R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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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(3
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+ ,dim=c(4
+ ,153)
+ ,dimnames=list(c('Popular'
+ ,'Friends(s)'
+ ,'Friends(f)'
+ ,'Future')
+ ,1:153))
> y <- array(NA,dim=c(4,153),dimnames=list(c('Popular','Friends(s)','Friends(f)','Future'),1:153))
> 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 = '4'
> #'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
Future Popular Friends(s) Friends(f)
1 4 3 3 3
2 4 3 3 3
3 3 4 4 3
4 4 3 3 3
5 3 3 2 2
6 4 3 3 2
7 4 3 4 4
8 4 2 2 2
9 4 3 3 3
10 2 3 4 2
11 2 3 3 4
12 4 3 3 3
13 4 3 4 3
14 4 2 2 2
15 4 3 2 3
16 3 3 3 2
17 4 2 2 3
18 4 3 4 3
19 3 2 2 2
20 2 1 1 3
21 2 2 3 2
22 4 3 4 3
23 3 3 2 3
24 4 3 3 2
25 4 3 3 3
26 3 3 4 3
27 3 2 3 4
28 2 3 3 3
29 3 3 4 3
30 4 4 4 2
31 3 3 4 2
32 3 3 3 4
33 4 3 4 4
34 4 2 2 3
35 3 3 4 4
36 4 3 3 3
37 2 3 2 2
38 3 3 4 3
39 4 4 4 4
40 4 3 4 3
41 3 3 4 3
42 5 1 2 2
43 3 2 2 2
44 3 3 3 3
45 4 4 4 4
46 4 4 5 4
47 5 2 2 2
48 3 1 3 3
49 4 3 3 3
50 4 3 2 3
51 2 1 2 2
52 4 3 3 4
53 4 2 2 3
54 4 3 4 4
55 3 3 3 3
56 3 2 3 3
57 3 4 4 4
58 2 1 1 1
59 4 3 4 4
60 4 2 2 2
61 4 4 4 3
62 3 3 4 3
63 4 4 4 3
64 3 3 2 3
65 4 3 4 4
66 4 3 2 2
67 4 3 4 2
68 4 3 4 4
69 4 1 1 1
70 4 3 4 4
71 4 3 4 4
72 4 3 3 3
73 2 2 3 2
74 3 3 3 3
75 4 3 3 3
76 4 3 3 3
77 3 2 3 3
78 4 3 4 4
79 4 2 1 2
80 4 2 3 3
81 3 3 4 3
82 3 3 3 3
83 3 2 3 2
84 3 2 4 2
85 4 3 3 3
86 4 2 2 2
87 2 3 3 3
88 4 4 4 3
89 3 2 3 3
90 4 3 4 3
91 3 2 3 4
92 4 4 4 4
93 3 3 4 4
94 3 3 3 3
95 4 3 2 2
96 3 3 1 3
97 4 2 2 2
98 2 3 2 3
99 4 4 3 3
100 4 4 4 4
101 4 4 4 3
102 4 3 3 3
103 4 3 3 2
104 3 1 1 1
105 4 4 3 3
106 3 1 3 3
107 4 3 4 4
108 3 2 2 2
109 3 3 3 3
110 2 3 3 4
111 4 2 3 3
112 4 3 4 4
113 4 3 4 4
114 1 4 4 3
115 4 4 4 3
116 4 3 2 3
117 4 3 3 3
118 3 3 4 3
119 4 3 3 3
120 4 3 4 3
121 3 1 2 3
122 4 2 4 4
123 4 4 4 3
124 4 3 3 2
125 4 4 4 4
126 4 3 3 3
127 3 2 3 3
128 3 1 1 1
129 4 4 4 4
130 4 3 4 3
131 4 3 2 2
132 4 3 3 2
133 4 4 4 4
134 4 3 3 3
135 4 3 4 4
136 4 1 2 2
137 4 4 5 4
138 3 2 3 4
139 3 2 4 3
140 4 3 3 3
141 4 3 4 3
142 2 2 2 2
143 4 3 3 3
144 4 3 3 3
145 4 2 2 3
146 4 3 4 4
147 4 4 4 3
148 4 4 3 3
149 4 4 4 4
150 4 2 2 2
151 4 3 4 3
152 4 3 4 4
153 4 3 3 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Popular `Friends(s)` `Friends(f)`
2.880869 0.192485 0.003701 0.039144
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.7830 -0.4335 0.2561 0.4131 1.8410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.880869 0.249779 11.534 <2e-16 ***
Popular 0.192485 0.092765 2.075 0.0397 *
`Friends(s)` 0.003701 0.091270 0.041 0.9677
`Friends(f)` 0.039144 0.094358 0.415 0.6789
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6831 on 149 degrees of freedom
Multiple R-squared: 0.05898, Adjusted R-squared: 0.04004
F-statistic: 3.113 on 3 and 149 DF, p-value: 0.02814
> 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,] 0.11038892 2.207778e-01 8.896111e-01
[2,] 0.09801448 1.960290e-01 9.019855e-01
[3,] 0.05134182 1.026836e-01 9.486582e-01
