R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'q()' to quit R.
> x <- array(list(13
+ ,13
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+ ,3
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+ ,2)
+ ,dim=c(5
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity')
+ ,1:156))
> y <- array(NA,dim=c(5,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity'),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
1 13 13 14 13 3
2 12 12 8 13 5
3 15 10 12 16 6
4 12 9 7 12 6
5 10 10 10 11 5
6 12 12 7 12 3
7 15 13 16 18 8
8 9 12 11 11 4
9 12 12 14 14 4
10 11 6 6 9 4
11 11 5 16 14 6
12 11 12 11 12 6
13 15 11 16 11 5
14 7 14 12 12 4
15 11 14 7 13 6
16 11 12 13 11 4
17 10 12 11 12 6
18 14 11 15 16 6
19 10 11 7 9 4
20 6 7 9 11 4
21 11 9 7 13 2
22 15 11 14 15 7
23 11 11 15 10 5
24 12 12 7 11 4
25 14 12 15 13 6
26 15 11 17 16 6
27 9 11 15 15 7
28 13 8 14 14 5
29 13 9 14 14 6
30 16 12 8 14 4
31 13 10 8 8 4
32 12 10 14 13 7
33 14 12 14 15 7
34 11 8 8 13 4
35 9 12 11 11 4
36 16 11 16 15 6
37 12 12 10 15 6
38 10 7 8 9 5
39 13 11 14 13 6
40 16 11 16 16 7
41 14 12 13 13 6
42 15 9 5 11 3
43 5 15 8 12 3
44 8 11 10 12 4
45 11 11 8 12 6
46 16 11 13 14 7
47 17 11 15 14 5
48 9 15 6 8 4
49 9 11 12 13 5
50 13 12 16 16 6
51 10 12 5 13 6
52 6 9 15 11 6
53 12 12 12 14 5
54 8 12 8 13 4
55 14 13 13 13 5
56 12 11 14 13 5
57 11 9 12 12 4
58 16 9 16 16 6
59 8 11 10 15 2
60 15 11 15 15 8
61 7 12 8 12 3
62 16 12 16 14 6
63 14 9 19 12 6
64 16 11 14 15 6
65 9 9 6 12 5
66 14 12 13 13 5
67 11 12 15 12 6
68 13 12 7 12 5
69 15 12 13 13 6
70 5 14 4 5 2
71 15 11 14 13 5
72 13 12 13 13 5
73 11 11 11 14 5
74 11 6 14 17 6
75 12 10 12 13 6
76 12 12 15 13 6
77 12 13 14 12 5
78 12 8 13 13 5
79 14 12 8 14 4
80 6 12 6 11 2
81 7 12 7 12 4
82 14 6 13 12 6
83 14 11 13 16 6
84 10 10 11 12 5
85 13 12 5 12 3
86 12 13 12 12 6
87 9 11 8 10 4
88 12 7 11 15 5
89 16 11 14 15 8
90 10 11 9 12 4
91 14 11 10 16 6
92 10 11 13 15 6
93 16 12 16 16 7
94 15 10 16 13 6
95 12 11 11 12 5
96 10 12 8 11 4
97 8 7 4 13 6
98 8 13 7 10 3
99 11 8 14 15 5
100 13 12 11 13 6
101 16 11 17 16 7
102 16 12 15 15 7
103 14 14 17 18 6
104 11 10 5 13 3
105 4 10 4 10 2
106 14 13 10 16 8
107 9 10 11 13 3
108 14 11 15 15 8
109 8 10 10 14 3
110 8 7 9 15 4
111 11 10 12 14 5
112 12 8 15 13 7
113 11 12 7 13 6
114 14 12 13 15 6
115 15 12 12 16 7
116 16 11 14 14 6
117 16 12 14 14 6
118 11 12 8 16 6
119 14 12 15 14 6
120 14 11 12 12 4
121 12 12 12 13 4
122 14 11 16 12 5
123 8 11 9 12 4
124 13 13 15 14 6
125 16 12 15 14 6
126 12 12 6 14 5
127 16 12 14 16 8
128 12 12 15 13 6
129 11 8 10 14 5
130 4 8 6 4 4
131 16 12 14 16 8
132 15 11 12 13 6
133 10 12 8 16 4
134 13 13 11 15 6
135 15 12 13 14 6
136 12 12 9 13 4
137 14 11 15 14 6
138 7 12 13 12 3
139 19 12 15 15 6
140 12 10 14 14 5
141 12 11 16 13 4
142 13 12 14 14 6
143 15 12 14 16 4
144 8 10 10 6 4
145 12 12 10 13 4
146 10 13 4 13 6
147 8 12 8 14 5
148 10 15 15 15 6
149 15 11 16 14 6
150 16 12 12 15 8
151 13 11 12 13 7
152 16 12 15 16 7
153 9 11 9 12 4
154 14 10 12 15 6
155 14 11 14 12 6
156 12 11 11 14 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
0.30358 0.09455 0.24382 0.34890 0.62709
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.41228 -1.27704 -0.03589 1.29546 6.90720
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.30358 1.42512 0.213 0.831599
FindingFriends 0.09455 0.09596 0.985 0.326054
