R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1910
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+ ,0
+ ,0)
+ ,dim=c(4
+ ,149)
+ ,dimnames=list(c('pg'
+ ,'blogs'
+ ,'LFM'
+ ,'hours')
+ ,1:149))
> y <- array(NA,dim=c(4,149),dimnames=list(c('pg','blogs','LFM','hours'),1:149))
> 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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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, 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
LFM pg blogs hours
1 56 1910 61 51
2 73 2598 74 48
3 62 2144 57 46
4 42 1331 50 42
5 59 1431 48 38
6 27 7334 2 38
7 59 1133 41 36
8 56 1535 61 36
9 78 1196 31 35
10 47 1551 12 35
11 51 2108 46 34
12 47 1335 31 34
13 55 1532 60 32
14 35 842 49 31
15 47 1539 15 31
16 48 1065 33 31
17 47 1474 36 31
18 55 1226 55 30
19 42 1598 28 30
20 54 1546 44 30
21 60 914 41 30
22 51 1371 26 28
23 47 1318 28 27
24 52 1313 40 27
25 38 1743 28 27
26 46 1060 57 26
27 12 1102 67 26
28 48 1275 56 26
29 48 1253 54 26
30 32 1487 25 26
31 27 1098 19 26
32 60 930 28 25
33 47 1176 36 25
34 47 1290 19 25
35 58 903 42 25
36 47 1240 30 24
37 45 1402 28 24
38 48 1495 41 24
39 42 1493 35 24
40 41 826 10 24
41 48 1469 57 24
42 60 1064 48 24
43 56 821 32 24
44 41 1317 39 23
45 52 873 49 23
46 50 982 22 23
47 49 708 17 23
48 42 1174 30 23
49 39 853 55 23
50 39 872 33 23
51 41 1202 42 23
52 46 793 24 22
53 36 1000 13 22
54 49 1205 35 22
55 48 1671 37 22
56 55 1106 3 22
57 45 1131 15 22
58 48 775 19 21
59 52 1224 29 21
60 39 1375 28 21
61 45 804 38 21
62 32 923 23 20
63 51 1233 38 20
64 41 1170 35 20
65 52 613 27 20
66 22 987 23 20
67 54 1204 32 19
68 27 933 7 18
69 41 861 57 18
70 45 932 39 18
71 52 705 18 18
72 57 828 18 17
73 47 1083 41 17
74 41 779 33 16
75 46 792 0 16
76 43 587 35 16
77 31 918 37 16
78 40 649 16 16
79 24 843 34 16
80 30 1060 35 16
81 45 575 25 15
82 32 548 26 15
83 46 503 13 15
84 9 743 30 15
85 44 846 17 15
86 32 861 54 15
87 37 486 40 15
88 64 634 9 15
89 21 871 25 14
90 20 715 29 14
91 33 812 40 14
92 26 970 32 14
93 36 959 17 13
94 33 960 18 13
95 20 646 17 13
96 31 562 15 13
97 13 636 28 13
98 35 646 18 13
99 24 428 10 12
100 40 830 10 12
101 19 460 4 12
102 15 781 10 12
103 34 567 2 12
104 32 694 25 12
105 58 475 16 12
106 21 485 28 12
107 31 613 25 11
108 21 480 7 11
109 26 582 27 11
110 47 569 16 11
111 37 559 7 11
112 28 508 16 10
113 9 488 0 10
114 45 475 36 9
115 32 630 15 9
116 35 386 5 9
117 29 511 14 9
118 1 585 43 9
119 20 580 10 9
120 15 516 0 9
121 11 413 8 8
122 33 495 12 8
123 18 478 10 8
124 10 350 39 7
125 41 427 0 7
126 10 349 10 6
127 0 335 7 6
128 28 470 8 5
129 31 250 0 5
130 24 308 3 5
131 38 229 0 5
132 0 244 8 5
133 25 242 1 5
134 40 352 0 5
135 4 428 8 5
136 23 270 3 5
137 13 242 0 4
138 6 291 0 4
139 0 135 0 3
140 3 210 3 3
141 0 231 0 2
142 7 268 0 2
143 2 126 0 2
144 0 340 0 2
145 0 44 0 2
146 0 25 0 1
147 0 104 0 1
148 5 142 2 1
149 0 11 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pg blogs hours
13.217050 -0.004963 0.006620 1.481203
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-34.702 -8.796 1.217 7.732 31.652
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.217050 1.938188 6.819 2.29e-10 ***
pg -0.004963 0.001994 -2.489 0.0139 *
blogs 0.006620 0.078269 0.085 0.9327
hours 1.481203 0.176157 8.408 3.56e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.82 on 145 degrees of freedom
Multiple R-squared: 0.5634, Adjusted R-squared: 0.5544