[4,] 0.24611182 4.922236e-01 7.538882e-01
[5,] 0.93100903 1.379819e-01 6.899097e-02
[6,] 0.90683647 1.863271e-01 9.316353e-02
[7,] 0.88691250 2.261750e-01 1.130875e-01
[8,] 0.84083305 3.183339e-01 1.591670e-01
[9,] 0.79275445 4.144911e-01 2.072456e-01
[10,] 0.74973646 5.005271e-01 2.502635e-01
[11,] 0.68922789 6.215442e-01 3.107721e-01
[12,] 0.65317969 6.936406e-01 3.468203e-01
[13,] 0.67232366 6.553527e-01 3.276763e-01
[14,] 0.87845622 2.430876e-01 1.215438e-01
[15,] 0.92249419 1.550116e-01 7.750581e-02
[16,] 0.91043887 1.791223e-01 8.956113e-02
[17,] 0.90299844 1.940031e-01 9.700156e-02
[18,] 0.88753700 2.249260e-01 1.124630e-01
[19,] 0.86519033 2.696193e-01 1.348097e-01
[20,] 0.84717601 3.056480e-01 1.528240e-01
[21,] 0.81244121 3.751176e-01 1.875588e-01
[22,] 0.92228107 1.554379e-01 7.771893e-02
[23,] 0.90759714 1.848057e-01 9.240286e-02
[24,] 0.88317860 2.336428e-01 1.168214e-01
[25,] 0.86302753 2.739449e-01 1.369725e-01
[26,] 0.84644841 3.071032e-01 1.535516e-01
[27,] 0.83449144 3.310171e-01 1.655086e-01
[28,] 0.83697157 3.260569e-01 1.630284e-01
[29,] 0.81713611 3.657278e-01 1.828639e-01
[30,] 0.79601827 4.079635e-01 2.039817e-01
[31,] 0.90637548 1.872490e-01 9.362452e-02
[32,] 0.89374980 2.125004e-01 1.062502e-01
[33,] 0.86979598 2.604080e-01 1.302040e-01
[34,] 0.85626317 2.874737e-01 1.437368e-01
[35,] 0.84099365 3.180127e-01 1.590063e-01
[36,] 0.95977050 8.045900e-02 4.022950e-02
[37,] 0.95081581 9.836838e-02 4.918419e-02
[38,] 0.94397942 1.120412e-01 5.602058e-02
[39,] 0.93342622 1.331476e-01 6.657378e-02
[40,] 0.91898900 1.620220e-01 8.101100e-02
[41,] 0.97413432 5.173136e-02 2.586568e-02
[42,] 0.96777810 6.444380e-02 3.222190e-02
[43,] 0.96233819 7.532361e-02 3.766181e-02
[44,] 0.95611081 8.777838e-02 4.388919e-02
[45,] 0.97350795 5.298410e-02 2.649205e-02
[46,] 0.96805362 6.389276e-02 3.194638e-02
[47,] 0.96586262 6.827476e-02 3.413738e-02
[48,] 0.95921750 8.156499e-02 4.078250e-02
[49,] 0.95534197 8.931606e-02 4.465803e-02
[50,] 0.94663033 1.067393e-01 5.336967e-02
[51,] 0.94976334 1.004733e-01 5.023666e-02
[52,] 0.96636492 6.727016e-02 3.363508e-02
[53,] 0.95965588 8.068824e-02 4.034412e-02
[54,] 0.95893452 8.213096e-02 4.106548e-02
[55,] 0.94913789 1.017242e-01 5.086211e-02
[56,] 0.94539721 1.092056e-01 5.460279e-02
[57,] 0.93304060 1.339188e-01 6.695940e-02
[58,] 0.92791621 1.441676e-01 7.208379e-02
[59,] 0.91576275 1.684745e-01 8.423725e-02
[60,] 0.90559696 1.888061e-01 9.440304e-02
[61,] 0.89435448 2.112910e-01 1.056455e-01
[62,] 0.87834301 2.433140e-01 1.216570e-01
[63,] 0.89230474 2.153905e-01 1.076953e-01
[64,] 0.87610643 2.477871e-01 1.238936e-01
[65,] 0.85813194 2.837361e-01 1.418681e-01
[66,] 0.84054325 3.189135e-01 1.594567e-01
[67,] 0.91119909 1.776018e-01 8.880091e-02
[68,] 0.90605778 1.878844e-01 9.394222e-02
[69,] 0.89270933 2.145813e-01 1.072907e-01
[70,] 0.87800032 2.439994e-01 1.219997e-01
[71,] 0.86137202 2.772560e-01 1.386280e-01
[72,] 0.84225480 3.154904e-01 1.577452e-01
[73,] 0.84046212 3.190758e-01 1.595379e-01
[74,] 0.83412719 3.317456e-01 1.658728e-01
[75,] 0.82996756 3.400649e-01 1.700324e-01
[76,] 0.82204932 3.559014e-01 1.779507e-01