KnowingPeople 0.24382 0.06137 3.973 0.000110 ***
Liked 0.34890 0.09648 3.616 0.000407 ***
Celebrity 0.62709 0.15603 4.019 9.2e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.106 on 151 degrees of freedom
Multiple R-squared: 0.4992, Adjusted R-squared: 0.4859
F-statistic: 37.63 on 4 and 151 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,] 0.06025483 0.120509656 0.939745172
[2,] 0.04069541 0.081390820 0.959304590
[3,] 0.01701945 0.034038908 0.982980546
[4,] 0.03324462 0.066489248 0.966755376
[5,] 0.01685239 0.033704781 0.983147609
[6,] 0.34006225 0.680124498 0.659937751
[7,] 0.68738350 0.625232997 0.312616498
[8,] 0.60120923 0.797581532 0.398790766
[9,] 0.51058217 0.978835666 0.489417833
[10,] 0.45359875 0.907197493 0.546401253
[11,] 0.37072351 0.741447019 0.629276490
[12,] 0.30131800 0.602635999 0.698682000
[13,] 0.59754629 0.804907428 0.402453714
[14,] 0.53486277 0.930274467 0.465137233
[15,] 0.50294040 0.994119210 0.497059605
[16,] 0.43280180 0.865603609 0.567198196
[17,] 0.41858398 0.837167966 0.581416017
[18,] 0.38354803 0.767096051 0.616451975
[19,] 0.32900645 0.658012901 0.670993549
[20,] 0.58995252 0.820094955 0.410047478
[21,] 0.53178345 0.936433110 0.468216555
[22,] 0.47049945 0.940998900 0.529500550
[23,] 0.64591484 0.708170311 0.354085155
[24,] 0.77761946 0.444761071 0.222380535
[25,] 0.73813229 0.523735420 0.261867710
[26,] 0.69454793 0.610904134 0.305452067
[27,] 0.65024830 0.699503407 0.349751704
[28,] 0.64287953 0.714240950 0.357120475
[29,] 0.66452680 0.670946396 0.335473198
[30,] 0.62364988 0.752700248 0.376350124
[31,] 0.57427048 0.851459035 0.425729518
[32,] 0.52341717 0.953165663 0.476582832
[33,] 0.50655459 0.986890813 0.493445406
[34,] 0.47828648 0.956572952 0.521713524
[35,] 0.78471792 0.430564168 0.215282084
[36,] 0.94630419 0.107391626 0.053695813
[37,] 0.95742802 0.085143966 0.042571983
[38,] 0.94501850 0.109962993 0.054981497
[39,] 0.95191475 0.096170501 0.048085251
[40,] 0.97747740 0.045045209 0.022522604
[41,] 0.97040463 0.059190734 0.029595367
[42,] 0.97804643 0.043907137 0.021953569
[43,] 0.97497374 0.050052524 0.025026262
[44,] 0.96972042 0.060559161 0.030279580
[45,] 0.99719509 0.005609811 0.002804906
[46,] 0.99601519 0.007969628 0.003984814
[47,] 0.99706646 0.005867084 0.002933542
[48,] 0.99677705 0.006445892 0.003222946
[49,] 0.99545216 0.009095673 0.004547837
[50,] 0.99369831 0.012603381 0.006301690
[51,] 0.99282449 0.014351015 0.007175508
[52,] 0.99487554 0.010248917 0.005124459
[53,] 0.99310784 0.013784327 0.006892163
[54,] 0.99430356 0.011392880 0.005696440
[55,] 0.99461821 0.010763579 0.005381789
[56,] 0.99271170 0.014576606 0.007288303
[57,] 0.99314526 0.013709475 0.006854737
[58,] 0.99152103 0.016957933 0.008478966
[59,] 0.99064003 0.018719938 0.009359969
[60,] 0.99057927 0.018841465 0.009420732
[61,] 0.99205278 0.015894435 0.007947217
[62,] 0.99217941 0.015641177 0.007820589
[63,] 0.98953397 0.020932054 0.010466027
[64,] 0.99111882 0.017762368 0.008881184
[65,] 0.98829900 0.023401992 0.011700996
[66,] 0.98538473 0.029230541 0.014615270
[67,] 0.98967474 0.020650530 0.010325265
[68,] 0.98615187 0.027696253 0.013848127
[69,] 0.98395739 0.032085216 0.016042608
[70,] 0.97884824 0.042303529 0.021151764
[71,] 0.97214544 0.055709126 0.027854563
[72,] 0.98227278 0.035454444 0.017727222
[73,] 0.98183755 0.036324891 0.018162446
[74,] 0.98542068 0.029158630 0.014579315
