R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(114468
+ ,47
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+ ,1032)
+ ,dim=c(4
+ ,156)
+ ,dimnames=list(c('CWS'
+ ,'NOL'
+ ,'CWC'
+ ,'TNOP')
+ ,1:156))
> y <- array(NA,dim=c(4,156),dimnames=list(c('CWS','NOL','CWC','TNOP'),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
CWS NOL CWC TNOP
1 114468 47 95556 1173
2 88594 48 54565 669
3 74151 42 63016 1154
4 77921 77 79774 1948
5 53212 33 31258 705
6 34956 20 52491 332
7 149703 80 91256 2726
8 6853 16 22807 345
9 58907 39 77411 1385
10 67067 26 48821 1162
11 110563 67 52295 1431
12 58126 76 63262 1228
13 57113 46 50466 1205
14 77993 44 62932 1732
15 68091 57 38439 1214
16 124676 125 70817 3221
17 109522 43 105965 1385
18 75865 107 73795 1992
19 79746 36 82043 883
20 77844 51 74349 1631
21 98681 49 82204 1459
22 105531 58 55709 1929
23 51428 22 37137 860
24 65703 33 70780 1165
25 72562 77 55027 2115
26 81728 93 56699 1939
27 95580 86 65911 1844
28 98278 56 56316 1346
29 46629 35 26982 1093
30 115189 40 54628 1626
31 124865 56 96750 1551
32 59392 50 53009 1267
33 127818 66 64664 1478
34 17821 27 36990 670
35 154076 58 85224 2040
36 64881 38 37048 1561
37 136506 85 59635 2079
38 66524 52 42051 1113
39 45988 26 26998 686
40 107445 111 63717 2065
41 102772 57 55071 2251
42 46657 43 40001 1106
43 97563 50 54506 1244
44 36663 32 35838 1021
45 55369 49 50838 1735
46 77921 99 86997 3681
47 56968 42 33032 918
48 77519 56 61704 1582
49 129805 73 117986 2900
50 72761 39 56733 1496
51 81278 55 55064 1116
52 15049 24 5950 496
53 113935 215 84607 1777
54 25109 17 32551 744
55 45824 61 31701 1104
56 89644 27 71170 1612
57 109011 60 101773 1849
58 134245 114 101653 2460
59 136692 79 81493 1701
60 50741 57 55901 1334
61 149510 89 109104 2549
62 147888 78 114425 2218
63 54987 62 36311 1633
64 74467 64 70027 1741
65 100033 43 73713 982
66 85505 38 40671 1171
67 62426 88 89041 1282
68 82932 104 57231 1977
69 72002 50 68608 1521
70 65469 37 59155 1071
71 63572 37 55827 1425
72 23824 29 22618 852
73 73831 46 58425 1363
74 63551 40 65724 1152
75 56756 36 56979 1100
76 81399 48 72369 1393
77 117881 60 79194 1521
78 70711 62 202316 1015
79 50495 38 44970 993
80 53845 45 49319 1190
81 51390 33 36252 1244
82 104953 79 75741 2648
83 65983 54 38417 1177
84 76839 61 64102 1333
85 55792 25 56622 870
86 25155 39 15430 1473
87 55291 38 72571 881
88 84279 116 67271 2489
89 99692 57 43460 1429
90 59633 72 99501 1995
91 63249 55 28340 1247
92 82928 50 76013 1357
93 50000 45 37361 1317
94 69455 54 48204 2041
95 84068 53 76168 1454
96 76195 28 85168 1031
97 114634 30 125410 1154
98 139357 57 123328 1521
99 110044 98 83038 2314
100 155118 75 120087 2274
101 83061 71 91939 1371
102 127122 42 103646 1624
103 45653 112 29467 999
104 19630 14 43750 602
105 67229 46 34497 1380
106 86060 93 66477 1207
107 88003 30 71181 1405
108 95815 66 74482 1800
109 85499 37 174949 705
110 27220 66 46765 1151
111 109882 43 90257 1270
112 72579 57 51370 1381
113 5841 10 1168 391
114 68369 53 51360 1264
115 24610 25 25162 530
116 30995 36 21067 1123
117 150662 69 58233 1981
118 6622 16 855 387
119 93694 38 85903 1485
120 13155 19 14116 449
121 111908 79 57637 2209
122 57550 36 94137 1135
123 16356 48 62147 814
124 40174 31 62832 1015
125 13983 34 8773 568
126 52316 26 63785 936
127 99585 50 65196 1585
128 86271 40 73087 871
129 131012 52 72631 2275
