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(4.3,96.2,4.1,96.8,3.9,109.9,3.8,88,3.7,91.1,3.7,106.4,4.1,68.6,4.1,100.1,3.8,108,3.7,106,3.5,108.6,3.6,91.5,4.1,99.2,3.8,98,3.7,96.6,3.6,102.8,3.3,96.9,3.4,110,3.7,70.5,3.7,101.9,3.4,109.6,3.3,107.8,3,113,3,93.8,3.3,108,3,102.8,2.9,116.3,2.8,89.2,2.5,106.7,2.6,112.1,2.8,74.2,2.7,108.8,2.4,111.5,2.2,118.8,2.1,118.9,2.1,97.6,2.3,116.4,2.1,107.9,2,121.2,1.9,97.9,1.7,113.4,1.8,117.6,2.1,79.6,2,115.9,1.8,115.7,1.7,129.1,1.6,123.3,1.6,96.7,1.8,121.2,1.7,118.2,1.7,102.1,1.5,125.4,1.5,116.7,1.5,121.3,1.8,85.3,1.8,114.2,1.7,124.4,1.7,131,1.8,118.3,2,99.6),dim=c(2,60),dimnames=list(c('unempl','proman'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('unempl','proman'),1:60))
> 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
unempl proman M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 4.3 96.2 1 0 0 0 0 0 0 0 0 0 0 1
2 4.1 96.8 0 1 0 0 0 0 0 0 0 0 0 2
3 3.9 109.9 0 0 1 0 0 0 0 0 0 0 0 3
4 3.8 88.0 0 0 0 1 0 0 0 0 0 0 0 4
5 3.7 91.1 0 0 0 0 1 0 0 0 0 0 0 5
6 3.7 106.4 0 0 0 0 0 1 0 0 0 0 0 6
7 4.1 68.6 0 0 0 0 0 0 1 0 0 0 0 7
8 4.1 100.1 0 0 0 0 0 0 0 1 0 0 0 8
9 3.8 108.0 0 0 0 0 0 0 0 0 1 0 0 9
10 3.7 106.0 0 0 0 0 0 0 0 0 0 1 0 10
11 3.5 108.6 0 0 0 0 0 0 0 0 0 0 1 11
12 3.6 91.5 0 0 0 0 0 0 0 0 0 0 0 12
13 4.1 99.2 1 0 0 0 0 0 0 0 0 0 0 13
14 3.8 98.0 0 1 0 0 0 0 0 0 0 0 0 14
15 3.7 96.6 0 0 1 0 0 0 0 0 0 0 0 15
16 3.6 102.8 0 0 0 1 0 0 0 0 0 0 0 16
17 3.3 96.9 0 0 0 0 1 0 0 0 0 0 0 17
18 3.4 110.0 0 0 0 0 0 1 0 0 0 0 0 18
19 3.7 70.5 0 0 0 0 0 0 1 0 0 0 0 19
20 3.7 101.9 0 0 0 0 0 0 0 1 0 0 0 20
21 3.4 109.6 0 0 0 0 0 0 0 0 1 0 0 21
22 3.3 107.8 0 0 0 0 0 0 0 0 0 1 0 22
23 3.0 113.0 0 0 0 0 0 0 0 0 0 0 1 23
24 3.0 93.8 0 0 0 0 0 0 0 0 0 0 0 24
25 3.3 108.0 1 0 0 0 0 0 0 0 0 0 0 25
26 3.0 102.8 0 1 0 0 0 0 0 0 0 0 0 26
27 2.9 116.3 0 0 1 0 0 0 0 0 0 0 0 27
28 2.8 89.2 0 0 0 1 0 0 0 0 0 0 0 28
29 2.5 106.7 0 0 0 0 1 0 0 0 0 0 0 29
30 2.6 112.1 0 0 0 0 0 1 0 0 0 0 0 30
31 2.8 74.2 0 0 0 0 0 0 1 0 0 0 0 31
32 2.7 108.8 0 0 0 0 0 0 0 1 0 0 0 32
33 2.4 111.5 0 0 0 0 0 0 0 0 1 0 0 33
34 2.2 118.8 0 0 0 0 0 0 0 0 0 1 0 34
35 2.1 118.9 0 0 0 0 0 0 0 0 0 0 1 35
36 2.1 97.6 0 0 0 0 0 0 0 0 0 0 0 36
37 2.3 116.4 1 0 0 0 0 0 0 0 0 0 0 37
38 2.1 107.9 0 1 0 0 0 0 0 0 0 0 0 38
39 2.0 121.2 0 0 1 0 0 0 0 0 0 0 0 39
40 1.9 97.9 0 0 0 1 0 0 0 0 0 0 0 40
41 1.7 113.4 0 0 0 0 1 0 0 0 0 0 0 41
42 1.8 117.6 0 0 0 0 0 1 0 0 0 0 0 42
43 2.1 79.6 0 0 0 0 0 0 1 0 0 0 0 43
44 2.0 115.9 0 0 0 0 0 0 0 1 0 0 0 44
45 1.8 115.7 0 0 0 0 0 0 0 0 1 0 0 45
46 1.7 129.1 0 0 0 0 0 0 0 0 0 1 0 46
47 1.6 123.3 0 0 0 0 0 0 0 0 0 0 1 47
48 1.6 96.7 0 0 0 0 0 0 0 0 0 0 0 48
49 1.8 121.2 1 0 0 0 0 0 0 0 0 0 0 49
50 1.7 118.2 0 1 0 0 0 0 0 0 0 0 0 50
51 1.7 102.1 0 0 1 0 0 0 0 0 0 0 0 51