[77,] 0.80320533 3.935893e-01 1.967947e-01
[78,] 0.79565304 4.086939e-01 2.043470e-01
[79,] 0.77336516 4.532697e-01 2.266348e-01
[80,] 0.76756442 4.648712e-01 2.324356e-01
[81,] 0.89884710 2.023058e-01 1.011529e-01
[82,] 0.87803802 2.439240e-01 1.219620e-01
[83,] 0.86298585 2.740283e-01 1.370142e-01
[84,] 0.84220592 3.155882e-01 1.577941e-01
[85,] 0.82139190 3.572162e-01 1.786081e-01
[86,] 0.79042635 4.191473e-01 2.095737e-01
[87,] 0.78831404 4.233719e-01 2.116860e-01
[88,] 0.78196356 4.360729e-01 2.180364e-01
[89,] 0.76076977 4.784605e-01 2.392302e-01
[90,] 0.73918758 5.216248e-01 2.608124e-01
[91,] 0.73195562 5.360888e-01 2.680444e-01
[92,] 0.87699158 2.460168e-01 1.230084e-01
[93,] 0.85172367 2.965527e-01 1.482763e-01
[94,] 0.82219151 3.556170e-01 1.778085e-01
[95,] 0.78955784 4.208843e-01 2.104422e-01
[96,] 0.76237165 4.752567e-01 2.376283e-01
[97,] 0.73483552 5.303290e-01 2.651645e-01
[98,] 0.69450316 6.109937e-01 3.054968e-01
[99,] 0.65121309 6.975738e-01 3.487869e-01
[100,] 0.61378806 7.724239e-01 3.862119e-01
[101,] 0.57396490 8.520702e-01 4.260351e-01
[102,] 0.54433738 9.113252e-01 4.556626e-01
[103,] 0.53995571 9.200886e-01 4.600443e-01
[104,] 0.81078032 3.784394e-01 1.892197e-01
[105,] 0.79290535 4.141893e-01 2.070946e-01
[106,] 0.75824518 4.835096e-01 2.417548e-01
[107,] 0.72042843 5.591431e-01 2.795716e-01
[108,] 0.99992342 1.531656e-04 7.658278e-05
[109,] 0.99986512 2.697558e-04 1.348779e-04
[110,] 0.99977077 4.584589e-04 2.292295e-04
[111,] 0.99962410 7.518001e-04 3.759001e-04
[112,] 0.99982321 3.535897e-04 1.767948e-04
[113,] 0.99970328 5.934342e-04 2.967171e-04
[114,] 0.99949641 1.007174e-03 5.035869e-04
[115,] 0.99918560 1.628806e-03 8.144028e-04
[116,] 0.99909863 1.802738e-03 9.013692e-04
[117,] 0.99849664 3.006720e-03 1.503360e-03
[118,] 0.99753302 4.933969e-03 2.466985e-03
[119,] 0.99592563 8.148737e-03 4.074368e-03
[120,] 0.99360066 1.279869e-02 6.399343e-03
[121,] 0.99293432 1.413137e-02 7.065684e-03
[122,] 0.99002099 1.995802e-02 9.979008e-03
[123,] 0.98401624 3.196753e-02 1.598376e-02
[124,] 0.97580887 4.838227e-02 2.419113e-02
[125,] 0.96341359 7.317281e-02 3.658641e-02
[126,] 0.94647177 1.070565e-01 5.352823e-02
[127,] 0.92157794 1.568441e-01 7.842206e-02
[128,] 0.88993863 2.201227e-01 1.100614e-01
[129,] 0.84988288 3.002342e-01 1.501171e-01
[130,] 0.89689649 2.062070e-01 1.031035e-01
[131,] 0.85567523 2.886495e-01 1.443248e-01
[132,] 0.84464220 3.107156e-01 1.553578e-01
[133,] 0.82741451 3.451710e-01 1.725855e-01
[134,] 0.75960329 4.807934e-01 2.403967e-01
[135,] 0.67138592 6.572282e-01 3.286141e-01
[136,] 1.00000000 2.556449e-103 1.278225e-103
[137,] 1.00000000 0.000000e+00 0.000000e+00
[138,] 1.00000000 0.000000e+00 0.000000e+00
[139,] 1.00000000 1.586687e-59 7.933437e-60
[140,] 1.00000000 0.000000e+00 0.000000e+00
> postscript(file="/var/www/html/rcomp/tmp/1r7xx1290508224.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2r7xx1290508224.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r7xx1290508224.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41gwi1290508224.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/51gwi1290508224.ps",horizontal=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 = 153
