R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(8.7
+ ,110.3
+ ,9.3
+ ,9.3
+ ,8.2
+ ,103.9
+ ,8.7
+ ,9.3
+ ,8.3
+ ,101.6
+ ,8.2
+ ,8.7
+ ,8.5
+ ,94.6
+ ,8.3
+ ,8.2
+ ,8.6
+ ,95.9
+ ,8.5
+ ,8.3
+ ,8.5
+ ,104.7
+ ,8.6
+ ,8.5
+ ,8.2
+ ,102.8
+ ,8.5
+ ,8.6
+ ,8.1
+ ,98.1
+ ,8.2
+ ,8.5
+ ,7.9
+ ,113.9
+ ,8.1
+ ,8.2
+ ,8.6
+ ,80.9
+ ,7.9
+ ,8.1
+ ,8.7
+ ,95.7
+ ,8.6
+ ,7.9
+ ,8.7
+ ,113.2
+ ,8.7
+ ,8.6
+ ,8.5
+ ,105.9
+ ,8.7
+ ,8.7
+ ,8.4
+ ,108.8
+ ,8.5
+ ,8.7
+ ,8.5
+ ,102.3
+ ,8.4
+ ,8.5
+ ,8.7
+ ,99
+ ,8.5
+ ,8.4
+ ,8.7
+ ,100.7
+ ,8.7
+ ,8.5
+ ,8.6
+ ,115.5
+ ,8.7
+ ,8.7
+ ,8.5
+ ,100.7
+ ,8.6
+ ,8.7
+ ,8.3
+ ,109.9
+ ,8.5
+ ,8.6
+ ,8
+ ,114.6
+ ,8.3
+ ,8.5
+ ,8.2
+ ,85.4
+ ,8
+ ,8.3
+ ,8.1
+ ,100.5
+ ,8.2
+ ,8
+ ,8.1
+ ,114.8
+ ,8.1
+ ,8.2
+ ,8
+ ,116.5
+ ,8.1
+ ,8.1
+ ,7.9
+ ,112.9
+ ,8
+ ,8.1
+ ,7.9
+ ,102
+ ,7.9
+ ,8
+ ,8
+ ,106
+ ,7.9
+ ,7.9
+ ,8
+ ,105.3
+ ,8
+ ,7.9
+ ,7.9
+ ,118.8
+ ,8
+ ,8
+ ,8
+ ,106.1
+ ,7.9
+ ,8
+ ,7.7
+ ,109.3
+ ,8
+ ,7.9
+ ,7.2
+ ,117.2
+ ,7.7
+ ,8
+ ,7.5
+ ,92.5
+ ,7.2
+ ,7.7
+ ,7.3
+ ,104.2
+ ,7.5
+ ,7.2
+ ,7
+ ,112.5
+ ,7.3
+ ,7.5
+ ,7
+ ,122.4
+ ,7
+ ,7.3
+ ,7
+ ,113.3
+ ,7
+ ,7
+ ,7.2
+ ,100
+ ,7
+ ,7
+ ,7.3
+ ,110.7
+ ,7.2
+ ,7
+ ,7.1
+ ,112.8
+ ,7.3
+ ,7.2
+ ,6.8
+ ,109.8
+ ,7.1
+ ,7.3
+ ,6.4
+ ,117.3
+ ,6.8
+ ,7.1
+ ,6.1
+ ,109.1
+ ,6.4
+ ,6.8
+ ,6.5
+ ,115.9
+ ,6.1
+ ,6.4
+ ,7.7
+ ,96
+ ,6.5
+ ,6.1
+ ,7.9
+ ,99.8
+ ,7.7
+ ,6.5
+ ,7.5
+ ,116.8
+ ,7.9
+ ,7.7
+ ,6.9
+ ,115.7
+ ,7.5
+ ,7.9
+ ,6.6
+ ,99.4
+ ,6.9
+ ,7.5
+ ,6.9
+ ,94.3
+ ,6.6
+ ,6.9
+ ,7.7
+ ,91
+ ,6.9
+ ,6.6
+ ,8
+ ,93.2
+ ,7.7
+ ,6.9
+ ,8
+ ,103.1
+ ,8
+ ,7.7
+ ,7.7
+ ,94.1
+ ,8
+ ,8
+ ,7.3
+ ,91.8
+ ,7.7
+ ,8
+ ,7.4
+ ,102.7
+ ,7.3
+ ,7.7
+ ,8.1
+ ,82.6
+ ,7.4
+ ,7.3)
+ ,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 = 'Do not include Seasonal 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 t
1 8.7 110.3 9.3 9.3 1
2 8.2 103.9 8.7 9.3 2
3 8.3 101.6 8.2 8.7 3
4 8.5 94.6 8.3 8.2 4
5 8.6 95.9 8.5 8.3 5
6 8.5 104.7 8.6 8.5 6
7 8.2 102.8 8.5 8.6 7
8 8.1 98.1 8.2 8.5 8
9 7.9 113.9 8.1 8.2 9
10 8.6 80.9 7.9 8.1 10
11 8.7 95.7 8.6 7.9 11
12 8.7 113.2 8.7 8.6 12
13 8.5 105.9 8.7 8.7 13
14 8.4 108.8 8.5 8.7 14
15 8.5 102.3 8.4 8.5 15
16 8.7 99.0 8.5 8.4 16
17 8.7 100.7 8.7 8.5 17
18 8.6 115.5 8.7 8.7 18
19 8.5 100.7 8.6 8.7 19
20 8.3 109.9 8.5 8.6 20
21 8.0 114.6 8.3 8.5 21
22 8.2 85.4 8.0 8.3 22
23 8.1 100.5 8.2 8.0 23
24 8.1 114.8 8.1 8.2 24
25 8.0 116.5 8.1 8.1 25
26 7.9 112.9 8.0 8.1 26
27 7.9 102.0 7.9 8.0 27
28 8.0 106.0 7.9 7.9 28
29 8.0 105.3 8.0 7.9 29
30 7.9 118.8 8.0 8.0 30
31 8.0 106.1 7.9 8.0 31
32 7.7 109.3 8.0 7.9 32
33 7.2 117.2 7.7 8.0 33
34 7.5 92.5 7.2 7.7 34
35 7.3 104.2 7.5 7.2 35
36 7.0 112.5 7.3 7.5 36
37 7.0 122.4 7.0 7.3 37
38 7.0 113.3 7.0 7.0 38
39 7.2 100.0 7.0 7.0 39
40 7.3 110.7 7.2 7.0 40
41 7.1 112.8 7.3 7.2 41