F-statistic: 62.38 on 3 and 145 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,] 0.32507464 0.6501492716 6.749254e-01
[2,] 0.37160778 0.7432155649 6.283922e-01
[3,] 0.80017643 0.3996471357 1.998236e-01
[4,] 0.72540244 0.5491951257 2.745976e-01
[5,] 0.65214320 0.6957135918 3.478568e-01
[6,] 0.59880330 0.8023933934 4.011967e-01
[7,] 0.50873704 0.9825259240 4.912630e-01
[8,] 0.69407879 0.6118424131 3.059212e-01
[9,] 0.61651976 0.7669604809 3.834802e-01
[10,] 0.54073207 0.9185358626 4.592679e-01
[11,] 0.46431423 0.9286284586 5.356858e-01
[12,] 0.38962839 0.7792567745 6.103716e-01
[13,] 0.33841050 0.6768210089 6.615895e-01
[14,] 0.28068294 0.5613658864 7.193171e-01
[15,] 0.25251600 0.5050320098 7.474840e-01
[16,] 0.20256901 0.4051380129 7.974310e-01
[17,] 0.15548533 0.3109706552 8.445147e-01
[18,] 0.11755423 0.2351084506 8.824458e-01
[19,] 0.10704453 0.2140890587 8.929555e-01
[20,] 0.08419373 0.1683874672 9.158063e-01
[21,] 0.57105980 0.8578803937 4.289402e-01
[22,] 0.52427319 0.9514536107 4.757268e-01
[23,] 0.47322867 0.9464573476 5.267713e-01
[24,] 0.50060431 0.9987913719 4.993957e-01
[25,] 0.62836423 0.7432715358 3.716358e-01
[26,] 0.67539575 0.6492085086 3.246043e-01
[27,] 0.62964852 0.7407029640 3.703515e-01
[28,] 0.58399854 0.8320029113 4.160015e-01
[29,] 0.59154977 0.8169004670 4.084502e-01
[30,] 0.54254837 0.9149032510 4.574516e-01
[31,] 0.49180902 0.9836180406 5.081910e-01
[32,] 0.44802003 0.8960400622 5.519800e-01
[33,] 0.40076807 0.8015361434 5.992319e-01
[34,] 0.37464340 0.7492867904 6.253566e-01
[35,] 0.33182576 0.6636515116 6.681742e-01
[36,] 0.36195185 0.7239036906 6.380482e-01
[37,] 0.34348078 0.6869615691 6.565192e-01
[38,] 0.30329113 0.6065822683 6.967089e-01
[39,] 0.26902345 0.5380468922 7.309766e-01
[40,] 0.23425243 0.4685048556 7.657476e-01
[41,] 0.19881390 0.3976278067 8.011861e-01
[42,] 0.17064852 0.3412970380 8.293515e-01
[43,] 0.15570982 0.3114196393 8.442902e-01
[44,] 0.14481231 0.2896246202 8.551877e-01
[45,] 0.12359861 0.2471972298 8.764014e-01
[46,] 0.10122754 0.2024550749 8.987725e-01
[47,] 0.10247983 0.2049596664 8.975202e-01
[48,] 0.08614700 0.1722939936 9.138530e-01
[49,] 0.07291964 0.1458392802 9.270804e-01
[50,] 0.07147152 0.1429430497 9.285285e-01
[51,] 0.05827033 0.1165406616 9.417297e-01
[52,] 0.04640187 0.0928037446 9.535981e-01
[53,] 0.04128732 0.0825746383 9.587127e-01
[54,] 0.03374450 0.0674889928 9.662555e-01
[55,] 0.02570341 0.0514068138 9.742966e-01
[56,] 0.03056922 0.0611384445 9.694308e-01
[57,] 0.02739267 0.0547853313 9.726073e-01
[58,] 0.02088840 0.0417767981 9.791116e-01
[59,] 0.01706369 0.0341273831 9.829363e-01
[60,] 0.05152910 0.1030582092 9.484709e-01
[61,] 0.05473023 0.1094604605 9.452698e-01
[62,] 0.08230463 0.1646092533 9.176954e-01
[63,] 0.06695070 0.1339014068 9.330493e-01
[64,] 0.05401634 0.1080326878 9.459837e-01
[65,] 0.04893951 0.0978790239 9.510605e-01
[66,] 0.06091794 0.1218358745 9.390821e-01
[67,] 0.05564089 0.1112817845 9.443591e-01
[68,] 0.04430464 0.0886092780 9.556954e-01
[69,] 0.03550657 0.0710131437 9.644934e-01
[70,] 0.02816940 0.0563388045 9.718306e-01
[71,] 0.02530021 0.0506004214 9.746998e-01
[72,] 0.01918398 0.0383679527 9.808160e-01
[73,] 0.02562255 0.0512451093 9.743774e-01
[74,] 0.02256472 0.0451294397 9.774353e-01
[75,] 0.01861099 0.0372219756 9.813890e-01
[76,] 0.01585249 0.0317049785 9.841475e-01
[77,] 0.01258590 0.0251717986 9.874141e-01
[78,] 0.06430839 0.1286167725 9.356916e-01
[79,] 0.05343398 0.1068679613 9.465660e-01
