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)
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> x <- array(list(13
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+ ,2)
+ ,dim=c(6
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'Sum_friends')
+ ,1:156))
> y <- array(NA,dim=c(6,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','Sum_friends'),1:156))
> 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 = '1'
> #'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
Popularity FindingFriends KnowingPeople Liked Celebrity Sum_friends
1 13 13 14 13 3 2
2 12 12 8 13 5 1
3 15 10 12 16 6 0
4 12 9 7 12 6 3
5 10 10 10 11 5 3
6 12 12 7 12 3 1
7 15 13 16 18 8 3
8 9 12 11 11 4 1
9 12 12 14 14 4 4
10 11 6 6 9 4 0
11 11 5 16 14 6 3
12 11 12 11 12 6 2
13 15 11 16 11 5 4
14 7 14 12 12 4 3
15 11 14 7 13 6 1
16 11 12 13 11 4 1
17 10 12 11 12 6 2
18 14 11 15 16 6 3
19 10 11 7 9 4 1
20 6 7 9 11 4 1
21 11 9 7 13 2 2
22 15 11 14 15 7 3
23 11 11 15 10 5 4
24 12 12 7 11 4 2
25 14 12 15 13 6 1
26 15 11 17 16 6 2
27 9 11 15 15 7 2
28 13 8 14 14 5 4
29 13 9 14 14 6 2
30 16 12 8 14 4 3
31 13 10 8 8 4 3
32 12 10 14 13 7 3
33 14 12 14 15 7 4
34 11 8 8 13 4 2
35 9 12 11 11 4 2
36 16 11 16 15 6 4
37 12 12 10 15 6 3
38 10 7 8 9 5 4
39 13 11 14 13 6 2
40 16 11 16 16 7 5
41 14 12 13 13 6 3
42 15 9 5 11 3 1
43 5 15 8 12 3 1
44 8 11 10 12 4 1
45 11 11 8 12 6 2
46 16 11 13 14 7 3
47 17 11 15 14 5 9
48 9 15 6 8 4 0
49 9 11 12 13 5 0
50 13 12 16 16 6 2
51 10 12 5 13 6 2
52 6 9 15 11 6 3
53 12 12 12 14 5 1
54 8 12 8 13 4 2
55 14 13 13 13 5 0
56 12 11 14 13 5 5
57 11 9 12 12 4 2
58 16 9 16 16 6 4
59 8 11 10 15 2 3
60 15 11 15 15 8 0
61 7 12 8 12 3 0
62 16 12 16 14 6 4
63 14 9 19 12 6 1
64 16 11 14 15 6 1
65 9 9 6 12 5 4
66 14 12 13 13 5 2
67 11 12 15 12 6 4
68 13 12 7 12 5 1
69 15 12 13 13 6 4
70 5 14 4 5 2 2
71 15 11 14 13 5 5
72 13 12 13 13 5 4
73 11 11 11 14 5 4
74 11 6 14 17 6 4
75 12 10 12 13 6 4
76 12 12 15 13 6 3
77 12 13 14 12 5 3
78 12 8 13 13 5 3
79 14 12 8 14 4 2
80 6 12 6 11 2 1
81 7 12 7 12 4 1
82 14 6 13 12 6 5
83 14 11 13 16 6 4
84 10 10 11 12 5 2
85 13 12 5 12 3 3
86 12 13 12 12 6 2
87 9 11 8 10 4 2
88 12 7 11 15 5 2
89 16 11 14 15 8 2
90 10 11 9 12 4 3
91 14 11 10 16 6 2
92 10 11 13 15 6 3
93 16 12 16 16 7 4
94 15 10 16 13 6 3
95 12 11 11 12 5 3
96 10 12 8 11 4 0
97 8 7 4 13 6 1
98 8 13 7 10 3 2
99 11 8 14 15 5 2
100 13 12 11 13 6 3
101 16 11 17 16 7 4
102 16 12 15 15 7 4
103 14 14 17 18 6 1
104 11 10 5 13 3 2
105 4 10 4 10 2 2
106 14 13 10 16 8 3
107 9 10 11 13 3 3
108 14 11 15 15 8 3
109 8 10 10 14 3 1
110 8 7 9 15 4 1
111 11 10 12 14 5 1
112 12 8 15 13 7 1
113 11 12 7 13 6 0
114 14 12 13 15 6 1
115 15 12 12 16 7 3
116 16 11 14 14 6 3
117 16 12 14 14 6 0
118 11 12 8 16 6 2
119 14 12 15 14 6 5
120 14 11 12 12 4 2
121 12 12 12 13 4 3
122 14 11 16 12 5 3
123 8 11 9 12 4 5
124 13 13 15 14 6 4
125 16 12 15 14 6 4
126 12 12 6 14 5 0
127 16 12 14 16 8 3
128 12 12 15 13 6 0
129 11 8 10 14 5 2
130 4 8 6 4 4 0
131 16 12 14 16 8 6
132 15 11 12 13 6 3
133 10 12 8 16 4 1
134 13 13 11 15 6 6
135 15 12 13 14 6 2
136 12 12 9 13 4 1
137 14 11 15 14 6 3
138 7 12 13 12 3 1
139 19 12 15 15 6 2
140 12 10 14 14 5 4
141 12 11 16 13 4 1
142 13 12 14 14 6 2
143 15 12 14 16 4 0
144 8 10 10 6 4 5
145 12 12 10 13 4 2
146 10 13 4 13 6 1
147 8 12 8 14 5 1
148 10 15 15 15 6 4
149 15 11 16 14 6 3
150 16 12 12 15 8 0
151 13 11 12 13 7 3
