605
22 -0.90900207 0.25668262
23 0.29499006 -0.90900207
24 -3.81612377 0.29499006
25 -0.01585765 -3.81612377
26 4.42883010 -0.01585765
27 3.40941034 4.42883010
28 -0.08511410 3.40941034
29 1.39740747 -0.08511410
30 0.40114427 1.39740747
31 -1.43654106 0.40114427
32 1.27855042 -1.43654106
33 -0.86940217 1.27855042
34 1.41432073 -0.86940217
35 -0.34876371 1.41432073
36 0.42816593 -0.34876371
37 0.72205254 0.42816593
38 5.58000172 0.72205254
39 -3.17860636 5.58000172
40 -2.33903403 -3.17860636
41 -0.52340707 -2.33903403
42 1.58623808 -0.52340707
43 3.19975115 1.58623808
44 0.40733279 3.19975115
45 -2.75919599 0.40733279
46 -1.55204382 -2.75919599
47 -0.44608017 -1.55204382
48 -2.17071076 -0.44608017
49 3.31170768 -2.17071076
50 -0.61440345 3.31170768
51 -0.19424149 -0.61440345
52 1.01981120 -0.19424149
53 -1.93196112 1.01981120
54 -0.54849390 -1.93196112
55 -2.17761129 -0.54849390
56 3.82072947 -2.17761129
57 -0.30039570 3.82072947
58 -1.21926933 -0.30039570
59 1.31369781 -1.21926933
60 -2.01812576 1.31369781
61 3.72006241 -2.01812576
62 1.95745158 3.72006241
63 0.45641281 1.95745158
64 -1.08983394 0.45641281
65 -1.52842568 -1.08983394
66 -0.77898623 -1.52842568
67 -1.41957996 -0.77898623
68 -0.35566424 -1.41957996
69 -0.40087220 -0.35566424
70 2.71426009 -0.40087220
71 -1.75744933 2.71426009
72 -2.55572532 -1.75744933
73 -0.05416509 -2.55572532
74 -1.42964053 -0.05416509
75 5.46198737 -1.42964053
76 -0.52024702 5.46198737
77 -0.53720812 -0.52024702
78 1.22129174 -0.53720812
79 0.52232236 1.22129174
80 -0.41093277 0.52232236
81 -3.22456592 -0.41093277
82 2.71160578 -3.22456592
83 0.93874383 2.71160578
84 1.48848758 0.93874383
85 -2.49987634 1.48848758
86 -0.58931662 -2.49987634
87 -1.98916688 -0.58931662
88 0.29183002 -1.98916688
89 0.21463469 0.29183002
90 -1.42576848 0.21463469
91 -1.36237421 -1.42576848
92 -2.26486718 -1.36237421
93 -2.90172042 -2.26486718
94 -1.30336518 -2.90172042
95 -2.01122523 -1.30336518
96 0.20457412 -2.01122523
97 0.42500589 0.20457412
98 1.96435211 0.42500589
99 1.96435211 1.96435211
100 -1.55836391 1.96435211
101 0.03625085 -1.55836391
102 2.37761354 0.03625085
103 0.40430431 2.37761354
104 -0.82103416 0.40430431
105 2.54768716 -0.82103416
106 0.26358314 2.54768716
107 -1.41957996 0.26358314
108 -0.87630270 -1.41957996
109 -2.84413542 -0.87630270
110 0.26358314 -2.84413542
111 -1.23235837 0.26358314
112 -0.22564840 -1.23235837
113 0.03625085 -0.22564840
114 -3.24526750 0.03625085
115 -0.87314266 -3.24526750
116 1.26275923 -0.87314266
117 -0.60750292 1.26275923
118 1.77287922 -0.60750292
119 0.83136679 1.77287922
120 -0.78530632 0.83136679
121 -3.02109524 -0.78530632
122 -3.65162840 -3.02109524
123 0.68000462 -3.65162840
124 0.94938484 0.68000462
125 -0.51334650 0.94938484
126 0.64169717 -0.51334650
127 -1.26821778 0.64169717
128 0.41810536 -1.26821778
129 0.76726051 0.41810536
130 1.54238688 0.76726051
131 NA 1.54238688
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.37936389 1.42617211
