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(1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0),dim=c(2,154),dimnames=list(c('Treatment','CorrectAnalysis'),1:154))
> y <- array(NA,dim=c(2,154),dimnames=list(c('Treatment','CorrectAnalysis'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
CorrectAnalysis Treatment
1 0 1
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 1
9 0 0
10 0 0
11 0 1
12 0 0
13 0 0
14 0 1
15 0 0
16 0 1
17 1 1
18 0 1
19 0 0
20 1 1
21 0 0
22 0 0
23 0 0
24 0 0
25 0 1
26 0 0
27 0 0
28 0 0
29 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 0 1
35 0 0
36 0 0
37 0 1
38 0 0
39 0 0
40 0 1
41 1 0
42 0 0
43 0 0
44 0 1
45 0 0
46 0 0
47 0 0
48 0 0
49 0 0
50 0 0
51 0 1
52 1 1
53 0 0
54 1 0
55 0 0
56 0 1
57 0 0
58 0 0
59 0 0
60 1 1
61 0 1
62 0 0
63 0 0
64 0 1
65 0 0
66 0 0
67 1 1
68 0 0
69 0 0
70 0 0
71 0 0
72 0 0
73 0 0
74 0 0
75 0 0
76 0 1
77 0 0
78 0 0
79 1 1
80 0 1
81 0 0
82 0 0
83 0 0
84 1 0
85 0 0
86 0 0
87 0 0
88 0 1
89 0 0
90 0 0
91 0 0
92 0 1
93 0 0
94 0 0
95 0 1
96 0 0
97 0 1
98 0 0
99 0 0
100 0 0
101 0 0
102 0 0
103 0 0
104 0 0
105 0 1
106 0 0
107 0 0
108 0 1
109 0 0
110 0 0
111 0 1
112 0 1
113 0 0
114 0 1
115 0 0
116 0 0
117 0 0
118 0 0
119 0 0
120 0 0
121 0 0
122 0 0
123 0 1
124 0 0
125 0 0
126 0 1
127 0 0
128 0 0
129 0 0
130 0 0
131 0 0
132 0 0
133 0 0
134 0 0
135 0 0
136 0 0
137 0 0
138 0 1
139 0 1
140 0 0
141 1 0
142 0 1
143 0 0
144 0 0
145 0 0
146 0 1
147 0 1
148 0 1
149 0 0
150 0 0
151 0 0
152 1 0
153 1 0
154 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Treatment
0.05263 0.09737
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.15000 -0.05263 -0.05263 -0.05263 0.94737
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05263 0.02495 2.110 0.0365 *
Treatment 0.09737 0.04895 1.989 0.0485 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2664 on 152 degrees of freedom
Multiple R-squared: 0.02537, Adjusted R-squared: 0.01896
F-statistic: 3.957 on 1 and 152 DF, p-value: 0.04848
> 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,] 0.0000000000 0.0000000000 1.0000000000
[2,] 0.0000000000 0.0000000000 1.0000000000
[3,] 0.0000000000 0.0000000000 1.0000000000
[4,] 0.0000000000 0.0000000000 1.0000000000
[5,] 0.0000000000 0.0000000000 1.0000000000
[6,] 0.0000000000 0.0000000000 1.0000000000
[7,] 0.0000000000 0.0000000000 1.0000000000
[8,] 0.0000000000 0.0000000000 1.0000000000
[9,] 0.0000000000 0.0000000000 1.0000000000
[10,] 0.0000000000 0.0000000000 1.0000000000
[11,] 0.0000000000 0.0000000000 1.0000000000
[12,] 0.0000000000 0.0000000000 1.0000000000
[13,] 0.2769984396 0.5539968792 0.7230015604
[14,] 0.2360283048 0.4720566095 0.7639716952
[15,] 0.1801315917 0.3602631835 0.8198684083
[16,] 0.7319990405 0.5360019190 0.2680009595
[17,] 0.6718749321 0.6562501358 0.3281250679
[18,] 0.6079768716 0.7840462568 0.3920231284
[19,] 0.5420291324 0.9159417352 0.4579708676
[20,] 0.4758484800 0.9516969599 0.5241515200
[21,] 0.4564318029 0.9128636059 0.5435681971
[22,] 0.3933802403 0.7867604806 0.6066197597
[23,] 0.3337252892 0.6674505784 0.6662747108
[24,] 0.2786273676 0.5572547352 0.7213726324
[25,] 0.2289101964 0.4578203929 0.7710898036
[26,] 0.1850492886 0.3700985771 0.8149507114
[27,] 0.1471926098 0.2943852196 0.8528073902
[28,] 0.1152056153 0.2304112307 0.8847943847