152 16 12 15 16 7 3
153 9 11 9 12 4 0
154 14 10 12 15 6 2
155 14 11 14 12 6 5
156 12 11 11 14 2 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
0.03428 0.10631 0.21144 0.35765 0.60600
Sum_friends
0.21260
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.3707 -1.2114 0.0148 1.3925 6.9870
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03428 1.42328 0.024 0.980818
FindingFriends 0.10631 0.09552 1.113 0.267509
KnowingPeople 0.21144 0.06363 3.323 0.001118 **
Liked 0.35765 0.09593 3.728 0.000273 ***
Celebrity 0.60600 0.15540 3.900 0.000145 ***
Sum_friends 0.21260 0.12003 1.771 0.078554 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.091 on 150 degrees of freedom
Multiple R-squared: 0.5095, Adjusted R-squared: 0.4931
F-statistic: 31.16 on 5 and 150 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,] 0.11304340 0.22608680 0.886956601
[2,] 0.05013383 0.10026766 0.949866172
[3,] 0.07267912 0.14535824 0.927320880
[4,] 0.03629399 0.07258798 0.963706011
[5,] 0.45572322 0.91144644 0.544276780
[6,] 0.76911690 0.46176621 0.230883104
[7,] 0.69084332 0.61831336 0.309156678
[8,] 0.60249265 0.79501471 0.397507353
[9,] 0.54677609 0.90644783 0.453223915
[10,] 0.46048375 0.92096750 0.539516251
[11,] 0.38396188 0.76792377 0.616038115
[12,] 0.68164900 0.63670200 0.318351000
[13,] 0.62248048 0.75503905 0.377519525
[14,] 0.59138436 0.81723128 0.408615640
[15,] 0.52172208 0.95655583 0.478277916
[16,] 0.50547172 0.98905655 0.494528276
[17,] 0.46660037 0.93320075 0.533399626
[18,] 0.40570234 0.81140469 0.594297657
[19,] 0.66565254 0.66869492 0.334347461
[20,] 0.60924066 0.78151868 0.390759341
[21,] 0.54821781 0.90356439 0.451782194
[22,] 0.69672924 0.60654151 0.303270756
[23,] 0.79677970 0.40644061 0.203220305
[24,] 0.75901583 0.48196834 0.240984169
[25,] 0.71263249 0.57473503 0.287367513
[26,] 0.67086218 0.65827564 0.329137821
[27,] 0.66048547 0.67902906 0.339514532
[28,] 0.66208648 0.67582705 0.337913525
[29,] 0.63156704 0.73686592 0.368432960
[30,] 0.58145829 0.83708341 0.418541706
[31,] 0.53426092 0.93147816 0.465739081
[32,] 0.49375292 0.98750584 0.506247079
[33,] 0.46102623 0.92205245 0.538973774
[34,] 0.78697458 0.42605084 0.213025420
[35,] 0.94415076 0.11169847 0.055849236
[36,] 0.95016026 0.09967948 0.049839742
[37,] 0.93608557 0.12782886 0.063914431
[38,] 0.94292614 0.11414773 0.057073863
[39,] 0.94101550 0.11796899 0.058984496
[40,] 0.92861446 0.14277109 0.071385543
[41,] 0.92821674 0.14356651 0.071783255
[42,] 0.91721158 0.16557684 0.082788418
[43,] 0.91100894 0.17798211 0.088991057
[44,] 0.98813680 0.02372640 0.011863199
[45,] 0.98391564 0.03216872 0.016084359
[46,] 0.98911419 0.02177162 0.010885812
[47,] 0.99160499 0.01679001 0.008395007
[48,] 0.98927482 0.02145036 0.010725182
[49,] 0.98551005 0.02897991 0.014489953
[50,] 0.98309982 0.03380035 0.016900175
[51,] 0.98973176 0.02053648 0.010268240
[52,] 0.98791679 0.02416642 0.012083208
[53,] 0.98839547 0.02320905 0.011604527
[54,] 0.98818156 0.02363688 0.011818439
[55,] 0.98619508 0.02760983 0.013804916
[56,] 0.98883872 0.02232257 0.011161283
[57,] 0.98792790 0.02414419 0.012072096
[58,] 0.98704719 0.02590563 0.012952814
[59,] 0.98768991 0.02462018 0.012310091
[60,] 0.99003550 0.01992899 0.009964497
[61,] 0.98933713 0.02132573 0.010662867
[62,] 0.98621897 0.02756207 0.013781034
[63,] 0.98649015 0.02701970 0.013509852
[64,] 0.98204296 0.03591409 0.017957043
