R version 2.6.2 (2008-02-08)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(99.2,96.7,101.0,56421,53152,53536,52408,41454,38271,35306,26414,31917,38030,27534,18387,50556,43901,48572,43899,37532,40357,35489,29027,34485,42598,30306,26451,47460,50104,61465,53726,39477,43895,31481,29896,33842,39120,33702,25094,51442,45594,52518,48564,41745,49585,32747,33379,35645,37034,35681,20972,58552,54955,65540,51570,51145,46641,35704,33253,35193,41668,34865,21210,56126,49231,59723,48103,47472,50497,40059,34149,36860,46356,36577),dim=c(3,72),dimnames=list(c('Cons','Inc','Price'),1:72))
> y <- array(NA,dim=c(3,72),dimnames=list(c('Cons','Inc','Price'),1:72))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
Cons Inc Price M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 99.2 96.7 101.0 1 0 0 0 0 0 0 0 0 0 0 1
2 56421.0 53152.0 53536.0 0 1 0 0 0 0 0 0 0 0 0 2
3 52408.0 41454.0 38271.0 0 0 1 0 0 0 0 0 0 0 0 3
4 35306.0 26414.0 31917.0 0 0 0 1 0 0 0 0 0 0 0 4
5 38030.0 27534.0 18387.0 0 0 0 0 1 0 0 0 0 0 0 5
6 50556.0 43901.0 48572.0 0 0 0 0 0 1 0 0 0 0 0 6
7 43899.0 37532.0 40357.0 0 0 0 0 0 0 1 0 0 0 0 7
8 35489.0 29027.0 34485.0 0 0 0 0 0 0 0 1 0 0 0 8
9 42598.0 30306.0 26451.0 0 0 0 0 0 0 0 0 1 0 0 9
10 47460.0 50104.0 61465.0 0 0 0 0 0 0 0 0 0 1 0 10
11 53726.0 39477.0 43895.0 0 0 0 0 0 0 0 0 0 0 1 11
12 31481.0 29896.0 33842.0 0 0 0 0 0 0 0 0 0 0 0 12
13 39120.0 33702.0 25094.0 1 0 0 0 0 0 0 0 0 0 0 13
14 51442.0 45594.0 52518.0 0 1 0 0 0 0 0 0 0 0 0 14
15 48564.0 41745.0 49585.0 0 0 1 0 0 0 0 0 0 0 0 15
16 32747.0 33379.0 35645.0 0 0 0 1 0 0 0 0 0 0 0 16
17 37034.0 35681.0 20972.0 0 0 0 0 1 0 0 0 0 0 0 17
18 58552.0 54955.0 65540.0 0 0 0 0 0 1 0 0 0 0 0 18
19 51570.0 51145.0 46641.0 0 0 0 0 0 0 1 0 0 0 0 19
20 35704.0 33253.0 35193.0 0 0 0 0 0 0 0 1 0 0 0 20
21 41668.0 34865.0 21210.0 0 0 0 0 0 0 0 0 1 0 0 21
22 56126.0 49231.0 59723.0 0 0 0 0 0 0 0 0 0 1 0 22
23 48103.0 47472.0 50497.0 0 0 0 0 0 0 0 0 0 0 1 23
24 40059.0 34149.0 36860.0 0 0 0 0 0 0 0 0 0 0 0 24
25 46356.0 36577.0 99.2 1 0 0 0 0 0 0 0 0 0 0 25
26 96.7 101.0 56421.0 0 1 0 0 0 0 0 0 0 0 0 26
27 53152.0 53536.0 52408.0 0 0 1 0 0 0 0 0 0 0 0 27
28 41454.0 38271.0 35306.0 0 0 0 1 0 0 0 0 0 0 0 28
29 26414.0 31917.0 38030.0 0 0 0 0 1 0 0 0 0 0 0 29
30 27534.0 18387.0 50556.0 0 0 0 0 0 1 0 0 0 0 0 30
31 43901.0 48572.0 43899.0 0 0 0 0 0 0 1 0 0 0 0 31
32 37532.0 40357.0 35489.0 0 0 0 0 0 0 0 1 0 0 0 32
33 29027.0 34485.0 42598.0 0 0 0 0 0 0 0 0 1 0 0 33
34 30306.0 26451.0 47460.0 0 0 0 0 0 0 0 0 0 1 0 34
35 50104.0 61465.0 53726.0 0 0 0 0 0 0 0 0 0 0 1 35
36 39477.0 43895.0 31481.0 0 0 0 0 0 0 0 0 0 0 0 36
37 29896.0 33842.0 39120.0 1 0 0 0 0 0 0 0 0 0 0 37
38 33702.0 25094.0 51442.0 0 1 0 0 0 0 0 0 0 0 0 38
