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.
You are welcome to redistribute it under certain conditions.
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(8.1
+ ,10.9
+ ,115.6
+ ,92.9
+ ,7.7
+ ,10
+ ,127.1
+ ,107.7
+ ,7.5
+ ,9.2
+ ,123
+ ,103.5
+ ,7.6
+ ,9.2
+ ,122.2
+ ,91.1
+ ,7.8
+ ,9.5
+ ,126.4
+ ,79.8
+ ,7.8
+ ,9.6
+ ,112.7
+ ,71.9
+ ,7.8
+ ,9.5
+ ,105.8
+ ,82.9
+ ,7.5
+ ,9.1
+ ,120.9
+ ,90.1
+ ,7.5
+ ,8.9
+ ,116.3
+ ,100.7
+ ,7.1
+ ,9
+ ,115.7
+ ,90.7
+ ,7.5
+ ,10.1
+ ,127.9
+ ,108.8
+ ,7.5
+ ,10.3
+ ,108.3
+ ,44.1
+ ,7.6
+ ,10.2
+ ,121.1
+ ,93.6
+ ,7.7
+ ,9.6
+ ,128.6
+ ,107.4
+ ,7.7
+ ,9.2
+ ,123.1
+ ,96.5
+ ,7.9
+ ,9.3
+ ,127.7
+ ,93.6
+ ,8.1
+ ,9.4
+ ,126.6
+ ,76.5
+ ,8.2
+ ,9.4
+ ,118.4
+ ,76.7
+ ,8.2
+ ,9.2
+ ,110
+ ,84
+ ,8.2
+ ,9
+ ,129.6
+ ,103.3
+ ,7.9
+ ,9
+ ,115.8
+ ,88.5
+ ,7.3
+ ,9
+ ,125.9
+ ,99
+ ,6.9
+ ,9.8
+ ,128.4
+ ,105.9
+ ,6.6
+ ,10
+ ,114
+ ,44.7
+ ,6.7
+ ,9.8
+ ,125.6
+ ,94
+ ,6.9
+ ,9.3
+ ,128.5
+ ,107.1
+ ,7
+ ,9
+ ,136.6
+ ,104.8
+ ,7.1
+ ,9
+ ,133.1
+ ,102.5
+ ,7.2
+ ,9.1
+ ,124.6
+ ,77.7
+ ,7.1
+ ,9.1
+ ,123.5
+ ,85.2
+ ,6.9
+ ,9.1
+ ,117.2
+ ,91.3
+ ,7
+ ,9.2
+ ,135.5
+ ,106.5
+ ,6.8
+ ,8.8
+ ,124.8
+ ,92.4
+ ,6.4
+ ,8.3
+ ,127.8
+ ,97.5
+ ,6.7
+ ,8.4
+ ,133.1
+ ,107
+ ,6.6
+ ,8.1
+ ,125.7
+ ,51.1
+ ,6.4
+ ,7.7
+ ,128.4
+ ,98.6
+ ,6.3
+ ,7.9
+ ,131.9
+ ,102.2
+ ,6.2
+ ,7.9
+ ,146.3
+ ,114.3
+ ,6.5
+ ,8
+ ,140.6
+ ,99.4
+ ,6.8
+ ,7.9
+ ,129.5
+ ,72.5
+ ,6.8
+ ,7.6
+ ,132.4
+ ,92.3
+ ,6.4
+ ,7.1
+ ,125.9
+ ,99.4
+ ,6.1
+ ,6.8
+ ,126.9
+ ,85.9
+ ,5.8
+ ,6.5
+ ,135.8
+ ,109.4
+ ,6.1
+ ,6.9
+ ,129.5
+ ,97.6
+ ,7.2
+ ,8.2
+ ,130.2
+ ,104.7
+ ,7.3
+ ,8.7
+ ,133.8
+ ,56.9
+ ,6.9
+ ,8.3
+ ,123.3
+ ,86.7
+ ,6.1
+ ,7.9
+ ,140.7
+ ,108.5
+ ,5.8
+ ,7.5
+ ,145.9
+ ,103.4
+ ,6.2
+ ,7.8
+ ,128.5
+ ,86.2
+ ,7.1
+ ,8.3
+ ,135.9
+ ,71
+ ,7.7
+ ,8.4
+ ,120.2
+ ,75.9
+ ,7.9
+ ,8.2
+ ,119.2
+ ,87.1
+ ,7.7
+ ,7.7
+ ,132.5
+ ,102
+ ,7.4
+ ,7.2
+ ,130.5
+ ,88.5
+ ,7.5
+ ,7.3
+ ,124.8
+ ,87.8
+ ,8
+ ,8.1
+ ,136.7
+ ,100.8
+ ,8.1
+ ,8.5
+ ,129.2
+ ,50.6
+ ,8
+ ,8.4
+ ,127.9
+ ,85.9)
+ ,dim=c(4
+ ,61)
+ ,dimnames=list(c('mannen'
+ ,'vrouwen'
+ ,'voeding'
+ ,'bouw')
+ ,1:61))
> y <- array(NA,dim=c(4,61),dimnames=list(c('mannen','vrouwen','voeding','bouw'),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 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '4'
> #'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
bouw mannen vrouwen voeding M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 92.9 8.1 10.9 115.6 1 0 0 0 0 0 0 0 0 0 0