[65,] 0.97909896 0.04180208 0.020901039
[66,] 0.98622214 0.02755572 0.013777861
[67,] 0.98208894 0.03582212 0.017911061
[68,] 0.97937542 0.04124916 0.020624580
[69,] 0.97306390 0.05387221 0.026936103
[70,] 0.96481255 0.07037491 0.035187453
[71,] 0.97679718 0.04640564 0.023202822
[72,] 0.97583366 0.04833269 0.024166343
[73,] 0.97969665 0.04060671 0.020303353
[74,] 0.97895084 0.04209831 0.021049156
[75,] 0.97211565 0.05576871 0.027884353
[76,] 0.96615919 0.06768163 0.033840814
[77,] 0.98874936 0.02250128 0.011250639
[78,] 0.98500097 0.02999806 0.014999032
[79,] 0.98008463 0.03983075 0.019915373
[80,] 0.97418620 0.05162760 0.025813798
[81,] 0.96854269 0.06291462 0.031457312
[82,] 0.95970275 0.08059451 0.040297254
[83,] 0.95182776 0.09634448 0.048172242
[84,] 0.97287740 0.05424520 0.027122602
[85,] 0.96483251 0.07033498 0.035167490
[86,] 0.96001348 0.07997303 0.039986516
[87,] 0.94984346 0.10031307 0.050156537
[88,] 0.93770790 0.12458421 0.062292104
[89,] 0.93266633 0.13466734 0.067333671
[90,] 0.91641257 0.16717487 0.083587433
[91,] 0.91116538 0.17766924 0.088834618
[92,] 0.89111134 0.21777733 0.108888663
[93,] 0.86694616 0.26610767 0.133053837
[94,] 0.84385114 0.31229772 0.156148860
[95,] 0.85640740 0.28718520 0.143592601
[96,] 0.89709608 0.20580784 0.102903922
[97,] 0.89919149 0.20161702 0.100808512
[98,] 0.87691450 0.24617100 0.123085499
[99,] 0.85625313 0.28749374 0.143746870
[100,] 0.85173783 0.29652434 0.148262172
[101,] 0.84924068 0.30151865 0.150759324
[102,] 0.87388402 0.25223196 0.126115979
[103,] 0.86209912 0.27580176 0.137900878
[104,] 0.90242470 0.19515061 0.097575304
[105,] 0.87652872 0.24694256 0.123471278
[106,] 0.84869105 0.30261791 0.151308953
[107,] 0.81393019 0.37213963 0.186069813
[108,] 0.81280926 0.37438149 0.187190743
[109,] 0.82538322 0.34923355 0.174616776
[110,] 0.81493250 0.37013499 0.185067497
[111,] 0.77335508 0.45328984 0.226644918
[112,] 0.83456194 0.33087612 0.165438060
[113,] 0.80460573 0.39078854 0.195394272
[114,] 0.77741150 0.44517699 0.222588497
[115,] 0.77577687 0.44844627 0.224223134
[116,] 0.73424285 0.53151431 0.265757154
[117,] 0.73035881 0.53928239 0.269641195
[118,] 0.71378386 0.57243227 0.286216137
[119,] 0.65818546 0.68362908 0.341814541
[120,] 0.62702138 0.74595724 0.372978620
[121,] 0.64415479 0.71169042 0.355845212
[122,] 0.63879873 0.72240254 0.361201270
[123,] 0.57307075 0.85385850 0.426929248
[124,] 0.56002562 0.87994876 0.439974380
[125,] 0.52771348 0.94457304 0.472286522
[126,] 0.46011729 0.92023457 0.539882714
[127,] 0.43375822 0.86751645 0.566241777
[128,] 0.42831495 0.85662991 0.571685045
[129,] 0.35592945 0.71185891 0.644070547
[130,] 0.44005218 0.88010436 0.559947821
[131,] 0.78911606 0.42176788 0.210883938
[132,] 0.81133831 0.37732338 0.188661690
[133,] 0.76831239 0.46337522 0.231687609
[134,] 0.68483084 0.63033833 0.315169164
[135,] 0.64226336 0.71547327 0.357736637
[136,] 0.52794501 0.94410997 0.472054986
[137,] 0.50804577 0.98390846 0.491954232
[138,] 0.67279669 0.65440661 0.327203307
[139,] 0.57144807 0.85710386 0.428551928
> postscript(file="/var/wessaorg/rcomp/tmp/1n5qa1322004801.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/254op1322004801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3dn501322004801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/44t611322004801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ugbx1322004801.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 = 156
