R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
Natural language support but running in an English locale
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
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+ ,6282154
+ ,1
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+ ,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 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/freestat/rcomp/tmp/1zlj71291209825.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2au0a1291209825.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3au0a1291209825.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4au0a1291209825.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5au0a1291209825.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/freestat/rcomp/tmp/6llzd1291209825.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/freestat/rcomp/tmp/7ddhy1291209825.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8ddhy1291209825.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9ddhy1291209825.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10o4yj1291209825.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11r4e71291209825.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/freestat/rcomp/tmp/12d5vv1291209825.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/freestat/rcomp/tmp/13rftm1291209825.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/freestat/rcomp/tmp/14cx9a1291209825.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/freestat/rcomp/tmp/15ggqx1291209825.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/freestat/rcomp/tmp/16jg6l1291209825.tab")
+ }
>
> try(system("convert tmp/1zlj71291209825.ps tmp/1zlj71291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/2au0a1291209825.ps tmp/2au0a1291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/3au0a1291209825.ps tmp/3au0a1291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/4au0a1291209825.ps tmp/4au0a1291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/5au0a1291209825.ps tmp/5au0a1291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/6llzd1291209825.ps tmp/6llzd1291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ddhy1291209825.ps tmp/7ddhy1291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ddhy1291209825.ps tmp/8ddhy1291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ddhy1291209825.ps tmp/9ddhy1291209825.png",intern=TRUE))
character(0)
> try(system("convert tmp/10o4yj1291209825.ps tmp/10o4yj1291209825.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.445 2.532 4.760