2 107.7 7.7 10.0 127.1 0 1 0 0 0 0 0 0 0 0 0
3 103.5 7.5 9.2 123.0 0 0 1 0 0 0 0 0 0 0 0
4 91.1 7.6 9.2 122.2 0 0 0 1 0 0 0 0 0 0 0
5 79.8 7.8 9.5 126.4 0 0 0 0 1 0 0 0 0 0 0
6 71.9 7.8 9.6 112.7 0 0 0 0 0 1 0 0 0 0 0
7 82.9 7.8 9.5 105.8 0 0 0 0 0 0 1 0 0 0 0
8 90.1 7.5 9.1 120.9 0 0 0 0 0 0 0 1 0 0 0
9 100.7 7.5 8.9 116.3 0 0 0 0 0 0 0 0 1 0 0
10 90.7 7.1 9.0 115.7 0 0 0 0 0 0 0 0 0 1 0
11 108.8 7.5 10.1 127.9 0 0 0 0 0 0 0 0 0 0 1
12 44.1 7.5 10.3 108.3 0 0 0 0 0 0 0 0 0 0 0
13 93.6 7.6 10.2 121.1 1 0 0 0 0 0 0 0 0 0 0
14 107.4 7.7 9.6 128.6 0 1 0 0 0 0 0 0 0 0 0
15 96.5 7.7 9.2 123.1 0 0 1 0 0 0 0 0 0 0 0
16 93.6 7.9 9.3 127.7 0 0 0 1 0 0 0 0 0 0 0
17 76.5 8.1 9.4 126.6 0 0 0 0 1 0 0 0 0 0 0
18 76.7 8.2 9.4 118.4 0 0 0 0 0 1 0 0 0 0 0
19 84.0 8.2 9.2 110.0 0 0 0 0 0 0 1 0 0 0 0
20 103.3 8.2 9.0 129.6 0 0 0 0 0 0 0 1 0 0 0
21 88.5 7.9 9.0 115.8 0 0 0 0 0 0 0 0 1 0 0
22 99.0 7.3 9.0 125.9 0 0 0 0 0 0 0 0 0 1 0
23 105.9 6.9 9.8 128.4 0 0 0 0 0 0 0 0 0 0 1
24 44.7 6.6 10.0 114.0 0 0 0 0 0 0 0 0 0 0 0
25 94.0 6.7 9.8 125.6 1 0 0 0 0 0 0 0 0 0 0
26 107.1 6.9 9.3 128.5 0 1 0 0 0 0 0 0 0 0 0
27 104.8 7.0 9.0 136.6 0 0 1 0 0 0 0 0 0 0 0
28 102.5 7.1 9.0 133.1 0 0 0 1 0 0 0 0 0 0 0
29 77.7 7.2 9.1 124.6 0 0 0 0 1 0 0 0 0 0 0
30 85.2 7.1 9.1 123.5 0 0 0 0 0 1 0 0 0 0 0
31 91.3 6.9 9.1 117.2 0 0 0 0 0 0 1 0 0 0 0
32 106.5 7.0 9.2 135.5 0 0 0 0 0 0 0 1 0 0 0
33 92.4 6.8 8.8 124.8 0 0 0 0 0 0 0 0 1 0 0
34 97.5 6.4 8.3 127.8 0 0 0 0 0 0 0 0 0 1 0
35 107.0 6.7 8.4 133.1 0 0 0 0 0 0 0 0 0 0 1
36 51.1 6.6 8.1 125.7 0 0 0 0 0 0 0 0 0 0 0
37 98.6 6.4 7.7 128.4 1 0 0 0 0 0 0 0 0 0 0
38 102.2 6.3 7.9 131.9 0 1 0 0 0 0 0 0 0 0 0
39 114.3 6.2 7.9 146.3 0 0 1 0 0 0 0 0 0 0 0
40 99.4 6.5 8.0 140.6 0 0 0 1 0 0 0 0 0 0 0
41 72.5 6.8 7.9 129.5 0 0 0 0 1 0 0 0 0 0 0
42 92.3 6.8 7.6 132.4 0 0 0 0 0 1 0 0 0 0 0
43 99.4 6.4 7.1 125.9 0 0 0 0 0 0 1 0 0 0 0
44 85.9 6.1 6.8 126.9 0 0 0 0 0 0 0 1 0 0 0
45 109.4 5.8 6.5 135.8 0 0 0 0 0 0 0 0 1 0 0
46 97.6 6.1 6.9 129.5 0 0 0 0 0 0 0 0 0 1 0
47 104.7 7.2 8.2 130.2 0 0 0 0 0 0 0 0 0 0 1
48 56.9 7.3 8.7 133.8 0 0 0 0 0 0 0 0 0 0 0
49 86.7 6.9 8.3 123.3 1 0 0 0 0 0 0 0 0 0 0
50 108.5 6.1 7.9 140.7 0 1 0 0 0 0 0 0 0 0 0
51 103.4 5.8 7.5 145.9 0 0 1 0 0 0 0 0 0 0 0
52 86.2 6.2 7.8 128.5 0 0 0 1 0 0 0 0 0 0 0
53 71.0 7.1 8.3 135.9 0 0 0 0 1 0 0 0 0 0 0
54 75.9 7.7 8.4 120.2 0 0 0 0 0 1 0 0 0 0 0
55 87.1 7.9 8.2 119.2 0 0 0 0 0 0 1 0 0 0 0
56 102.0 7.7 7.7 132.5 0 0 0 0 0 0 0 1 0 0 0
57 88.5 7.4 7.2 130.5 0 0 0 0 0 0 0 0 1 0 0
