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(7.5,9.2,7.7,8.1,7.6,9.2,7.5,7.7,7.8,9.5,7.6,7.5,7.8,9.6,7.8,7.6,7.8,9.5,7.8,7.8,7.5,9.1,7.8,7.8,7.5,8.9,7.5,7.8,7.1,9,7.5,7.5,7.5,10.1,7.1,7.5,7.5,10.3,7.5,7.1,7.6,10.2,7.5,7.5,7.7,9.6,7.6,7.5,7.7,9.2,7.7,7.6,7.9,9.3,7.7,7.7,8.1,9.4,7.9,7.7,8.2,9.4,8.1,7.9,8.2,9.2,8.2,8.1,8.2,9,8.2,8.2,7.9,9,8.2,8.2,7.3,9,7.9,8.2,6.9,9.8,7.3,7.9,6.6,10,6.9,7.3,6.7,9.8,6.6,6.9,6.9,9.3,6.7,6.6,7,9,6.9,6.7,7.1,9,7,6.9,7.2,9.1,7.1,7,7.1,9.1,7.2,7.1,6.9,9.1,7.1,7.2,7,9.2,6.9,7.1,6.8,8.8,7,6.9,6.4,8.3,6.8,7,6.7,8.4,6.4,6.8,6.6,8.1,6.7,6.4,6.4,7.7,6.6,6.7,6.3,7.9,6.4,6.6,6.2,7.9,6.3,6.4,6.5,8,6.2,6.3,6.8,7.9,6.5,6.2,6.8,7.6,6.8,6.5,6.4,7.1,6.8,6.8,6.1,6.8,6.4,6.8,5.8,6.5,6.1,6.4,6.1,6.9,5.8,6.1,7.2,8.2,6.1,5.8,7.3,8.7,7.2,6.1,6.9,8.3,7.3,7.2,6.1,7.9,6.9,7.3,5.8,7.5,6.1,6.9,6.2,7.8,5.8,6.1,7.1,8.3,6.2,5.8,7.7,8.4,7.1,6.2,7.9,8.2,7.7,7.1,7.7,7.7,7.9,7.7,7.4,7.2,7.7,7.9,7.5,7.3,7.4,7.7,8,8.1,7.5,7.4,8.1,8.5,8,7.5),dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> 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 Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.5 9.2 7.7 8.1 1 0 0 0 0 0 0 0 0 0 0 1
2 7.6 9.2 7.5 7.7 0 1 0 0 0 0 0 0 0 0 0 2
3 7.8 9.5 7.6 7.5 0 0 1 0 0 0 0 0 0 0 0 3
4 7.8 9.6 7.8 7.6 0 0 0 1 0 0 0 0 0 0 0 4
5 7.8 9.5 7.8 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 9.1 7.8 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.5 8.9 7.5 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.1 9.0 7.5 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 10.1 7.1 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 10.3 7.5 7.1 0 0 0 0 0 0 0 0 0 1 0 10
11 7.6 10.2 7.5 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 7.7 9.6 7.6 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 7.7 9.2 7.7 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 9.3 7.7 7.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.1 9.4 7.9 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 9.4 8.1 7.9 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 9.2 8.2 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 9.0 8.2 8.2 0 0 0 0 0 1 0 0 0 0 0 18
19 7.9 9.0 8.2 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 7.3 9.0 7.9 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 6.9 9.8 7.3 7.9 0 0 0 0 0 0 0 0 1 0 0 21
22 6.6 10.0 6.9 7.3 0 0 0 0 0 0 0 0 0 1 0 22
23 6.7 9.8 6.6 6.9 0 0 0 0 0 0 0 0 0 0 1 23
24 6.9 9.3 6.7 6.6 0 0 0 0 0 0 0 0 0 0 0 24