Frequency = 1
1 2 3 4 5 6 7
0.4131409 0.4131409 -0.7830454 0.4131409 -0.5440142 0.4522845 0.3702959
8 9 10 11 12 13 14
0.6484708 0.4131409 -1.5514169 -1.6260027 0.4131409 0.4094395 0.6484708
15 16 17 18 19 20 21
0.4168423 -0.5477155 0.6093272 0.4094395 -0.3515292 -1.1944865 -1.3552306
22 23 24 25 26 27 28
0.4094395 -0.5831577 0.4522845 0.4131409 -0.5905605 -0.4335177 -1.5868591
29 30 31 32 33 34 35
-0.5905605 0.2560982 -0.5514169 -0.6260027 0.3702959 0.6093272 -0.6297041
36 37 38 39 40 41 42
0.4131409 -1.5440142 -0.5905605 0.1778110 0.4094395 -0.5905605 1.8409557
43 44 45 46 47 48 49
-0.3515292 -0.5868591 0.1778110 0.1741096 1.6484708 -0.2018893 0.4131409
50 51 52 53 54 55 56
0.4168423 -1.1590443 0.3739973 0.6093272 0.3702959 -0.5868591 -0.3943742
57 58 59 60 61 62 63
-0.8221890 -1.1161994 0.3702959 0.6484708 0.2169546 -0.5905605 0.2169546
64 65 66 67 68 69 70
-0.5831577 0.3702959 0.4559858 0.4485831 0.3702959 0.8838006 0.3702959
71 72 73 74 75 76 77
0.3702959 0.4131409 -1.3552306 -0.5868591 0.4131409 0.4131409 -0.3943742
78 79 80 81 82 83 84
0.3702959 0.6521722 0.6056258 -0.5905605 -0.5868591 -0.3552306 -0.3589320
85 86 87 88 89 90 91
0.4131409 0.6484708 -1.5868591 0.2169546 -0.3943742 0.4094395 -0.4335177
92 93 94 95 96 97 98
0.1778110 -0.6297041 -0.5868591 0.4559858 -0.5794563 0.6484708 -1.5831577
99 100 101 102 103 104 105
0.2206560 0.1778110 0.2169546 0.4131409 0.4522845 -0.1161994 0.2206560
106 107 108 109 110 111 112
-0.2018893 0.3702959 -0.3515292 -0.5868591 -1.6260027 0.6056258 0.3702959
113 114 115 116 117 118 119
0.3702959 -2.7830454 0.2169546 0.4168423 0.4131409 -0.5905605 0.4131409
120 121 122 123 124 125 126
0.4094395 -0.1981879 0.5627809 0.2169546 0.4522845 0.1778110 0.4131409
127 128 129 130 131 132 133
-0.3943742 -0.1161994 0.1778110 0.4094395 0.4559858 0.4522845 0.1778110
134 135 136 137 138 139 140
0.4131409 0.3702959 0.8409557 0.1741096 -0.4335177 -0.3980756 0.4131409
141 142 143 144 145 146 147
0.4094395 -1.3515292 0.4131409 0.4131409 0.6093272 0.3702959 0.2169546
148 149 150 151 152 153
0.2206560 0.1778110 0.6484708 0.4094395 0.3702959 0.4522845
> postscript(file="/var/www/html/rcomp/tmp/61gwi1290508224.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 153
Frequency = 1
lag(myerror, k = 1) myerror
0 0.4131409 NA
1 0.4131409 0.4131409
2 -0.7830454 0.4131409
3 0.4131409 -0.7830454
4 -0.5440142 0.4131409
5 0.4522845 -0.5440142
6 0.3702959 0.4522845
7 0.6484708 0.3702959
8 0.4131409 0.6484708
9 -1.5514169 0.4131409
10 -1.6260027 -1.5514169
11 0.4131409 -1.6260027
12 0.4094395 0.4131409
13 0.6484708 0.4094395
14 0.4168423 0.6484708
15 -0.5477155 0.4168423
16 0.6093272 -0.5477155
17 0.4094395 0.6093272
18 -0.3515292 0.4094395
19 -1.1944865 -0.3515292
20 -1.3552306 -1.1944865
21 0.4094395 -1.3552306
22 -0.5831577 0.4094395
23 0.4522845 -0.5831577
24 0.4131409 0.4522845
25 -0.5905605 0.4131409
26 -0.4335177 -0.5905605
27 -1.5868591 -0.4335177
28 -0.5905605 -1.5868591
29 0.2560982 -0.5905605
30 -0.5514169 0.2560982
31 -0.6260027 -0.5514169
32 0.3702959 -0.6260027