58 87.8 7.5 7.3 124.8 0 0 0 0 0 0 0 0 0 1 0
59 100.8 8.0 8.1 136.7 0 0 0 0 0 0 0 0 0 0 1
60 50.6 8.1 8.5 129.2 0 0 0 0 0 0 0 0 0 0 0
61 85.9 8.0 8.4 127.9 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) mannen vrouwen voeding M1 M2
-70.0163 -1.8100 4.0886 0.7797 41.0589 50.1873
M3 M4 M5 M6 M7 M8
46.6575 40.2622 22.5007 33.2821 47.0328 45.9679
M9 M10 M11
48.5163 46.6146 49.7862
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.52684 -2.47033 -0.04297 1.82217 8.94188
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -70.0163 23.6218 -2.964 0.004797 **
mannen -1.8100 1.1742 -1.542 0.130044
vrouwen 4.0886 1.0207 4.006 0.000224 ***
voeding 0.7797 0.1279 6.095 2.08e-07 ***
M1 41.0589 2.7591 14.881 < 2e-16 ***
M2 50.1873 3.0534 16.436 < 2e-16 ***
M3 46.6575 3.1485 14.819 < 2e-16 ***
M4 40.2622 2.9810 13.506 < 2e-16 ***
M5 22.5007 2.9758 7.561 1.32e-09 ***
M6 33.2821 2.9261 11.374 5.80e-15 ***
M7 47.0328 3.1295 15.029 < 2e-16 ***
M8 45.9679 2.9840 15.405 < 2e-16 ***
M9 48.5163 2.9909 16.221 < 2e-16 ***
M10 46.6146 2.9707 15.692 < 2e-16 ***
M11 49.7862 3.0604 16.268 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.536 on 46 degrees of freedom
Multiple R-squared: 0.9405, Adjusted R-squared: 0.9223
F-statistic: 51.9 on 14 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,] 0.25882608 0.51765216 0.7411739
[2,] 0.13147794 0.26295588 0.8685221
[3,] 0.17095622 0.34191245 0.8290438
[4,] 0.36595550 0.73191101 0.6340445
[5,] 0.31604517 0.63209033 0.6839548
[6,] 0.24229138 0.48458277 0.7577086
[7,] 0.19819865 0.39639731 0.8018013
[8,] 0.13662607 0.27325215 0.8633739
[9,] 0.10246131 0.20492263 0.8975387
[10,] 0.08685476 0.17370952 0.9131452
[11,] 0.13369887 0.26739774 0.8663011
[12,] 0.21967013 0.43934026 0.7803299
[13,] 0.21327187 0.42654373 0.7867281
[14,] 0.14591896 0.29183792 0.8540810
[15,] 0.09513545 0.19027091 0.9048645
[16,] 0.13054966 0.26109932 0.8694503
[17,] 0.08511567 0.17023135 0.9148843
[18,] 0.05170278 0.10340556 0.9482972
[19,] 0.02930997 0.05861994 0.9706900
[20,] 0.02971330 0.05942661 0.9702867
[21,] 0.02658809 0.05317619 0.9734119
[22,] 0.03581727 0.07163455 0.9641827
[23,] 0.02056086 0.04112171 0.9794391
[24,] 0.05027197 0.10054393 0.9497280
[25,] 0.03743779 0.07487559 0.9625622
[26,] 0.02318782 0.04637564 0.9768122
> postscript(file="/var/www/html/rcomp/tmp/1sowk1258714878.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29dx51258714878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/331h61258714878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a90z1258714878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56sny1258714878.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
