R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(20.3,3016,20,2155,19.2,2172,21.8,2150,21.3,2533,21.5,2058,19.5,2160,19.5,2260,19.7,2498,18.7,2695,19.7,2799,20,2946,19.7,2930,19.2,2318,19.7,2540,22,2570,21.8,2669,22.8,2450,21,2842,25,3440,23.3,2678,25,2981,26.8,2260,25.3,2844,26.5,2546,27.8,2456,22,2295,22.3,2379,28,2479,25,2057,27.3,2280,25.8,2351,27.3,2276,23.5,2548,24.5,2311,18,2201,21.3,2725,21.8,2408,20.5,2139,22.3,1898,18.7,2537,22.3,2068,17.7,2063,19.7,2520,20.5,2434,18.5,2190,10,2794,14.2,2070,15.5,2615,16.5,2265,20.5,2139,15.7,2428,11.7,2137,7.5,1823,3.5,2063,4.5,1806,2.2,1758,5,2243,2.3,1993,6.1,1932,3.3,2465),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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
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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 20.3 3016 1 0 0 0 0 0 0 0 0 0 0 1
2 20.0 2155 0 1 0 0 0 0 0 0 0 0 0 2
3 19.2 2172 0 0 1 0 0 0 0 0 0 0 0 3
4 21.8 2150 0 0 0 1 0 0 0 0 0 0 0 4
5 21.3 2533 0 0 0 0 1 0 0 0 0 0 0 5
6 21.5 2058 0 0 0 0 0 1 0 0 0 0 0 6
7 19.5 2160 0 0 0 0 0 0 1 0 0 0 0 7
8 19.5 2260 0 0 0 0 0 0 0 1 0 0 0 8
9 19.7 2498 0 0 0 0 0 0 0 0 1 0 0 9
10 18.7 2695 0 0 0 0 0 0 0 0 0 1 0 10
11 19.7 2799 0 0 0 0 0 0 0 0 0 0 1 11
12 20.0 2946 0 0 0 0 0 0 0 0 0 0 0 12
13 19.7 2930 1 0 0 0 0 0 0 0 0 0 0 13
14 19.2 2318 0 1 0 0 0 0 0 0 0 0 0 14
15 19.7 2540 0 0 1 0 0 0 0 0 0 0 0 15
16 22.0 2570 0 0 0 1 0 0 0 0 0 0 0 16
17 21.8 2669 0 0 0 0 1 0 0 0 0 0 0 17
18 22.8 2450 0 0 0 0 0 1 0 0 0 0 0 18
19 21.0 2842 0 0 0 0 0 0 1 0 0 0 0 19
20 25.0 3440 0 0 0 0 0 0 0 1 0 0 0 20
21 23.3 2678 0 0 0 0 0 0 0 0 1 0 0 21
22 25.0 2981 0 0 0 0 0 0 0 0 0 1 0 22
23 26.8 2260 0 0 0 0 0 0 0 0 0 0 1 23
24 25.3 2844 0 0 0 0 0 0 0 0 0 0 0 24
25 26.5 2546 1 0 0 0 0 0 0 0 0 0 0 25
26 27.8 2456 0 1 0 0 0 0 0 0 0 0 0 26
27 22.0 2295 0 0 1 0 0 0 0 0 0 0 0 27
28 22.3 2379 0 0 0 1 0 0 0 0 0 0 0 28
29 28.0 2479 0 0 0 0 1 0 0 0 0 0 0 29
30 25.0 2057 0 0 0 0 0 1 0 0 0 0 0 30
31 27.3 2280 0 0 0 0 0 0 1 0 0 0 0 31
32 25.8 2351 0 0 0 0 0 0 0 1 0 0 0 32
33 27.3 2276 0 0 0 0 0 0 0 0 1 0 0 33
34 23.5 2548 0 0 0 0 0 0 0 0 0 1 0 34
35 24.5 2311 0 0 0 0 0 0 0 0 0 0 1 35
36 18.0 2201 0 0 0 0 0 0 0 0 0 0 0 36
37 21.3 2725 1 0 0 0 0 0 0 0 0 0 0 37
38 21.8 2408 0 1 0 0 0 0 0 0 0 0 0 38
39 20.5 2139 0 0 1 0 0 0 0 0 0 0 0 39
40 22.3 1898 0 0 0 1 0 0 0 0 0 0 0 40
41 18.7 2537 0 0 0 0 1 0 0 0 0 0 0 41
42 22.3 2068 0 0 0 0 0 1 0 0 0 0 0 42
43 17.7 2063 0 0 0 0 0 0 1 0 0 0 0 43
44 19.7 2520 0 0 0 0 0 0 0 1 0 0 0 44
45 20.5 2434 0 0 0 0 0 0 0 0 1 0 0 45
46 18.5 2190 0 0 0 0 0 0 0 0 0 1 0 46
47 10.0 2794 0 0 0 0 0 0 0 0 0 0 1 47
48 14.2 2070 0 0 0 0 0 0 0 0 0 0 0 48
49 15.5 2615 1 0 0 0 0 0 0 0 0 0 0 49
50 16.5 2265 0 1 0 0 0 0 0 0 0 0 0 50
51 20.5 2139 0 0 1 0 0 0 0 0 0 0 0 51
52 15.7 2428 0 0 0 1 0 0 0 0 0 0 0 52
53 11.7 2137 0 0 0 0 1 0 0 0 0 0 0 53
54 7.5 1823 0 0 0 0 0 1 0 0 0 0 0 54
55 3.5 2063 0 0 0 0 0 0 1 0 0 0 0 55
56 4.5 1806 0 0 0 0 0 0 0 1 0 0 0 56
57 2.2 1758 0 0 0 0 0 0 0 0 1 0 0 57
58 5.0 2243 0 0 0 0 0 0 0 0 0 1 0 58
59 2.3 1993 0 0 0 0 0 0 0 0 0 0 1 59
60 6.1 1932 0 0 0 0 0 0 0 0 0 0 0 60
61 3.3 2465 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
7.229534 0.006485 -1.854800 3.162772 3.062330 3.489193
M5 M6 M7 M8 M9 M10
1.931484 4.082780 0.996539 1.008249 1.827329 0.221975
M11 t
-0.441129 -0.168433