130 130274 67 86281 1638
131 159051 75 162365 2238
132 76506 24 56530 829
133 49145 29 35606 809
134 66398 197 70111 1904
135 127546 115 92046 3053
136 6802 17 63989 655
137 99509 88 104911 2617
138 43106 52 43448 1314
139 108303 35 60029 1154
140 64167 54 38650 1496
141 8579 77 47261 754
142 97811 82 73586 2831
143 84365 54 83042 1281
144 10901 63 37238 2035
145 91346 72 63958 1894
146 33660 42 78956 1268
147 93634 50 99518 1713
148 109348 69 111436 1568
149 7953 10 6023 207
150 63538 59 42564 1302
151 108281 79 38885 1761
152 4245 5 1644 151
153 21509 20 6179 474
154 7670 5 3926 141
155 10641 27 23238 705
156 41243 35 49288 1032
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) NOL CWC TNOP
1363.805 0.369 0.523 29.023
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-75810 -12188 -511 12106 61324
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.364e+03 4.942e+03 0.276 0.783
NOL 3.690e-01 8.407e+01 0.004 0.997
CWC 5.230e-01 6.651e-02 7.863 6.44e-13 ***
TNOP 2.902e+01 4.290e+00 6.766 2.69e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22870 on 152 degrees of freedom
Multiple R-squared: 0.6328, Adjusted R-squared: 0.6256
F-statistic: 87.32 on 3 and 152 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,] 0.79233778 0.41532444 0.20766222
[2,] 0.72122951 0.55754098 0.27877049
[3,] 0.70747740 0.58504519 0.29252260
[4,] 0.65660541 0.68678919 0.34339459
[5,] 0.63846877 0.72306245 0.36153123
[6,] 0.75027619 0.49944761 0.24972381
[7,] 0.68077681 0.63844638 0.31922319
[8,] 0.59623176 0.80753648 0.40376824
[9,] 0.50873077 0.98253846 0.49126923
[10,] 0.45075167 0.90150333 0.54924833
[11,] 0.36960948 0.73921896 0.63039052
[12,] 0.41394793 0.82789586 0.58605207
[13,] 0.33830058 0.67660116 0.66169942
[14,] 0.29046862 0.58093723 0.70953138
[15,] 0.23440625 0.46881250 0.76559375
[16,] 0.22051343 0.44102686 0.77948657
[17,] 0.16974295 0.33948589 0.83025705
[18,] 0.14016721 0.28033441 0.85983279
[19,] 0.12831275 0.25662551 0.87168725
[20,] 0.09597188 0.19194376 0.90402812
[21,] 0.07162919 0.14325837 0.92837081
[22,] 0.08415025 0.16830051 0.91584975
[23,] 0.06132722 0.12265445 0.93867278
[24,] 0.10332853 0.20665706 0.89667147
[25,] 0.10215976 0.20431952 0.89784024
[26,] 0.08241897 0.16483793 0.91758103
[27,] 0.19731987 0.39463974 0.80268013
[28,] 0.21727401 0.43454802 0.78272599
[29,] 0.33315169 0.66630339 0.66684831
[30,] 0.28346397 0.56692794 0.71653603
[31,] 0.40304221 0.80608442 0.59695779
[32,] 0.35773274 0.71546549 0.64226726
[33,] 0.31573728 0.63147456 0.68426272
[34,] 0.27624470 0.55248940 0.72375530
[35,] 0.23419166 0.46838331 0.76580834
[36,] 0.20341934 0.40683867 0.79658066
[37,] 0.22401220 0.44802441 0.77598780
[38,] 0.20425399 0.40850799 0.79574601
[39,] 0.22048602 0.44097204 0.77951398
[40,] 0.67015016 0.65969967 0.32984984
[41,] 0.63097820 0.73804359 0.36902180
[42,] 0.58445018 0.83109963 0.41554982
[43,] 0.56199616 0.87600768 0.43800384
[44,] 0.51193175 0.97613651 0.48806825
[45,] 0.48098085 0.96196171 0.51901915
[46,] 0.43528833 0.87057666 0.56471167
[47,] 0.43568572 0.87137143 0.56431428
[48,] 0.41761621 0.83523243 0.58238379
[49,] 0.37437235 0.74874471 0.62562765
[50,] 0.32916563 0.65833126 0.67083437
[51,] 0.29027887 0.58055774 0.70972113
[52,] 0.25269921 0.50539843 0.74730079
[53,] 0.34107651 0.68215303 0.65892349
[54,] 0.33991091 0.67982182 0.66008909
[55,] 0.31262219 0.62524438 0.68737781