[2,] 3.15737602 2.37936389
[3,] 0.54740919 3.15737602
[4,] -0.95737008 0.54740919
[5,] 2.32234500 -0.95737008
[6,] 0.60248209 2.32234500
[7,] -0.15210614 0.60248209
[8,] -0.28149739 -0.15210614
[9,] 3.43302849 -0.28149739
[10,] -1.17927053 3.43302849
[11,] 0.40586410 -1.17927053
[12,] 2.04786752 0.40586410
[13,] -3.62338153 2.04786752
[14,] -0.56861503 -3.62338153
[15,] 1.42083137 -0.56861503
[16,] -2.02128580 1.42083137
[17,] 1.51678229 -2.02128580
[18,] 0.79234734 1.51678229
[19,] -3.58133360 0.79234734
[20,] 1.79796605 -3.58133360
[21,] 0.25668262 1.79796605
[22,] -0.90900207 0.25668262
[23,] 0.29499006 -0.90900207
[24,] -3.81612377 0.29499006
[25,] -0.01585765 -3.81612377
[26,] 4.42883010 -0.01585765
[27,] 3.40941034 4.42883010
[28,] -0.08511410 3.40941034
[29,] 1.39740747 -0.08511410
[30,] 0.40114427 1.39740747
[31,] -1.43654106 0.40114427
[32,] 1.27855042 -1.43654106
[33,] -0.86940217 1.27855042
[34,] 1.41432073 -0.86940217
[35,] -0.34876371 1.41432073
[36,] 0.42816593 -0.34876371
[37,] 0.72205254 0.42816593
[38,] 5.58000172 0.72205254
[39,] -3.17860636 5.58000172
[40,] -2.33903403 -3.17860636
[41,] -0.52340707 -2.33903403
[42,] 1.58623808 -0.52340707
[43,] 3.19975115 1.58623808
[44,] 0.40733279 3.19975115
[45,] -2.75919599 0.40733279
[46,] -1.55204382 -2.75919599
[47,] -0.44608017 -1.55204382
[48,] -2.17071076 -0.44608017
[49,] 3.31170768 -2.17071076
[50,] -0.61440345 3.31170768
[51,] -0.19424149 -0.61440345
[52,] 1.01981120 -0.19424149
[53,] -1.93196112 1.01981120
[54,] -0.54849390 -1.93196112
[55,] -2.17761129 -0.54849390
[56,] 3.82072947 -2.17761129
[57,] -0.30039570 3.82072947
[58,] -1.21926933 -0.30039570
[59,] 1.31369781 -1.21926933
[60,] -2.01812576 1.31369781
[61,] 3.72006241 -2.01812576
[62,] 1.95745158 3.72006241
[63,] 0.45641281 1.95745158
[64,] -1.08983394 0.45641281
[65,] -1.52842568 -1.08983394
[66,] -0.77898623 -1.52842568
[67,] -1.41957996 -0.77898623
[68,] -0.35566424 -1.41957996
[69,] -0.40087220 -0.35566424
[70,] 2.71426009 -0.40087220
[71,] -1.75744933 2.71426009
[72,] -2.55572532 -1.75744933
[73,] -0.05416509 -2.55572532
[74,] -1.42964053 -0.05416509
[75,] 5.46198737 -1.42964053
[76,] -0.52024702 5.46198737
[77,] -0.53720812 -0.52024702
[78,] 1.22129174 -0.53720812
[79,] 0.52232236 1.22129174
[80,] -0.41093277 0.52232236
[81,] -3.22456592 -0.41093277
[82,] 2.71160578 -3.22456592
[83,] 0.93874383 2.71160578
[84,] 1.48848758 0.93874383
[85,] -2.49987634 1.48848758
[86,] -0.58931662 -2.49987634
[87,] -1.98916688 -0.58931662
[88,] 0.29183002 -1.98916688
[89,] 0.21463469 0.29183002
[90,] -1.42576848 0.21463469
[91,] -1.36237421 -1.42576848
[92,] -2.26486718 -1.36237421
[93,] -2.90172042 -2.26486718
[94,] -1.30336518 -2.90172042
[95,] -2.01122523 -1.30336518
[96,] 0.20457412 -2.01122523
[97,] 0.42500589 0.20457412
[98,] 1.96435211 0.42500589
[99,] 1.96435211 1.96435211
[100,] -1.55836391 1.96435211
[101,] 0.03625085 -1.55836391
[102,] 2.37761354 0.03625085
