R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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
+ ,162556
+ ,807
+ ,213118
+ ,6282154
+ ,1
+ ,29790
+ ,444
+ ,81767
+ ,4321023
+ ,1
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+ ,19
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+ ,403560
+ ,1
+ ,1383
+ ,9
+ ,73221
+ ,317892)
+ ,dim=c(5
+ ,100)
+ ,dimnames=list(c('Group'
+ ,'Costs'
+ ,'Orders'
+ ,'Dividends'
+ ,'Wealth
')
+ ,1:100))
> y <- array(NA,dim=c(5,100),dimnames=list(c('Group','Costs','Orders','Dividends','Wealth
'),1:100))
> 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 = '5'
> #'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.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
Wealth\r Group Costs Orders Dividends
1 6282154 1 162556 807 213118
2 4321023 1 29790 444 81767
3 4111912 1 87550 412 153198
4 223193 0 84738 428 -26007
5 1491348 1 54660 315 126942
6 1629616 1 42634 168 157214
7 1398893 0 40949 263 129352
8 1926517 1 45187 267 234817
9 983660 1 37704 228 60448
10 1443586 1 16275 129 47818
11 1073089 0 25830 104 245546
12 984885 0 12679 122 48020
13 1405225 1 18014 393 -1710
14 227132 0 43556 190 32648
15 929118 1 24811 280 95350
16 1071292 0 6575 63 151352
17 638830 0 7123 102 288170
18 856956 1 21950 265 114337
19 992426 1 37597 234 37884
20 444477 0 17821 277 122844
21 857217 1 12988 73 82340
22 711969 1 22330 67 79801
23 702380 0 13326 103 165548
24 358589 0 16189 290 116384
25 297978 0 7146 83 134028
26 585715 0 15824 56 63838
27 657954 1 27664 236 74996
28 209458 0 11920 73 31080
29 786690 0 8568 34 32168
30 439798 0 14416 139 49857
31 688779 1 3369 26 87161
32 574339 1 11819 70 106113
33 741409 1 6984 40 80570
34 597793 1 4519 42 102129
35 644190 0 2220 12 301670
36 377934 0 18562 211 102313
37 640273 0 10327 74 88577
38 697458 1 5336 80 112477
39 550608 1 2365 83 191778
40 207393 0 4069 131 79804
41 301607 0 8636 203 128294
42 345783 0 13718 56 96448
43 501749 0 4525 89 93811
44 379983 0 6869 88 117520
45 387475 0 4628 39 69159
46 377305 1 3689 25 101792
47 370837 1 4891 49 210568
48 430866 1 7489 149 136996
49 469107 0 4901 58 121920
50 194493 0 2284 41 76403
51 530670 1 3160 90 108094
52 518365 1 4150 136 134759
53 491303 1 7285 97 188873
54 527021 1 1134 63 146216
55 233773 1 4658 114 156608
56 405972 0 2384 77 61348
57 652925 0 3748 6 50350
58 446211 0 5371 47 87720
59 341340 0 1285 51 99489
60 387699 1 9327 85 87419
61 493408 1 5565 43 94355
62 146494 0 1528 32 60326
63 414462 1 3122 25 94670
64 364304 1 7561 77 82425
65 355178 0 2675 54 59017
66 357760 0 13253 251 90829
67 261216 0 880 15 80791
68 397144 1 2053 44 100423
69 374943 0 1424 73 131116
70 424898 1 4036 85 100269
71 202055 1 3045 49 27330
72 378525 0 5119 38 39039
73 310768 0 1431 35 106885
74 325738 0 554 9 79285
75 394510 0 1975 34 118881
76 247060 1 1765 20 77623
77 368078 0 1012 29 114768
78 236761 0 810 11 74015
79 312378 0 1280 52 69465
80 339836 1 666 13 117869
81 347385 0 1380 29 60982
82 426280 1 4677 66 90131
83 352850 0 876 33 138971
84 301881 0 814 15 39625
85 377516 0 514 15 102725
86 357312 1 5692 68 64239
87 458343 0 3642 100 90262
88 354228 0 540 13 103960
89 308636 0 2099 45 106611
90 386212 0 567 14 103345
91 393343 0 2001 36 95551
92 378509 1 2949 40 82903
93 452469 0 2253 68 63593
94 364839 1 6533 29 126910