[56,] 0.29482805 0.58965610 0.70517195
[57,] 0.26261451 0.52522903 0.73738549
[58,] 0.24587172 0.49174345 0.75412828
[59,] 0.26223133 0.52446267 0.73776867
[60,] 0.29120655 0.58241309 0.70879345
[61,] 0.34990767 0.69981534 0.65009233
[62,] 0.30950908 0.61901816 0.69049092
[63,] 0.28142466 0.56284933 0.71857534
[64,] 0.24559668 0.49119336 0.75440332
[65,] 0.21623115 0.43246230 0.78376885
[66,] 0.19487636 0.38975273 0.80512364
[67,] 0.16466950 0.32933900 0.83533050
[68,] 0.14333521 0.28667042 0.85666479
[69,] 0.12288253 0.24576507 0.87711747
[70,] 0.10166628 0.20333256 0.89833372
[71,] 0.11619170 0.23238339 0.88380830
[72,] 0.46385717 0.92771435 0.53614283
[73,] 0.42080328 0.84160656 0.57919672
[74,] 0.38286581 0.76573163 0.61713419
[75,] 0.34245506 0.68491012 0.65754494
[76,] 0.31311693 0.62623386 0.68688307
[77,] 0.28246020 0.56492040 0.71753980
[78,] 0.24626463 0.49252927 0.75373537
[79,] 0.21138805 0.42277609 0.78861195
[80,] 0.22880373 0.45760747 0.77119627
[81,] 0.20087364 0.40174728 0.79912636
[82,] 0.20513237 0.41026474 0.79486763
[83,] 0.24848713 0.49697425 0.75151287
[84,] 0.41855748 0.83711496 0.58144252
[85,] 0.38591836 0.77183671 0.61408164
[86,] 0.34221125 0.68442251 0.65778875
[87,] 0.30700539 0.61401079 0.69299461
[88,] 0.29025229 0.58050458 0.70974771
[89,] 0.25085955 0.50171909 0.74914045
[90,] 0.21441051 0.42882103 0.78558949
[91,] 0.19537187 0.39074374 0.80462813
[92,] 0.22210095 0.44420191 0.77789905
[93,] 0.18812968 0.37625937 0.81187032
[94,] 0.19455251 0.38910502 0.80544749
[95,] 0.16528899 0.33057799 0.83471101
[96,] 0.17144470 0.34288941 0.82855530
[97,] 0.14993527 0.29987055 0.85006473
[98,] 0.14533074 0.29066148 0.85466926
[99,] 0.12195250 0.24390500 0.87804750
[100,] 0.12101956 0.24203912 0.87898044
[101,] 0.10046835 0.20093671 0.89953165
[102,] 0.08097260 0.16194520 0.91902740
[103,] 0.08072166 0.16144333 0.91927834
[104,] 0.09098370 0.18196739 0.90901630
[105,] 0.09624270 0.19248539 0.90375730
[106,] 0.07796035 0.15592070 0.92203965
[107,] 0.06238377 0.12476753 0.93761623
[108,] 0.04917437 0.09834874 0.95082563
[109,] 0.03788797 0.07577594 0.96211203
[110,] 0.03087005 0.06174009 0.96912995
[111,] 0.14438153 0.28876307 0.85561847
[112,] 0.11722321 0.23444643 0.88277679
[113,] 0.09423830 0.18847660 0.90576170
[114,] 0.07459802 0.14919603 0.92540198
[115,] 0.07045640 0.14091281 0.92954360
[116,] 0.07078565 0.14157129 0.92921435
[117,] 0.10391562 0.20783124 0.89608438
[118,] 0.10090472 0.20180944 0.89909528
[119,] 0.07858634 0.15717267 0.92141366
[120,] 0.06215314 0.12430627 0.93784686
[121,] 0.05761700 0.11523400 0.94238300
[122,] 0.05472172 0.10944345 0.94527828
[123,] 0.06661623 0.13323245 0.93338377
[124,] 0.11756986 0.23513972 0.88243014
[125,] 0.10099134 0.20198267 0.89900866
[126,] 0.11030457 0.22060913 0.88969543
[127,] 0.08960041 0.17920082 0.91039959
[128,] 0.07924011 0.15848022 0.92075989
[129,] 0.05895162 0.11790324 0.94104838
[130,] 0.10602403 0.21204807 0.89397597
[131,] 0.09412234 0.18824469 0.90587766
[132,] 0.07192579 0.14385159 0.92807421
[133,] 0.18895742 0.37791484 0.81104258
[134,] 0.14906577 0.29813154 0.85093423
[135,] 0.65061465 0.69877069 0.34938535
[136,] 0.75691444 0.48617113 0.24308556
[137,] 0.67191410 0.65617180 0.32808590
[138,] 0.89322061 0.21355878 0.10677939
[139,] 0.82847090 0.34305820 0.17152910
[140,] 0.94022062 0.11955876 0.05977938