[29,] 0.0887313680 0.1774627360 0.9112686320
[30,] 0.0805981450 0.1611962900 0.9194018550
[31,] 0.0609010650 0.1218021300 0.9390989350
[32,] 0.0453042922 0.0906085844 0.9546957078
[33,] 0.0394608303 0.0789216605 0.9605391697
[34,] 0.0287650670 0.0575301341 0.9712349330
[35,] 0.0206529587 0.0413059174 0.9793470413
[36,] 0.0172731832 0.0345463664 0.9827268168
[37,] 0.3946317187 0.7892634375 0.6053682813
[38,] 0.3456168899 0.6912337797 0.6543831101
[39,] 0.2993054104 0.5986108208 0.7006945896
[40,] 0.2703576914 0.5407153829 0.7296423086
[41,] 0.2298143064 0.4596286127 0.7701856936
[42,] 0.1930920887 0.3861841773 0.8069079113
[43,] 0.1603433740 0.3206867480 0.8396566260
[44,] 0.1315820301 0.2631640602 0.8684179699
[45,] 0.1067013259 0.2134026518 0.8932986741
[46,] 0.0854962465 0.1709924931 0.9145037535
[47,] 0.0729956050 0.1459912099 0.9270043950
[48,] 0.3817635362 0.7635270724 0.6182364638
[49,] 0.3367784462 0.6735568923 0.6632215538
[50,] 0.8377203390 0.3245593219 0.1622796610
[51,] 0.8075549317 0.3848901367 0.1924450683
[52,] 0.7866907315 0.4266185370 0.2133092685
[53,] 0.7515558911 0.4968882177 0.2484441089
[54,] 0.7136357917 0.5727284166 0.2863642083
[55,] 0.6732545710 0.6534908580 0.3267454290
[56,] 0.9287495040 0.1425009919 0.0712504960
[57,] 0.9191824035 0.1616351931 0.0808175965
[58,] 0.9008128863 0.1983742274 0.0991871137
[59,] 0.8795981854 0.2408036292 0.1204018146
[60,] 0.8648029739 0.2703940522 0.1351970261
[61,] 0.8387385359 0.3225229281 0.1612614641
[62,] 0.8096414237 0.3807171525 0.1903585763
[63,] 0.9748590754 0.0502818491 0.0251409246
[64,] 0.9675371132 0.0649257737 0.0324628868
[65,] 0.9585499389 0.0829001221 0.0414500611
[66,] 0.9476574509 0.1046850983 0.0523425491
[67,] 0.9346210360 0.1307579280 0.0653789640
[68,] 0.9192134551 0.1615730898 0.0807865449
[69,] 0.9012299980 0.1975400041 0.0987700020
[70,] 0.8805004032 0.2389991936 0.1194995968
[71,] 0.8569008895 0.2861982209 0.1430991105
[72,] 0.8407764711 0.3184470578 0.1592235289
[73,] 0.8123882581 0.3752234839 0.1876117419
[74,] 0.7810961539 0.4378076922 0.2189038461
[75,] 0.9784853276 0.0430293448 0.0215146724
[76,] 0.9746515912 0.0506968176 0.0253484088
[77,] 0.9673669301 0.0652661399 0.0326330699
[78,] 0.9584422854 0.0831154292 0.0415577146
[79,] 0.9476425275 0.1047149450 0.0523574725
[80,] 0.9992596656 0.0014806689 0.0007403344
[81,] 0.9989116974 0.0021766053 0.0010883026
[82,] 0.9984188072 0.0031623856 0.0015811928
[83,] 0.9977293962 0.0045412077 0.0022706038
[84,] 0.9970533925 0.0058932149 0.0029466075
[85,] 0.9958504183 0.0082991634 0.0041495817
[86,] 0.9942239591 0.0115520818 0.0057760409
[87,] 0.9920528521 0.0158942957 0.0079471479
[88,] 0.9898969771 0.0202060459 0.0101030229
[89,] 0.9863714181 0.0272571638 0.0136285819
[90,] 0.9818265393 0.0363469214 0.0181734607
[91,] 0.9772612965 0.0454774069 0.0227387035
[92,] 0.9702775368 0.0594449265 0.0297224632
[93,] 0.9631741501 0.0736516997 0.0368258499
[94,] 0.9528235754 0.0943528492 0.0471764246
[95,] 0.9402459203 0.1195081595 0.0597540797
[96,] 0.9251639556 0.1496720887 0.0748360444
[97,] 0.9073192170 0.1853615659 0.0926807830
[98,] 0.8864874057 0.2270251886 0.1135125943
[99,] 0.8624947333 0.2750105335 0.1375052667
[100,] 0.8352341619 0.3295316762 0.1647658381