[80,] 0.04375331 0.0875066270 9.562467e-01
[81,] 0.03442804 0.0688560842 9.655720e-01
[82,] 0.08501489 0.1700297824 9.149851e-01
[83,] 0.10028860 0.2005771905 8.997114e-01
[84,] 0.12059665 0.2411932952 8.794034e-01
[85,] 0.09910345 0.1982068947 9.008966e-01
[86,] 0.09094487 0.1818897384 9.090551e-01
[87,] 0.07260227 0.1452045369 9.273977e-01
[88,] 0.05761343 0.1152268654 9.423866e-01
[89,] 0.06999588 0.1399917560 9.300041e-01
[90,] 0.05706385 0.1141277035 9.429361e-01
[91,] 0.10024031 0.2004806290 8.997597e-01
[92,] 0.08002754 0.1600550822 9.199725e-01
[93,] 0.07761068 0.1552213507 9.223893e-01
[94,] 0.06587300 0.1317459932 9.341270e-01
[95,] 0.09106007 0.1821201381 9.089399e-01
[96,] 0.13836029 0.2767205705 8.616397e-01
[97,] 0.11631681 0.2326336159 8.836832e-01
[98,] 0.09325995 0.1865199085 9.067400e-01
[99,] 0.18738570 0.3747714082 8.126143e-01
[100,] 0.18303989 0.3660797753 8.169601e-01
[101,] 0.15048288 0.3009657633 8.495171e-01
[102,] 0.16103439 0.3220687892 8.389656e-01
[103,] 0.13533160 0.2706632007 8.646684e-01
[104,] 0.15395098 0.3079019681 8.460490e-01
[105,] 0.12610754 0.2522150888 8.738925e-01
[106,] 0.10067823 0.2013564634 8.993218e-01
[107,] 0.23203318 0.4640663667 7.679668e-01
[108,] 0.49133449 0.9826689777 5.086655e-01
[109,] 0.45495644 0.9099128822 5.450436e-01
[110,] 0.40634791 0.8126958213 5.936521e-01
[111,] 0.35906246 0.7181249163 6.409375e-01
[112,] 0.39107951 0.7821590261 6.089205e-01
[113,] 0.36413301 0.7282660129 6.358670e-01
[114,] 0.51103887 0.9779222666 4.889611e-01
[115,] 0.63284775 0.7343045097 3.671523e-01
[116,] 0.58376111 0.8324777846 4.162389e-01
[117,] 0.59923531 0.8015293756 4.007647e-01
[118,] 0.82933410 0.3413318095 1.706659e-01
[119,] 0.79088691 0.4182261808 2.091131e-01
[120,] 0.75054269 0.4989146209 2.494573e-01
[121,] 0.86167137 0.2766572608 1.383286e-01
[122,] 0.94036315 0.1192736981 5.963685e-02
[123,] 0.91752342 0.1649531629 8.247658e-02
[124,] 0.89390435 0.2121913085 1.060957e-01
[125,] 0.92664250 0.1467149915 7.335750e-02
[126,] 0.91739130 0.1652173991 8.260870e-02
[127,] 0.88564140 0.2287171900 1.143586e-01
[128,] 0.99572153 0.0085569373 4.278469e-03
[129,] 0.99835498 0.0032900402 1.645020e-03
[130,] 0.99950577 0.0009884649 4.942325e-04
[131,] 0.99992620 0.0001475990 7.379951e-05
[132,] 0.99986855 0.0002629083 1.314541e-04
[133,] 0.99940833 0.0011833422 5.916711e-04
[134,] 0.99819037 0.0036192514 1.809626e-03
[135,] 0.99380542 0.0123891534 6.194577e-03
[136,] 0.99925953 0.0014809343 7.404671e-04
> postscript(file="/var/fisher/rcomp/tmp/12riz1352131957.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2enf61352131957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3t9iz1352131957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4177f1352131957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5jqfj1352131957.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 = 149
Frequency = 1
1 2 3 4 5
-23.682431401 1.089820238 -9.088542517 -27.152498655 -3.718125114
6 7 8 9 10
-6.115661223 -2.188423025 -3.325602615 18.671662230 -10.440612791
11 12 13 14 15
-2.419967721 -10.157246091 1.590938303 -20.279668910 -4.595220575
16 17 18 19 20
-6.066950046 -5.056848598 3.067694735 -7.907246112 3.728747396
21 22 23 24 25
6.611845602 2.941745787 0.146657444 5.042402404 -6.743969700
26 27 28 29 30
-0.844630600 -34.702374003 2.229083811 2.133132560 -12.513494246
31 32 33 34 35
-19.404471368 14.183329459 2.351324840 3.029671515 11.956643538