[141,] 0.93757582 0.12484836 0.06242418
[142,] 0.91774906 0.16450189 0.08225094
[143,] 0.81757840 0.36484321 0.18242160
> postscript(file="/var/wessaorg/rcomp/tmp/1z7b61321974063.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2kgao1321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3rttb1321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/43tb71321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5hfdm1321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 156
Frequency = 1
1 2 3 4 5 6
29070.5593 39260.5857 6324.0299 -21727.0464 15027.9650 -3501.6655
7 8 9 10 11 12
21469.2023 -16456.8838 -23151.2450 6437.1952 40294.0756 -11989.8753
13 14 15 16 17 18
-5632.4514 -6566.1473 11369.9468 -7252.0277 12529.6197 -21944.3429
19 20 21 22 23 24
9836.1118 -9757.0523 11964.7965 19026.4860 5674.9448 -6500.1814
25 26 27 28 29 30
-18991.1661 -5597.3917 6196.8615 28377.2697 -580.5607 38050.4574
31 32 33 34 35 36
27869.0794 -6484.2538 49716.8395 -22342.6303 48914.6929 -1176.6279
37 38 39 40 41 42
43584.9055 10847.1885 10585.8058 12785.9047 7256.0576 -7741.2565
43 44 45 46 47 48
31571.4039 -13087.1408 -22954.3580 -75809.5999 11670.9833 -2048.9083
49 50 51 52 53 54
-17455.0157 -1705.0022 18707.7066 -3830.7560 16671.5146 -14877.2234
55 56 57 58 59 60
-4182.2665 4265.7390 737.8728 8281.5726 43312.9564 -18594.7925
61 62 63 64 65 66
17076.1480 22282.1808 -12783.7254 -14071.1509 31603.5135 28871.7004
67 68 69 70 71 72
-22742.9915 -5778.5451 -9403.8139 2071.9510 -8358.8185 -14106.5665
73 74 75 76 77 78
2337.6392 -5633.3673 -6344.3853 1742.0241 30935.4198 -65937.7533
79 80 81 82 83 84
-3220.3968 -7864.8980 -5049.1736 -12902.8679 10348.4146 3241.9427
85 86 87 88 89 90
-442.3097 -27043.5672 -9608.0830 -24546.3537 34105.1816 -51693.7617
91 92 93 94 95 96
10852.3156 2409.4449 -9142.2546 -16373.7980 652.0354 358.3841
97 98 99 100 101 102
14181.7612 29332.1180 -1941.2080 24926.9812 -6200.3215 24406.2101
103 104 105 106 107 108
-156.3583 -22090.4847 7755.6828 14866.0148 8625.6670 3233.9506
109 110 111 112 113 114
-27831.4559 -32030.0992 24441.9679 4248.6617 -7485.3572 3441.0732
115 116 117 118 119 120
-5304.0589 -13992.3028 61324.2684 -6426.7914 4292.8171 -8629.3325
121 122 123 124 125 126
16260.9909 -25998.4199 -41150.8664 -23518.4716 -8466.4329 -9580.1825
127 128 129 130 131 132
18106.0526 21391.5619 25618.1332 36223.8987 7795.0227 21510.1201
133 134 135 136 137 138
5670.1965 -26963.9228 -10604.4146 -47042.0470 -32705.2758 -19135.0382
139 140 141 142 143 144
42040.7004 -847.8134 -39412.3653 -24230.2240 2374.8262 -69022.1777
145 146 147 148 149 150
1538.2157 -45811.6224 -9509.0125 4173.6684 -2572.0826 2104.9550
151 152 153 154 155 156
35442.9373 -2362.8916 3149.4704 158.9402 -23346.6652 -15861.3422
> postscript(file="/var/wessaorg/rcomp/tmp/6ewmw1321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 29070.5593 NA
1 39260.5857 29070.5593
2 6324.0299 39260.5857
3 -21727.0464 6324.0299
4 15027.9650 -21727.0464
5 -3501.6655 15027.9650
6 21469.2023 -3501.6655
7 -16456.8838 21469.2023
8 -23151.2450 -16456.8838
9 6437.1952 -23151.2450
10 40294.0756 6437.1952
11 -11989.8753 40294.0756
12 -5632.4514 -11989.8753
13 -6566.1473 -5632.4514
14 11369.9468 -6566.1473
15 -7252.0277 11369.9468
16 12529.6197 -7252.0277
17 -21944.3429 12529.6197
18 9836.1118 -21944.3429
19 -9757.0523 9836.1118
20 11964.7965 -9757.0523