39 45594.0 52518.0 48564.0 0 0 1 0 0 0 0 0 0 0 0 39
40 41745.0 49585.0 32747.0 0 0 0 1 0 0 0 0 0 0 0 40
41 33379.0 35645.0 37034.0 0 0 0 0 1 0 0 0 0 0 0 41
42 35681.0 20972.0 58552.0 0 0 0 0 0 1 0 0 0 0 0 42
43 54955.0 65540.0 51570.0 0 0 0 0 0 0 1 0 0 0 0 43
44 51145.0 46641.0 35704.0 0 0 0 0 0 0 0 1 0 0 0 44
45 33253.0 35193.0 41668.0 0 0 0 0 0 0 0 0 1 0 0 45
46 34865.0 21210.0 56126.0 0 0 0 0 0 0 0 0 0 1 0 46
47 49231.0 59723.0 48103.0 0 0 0 0 0 0 0 0 0 0 1 47
48 47472.0 50497.0 40059.0 0 0 0 0 0 0 0 0 0 0 0 48
49 34149.0 36860.0 46356.0 1 0 0 0 0 0 0 0 0 0 0 49
50 36577.0 99.2 96.7 0 1 0 0 0 0 0 0 0 0 0 50
51 101.0 56421.0 53152.0 0 0 1 0 0 0 0 0 0 0 0 51
52 53536.0 52408.0 41454.0 0 0 0 1 0 0 0 0 0 0 0 52
53 38271.0 35306.0 26414.0 0 0 0 0 1 0 0 0 0 0 0 53
54 31917.0 38030.0 27534.0 0 0 0 0 0 1 0 0 0 0 0 54
55 18387.0 50556.0 43901.0 0 0 0 0 0 0 1 0 0 0 0 55
56 48572.0 43899.0 37532.0 0 0 0 0 0 0 0 1 0 0 0 56
57 40357.0 35489.0 29027.0 0 0 0 0 0 0 0 0 1 0 0 57
58 34485.0 42598.0 30306.0 0 0 0 0 0 0 0 0 0 1 0 58
59 26451.0 47460.0 50104.0 0 0 0 0 0 0 0 0 0 0 1 59
60 61465.0 53726.0 39477.0 0 0 0 0 0 0 0 0 0 0 0 60
61 43895.0 31481.0 29896.0 1 0 0 0 0 0 0 0 0 0 0 61
62 33842.0 39120.0 33702.0 0 1 0 0 0 0 0 0 0 0 0 62
63 25094.0 51442.0 45594.0 0 0 1 0 0 0 0 0 0 0 0 63
64 52518.0 48564.0 41745.0 0 0 0 1 0 0 0 0 0 0 0 64
65 49585.0 32747.0 33379.0 0 0 0 0 1 0 0 0 0 0 0 65
66 35645.0 37034.0 35681.0 0 0 0 0 0 1 0 0 0 0 0 66
67 20972.0 58552.0 54955.0 0 0 0 0 0 0 1 0 0 0 0 67
68 65540.0 51570.0 51145.0 0 0 0 0 0 0 0 1 0 0 0 68
69 46641.0 35704.0 33253.0 0 0 0 0 0 0 0 0 1 0 0 69
70 35193.0 41668.0 34865.0 0 0 0 0 0 0 0 0 0 1 0 70
71 21210.0 56126.0 49231.0 0 0 0 0 0 0 0 0 0 0 1 71
72 59723.0 48103.0 47472.0 0 0 0 0 0 0 0 0 0 0 0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Inc Price M1 M2 M3
2.430e+04 7.388e-01 -8.985e-02 -6.537e+03 -5.320e+02 -1.414e+04
M4 M5 M6 M7 M8 M9
-3.651e+03 -3.803e+03 -8.893e+02 -1.399e+04 3.669e+02 -1.986e+03
M10 M11 t
-2.693e+03 -1.063e+04 -1.501e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-39313.05 -6520.18 -60.45 6965.99 20248.40
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.430e+04 8.122e+03 2.992 0.00409 **
Inc 7.388e-01 1.470e-01 5.024 5.31e-06 ***
Price -8.985e-02 1.430e-01 -0.629 0.53218
M1 -6.537e+03 7.042e+03 -0.928 0.35720
M2 -5.320e+02 7.098e+03 -0.075 0.94052
M3 -1.414e+04 6.819e+03 -2.074 0.04263 *
M4 -3.651e+03 6.668e+03 -0.547 0.58620
M5 -3.803e+03 6.820e+03 -0.558 0.57933
M6 -8.893e+02 6.957e+03 -0.128 0.89873
M7 -1.399e+04 6.809e+03 -2.055 0.04448 *
M8 3.669e+02 6.657e+03 0.055 0.95624
M9 -1.986e+03 6.757e+03 -0.294 0.76991
M10 -2.693e+03 6.891e+03 -0.391 0.69745