33 0.6093272 0.3702959
34 -0.6297041 0.6093272
35 0.4131409 -0.6297041
36 -1.5440142 0.4131409
37 -0.5905605 -1.5440142
38 0.1778110 -0.5905605
39 0.4094395 0.1778110
40 -0.5905605 0.4094395
41 1.8409557 -0.5905605
42 -0.3515292 1.8409557
43 -0.5868591 -0.3515292
44 0.1778110 -0.5868591
45 0.1741096 0.1778110
46 1.6484708 0.1741096
47 -0.2018893 1.6484708
48 0.4131409 -0.2018893
49 0.4168423 0.4131409
50 -1.1590443 0.4168423
51 0.3739973 -1.1590443
52 0.6093272 0.3739973
53 0.3702959 0.6093272
54 -0.5868591 0.3702959
55 -0.3943742 -0.5868591
56 -0.8221890 -0.3943742
57 -1.1161994 -0.8221890
58 0.3702959 -1.1161994
59 0.6484708 0.3702959
60 0.2169546 0.6484708
61 -0.5905605 0.2169546
62 0.2169546 -0.5905605
63 -0.5831577 0.2169546
64 0.3702959 -0.5831577
65 0.4559858 0.3702959
66 0.4485831 0.4559858
67 0.3702959 0.4485831
68 0.8838006 0.3702959
69 0.3702959 0.8838006
70 0.3702959 0.3702959
71 0.4131409 0.3702959
72 -1.3552306 0.4131409
73 -0.5868591 -1.3552306
74 0.4131409 -0.5868591
75 0.4131409 0.4131409
76 -0.3943742 0.4131409
77 0.3702959 -0.3943742
78 0.6521722 0.3702959
79 0.6056258 0.6521722
80 -0.5905605 0.6056258
81 -0.5868591 -0.5905605
82 -0.3552306 -0.5868591
83 -0.3589320 -0.3552306
84 0.4131409 -0.3589320
85 0.6484708 0.4131409
86 -1.5868591 0.6484708
87 0.2169546 -1.5868591
88 -0.3943742 0.2169546
89 0.4094395 -0.3943742
90 -0.4335177 0.4094395
91 0.1778110 -0.4335177
92 -0.6297041 0.1778110
93 -0.5868591 -0.6297041
94 0.4559858 -0.5868591
95 -0.5794563 0.4559858
96 0.6484708 -0.5794563
97 -1.5831577 0.6484708
98 0.2206560 -1.5831577
99 0.1778110 0.2206560
100 0.2169546 0.1778110
101 0.4131409 0.2169546
102 0.4522845 0.4131409
103 -0.1161994 0.4522845
104 0.2206560 -0.1161994
105 -0.2018893 0.2206560
106 0.3702959 -0.2018893
107 -0.3515292 0.3702959
108 -0.5868591 -0.3515292
109 -1.6260027 -0.5868591
110 0.6056258 -1.6260027
111 0.3702959 0.6056258
112 0.3702959 0.3702959
113 -2.7830454 0.3702959
114 0.2169546 -2.7830454
115 0.4168423 0.2169546
116 0.4131409 0.4168423
117 -0.5905605 0.4131409
118 0.4131409 -0.5905605
119 0.4094395 0.4131409
120 -0.1981879 0.4094395
121 0.5627809 -0.1981879
122 0.2169546 0.5627809
123 0.4522845 0.2169546
124 0.1778110 0.4522845
125 0.4131409 0.1778110
126 -0.3943742 0.4131409
127 -0.1161994 -0.3943742
128 0.1778110 -0.1161994
129 0.4094395 0.1778110
130 0.4559858 0.4094395
131 0.4522845 0.4559858
132 0.1778110 0.4522845
133 0.4131409 0.1778110
134 0.3702959 0.4131409
135 0.8409557 0.3702959
136 0.1741096 0.8409557
137 -0.4335177 0.1741096
138 -0.3980756 -0.4335177
139 0.4131409 -0.3980756
140 0.4094395 0.4131409
141 -1.3515292 0.4094395
142 0.4131409 -1.3515292
143 0.4131409 0.4131409
144 0.6093272 0.4131409
145 0.3702959 0.6093272
146 0.2169546 0.3702959
147 0.2206560 0.2169546
148 0.1778110 0.2206560
149 0.6484708 0.1778110
150 0.4094395 0.6484708
151 0.3702959 0.4094395
152 0.4522845 0.3702959
153 NA 0.4522845
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.4131409 0.4131409
[2,] -0.7830454 0.4131409
[3,] 0.4131409 -0.7830454
[4,] -0.5440142 0.4131409
[5,] 0.4522845 -0.5440142
[6,] 0.3702959 0.4522845
[7,] 0.6484708 0.3702959