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.840 -3.866 -1.368 3.671 9.510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.229534 8.554042 0.845 0.40230
X 0.006485 0.002947 2.201 0.03270 *
M1 -1.854800 3.524316 -0.526 0.60116
M2 3.162772 3.636444 0.870 0.38886
M3 3.062330 3.657280 0.837 0.40665
M4 3.489193 3.635122 0.960 0.34204
M5 1.931484 3.589874 0.538 0.59309
M6 4.082780 3.741828 1.091 0.28078
M7 0.996539 3.615776 0.276 0.78406
M8 1.008249 3.583742 0.281 0.77968
M9 1.827329 3.591465 0.509 0.61327
M10 0.221975 3.594739 0.062 0.95102
M11 -0.441129 3.578791 -0.123 0.90243
t -0.168433 0.049605 -3.395 0.00140 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.657 on 47 degrees of freedom
Multiple R-squared: 0.4513, Adjusted R-squared: 0.2995
F-statistic: 2.973 on 13 and 47 DF, p-value: 0.003084
> 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,] 1.082140e-03 2.164279e-03 0.9989179
[2,] 2.690984e-04 5.381969e-04 0.9997309
[3,] 3.044990e-05 6.089980e-05 0.9999696
[4,] 3.460766e-05 6.921533e-05 0.9999654
[5,] 6.196790e-04 1.239358e-03 0.9993803
[6,] 6.704508e-03 1.340902e-02 0.9932955
[7,] 2.707253e-02 5.414506e-02 0.9729275
[8,] 2.100556e-02 4.201112e-02 0.9789944
[9,] 1.157409e-02 2.314818e-02 0.9884259
[10,] 1.136695e-02 2.273391e-02 0.9886330
[11,] 2.249582e-02 4.499164e-02 0.9775042
[12,] 7.425571e-02 1.485114e-01 0.9257443
[13,] 5.096524e-02 1.019305e-01 0.9490348
[14,] 3.892794e-02 7.785588e-02 0.9610721
[15,] 2.999624e-02 5.999248e-02 0.9700038
[16,] 1.766691e-02 3.533383e-02 0.9823331
[17,] 1.046108e-02 2.092215e-02 0.9895389
[18,] 8.696268e-03 1.739254e-02 0.9913037
[19,] 1.355566e-02 2.711132e-02 0.9864443
[20,] 8.165914e-02 1.633183e-01 0.9183409
[21,] 9.302456e-02 1.860491e-01 0.9069754
[22,] 1.086026e-01 2.172053e-01 0.8913974
[23,] 3.477339e-01 6.954678e-01 0.6522661
[24,] 3.274197e-01 6.548395e-01 0.6725803
[25,] 5.288169e-01 9.423662e-01 0.4711831
[26,] 4.334959e-01 8.669918e-01 0.5665041
[27,] 3.806542e-01 7.613085e-01 0.6193458
[28,] 2.971198e-01 5.942396e-01 0.7028802
> postscript(file="/var/www/html/rcomp/tmp/15udr1258727146.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lplt1258727146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lwmk1258727146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/426261258727146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/534q61258727146.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 = 61
Frequency = 1
1 2 3 4 5 6
-4.463956147 -4.029825783 -4.671190316 -2.186958641 -3.444431440 -2.147094331
7 8 9 10 11 12
-1.553852730 -2.045594108 -4.039584151 -4.543270203 -3.386135436 -4.312072538
13 14 15 16 17 18
-2.485085585 -3.865628961 -4.536343361 -2.689312628 -1.805149514 -1.367878580
19 20 21 22 23 24
-2.455180692 -2.176269551 0.414373902 1.923316699 9.230274099 3.670552160
25 26 27 28 29 30
8.826205759 5.860683698 1.373583927 0.870444451 7.648122932 5.401774466
31 32 33 34 35 36
9.510375414 7.706688407 9.042388649 5.252355084 8.620749873 2.561363579
37 38 39 40 41 42
4.486648445 2.193138187 2.906378834 6.010745244 -0.006793729 4.651635579
43 44 45 46 47 48
3.338732964 2.531977428 3.239008638 4.595045957 -6.990136024 1.632042648
49 50 51 52 53 54
1.421150211 -0.158367141 4.927570916 -2.004918427 -2.391748250 -6.538437133
55 56 57 58 59 60
-8.840074955 -6.016802175 -8.656187038 -7.227447537 -7.474752513 -3.551885848
61
-7.784962683
> postscript(file="/var/www/html/rcomp/tmp/618xw1258727146.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.463956147 NA