1.82217357 1.48316778 6.91849779 1.71855495 4.04084648 -4.36788132
7 8 9 10 11 12
-1.32988222 -3.74576183 8.71010875 -0.05320958 1.58960599 1.13983398
13 14 15 16 17 18
0.19092281 1.64908102 0.20253746 0.06446854 1.53678026 -2.47032504
19 20 21 22 23 24
-1.55394776 4.34690787 -2.78489426 0.65606280 -1.55968208 -3.10680122
25 26 27 28 29 30
-2.91123818 1.20559324 -2.47245181 4.53274116 3.89368052 1.28884007
31 32 33 34 35 36
-1.81183472 -0.04296621 -7.07533915 -1.09235552 1.23783057 1.93925102
37 38 39 40 41 42
7.54867050 -1.70732550 2.51406338 -1.41229844 -0.94446703 7.03955149
43 44 45 46 47 48
6.77709644 -5.75415445 8.94188389 2.86319309 2.92163807 0.23772068
49 50 51 52 53 54
-1.92309243 -2.63051653 -7.16264682 -4.90346621 -8.52684023 -1.49018520
55 56 57 58 59 60
-2.08143174 5.19597463 -7.79175923 -2.37369079 -4.18939256 -0.21000446
61
-4.72743628
> postscript(file="/var/www/html/rcomp/tmp/6hlnw1258714878.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 1.82217357 NA
1 1.48316778 1.82217357
2 6.91849779 1.48316778
3 1.71855495 6.91849779
4 4.04084648 1.71855495
5 -4.36788132 4.04084648
6 -1.32988222 -4.36788132
7 -3.74576183 -1.32988222
8 8.71010875 -3.74576183
9 -0.05320958 8.71010875
10 1.58960599 -0.05320958
11 1.13983398 1.58960599
12 0.19092281 1.13983398
13 1.64908102 0.19092281
14 0.20253746 1.64908102
15 0.06446854 0.20253746
16 1.53678026 0.06446854
17 -2.47032504 1.53678026
18 -1.55394776 -2.47032504
19 4.34690787 -1.55394776
20 -2.78489426 4.34690787
21 0.65606280 -2.78489426
22 -1.55968208 0.65606280
23 -3.10680122 -1.55968208
24 -2.91123818 -3.10680122
25 1.20559324 -2.91123818
26 -2.47245181 1.20559324
27 4.53274116 -2.47245181
28 3.89368052 4.53274116
29 1.28884007 3.89368052
30 -1.81183472 1.28884007
31 -0.04296621 -1.81183472
32 -7.07533915 -0.04296621
33 -1.09235552 -7.07533915
34 1.23783057 -1.09235552
35 1.93925102 1.23783057
36 7.54867050 1.93925102
37 -1.70732550 7.54867050
38 2.51406338 -1.70732550
39 -1.41229844 2.51406338
40 -0.94446703 -1.41229844
41 7.03955149 -0.94446703
42 6.77709644 7.03955149
43 -5.75415445 6.77709644
44 8.94188389 -5.75415445
45 2.86319309 8.94188389
46 2.92163807 2.86319309
47 0.23772068 2.92163807
48 -1.92309243 0.23772068
49 -2.63051653 -1.92309243
50 -7.16264682 -2.63051653
51 -4.90346621 -7.16264682
52 -8.52684023 -4.90346621
53 -1.49018520 -8.52684023