36 37 38 39 40
4.189893696 3.007176811 6.382698427 0.412491412 -3.732485478
41 42 43 44 45
6.147735922 16.197206839 11.097060408 -0.006314066 8.723812630
46 47 48 49 50
7.443542227 5.116716676 0.343523175 -4.415171420 -4.175232082
51 52 53 54 55
-0.596945267 3.973454571 -4.926337786 8.945486451 10.245111931
56 57 58 59 60
14.665963694 4.710605561 7.398418639 13.560709945 1.316777620
61 62 63 64 65
4.416574075 -6.412300239 14.027002526 3.734178743 12.022618755
66 67 68 69 70
-16.094653503 18.403990969 -8.294344067 5.017307984 9.488855665
71 72 73 74 75
15.501220455 22.592900450 13.706266293 7.731606232 13.014585171
76 77 78 79 80
8.765426210 -1.604984393 6.198924726 -8.957366939 -1.886965883
81 82 83 84 85
12.253269196 -0.887357928 12.975355508 -22.946007659 12.651263853
86 87 88 89 90
0.480775732 3.712243093 31.652018300 -8.796411975 -10.597155523
91 92 93 94 95
2.811458829 -3.351391533 8.174514221 5.172857544 -9.378976846
96 97 98 99 100
1.217351628 -16.501428130 5.614403247 -4.933419013 13.061799547
101 102 103 104 105
-9.734876200 -12.181398735 5.809429251 4.287501622 29.260133364
106 107 108 109 110
-7.769673222 4.366682647 -6.174268642 -0.800417306 20.207879683
111 112 113 114 115
10.217826547 2.386325313 -16.607020773 20.571343242 8.479661986
116 117 118 119 120
10.334832880 4.895657494 -22.929040783 -3.735399989 -8.986847651
121 122 123 124 125
-12.069816950 10.310688300 -4.760446799 -12.106514911 19.533830208
126 127 128 129 130
-10.438298150 -20.487923652 9.656695200 11.617743805 4.885751437
131 132 133 134 135
18.513515969 -19.464994836 5.571418055 21.123993290 -14.551760471
136 137 138 139 140
3.697148687 -4.940759362 -11.697561080 -16.990622323 -13.638239777
141 142 143 144 145
-15.032949544 -7.849310025 -13.554088720 -14.491957447 -15.961073600
146 147 148 149
-14.574172300 -14.182077110 -9.006714176 -13.162454848
> postscript(file="/var/fisher/rcomp/tmp/6gt491352131957.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 = 149
Frequency = 1
lag(myerror, k = 1) myerror
0 -23.682431401 NA
1 1.089820238 -23.682431401
2 -9.088542517 1.089820238
3 -27.152498655 -9.088542517
4 -3.718125114 -27.152498655
5 -6.115661223 -3.718125114
6 -2.188423025 -6.115661223
7 -3.325602615 -2.188423025
8 18.671662230 -3.325602615
9 -10.440612791 18.671662230
10 -2.419967721 -10.440612791
11 -10.157246091 -2.419967721
12 1.590938303 -10.157246091
13 -20.279668910 1.590938303
14 -4.595220575 -20.279668910
15 -6.066950046 -4.595220575
16 -5.056848598 -6.066950046
17 3.067694735 -5.056848598
18 -7.907246112 3.067694735
19 3.728747396 -7.907246112
20 6.611845602 3.728747396
21 2.941745787 6.611845602
22 0.146657444 2.941745787
23 5.042402404 0.146657444
24 -6.743969700 5.042402404
25 -0.844630600 -6.743969700
26 -34.702374003 -0.844630600
27 2.229083811 -34.702374003
28 2.133132560 2.229083811
29 -12.513494246 2.133132560
30 -19.404471368 -12.513494246
31 14.183329459 -19.404471368
32 2.351324840 14.183329459
33 3.029671515 2.351324840
34 11.956643538 3.029671515
35 4.189893696 11.956643538
36 3.007176811 4.189893696
37 6.382698427 3.007176811
38 0.412491412 6.382698427
39 -3.732485478 0.412491412
40 6.147735922 -3.732485478
41 16.197206839 6.147735922
42 11.097060408 16.197206839
43 -0.006314066 11.097060408
44 8.723812630 -0.006314066
45 7.443542227 8.723812630
46 5.116716676 7.443542227
47 0.343523175 5.116716676
48 -4.415171420 0.343523175
49 -4.175232082 -4.415171420