[75,] 0.98609320 0.027813603 0.013906801
[76,] 0.98130736 0.037385271 0.018692636
[77,] 0.97727777 0.045444460 0.022722230
[78,] 0.99378184 0.012436326 0.006218163
[79,] 0.99153828 0.016923432 0.008461716
[80,] 0.98850469 0.022990624 0.011495312
[81,] 0.98491022 0.030179555 0.015089777
[82,] 0.98089719 0.038205613 0.019102807
[83,] 0.97477537 0.050449267 0.025224634
[84,] 0.96933891 0.061322177 0.030661088
[85,] 0.98317308 0.033653837 0.016826918
[86,] 0.97806704 0.043865917 0.021932959
[87,] 0.97458622 0.050827566 0.025413783
[88,] 0.96769123 0.064617533 0.032308766
[89,] 0.95887106 0.082257878 0.041128939
[90,] 0.95604341 0.087913190 0.043956595
[91,] 0.94400239 0.111995218 0.055997609
[92,] 0.94153104 0.116937917 0.058468958
[93,] 0.92723554 0.145528928 0.072764464
[94,] 0.90964235 0.180715296 0.090357648
[95,] 0.89362610 0.212747799 0.106373899
[96,] 0.90469213 0.190615732 0.095307866
[97,] 0.93325305 0.133493904 0.066746952
[98,] 0.93307863 0.133842748 0.066921374
[99,] 0.91632909 0.167341816 0.083670908
[100,] 0.90033130 0.199337407 0.099668703
[101,] 0.89684129 0.206317411 0.103158705
[102,] 0.89751901 0.204961984 0.102480992
[103,] 0.91956214 0.160875722 0.080437861
[104,] 0.91161627 0.176767460 0.088383730
[105,] 0.93837692 0.123246162 0.061623081
[106,] 0.92072731 0.158545386 0.079272693
[107,] 0.89861394 0.202772124 0.101386062
[108,] 0.87250937 0.254981256 0.127490628
[109,] 0.87119709 0.257605823 0.128802912
[110,] 0.87795248 0.244095041 0.122047521
[111,] 0.86972170 0.260556595 0.130278297
[112,] 0.83681688 0.326366244 0.163183122
[113,] 0.88446731 0.231065385 0.115532693
[114,] 0.86066277 0.278674455 0.139337227
[115,] 0.83872555 0.322548906 0.161274453
[116,] 0.83116046 0.337679085 0.168839542
[117,] 0.79484977 0.410300457 0.205150228
[118,] 0.79485241 0.410295179 0.205147589
[119,] 0.77850307 0.442993856 0.221496928
[120,] 0.73005420 0.539891603 0.269945802
[121,] 0.70500933 0.589981343 0.294990671
[122,] 0.72294830 0.554103399 0.277051700
[123,] 0.71864931 0.562701379 0.281350689
[124,] 0.66253617 0.674927654 0.337463827
[125,] 0.65120502 0.697589960 0.348794980
[126,] 0.62502407 0.749951851 0.374975926
[127,] 0.55383536 0.892329282 0.446164641
[128,] 0.52903167 0.941936654 0.470968327
[129,] 0.52496898 0.950062040 0.475031020
[130,] 0.45183759 0.903675184 0.548162408
[131,] 0.52185120 0.956297608 0.478148804
[132,] 0.85005544 0.299889111 0.149944555
[133,] 0.87775098 0.244498046 0.122249023
[134,] 0.84421312 0.311573758 0.155786879
[135,] 0.77723332 0.445533369 0.222766685
[136,] 0.74931269 0.501374627 0.250687314
[137,] 0.65222032 0.695559366 0.347779683
[138,] 0.64287700 0.714245990 0.357122995
[139,] 0.76391824 0.472163518 0.236081759
[140,] 0.73167419 0.536651614 0.268325807
[141,] 0.85246245 0.295075102 0.147537551
> postscript(file="/var/www/rcomp/tmp/1zi7c1290096281.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2zi7c1290096281.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/39r7f1290096281.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/49r7f1290096281.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/59r7f1290096281.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 = 156
Frequency = 1
1 2 3 4 5 6
1.63680998 0.94011353 1.48012566 1.18939279 -0.66062943 2.78701106
7 8 9 10 11 12
-1.73078073 -1.46645861 -0.24463251 3.01773753 -2.32461937 -1.06953457