[101,] 0.8085509212 0.3828981575 0.1914490788
[102,] 0.7749770654 0.4500458692 0.2250229346
[103,] 0.7382769908 0.5234460184 0.2617230092
[104,] 0.7025465313 0.5949069374 0.2974534687
[105,] 0.6604507142 0.6790985716 0.3395492858
[106,] 0.6162337355 0.7675325290 0.3837662645
[107,] 0.5735678081 0.8528643838 0.4264321919
[108,] 0.5291305603 0.9417388795 0.4708694397
[109,] 0.4814017383 0.9628034766 0.5185982617
[110,] 0.4356581737 0.8713163473 0.5643418263
[111,] 0.3887244453 0.7774488905 0.6112755547
[112,] 0.3433455241 0.6866910481 0.6566544759
[113,] 0.3001208115 0.6002416230 0.6998791885
[114,] 0.2595613304 0.5191226608 0.7404386696
[115,] 0.2220714026 0.4441428052 0.7779285974
[116,] 0.1879374959 0.3758749918 0.8120625041
[117,] 0.1573246877 0.3146493754 0.8426753123
[118,] 0.1302804936 0.2605609873 0.8697195064
[119,] 0.1054155922 0.2108311844 0.8945844078
[120,] 0.0850903383 0.1701806767 0.9149096617
[121,] 0.0679462957 0.1358925913 0.9320537043
[122,] 0.0523161698 0.1046323396 0.9476838302
[123,] 0.0406545858 0.0813091716 0.9593454142
[124,] 0.0312749996 0.0625499992 0.9687250004
[125,] 0.0238424541 0.0476849082 0.9761575459
[126,] 0.0180380482 0.0360760965 0.9819619518
[127,] 0.0135691775 0.0271383550 0.9864308225
[128,] 0.0101760744 0.0203521488 0.9898239256
[129,] 0.0076350455 0.0152700911 0.9923649545
[130,] 0.0057590359 0.0115180717 0.9942409641
[131,] 0.0043963096 0.0087926192 0.9956036904
[132,] 0.0034281739 0.0068563478 0.9965718261
[133,] 0.0027669402 0.0055338804 0.9972330598
[134,] 0.0016567746 0.0033135493 0.9983432254
[135,] 0.0009534765 0.0019069530 0.9990465235
[136,] 0.0007728636 0.0015457273 0.9992271364
[137,] 0.0164201174 0.0328402348 0.9835798826
[138,] 0.0098814084 0.0197628167 0.9901185916
[139,] 0.0072845756 0.0145691512 0.9927154244
[140,] 0.0056541193 0.0113082385 0.9943458807
[141,] 0.0048596286 0.0097192572 0.9951403714
[142,] 0.0023816618 0.0047633236 0.9976183382
[143,] 0.0010572863 0.0021145725 0.9989427137
[144,] 0.0004147927 0.0008295855 0.9995852073
[145,] 0.0003384572 0.0006769145 0.9996615428
> postscript(file="/var/wessaorg/rcomp/tmp/1q8oc1355496697.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2hluz1355496697.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3q21f1355496697.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4n0o21355496697.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/57dzt1355496697.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 = 154
Frequency = 1
1 2 3 4 5 6
-0.15000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
7 8 9 10 11 12
-0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.15000000 -0.05263158
13 14 15 16 17 18
-0.05263158 -0.15000000 -0.05263158 -0.15000000 0.85000000 -0.15000000
19 20 21 22 23 24
-0.05263158 0.85000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158
25 26 27 28 29 30
-0.15000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
31 32 33 34 35 36
-0.05263158 -0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158
37 38 39 40 41 42
-0.15000000 -0.05263158 -0.05263158 -0.15000000 0.94736842 -0.05263158
43 44 45 46 47 48
-0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158
49 50 51 52 53 54
-0.05263158 -0.05263158 -0.15000000 0.85000000 -0.05263158 0.94736842