21 19026.4860 11964.7965
22 5674.9448 19026.4860
23 -6500.1814 5674.9448
24 -18991.1661 -6500.1814
25 -5597.3917 -18991.1661
26 6196.8615 -5597.3917
27 28377.2697 6196.8615
28 -580.5607 28377.2697
29 38050.4574 -580.5607
30 27869.0794 38050.4574
31 -6484.2538 27869.0794
32 49716.8395 -6484.2538
33 -22342.6303 49716.8395
34 48914.6929 -22342.6303
35 -1176.6279 48914.6929
36 43584.9055 -1176.6279
37 10847.1885 43584.9055
38 10585.8058 10847.1885
39 12785.9047 10585.8058
40 7256.0576 12785.9047
41 -7741.2565 7256.0576
42 31571.4039 -7741.2565
43 -13087.1408 31571.4039
44 -22954.3580 -13087.1408
45 -75809.5999 -22954.3580
46 11670.9833 -75809.5999
47 -2048.9083 11670.9833
48 -17455.0157 -2048.9083
49 -1705.0022 -17455.0157
50 18707.7066 -1705.0022
51 -3830.7560 18707.7066
52 16671.5146 -3830.7560
53 -14877.2234 16671.5146
54 -4182.2665 -14877.2234
55 4265.7390 -4182.2665
56 737.8728 4265.7390
57 8281.5726 737.8728
58 43312.9564 8281.5726
59 -18594.7925 43312.9564
60 17076.1480 -18594.7925
61 22282.1808 17076.1480
62 -12783.7254 22282.1808
63 -14071.1509 -12783.7254
64 31603.5135 -14071.1509
65 28871.7004 31603.5135
66 -22742.9915 28871.7004
67 -5778.5451 -22742.9915
68 -9403.8139 -5778.5451
69 2071.9510 -9403.8139
70 -8358.8185 2071.9510
71 -14106.5665 -8358.8185
72 2337.6392 -14106.5665
73 -5633.3673 2337.6392
74 -6344.3853 -5633.3673
75 1742.0241 -6344.3853
76 30935.4198 1742.0241
77 -65937.7533 30935.4198
78 -3220.3968 -65937.7533
79 -7864.8980 -3220.3968
80 -5049.1736 -7864.8980
81 -12902.8679 -5049.1736
82 10348.4146 -12902.8679
83 3241.9427 10348.4146
84 -442.3097 3241.9427
85 -27043.5672 -442.3097
86 -9608.0830 -27043.5672
87 -24546.3537 -9608.0830
88 34105.1816 -24546.3537
89 -51693.7617 34105.1816
90 10852.3156 -51693.7617
91 2409.4449 10852.3156
92 -9142.2546 2409.4449
93 -16373.7980 -9142.2546
94 652.0354 -16373.7980
95 358.3841 652.0354
96 14181.7612 358.3841
97 29332.1180 14181.7612
98 -1941.2080 29332.1180
99 24926.9812 -1941.2080
100 -6200.3215 24926.9812
101 24406.2101 -6200.3215
102 -156.3583 24406.2101
103 -22090.4847 -156.3583
104 7755.6828 -22090.4847
105 14866.0148 7755.6828
106 8625.6670 14866.0148
107 3233.9506 8625.6670
108 -27831.4559 3233.9506
109 -32030.0992 -27831.4559
110 24441.9679 -32030.0992
111 4248.6617 24441.9679
112 -7485.3572 4248.6617
113 3441.0732 -7485.3572
114 -5304.0589 3441.0732
115 -13992.3028 -5304.0589
116 61324.2684 -13992.3028
117 -6426.7914 61324.2684
118 4292.8171 -6426.7914
119 -8629.3325 4292.8171
120 16260.9909 -8629.3325
121 -25998.4199 16260.9909
122 -41150.8664 -25998.4199
123 -23518.4716 -41150.8664
124 -8466.4329 -23518.4716
125 -9580.1825 -8466.4329
126 18106.0526 -9580.1825
127 21391.5619 18106.0526
128 25618.1332 21391.5619
129 36223.8987 25618.1332
130 7795.0227 36223.8987
131 21510.1201 7795.0227
132 5670.1965 21510.1201
133 -26963.9228 5670.1965
134 -10604.4146 -26963.9228
135 -47042.0470 -10604.4146
136 -32705.2758 -47042.0470
137 -19135.0382 -32705.2758
138 42040.7004 -19135.0382
139 -847.8134 42040.7004
140 -39412.3653 -847.8134
141 -24230.2240 -39412.3653
142 2374.8262 -24230.2240
143 -69022.1777 2374.8262
144 1538.2157 -69022.1777
145 -45811.6224 1538.2157
146 -9509.0125 -45811.6224
147 4173.6684 -9509.0125
148 -2572.0826 4173.6684
149 2104.9550 -2572.0826
150 35442.9373 2104.9550
151 -2362.8916 35442.9373
152 3149.4704 -2362.8916
153 158.9402 3149.4704