13 14 15 16 17 18
2.78189412 -4.24827729 -0.63224540 0.04589825 -2.06953457 -0.34588608
19 20 21 22 23 24
1.30118083 -3.50608034 2.34883552 0.61975211 -0.62538125 2.50882767
25 26 27 28 29 30
0.60627609 0.16647078 -5.62406946 0.50646914 -0.21516433 5.21829691
31 32 33 34 35 36
4.50080935 -1.58789474 -0.47479492 0.94538808 -1.46645861 1.75919541
37 38 39 40 41 42
-0.87242219 0.80846091 -0.05535532 0.78320590 1.09391923 6.90719834
43 44 45 46 47 48
-4.74045159 -2.47699307 -0.24352283 2.21247674 3.97900650 0.51571736
49 50 51 52 53 54
-2.94062573 -1.68425468 -0.95550821 -6.41227671 -0.38407582 -2.43280002
55 56 57 58 59 60
1.62645865 -0.42826887 0.22445784 1.59938640 -2.26952937 -0.25115591
61 62 63 64 65 66
-2.45681051 2.01355145 0.26353395 2.24683855 -0.93969919 1.72100568
67 68 69 70 71 72
-2.04482085 2.53283816 2.09391923 -0.60121038 2.57173113 0.72100568
73 74 75 76 77 78
-1.04570722 -2.97823245 -0.47316515 -1.39372391 -0.26845985 0.09919378
79 80 81 82 83 84
3.21829691 -1.99317786 -2.84007539 2.01010444 0.14175706 -1.25335407
85 86 87 88 89 90
4.27465420 -0.40790316 -0.29154381 -0.01642218 0.99266566 -0.23317150
91 92 93 94 95 96
0.87322177 -3.50933987 0.68865888 1.55154857 0.65209891 0.26500610
97 98 99 100 101 102
-2.23895151 -0.60972984 -1.84243392 0.58156237 0.53938433 1.28138351
103 104 105 106 107 108
-1.81497642 2.11484519 -2.96753760 -0.57004518 -1.34808424 -1.25115591
109 110 111 112 113 114
-2.45316573 -2.90169260 -1.19498177 -1.64262226 -0.44315135 0.39611310
115 116 117 118 119 120
0.66394516 2.59574162 2.50119459 -1.73368211 0.25737302 3.03536378
121 122 123 124 125 126
0.59191370 1.43299106 -2.23317150 -0.83717400 2.25737302 1.07885360
127 128 129 130 131 132
0.54921557 -1.39372391 -0.51824458 -2.42684120 0.54921557 2.43228782
133 134 135 136 137 138
-1.47950922 -0.21079079 1.74501616 1.32337841 0.35192005 -3.67591836
139 140 141 142 143 144
4.90846996 -0.68262491 -0.28882556 -0.49880541 2.05756136 -0.28902766
145 146 147 148 149 150
1.07955684 -0.80623367 -3.40878954 -4.37517112 1.10809848 1.38576177
151 152 153 154 155 156
-0.19479862 0.93248045 -1.23317150 0.82902872 1.29354775 1.83555212
> postscript(file="/var/www/rcomp/tmp/6k0oi1290096281.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.63680998 NA
1 0.94011353 1.63680998
2 1.48012566 0.94011353
3 1.18939279 1.48012566
4 -0.66062943 1.18939279
5 2.78701106 -0.66062943
6 -1.73078073 2.78701106
7 -1.46645861 -1.73078073
8 -0.24463251 -1.46645861
9 3.01773753 -0.24463251
10 -2.32461937 3.01773753
11 -1.06953457 -2.32461937
12 2.78189412 -1.06953457
13 -4.24827729 2.78189412
14 -0.63224540 -4.24827729
15 0.04589825 -0.63224540
16 -2.06953457 0.04589825
17 -0.34588608 -2.06953457
18 1.30118083 -0.34588608
19 -3.50608034 1.30118083
20 2.34883552 -3.50608034
21 0.61975211 2.34883552
22 -0.62538125 0.61975211
23 2.50882767 -0.62538125
24 0.60627609 2.50882767
25 0.16647078 0.60627609
26 -5.62406946 0.16647078
27 0.50646914 -5.62406946
28 -0.21516433 0.50646914
29 5.21829691 -0.21516433
30 4.50080935 5.21829691
31 -1.58789474 4.50080935
32 -0.47479492 -1.58789474
33 0.94538808 -0.47479492
34 -1.46645861 0.94538808
35 1.75919541 -1.46645861
36 -0.87242219 1.75919541
37 0.80846091 -0.87242219
38 -0.05535532 0.80846091
39 0.78320590 -0.05535532
40 1.09391923 0.78320590
41 6.90719834 1.09391923
42 -4.74045159 6.90719834