54 -2.08143174 -1.49018520
55 5.19597463 -2.08143174
56 -7.79175923 5.19597463
57 -2.37369079 -7.79175923
58 -4.18939256 -2.37369079
59 -0.21000446 -4.18939256
60 -4.72743628 -0.21000446
61 NA -4.72743628
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.48316778 1.82217357
[2,] 6.91849779 1.48316778
[3,] 1.71855495 6.91849779
[4,] 4.04084648 1.71855495
[5,] -4.36788132 4.04084648
[6,] -1.32988222 -4.36788132
[7,] -3.74576183 -1.32988222
[8,] 8.71010875 -3.74576183
[9,] -0.05320958 8.71010875
[10,] 1.58960599 -0.05320958
[11,] 1.13983398 1.58960599
[12,] 0.19092281 1.13983398
[13,] 1.64908102 0.19092281
[14,] 0.20253746 1.64908102
[15,] 0.06446854 0.20253746
[16,] 1.53678026 0.06446854
[17,] -2.47032504 1.53678026
[18,] -1.55394776 -2.47032504
[19,] 4.34690787 -1.55394776
[20,] -2.78489426 4.34690787
[21,] 0.65606280 -2.78489426
[22,] -1.55968208 0.65606280
[23,] -3.10680122 -1.55968208
[24,] -2.91123818 -3.10680122
[25,] 1.20559324 -2.91123818
[26,] -2.47245181 1.20559324
[27,] 4.53274116 -2.47245181
[28,] 3.89368052 4.53274116
[29,] 1.28884007 3.89368052
[30,] -1.81183472 1.28884007
[31,] -0.04296621 -1.81183472
[32,] -7.07533915 -0.04296621
[33,] -1.09235552 -7.07533915
[34,] 1.23783057 -1.09235552
[35,] 1.93925102 1.23783057
[36,] 7.54867050 1.93925102
[37,] -1.70732550 7.54867050
[38,] 2.51406338 -1.70732550
[39,] -1.41229844 2.51406338
[40,] -0.94446703 -1.41229844
[41,] 7.03955149 -0.94446703
[42,] 6.77709644 7.03955149
[43,] -5.75415445 6.77709644
[44,] 8.94188389 -5.75415445
[45,] 2.86319309 8.94188389
[46,] 2.92163807 2.86319309
[47,] 0.23772068 2.92163807
[48,] -1.92309243 0.23772068
[49,] -2.63051653 -1.92309243
[50,] -7.16264682 -2.63051653
[51,] -4.90346621 -7.16264682
[52,] -8.52684023 -4.90346621
[53,] -1.49018520 -8.52684023
[54,] -2.08143174 -1.49018520
[55,] 5.19597463 -2.08143174
[56,] -7.79175923 5.19597463
[57,] -2.37369079 -7.79175923
[58,] -4.18939256 -2.37369079
[59,] -0.21000446 -4.18939256
[60,] -4.72743628 -0.21000446
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.48316778 1.82217357
2 6.91849779 1.48316778
3 1.71855495 6.91849779
4 4.04084648 1.71855495
5 -4.36788132 4.04084648
6 -1.32988222 -4.36788132
7 -3.74576183 -1.32988222
8 8.71010875 -3.74576183
9 -0.05320958 8.71010875
10 1.58960599 -0.05320958
11 1.13983398 1.58960599
12 0.19092281 1.13983398
13 1.64908102 0.19092281
14 0.20253746 1.64908102
15 0.06446854 0.20253746
16 1.53678026 0.06446854
17 -2.47032504 1.53678026
18 -1.55394776 -2.47032504
19 4.34690787 -1.55394776