95 358649 0 1889 43 37527
96 376641 1 3055 30 60247
97 429112 0 272 9 112995
98 330546 1 1414 22 70184
99 403560 0 2564 19 130140
100 317892 1 1383 9 73221
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Group Costs Orders Dividends
-1.192e+05 1.948e+05 1.905e+01 1.974e+03 2.423e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2053625 -155094 4637 173837 2603268
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.192e+05 1.147e+05 -1.039 0.30146
Group 1.948e+05 9.782e+04 1.991 0.04930 *
Costs 1.905e+01 4.504e+00 4.229 5.4e-05 ***
Orders 1.974e+03 8.010e+02 2.465 0.01550 *
Dividends 2.423e+00 9.023e-01 2.686 0.00855 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 474700 on 95 degrees of freedom
Multiple R-squared: 0.691, Adjusted R-squared: 0.678
F-statistic: 53.11 on 4 and 95 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,] 1.0000000 3.188291e-12 1.594146e-12
[2,] 1.0000000 1.445316e-11 7.226581e-12
[3,] 1.0000000 1.422800e-14 7.114001e-15
[4,] 1.0000000 4.736008e-15 2.368004e-15
[5,] 1.0000000 4.465912e-18 2.232956e-18
[6,] 1.0000000 4.752790e-24 2.376395e-24
[7,] 1.0000000 1.070465e-26 5.352324e-27
[8,] 1.0000000 1.135616e-28 5.678079e-29
[9,] 1.0000000 4.888335e-32 2.444167e-32
[10,] 1.0000000 1.159319e-32 5.796595e-33
[11,] 1.0000000 4.431863e-34 2.215931e-34
[12,] 1.0000000 1.777156e-33 8.885778e-34
[13,] 1.0000000 1.908190e-33 9.540950e-34
[14,] 1.0000000 7.049539e-34 3.524769e-34
[15,] 1.0000000 5.057740e-33 2.528870e-33
[16,] 1.0000000 1.506711e-32 7.533556e-33
[17,] 1.0000000 2.865691e-32 1.432846e-32
[18,] 1.0000000 9.711276e-32 4.855638e-32
[19,] 1.0000000 1.107949e-31 5.539746e-32
[20,] 1.0000000 1.865000e-31 9.325000e-32
[21,] 1.0000000 1.102194e-31 5.510969e-32
[22,] 1.0000000 5.240546e-34 2.620273e-34
[23,] 1.0000000 3.523963e-33 1.761982e-33
[24,] 1.0000000 1.607923e-33 8.039617e-34
[25,] 1.0000000 8.842436e-33 4.421218e-33
[26,] 1.0000000 1.016900e-33 5.084500e-34
[27,] 1.0000000 1.572701e-33 7.863507e-34
[28,] 1.0000000 7.866485e-33 3.933243e-33
[29,] 1.0000000 3.238139e-32 1.619070e-32
[30,] 1.0000000 1.228866e-32 6.144328e-33
[31,] 1.0000000 8.360904e-34 4.180452e-34
[32,] 1.0000000 1.842082e-33 9.210411e-34
[33,] 1.0000000 3.688351e-33 1.844176e-33
[34,] 1.0000000 9.325898e-33 4.662949e-33
[35,] 1.0000000 4.388534e-32 2.194267e-32
[36,] 1.0000000 1.182747e-31 5.913737e-32
[37,] 1.0000000 8.481695e-31 4.240847e-31
[38,] 1.0000000 4.831213e-30 2.415606e-30
[39,] 1.0000000 3.512854e-29 1.756427e-29
[40,] 1.0000000 8.410415e-29 4.205208e-29
[41,] 1.0000000 3.988873e-28 1.994436e-28
[42,] 1.0000000 2.184765e-27 1.092382e-27
[43,] 1.0000000 2.997141e-27 1.498570e-27
[44,] 1.0000000 4.400488e-27 2.200244e-27
[45,] 1.0000000 5.566117e-27 2.783059e-27
[46,] 1.0000000 2.928824e-26 1.464412e-26
[47,] 1.0000000 1.166038e-26 5.830189e-27
[48,] 1.0000000 2.094244e-26 1.047122e-26
[49,] 1.0000000 8.770881e-26 4.385440e-26
[50,] 1.0000000 1.234581e-28 6.172903e-29
[51,] 1.0000000 5.906857e-28 2.953429e-28
[52,] 1.0000000 5.424628e-27 2.712314e-27
[53,] 1.0000000 4.849997e-26 2.424998e-26
[54,] 1.0000000 8.773622e-26 4.386811e-26
[55,] 1.0000000 8.071086e-27 4.035543e-27
[56,] 1.0000000 4.904336e-26 2.452168e-26
[57,] 1.0000000 5.171989e-25 2.585995e-25
[58,] 1.0000000 4.951253e-24 2.475627e-24
[59,] 1.0000000 1.394709e-24 6.973547e-25