[8,] 0.4131409 0.6484708
[9,] -1.5514169 0.4131409
[10,] -1.6260027 -1.5514169
[11,] 0.4131409 -1.6260027
[12,] 0.4094395 0.4131409
[13,] 0.6484708 0.4094395
[14,] 0.4168423 0.6484708
[15,] -0.5477155 0.4168423
[16,] 0.6093272 -0.5477155
[17,] 0.4094395 0.6093272
[18,] -0.3515292 0.4094395
[19,] -1.1944865 -0.3515292
[20,] -1.3552306 -1.1944865
[21,] 0.4094395 -1.3552306
[22,] -0.5831577 0.4094395
[23,] 0.4522845 -0.5831577
[24,] 0.4131409 0.4522845
[25,] -0.5905605 0.4131409
[26,] -0.4335177 -0.5905605
[27,] -1.5868591 -0.4335177
[28,] -0.5905605 -1.5868591
[29,] 0.2560982 -0.5905605
[30,] -0.5514169 0.2560982
[31,] -0.6260027 -0.5514169
[32,] 0.3702959 -0.6260027
[33,] 0.6093272 0.3702959
[34,] -0.6297041 0.6093272
[35,] 0.4131409 -0.6297041
[36,] -1.5440142 0.4131409
[37,] -0.5905605 -1.5440142
[38,] 0.1778110 -0.5905605
[39,] 0.4094395 0.1778110
[40,] -0.5905605 0.4094395
[41,] 1.8409557 -0.5905605
[42,] -0.3515292 1.8409557
[43,] -0.5868591 -0.3515292
[44,] 0.1778110 -0.5868591
[45,] 0.1741096 0.1778110
[46,] 1.6484708 0.1741096
[47,] -0.2018893 1.6484708
[48,] 0.4131409 -0.2018893
[49,] 0.4168423 0.4131409
[50,] -1.1590443 0.4168423
[51,] 0.3739973 -1.1590443
[52,] 0.6093272 0.3739973
[53,] 0.3702959 0.6093272
[54,] -0.5868591 0.3702959
[55,] -0.3943742 -0.5868591
[56,] -0.8221890 -0.3943742
[57,] -1.1161994 -0.8221890
[58,] 0.3702959 -1.1161994
[59,] 0.6484708 0.3702959
[60,] 0.2169546 0.6484708
[61,] -0.5905605 0.2169546
[62,] 0.2169546 -0.5905605
[63,] -0.5831577 0.2169546
[64,] 0.3702959 -0.5831577
[65,] 0.4559858 0.3702959
[66,] 0.4485831 0.4559858
[67,] 0.3702959 0.4485831
[68,] 0.8838006 0.3702959
[69,] 0.3702959 0.8838006
[70,] 0.3702959 0.3702959
[71,] 0.4131409 0.3702959
[72,] -1.3552306 0.4131409
[73,] -0.5868591 -1.3552306
[74,] 0.4131409 -0.5868591
[75,] 0.4131409 0.4131409
[76,] -0.3943742 0.4131409
[77,] 0.3702959 -0.3943742
[78,] 0.6521722 0.3702959
[79,] 0.6056258 0.6521722
[80,] -0.5905605 0.6056258
[81,] -0.5868591 -0.5905605
[82,] -0.3552306 -0.5868591
[83,] -0.3589320 -0.3552306
[84,] 0.4131409 -0.3589320
[85,] 0.6484708 0.4131409
[86,] -1.5868591 0.6484708
[87,] 0.2169546 -1.5868591
[88,] -0.3943742 0.2169546
[89,] 0.4094395 -0.3943742
[90,] -0.4335177 0.4094395
[91,] 0.1778110 -0.4335177
[92,] -0.6297041 0.1778110
[93,] -0.5868591 -0.6297041
[94,] 0.4559858 -0.5868591
[95,] -0.5794563 0.4559858
[96,] 0.6484708 -0.5794563
[97,] -1.5831577 0.6484708
[98,] 0.2206560 -1.5831577
[99,] 0.1778110 0.2206560
[100,] 0.2169546 0.1778110
[101,] 0.4131409 0.2169546
[102,] 0.4522845 0.4131409
[103,] -0.1161994 0.4522845
[104,] 0.2206560 -0.1161994
[105,] -0.2018893 0.2206560
[106,] 0.3702959 -0.2018893
[107,] -0.3515292 0.3702959
[108,] -0.5868591 -0.3515292
[109,] -1.6260027 -0.5868591
[110,] 0.6056258 -1.6260027
[111,] 0.3702959 0.6056258
[112,] 0.3702959 0.3702959
[113,] -2.7830454 0.3702959
[114,] 0.2169546 -2.7830454
[115,] 0.4168423 0.2169546
[116,] 0.4131409 0.4168423
[117,] -0.5905605 0.4131409
[118,] 0.4131409 -0.5905605
[119,] 0.4094395 0.4131409
[120,] -0.1981879 0.4094395
[121,] 0.5627809 -0.1981879
[122,] 0.2169546 0.5627809