25 7.0 9.0 6.9 6.7 1 0 0 0 0 0 0 0 0 0 0 25
26 7.1 9.0 7.0 6.9 0 1 0 0 0 0 0 0 0 0 0 26
27 7.2 9.1 7.1 7.0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.1 9.1 7.2 7.1 0 0 0 1 0 0 0 0 0 0 0 28
29 6.9 9.1 7.1 7.2 0 0 0 0 1 0 0 0 0 0 0 29
30 7.0 9.2 6.9 7.1 0 0 0 0 0 1 0 0 0 0 0 30
31 6.8 8.8 7.0 6.9 0 0 0 0 0 0 1 0 0 0 0 31
32 6.4 8.3 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 32
33 6.7 8.4 6.4 6.8 0 0 0 0 0 0 0 0 1 0 0 33
34 6.6 8.1 6.7 6.4 0 0 0 0 0 0 0 0 0 1 0 34
35 6.4 7.7 6.6 6.7 0 0 0 0 0 0 0 0 0 0 1 35
36 6.3 7.9 6.4 6.6 0 0 0 0 0 0 0 0 0 0 0 36
37 6.2 7.9 6.3 6.4 1 0 0 0 0 0 0 0 0 0 0 37
38 6.5 8.0 6.2 6.3 0 1 0 0 0 0 0 0 0 0 0 38
39 6.8 7.9 6.5 6.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 7.6 6.8 6.5 0 0 0 1 0 0 0 0 0 0 0 40
41 6.4 7.1 6.8 6.8 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 6.8 6.4 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 5.8 6.5 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 6.1 6.9 5.8 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 7.2 8.2 6.1 5.8 0 0 0 0 0 0 0 0 1 0 0 45
46 7.3 8.7 7.2 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 8.3 7.3 7.2 0 0 0 0 0 0 0 0 0 0 1 47
48 6.1 7.9 6.9 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 5.8 7.5 6.1 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 6.2 7.8 5.8 6.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.1 8.3 6.2 5.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.7 8.4 7.1 6.2 0 0 0 1 0 0 0 0 0 0 0 52
53 7.9 8.2 7.7 7.1 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 7.7 7.9 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 7.4 7.2 7.7 7.9 0 0 0 0 0 0 1 0 0 0 0 55
56 7.5 7.3 7.4 7.7 0 0 0 0 0 0 0 1 0 0 0 56
57 8.0 8.1 7.5 7.4 0 0 0 0 0 0 0 0 1 0 0 57
58 8.1 8.5 8.0 7.5 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
0.253787 0.117297 1.387180 -0.594692 0.142919 0.368637
M3 M4 M5 M6 M7 M8
0.320543 0.099748 0.076609 0.147112 0.104253 0.121497
M9 M10 M11 t
0.549585 -0.162274 0.020094 0.002332
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.53020 -0.12985 0.01636 0.12969 0.45185
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.253787 0.737165 0.344 0.73236
X 0.117297 0.073294 1.600 0.11701
Y1 1.387180 0.128288 10.813 1.03e-13 ***
Y2 -0.594692 0.129254 -4.601 3.85e-05 ***
M1 0.142919 0.159059 0.899 0.37403
M2 0.368637 0.157806 2.336 0.02434 *
M3 0.320543 0.162329 1.975 0.05491 .