50 -0.596945267 -4.175232082
51 3.973454571 -0.596945267
52 -4.926337786 3.973454571
53 8.945486451 -4.926337786
54 10.245111931 8.945486451
55 14.665963694 10.245111931
56 4.710605561 14.665963694
57 7.398418639 4.710605561
58 13.560709945 7.398418639
59 1.316777620 13.560709945
60 4.416574075 1.316777620
61 -6.412300239 4.416574075
62 14.027002526 -6.412300239
63 3.734178743 14.027002526
64 12.022618755 3.734178743
65 -16.094653503 12.022618755
66 18.403990969 -16.094653503
67 -8.294344067 18.403990969
68 5.017307984 -8.294344067
69 9.488855665 5.017307984
70 15.501220455 9.488855665
71 22.592900450 15.501220455
72 13.706266293 22.592900450
73 7.731606232 13.706266293
74 13.014585171 7.731606232
75 8.765426210 13.014585171
76 -1.604984393 8.765426210
77 6.198924726 -1.604984393
78 -8.957366939 6.198924726
79 -1.886965883 -8.957366939
80 12.253269196 -1.886965883
81 -0.887357928 12.253269196
82 12.975355508 -0.887357928
83 -22.946007659 12.975355508
84 12.651263853 -22.946007659
85 0.480775732 12.651263853
86 3.712243093 0.480775732
87 31.652018300 3.712243093
88 -8.796411975 31.652018300
89 -10.597155523 -8.796411975
90 2.811458829 -10.597155523
91 -3.351391533 2.811458829
92 8.174514221 -3.351391533
93 5.172857544 8.174514221
94 -9.378976846 5.172857544
95 1.217351628 -9.378976846
96 -16.501428130 1.217351628
97 5.614403247 -16.501428130
98 -4.933419013 5.614403247
99 13.061799547 -4.933419013
100 -9.734876200 13.061799547
101 -12.181398735 -9.734876200
102 5.809429251 -12.181398735
103 4.287501622 5.809429251
104 29.260133364 4.287501622
105 -7.769673222 29.260133364
106 4.366682647 -7.769673222
107 -6.174268642 4.366682647
108 -0.800417306 -6.174268642
109 20.207879683 -0.800417306
110 10.217826547 20.207879683
111 2.386325313 10.217826547
112 -16.607020773 2.386325313
113 20.571343242 -16.607020773
114 8.479661986 20.571343242
115 10.334832880 8.479661986
116 4.895657494 10.334832880
117 -22.929040783 4.895657494
118 -3.735399989 -22.929040783
119 -8.986847651 -3.735399989
120 -12.069816950 -8.986847651
121 10.310688300 -12.069816950
122 -4.760446799 10.310688300
123 -12.106514911 -4.760446799
124 19.533830208 -12.106514911
125 -10.438298150 19.533830208
126 -20.487923652 -10.438298150
127 9.656695200 -20.487923652
128 11.617743805 9.656695200
129 4.885751437 11.617743805
130 18.513515969 4.885751437
131 -19.464994836 18.513515969
132 5.571418055 -19.464994836
133 21.123993290 5.571418055
134 -14.551760471 21.123993290
135 3.697148687 -14.551760471
136 -4.940759362 3.697148687
137 -11.697561080 -4.940759362
138 -16.990622323 -11.697561080
139 -13.638239777 -16.990622323
140 -15.032949544 -13.638239777
141 -7.849310025 -15.032949544
142 -13.554088720 -7.849310025
143 -14.491957447 -13.554088720
144 -15.961073600 -14.491957447
145 -14.574172300 -15.961073600
146 -14.182077110 -14.574172300
147 -9.006714176 -14.182077110
148 -13.162454848 -9.006714176
149 NA -13.162454848
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.089820238 -23.682431401
[2,] -9.088542517 1.089820238
[3,] -27.152498655 -9.088542517
[4,] -3.718125114 -27.152498655
[5,] -6.115661223 -3.718125114
[6,] -2.188423025 -6.115661223
[7,] -3.325602615 -2.188423025
[8,] 18.671662230 -3.325602615
[9,] -10.440612791 18.671662230
[10,] -2.419967721 -10.440612791
[11,] -10.157246091 -2.419967721
[12,] 1.590938303 -10.157246091
[13,] -20.279668910 1.590938303
[14,] -4.595220575 -20.279668910
[15,] -6.066950046 -4.595220575