43 -2.47699307 -4.74045159
44 -0.24352283 -2.47699307
45 2.21247674 -0.24352283
46 3.97900650 2.21247674
47 0.51571736 3.97900650
48 -2.94062573 0.51571736
49 -1.68425468 -2.94062573
50 -0.95550821 -1.68425468
51 -6.41227671 -0.95550821
52 -0.38407582 -6.41227671
53 -2.43280002 -0.38407582
54 1.62645865 -2.43280002
55 -0.42826887 1.62645865
56 0.22445784 -0.42826887
57 1.59938640 0.22445784
58 -2.26952937 1.59938640
59 -0.25115591 -2.26952937
60 -2.45681051 -0.25115591
61 2.01355145 -2.45681051
62 0.26353395 2.01355145
63 2.24683855 0.26353395
64 -0.93969919 2.24683855
65 1.72100568 -0.93969919
66 -2.04482085 1.72100568
67 2.53283816 -2.04482085
68 2.09391923 2.53283816
69 -0.60121038 2.09391923
70 2.57173113 -0.60121038
71 0.72100568 2.57173113
72 -1.04570722 0.72100568
73 -2.97823245 -1.04570722
74 -0.47316515 -2.97823245
75 -1.39372391 -0.47316515
76 -0.26845985 -1.39372391
77 0.09919378 -0.26845985
78 3.21829691 0.09919378
79 -1.99317786 3.21829691
80 -2.84007539 -1.99317786
81 2.01010444 -2.84007539
82 0.14175706 2.01010444
83 -1.25335407 0.14175706
84 4.27465420 -1.25335407
85 -0.40790316 4.27465420
86 -0.29154381 -0.40790316
87 -0.01642218 -0.29154381
88 0.99266566 -0.01642218
89 -0.23317150 0.99266566
90 0.87322177 -0.23317150
91 -3.50933987 0.87322177
92 0.68865888 -3.50933987
93 1.55154857 0.68865888
94 0.65209891 1.55154857
95 0.26500610 0.65209891
96 -2.23895151 0.26500610
97 -0.60972984 -2.23895151
98 -1.84243392 -0.60972984
99 0.58156237 -1.84243392
100 0.53938433 0.58156237
101 1.28138351 0.53938433
102 -1.81497642 1.28138351
103 2.11484519 -1.81497642
104 -2.96753760 2.11484519
105 -0.57004518 -2.96753760
106 -1.34808424 -0.57004518
107 -1.25115591 -1.34808424
108 -2.45316573 -1.25115591
109 -2.90169260 -2.45316573
110 -1.19498177 -2.90169260
111 -1.64262226 -1.19498177
112 -0.44315135 -1.64262226
113 0.39611310 -0.44315135
114 0.66394516 0.39611310
115 2.59574162 0.66394516
116 2.50119459 2.59574162
117 -1.73368211 2.50119459
118 0.25737302 -1.73368211
119 3.03536378 0.25737302
120 0.59191370 3.03536378
121 1.43299106 0.59191370
122 -2.23317150 1.43299106
123 -0.83717400 -2.23317150
124 2.25737302 -0.83717400
125 1.07885360 2.25737302
126 0.54921557 1.07885360
127 -1.39372391 0.54921557
128 -0.51824458 -1.39372391
129 -2.42684120 -0.51824458
130 0.54921557 -2.42684120
131 2.43228782 0.54921557
132 -1.47950922 2.43228782
133 -0.21079079 -1.47950922
134 1.74501616 -0.21079079
135 1.32337841 1.74501616
136 0.35192005 1.32337841
137 -3.67591836 0.35192005
138 4.90846996 -3.67591836
139 -0.68262491 4.90846996
140 -0.28882556 -0.68262491
141 -0.49880541 -0.28882556
142 2.05756136 -0.49880541
143 -0.28902766 2.05756136
144 1.07955684 -0.28902766
145 -0.80623367 1.07955684
146 -3.40878954 -0.80623367
147 -4.37517112 -3.40878954
148 1.10809848 -4.37517112
149 1.38576177 1.10809848
150 -0.19479862 1.38576177
151 0.93248045 -0.19479862
152 -1.23317150 0.93248045
153 0.82902872 -1.23317150
154 1.29354775 0.82902872
155 1.83555212 1.29354775
156 NA 1.83555212
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.94011353 1.63680998
[2,] 1.48012566 0.94011353
[3,] 1.18939279 1.48012566
[4,] -0.66062943 1.18939279
[5,] 2.78701106 -0.66062943
[6,] -1.73078073 2.78701106
[7,] -1.46645861 -1.73078073
[8,] -0.24463251 -1.46645861
[9,] 3.01773753 -0.24463251
[10,] -2.32461937 3.01773753