42 6.8 109.8 7.1 7.3 42
43 6.4 117.3 6.8 7.1 43
44 6.1 109.1 6.4 6.8 44
45 6.5 115.9 6.1 6.4 45
46 7.7 96.0 6.5 6.1 46
47 7.9 99.8 7.7 6.5 47
48 7.5 116.8 7.9 7.7 48
49 6.9 115.7 7.5 7.9 49
50 6.6 99.4 6.9 7.5 50
51 6.9 94.3 6.6 6.9 51
52 7.7 91.0 6.9 6.6 52
53 8.0 93.2 7.7 6.9 53
54 8.0 103.1 8.0 7.7 54
55 7.7 94.1 8.0 8.0 55
56 7.3 91.8 7.7 8.0 56
57 7.4 102.7 7.3 7.7 57
58 8.1 82.6 7.4 7.3 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 t
5.05137 -0.01873 1.05792 -0.41866 -0.00924
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.424773 -0.166898 -0.008002 0.173189 0.549440
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.051369 0.695912 7.259 1.73e-09 ***
X -0.018734 0.003139 -5.969 2.03e-07 ***
Y1 1.057922 0.099071 10.678 8.06e-15 ***
Y2 -0.418658 0.098669 -4.243 8.90e-05 ***
t -0.009240 0.002802 -3.298 0.00174 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2216 on 53 degrees of freedom
Multiple R-squared: 0.8972, Adjusted R-squared: 0.8894
F-statistic: 115.6 on 4 and 53 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,] 0.035975585 0.071951170 0.9640244
[2,] 0.041327017 0.082654034 0.9586730
[3,] 0.106252714 0.212505428 0.8937473
[4,] 0.058728762 0.117457524 0.9412712
[5,] 0.309462315 0.618924630 0.6905377
[6,] 0.220460737 0.440921474 0.7795393
[7,] 0.146908896 0.293817791 0.8530911
[8,] 0.092896521 0.185793042 0.9071035
[9,] 0.057007907 0.114015814 0.9429921
[10,] 0.034860605 0.069721209 0.9651394
[11,] 0.027666693 0.055333386 0.9723333
[12,] 0.023340995 0.046681989 0.9766590
[13,] 0.017571471 0.035142943 0.9824285
[14,] 0.016722534 0.033445068 0.9832775
[15,] 0.018727121 0.037454243 0.9812729
[16,] 0.034360161 0.068720322 0.9656398
[17,] 0.026787931 0.053575863 0.9732121
[18,] 0.018382511 0.036765023 0.9816175
[19,] 0.012726975 0.025453951 0.9872730
[20,] 0.009677158 0.019354317 0.9903228
[21,] 0.006027415 0.012054829 0.9939726
[22,] 0.003859349 0.007718698 0.9961407
[23,] 0.004226187 0.008452373 0.9957738
[24,] 0.005274303 0.010548607 0.9947257
[25,] 0.009822588 0.019645176 0.9901774
[26,] 0.019084197 0.038168395 0.9809158
[27,] 0.025498848 0.050997697 0.9745012
[28,] 0.045853509 0.091707018 0.9541465
[29,] 0.041067997 0.082135994 0.9589320
[30,] 0.093083231 0.186166462 0.9069168
[31,] 0.074582580 0.149165159 0.9254174
[32,] 0.068918131 0.137836262 0.9310819
[33,] 0.091030445 0.182060891 0.9089696
[34,] 0.109148256 0.218296512 0.8908517
[35,] 0.190288939 0.380577878 0.8097111
[36,] 0.160804445 0.321608890 0.8391956
[37,] 0.241426492 0.482852985 0.7585735
[38,] 0.294824929 0.589649857 0.7051751
[39,] 0.825666411 0.348667177 0.1743336
[40,] 0.800972926 0.398054147 0.1990271
[41,] 0.739952490 0.520095021 0.2600475
[42,] 0.655390745 0.689218510 0.3446093
[43,] 0.498680873 0.997361745 0.5013191