[16,] -5.056848598 -6.066950046
[17,] 3.067694735 -5.056848598
[18,] -7.907246112 3.067694735
[19,] 3.728747396 -7.907246112
[20,] 6.611845602 3.728747396
[21,] 2.941745787 6.611845602
[22,] 0.146657444 2.941745787
[23,] 5.042402404 0.146657444
[24,] -6.743969700 5.042402404
[25,] -0.844630600 -6.743969700
[26,] -34.702374003 -0.844630600
[27,] 2.229083811 -34.702374003
[28,] 2.133132560 2.229083811
[29,] -12.513494246 2.133132560
[30,] -19.404471368 -12.513494246
[31,] 14.183329459 -19.404471368
[32,] 2.351324840 14.183329459
[33,] 3.029671515 2.351324840
[34,] 11.956643538 3.029671515
[35,] 4.189893696 11.956643538
[36,] 3.007176811 4.189893696
[37,] 6.382698427 3.007176811
[38,] 0.412491412 6.382698427
[39,] -3.732485478 0.412491412
[40,] 6.147735922 -3.732485478
[41,] 16.197206839 6.147735922
[42,] 11.097060408 16.197206839
[43,] -0.006314066 11.097060408
[44,] 8.723812630 -0.006314066
[45,] 7.443542227 8.723812630
[46,] 5.116716676 7.443542227
[47,] 0.343523175 5.116716676
[48,] -4.415171420 0.343523175
[49,] -4.175232082 -4.415171420
[50,] -0.596945267 -4.175232082
[51,] 3.973454571 -0.596945267
[52,] -4.926337786 3.973454571
[53,] 8.945486451 -4.926337786
[54,] 10.245111931 8.945486451
[55,] 14.665963694 10.245111931
[56,] 4.710605561 14.665963694
[57,] 7.398418639 4.710605561
[58,] 13.560709945 7.398418639
[59,] 1.316777620 13.560709945
[60,] 4.416574075 1.316777620
[61,] -6.412300239 4.416574075
[62,] 14.027002526 -6.412300239
[63,] 3.734178743 14.027002526
[64,] 12.022618755 3.734178743
[65,] -16.094653503 12.022618755
[66,] 18.403990969 -16.094653503
[67,] -8.294344067 18.403990969
[68,] 5.017307984 -8.294344067
[69,] 9.488855665 5.017307984
[70,] 15.501220455 9.488855665
[71,] 22.592900450 15.501220455
[72,] 13.706266293 22.592900450
[73,] 7.731606232 13.706266293
[74,] 13.014585171 7.731606232
[75,] 8.765426210 13.014585171
[76,] -1.604984393 8.765426210
[77,] 6.198924726 -1.604984393
[78,] -8.957366939 6.198924726
[79,] -1.886965883 -8.957366939
[80,] 12.253269196 -1.886965883
[81,] -0.887357928 12.253269196
[82,] 12.975355508 -0.887357928
[83,] -22.946007659 12.975355508
[84,] 12.651263853 -22.946007659
[85,] 0.480775732 12.651263853
[86,] 3.712243093 0.480775732
[87,] 31.652018300 3.712243093
[88,] -8.796411975 31.652018300
[89,] -10.597155523 -8.796411975
[90,] 2.811458829 -10.597155523
[91,] -3.351391533 2.811458829
[92,] 8.174514221 -3.351391533
[93,] 5.172857544 8.174514221
[94,] -9.378976846 5.172857544
[95,] 1.217351628 -9.378976846
[96,] -16.501428130 1.217351628
[97,] 5.614403247 -16.501428130
[98,] -4.933419013 5.614403247
[99,] 13.061799547 -4.933419013
[100,] -9.734876200 13.061799547
[101,] -12.181398735 -9.734876200
[102,] 5.809429251 -12.181398735
[103,] 4.287501622 5.809429251
[104,] 29.260133364 4.287501622
[105,] -7.769673222 29.260133364
[106,] 4.366682647 -7.769673222
[107,] -6.174268642 4.366682647
[108,] -0.800417306 -6.174268642
[109,] 20.207879683 -0.800417306
[110,] 10.217826547 20.207879683
[111,] 2.386325313 10.217826547
[112,] -16.607020773 2.386325313
[113,] 20.571343242 -16.607020773
[114,] 8.479661986 20.571343242
[115,] 10.334832880 8.479661986
[116,] 4.895657494 10.334832880
[117,] -22.929040783 4.895657494
[118,] -3.735399989 -22.929040783
[119,] -8.986847651 -3.735399989
[120,] -12.069816950 -8.986847651
[121,] 10.310688300 -12.069816950
[122,] -4.760446799 10.310688300
[123,] -12.106514911 -4.760446799
[124,] 19.533830208 -12.106514911