55 56 57 58 59 60
-0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.05263158 0.85000000
61 62 63 64 65 66
-0.15000000 -0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158
67 68 69 70 71 72
0.85000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
73 74 75 76 77 78
-0.05263158 -0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158
79 80 81 82 83 84
0.85000000 -0.15000000 -0.05263158 -0.05263158 -0.05263158 0.94736842
85 86 87 88 89 90
-0.05263158 -0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158
91 92 93 94 95 96
-0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.15000000 -0.05263158
97 98 99 100 101 102
-0.15000000 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
103 104 105 106 107 108
-0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.15000000
109 110 111 112 113 114
-0.05263158 -0.05263158 -0.15000000 -0.15000000 -0.05263158 -0.15000000
115 116 117 118 119 120
-0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
121 122 123 124 125 126
-0.05263158 -0.05263158 -0.15000000 -0.05263158 -0.05263158 -0.15000000
127 128 129 130 131 132
-0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158
133 134 135 136 137 138
-0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.05263158 -0.15000000
139 140 141 142 143 144
-0.15000000 -0.05263158 0.94736842 -0.15000000 -0.05263158 -0.05263158
145 146 147 148 149 150
-0.05263158 -0.15000000 -0.15000000 -0.15000000 -0.05263158 -0.05263158
151 152 153 154
-0.05263158 0.94736842 0.94736842 -0.05263158
> postscript(file="/var/wessaorg/rcomp/tmp/6pe7e1355496697.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.15000000 NA
1 -0.05263158 -0.15000000
2 -0.05263158 -0.05263158
3 -0.05263158 -0.05263158
4 -0.05263158 -0.05263158
5 -0.05263158 -0.05263158
6 -0.05263158 -0.05263158
7 -0.15000000 -0.05263158
8 -0.05263158 -0.15000000
9 -0.05263158 -0.05263158
10 -0.15000000 -0.05263158
11 -0.05263158 -0.15000000
12 -0.05263158 -0.05263158
13 -0.15000000 -0.05263158
14 -0.05263158 -0.15000000
15 -0.15000000 -0.05263158
16 0.85000000 -0.15000000
17 -0.15000000 0.85000000
18 -0.05263158 -0.15000000
19 0.85000000 -0.05263158
20 -0.05263158 0.85000000
21 -0.05263158 -0.05263158
22 -0.05263158 -0.05263158
23 -0.05263158 -0.05263158
24 -0.15000000 -0.05263158
25 -0.05263158 -0.15000000
26 -0.05263158 -0.05263158
27 -0.05263158 -0.05263158
28 -0.05263158 -0.05263158
29 -0.05263158 -0.05263158
30 -0.05263158 -0.05263158
31 -0.05263158 -0.05263158
32 -0.05263158 -0.05263158
33 -0.15000000 -0.05263158
34 -0.05263158 -0.15000000
35 -0.05263158 -0.05263158
36 -0.15000000 -0.05263158
37 -0.05263158 -0.15000000
38 -0.05263158 -0.05263158
39 -0.15000000 -0.05263158
40 0.94736842 -0.15000000
41 -0.05263158 0.94736842
42 -0.05263158 -0.05263158
43 -0.15000000 -0.05263158
44 -0.05263158 -0.15000000
45 -0.05263158 -0.05263158
46 -0.05263158 -0.05263158
47 -0.05263158 -0.05263158
48 -0.05263158 -0.05263158
49 -0.05263158 -0.05263158
50 -0.15000000 -0.05263158
51 0.85000000 -0.15000000
52 -0.05263158 0.85000000
53 0.94736842 -0.05263158
54 -0.05263158 0.94736842
55 -0.15000000 -0.05263158
56 -0.05263158 -0.15000000
57 -0.05263158 -0.05263158
58 -0.05263158 -0.05263158
59 0.85000000 -0.05263158
60 -0.15000000 0.85000000
61 -0.05263158 -0.15000000