[11,] -1.06953457 -2.32461937
[12,] 2.78189412 -1.06953457
[13,] -4.24827729 2.78189412
[14,] -0.63224540 -4.24827729
[15,] 0.04589825 -0.63224540
[16,] -2.06953457 0.04589825
[17,] -0.34588608 -2.06953457
[18,] 1.30118083 -0.34588608
[19,] -3.50608034 1.30118083
[20,] 2.34883552 -3.50608034
[21,] 0.61975211 2.34883552
[22,] -0.62538125 0.61975211
[23,] 2.50882767 -0.62538125
[24,] 0.60627609 2.50882767
[25,] 0.16647078 0.60627609
[26,] -5.62406946 0.16647078
[27,] 0.50646914 -5.62406946
[28,] -0.21516433 0.50646914
[29,] 5.21829691 -0.21516433
[30,] 4.50080935 5.21829691
[31,] -1.58789474 4.50080935
[32,] -0.47479492 -1.58789474
[33,] 0.94538808 -0.47479492
[34,] -1.46645861 0.94538808
[35,] 1.75919541 -1.46645861
[36,] -0.87242219 1.75919541
[37,] 0.80846091 -0.87242219
[38,] -0.05535532 0.80846091
[39,] 0.78320590 -0.05535532
[40,] 1.09391923 0.78320590
[41,] 6.90719834 1.09391923
[42,] -4.74045159 6.90719834
[43,] -2.47699307 -4.74045159
[44,] -0.24352283 -2.47699307
[45,] 2.21247674 -0.24352283
[46,] 3.97900650 2.21247674
[47,] 0.51571736 3.97900650
[48,] -2.94062573 0.51571736
[49,] -1.68425468 -2.94062573
[50,] -0.95550821 -1.68425468
[51,] -6.41227671 -0.95550821
[52,] -0.38407582 -6.41227671
[53,] -2.43280002 -0.38407582
[54,] 1.62645865 -2.43280002
[55,] -0.42826887 1.62645865
[56,] 0.22445784 -0.42826887
[57,] 1.59938640 0.22445784
[58,] -2.26952937 1.59938640
[59,] -0.25115591 -2.26952937
[60,] -2.45681051 -0.25115591
[61,] 2.01355145 -2.45681051
[62,] 0.26353395 2.01355145
[63,] 2.24683855 0.26353395
[64,] -0.93969919 2.24683855
[65,] 1.72100568 -0.93969919
[66,] -2.04482085 1.72100568
[67,] 2.53283816 -2.04482085
[68,] 2.09391923 2.53283816
[69,] -0.60121038 2.09391923
[70,] 2.57173113 -0.60121038
[71,] 0.72100568 2.57173113
[72,] -1.04570722 0.72100568
[73,] -2.97823245 -1.04570722
[74,] -0.47316515 -2.97823245
[75,] -1.39372391 -0.47316515
[76,] -0.26845985 -1.39372391
[77,] 0.09919378 -0.26845985
[78,] 3.21829691 0.09919378
[79,] -1.99317786 3.21829691
[80,] -2.84007539 -1.99317786
[81,] 2.01010444 -2.84007539
[82,] 0.14175706 2.01010444
[83,] -1.25335407 0.14175706
[84,] 4.27465420 -1.25335407
[85,] -0.40790316 4.27465420
[86,] -0.29154381 -0.40790316
[87,] -0.01642218 -0.29154381
[88,] 0.99266566 -0.01642218
[89,] -0.23317150 0.99266566
[90,] 0.87322177 -0.23317150
[91,] -3.50933987 0.87322177
[92,] 0.68865888 -3.50933987
[93,] 1.55154857 0.68865888
[94,] 0.65209891 1.55154857
[95,] 0.26500610 0.65209891
[96,] -2.23895151 0.26500610
[97,] -0.60972984 -2.23895151
[98,] -1.84243392 -0.60972984
[99,] 0.58156237 -1.84243392
[100,] 0.53938433 0.58156237
[101,] 1.28138351 0.53938433
[102,] -1.81497642 1.28138351
[103,] 2.11484519 -1.81497642
[104,] -2.96753760 2.11484519
[105,] -0.57004518 -2.96753760
[106,] -1.34808424 -0.57004518
[107,] -1.25115591 -1.34808424
[108,] -2.45316573 -1.25115591
[109,] -2.90169260 -2.45316573
[110,] -1.19498177 -2.90169260
[111,] -1.64262226 -1.19498177
[112,] -0.44315135 -1.64262226
[113,] 0.39611310 -0.44315135
[114,] 0.66394516 0.39611310
[115,] 2.59574162 0.66394516
[116,] 2.50119459 2.59574162
[117,] -1.73368211 2.50119459
[118,] 0.25737302 -1.73368211
[119,] 3.03536378 0.25737302
[120,] 0.59191370 3.03536378
[121,] 1.43299106 0.59191370
[122,] -2.23317150 1.43299106
[123,] -0.83717400 -2.23317150
[124,] 2.25737302 -0.83717400
[125,] 1.07885360 2.25737302