[123,] 0.4522845 0.2169546
[124,] 0.1778110 0.4522845
[125,] 0.4131409 0.1778110
[126,] -0.3943742 0.4131409
[127,] -0.1161994 -0.3943742
[128,] 0.1778110 -0.1161994
[129,] 0.4094395 0.1778110
[130,] 0.4559858 0.4094395
[131,] 0.4522845 0.4559858
[132,] 0.1778110 0.4522845
[133,] 0.4131409 0.1778110
[134,] 0.3702959 0.4131409
[135,] 0.8409557 0.3702959
[136,] 0.1741096 0.8409557
[137,] -0.4335177 0.1741096
[138,] -0.3980756 -0.4335177
[139,] 0.4131409 -0.3980756
[140,] 0.4094395 0.4131409
[141,] -1.3515292 0.4094395
[142,] 0.4131409 -1.3515292
[143,] 0.4131409 0.4131409
[144,] 0.6093272 0.4131409
[145,] 0.3702959 0.6093272
[146,] 0.2169546 0.3702959
[147,] 0.2206560 0.2169546
[148,] 0.1778110 0.2206560
[149,] 0.6484708 0.1778110
[150,] 0.4094395 0.6484708
[151,] 0.3702959 0.4094395
[152,] 0.4522845 0.3702959
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.4131409 0.4131409
2 -0.7830454 0.4131409
3 0.4131409 -0.7830454
4 -0.5440142 0.4131409
5 0.4522845 -0.5440142
6 0.3702959 0.4522845
7 0.6484708 0.3702959
8 0.4131409 0.6484708
9 -1.5514169 0.4131409
10 -1.6260027 -1.5514169
11 0.4131409 -1.6260027
12 0.4094395 0.4131409
13 0.6484708 0.4094395
14 0.4168423 0.6484708
15 -0.5477155 0.4168423
16 0.6093272 -0.5477155
17 0.4094395 0.6093272
18 -0.3515292 0.4094395
19 -1.1944865 -0.3515292
20 -1.3552306 -1.1944865
21 0.4094395 -1.3552306
22 -0.5831577 0.4094395
23 0.4522845 -0.5831577
24 0.4131409 0.4522845
25 -0.5905605 0.4131409
26 -0.4335177 -0.5905605
27 -1.5868591 -0.4335177
28 -0.5905605 -1.5868591
29 0.2560982 -0.5905605
30 -0.5514169 0.2560982
31 -0.6260027 -0.5514169
32 0.3702959 -0.6260027
33 0.6093272 0.3702959
34 -0.6297041 0.6093272
35 0.4131409 -0.6297041
36 -1.5440142 0.4131409
37 -0.5905605 -1.5440142
38 0.1778110 -0.5905605
39 0.4094395 0.1778110
40 -0.5905605 0.4094395
41 1.8409557 -0.5905605
42 -0.3515292 1.8409557
43 -0.5868591 -0.3515292
44 0.1778110 -0.5868591
45 0.1741096 0.1778110
46 1.6484708 0.1741096
47 -0.2018893 1.6484708
48 0.4131409 -0.2018893
49 0.4168423 0.4131409
50 -1.1590443 0.4168423
51 0.3739973 -1.1590443
52 0.6093272 0.3739973
53 0.3702959 0.6093272
54 -0.5868591 0.3702959
55 -0.3943742 -0.5868591
56 -0.8221890 -0.3943742
57 -1.1161994 -0.8221890
58 0.3702959 -1.1161994
59 0.6484708 0.3702959
60 0.2169546 0.6484708
61 -0.5905605 0.2169546
62 0.2169546 -0.5905605
63 -0.5831577 0.2169546
64 0.3702959 -0.5831577
65 0.4559858 0.3702959
66 0.4485831 0.4559858
67 0.3702959 0.4485831
68 0.8838006 0.3702959
69 0.3702959 0.8838006
70 0.3702959 0.3702959
71 0.4131409 0.3702959
72 -1.3552306 0.4131409
73 -0.5868591 -1.3552306
74 0.4131409 -0.5868591
75 0.4131409 0.4131409
76 -0.3943742 0.4131409
77 0.3702959 -0.3943742
78 0.6521722 0.3702959
79 0.6056258 0.6521722
80 -0.5905605 0.6056258
81 -0.5868591 -0.5905605
82 -0.3552306 -0.5868591
83 -0.3589320 -0.3552306
84 0.4131409 -0.3589320
85 0.6484708 0.4131409
86 -1.5868591 0.6484708
87 0.2169546 -1.5868591
88 -0.3943742 0.2169546
89 0.4094395 -0.3943742
90 -0.4335177 0.4094395
91 0.1778110 -0.4335177
92 -0.6297041 0.1778110
93 -0.5868591 -0.6297041
94 0.4559858 -0.5868591
95 -0.5794563 0.4559858