62 -0.05263158 -0.05263158
63 -0.15000000 -0.05263158
64 -0.05263158 -0.15000000
65 -0.05263158 -0.05263158
66 0.85000000 -0.05263158
67 -0.05263158 0.85000000
68 -0.05263158 -0.05263158
69 -0.05263158 -0.05263158
70 -0.05263158 -0.05263158
71 -0.05263158 -0.05263158
72 -0.05263158 -0.05263158
73 -0.05263158 -0.05263158
74 -0.05263158 -0.05263158
75 -0.15000000 -0.05263158
76 -0.05263158 -0.15000000
77 -0.05263158 -0.05263158
78 0.85000000 -0.05263158
79 -0.15000000 0.85000000
80 -0.05263158 -0.15000000
81 -0.05263158 -0.05263158
82 -0.05263158 -0.05263158
83 0.94736842 -0.05263158
84 -0.05263158 0.94736842
85 -0.05263158 -0.05263158
86 -0.05263158 -0.05263158
87 -0.15000000 -0.05263158
88 -0.05263158 -0.15000000
89 -0.05263158 -0.05263158
90 -0.05263158 -0.05263158
91 -0.15000000 -0.05263158
92 -0.05263158 -0.15000000
93 -0.05263158 -0.05263158
94 -0.15000000 -0.05263158
95 -0.05263158 -0.15000000
96 -0.15000000 -0.05263158
97 -0.05263158 -0.15000000
98 -0.05263158 -0.05263158
99 -0.05263158 -0.05263158
100 -0.05263158 -0.05263158
101 -0.05263158 -0.05263158
102 -0.05263158 -0.05263158
103 -0.05263158 -0.05263158
104 -0.15000000 -0.05263158
105 -0.05263158 -0.15000000
106 -0.05263158 -0.05263158
107 -0.15000000 -0.05263158
108 -0.05263158 -0.15000000
109 -0.05263158 -0.05263158
110 -0.15000000 -0.05263158
111 -0.15000000 -0.15000000
112 -0.05263158 -0.15000000
113 -0.15000000 -0.05263158
114 -0.05263158 -0.15000000
115 -0.05263158 -0.05263158
116 -0.05263158 -0.05263158
117 -0.05263158 -0.05263158
118 -0.05263158 -0.05263158
119 -0.05263158 -0.05263158
120 -0.05263158 -0.05263158
121 -0.05263158 -0.05263158
122 -0.15000000 -0.05263158
123 -0.05263158 -0.15000000
124 -0.05263158 -0.05263158
125 -0.15000000 -0.05263158
126 -0.05263158 -0.15000000
127 -0.05263158 -0.05263158
128 -0.05263158 -0.05263158
129 -0.05263158 -0.05263158
130 -0.05263158 -0.05263158
131 -0.05263158 -0.05263158
132 -0.05263158 -0.05263158
133 -0.05263158 -0.05263158
134 -0.05263158 -0.05263158
135 -0.05263158 -0.05263158
136 -0.05263158 -0.05263158
137 -0.15000000 -0.05263158
138 -0.15000000 -0.15000000
139 -0.05263158 -0.15000000
140 0.94736842 -0.05263158
141 -0.15000000 0.94736842
142 -0.05263158 -0.15000000
143 -0.05263158 -0.05263158
144 -0.05263158 -0.05263158
145 -0.15000000 -0.05263158
146 -0.15000000 -0.15000000
147 -0.15000000 -0.15000000
148 -0.05263158 -0.15000000
149 -0.05263158 -0.05263158
150 -0.05263158 -0.05263158
151 0.94736842 -0.05263158
152 0.94736842 0.94736842
153 -0.05263158 0.94736842
154 NA -0.05263158
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.05263158 -0.15000000
[2,] -0.05263158 -0.05263158
[3,] -0.05263158 -0.05263158
[4,] -0.05263158 -0.05263158
[5,] -0.05263158 -0.05263158
[6,] -0.05263158 -0.05263158
[7,] -0.15000000 -0.05263158
[8,] -0.05263158 -0.15000000
[9,] -0.05263158 -0.05263158
[10,] -0.15000000 -0.05263158
[11,] -0.05263158 -0.15000000
[12,] -0.05263158 -0.05263158
[13,] -0.15000000 -0.05263158
[14,] -0.05263158 -0.15000000
[15,] -0.15000000 -0.05263158
[16,] 0.85000000 -0.15000000
[17,] -0.15000000 0.85000000
[18,] -0.05263158 -0.15000000
[19,] 0.85000000 -0.05263158
[20,] -0.05263158 0.85000000
[21,] -0.05263158 -0.05263158
[22,] -0.05263158 -0.05263158
[23,] -0.05263158 -0.05263158