M11 -1.063e+04 6.836e+03 -1.556 0.12530
t -1.501e+02 6.871e+01 -2.184 0.03308 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11510 on 57 degrees of freedom
Multiple R-squared: 0.389, Adjusted R-squared: 0.2389
F-statistic: 2.592 on 14 and 57 DF, p-value: 0.005816
> 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,] 7.345487e-02 0.1469097468 0.92654513
[2,] 2.457061e-02 0.0491412127 0.97542939
[3,] 7.864819e-03 0.0157296375 0.99213518
[4,] 2.285423e-03 0.0045708460 0.99771458
[5,] 8.883984e-03 0.0177679683 0.99111602
[6,] 9.584040e-03 0.0191680791 0.99041596
[7,] 5.846264e-03 0.0116925286 0.99415374
[8,] 4.015246e-03 0.0080304926 0.99598475
[9,] 2.604757e-03 0.0052095144 0.99739524
[10,] 3.201278e-03 0.0064025560 0.99679872
[11,] 1.358992e-03 0.0027179836 0.99864101
[12,] 7.831021e-04 0.0015662041 0.99921690
[13,] 4.540608e-04 0.0009081215 0.99954594
[14,] 3.602927e-04 0.0007205854 0.99963971
[15,] 2.364328e-04 0.0004728657 0.99976357
[16,] 1.690126e-04 0.0003380253 0.99983099
[17,] 6.581286e-05 0.0001316257 0.99993419
[18,] 2.275949e-04 0.0004551898 0.99977241
[19,] 1.766739e-04 0.0003533478 0.99982333
[20,] 1.112221e-04 0.0002224441 0.99988878
[21,] 1.070896e-04 0.0002141792 0.99989291
[22,] 6.636770e-04 0.0013273539 0.99933632
[23,] 4.964004e-04 0.0009928007 0.99950360
[24,] 3.370562e-04 0.0006741124 0.99966294
[25,] 5.028876e-04 0.0010057751 0.99949711
[26,] 1.184249e-02 0.0236849721 0.98815751
[27,] 9.390353e-03 0.0187807063 0.99060965
[28,] 5.685705e-03 0.0113714109 0.99431429
[29,] 7.847972e-03 0.0156959437 0.99215203
[30,] 3.301142e-01 0.6602283649 0.66988582
[31,] 2.563556e-01 0.5127111080 0.74364445
[32,] 1.914662e-01 0.3829323554 0.80853382
[33,] 1.922175e-01 0.3844349254 0.80778254
[34,] 9.811228e-01 0.0377543277 0.01887716
[35,] 9.559128e-01 0.0881744205 0.04408721
[36,] 9.479017e-01 0.1041966636 0.05209833
[37,] 8.760260e-01 0.2479480162 0.12397401
> postscript(file="/var/www/html/rcomp/tmp/18bhm1210362330.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/27w0g1210362330.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3dgxr1210362330.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4eugf1210362330.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pzdp1210362330.ps",horizontal=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 = 72
Frequency = 1
1 2 3 4 5 6
-17575.91705 -1506.09258 15511.45042 -1390.22917 -407.17282 -24.63646
7 8 9 10 11 12
10539.42154 -6324.15039 1620.89970 -4141.62227 16489.23436 -10064.81467
13 14 15 16 17 18
662.98312 808.32359 14269.88579 -6959.32318 -5389.25781 3129.85382
19 20 21 22 23 24
10518.28407 -7366.95670 -1347.46321 6813.70019 7353.38517 -2557.01168
25 26 27 28 29 30
5329.88691 -14774.09657 12200.90945 -96.26131 -9894.78107 -416.30408