[60,] 1.0000000 5.915708e-24 2.957854e-24
[61,] 1.0000000 4.657761e-23 2.328881e-23
[62,] 1.0000000 3.142255e-22 1.571128e-22
[63,] 1.0000000 3.308446e-21 1.654223e-21
[64,] 1.0000000 2.156317e-21 1.078158e-21
[65,] 1.0000000 2.194576e-20 1.097288e-20
[66,] 1.0000000 9.681695e-20 4.840847e-20
[67,] 1.0000000 1.121448e-18 5.607242e-19
[68,] 1.0000000 1.289507e-17 6.447536e-18
[69,] 1.0000000 2.910542e-17 1.455271e-17
[70,] 1.0000000 3.433684e-16 1.716842e-16
[71,] 1.0000000 3.282798e-16 1.641399e-16
[72,] 1.0000000 8.653895e-16 4.326947e-16
[73,] 1.0000000 1.097634e-14 5.488168e-15
[74,] 1.0000000 1.395901e-13 6.979505e-14
[75,] 1.0000000 1.371864e-12 6.859322e-13
[76,] 1.0000000 6.605460e-12 3.302730e-12
[77,] 1.0000000 5.747628e-11 2.873814e-11
[78,] 1.0000000 7.583006e-10 3.791503e-10
[79,] 1.0000000 8.534733e-09 4.267367e-09
[80,] 1.0000000 9.675454e-08 4.837727e-08
[81,] 0.9999995 9.709159e-07 4.854580e-07
[82,] 0.9999999 1.509184e-07 7.545918e-08
[83,] 0.9999985 2.980863e-06 1.490432e-06
[84,] 0.9999797 4.052477e-05 2.026239e-05
[85,] 0.9996236 7.527618e-04 3.763809e-04
> postscript(file="/var/www/html/rcomp/tmp/1fw2c1291216634.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fw2c1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/385kx1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/485kx1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/585kx1291216634.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 = 100
Frequency = 1
1 2 3 4 5
1000593.8580 2603268.0642 1184070.5633 -2053625.2042 -554894.3964
6 7 8 9 10
29306.2774 -94568.6500 -105927.6053 -406720.7226 687433.1069
11 12 13 14 15
-100042.6567 505355.3482 214744.6226 -937525.3218 -402925.9541
16 17 18 19 20
574116.0414 -277322.5262 -436988.4486 -353085.0820 -620328.9841
21 22 23 24 25
190580.3178 -114609.0351 -36757.2287 -685144.0058 -207580.5859
26 27 28 29 30
138256.4761 -592239.4450 -117823.5585 597617.1407 -110831.7627
31 32 33 34 35
286469.1387 -121715.6149 258574.0677 105718.8214 -33597.9619
36 37 38 39 40
-520929.5537 202031.6183 89722.4071 -198624.7630 -202927.9415
41 42 43 44 45
-455356.1565 -140583.0998 131722.0507 -90168.5977 173939.5653
46 47 48 49 50
-64579.7952 -404914.5230 -413521.6707 85008.0271 4101.9811
51 52 53 54 55
-44740.9406 -231336.7968 -372243.5432 -48871.3469 -535114.6232
56 57 58 59 60
179082.1676 566882.2001 157751.4598 94291.5522 -245204.9428
61 62 63 64 65
-1725.8676 27229.7482 635.1905 -207066.2696 173803.3724
66 67 68 69 70
-491126.1486 138266.7145 -47774.1793 5172.6989 -138365.0631
71 72 73 74 75
-94509.3434 230599.0333 74605.6948 224493.4435 120891.3494
76 77 78 79 80
-89739.3887 132640.2159 139462.0542 136205.2031 -59735.0302
81 82 83 84 85
235273.0872 -87113.6332 53456.1755 279943.0843 208387.1317
86 87 88 89 90
-116621.2913 92017.2594 185559.8734 40670.8260 216545.5526
91 92 93 94 95
171814.1801 -33123.4596 240403.1488 -199980.0264 266038.0921
96 97 98 99 100
37633.2213 251552.0566 14509.8941 121054.3194 20753.2505
> postscript(file="/var/www/html/rcomp/tmp/6ie101291216634.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 = 100
Frequency = 1
lag(myerror, k = 1) myerror
0 1000593.8580 NA
1 2603268.0642 1000593.8580
2 1184070.5633 2603268.0642
3 -2053625.2042 1184070.5633
4 -554894.3964 -2053625.2042
5 29306.2774 -554894.3964