[24,] -0.15000000 -0.05263158
[25,] -0.05263158 -0.15000000
[26,] -0.05263158 -0.05263158
[27,] -0.05263158 -0.05263158
[28,] -0.05263158 -0.05263158
[29,] -0.05263158 -0.05263158
[30,] -0.05263158 -0.05263158
[31,] -0.05263158 -0.05263158
[32,] -0.05263158 -0.05263158
[33,] -0.15000000 -0.05263158
[34,] -0.05263158 -0.15000000
[35,] -0.05263158 -0.05263158
[36,] -0.15000000 -0.05263158
[37,] -0.05263158 -0.15000000
[38,] -0.05263158 -0.05263158
[39,] -0.15000000 -0.05263158
[40,] 0.94736842 -0.15000000
[41,] -0.05263158 0.94736842
[42,] -0.05263158 -0.05263158
[43,] -0.15000000 -0.05263158
[44,] -0.05263158 -0.15000000
[45,] -0.05263158 -0.05263158
[46,] -0.05263158 -0.05263158
[47,] -0.05263158 -0.05263158
[48,] -0.05263158 -0.05263158
[49,] -0.05263158 -0.05263158
[50,] -0.15000000 -0.05263158
[51,] 0.85000000 -0.15000000
[52,] -0.05263158 0.85000000
[53,] 0.94736842 -0.05263158
[54,] -0.05263158 0.94736842
[55,] -0.15000000 -0.05263158
[56,] -0.05263158 -0.15000000
[57,] -0.05263158 -0.05263158
[58,] -0.05263158 -0.05263158
[59,] 0.85000000 -0.05263158
[60,] -0.15000000 0.85000000
[61,] -0.05263158 -0.15000000
[62,] -0.05263158 -0.05263158
[63,] -0.15000000 -0.05263158
[64,] -0.05263158 -0.15000000
[65,] -0.05263158 -0.05263158
[66,] 0.85000000 -0.05263158
[67,] -0.05263158 0.85000000
[68,] -0.05263158 -0.05263158
[69,] -0.05263158 -0.05263158
[70,] -0.05263158 -0.05263158
[71,] -0.05263158 -0.05263158
[72,] -0.05263158 -0.05263158
[73,] -0.05263158 -0.05263158
[74,] -0.05263158 -0.05263158
[75,] -0.15000000 -0.05263158
[76,] -0.05263158 -0.15000000
[77,] -0.05263158 -0.05263158
[78,] 0.85000000 -0.05263158
[79,] -0.15000000 0.85000000
[80,] -0.05263158 -0.15000000
[81,] -0.05263158 -0.05263158
[82,] -0.05263158 -0.05263158
[83,] 0.94736842 -0.05263158
[84,] -0.05263158 0.94736842
[85,] -0.05263158 -0.05263158
[86,] -0.05263158 -0.05263158
[87,] -0.15000000 -0.05263158
[88,] -0.05263158 -0.15000000
[89,] -0.05263158 -0.05263158
[90,] -0.05263158 -0.05263158
[91,] -0.15000000 -0.05263158
[92,] -0.05263158 -0.15000000
[93,] -0.05263158 -0.05263158
[94,] -0.15000000 -0.05263158
[95,] -0.05263158 -0.15000000
[96,] -0.15000000 -0.05263158
[97,] -0.05263158 -0.15000000
[98,] -0.05263158 -0.05263158
[99,] -0.05263158 -0.05263158
[100,] -0.05263158 -0.05263158
[101,] -0.05263158 -0.05263158
[102,] -0.05263158 -0.05263158
[103,] -0.05263158 -0.05263158
[104,] -0.15000000 -0.05263158
[105,] -0.05263158 -0.15000000
[106,] -0.05263158 -0.05263158
[107,] -0.15000000 -0.05263158
[108,] -0.05263158 -0.15000000
[109,] -0.05263158 -0.05263158
[110,] -0.15000000 -0.05263158
[111,] -0.15000000 -0.15000000
[112,] -0.05263158 -0.15000000
[113,] -0.15000000 -0.05263158
[114,] -0.05263158 -0.15000000
[115,] -0.05263158 -0.05263158
[116,] -0.05263158 -0.05263158
[117,] -0.05263158 -0.05263158
[118,] -0.05263158 -0.05263158
[119,] -0.05263158 -0.05263158
[120,] -0.05263158 -0.05263158
[121,] -0.05263158 -0.05263158
[122,] -0.15000000 -0.05263158
[123,] -0.05263158 -0.15000000
[124,] -0.05263158 -0.05263158
[125,] -0.15000000 -0.05263158
[126,] -0.05263158 -0.15000000
[127,] -0.05263158 -0.05263158
[128,] -0.05263158 -0.05263158
[129,] -0.05263158 -0.05263158
[130,] -0.05263158 -0.05263158
[131,] -0.05263158 -0.05263158