M4 0.099748 0.166858 0.598 0.55318
M5 0.076609 0.163819 0.468 0.64246
M6 0.147112 0.164765 0.893 0.37702
M7 0.104253 0.167459 0.623 0.53694
M8 0.121497 0.163832 0.742 0.46246
M9 0.549585 0.160308 3.428 0.00137 **
M10 -0.162274 0.169222 -0.959 0.34308
M11 0.020094 0.166615 0.121 0.90458
t 0.002332 0.003531 0.660 0.51261
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2341 on 42 degrees of freedom
Multiple R-squared: 0.9062, Adjusted R-squared: 0.8726
F-statistic: 27.04 on 15 and 42 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.1385127 0.27702531 0.86148735
[2,] 0.1501454 0.30029078 0.84985461
[3,] 0.7888059 0.42238821 0.21119410
[4,] 0.6763820 0.64723606 0.32361803
[5,] 0.5872735 0.82545294 0.41272647
[6,] 0.5690825 0.86183505 0.43091752
[7,] 0.5048862 0.99022762 0.49511381
[8,] 0.4246319 0.84926381 0.57536810
[9,] 0.3191855 0.63837095 0.68081452
[10,] 0.2298083 0.45961664 0.77019168
[11,] 0.1715683 0.34313667 0.82843166
[12,] 0.1740250 0.34805008 0.82597496
[13,] 0.1434654 0.28693088 0.85653456
[14,] 0.2094326 0.41886529 0.79056735
[15,] 0.2880312 0.57606238 0.71196881
[16,] 0.2415673 0.48313455 0.75843273
[17,] 0.3917488 0.78349757 0.60825122
[18,] 0.8986949 0.20261016 0.10130508
[19,] 0.9678068 0.06438644 0.03219322
[20,] 0.9485961 0.10280781 0.05140390
[21,] 0.9590827 0.08183459 0.04091729
> postscript(file="/var/www/html/rcomp/tmp/19iq41259259666.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2sza31259259666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qmbq1259259666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4imtb1259259666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tm1o1259259666.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 = 58
Frequency = 1
1 2 3 4 5
0.1575477537 0.0690570299 0.0219736750 0.0107400701 0.1622149756
6 7 8 9 10
-0.1637008226 0.3164395153 -0.2932745863 0.1021503549 -0.0045316879
11 12 13 14 15
0.1603749866 0.2097977686 0.0322165302 0.0519062279 0.0085028467
16 17 18 19 20
0.1684682158 0.1929548971 0.2030488229 -0.0564242617 -0.2598470187
21 22 23 24 25
-0.5302045511 0.0539187517 0.1709553068 0.1302406669 -0.0977882958
26 27 28 29 30
-0.2456175982 -0.1908337812 -0.1516196619 -0.1326257562 0.1007764201
31 32 33 34 35
-0.2694340868 -0.2934568442 0.0003271489 -0.0089881699 -0.0296434952
36 37 38 39 40
0.0826259856 -0.1428459764 -0.0033767524 -0.1215078318 -0.1056019630
41 42 43 44 45
-0.2477388336 -0.0305124850 -0.0765192930 0.3947312780 0.3172632279
46 47 48 49 50
-0.2793493163 -0.3016867981 -0.4226644211 0.0508699883 0.1280310928
51 52 53 54 55
0.2818650913 0.0780133390 0.0251947172 -0.1096119354 0.0859381262
56 57 58
0.4518471712 0.1104638194 0.2389504224
> postscript(file="/var/www/html/rcomp/tmp/63m531259259666.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 0.1575477537 NA
1 0.0690570299 0.1575477537
2 0.0219736750 0.0690570299
3 0.0107400701 0.0219736750
4 0.1622149756 0.0107400701
5 -0.1637008226 0.1622149756
6 0.3164395153 -0.1637008226
7 -0.2932745863 0.3164395153
8 0.1021503549 -0.2932745863
9 -0.0045316879 0.1021503549
10 0.1603749866 -0.0045316879
11 0.2097977686 0.1603749866
12 0.0322165302 0.2097977686
13 0.0519062279 0.0322165302
14 0.0085028467 0.0519062279