154 -23346.6652 158.9402
155 -15861.3422 -23346.6652
156 NA -15861.3422
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 39260.5857 29070.5593
[2,] 6324.0299 39260.5857
[3,] -21727.0464 6324.0299
[4,] 15027.9650 -21727.0464
[5,] -3501.6655 15027.9650
[6,] 21469.2023 -3501.6655
[7,] -16456.8838 21469.2023
[8,] -23151.2450 -16456.8838
[9,] 6437.1952 -23151.2450
[10,] 40294.0756 6437.1952
[11,] -11989.8753 40294.0756
[12,] -5632.4514 -11989.8753
[13,] -6566.1473 -5632.4514
[14,] 11369.9468 -6566.1473
[15,] -7252.0277 11369.9468
[16,] 12529.6197 -7252.0277
[17,] -21944.3429 12529.6197
[18,] 9836.1118 -21944.3429
[19,] -9757.0523 9836.1118
[20,] 11964.7965 -9757.0523
[21,] 19026.4860 11964.7965
[22,] 5674.9448 19026.4860
[23,] -6500.1814 5674.9448
[24,] -18991.1661 -6500.1814
[25,] -5597.3917 -18991.1661
[26,] 6196.8615 -5597.3917
[27,] 28377.2697 6196.8615
[28,] -580.5607 28377.2697
[29,] 38050.4574 -580.5607
[30,] 27869.0794 38050.4574
[31,] -6484.2538 27869.0794
[32,] 49716.8395 -6484.2538
[33,] -22342.6303 49716.8395
[34,] 48914.6929 -22342.6303
[35,] -1176.6279 48914.6929
[36,] 43584.9055 -1176.6279
[37,] 10847.1885 43584.9055
[38,] 10585.8058 10847.1885
[39,] 12785.9047 10585.8058
[40,] 7256.0576 12785.9047
[41,] -7741.2565 7256.0576
[42,] 31571.4039 -7741.2565
[43,] -13087.1408 31571.4039
[44,] -22954.3580 -13087.1408
[45,] -75809.5999 -22954.3580
[46,] 11670.9833 -75809.5999
[47,] -2048.9083 11670.9833
[48,] -17455.0157 -2048.9083
[49,] -1705.0022 -17455.0157
[50,] 18707.7066 -1705.0022
[51,] -3830.7560 18707.7066
[52,] 16671.5146 -3830.7560
[53,] -14877.2234 16671.5146
[54,] -4182.2665 -14877.2234
[55,] 4265.7390 -4182.2665
[56,] 737.8728 4265.7390
[57,] 8281.5726 737.8728
[58,] 43312.9564 8281.5726
[59,] -18594.7925 43312.9564
[60,] 17076.1480 -18594.7925
[61,] 22282.1808 17076.1480
[62,] -12783.7254 22282.1808
[63,] -14071.1509 -12783.7254
[64,] 31603.5135 -14071.1509
[65,] 28871.7004 31603.5135
[66,] -22742.9915 28871.7004
[67,] -5778.5451 -22742.9915
[68,] -9403.8139 -5778.5451
[69,] 2071.9510 -9403.8139
[70,] -8358.8185 2071.9510
[71,] -14106.5665 -8358.8185
[72,] 2337.6392 -14106.5665
[73,] -5633.3673 2337.6392
[74,] -6344.3853 -5633.3673
[75,] 1742.0241 -6344.3853
[76,] 30935.4198 1742.0241
[77,] -65937.7533 30935.4198
[78,] -3220.3968 -65937.7533
[79,] -7864.8980 -3220.3968
[80,] -5049.1736 -7864.8980
[81,] -12902.8679 -5049.1736
[82,] 10348.4146 -12902.8679
[83,] 3241.9427 10348.4146
[84,] -442.3097 3241.9427
[85,] -27043.5672 -442.3097
[86,] -9608.0830 -27043.5672
[87,] -24546.3537 -9608.0830
[88,] 34105.1816 -24546.3537
[89,] -51693.7617 34105.1816
[90,] 10852.3156 -51693.7617
[91,] 2409.4449 10852.3156
[92,] -9142.2546 2409.4449
[93,] -16373.7980 -9142.2546
[94,] 652.0354 -16373.7980
[95,] 358.3841 652.0354
[96,] 14181.7612 358.3841
[97,] 29332.1180 14181.7612
[98,] -1941.2080 29332.1180
[99,] 24926.9812 -1941.2080
[100,] -6200.3215 24926.9812
[101,] 24406.2101 -6200.3215
[102,] -156.3583 24406.2101
[103,] -22090.4847 -156.3583
[104,] 7755.6828 -22090.4847
[105,] 14866.0148 7755.6828
[106,] 8625.6670 14866.0148
[107,] 3233.9506 8625.6670
[108,] -27831.4559 3233.9506
[109,] -32030.0992 -27831.4559
[110,] 24441.9679 -32030.0992
[111,] 4248.6617 24441.9679
[112,] -7485.3572 4248.6617
[113,] 3441.0732 -7485.3572
[114,] -5304.0589 3441.0732
[115,] -13992.3028 -5304.0589