[103,] 0.40430431 2.37761354
[104,] -0.82103416 0.40430431
[105,] 2.54768716 -0.82103416
[106,] 0.26358314 2.54768716
[107,] -1.41957996 0.26358314
[108,] -0.87630270 -1.41957996
[109,] -2.84413542 -0.87630270
[110,] 0.26358314 -2.84413542
[111,] -1.23235837 0.26358314
[112,] -0.22564840 -1.23235837
[113,] 0.03625085 -0.22564840
[114,] -3.24526750 0.03625085
[115,] -0.87314266 -3.24526750
[116,] 1.26275923 -0.87314266
[117,] -0.60750292 1.26275923
[118,] 1.77287922 -0.60750292
[119,] 0.83136679 1.77287922
[120,] -0.78530632 0.83136679
[121,] -3.02109524 -0.78530632
[122,] -3.65162840 -3.02109524
[123,] 0.68000462 -3.65162840
[124,] 0.94938484 0.68000462
[125,] -0.51334650 0.94938484
[126,] 0.64169717 -0.51334650
[127,] -1.26821778 0.64169717
[128,] 0.41810536 -1.26821778
[129,] 0.76726051 0.41810536
[130,] 1.54238688 0.76726051
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.37936389 1.42617211
2 3.15737602 2.37936389
3 0.54740919 3.15737602
4 -0.95737008 0.54740919
5 2.32234500 -0.95737008
6 0.60248209 2.32234500
7 -0.15210614 0.60248209
8 -0.28149739 -0.15210614
9 3.43302849 -0.28149739
10 -1.17927053 3.43302849
11 0.40586410 -1.17927053
12 2.04786752 0.40586410
13 -3.62338153 2.04786752
14 -0.56861503 -3.62338153
15 1.42083137 -0.56861503
16 -2.02128580 1.42083137
17 1.51678229 -2.02128580
18 0.79234734 1.51678229
19 -3.58133360 0.79234734
20 1.79796605 -3.58133360
21 0.25668262 1.79796605
22 -0.90900207 0.25668262
23 0.29499006 -0.90900207
24 -3.81612377 0.29499006
25 -0.01585765 -3.81612377
26 4.42883010 -0.01585765
27 3.40941034 4.42883010
28 -0.08511410 3.40941034
29 1.39740747 -0.08511410
30 0.40114427 1.39740747
31 -1.43654106 0.40114427
32 1.27855042 -1.43654106
33 -0.86940217 1.27855042
34 1.41432073 -0.86940217
35 -0.34876371 1.41432073
36 0.42816593 -0.34876371
37 0.72205254 0.42816593
38 5.58000172 0.72205254
39 -3.17860636 5.58000172
40 -2.33903403 -3.17860636
41 -0.52340707 -2.33903403
42 1.58623808 -0.52340707
43 3.19975115 1.58623808
44 0.40733279 3.19975115
45 -2.75919599 0.40733279
46 -1.55204382 -2.75919599
47 -0.44608017 -1.55204382
48 -2.17071076 -0.44608017
49 3.31170768 -2.17071076
50 -0.61440345 3.31170768
51 -0.19424149 -0.61440345
52 1.01981120 -0.19424149
53 -1.93196112 1.01981120
54 -0.54849390 -1.93196112
55 -2.17761129 -0.54849390
56 3.82072947 -2.17761129
57 -0.30039570 3.82072947
58 -1.21926933 -0.30039570
59 1.31369781 -1.21926933
60 -2.01812576 1.31369781
61 3.72006241 -2.01812576
62 1.95745158 3.72006241
63 0.45641281 1.95745158
64 -1.08983394 0.45641281
65 -1.52842568 -1.08983394
66 -0.77898623 -1.52842568
67 -1.41957996 -0.77898623
68 -0.35566424 -1.41957996
69 -0.40087220 -0.35566424
70 2.71426009 -0.40087220
71 -1.75744933 2.71426009
72 -2.55572532 -1.75744933
73 -0.05416509 -2.55572532
74 -1.42964053 -0.05416509
75 5.46198737 -1.42964053
76 -0.52024702 5.46198737
77 -0.53720812 -0.52024702
78 1.22129174 -0.53720812
79 0.52232236 1.22129174