15 0.1684682158 0.0085028467
16 0.1929548971 0.1684682158
17 0.2030488229 0.1929548971
18 -0.0564242617 0.2030488229
19 -0.2598470187 -0.0564242617
20 -0.5302045511 -0.2598470187
21 0.0539187517 -0.5302045511
22 0.1709553068 0.0539187517
23 0.1302406669 0.1709553068
24 -0.0977882958 0.1302406669
25 -0.2456175982 -0.0977882958
26 -0.1908337812 -0.2456175982
27 -0.1516196619 -0.1908337812
28 -0.1326257562 -0.1516196619
29 0.1007764201 -0.1326257562
30 -0.2694340868 0.1007764201
31 -0.2934568442 -0.2694340868
32 0.0003271489 -0.2934568442
33 -0.0089881699 0.0003271489
34 -0.0296434952 -0.0089881699
35 0.0826259856 -0.0296434952
36 -0.1428459764 0.0826259856
37 -0.0033767524 -0.1428459764
38 -0.1215078318 -0.0033767524
39 -0.1056019630 -0.1215078318
40 -0.2477388336 -0.1056019630
41 -0.0305124850 -0.2477388336
42 -0.0765192930 -0.0305124850
43 0.3947312780 -0.0765192930
44 0.3172632279 0.3947312780
45 -0.2793493163 0.3172632279
46 -0.3016867981 -0.2793493163
47 -0.4226644211 -0.3016867981
48 0.0508699883 -0.4226644211
49 0.1280310928 0.0508699883
50 0.2818650913 0.1280310928
51 0.0780133390 0.2818650913
52 0.0251947172 0.0780133390
53 -0.1096119354 0.0251947172
54 0.0859381262 -0.1096119354
55 0.4518471712 0.0859381262
56 0.1104638194 0.4518471712
57 0.2389504224 0.1104638194
58 NA 0.2389504224
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0690570299 0.1575477537
[2,] 0.0219736750 0.0690570299
[3,] 0.0107400701 0.0219736750
[4,] 0.1622149756 0.0107400701
[5,] -0.1637008226 0.1622149756
[6,] 0.3164395153 -0.1637008226
[7,] -0.2932745863 0.3164395153
[8,] 0.1021503549 -0.2932745863
[9,] -0.0045316879 0.1021503549
[10,] 0.1603749866 -0.0045316879
[11,] 0.2097977686 0.1603749866
[12,] 0.0322165302 0.2097977686
[13,] 0.0519062279 0.0322165302
[14,] 0.0085028467 0.0519062279
[15,] 0.1684682158 0.0085028467
[16,] 0.1929548971 0.1684682158
[17,] 0.2030488229 0.1929548971
[18,] -0.0564242617 0.2030488229
[19,] -0.2598470187 -0.0564242617
[20,] -0.5302045511 -0.2598470187
[21,] 0.0539187517 -0.5302045511
[22,] 0.1709553068 0.0539187517
[23,] 0.1302406669 0.1709553068
[24,] -0.0977882958 0.1302406669
[25,] -0.2456175982 -0.0977882958
[26,] -0.1908337812 -0.2456175982
[27,] -0.1516196619 -0.1908337812
[28,] -0.1326257562 -0.1516196619
[29,] 0.1007764201 -0.1326257562
[30,] -0.2694340868 0.1007764201
[31,] -0.2934568442 -0.2694340868
[32,] 0.0003271489 -0.2934568442
[33,] -0.0089881699 0.0003271489
[34,] -0.0296434952 -0.0089881699
[35,] 0.0826259856 -0.0296434952
[36,] -0.1428459764 0.0826259856
[37,] -0.0033767524 -0.1428459764
[38,] -0.1215078318 -0.0033767524
[39,] -0.1056019630 -0.1215078318
[40,] -0.2477388336 -0.1056019630
[41,] -0.0305124850 -0.2477388336
[42,] -0.0765192930 -0.0305124850
[43,] 0.3947312780 -0.0765192930
[44,] 0.3172632279 0.3947312780
[45,] -0.2793493163 0.3172632279
[46,] -0.3016867981 -0.2793493163
[47,] -0.4226644211 -0.3016867981
[48,] 0.0508699883 -0.4226644211
[49,] 0.1280310928 0.0508699883
[50,] 0.2818650913 0.1280310928
[51,] 0.0780133390 0.2818650913
[52,] 0.0251947172 0.0780133390
[53,] -0.1096119354 0.0251947172
[54,] 0.0859381262 -0.1096119354
[55,] 0.4518471712 0.0859381262
[56,] 0.1104638194 0.4518471712