Frequency = 1
1 2 3 4 5 6
1.73086477 1.10642240 2.00690323 0.96323097 -0.81374830 2.88752260
7 8 9 10 11 12
-1.72289501 -1.20659915 -0.55168494 3.41635289 -2.22983677 -0.98885675
13 14 15 16 17 18
2.59868890 -4.41350573 -0.50074843 0.37051518 -1.98885675 -0.37153155
19 20 21 22 23 24
1.46078211 -3.25218578 2.24218937 0.59156089 -0.83221607 2.42657196
25 26 27 28 29 30
1.02031996 0.41818302 -5.40728170 0.26753463 -0.01957301 4.92957231
31 32 33 34 35 36
4.28809652 -1.58682919 -0.72734489 0.92504690 -1.41919939 1.56207756
37 38 39 40 41 42
-1.06297072 0.43075812 0.12546810 0.38582255 1.01800515 6.98697707
43 44 45 46 47 48
-4.64283686 -2.24650296 -0.24822270 2.16065592 2.67417396 0.81725522
49 50 51 52 53 54
-2.42044316 -1.47667968 -1.07785192 -6.37065953 -0.09700113 -2.50017526
55 56 57 58 59 60
2.15550292 -0.90633004 0.33062221 1.41703646 -2.53265484 0.41191620
61 62 63 64 65 66
-2.11132000 1.81342421 0.85111743 2.62276396 -1.43192385 1.83660798
67 68 69 70 71 72
-2.25982858 2.67551742 1.80540491 -0.79379232 2.09366996 0.41140749
73 74 75 76 77 78
-1.41705348 -3.19881344 -0.77054118 -1.40488052 -0.33608845 0.04922989
79 80 81 82 83 84
3.14217256 -1.93737980 -2.71847999 1.58829009 -0.16124612 -1.17024308
85 86 87 88 89 90
3.88520778 -0.30660512 -0.32091315 0.07571697 1.19815855 -0.46026061
91 92 93 94 95 96
0.89828288 -3.59099369 0.49211725 1.59628772 0.51085114 0.64032960
97 98 99 100 101 102
-2.12228114 -0.71607881 -1.66491708 0.44089082 0.38697995 1.06121228
103 104 105 106 107 108
-1.40343773 1.95276692 -3.15683110 -0.73893362 -1.52849034 -1.22588453
109 110 111 112 113 114
-2.24949921 -2.68279453 -0.88439005 -1.16046046 -0.07553711 0.72790126
115 116 117 118 119 120
0.55048884 2.55521567 3.08671085 -1.78513699 -0.18773319 3.11801113
121 122 123 124 125 126
0.44145316 1.45363696 -2.88546109 -1.08143849 2.02486705 1.38425613
127 128 129 130 131 132
0.52160058 -0.76707980 -0.46149354 -2.00799725 -0.11620015 2.33575353
133 134 135 136 137 138
-1.36053158 -1.01851982 1.87295320 1.50098215 0.34377283 -3.38113442
139 140 141 142 143 144
5.09241534 -0.94507645 0.12718784 -0.33848963 2.58341165 -0.84468526
145 146 147 148 149 150
1.07693907 -0.76011438 -3.25122979 -4.65170176 1.13232999 1.93993916
151 152 153 154 155 156
-0.27024906 0.91616033 -0.82245988 0.93935493 0.84531956 1.82615476
> postscript(file="/var/wessaorg/rcomp/tmp/6cw701322004801.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.73086477 NA
1 1.10642240 1.73086477
2 2.00690323 1.10642240
3 0.96323097 2.00690323
4 -0.81374830 0.96323097
5 2.88752260 -0.81374830
6 -1.72289501 2.88752260
7 -1.20659915 -1.72289501
8 -0.55168494 -1.20659915
9 3.41635289 -0.55168494
10 -2.22983677 3.41635289
11 -0.98885675 -2.22983677
12 2.59868890 -0.98885675
13 -4.41350573 2.59868890
14 -0.50074843 -4.41350573
15 0.37051518 -0.50074843
16 -1.98885675 0.37051518
17 -0.37153155 -1.98885675
18 1.46078211 -0.37153155
19 -3.25218578 1.46078211
20 2.24218937 -3.25218578
21 0.59156089 2.24218937
22 -0.83221607 0.59156089
23 2.42657196 -0.83221607
24 1.02031996 2.42657196
25 0.41818302 1.02031996
26 -5.40728170 0.41818302
27 0.26753463 -5.40728170
28 -0.01957301 0.26753463
29 4.92957231 -0.01957301
30 4.28809652 4.92957231
31 -1.58682919 4.28809652
32 -0.72734489 -1.58682919
33 0.92504690 -0.72734489
34 -1.41919939 0.92504690
35 1.56207756 -1.41919939