96 0.6484708 -0.5794563
97 -1.5831577 0.6484708
98 0.2206560 -1.5831577
99 0.1778110 0.2206560
100 0.2169546 0.1778110
101 0.4131409 0.2169546
102 0.4522845 0.4131409
103 -0.1161994 0.4522845
104 0.2206560 -0.1161994
105 -0.2018893 0.2206560
106 0.3702959 -0.2018893
107 -0.3515292 0.3702959
108 -0.5868591 -0.3515292
109 -1.6260027 -0.5868591
110 0.6056258 -1.6260027
111 0.3702959 0.6056258
112 0.3702959 0.3702959
113 -2.7830454 0.3702959
114 0.2169546 -2.7830454
115 0.4168423 0.2169546
116 0.4131409 0.4168423
117 -0.5905605 0.4131409
118 0.4131409 -0.5905605
119 0.4094395 0.4131409
120 -0.1981879 0.4094395
121 0.5627809 -0.1981879
122 0.2169546 0.5627809
123 0.4522845 0.2169546
124 0.1778110 0.4522845
125 0.4131409 0.1778110
126 -0.3943742 0.4131409
127 -0.1161994 -0.3943742
128 0.1778110 -0.1161994
129 0.4094395 0.1778110
130 0.4559858 0.4094395
131 0.4522845 0.4559858
132 0.1778110 0.4522845
133 0.4131409 0.1778110
134 0.3702959 0.4131409
135 0.8409557 0.3702959
136 0.1741096 0.8409557
137 -0.4335177 0.1741096
138 -0.3980756 -0.4335177
139 0.4131409 -0.3980756
140 0.4094395 0.4131409
141 -1.3515292 0.4094395
142 0.4131409 -1.3515292
143 0.4131409 0.4131409
144 0.6093272 0.4131409
145 0.3702959 0.6093272
146 0.2169546 0.3702959
147 0.2206560 0.2169546
148 0.1778110 0.2206560
149 0.6484708 0.1778110
150 0.4094395 0.6484708
151 0.3702959 0.4094395
152 0.4522845 0.3702959
> 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/7c8dl1290508224.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8c8dl1290508224.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9x9x11290508225.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10x9x11290508225.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jaeo1290508225.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/124suc1290508225.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/13bt961290508225.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/144k891290508225.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/15737x1290508225.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/163cn61290508225.tab")
+ }
>
> try(system("convert tmp/1r7xx1290508224.ps tmp/1r7xx1290508224.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r7xx1290508224.ps tmp/2r7xx1290508224.png",intern=TRUE))
character(0)
> try(system("convert tmp/3r7xx1290508224.ps tmp/3r7xx1290508224.png",intern=TRUE))
character(0)
> try(system("convert tmp/41gwi1290508224.ps tmp/41gwi1290508224.png",intern=TRUE))
character(0)
> try(system("convert tmp/51gwi1290508224.ps tmp/51gwi1290508224.png",intern=TRUE))
character(0)
> try(system("convert tmp/61gwi1290508224.ps tmp/61gwi1290508224.png",intern=TRUE))
character(0)
> try(system("convert tmp/7c8dl1290508224.ps tmp/7c8dl1290508224.png",intern=TRUE))
character(0)
> try(system("convert tmp/8c8dl1290508224.ps tmp/8c8dl1290508224.png",intern=TRUE))
character(0)
> try(system("convert tmp/9x9x11290508225.ps tmp/9x9x11290508225.png",intern=TRUE))
character(0)
> try(system("convert tmp/10x9x11290508225.ps tmp/10x9x11290508225.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.839 1.738 8.583