> postscript(file="/var/www/html/rcomp/tmp/1pxcu1261078666.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2d6da1261078666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3s4jp1261078666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/42s951261078666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5h3df1261078666.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
-0.2209403159 -0.1968439618 0.1470744447 -0.0899438647 -0.1260684986
6 7 8 9 10
-0.0740310300 -0.2527274655 -0.1560259099 -0.0705957309 0.1901447427
11 12 13 14 15
-0.2476308438 0.2767202844 -0.0089313241 0.1662213074 0.1757516832
16 17 18 19 20
0.1755118445 0.0468807637 0.3171137347 0.0548844386 0.1004025459
21 22 23 24 25
0.0674103895 -0.0367339524 -0.1817941134 0.2848641238 0.1840859051
26 27 28 29 30
0.1316760929 0.0006431621 0.1429528731 0.0332869053 0.2373000527
31 32 33 34 35
0.2144119098 -0.1640576926 -0.1475776649 0.1022991029 -0.3959800187
36 37 38 39 40
-0.1940670927 0.2342833709 -0.0525523315 -0.0924730113 0.0056350739
41 42 43 44 45
-0.1678444702 -0.2613559691 -0.2779668235 -0.4247732019 0.2617706732
46 47 48 49 50
0.5494402101 -0.2721744628 -0.0536536014 -0.1581205323 -0.2869527205
51 52 53 54 55
-0.0070735998 0.2973705939 -0.0729152376 0.1393397452 -0.1944279108
56 57 58
-0.3108992608 0.3001115163 0.3595451226
> postscript(file="/var/www/html/rcomp/tmp/60f9j1261078666.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 -0.2209403159 NA
1 -0.1968439618 -0.2209403159
2 0.1470744447 -0.1968439618
3 -0.0899438647 0.1470744447
4 -0.1260684986 -0.0899438647
5 -0.0740310300 -0.1260684986
6 -0.2527274655 -0.0740310300
7 -0.1560259099 -0.2527274655
8 -0.0705957309 -0.1560259099
9 0.1901447427 -0.0705957309
10 -0.2476308438 0.1901447427
11 0.2767202844 -0.2476308438
12 -0.0089313241 0.2767202844
13 0.1662213074 -0.0089313241
14 0.1757516832 0.1662213074
15 0.1755118445 0.1757516832
16 0.0468807637 0.1755118445
17 0.3171137347 0.0468807637
18 0.0548844386 0.3171137347
19 0.1004025459 0.0548844386
20 0.0674103895 0.1004025459
21 -0.0367339524 0.0674103895
22 -0.1817941134 -0.0367339524
23 0.2848641238 -0.1817941134
24 0.1840859051 0.2848641238
25 0.1316760929 0.1840859051
26 0.0006431621 0.1316760929
27 0.1429528731 0.0006431621
28 0.0332869053 0.1429528731
29 0.2373000527 0.0332869053
30 0.2144119098 0.2373000527
31 -0.1640576926 0.2144119098
32 -0.1475776649 -0.1640576926
33 0.1022991029 -0.1475776649
34 -0.3959800187 0.1022991029
35 -0.1940670927 -0.3959800187
36 0.2342833709 -0.1940670927
37 -0.0525523315 0.2342833709
38 -0.0924730113 -0.0525523315
39 0.0056350739 -0.0924730113
40 -0.1678444702 0.0056350739
41 -0.2613559691 -0.1678444702
42 -0.2779668235 -0.2613559691
43 -0.4247732019 -0.2779668235
44 0.2617706732 -0.4247732019
45 0.5494402101 0.2617706732
46 -0.2721744628 0.5494402101
47 -0.0536536014 -0.2721744628