52 1.5 125.4 0 0 0 1 0 0 0 0 0 0 0 52
53 1.5 116.7 0 0 0 0 1 0 0 0 0 0 0 53
54 1.5 121.3 0 0 0 0 0 1 0 0 0 0 0 54
55 1.8 85.3 0 0 0 0 0 0 1 0 0 0 0 55
56 1.8 114.2 0 0 0 0 0 0 0 1 0 0 0 56
57 1.7 124.4 0 0 0 0 0 0 0 0 1 0 0 57
58 1.7 131.0 0 0 0 0 0 0 0 0 0 1 0 58
59 1.8 118.3 0 0 0 0 0 0 0 0 0 0 1 59
60 2.0 99.6 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) proman M1 M2 M3 M4
5.27351 -0.01264 0.36677 0.14753 0.14866 -0.03505
M5 M6 M7 M8 M9 M10
-0.11620 0.09601 -0.03785 0.37800 0.25405 0.25797
M11 t
0.15567 -0.04450
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.33775 -0.15644 -0.01289 0.14048 0.65549
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.273510 0.534087 9.874 6.08e-13 ***
proman -0.012642 0.006315 -2.002 0.0512 .
M1 0.366770 0.186394 1.968 0.0551 .
M2 0.147528 0.173759 0.849 0.4003
M3 0.148661 0.186955 0.795 0.4306
M4 -0.035053 0.161555 -0.217 0.8292
M5 -0.116196 0.170646 -0.681 0.4993
M6 0.096009 0.198414 0.484 0.6308
M7 -0.037852 0.191981 -0.197 0.8446
M8 0.378005 0.176450 2.142 0.0375 *
M9 0.254055 0.195090 1.302 0.1993
M10 0.257968 0.213032 1.211 0.2321
M11 0.155666 0.202344 0.769 0.4456
t -0.044498 0.003026 -14.706 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.242 on 46 degrees of freedom
Multiple R-squared: 0.9437, Adjusted R-squared: 0.9278
F-statistic: 59.28 on 13 and 46 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,] 0.0236747284 0.0473494567 0.9763253
[2,] 0.0056961910 0.0113923821 0.9943038
[3,] 0.0031426033 0.0062852065 0.9968574
[4,] 0.0015781763 0.0031563525 0.9984218
[5,] 0.0007512909 0.0015025818 0.9992487
[6,] 0.0003111785 0.0006223571 0.9996888
[7,] 0.0003078869 0.0006157738 0.9996921
[8,] 0.0007586627 0.0015173255 0.9992413
[9,] 0.0049028818 0.0098057635 0.9950971
[10,] 0.0151697409 0.0303394819 0.9848303
[11,] 0.0249892238 0.0499784476 0.9750108
[12,] 0.0406834782 0.0813669563 0.9593165
[13,] 0.0532691389 0.1065382778 0.9467309
[14,] 0.0875458855 0.1750917710 0.9124541
[15,] 0.1785072759 0.3570145519 0.8214927
[16,] 0.3672345792 0.7344691584 0.6327654
[17,] 0.4954330921 0.9908661841 0.5045669
[18,] 0.5347150980 0.9305698040 0.4652849
[19,] 0.4883566255 0.9767132510 0.5116434
[20,] 0.4404501429 0.8809002858 0.5595499
[21,] 0.4943203795 0.9886407591 0.5056796
[22,] 0.4752597747 0.9505195494 0.5247402
[23,] 0.5823879435 0.8352241130 0.4176121
[24,] 0.4872443496 0.9744886991 0.5127557
[25,] 0.3769708958 0.7539417915 0.6230291
[26,] 0.3377185971 0.6754371942 0.6622814
[27,] 0.3229813290 0.6459626580 0.6770187
> postscript(file="/var/www/html/rcomp/tmp/1oqon1258665376.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fx8l1258665376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34iq31258665376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40aj91258665376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qan61258665376.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 = 60