[126,] 0.54921557 1.07885360
[127,] -1.39372391 0.54921557
[128,] -0.51824458 -1.39372391
[129,] -2.42684120 -0.51824458
[130,] 0.54921557 -2.42684120
[131,] 2.43228782 0.54921557
[132,] -1.47950922 2.43228782
[133,] -0.21079079 -1.47950922
[134,] 1.74501616 -0.21079079
[135,] 1.32337841 1.74501616
[136,] 0.35192005 1.32337841
[137,] -3.67591836 0.35192005
[138,] 4.90846996 -3.67591836
[139,] -0.68262491 4.90846996
[140,] -0.28882556 -0.68262491
[141,] -0.49880541 -0.28882556
[142,] 2.05756136 -0.49880541
[143,] -0.28902766 2.05756136
[144,] 1.07955684 -0.28902766
[145,] -0.80623367 1.07955684
[146,] -3.40878954 -0.80623367
[147,] -4.37517112 -3.40878954
[148,] 1.10809848 -4.37517112
[149,] 1.38576177 1.10809848
[150,] -0.19479862 1.38576177
[151,] 0.93248045 -0.19479862
[152,] -1.23317150 0.93248045
[153,] 0.82902872 -1.23317150
[154,] 1.29354775 0.82902872
[155,] 1.83555212 1.29354775
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.94011353 1.63680998
2 1.48012566 0.94011353
3 1.18939279 1.48012566
4 -0.66062943 1.18939279
5 2.78701106 -0.66062943
6 -1.73078073 2.78701106
7 -1.46645861 -1.73078073
8 -0.24463251 -1.46645861
9 3.01773753 -0.24463251
10 -2.32461937 3.01773753
11 -1.06953457 -2.32461937
12 2.78189412 -1.06953457
13 -4.24827729 2.78189412
14 -0.63224540 -4.24827729
15 0.04589825 -0.63224540
16 -2.06953457 0.04589825
17 -0.34588608 -2.06953457
18 1.30118083 -0.34588608
19 -3.50608034 1.30118083
20 2.34883552 -3.50608034
21 0.61975211 2.34883552
22 -0.62538125 0.61975211
23 2.50882767 -0.62538125
24 0.60627609 2.50882767
25 0.16647078 0.60627609
26 -5.62406946 0.16647078
27 0.50646914 -5.62406946
28 -0.21516433 0.50646914
29 5.21829691 -0.21516433
30 4.50080935 5.21829691
31 -1.58789474 4.50080935
32 -0.47479492 -1.58789474
33 0.94538808 -0.47479492
34 -1.46645861 0.94538808
35 1.75919541 -1.46645861
36 -0.87242219 1.75919541
37 0.80846091 -0.87242219
38 -0.05535532 0.80846091
39 0.78320590 -0.05535532
40 1.09391923 0.78320590
41 6.90719834 1.09391923
42 -4.74045159 6.90719834
43 -2.47699307 -4.74045159
44 -0.24352283 -2.47699307
45 2.21247674 -0.24352283
46 3.97900650 2.21247674
47 0.51571736 3.97900650
48 -2.94062573 0.51571736
49 -1.68425468 -2.94062573
50 -0.95550821 -1.68425468
51 -6.41227671 -0.95550821
52 -0.38407582 -6.41227671
53 -2.43280002 -0.38407582
54 1.62645865 -2.43280002
55 -0.42826887 1.62645865
56 0.22445784 -0.42826887
57 1.59938640 0.22445784
58 -2.26952937 1.59938640
59 -0.25115591 -2.26952937
60 -2.45681051 -0.25115591
61 2.01355145 -2.45681051
62 0.26353395 2.01355145
63 2.24683855 0.26353395
64 -0.93969919 2.24683855
65 1.72100568 -0.93969919
66 -2.04482085 1.72100568
67 2.53283816 -2.04482085
68 2.09391923 2.53283816
69 -0.60121038 2.09391923
70 2.57173113 -0.60121038
71 0.72100568 2.57173113
72 -1.04570722 0.72100568
73 -2.97823245 -1.04570722
74 -0.47316515 -2.97823245
75 -1.39372391 -0.47316515
76 -0.26845985 -1.39372391
77 0.09919378 -0.26845985
78 3.21829691 0.09919378
79 -1.99317786 3.21829691
80 -2.84007539 -1.99317786
81 2.01010444 -2.84007539
82 0.14175706 2.01010444
83 -1.25335407 0.14175706
84 4.27465420 -1.25335407
85 -0.40790316 4.27465420
86 -0.29154381 -0.40790316
87 -0.01642218 -0.29154381
88 0.99266566 -0.01642218
89 -0.23317150 0.99266566
90 0.87322177 -0.23317150
91 -3.50933987 0.87322177
92 0.68865888 -3.50933987