48 -0.1581205323 -0.0536536014
49 -0.2869527205 -0.1581205323
50 -0.0070735998 -0.2869527205
51 0.2973705939 -0.0070735998
52 -0.0729152376 0.2973705939
53 0.1393397452 -0.0729152376
54 -0.1944279108 0.1393397452
55 -0.3108992608 -0.1944279108
56 0.3001115163 -0.3108992608
57 0.3595451226 0.3001115163
58 NA 0.3595451226
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1968439618 -0.2209403159
[2,] 0.1470744447 -0.1968439618
[3,] -0.0899438647 0.1470744447
[4,] -0.1260684986 -0.0899438647
[5,] -0.0740310300 -0.1260684986
[6,] -0.2527274655 -0.0740310300
[7,] -0.1560259099 -0.2527274655
[8,] -0.0705957309 -0.1560259099
[9,] 0.1901447427 -0.0705957309
[10,] -0.2476308438 0.1901447427
[11,] 0.2767202844 -0.2476308438
[12,] -0.0089313241 0.2767202844
[13,] 0.1662213074 -0.0089313241
[14,] 0.1757516832 0.1662213074
[15,] 0.1755118445 0.1757516832
[16,] 0.0468807637 0.1755118445
[17,] 0.3171137347 0.0468807637
[18,] 0.0548844386 0.3171137347
[19,] 0.1004025459 0.0548844386
[20,] 0.0674103895 0.1004025459
[21,] -0.0367339524 0.0674103895
[22,] -0.1817941134 -0.0367339524
[23,] 0.2848641238 -0.1817941134
[24,] 0.1840859051 0.2848641238
[25,] 0.1316760929 0.1840859051
[26,] 0.0006431621 0.1316760929
[27,] 0.1429528731 0.0006431621
[28,] 0.0332869053 0.1429528731
[29,] 0.2373000527 0.0332869053
[30,] 0.2144119098 0.2373000527
[31,] -0.1640576926 0.2144119098
[32,] -0.1475776649 -0.1640576926
[33,] 0.1022991029 -0.1475776649
[34,] -0.3959800187 0.1022991029
[35,] -0.1940670927 -0.3959800187
[36,] 0.2342833709 -0.1940670927
[37,] -0.0525523315 0.2342833709
[38,] -0.0924730113 -0.0525523315
[39,] 0.0056350739 -0.0924730113
[40,] -0.1678444702 0.0056350739
[41,] -0.2613559691 -0.1678444702
[42,] -0.2779668235 -0.2613559691
[43,] -0.4247732019 -0.2779668235
[44,] 0.2617706732 -0.4247732019
[45,] 0.5494402101 0.2617706732
[46,] -0.2721744628 0.5494402101
[47,] -0.0536536014 -0.2721744628
[48,] -0.1581205323 -0.0536536014
[49,] -0.2869527205 -0.1581205323
[50,] -0.0070735998 -0.2869527205
[51,] 0.2973705939 -0.0070735998
[52,] -0.0729152376 0.2973705939
[53,] 0.1393397452 -0.0729152376
[54,] -0.1944279108 0.1393397452
[55,] -0.3108992608 -0.1944279108
[56,] 0.3001115163 -0.3108992608
[57,] 0.3595451226 0.3001115163
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1968439618 -0.2209403159
2 0.1470744447 -0.1968439618
3 -0.0899438647 0.1470744447
4 -0.1260684986 -0.0899438647
5 -0.0740310300 -0.1260684986
6 -0.2527274655 -0.0740310300
7 -0.1560259099 -0.2527274655
8 -0.0705957309 -0.1560259099
9 0.1901447427 -0.0705957309
10 -0.2476308438 0.1901447427
11 0.2767202844 -0.2476308438
12 -0.0089313241 0.2767202844
13 0.1662213074 -0.0089313241
14 0.1757516832 0.1662213074
15 0.1755118445 0.1757516832
16 0.0468807637 0.1755118445
17 0.3171137347 0.0468807637
18 0.0548844386 0.3171137347