20 -2.78489426 4.34690787
21 0.65606280 -2.78489426
22 -1.55968208 0.65606280
23 -3.10680122 -1.55968208
24 -2.91123818 -3.10680122
25 1.20559324 -2.91123818
26 -2.47245181 1.20559324
27 4.53274116 -2.47245181
28 3.89368052 4.53274116
29 1.28884007 3.89368052
30 -1.81183472 1.28884007
31 -0.04296621 -1.81183472
32 -7.07533915 -0.04296621
33 -1.09235552 -7.07533915
34 1.23783057 -1.09235552
35 1.93925102 1.23783057
36 7.54867050 1.93925102
37 -1.70732550 7.54867050
38 2.51406338 -1.70732550
39 -1.41229844 2.51406338
40 -0.94446703 -1.41229844
41 7.03955149 -0.94446703
42 6.77709644 7.03955149
43 -5.75415445 6.77709644
44 8.94188389 -5.75415445
45 2.86319309 8.94188389
46 2.92163807 2.86319309
47 0.23772068 2.92163807
48 -1.92309243 0.23772068
49 -2.63051653 -1.92309243
50 -7.16264682 -2.63051653
51 -4.90346621 -7.16264682
52 -8.52684023 -4.90346621
53 -1.49018520 -8.52684023
54 -2.08143174 -1.49018520
55 5.19597463 -2.08143174
56 -7.79175923 5.19597463
57 -2.37369079 -7.79175923
58 -4.18939256 -2.37369079
59 -0.21000446 -4.18939256
60 -4.72743628 -0.21000446
> 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/7rtpk1258714878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83cw51258714878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9s6co1258714878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nf4x1258714878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rcx11258714878.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/12glnh1258714878.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/13mpby1258714878.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/144d661258714878.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/15uxum1258714878.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/165fy91258714878.tab")
+ }
>
> system("convert tmp/1sowk1258714878.ps tmp/1sowk1258714878.png")
> system("convert tmp/29dx51258714878.ps tmp/29dx51258714878.png")
> system("convert tmp/331h61258714878.ps tmp/331h61258714878.png")
> system("convert tmp/4a90z1258714878.ps tmp/4a90z1258714878.png")
> system("convert tmp/56sny1258714878.ps tmp/56sny1258714878.png")
> system("convert tmp/6hlnw1258714878.ps tmp/6hlnw1258714878.png")
> system("convert tmp/7rtpk1258714878.ps tmp/7rtpk1258714878.png")
> system("convert tmp/83cw51258714878.ps tmp/83cw51258714878.png")
> system("convert tmp/9s6co1258714878.ps tmp/9s6co1258714878.png")
> system("convert tmp/10nf4x1258714878.ps tmp/10nf4x1258714878.png")
>
>
> proc.time()
user system elapsed
2.386 1.521 2.805