[132,] -0.05263158 -0.05263158
[133,] -0.05263158 -0.05263158
[134,] -0.05263158 -0.05263158
[135,] -0.05263158 -0.05263158
[136,] -0.05263158 -0.05263158
[137,] -0.15000000 -0.05263158
[138,] -0.15000000 -0.15000000
[139,] -0.05263158 -0.15000000
[140,] 0.94736842 -0.05263158
[141,] -0.15000000 0.94736842
[142,] -0.05263158 -0.15000000
[143,] -0.05263158 -0.05263158
[144,] -0.05263158 -0.05263158
[145,] -0.15000000 -0.05263158
[146,] -0.15000000 -0.15000000
[147,] -0.15000000 -0.15000000
[148,] -0.05263158 -0.15000000
[149,] -0.05263158 -0.05263158
[150,] -0.05263158 -0.05263158
[151,] 0.94736842 -0.05263158
[152,] 0.94736842 0.94736842
[153,] -0.05263158 0.94736842
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.05263158 -0.15000000
2 -0.05263158 -0.05263158
3 -0.05263158 -0.05263158
4 -0.05263158 -0.05263158
5 -0.05263158 -0.05263158
6 -0.05263158 -0.05263158
7 -0.15000000 -0.05263158
8 -0.05263158 -0.15000000
9 -0.05263158 -0.05263158
10 -0.15000000 -0.05263158
11 -0.05263158 -0.15000000
12 -0.05263158 -0.05263158
13 -0.15000000 -0.05263158
14 -0.05263158 -0.15000000
15 -0.15000000 -0.05263158
16 0.85000000 -0.15000000
17 -0.15000000 0.85000000
18 -0.05263158 -0.15000000
19 0.85000000 -0.05263158
20 -0.05263158 0.85000000
21 -0.05263158 -0.05263158
22 -0.05263158 -0.05263158
23 -0.05263158 -0.05263158
24 -0.15000000 -0.05263158
25 -0.05263158 -0.15000000
26 -0.05263158 -0.05263158
27 -0.05263158 -0.05263158
28 -0.05263158 -0.05263158
29 -0.05263158 -0.05263158
30 -0.05263158 -0.05263158
31 -0.05263158 -0.05263158
32 -0.05263158 -0.05263158
33 -0.15000000 -0.05263158
34 -0.05263158 -0.15000000
35 -0.05263158 -0.05263158
36 -0.15000000 -0.05263158
37 -0.05263158 -0.15000000
38 -0.05263158 -0.05263158
39 -0.15000000 -0.05263158
40 0.94736842 -0.15000000
41 -0.05263158 0.94736842
42 -0.05263158 -0.05263158
43 -0.15000000 -0.05263158
44 -0.05263158 -0.15000000
45 -0.05263158 -0.05263158
46 -0.05263158 -0.05263158
47 -0.05263158 -0.05263158
48 -0.05263158 -0.05263158
49 -0.05263158 -0.05263158
50 -0.15000000 -0.05263158
51 0.85000000 -0.15000000
52 -0.05263158 0.85000000
53 0.94736842 -0.05263158
54 -0.05263158 0.94736842
55 -0.15000000 -0.05263158
56 -0.05263158 -0.15000000
57 -0.05263158 -0.05263158
58 -0.05263158 -0.05263158
59 0.85000000 -0.05263158
60 -0.15000000 0.85000000
61 -0.05263158 -0.15000000
62 -0.05263158 -0.05263158
63 -0.15000000 -0.05263158
64 -0.05263158 -0.15000000
65 -0.05263158 -0.05263158
66 0.85000000 -0.05263158
67 -0.05263158 0.85000000
68 -0.05263158 -0.05263158
69 -0.05263158 -0.05263158
70 -0.05263158 -0.05263158
71 -0.05263158 -0.05263158
72 -0.05263158 -0.05263158
73 -0.05263158 -0.05263158
74 -0.05263158 -0.05263158
75 -0.15000000 -0.05263158
76 -0.05263158 -0.15000000
77 -0.05263158 -0.05263158
78 0.85000000 -0.05263158
79 -0.15000000 0.85000000
80 -0.05263158 -0.15000000
81 -0.05263158 -0.05263158
82 -0.05263158 -0.05263158
83 0.94736842 -0.05263158
84 -0.05263158 0.94736842
85 -0.05263158 -0.05263158
86 -0.05263158 -0.05263158
87 -0.15000000 -0.05263158
88 -0.05263158 -0.15000000
89 -0.05263158 -0.05263158
90 -0.05263158 -0.05263158
91 -0.15000000 -0.05263158
92 -0.05263158 -0.15000000
93 -0.05263158 -0.05263158
94 -0.15000000 -0.05263158