19 0.1004025459 0.0548844386
20 0.0674103895 0.1004025459
21 -0.0367339524 0.0674103895
22 -0.1817941134 -0.0367339524
23 0.2848641238 -0.1817941134
24 0.1840859051 0.2848641238
25 0.1316760929 0.1840859051
26 0.0006431621 0.1316760929
27 0.1429528731 0.0006431621
28 0.0332869053 0.1429528731
29 0.2373000527 0.0332869053
30 0.2144119098 0.2373000527
31 -0.1640576926 0.2144119098
32 -0.1475776649 -0.1640576926
33 0.1022991029 -0.1475776649
34 -0.3959800187 0.1022991029
35 -0.1940670927 -0.3959800187
36 0.2342833709 -0.1940670927
37 -0.0525523315 0.2342833709
38 -0.0924730113 -0.0525523315
39 0.0056350739 -0.0924730113
40 -0.1678444702 0.0056350739
41 -0.2613559691 -0.1678444702
42 -0.2779668235 -0.2613559691
43 -0.4247732019 -0.2779668235
44 0.2617706732 -0.4247732019
45 0.5494402101 0.2617706732
46 -0.2721744628 0.5494402101
47 -0.0536536014 -0.2721744628
48 -0.1581205323 -0.0536536014
49 -0.2869527205 -0.1581205323
50 -0.0070735998 -0.2869527205
51 0.2973705939 -0.0070735998
52 -0.0729152376 0.2973705939
53 0.1393397452 -0.0729152376
54 -0.1944279108 0.1393397452
55 -0.3108992608 -0.1944279108
56 0.3001115163 -0.3108992608
57 0.3595451226 0.3001115163
> 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/7hw3x1261078666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8upbw1261078666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9f0201261078666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107afw1261078666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fo3l1261078666.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/12lm5y1261078666.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/135x361261078666.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/14xclt1261078666.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/15o28i1261078666.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/167ocq1261078666.tab")
+ }
>
> try(system("convert tmp/1pxcu1261078666.ps tmp/1pxcu1261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d6da1261078666.ps tmp/2d6da1261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/3s4jp1261078666.ps tmp/3s4jp1261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/42s951261078666.ps tmp/42s951261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h3df1261078666.ps tmp/5h3df1261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/60f9j1261078666.ps tmp/60f9j1261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hw3x1261078666.ps tmp/7hw3x1261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/8upbw1261078666.ps tmp/8upbw1261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/9f0201261078666.ps tmp/9f0201261078666.png",intern=TRUE))
character(0)
> try(system("convert tmp/107afw1261078666.ps tmp/107afw1261078666.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.438 1.546 4.570