[116,] 61324.2684 -13992.3028
[117,] -6426.7914 61324.2684
[118,] 4292.8171 -6426.7914
[119,] -8629.3325 4292.8171
[120,] 16260.9909 -8629.3325
[121,] -25998.4199 16260.9909
[122,] -41150.8664 -25998.4199
[123,] -23518.4716 -41150.8664
[124,] -8466.4329 -23518.4716
[125,] -9580.1825 -8466.4329
[126,] 18106.0526 -9580.1825
[127,] 21391.5619 18106.0526
[128,] 25618.1332 21391.5619
[129,] 36223.8987 25618.1332
[130,] 7795.0227 36223.8987
[131,] 21510.1201 7795.0227
[132,] 5670.1965 21510.1201
[133,] -26963.9228 5670.1965
[134,] -10604.4146 -26963.9228
[135,] -47042.0470 -10604.4146
[136,] -32705.2758 -47042.0470
[137,] -19135.0382 -32705.2758
[138,] 42040.7004 -19135.0382
[139,] -847.8134 42040.7004
[140,] -39412.3653 -847.8134
[141,] -24230.2240 -39412.3653
[142,] 2374.8262 -24230.2240
[143,] -69022.1777 2374.8262
[144,] 1538.2157 -69022.1777
[145,] -45811.6224 1538.2157
[146,] -9509.0125 -45811.6224
[147,] 4173.6684 -9509.0125
[148,] -2572.0826 4173.6684
[149,] 2104.9550 -2572.0826
[150,] 35442.9373 2104.9550
[151,] -2362.8916 35442.9373
[152,] 3149.4704 -2362.8916
[153,] 158.9402 3149.4704
[154,] -23346.6652 158.9402
[155,] -15861.3422 -23346.6652
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 39260.5857 29070.5593
2 6324.0299 39260.5857
3 -21727.0464 6324.0299
4 15027.9650 -21727.0464
5 -3501.6655 15027.9650
6 21469.2023 -3501.6655
7 -16456.8838 21469.2023
8 -23151.2450 -16456.8838
9 6437.1952 -23151.2450
10 40294.0756 6437.1952
11 -11989.8753 40294.0756
12 -5632.4514 -11989.8753
13 -6566.1473 -5632.4514
14 11369.9468 -6566.1473
15 -7252.0277 11369.9468
16 12529.6197 -7252.0277
17 -21944.3429 12529.6197
18 9836.1118 -21944.3429
19 -9757.0523 9836.1118
20 11964.7965 -9757.0523
21 19026.4860 11964.7965
22 5674.9448 19026.4860
23 -6500.1814 5674.9448
24 -18991.1661 -6500.1814
25 -5597.3917 -18991.1661
26 6196.8615 -5597.3917
27 28377.2697 6196.8615
28 -580.5607 28377.2697
29 38050.4574 -580.5607
30 27869.0794 38050.4574
31 -6484.2538 27869.0794
32 49716.8395 -6484.2538
33 -22342.6303 49716.8395
34 48914.6929 -22342.6303
35 -1176.6279 48914.6929
36 43584.9055 -1176.6279
37 10847.1885 43584.9055
38 10585.8058 10847.1885
39 12785.9047 10585.8058
40 7256.0576 12785.9047
41 -7741.2565 7256.0576
42 31571.4039 -7741.2565
43 -13087.1408 31571.4039
44 -22954.3580 -13087.1408
45 -75809.5999 -22954.3580
46 11670.9833 -75809.5999
47 -2048.9083 11670.9833
48 -17455.0157 -2048.9083
49 -1705.0022 -17455.0157
50 18707.7066 -1705.0022
51 -3830.7560 18707.7066
52 16671.5146 -3830.7560
53 -14877.2234 16671.5146
54 -4182.2665 -14877.2234
55 4265.7390 -4182.2665
56 737.8728 4265.7390
57 8281.5726 737.8728
58 43312.9564 8281.5726
59 -18594.7925 43312.9564
60 17076.1480 -18594.7925
61 22282.1808 17076.1480
62 -12783.7254 22282.1808
63 -14071.1509 -12783.7254
64 31603.5135 -14071.1509
65 28871.7004 31603.5135
66 -22742.9915 28871.7004
67 -5778.5451 -22742.9915
68 -9403.8139 -5778.5451
69 2071.9510 -9403.8139
70 -8358.8185 2071.9510
71 -14106.5665 -8358.8185
72 2337.6392 -14106.5665
73 -5633.3673 2337.6392
74 -6344.3853 -5633.3673
75 1742.0241 -6344.3853
76 30935.4198 1742.0241
77 -65937.7533 30935.4198
78 -3220.3968 -65937.7533
79 -7864.8980 -3220.3968
80 -5049.1736 -7864.8980
81 -12902.8679 -5049.1736
82 10348.4146 -12902.8679
83 3241.9427 10348.4146
84 -442.3097 3241.9427
85 -27043.5672 -442.3097
86 -9608.0830 -27043.5672