[57,] 0.2389504224 0.1104638194
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0690570299 0.1575477537
2 0.0219736750 0.0690570299
3 0.0107400701 0.0219736750
4 0.1622149756 0.0107400701
5 -0.1637008226 0.1622149756
6 0.3164395153 -0.1637008226
7 -0.2932745863 0.3164395153
8 0.1021503549 -0.2932745863
9 -0.0045316879 0.1021503549
10 0.1603749866 -0.0045316879
11 0.2097977686 0.1603749866
12 0.0322165302 0.2097977686
13 0.0519062279 0.0322165302
14 0.0085028467 0.0519062279
15 0.1684682158 0.0085028467
16 0.1929548971 0.1684682158
17 0.2030488229 0.1929548971
18 -0.0564242617 0.2030488229
19 -0.2598470187 -0.0564242617
20 -0.5302045511 -0.2598470187
21 0.0539187517 -0.5302045511
22 0.1709553068 0.0539187517
23 0.1302406669 0.1709553068
24 -0.0977882958 0.1302406669
25 -0.2456175982 -0.0977882958
26 -0.1908337812 -0.2456175982
27 -0.1516196619 -0.1908337812
28 -0.1326257562 -0.1516196619
29 0.1007764201 -0.1326257562
30 -0.2694340868 0.1007764201
31 -0.2934568442 -0.2694340868
32 0.0003271489 -0.2934568442
33 -0.0089881699 0.0003271489
34 -0.0296434952 -0.0089881699
35 0.0826259856 -0.0296434952
36 -0.1428459764 0.0826259856
37 -0.0033767524 -0.1428459764
38 -0.1215078318 -0.0033767524
39 -0.1056019630 -0.1215078318
40 -0.2477388336 -0.1056019630
41 -0.0305124850 -0.2477388336
42 -0.0765192930 -0.0305124850
43 0.3947312780 -0.0765192930
44 0.3172632279 0.3947312780
45 -0.2793493163 0.3172632279
46 -0.3016867981 -0.2793493163
47 -0.4226644211 -0.3016867981
48 0.0508699883 -0.4226644211
49 0.1280310928 0.0508699883
50 0.2818650913 0.1280310928
51 0.0780133390 0.2818650913
52 0.0251947172 0.0780133390
53 -0.1096119354 0.0251947172
54 0.0859381262 -0.1096119354
55 0.4518471712 0.0859381262
56 0.1104638194 0.4518471712
57 0.2389504224 0.1104638194
> 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/7xd731259259666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8pubd1259259666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99lrt1259259666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107zpn1259259666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jh1p1259259666.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/12sv871259259666.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/13k9581259259666.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/148u9y1259259666.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/15tx0t1259259667.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/16txrr1259259667.tab")
+ }
>
> system("convert tmp/19iq41259259666.ps tmp/19iq41259259666.png")
> system("convert tmp/2sza31259259666.ps tmp/2sza31259259666.png")
> system("convert tmp/3qmbq1259259666.ps tmp/3qmbq1259259666.png")
> system("convert tmp/4imtb1259259666.ps tmp/4imtb1259259666.png")
> system("convert tmp/5tm1o1259259666.ps tmp/5tm1o1259259666.png")
> system("convert tmp/63m531259259666.ps tmp/63m531259259666.png")
> system("convert tmp/7xd731259259666.ps tmp/7xd731259259666.png")
> system("convert tmp/8pubd1259259666.ps tmp/8pubd1259259666.png")
> system("convert tmp/99lrt1259259666.ps tmp/99lrt1259259666.png")
> system("convert tmp/107zpn1259259666.ps tmp/107zpn1259259666.png")
>
>
> proc.time()
user system elapsed
2.368 1.588 3.111