95 -0.05263158 -0.15000000
96 -0.15000000 -0.05263158
97 -0.05263158 -0.15000000
98 -0.05263158 -0.05263158
99 -0.05263158 -0.05263158
100 -0.05263158 -0.05263158
101 -0.05263158 -0.05263158
102 -0.05263158 -0.05263158
103 -0.05263158 -0.05263158
104 -0.15000000 -0.05263158
105 -0.05263158 -0.15000000
106 -0.05263158 -0.05263158
107 -0.15000000 -0.05263158
108 -0.05263158 -0.15000000
109 -0.05263158 -0.05263158
110 -0.15000000 -0.05263158
111 -0.15000000 -0.15000000
112 -0.05263158 -0.15000000
113 -0.15000000 -0.05263158
114 -0.05263158 -0.15000000
115 -0.05263158 -0.05263158
116 -0.05263158 -0.05263158
117 -0.05263158 -0.05263158
118 -0.05263158 -0.05263158
119 -0.05263158 -0.05263158
120 -0.05263158 -0.05263158
121 -0.05263158 -0.05263158
122 -0.15000000 -0.05263158
123 -0.05263158 -0.15000000
124 -0.05263158 -0.05263158
125 -0.15000000 -0.05263158
126 -0.05263158 -0.15000000
127 -0.05263158 -0.05263158
128 -0.05263158 -0.05263158
129 -0.05263158 -0.05263158
130 -0.05263158 -0.05263158
131 -0.05263158 -0.05263158
132 -0.05263158 -0.05263158
133 -0.05263158 -0.05263158
134 -0.05263158 -0.05263158
135 -0.05263158 -0.05263158
136 -0.05263158 -0.05263158
137 -0.15000000 -0.05263158
138 -0.15000000 -0.15000000
139 -0.05263158 -0.15000000
140 0.94736842 -0.05263158
141 -0.15000000 0.94736842
142 -0.05263158 -0.15000000
143 -0.05263158 -0.05263158
144 -0.05263158 -0.05263158
145 -0.15000000 -0.05263158
146 -0.15000000 -0.15000000
147 -0.15000000 -0.15000000
148 -0.05263158 -0.15000000
149 -0.05263158 -0.05263158
150 -0.05263158 -0.05263158
151 0.94736842 -0.05263158
152 0.94736842 0.94736842
153 -0.05263158 0.94736842
> 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/7uwr91355496697.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8wee01355496697.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9oh301355496697.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10ti771355496697.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wlqf1355496697.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/126r2s1355496697.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/13f4091355496698.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/14i5mx1355496698.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/154qiz1355496698.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/162jve1355496698.tab")
+ }
>
> try(system("convert tmp/1q8oc1355496697.ps tmp/1q8oc1355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hluz1355496697.ps tmp/2hluz1355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/3q21f1355496697.ps tmp/3q21f1355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n0o21355496697.ps tmp/4n0o21355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/57dzt1355496697.ps tmp/57dzt1355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pe7e1355496697.ps tmp/6pe7e1355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uwr91355496697.ps tmp/7uwr91355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wee01355496697.ps tmp/8wee01355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/9oh301355496697.ps tmp/9oh301355496697.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ti771355496697.ps tmp/10ti771355496697.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
13.222 2.154 15.436