Frequency = 1
1 2 3 4 5
-0.0796569376 -0.0083319376 0.0006388753 -0.1480004105 -0.0831703613
6 7 8 9 10
-0.0574601396 0.0430455255 0.0698982348 0.0382154762 -0.0464834155
11 12 13 14 15
-0.0668149425 0.0171779147 0.2922466543 0.2408167282 0.1664839695
16 17 18 19 20
0.3730743638 0.1241297824 0.2220284278 0.2010433292 0.2266318760
21 22 23 24 25
0.1924207923 0.1102502257 0.0227869253 -0.0197676313 0.1374716749
26 27 28 29 30
0.0354752463 0.1495027094 -0.0648730297 -0.0180035714 -0.0174454434
31 32 33 34 35
-0.1182039409 -0.1521621921 -0.2495814040 -0.3167131773 -0.2686487684
36 37 38 39 40
-0.3377507389 -0.2223599548 -0.2660737480 -0.1545746099 -0.3209121717
41 42 43 44 45
-0.1993259647 -0.2139377874 -0.2159604475 -0.2284279351 -0.2625078613
46 47 48 49 50
-0.1525257183 -0.1790469006 -0.3151494869 -0.1277014367 -0.0018862889
51 52 53 54 55
-0.1620509444 0.1607112481 0.1763701150 0.0668149425 0.0900755337
56 57 58 59 60
0.0840600164 0.2814529967 0.4054720854 0.4917236863 0.6554899425
> postscript(file="/var/www/html/rcomp/tmp/64hzb1258665376.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0796569376 NA
1 -0.0083319376 -0.0796569376
2 0.0006388753 -0.0083319376
3 -0.1480004105 0.0006388753
4 -0.0831703613 -0.1480004105
5 -0.0574601396 -0.0831703613
6 0.0430455255 -0.0574601396
7 0.0698982348 0.0430455255
8 0.0382154762 0.0698982348
9 -0.0464834155 0.0382154762
10 -0.0668149425 -0.0464834155
11 0.0171779147 -0.0668149425
12 0.2922466543 0.0171779147
13 0.2408167282 0.2922466543
14 0.1664839695 0.2408167282
15 0.3730743638 0.1664839695
16 0.1241297824 0.3730743638
17 0.2220284278 0.1241297824
18 0.2010433292 0.2220284278
19 0.2266318760 0.2010433292
20 0.1924207923 0.2266318760
21 0.1102502257 0.1924207923
22 0.0227869253 0.1102502257
23 -0.0197676313 0.0227869253
24 0.1374716749 -0.0197676313
25 0.0354752463 0.1374716749
26 0.1495027094 0.0354752463
27 -0.0648730297 0.1495027094
28 -0.0180035714 -0.0648730297
29 -0.0174454434 -0.0180035714
30 -0.1182039409 -0.0174454434
31 -0.1521621921 -0.1182039409
32 -0.2495814040 -0.1521621921
33 -0.3167131773 -0.2495814040
34 -0.2686487684 -0.3167131773
35 -0.3377507389 -0.2686487684
36 -0.2223599548 -0.3377507389
37 -0.2660737480 -0.2223599548
38 -0.1545746099 -0.2660737480
39 -0.3209121717 -0.1545746099
40 -0.1993259647 -0.3209121717
41 -0.2139377874 -0.1993259647
42 -0.2159604475 -0.2139377874
43 -0.2284279351 -0.2159604475
44 -0.2625078613 -0.2284279351
45 -0.1525257183 -0.2625078613
46 -0.1790469006 -0.1525257183
47 -0.3151494869 -0.1790469006
48 -0.1277014367 -0.3151494869
49 -0.0018862889 -0.1277014367
50 -0.1620509444 -0.0018862889