1 -4.029825783 -4.463956147
2 -4.671190316 -4.029825783
3 -2.186958641 -4.671190316
4 -3.444431440 -2.186958641
5 -2.147094331 -3.444431440
6 -1.553852730 -2.147094331
7 -2.045594108 -1.553852730
8 -4.039584151 -2.045594108
9 -4.543270203 -4.039584151
10 -3.386135436 -4.543270203
11 -4.312072538 -3.386135436
12 -2.485085585 -4.312072538
13 -3.865628961 -2.485085585
14 -4.536343361 -3.865628961
15 -2.689312628 -4.536343361
16 -1.805149514 -2.689312628
17 -1.367878580 -1.805149514
18 -2.455180692 -1.367878580
19 -2.176269551 -2.455180692
20 0.414373902 -2.176269551
21 1.923316699 0.414373902
22 9.230274099 1.923316699
23 3.670552160 9.230274099
24 8.826205759 3.670552160
25 5.860683698 8.826205759
26 1.373583927 5.860683698
27 0.870444451 1.373583927
28 7.648122932 0.870444451
29 5.401774466 7.648122932
30 9.510375414 5.401774466
31 7.706688407 9.510375414
32 9.042388649 7.706688407
33 5.252355084 9.042388649
34 8.620749873 5.252355084
35 2.561363579 8.620749873
36 4.486648445 2.561363579
37 2.193138187 4.486648445
38 2.906378834 2.193138187
39 6.010745244 2.906378834
40 -0.006793729 6.010745244
41 4.651635579 -0.006793729
42 3.338732964 4.651635579
43 2.531977428 3.338732964
44 3.239008638 2.531977428
45 4.595045957 3.239008638
46 -6.990136024 4.595045957
47 1.632042648 -6.990136024
48 1.421150211 1.632042648
49 -0.158367141 1.421150211
50 4.927570916 -0.158367141
51 -2.004918427 4.927570916
52 -2.391748250 -2.004918427
53 -6.538437133 -2.391748250
54 -8.840074955 -6.538437133
55 -6.016802175 -8.840074955
56 -8.656187038 -6.016802175
57 -7.227447537 -8.656187038
58 -7.474752513 -7.227447537
59 -3.551885848 -7.474752513
60 -7.784962683 -3.551885848
61 NA -7.784962683
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.029825783 -4.463956147
[2,] -4.671190316 -4.029825783
[3,] -2.186958641 -4.671190316
[4,] -3.444431440 -2.186958641
[5,] -2.147094331 -3.444431440
[6,] -1.553852730 -2.147094331
[7,] -2.045594108 -1.553852730
[8,] -4.039584151 -2.045594108
[9,] -4.543270203 -4.039584151
[10,] -3.386135436 -4.543270203
[11,] -4.312072538 -3.386135436
[12,] -2.485085585 -4.312072538
[13,] -3.865628961 -2.485085585
[14,] -4.536343361 -3.865628961
[15,] -2.689312628 -4.536343361
[16,] -1.805149514 -2.689312628
[17,] -1.367878580 -1.805149514
[18,] -2.455180692 -1.367878580
[19,] -2.176269551 -2.455180692
[20,] 0.414373902 -2.176269551
[21,] 1.923316699 0.414373902
[22,] 9.230274099 1.923316699
[23,] 3.670552160 9.230274099
[24,] 8.826205759 3.670552160
[25,] 5.860683698 8.826205759
[26,] 1.373583927 5.860683698
[27,] 0.870444451 1.373583927
[28,] 7.648122932 0.870444451
[29,] 5.401774466 7.648122932
[30,] 9.510375414 5.401774466
[31,] 7.706688407 9.510375414
[32,] 9.042388649 7.706688407
[33,] 5.252355084 9.042388649
[34,] 8.620749873 5.252355084
[35,] 2.561363579 8.620749873
[36,] 4.486648445 2.561363579
[37,] 2.193138187 4.486648445
[38,] 2.906378834 2.193138187
[39,] 6.010745244 2.906378834
[40,] -0.006793729 6.010745244
[41,] 4.651635579 -0.006793729
[42,] 3.338732964 4.651635579
[43,] 2.531977428 3.338732964
[44,] 3.239008638 2.531977428
[45,] 4.595045957 3.239008638
[46,] -6.990136024 4.595045957
[47,] 1.632042648 -6.990136024
[48,] 1.421150211 1.632042648
[49,] -0.158367141 1.421150211
[50,] 4.927570916 -0.158367141
[51,] -2.004918427 4.927570916
[52,] -2.391748250 -2.004918427
[53,] -6.538437133 -2.391748250