6 -94568.6500 29306.2774
7 -105927.6053 -94568.6500
8 -406720.7226 -105927.6053
9 687433.1069 -406720.7226
10 -100042.6567 687433.1069
11 505355.3482 -100042.6567
12 214744.6226 505355.3482
13 -937525.3218 214744.6226
14 -402925.9541 -937525.3218
15 574116.0414 -402925.9541
16 -277322.5262 574116.0414
17 -436988.4486 -277322.5262
18 -353085.0820 -436988.4486
19 -620328.9841 -353085.0820
20 190580.3178 -620328.9841
21 -114609.0351 190580.3178
22 -36757.2287 -114609.0351
23 -685144.0058 -36757.2287
24 -207580.5859 -685144.0058
25 138256.4761 -207580.5859
26 -592239.4450 138256.4761
27 -117823.5585 -592239.4450
28 597617.1407 -117823.5585
29 -110831.7627 597617.1407
30 286469.1387 -110831.7627
31 -121715.6149 286469.1387
32 258574.0677 -121715.6149
33 105718.8214 258574.0677
34 -33597.9619 105718.8214
35 -520929.5537 -33597.9619
36 202031.6183 -520929.5537
37 89722.4071 202031.6183
38 -198624.7630 89722.4071
39 -202927.9415 -198624.7630
40 -455356.1565 -202927.9415
41 -140583.0998 -455356.1565
42 131722.0507 -140583.0998
43 -90168.5977 131722.0507
44 173939.5653 -90168.5977
45 -64579.7952 173939.5653
46 -404914.5230 -64579.7952
47 -413521.6707 -404914.5230
48 85008.0271 -413521.6707
49 4101.9811 85008.0271
50 -44740.9406 4101.9811
51 -231336.7968 -44740.9406
52 -372243.5432 -231336.7968
53 -48871.3469 -372243.5432
54 -535114.6232 -48871.3469
55 179082.1676 -535114.6232
56 566882.2001 179082.1676
57 157751.4598 566882.2001
58 94291.5522 157751.4598
59 -245204.9428 94291.5522
60 -1725.8676 -245204.9428
61 27229.7482 -1725.8676
62 635.1905 27229.7482
63 -207066.2696 635.1905
64 173803.3724 -207066.2696
65 -491126.1486 173803.3724
66 138266.7145 -491126.1486
67 -47774.1793 138266.7145
68 5172.6989 -47774.1793
69 -138365.0631 5172.6989
70 -94509.3434 -138365.0631
71 230599.0333 -94509.3434
72 74605.6948 230599.0333
73 224493.4435 74605.6948
74 120891.3494 224493.4435
75 -89739.3887 120891.3494
76 132640.2159 -89739.3887
77 139462.0542 132640.2159
78 136205.2031 139462.0542
79 -59735.0302 136205.2031
80 235273.0872 -59735.0302
81 -87113.6332 235273.0872
82 53456.1755 -87113.6332
83 279943.0843 53456.1755
84 208387.1317 279943.0843
85 -116621.2913 208387.1317
86 92017.2594 -116621.2913
87 185559.8734 92017.2594
88 40670.8260 185559.8734
89 216545.5526 40670.8260
90 171814.1801 216545.5526
91 -33123.4596 171814.1801
92 240403.1488 -33123.4596
93 -199980.0264 240403.1488
94 266038.0921 -199980.0264
95 37633.2213 266038.0921
96 251552.0566 37633.2213
97 14509.8941 251552.0566
98 121054.3194 14509.8941
99 20753.2505 121054.3194
100 NA 20753.2505
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2603268.0642 1000593.8580
[2,] 1184070.5633 2603268.0642
[3,] -2053625.2042 1184070.5633
[4,] -554894.3964 -2053625.2042
[5,] 29306.2774 -554894.3964
[6,] -94568.6500 29306.2774
[7,] -105927.6053 -94568.6500
[8,] -406720.7226 -105927.6053
[9,] 687433.1069 -406720.7226
[10,] -100042.6567 687433.1069
[11,] 505355.3482 -100042.6567
[12,] 214744.6226 505355.3482
[13,] -937525.3218 214744.6226
[14,] -402925.9541 -937525.3218
[15,] 574116.0414 -402925.9541
[16,] -277322.5262 574116.0414
[17,] -436988.4486 -277322.5262
[18,] -353085.0820 -436988.4486
[19,] -620328.9841 -353085.0820
[20,] 190580.3178 -620328.9841
[21,] -114609.0351 190580.3178
[22,] -36757.2287 -114609.0351
[23,] -685144.0058 -36757.2287