31 32 33 34 35 36
6304.75624 -8960.11851 -9985.11011 -1476.88176 1106.99734 -9022.05623
37 38 39 40 41 42
-3802.49055 1719.24413 6850.49306 -6593.40056 -3972.76021 8340.14002
43 44 45 46 47 48
7312.48734 1830.27768 -4564.90938 9533.80268 2816.64354 -3333.17598
49 50 51 52 53 54
671.75526 20248.39724 -39313.04816 5695.08794 2016.32615 -9012.89757
55 56 57 58 59 60
-17073.19059 3248.23368 2985.43310 -7167.29510 -8922.50910 10022.71687
61 62 63 64 65 66
14713.78230 -6495.77581 -9519.69056 9344.12627 17647.64575 -2016.15572
67 68 69 70 71 72
-17601.75860 17572.71424 11291.14990 -3561.70375 -18843.75131 14954.34170
> postscript(file="/var/www/html/rcomp/tmp/6f9vs1210362330.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 -17575.91705 NA
1 -1506.09258 -17575.91705
2 15511.45042 -1506.09258
3 -1390.22917 15511.45042
4 -407.17282 -1390.22917
5 -24.63646 -407.17282
6 10539.42154 -24.63646
7 -6324.15039 10539.42154
8 1620.89970 -6324.15039
9 -4141.62227 1620.89970
10 16489.23436 -4141.62227
11 -10064.81467 16489.23436
12 662.98312 -10064.81467
13 808.32359 662.98312
14 14269.88579 808.32359
15 -6959.32318 14269.88579
16 -5389.25781 -6959.32318
17 3129.85382 -5389.25781
18 10518.28407 3129.85382
19 -7366.95670 10518.28407
20 -1347.46321 -7366.95670
21 6813.70019 -1347.46321
22 7353.38517 6813.70019
23 -2557.01168 7353.38517
24 5329.88691 -2557.01168
25 -14774.09657 5329.88691
26 12200.90945 -14774.09657
27 -96.26131 12200.90945
28 -9894.78107 -96.26131
29 -416.30408 -9894.78107
30 6304.75624 -416.30408
31 -8960.11851 6304.75624
32 -9985.11011 -8960.11851
33 -1476.88176 -9985.11011
34 1106.99734 -1476.88176
35 -9022.05623 1106.99734
36 -3802.49055 -9022.05623
37 1719.24413 -3802.49055
38 6850.49306 1719.24413
39 -6593.40056 6850.49306
40 -3972.76021 -6593.40056
41 8340.14002 -3972.76021
42 7312.48734 8340.14002
43 1830.27768 7312.48734
44 -4564.90938 1830.27768
45 9533.80268 -4564.90938
46 2816.64354 9533.80268
47 -3333.17598 2816.64354
48 671.75526 -3333.17598
49 20248.39724 671.75526
50 -39313.04816 20248.39724
51 5695.08794 -39313.04816
52 2016.32615 5695.08794
53 -9012.89757 2016.32615
54 -17073.19059 -9012.89757
55 3248.23368 -17073.19059
56 2985.43310 3248.23368
57 -7167.29510 2985.43310
58 -8922.50910 -7167.29510
59 10022.71687 -8922.50910
60 14713.78230 10022.71687
61 -6495.77581 14713.78230
62 -9519.69056 -6495.77581
63 9344.12627 -9519.69056
64 17647.64575 9344.12627
65 -2016.15572 17647.64575
66 -17601.75860 -2016.15572
67 17572.71424 -17601.75860
68 11291.14990 17572.71424
69 -3561.70375 11291.14990
70 -18843.75131 -3561.70375
71 14954.34170 -18843.75131
72 NA 14954.34170
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1506.09258 -17575.91705