51 0.1607112481 -0.1620509444
52 0.1763701150 0.1607112481
53 0.0668149425 0.1763701150
54 0.0900755337 0.0668149425
55 0.0840600164 0.0900755337
56 0.2814529967 0.0840600164
57 0.4054720854 0.2814529967
58 0.4917236863 0.4054720854
59 0.6554899425 0.4917236863
60 NA 0.6554899425
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0083319376 -0.0796569376
[2,] 0.0006388753 -0.0083319376
[3,] -0.1480004105 0.0006388753
[4,] -0.0831703613 -0.1480004105
[5,] -0.0574601396 -0.0831703613
[6,] 0.0430455255 -0.0574601396
[7,] 0.0698982348 0.0430455255
[8,] 0.0382154762 0.0698982348
[9,] -0.0464834155 0.0382154762
[10,] -0.0668149425 -0.0464834155
[11,] 0.0171779147 -0.0668149425
[12,] 0.2922466543 0.0171779147
[13,] 0.2408167282 0.2922466543
[14,] 0.1664839695 0.2408167282
[15,] 0.3730743638 0.1664839695
[16,] 0.1241297824 0.3730743638
[17,] 0.2220284278 0.1241297824
[18,] 0.2010433292 0.2220284278
[19,] 0.2266318760 0.2010433292
[20,] 0.1924207923 0.2266318760
[21,] 0.1102502257 0.1924207923
[22,] 0.0227869253 0.1102502257
[23,] -0.0197676313 0.0227869253
[24,] 0.1374716749 -0.0197676313
[25,] 0.0354752463 0.1374716749
[26,] 0.1495027094 0.0354752463
[27,] -0.0648730297 0.1495027094
[28,] -0.0180035714 -0.0648730297
[29,] -0.0174454434 -0.0180035714
[30,] -0.1182039409 -0.0174454434
[31,] -0.1521621921 -0.1182039409
[32,] -0.2495814040 -0.1521621921
[33,] -0.3167131773 -0.2495814040
[34,] -0.2686487684 -0.3167131773
[35,] -0.3377507389 -0.2686487684
[36,] -0.2223599548 -0.3377507389
[37,] -0.2660737480 -0.2223599548
[38,] -0.1545746099 -0.2660737480
[39,] -0.3209121717 -0.1545746099
[40,] -0.1993259647 -0.3209121717
[41,] -0.2139377874 -0.1993259647
[42,] -0.2159604475 -0.2139377874
[43,] -0.2284279351 -0.2159604475
[44,] -0.2625078613 -0.2284279351
[45,] -0.1525257183 -0.2625078613
[46,] -0.1790469006 -0.1525257183
[47,] -0.3151494869 -0.1790469006
[48,] -0.1277014367 -0.3151494869
[49,] -0.0018862889 -0.1277014367
[50,] -0.1620509444 -0.0018862889
[51,] 0.1607112481 -0.1620509444
[52,] 0.1763701150 0.1607112481
[53,] 0.0668149425 0.1763701150
[54,] 0.0900755337 0.0668149425
[55,] 0.0840600164 0.0900755337
[56,] 0.2814529967 0.0840600164
[57,] 0.4054720854 0.2814529967
[58,] 0.4917236863 0.4054720854
[59,] 0.6554899425 0.4917236863
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0083319376 -0.0796569376
2 0.0006388753 -0.0083319376
3 -0.1480004105 0.0006388753
4 -0.0831703613 -0.1480004105
5 -0.0574601396 -0.0831703613
6 0.0430455255 -0.0574601396
7 0.0698982348 0.0430455255
8 0.0382154762 0.0698982348
9 -0.0464834155 0.0382154762
10 -0.0668149425 -0.0464834155
11 0.0171779147 -0.0668149425
12 0.2922466543 0.0171779147
13 0.2408167282 0.2922466543
14 0.1664839695 0.2408167282
15 0.3730743638 0.1664839695