[24,] -207580.5859 -685144.0058
[25,] 138256.4761 -207580.5859
[26,] -592239.4450 138256.4761
[27,] -117823.5585 -592239.4450
[28,] 597617.1407 -117823.5585
[29,] -110831.7627 597617.1407
[30,] 286469.1387 -110831.7627
[31,] -121715.6149 286469.1387
[32,] 258574.0677 -121715.6149
[33,] 105718.8214 258574.0677
[34,] -33597.9619 105718.8214
[35,] -520929.5537 -33597.9619
[36,] 202031.6183 -520929.5537
[37,] 89722.4071 202031.6183
[38,] -198624.7630 89722.4071
[39,] -202927.9415 -198624.7630
[40,] -455356.1565 -202927.9415
[41,] -140583.0998 -455356.1565
[42,] 131722.0507 -140583.0998
[43,] -90168.5977 131722.0507
[44,] 173939.5653 -90168.5977
[45,] -64579.7952 173939.5653
[46,] -404914.5230 -64579.7952
[47,] -413521.6707 -404914.5230
[48,] 85008.0271 -413521.6707
[49,] 4101.9811 85008.0271
[50,] -44740.9406 4101.9811
[51,] -231336.7968 -44740.9406
[52,] -372243.5432 -231336.7968
[53,] -48871.3469 -372243.5432
[54,] -535114.6232 -48871.3469
[55,] 179082.1676 -535114.6232
[56,] 566882.2001 179082.1676
[57,] 157751.4598 566882.2001
[58,] 94291.5522 157751.4598
[59,] -245204.9428 94291.5522
[60,] -1725.8676 -245204.9428
[61,] 27229.7482 -1725.8676
[62,] 635.1905 27229.7482
[63,] -207066.2696 635.1905
[64,] 173803.3724 -207066.2696
[65,] -491126.1486 173803.3724
[66,] 138266.7145 -491126.1486
[67,] -47774.1793 138266.7145
[68,] 5172.6989 -47774.1793
[69,] -138365.0631 5172.6989
[70,] -94509.3434 -138365.0631
[71,] 230599.0333 -94509.3434
[72,] 74605.6948 230599.0333
[73,] 224493.4435 74605.6948
[74,] 120891.3494 224493.4435
[75,] -89739.3887 120891.3494
[76,] 132640.2159 -89739.3887
[77,] 139462.0542 132640.2159
[78,] 136205.2031 139462.0542
[79,] -59735.0302 136205.2031
[80,] 235273.0872 -59735.0302
[81,] -87113.6332 235273.0872
[82,] 53456.1755 -87113.6332
[83,] 279943.0843 53456.1755
[84,] 208387.1317 279943.0843
[85,] -116621.2913 208387.1317
[86,] 92017.2594 -116621.2913
[87,] 185559.8734 92017.2594
[88,] 40670.8260 185559.8734
[89,] 216545.5526 40670.8260
[90,] 171814.1801 216545.5526
[91,] -33123.4596 171814.1801
[92,] 240403.1488 -33123.4596
[93,] -199980.0264 240403.1488
[94,] 266038.0921 -199980.0264
[95,] 37633.2213 266038.0921
[96,] 251552.0566 37633.2213
[97,] 14509.8941 251552.0566
[98,] 121054.3194 14509.8941
[99,] 20753.2505 121054.3194
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2603268.0642 1000593.8580
2 1184070.5633 2603268.0642
3 -2053625.2042 1184070.5633
4 -554894.3964 -2053625.2042
5 29306.2774 -554894.3964
6 -94568.6500 29306.2774
7 -105927.6053 -94568.6500
8 -406720.7226 -105927.6053
9 687433.1069 -406720.7226
10 -100042.6567 687433.1069
11 505355.3482 -100042.6567
12 214744.6226 505355.3482
13 -937525.3218 214744.6226
14 -402925.9541 -937525.3218
15 574116.0414 -402925.9541
16 -277322.5262 574116.0414
17 -436988.4486 -277322.5262
18 -353085.0820 -436988.4486
19 -620328.9841 -353085.0820
20 190580.3178 -620328.9841
21 -114609.0351 190580.3178
22 -36757.2287 -114609.0351
23 -685144.0058 -36757.2287
24 -207580.5859 -685144.0058
25 138256.4761 -207580.5859
26 -592239.4450 138256.4761
27 -117823.5585 -592239.4450
28 597617.1407 -117823.5585
29 -110831.7627 597617.1407
30 286469.1387 -110831.7627
31 -121715.6149 286469.1387
32 258574.0677 -121715.6149
33 105718.8214 258574.0677
34 -33597.9619 105718.8214
35 -520929.5537 -33597.9619