80 -0.41093277 0.52232236
81 -3.22456592 -0.41093277
82 2.71160578 -3.22456592
83 0.93874383 2.71160578
84 1.48848758 0.93874383
85 -2.49987634 1.48848758
86 -0.58931662 -2.49987634
87 -1.98916688 -0.58931662
88 0.29183002 -1.98916688
89 0.21463469 0.29183002
90 -1.42576848 0.21463469
91 -1.36237421 -1.42576848
92 -2.26486718 -1.36237421
93 -2.90172042 -2.26486718
94 -1.30336518 -2.90172042
95 -2.01122523 -1.30336518
96 0.20457412 -2.01122523
97 0.42500589 0.20457412
98 1.96435211 0.42500589
99 1.96435211 1.96435211
100 -1.55836391 1.96435211
101 0.03625085 -1.55836391
102 2.37761354 0.03625085
103 0.40430431 2.37761354
104 -0.82103416 0.40430431
105 2.54768716 -0.82103416
106 0.26358314 2.54768716
107 -1.41957996 0.26358314
108 -0.87630270 -1.41957996
109 -2.84413542 -0.87630270
110 0.26358314 -2.84413542
111 -1.23235837 0.26358314
112 -0.22564840 -1.23235837
113 0.03625085 -0.22564840
114 -3.24526750 0.03625085
115 -0.87314266 -3.24526750
116 1.26275923 -0.87314266
117 -0.60750292 1.26275923
118 1.77287922 -0.60750292
119 0.83136679 1.77287922
120 -0.78530632 0.83136679
121 -3.02109524 -0.78530632
122 -3.65162840 -3.02109524
123 0.68000462 -3.65162840
124 0.94938484 0.68000462
125 -0.51334650 0.94938484
126 0.64169717 -0.51334650
127 -1.26821778 0.64169717
128 0.41810536 -1.26821778
129 0.76726051 0.41810536
130 1.54238688 0.76726051
> 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/71o6q1291553896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8bf5b1291553896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bf5b1291553896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bf5b1291553896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118p3k1291553896.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/12t8181291553896.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/137zhh1291553896.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/14biy51291553896.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/15e1wt1291553896.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/162kwe1291553897.tab")
+ }
>
> try(system("convert tmp/1xopl1291553896.ps tmp/1xopl1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xopl1291553896.ps tmp/2xopl1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xopl1291553896.ps tmp/3xopl1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/48f7o1291553896.ps tmp/48f7o1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/58f7o1291553896.ps tmp/58f7o1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/68f7o1291553896.ps tmp/68f7o1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/71o6q1291553896.ps tmp/71o6q1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bf5b1291553896.ps tmp/8bf5b1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bf5b1291553896.ps tmp/9bf5b1291553896.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bf5b1291553896.ps tmp/10bf5b1291553896.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.571 1.824 8.333