[125,] -10.438298150 19.533830208
[126,] -20.487923652 -10.438298150
[127,] 9.656695200 -20.487923652
[128,] 11.617743805 9.656695200
[129,] 4.885751437 11.617743805
[130,] 18.513515969 4.885751437
[131,] -19.464994836 18.513515969
[132,] 5.571418055 -19.464994836
[133,] 21.123993290 5.571418055
[134,] -14.551760471 21.123993290
[135,] 3.697148687 -14.551760471
[136,] -4.940759362 3.697148687
[137,] -11.697561080 -4.940759362
[138,] -16.990622323 -11.697561080
[139,] -13.638239777 -16.990622323
[140,] -15.032949544 -13.638239777
[141,] -7.849310025 -15.032949544
[142,] -13.554088720 -7.849310025
[143,] -14.491957447 -13.554088720
[144,] -15.961073600 -14.491957447
[145,] -14.574172300 -15.961073600
[146,] -14.182077110 -14.574172300
[147,] -9.006714176 -14.182077110
[148,] -13.162454848 -9.006714176
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.089820238 -23.682431401
2 -9.088542517 1.089820238
3 -27.152498655 -9.088542517
4 -3.718125114 -27.152498655
5 -6.115661223 -3.718125114
6 -2.188423025 -6.115661223
7 -3.325602615 -2.188423025
8 18.671662230 -3.325602615
9 -10.440612791 18.671662230
10 -2.419967721 -10.440612791
11 -10.157246091 -2.419967721
12 1.590938303 -10.157246091
13 -20.279668910 1.590938303
14 -4.595220575 -20.279668910
15 -6.066950046 -4.595220575
16 -5.056848598 -6.066950046
17 3.067694735 -5.056848598
18 -7.907246112 3.067694735
19 3.728747396 -7.907246112
20 6.611845602 3.728747396
21 2.941745787 6.611845602
22 0.146657444 2.941745787
23 5.042402404 0.146657444
24 -6.743969700 5.042402404
25 -0.844630600 -6.743969700
26 -34.702374003 -0.844630600
27 2.229083811 -34.702374003
28 2.133132560 2.229083811
29 -12.513494246 2.133132560
30 -19.404471368 -12.513494246
31 14.183329459 -19.404471368
32 2.351324840 14.183329459
33 3.029671515 2.351324840
34 11.956643538 3.029671515
35 4.189893696 11.956643538
36 3.007176811 4.189893696
37 6.382698427 3.007176811
38 0.412491412 6.382698427
39 -3.732485478 0.412491412
40 6.147735922 -3.732485478
41 16.197206839 6.147735922
42 11.097060408 16.197206839
43 -0.006314066 11.097060408
44 8.723812630 -0.006314066
45 7.443542227 8.723812630
46 5.116716676 7.443542227
47 0.343523175 5.116716676
48 -4.415171420 0.343523175
49 -4.175232082 -4.415171420
50 -0.596945267 -4.175232082
51 3.973454571 -0.596945267
52 -4.926337786 3.973454571
53 8.945486451 -4.926337786
54 10.245111931 8.945486451
55 14.665963694 10.245111931
56 4.710605561 14.665963694
57 7.398418639 4.710605561
58 13.560709945 7.398418639
59 1.316777620 13.560709945
60 4.416574075 1.316777620
61 -6.412300239 4.416574075
62 14.027002526 -6.412300239
63 3.734178743 14.027002526
64 12.022618755 3.734178743
65 -16.094653503 12.022618755
66 18.403990969 -16.094653503
67 -8.294344067 18.403990969
68 5.017307984 -8.294344067
69 9.488855665 5.017307984
70 15.501220455 9.488855665
71 22.592900450 15.501220455
72 13.706266293 22.592900450
73 7.731606232 13.706266293
74 13.014585171 7.731606232
75 8.765426210 13.014585171
76 -1.604984393 8.765426210
77 6.198924726 -1.604984393
78 -8.957366939 6.198924726
79 -1.886965883 -8.957366939
80 12.253269196 -1.886965883
81 -0.887357928 12.253269196
82 12.975355508 -0.887357928
83 -22.946007659 12.975355508
84 12.651263853 -22.946007659
85 0.480775732 12.651263853
86 3.712243093 0.480775732
87 31.652018300 3.712243093
88 -8.796411975 31.652018300
89 -10.597155523 -8.796411975
90 2.811458829 -10.597155523
91 -3.351391533 2.811458829