36 202031.6183 -520929.5537
37 89722.4071 202031.6183
38 -198624.7630 89722.4071
39 -202927.9415 -198624.7630
40 -455356.1565 -202927.9415
41 -140583.0998 -455356.1565
42 131722.0507 -140583.0998
43 -90168.5977 131722.0507
44 173939.5653 -90168.5977
45 -64579.7952 173939.5653
46 -404914.5230 -64579.7952
47 -413521.6707 -404914.5230
48 85008.0271 -413521.6707
49 4101.9811 85008.0271
50 -44740.9406 4101.9811
51 -231336.7968 -44740.9406
52 -372243.5432 -231336.7968
53 -48871.3469 -372243.5432
54 -535114.6232 -48871.3469
55 179082.1676 -535114.6232
56 566882.2001 179082.1676
57 157751.4598 566882.2001
58 94291.5522 157751.4598
59 -245204.9428 94291.5522
60 -1725.8676 -245204.9428
61 27229.7482 -1725.8676
62 635.1905 27229.7482
63 -207066.2696 635.1905
64 173803.3724 -207066.2696
65 -491126.1486 173803.3724
66 138266.7145 -491126.1486
67 -47774.1793 138266.7145
68 5172.6989 -47774.1793
69 -138365.0631 5172.6989
70 -94509.3434 -138365.0631
71 230599.0333 -94509.3434
72 74605.6948 230599.0333
73 224493.4435 74605.6948
74 120891.3494 224493.4435
75 -89739.3887 120891.3494
76 132640.2159 -89739.3887
77 139462.0542 132640.2159
78 136205.2031 139462.0542
79 -59735.0302 136205.2031
80 235273.0872 -59735.0302
81 -87113.6332 235273.0872
82 53456.1755 -87113.6332
83 279943.0843 53456.1755
84 208387.1317 279943.0843
85 -116621.2913 208387.1317
86 92017.2594 -116621.2913
87 185559.8734 92017.2594
88 40670.8260 185559.8734
89 216545.5526 40670.8260
90 171814.1801 216545.5526
91 -33123.4596 171814.1801
92 240403.1488 -33123.4596
93 -199980.0264 240403.1488
94 266038.0921 -199980.0264
95 37633.2213 266038.0921
96 251552.0566 37633.2213
97 14509.8941 251552.0566
98 121054.3194 14509.8941
99 20753.2505 121054.3194
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7t50l1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8t50l1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9t50l1291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mfz61291216634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/117xgt1291216634.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12byeh1291216634.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13p8c81291216634.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14aqbe1291216634.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15dr9k1291216634.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16hr8p1291216634.tab")
+ }
>
> try(system("convert tmp/1fw2c1291216634.ps tmp/1fw2c1291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fw2c1291216634.ps tmp/2fw2c1291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/385kx1291216634.ps tmp/385kx1291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/485kx1291216634.ps tmp/485kx1291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/585kx1291216634.ps tmp/585kx1291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ie101291216634.ps tmp/6ie101291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/7t50l1291216634.ps tmp/7t50l1291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/8t50l1291216634.ps tmp/8t50l1291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/9t50l1291216634.ps tmp/9t50l1291216634.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mfz61291216634.ps tmp/10mfz61291216634.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.076 1.726 8.876