87 -24546.3537 -9608.0830
88 34105.1816 -24546.3537
89 -51693.7617 34105.1816
90 10852.3156 -51693.7617
91 2409.4449 10852.3156
92 -9142.2546 2409.4449
93 -16373.7980 -9142.2546
94 652.0354 -16373.7980
95 358.3841 652.0354
96 14181.7612 358.3841
97 29332.1180 14181.7612
98 -1941.2080 29332.1180
99 24926.9812 -1941.2080
100 -6200.3215 24926.9812
101 24406.2101 -6200.3215
102 -156.3583 24406.2101
103 -22090.4847 -156.3583
104 7755.6828 -22090.4847
105 14866.0148 7755.6828
106 8625.6670 14866.0148
107 3233.9506 8625.6670
108 -27831.4559 3233.9506
109 -32030.0992 -27831.4559
110 24441.9679 -32030.0992
111 4248.6617 24441.9679
112 -7485.3572 4248.6617
113 3441.0732 -7485.3572
114 -5304.0589 3441.0732
115 -13992.3028 -5304.0589
116 61324.2684 -13992.3028
117 -6426.7914 61324.2684
118 4292.8171 -6426.7914
119 -8629.3325 4292.8171
120 16260.9909 -8629.3325
121 -25998.4199 16260.9909
122 -41150.8664 -25998.4199
123 -23518.4716 -41150.8664
124 -8466.4329 -23518.4716
125 -9580.1825 -8466.4329
126 18106.0526 -9580.1825
127 21391.5619 18106.0526
128 25618.1332 21391.5619
129 36223.8987 25618.1332
130 7795.0227 36223.8987
131 21510.1201 7795.0227
132 5670.1965 21510.1201
133 -26963.9228 5670.1965
134 -10604.4146 -26963.9228
135 -47042.0470 -10604.4146
136 -32705.2758 -47042.0470
137 -19135.0382 -32705.2758
138 42040.7004 -19135.0382
139 -847.8134 42040.7004
140 -39412.3653 -847.8134
141 -24230.2240 -39412.3653
142 2374.8262 -24230.2240
143 -69022.1777 2374.8262
144 1538.2157 -69022.1777
145 -45811.6224 1538.2157
146 -9509.0125 -45811.6224
147 4173.6684 -9509.0125
148 -2572.0826 4173.6684
149 2104.9550 -2572.0826
150 35442.9373 2104.9550
151 -2362.8916 35442.9373
152 3149.4704 -2362.8916
153 158.9402 3149.4704
154 -23346.6652 158.9402
155 -15861.3422 -23346.6652
> 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/7rggy1321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8hsng1321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9zznk1321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10fx961321974063.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11q3hu1321974063.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/12wt2e1321974063.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/139f1o1321974063.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/14bok31321974063.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/15411m1321974063.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/16tp2t1321974063.tab")
+ }
>
> try(system("convert tmp/1z7b61321974063.ps tmp/1z7b61321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kgao1321974063.ps tmp/2kgao1321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rttb1321974063.ps tmp/3rttb1321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/43tb71321974063.ps tmp/43tb71321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hfdm1321974063.ps tmp/5hfdm1321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ewmw1321974063.ps tmp/6ewmw1321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rggy1321974063.ps tmp/7rggy1321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hsng1321974063.ps tmp/8hsng1321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zznk1321974063.ps tmp/9zznk1321974063.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fx961321974063.ps tmp/10fx961321974063.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.676 0.505 5.275