92 8.174514221 -3.351391533
93 5.172857544 8.174514221
94 -9.378976846 5.172857544
95 1.217351628 -9.378976846
96 -16.501428130 1.217351628
97 5.614403247 -16.501428130
98 -4.933419013 5.614403247
99 13.061799547 -4.933419013
100 -9.734876200 13.061799547
101 -12.181398735 -9.734876200
102 5.809429251 -12.181398735
103 4.287501622 5.809429251
104 29.260133364 4.287501622
105 -7.769673222 29.260133364
106 4.366682647 -7.769673222
107 -6.174268642 4.366682647
108 -0.800417306 -6.174268642
109 20.207879683 -0.800417306
110 10.217826547 20.207879683
111 2.386325313 10.217826547
112 -16.607020773 2.386325313
113 20.571343242 -16.607020773
114 8.479661986 20.571343242
115 10.334832880 8.479661986
116 4.895657494 10.334832880
117 -22.929040783 4.895657494
118 -3.735399989 -22.929040783
119 -8.986847651 -3.735399989
120 -12.069816950 -8.986847651
121 10.310688300 -12.069816950
122 -4.760446799 10.310688300
123 -12.106514911 -4.760446799
124 19.533830208 -12.106514911
125 -10.438298150 19.533830208
126 -20.487923652 -10.438298150
127 9.656695200 -20.487923652
128 11.617743805 9.656695200
129 4.885751437 11.617743805
130 18.513515969 4.885751437
131 -19.464994836 18.513515969
132 5.571418055 -19.464994836
133 21.123993290 5.571418055
134 -14.551760471 21.123993290
135 3.697148687 -14.551760471
136 -4.940759362 3.697148687
137 -11.697561080 -4.940759362
138 -16.990622323 -11.697561080
139 -13.638239777 -16.990622323
140 -15.032949544 -13.638239777
141 -7.849310025 -15.032949544
142 -13.554088720 -7.849310025
143 -14.491957447 -13.554088720
144 -15.961073600 -14.491957447
145 -14.574172300 -15.961073600
146 -14.182077110 -14.574172300
147 -9.006714176 -14.182077110
148 -13.162454848 -9.006714176
> 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/fisher/rcomp/tmp/76sj71352131957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8v6cu1352131957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9s0pb1352131957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10sn0r1352131957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11m5ll1352131957.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/fisher/rcomp/tmp/12ksiw1352131957.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/fisher/rcomp/tmp/13o6md1352131957.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/fisher/rcomp/tmp/146lp41352131957.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/fisher/rcomp/tmp/155txl1352131957.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/fisher/rcomp/tmp/16dca41352131957.tab")
+ }
>
> try(system("convert tmp/12riz1352131957.ps tmp/12riz1352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/2enf61352131957.ps tmp/2enf61352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t9iz1352131957.ps tmp/3t9iz1352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/4177f1352131957.ps tmp/4177f1352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jqfj1352131957.ps tmp/5jqfj1352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gt491352131957.ps tmp/6gt491352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/76sj71352131957.ps tmp/76sj71352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v6cu1352131957.ps tmp/8v6cu1352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/9s0pb1352131957.ps tmp/9s0pb1352131957.png",intern=TRUE))
character(0)
> try(system("convert tmp/10sn0r1352131957.ps tmp/10sn0r1352131957.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.429 1.069 8.499