36 -1.06297072 1.56207756
37 0.43075812 -1.06297072
38 0.12546810 0.43075812
39 0.38582255 0.12546810
40 1.01800515 0.38582255
41 6.98697707 1.01800515
42 -4.64283686 6.98697707
43 -2.24650296 -4.64283686
44 -0.24822270 -2.24650296
45 2.16065592 -0.24822270
46 2.67417396 2.16065592
47 0.81725522 2.67417396
48 -2.42044316 0.81725522
49 -1.47667968 -2.42044316
50 -1.07785192 -1.47667968
51 -6.37065953 -1.07785192
52 -0.09700113 -6.37065953
53 -2.50017526 -0.09700113
54 2.15550292 -2.50017526
55 -0.90633004 2.15550292
56 0.33062221 -0.90633004
57 1.41703646 0.33062221
58 -2.53265484 1.41703646
59 0.41191620 -2.53265484
60 -2.11132000 0.41191620
61 1.81342421 -2.11132000
62 0.85111743 1.81342421
63 2.62276396 0.85111743
64 -1.43192385 2.62276396
65 1.83660798 -1.43192385
66 -2.25982858 1.83660798
67 2.67551742 -2.25982858
68 1.80540491 2.67551742
69 -0.79379232 1.80540491
70 2.09366996 -0.79379232
71 0.41140749 2.09366996
72 -1.41705348 0.41140749
73 -3.19881344 -1.41705348
74 -0.77054118 -3.19881344
75 -1.40488052 -0.77054118
76 -0.33608845 -1.40488052
77 0.04922989 -0.33608845
78 3.14217256 0.04922989
79 -1.93737980 3.14217256
80 -2.71847999 -1.93737980
81 1.58829009 -2.71847999
82 -0.16124612 1.58829009
83 -1.17024308 -0.16124612
84 3.88520778 -1.17024308
85 -0.30660512 3.88520778
86 -0.32091315 -0.30660512
87 0.07571697 -0.32091315
88 1.19815855 0.07571697
89 -0.46026061 1.19815855
90 0.89828288 -0.46026061
91 -3.59099369 0.89828288
92 0.49211725 -3.59099369
93 1.59628772 0.49211725
94 0.51085114 1.59628772
95 0.64032960 0.51085114
96 -2.12228114 0.64032960
97 -0.71607881 -2.12228114
98 -1.66491708 -0.71607881
99 0.44089082 -1.66491708
100 0.38697995 0.44089082
101 1.06121228 0.38697995
102 -1.40343773 1.06121228
103 1.95276692 -1.40343773
104 -3.15683110 1.95276692
105 -0.73893362 -3.15683110
106 -1.52849034 -0.73893362
107 -1.22588453 -1.52849034
108 -2.24949921 -1.22588453
109 -2.68279453 -2.24949921
110 -0.88439005 -2.68279453
111 -1.16046046 -0.88439005
112 -0.07553711 -1.16046046
113 0.72790126 -0.07553711
114 0.55048884 0.72790126
115 2.55521567 0.55048884
116 3.08671085 2.55521567
117 -1.78513699 3.08671085
118 -0.18773319 -1.78513699
119 3.11801113 -0.18773319
120 0.44145316 3.11801113
121 1.45363696 0.44145316
122 -2.88546109 1.45363696
123 -1.08143849 -2.88546109
124 2.02486705 -1.08143849
125 1.38425613 2.02486705
126 0.52160058 1.38425613
127 -0.76707980 0.52160058
128 -0.46149354 -0.76707980
129 -2.00799725 -0.46149354
130 -0.11620015 -2.00799725
131 2.33575353 -0.11620015
132 -1.36053158 2.33575353
133 -1.01851982 -1.36053158
134 1.87295320 -1.01851982
135 1.50098215 1.87295320
136 0.34377283 1.50098215
137 -3.38113442 0.34377283
138 5.09241534 -3.38113442
139 -0.94507645 5.09241534
140 0.12718784 -0.94507645
141 -0.33848963 0.12718784
142 2.58341165 -0.33848963
143 -0.84468526 2.58341165
144 1.07693907 -0.84468526
145 -0.76011438 1.07693907
146 -3.25122979 -0.76011438
147 -4.65170176 -3.25122979
148 1.13232999 -4.65170176
149 1.93993916 1.13232999
150 -0.27024906 1.93993916
151 0.91616033 -0.27024906
152 -0.82245988 0.91616033
153 0.93935493 -0.82245988
154 0.84531956 0.93935493
155 1.82615476 0.84531956
156 NA 1.82615476
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.10642240 1.73086477
[2,] 2.00690323 1.10642240
[3,] 0.96323097 2.00690323
[4,] -0.81374830 0.96323097
[5,] 2.88752260 -0.81374830