[2,] 15511.45042 -1506.09258
[3,] -1390.22917 15511.45042
[4,] -407.17282 -1390.22917
[5,] -24.63646 -407.17282
[6,] 10539.42154 -24.63646
[7,] -6324.15039 10539.42154
[8,] 1620.89970 -6324.15039
[9,] -4141.62227 1620.89970
[10,] 16489.23436 -4141.62227
[11,] -10064.81467 16489.23436
[12,] 662.98312 -10064.81467
[13,] 808.32359 662.98312
[14,] 14269.88579 808.32359
[15,] -6959.32318 14269.88579
[16,] -5389.25781 -6959.32318
[17,] 3129.85382 -5389.25781
[18,] 10518.28407 3129.85382
[19,] -7366.95670 10518.28407
[20,] -1347.46321 -7366.95670
[21,] 6813.70019 -1347.46321
[22,] 7353.38517 6813.70019
[23,] -2557.01168 7353.38517
[24,] 5329.88691 -2557.01168
[25,] -14774.09657 5329.88691
[26,] 12200.90945 -14774.09657
[27,] -96.26131 12200.90945
[28,] -9894.78107 -96.26131
[29,] -416.30408 -9894.78107
[30,] 6304.75624 -416.30408
[31,] -8960.11851 6304.75624
[32,] -9985.11011 -8960.11851
[33,] -1476.88176 -9985.11011
[34,] 1106.99734 -1476.88176
[35,] -9022.05623 1106.99734
[36,] -3802.49055 -9022.05623
[37,] 1719.24413 -3802.49055
[38,] 6850.49306 1719.24413
[39,] -6593.40056 6850.49306
[40,] -3972.76021 -6593.40056
[41,] 8340.14002 -3972.76021
[42,] 7312.48734 8340.14002
[43,] 1830.27768 7312.48734
[44,] -4564.90938 1830.27768
[45,] 9533.80268 -4564.90938
[46,] 2816.64354 9533.80268
[47,] -3333.17598 2816.64354
[48,] 671.75526 -3333.17598
[49,] 20248.39724 671.75526
[50,] -39313.04816 20248.39724
[51,] 5695.08794 -39313.04816
[52,] 2016.32615 5695.08794
[53,] -9012.89757 2016.32615
[54,] -17073.19059 -9012.89757
[55,] 3248.23368 -17073.19059
[56,] 2985.43310 3248.23368
[57,] -7167.29510 2985.43310
[58,] -8922.50910 -7167.29510
[59,] 10022.71687 -8922.50910
[60,] 14713.78230 10022.71687
[61,] -6495.77581 14713.78230
[62,] -9519.69056 -6495.77581
[63,] 9344.12627 -9519.69056
[64,] 17647.64575 9344.12627
[65,] -2016.15572 17647.64575
[66,] -17601.75860 -2016.15572
[67,] 17572.71424 -17601.75860
[68,] 11291.14990 17572.71424
[69,] -3561.70375 11291.14990
[70,] -18843.75131 -3561.70375
[71,] 14954.34170 -18843.75131
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1506.09258 -17575.91705
2 15511.45042 -1506.09258
3 -1390.22917 15511.45042
4 -407.17282 -1390.22917
5 -24.63646 -407.17282
6 10539.42154 -24.63646
7 -6324.15039 10539.42154
8 1620.89970 -6324.15039
9 -4141.62227 1620.89970
10 16489.23436 -4141.62227
11 -10064.81467 16489.23436
12 662.98312 -10064.81467
13 808.32359 662.98312
14 14269.88579 808.32359
15 -6959.32318 14269.88579
16 -5389.25781 -6959.32318
17 3129.85382 -5389.25781
18 10518.28407 3129.85382
19 -7366.95670 10518.28407
20 -1347.46321 -7366.95670
21 6813.70019 -1347.46321
22 7353.38517 6813.70019
23 -2557.01168 7353.38517