[54,] -8.840074955 -6.538437133
[55,] -6.016802175 -8.840074955
[56,] -8.656187038 -6.016802175
[57,] -7.227447537 -8.656187038
[58,] -7.474752513 -7.227447537
[59,] -3.551885848 -7.474752513
[60,] -7.784962683 -3.551885848
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.029825783 -4.463956147
2 -4.671190316 -4.029825783
3 -2.186958641 -4.671190316
4 -3.444431440 -2.186958641
5 -2.147094331 -3.444431440
6 -1.553852730 -2.147094331
7 -2.045594108 -1.553852730
8 -4.039584151 -2.045594108
9 -4.543270203 -4.039584151
10 -3.386135436 -4.543270203
11 -4.312072538 -3.386135436
12 -2.485085585 -4.312072538
13 -3.865628961 -2.485085585
14 -4.536343361 -3.865628961
15 -2.689312628 -4.536343361
16 -1.805149514 -2.689312628
17 -1.367878580 -1.805149514
18 -2.455180692 -1.367878580
19 -2.176269551 -2.455180692
20 0.414373902 -2.176269551
21 1.923316699 0.414373902
22 9.230274099 1.923316699
23 3.670552160 9.230274099
24 8.826205759 3.670552160
25 5.860683698 8.826205759
26 1.373583927 5.860683698
27 0.870444451 1.373583927
28 7.648122932 0.870444451
29 5.401774466 7.648122932
30 9.510375414 5.401774466
31 7.706688407 9.510375414
32 9.042388649 7.706688407
33 5.252355084 9.042388649
34 8.620749873 5.252355084
35 2.561363579 8.620749873
36 4.486648445 2.561363579
37 2.193138187 4.486648445
38 2.906378834 2.193138187
39 6.010745244 2.906378834
40 -0.006793729 6.010745244
41 4.651635579 -0.006793729
42 3.338732964 4.651635579
43 2.531977428 3.338732964
44 3.239008638 2.531977428
45 4.595045957 3.239008638
46 -6.990136024 4.595045957
47 1.632042648 -6.990136024
48 1.421150211 1.632042648
49 -0.158367141 1.421150211
50 4.927570916 -0.158367141
51 -2.004918427 4.927570916
52 -2.391748250 -2.004918427
53 -6.538437133 -2.391748250
54 -8.840074955 -6.538437133
55 -6.016802175 -8.840074955
56 -8.656187038 -6.016802175
57 -7.227447537 -8.656187038
58 -7.474752513 -7.227447537
59 -3.551885848 -7.474752513
60 -7.784962683 -3.551885848
> 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/7dm231258727146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8r8pc1258727146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m0lf1258727147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10u01t1258727147.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u4o71258727147.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/12jlhn1258727147.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/13oz1m1258727147.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/14hmv01258727147.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/15aanr1258727147.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/16dept1258727147.tab")
+ }
>
> system("convert tmp/15udr1258727146.ps tmp/15udr1258727146.png")
> system("convert tmp/2lplt1258727146.ps tmp/2lplt1258727146.png")
> system("convert tmp/3lwmk1258727146.ps tmp/3lwmk1258727146.png")
> system("convert tmp/426261258727146.ps tmp/426261258727146.png")
> system("convert tmp/534q61258727146.ps tmp/534q61258727146.png")
> system("convert tmp/618xw1258727146.ps tmp/618xw1258727146.png")
> system("convert tmp/7dm231258727146.ps tmp/7dm231258727146.png")
> system("convert tmp/8r8pc1258727146.ps tmp/8r8pc1258727146.png")
> system("convert tmp/9m0lf1258727147.ps tmp/9m0lf1258727147.png")
> system("convert tmp/10u01t1258727147.ps tmp/10u01t1258727147.png")
>
>
> proc.time()
user system elapsed
2.403 1.545 2.808