[6,] -1.72289501 2.88752260
[7,] -1.20659915 -1.72289501
[8,] -0.55168494 -1.20659915
[9,] 3.41635289 -0.55168494
[10,] -2.22983677 3.41635289
[11,] -0.98885675 -2.22983677
[12,] 2.59868890 -0.98885675
[13,] -4.41350573 2.59868890
[14,] -0.50074843 -4.41350573
[15,] 0.37051518 -0.50074843
[16,] -1.98885675 0.37051518
[17,] -0.37153155 -1.98885675
[18,] 1.46078211 -0.37153155
[19,] -3.25218578 1.46078211
[20,] 2.24218937 -3.25218578
[21,] 0.59156089 2.24218937
[22,] -0.83221607 0.59156089
[23,] 2.42657196 -0.83221607
[24,] 1.02031996 2.42657196
[25,] 0.41818302 1.02031996
[26,] -5.40728170 0.41818302
[27,] 0.26753463 -5.40728170
[28,] -0.01957301 0.26753463
[29,] 4.92957231 -0.01957301
[30,] 4.28809652 4.92957231
[31,] -1.58682919 4.28809652
[32,] -0.72734489 -1.58682919
[33,] 0.92504690 -0.72734489
[34,] -1.41919939 0.92504690
[35,] 1.56207756 -1.41919939
[36,] -1.06297072 1.56207756
[37,] 0.43075812 -1.06297072
[38,] 0.12546810 0.43075812
[39,] 0.38582255 0.12546810
[40,] 1.01800515 0.38582255
[41,] 6.98697707 1.01800515
[42,] -4.64283686 6.98697707
[43,] -2.24650296 -4.64283686
[44,] -0.24822270 -2.24650296
[45,] 2.16065592 -0.24822270
[46,] 2.67417396 2.16065592
[47,] 0.81725522 2.67417396
[48,] -2.42044316 0.81725522
[49,] -1.47667968 -2.42044316
[50,] -1.07785192 -1.47667968
[51,] -6.37065953 -1.07785192
[52,] -0.09700113 -6.37065953
[53,] -2.50017526 -0.09700113
[54,] 2.15550292 -2.50017526
[55,] -0.90633004 2.15550292
[56,] 0.33062221 -0.90633004
[57,] 1.41703646 0.33062221
[58,] -2.53265484 1.41703646
[59,] 0.41191620 -2.53265484
[60,] -2.11132000 0.41191620
[61,] 1.81342421 -2.11132000
[62,] 0.85111743 1.81342421
[63,] 2.62276396 0.85111743
[64,] -1.43192385 2.62276396
[65,] 1.83660798 -1.43192385
[66,] -2.25982858 1.83660798
[67,] 2.67551742 -2.25982858
[68,] 1.80540491 2.67551742
[69,] -0.79379232 1.80540491
[70,] 2.09366996 -0.79379232
[71,] 0.41140749 2.09366996
[72,] -1.41705348 0.41140749
[73,] -3.19881344 -1.41705348
[74,] -0.77054118 -3.19881344
[75,] -1.40488052 -0.77054118
[76,] -0.33608845 -1.40488052
[77,] 0.04922989 -0.33608845
[78,] 3.14217256 0.04922989
[79,] -1.93737980 3.14217256
[80,] -2.71847999 -1.93737980
[81,] 1.58829009 -2.71847999
[82,] -0.16124612 1.58829009
[83,] -1.17024308 -0.16124612
[84,] 3.88520778 -1.17024308
[85,] -0.30660512 3.88520778
[86,] -0.32091315 -0.30660512
[87,] 0.07571697 -0.32091315
[88,] 1.19815855 0.07571697
[89,] -0.46026061 1.19815855
[90,] 0.89828288 -0.46026061
[91,] -3.59099369 0.89828288
[92,] 0.49211725 -3.59099369
[93,] 1.59628772 0.49211725
[94,] 0.51085114 1.59628772
[95,] 0.64032960 0.51085114
[96,] -2.12228114 0.64032960
[97,] -0.71607881 -2.12228114
[98,] -1.66491708 -0.71607881
[99,] 0.44089082 -1.66491708
[100,] 0.38697995 0.44089082
[101,] 1.06121228 0.38697995
[102,] -1.40343773 1.06121228
[103,] 1.95276692 -1.40343773
[104,] -3.15683110 1.95276692
[105,] -0.73893362 -3.15683110
[106,] -1.52849034 -0.73893362
[107,] -1.22588453 -1.52849034
[108,] -2.24949921 -1.22588453
[109,] -2.68279453 -2.24949921
[110,] -0.88439005 -2.68279453
[111,] -1.16046046 -0.88439005
[112,] -0.07553711 -1.16046046
[113,] 0.72790126 -0.07553711
[114,] 0.55048884 0.72790126
[115,] 2.55521567 0.55048884
[116,] 3.08671085 2.55521567
[117,] -1.78513699 3.08671085
[118,] -0.18773319 -1.78513699
[119,] 3.11801113 -0.18773319
[120,] 0.44145316 3.11801113
[121,] 1.45363696 0.44145316