24 5329.88691 -2557.01168
25 -14774.09657 5329.88691
26 12200.90945 -14774.09657
27 -96.26131 12200.90945
28 -9894.78107 -96.26131
29 -416.30408 -9894.78107
30 6304.75624 -416.30408
31 -8960.11851 6304.75624
32 -9985.11011 -8960.11851
33 -1476.88176 -9985.11011
34 1106.99734 -1476.88176
35 -9022.05623 1106.99734
36 -3802.49055 -9022.05623
37 1719.24413 -3802.49055
38 6850.49306 1719.24413
39 -6593.40056 6850.49306
40 -3972.76021 -6593.40056
41 8340.14002 -3972.76021
42 7312.48734 8340.14002
43 1830.27768 7312.48734
44 -4564.90938 1830.27768
45 9533.80268 -4564.90938
46 2816.64354 9533.80268
47 -3333.17598 2816.64354
48 671.75526 -3333.17598
49 20248.39724 671.75526
50 -39313.04816 20248.39724
51 5695.08794 -39313.04816
52 2016.32615 5695.08794
53 -9012.89757 2016.32615
54 -17073.19059 -9012.89757
55 3248.23368 -17073.19059
56 2985.43310 3248.23368
57 -7167.29510 2985.43310
58 -8922.50910 -7167.29510
59 10022.71687 -8922.50910
60 14713.78230 10022.71687
61 -6495.77581 14713.78230
62 -9519.69056 -6495.77581
63 9344.12627 -9519.69056
64 17647.64575 9344.12627
65 -2016.15572 17647.64575
66 -17601.75860 -2016.15572
67 17572.71424 -17601.75860
68 11291.14990 17572.71424
69 -3561.70375 11291.14990
70 -18843.75131 -3561.70375
71 14954.34170 -18843.75131
> 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/7k7xv1210362330.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dfru1210362330.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yhue1210362330.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vx0v1210362330.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11uu9j1210362330.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/129h8r1210362330.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/13wu761210362330.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/14pzu81210362330.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/15s7u71210362330.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/16l5tc1210362331.tab")
+ }
>
> system("convert tmp/18bhm1210362330.ps tmp/18bhm1210362330.png")
> system("convert tmp/27w0g1210362330.ps tmp/27w0g1210362330.png")
> system("convert tmp/3dgxr1210362330.ps tmp/3dgxr1210362330.png")
> system("convert tmp/4eugf1210362330.ps tmp/4eugf1210362330.png")
> system("convert tmp/5pzdp1210362330.ps tmp/5pzdp1210362330.png")
> system("convert tmp/6f9vs1210362330.ps tmp/6f9vs1210362330.png")
> system("convert tmp/7k7xv1210362330.ps tmp/7k7xv1210362330.png")
> system("convert tmp/8dfru1210362330.ps tmp/8dfru1210362330.png")
> system("convert tmp/9yhue1210362330.ps tmp/9yhue1210362330.png")
> system("convert tmp/10vx0v1210362330.ps tmp/10vx0v1210362330.png")
>
>
> proc.time()
user system elapsed
2.915 1.637 3.469