93 1.55154857 0.68865888
94 0.65209891 1.55154857
95 0.26500610 0.65209891
96 -2.23895151 0.26500610
97 -0.60972984 -2.23895151
98 -1.84243392 -0.60972984
99 0.58156237 -1.84243392
100 0.53938433 0.58156237
101 1.28138351 0.53938433
102 -1.81497642 1.28138351
103 2.11484519 -1.81497642
104 -2.96753760 2.11484519
105 -0.57004518 -2.96753760
106 -1.34808424 -0.57004518
107 -1.25115591 -1.34808424
108 -2.45316573 -1.25115591
109 -2.90169260 -2.45316573
110 -1.19498177 -2.90169260
111 -1.64262226 -1.19498177
112 -0.44315135 -1.64262226
113 0.39611310 -0.44315135
114 0.66394516 0.39611310
115 2.59574162 0.66394516
116 2.50119459 2.59574162
117 -1.73368211 2.50119459
118 0.25737302 -1.73368211
119 3.03536378 0.25737302
120 0.59191370 3.03536378
121 1.43299106 0.59191370
122 -2.23317150 1.43299106
123 -0.83717400 -2.23317150
124 2.25737302 -0.83717400
125 1.07885360 2.25737302
126 0.54921557 1.07885360
127 -1.39372391 0.54921557
128 -0.51824458 -1.39372391
129 -2.42684120 -0.51824458
130 0.54921557 -2.42684120
131 2.43228782 0.54921557
132 -1.47950922 2.43228782
133 -0.21079079 -1.47950922
134 1.74501616 -0.21079079
135 1.32337841 1.74501616
136 0.35192005 1.32337841
137 -3.67591836 0.35192005
138 4.90846996 -3.67591836
139 -0.68262491 4.90846996
140 -0.28882556 -0.68262491
141 -0.49880541 -0.28882556
142 2.05756136 -0.49880541
143 -0.28902766 2.05756136
144 1.07955684 -0.28902766
145 -0.80623367 1.07955684
146 -3.40878954 -0.80623367
147 -4.37517112 -3.40878954
148 1.10809848 -4.37517112
149 1.38576177 1.10809848
150 -0.19479862 1.38576177
151 0.93248045 -0.19479862
152 -1.23317150 0.93248045
153 0.82902872 -1.23317150
154 1.29354775 0.82902872
155 1.83555212 1.29354775
> 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/rcomp/tmp/7dsnl1290096281.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8dsnl1290096281.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9dsnl1290096281.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10514o1290096281.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/119jlc1290096281.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/rcomp/tmp/12ckji1290096281.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/rcomp/tmp/131lyt1290096281.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/rcomp/tmp/14cuge1290096281.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/rcomp/tmp/15fvw21290096281.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/rcomp/tmp/16t4cb1290096281.tab")
+ }
> try(system("convert tmp/1zi7c1290096281.ps tmp/1zi7c1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zi7c1290096281.ps tmp/2zi7c1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/39r7f1290096281.ps tmp/39r7f1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/49r7f1290096281.ps tmp/49r7f1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/59r7f1290096281.ps tmp/59r7f1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/6k0oi1290096281.ps tmp/6k0oi1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dsnl1290096281.ps tmp/7dsnl1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dsnl1290096281.ps tmp/8dsnl1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dsnl1290096281.ps tmp/9dsnl1290096281.png",intern=TRUE))
character(0)
> try(system("convert tmp/10514o1290096281.ps tmp/10514o1290096281.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.490 1.090 6.551