[122,] -2.88546109 1.45363696
[123,] -1.08143849 -2.88546109
[124,] 2.02486705 -1.08143849
[125,] 1.38425613 2.02486705
[126,] 0.52160058 1.38425613
[127,] -0.76707980 0.52160058
[128,] -0.46149354 -0.76707980
[129,] -2.00799725 -0.46149354
[130,] -0.11620015 -2.00799725
[131,] 2.33575353 -0.11620015
[132,] -1.36053158 2.33575353
[133,] -1.01851982 -1.36053158
[134,] 1.87295320 -1.01851982
[135,] 1.50098215 1.87295320
[136,] 0.34377283 1.50098215
[137,] -3.38113442 0.34377283
[138,] 5.09241534 -3.38113442
[139,] -0.94507645 5.09241534
[140,] 0.12718784 -0.94507645
[141,] -0.33848963 0.12718784
[142,] 2.58341165 -0.33848963
[143,] -0.84468526 2.58341165
[144,] 1.07693907 -0.84468526
[145,] -0.76011438 1.07693907
[146,] -3.25122979 -0.76011438
[147,] -4.65170176 -3.25122979
[148,] 1.13232999 -4.65170176
[149,] 1.93993916 1.13232999
[150,] -0.27024906 1.93993916
[151,] 0.91616033 -0.27024906
[152,] -0.82245988 0.91616033
[153,] 0.93935493 -0.82245988
[154,] 0.84531956 0.93935493
[155,] 1.82615476 0.84531956
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.10642240 1.73086477
2 2.00690323 1.10642240
3 0.96323097 2.00690323
4 -0.81374830 0.96323097
5 2.88752260 -0.81374830
6 -1.72289501 2.88752260
7 -1.20659915 -1.72289501
8 -0.55168494 -1.20659915
9 3.41635289 -0.55168494
10 -2.22983677 3.41635289
11 -0.98885675 -2.22983677
12 2.59868890 -0.98885675
13 -4.41350573 2.59868890
14 -0.50074843 -4.41350573
15 0.37051518 -0.50074843
16 -1.98885675 0.37051518
17 -0.37153155 -1.98885675
18 1.46078211 -0.37153155
19 -3.25218578 1.46078211
20 2.24218937 -3.25218578
21 0.59156089 2.24218937
22 -0.83221607 0.59156089
23 2.42657196 -0.83221607
24 1.02031996 2.42657196
25 0.41818302 1.02031996
26 -5.40728170 0.41818302
27 0.26753463 -5.40728170
28 -0.01957301 0.26753463
29 4.92957231 -0.01957301
30 4.28809652 4.92957231
31 -1.58682919 4.28809652
32 -0.72734489 -1.58682919
33 0.92504690 -0.72734489
34 -1.41919939 0.92504690
35 1.56207756 -1.41919939
36 -1.06297072 1.56207756
37 0.43075812 -1.06297072
38 0.12546810 0.43075812
39 0.38582255 0.12546810
40 1.01800515 0.38582255
41 6.98697707 1.01800515
42 -4.64283686 6.98697707
43 -2.24650296 -4.64283686
44 -0.24822270 -2.24650296
45 2.16065592 -0.24822270
46 2.67417396 2.16065592
47 0.81725522 2.67417396
48 -2.42044316 0.81725522
49 -1.47667968 -2.42044316
50 -1.07785192 -1.47667968
51 -6.37065953 -1.07785192
52 -0.09700113 -6.37065953
53 -2.50017526 -0.09700113
54 2.15550292 -2.50017526
55 -0.90633004 2.15550292
56 0.33062221 -0.90633004
57 1.41703646 0.33062221
58 -2.53265484 1.41703646
59 0.41191620 -2.53265484
60 -2.11132000 0.41191620
61 1.81342421 -2.11132000
62 0.85111743 1.81342421
63 2.62276396 0.85111743
64 -1.43192385 2.62276396
65 1.83660798 -1.43192385
66 -2.25982858 1.83660798
67 2.67551742 -2.25982858
68 1.80540491 2.67551742
69 -0.79379232 1.80540491
70 2.09366996 -0.79379232
71 0.41140749 2.09366996
72 -1.41705348 0.41140749
73 -3.19881344 -1.41705348
74 -0.77054118 -3.19881344
75 -1.40488052 -0.77054118
76 -0.33608845 -1.40488052
77 0.04922989 -0.33608845
78 3.14217256 0.04922989
79 -1.93737980 3.14217256
80 -2.71847999 -1.93737980
81 1.58829009 -2.71847999
82 -0.16124612 1.58829009
83 -1.17024308 -0.16124612
84 3.88520778 -1.17024308
85 -0.30660512 3.88520778
86 -0.32091315 -0.30660512
87 0.07571697 -0.32091315
88 1.19815855 0.07571697
89 -0.46026061 1.19815855