16 0.1241297824 0.3730743638
17 0.2220284278 0.1241297824
18 0.2010433292 0.2220284278
19 0.2266318760 0.2010433292
20 0.1924207923 0.2266318760
21 0.1102502257 0.1924207923
22 0.0227869253 0.1102502257
23 -0.0197676313 0.0227869253
24 0.1374716749 -0.0197676313
25 0.0354752463 0.1374716749
26 0.1495027094 0.0354752463
27 -0.0648730297 0.1495027094
28 -0.0180035714 -0.0648730297
29 -0.0174454434 -0.0180035714
30 -0.1182039409 -0.0174454434
31 -0.1521621921 -0.1182039409
32 -0.2495814040 -0.1521621921
33 -0.3167131773 -0.2495814040
34 -0.2686487684 -0.3167131773
35 -0.3377507389 -0.2686487684
36 -0.2223599548 -0.3377507389
37 -0.2660737480 -0.2223599548
38 -0.1545746099 -0.2660737480
39 -0.3209121717 -0.1545746099
40 -0.1993259647 -0.3209121717
41 -0.2139377874 -0.1993259647
42 -0.2159604475 -0.2139377874
43 -0.2284279351 -0.2159604475
44 -0.2625078613 -0.2284279351
45 -0.1525257183 -0.2625078613
46 -0.1790469006 -0.1525257183
47 -0.3151494869 -0.1790469006
48 -0.1277014367 -0.3151494869
49 -0.0018862889 -0.1277014367
50 -0.1620509444 -0.0018862889
51 0.1607112481 -0.1620509444
52 0.1763701150 0.1607112481
53 0.0668149425 0.1763701150
54 0.0900755337 0.0668149425
55 0.0840600164 0.0900755337
56 0.2814529967 0.0840600164
57 0.4054720854 0.2814529967
58 0.4917236863 0.4054720854
59 0.6554899425 0.4917236863
> 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/7fzvz1258665376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lp361258665376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9iws51258665376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100ofd1258665376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11inxk1258665376.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/12hszb1258665376.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/13q06s1258665376.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/14hikr1258665376.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/15h45l1258665376.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/16bnyr1258665376.tab")
+ }
>
> system("convert tmp/1oqon1258665376.ps tmp/1oqon1258665376.png")
> system("convert tmp/2fx8l1258665376.ps tmp/2fx8l1258665376.png")
> system("convert tmp/34iq31258665376.ps tmp/34iq31258665376.png")
> system("convert tmp/40aj91258665376.ps tmp/40aj91258665376.png")
> system("convert tmp/5qan61258665376.ps tmp/5qan61258665376.png")
> system("convert tmp/64hzb1258665376.ps tmp/64hzb1258665376.png")
> system("convert tmp/7fzvz1258665376.ps tmp/7fzvz1258665376.png")
> system("convert tmp/8lp361258665376.ps tmp/8lp361258665376.png")
> system("convert tmp/9iws51258665376.ps tmp/9iws51258665376.png")
> system("convert tmp/100ofd1258665376.ps tmp/100ofd1258665376.png")
>
>
> proc.time()
user system elapsed
2.365 1.545 2.824