90 0.89828288 -0.46026061
91 -3.59099369 0.89828288
92 0.49211725 -3.59099369
93 1.59628772 0.49211725
94 0.51085114 1.59628772
95 0.64032960 0.51085114
96 -2.12228114 0.64032960
97 -0.71607881 -2.12228114
98 -1.66491708 -0.71607881
99 0.44089082 -1.66491708
100 0.38697995 0.44089082
101 1.06121228 0.38697995
102 -1.40343773 1.06121228
103 1.95276692 -1.40343773
104 -3.15683110 1.95276692
105 -0.73893362 -3.15683110
106 -1.52849034 -0.73893362
107 -1.22588453 -1.52849034
108 -2.24949921 -1.22588453
109 -2.68279453 -2.24949921
110 -0.88439005 -2.68279453
111 -1.16046046 -0.88439005
112 -0.07553711 -1.16046046
113 0.72790126 -0.07553711
114 0.55048884 0.72790126
115 2.55521567 0.55048884
116 3.08671085 2.55521567
117 -1.78513699 3.08671085
118 -0.18773319 -1.78513699
119 3.11801113 -0.18773319
120 0.44145316 3.11801113
121 1.45363696 0.44145316
122 -2.88546109 1.45363696
123 -1.08143849 -2.88546109
124 2.02486705 -1.08143849
125 1.38425613 2.02486705
126 0.52160058 1.38425613
127 -0.76707980 0.52160058
128 -0.46149354 -0.76707980
129 -2.00799725 -0.46149354
130 -0.11620015 -2.00799725
131 2.33575353 -0.11620015
132 -1.36053158 2.33575353
133 -1.01851982 -1.36053158
134 1.87295320 -1.01851982
135 1.50098215 1.87295320
136 0.34377283 1.50098215
137 -3.38113442 0.34377283
138 5.09241534 -3.38113442
139 -0.94507645 5.09241534
140 0.12718784 -0.94507645
141 -0.33848963 0.12718784
142 2.58341165 -0.33848963
143 -0.84468526 2.58341165
144 1.07693907 -0.84468526
145 -0.76011438 1.07693907
146 -3.25122979 -0.76011438
147 -4.65170176 -3.25122979
148 1.13232999 -4.65170176
149 1.93993916 1.13232999
150 -0.27024906 1.93993916
151 0.91616033 -0.27024906
152 -0.82245988 0.91616033
153 0.93935493 -0.82245988
154 0.84531956 0.93935493
155 1.82615476 0.84531956
> 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/7vqka1322004801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/84p7s1322004801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ux301322004801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/102rpg1322004801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fugk1322004801.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/12omli1322004801.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/133wgf1322004801.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/14tngo1322004801.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/15e0zi1322004801.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/16p6ak1322004802.tab")
+ }
>
> try(system("convert tmp/1n5qa1322004801.ps tmp/1n5qa1322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/254op1322004801.ps tmp/254op1322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dn501322004801.ps tmp/3dn501322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/44t611322004801.ps tmp/44t611322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ugbx1322004801.ps tmp/5ugbx1322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cw701322004801.ps tmp/6cw701322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vqka1322004801.ps tmp/7vqka1322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/84p7s1322004801.ps tmp/84p7s1322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ux301322004801.ps tmp/9ux301322004801.png",intern=TRUE))
character(0)
> try(system("convert tmp/102rpg1322004801.ps tmp/102rpg1322004801.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.590 0.505 5.114