R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) 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. R is a collaborative project with many contributors. 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(15 + ,0 + ,13.6 + ,13.7 + ,13 + ,14.4 + ,15 + ,14.4 + ,0 + ,15.2 + ,13.6 + ,13.7 + ,13 + ,14.4 + ,13 + ,0 + ,12.9 + ,15.2 + ,13.6 + ,13.7 + ,13 + ,13.7 + ,0 + ,14 + ,12.9 + ,15.2 + ,13.6 + ,13.7 + ,13.6 + ,0 + ,14.1 + ,14 + ,12.9 + ,15.2 + ,13.6 + ,15.2 + ,0 + ,13.2 + ,14.1 + ,14 + ,12.9 + ,15.2 + ,12.9 + ,0 + ,11.3 + ,13.2 + ,14.1 + ,14 + ,12.9 + ,14 + ,0 + ,13.3 + ,11.3 + ,13.2 + ,14.1 + ,14 + ,14.1 + ,0 + ,14.4 + ,13.3 + ,11.3 + ,13.2 + ,14.1 + ,13.2 + ,0 + ,13.3 + ,14.4 + ,13.3 + ,11.3 + ,13.2 + ,11.3 + ,0 + ,11.6 + ,13.3 + ,14.4 + ,13.3 + ,11.3 + ,13.3 + ,0 + ,13.2 + ,11.6 + ,13.3 + ,14.4 + ,13.3 + ,14.4 + ,0 + ,13.1 + ,13.2 + ,11.6 + ,13.3 + ,14.4 + ,13.3 + ,0 + ,14.6 + ,13.1 + ,13.2 + ,11.6 + ,13.3 + ,11.6 + ,0 + ,14 + ,14.6 + ,13.1 + ,13.2 + ,11.6 + ,13.2 + ,0 + ,14.3 + ,14 + ,14.6 + ,13.1 + ,13.2 + ,13.1 + ,0 + ,13.8 + ,14.3 + ,14 + ,14.6 + ,13.1 + ,14.6 + ,0 + ,13.7 + ,13.8 + ,14.3 + ,14 + ,14.6 + ,14 + ,0 + ,11 + ,13.7 + ,13.8 + ,14.3 + ,14 + ,14.3 + ,0 + ,14.4 + ,11 + ,13.7 + ,13.8 + ,14.3 + ,13.8 + ,0 + ,15.6 + ,14.4 + ,11 + ,13.7 + ,13.8 + ,13.7 + ,0 + ,13.7 + ,15.6 + ,14.4 + ,11 + ,13.7 + ,11 + ,0 + ,12.6 + ,13.7 + ,15.6 + ,14.4 + ,11 + ,14.4 + ,0 + ,13.2 + ,12.6 + ,13.7 + ,15.6 + ,14.4 + ,15.6 + ,0 + ,13.3 + ,13.2 + ,12.6 + ,13.7 + ,15.6 + ,13.7 + ,0 + ,14.3 + ,13.3 + ,13.2 + ,12.6 + ,13.7 + ,12.6 + ,0 + ,14 + ,14.3 + ,13.3 + ,13.2 + ,12.6 + ,13.2 + ,0 + ,13.4 + ,14 + ,14.3 + ,13.3 + ,13.2 + ,13.3 + ,0 + ,13.9 + ,13.4 + ,14 + ,14.3 + ,13.3 + ,14.3 + ,0 + ,13.7 + ,13.9 + ,13.4 + ,14 + ,14.3 + ,14 + ,0 + ,10.5 + ,13.7 + ,13.9 + ,13.4 + ,14 + ,13.4 + ,0 + ,14.5 + ,10.5 + ,13.7 + ,13.9 + ,13.4 + ,13.9 + ,0 + ,15 + ,14.5 + ,10.5 + ,13.7 + ,13.9 + ,13.7 + ,0 + ,13.5 + ,15 + ,14.5 + ,10.5 + ,13.7 + ,10.5 + ,0 + ,13.5 + ,13.5 + ,15 + ,14.5 + ,10.5 + ,14.5 + ,0 + ,13.2 + ,13.5 + ,13.5 + ,15 + ,14.5 + ,15 + ,0 + ,13.8 + ,13.2 + ,13.5 + ,13.5 + ,15 + ,13.5 + ,0 + ,16.2 + ,13.8 + ,13.2 + ,13.5 + ,13.5 + ,13.5 + ,0 + ,14.7 + ,16.2 + ,13.8 + ,13.2 + ,13.5 + ,13.2 + ,0 + ,13.9 + ,14.7 + ,16.2 + ,13.8 + ,13.2 + ,13.8 + ,0 + ,16 + ,13.9 + ,14.7 + ,16.2 + ,13.8 + ,16.2 + ,0 + ,14.4 + ,16 + ,13.9 + ,14.7 + ,16.2 + ,14.7 + ,0 + ,12.3 + ,14.4 + ,16 + ,13.9 + ,14.7 + ,13.9 + ,0 + ,15.9 + ,12.3 + ,14.4 + ,16 + ,13.9 + ,16 + ,0 + ,15.9 + ,15.9 + ,12.3 + ,14.4 + ,16 + ,14.4 + ,0 + ,15.5 + ,15.9 + ,15.9 + ,12.3 + ,14.4 + ,12.3 + ,0 + ,15.1 + ,15.5 + ,15.9 + ,15.9 + ,12.3 + ,15.9 + ,0 + ,14.5 + ,15.1 + ,15.5 + ,15.9 + ,15.9 + ,15.9 + ,0 + ,15.1 + ,14.5 + ,15.1 + ,15.5 + ,15.9 + ,15.5 + ,0 + ,17.4 + ,15.1 + ,14.5 + ,15.1 + ,15.5 + ,15.1 + ,0 + ,16.2 + ,17.4 + ,15.1 + ,14.5 + ,15.1 + ,14.5 + ,0 + ,15.6 + ,16.2 + ,17.4 + ,15.1 + ,14.5 + ,15.1 + ,0 + ,17.2 + ,15.6 + ,16.2 + ,17.4 + ,15.1 + ,17.4 + ,0 + ,14.9 + ,17.2 + ,15.6 + ,16.2 + ,17.4 + ,16.2 + ,0 + ,13.8 + ,14.9 + ,17.2 + ,15.6 + ,16.2 + ,15.6 + ,0 + ,17.5 + ,13.8 + ,14.9 + ,17.2 + ,15.6 + ,17.2 + ,0 + ,16.2 + ,17.5 + ,13.8 + ,14.9 + ,17.2 + ,14.9 + ,0 + ,17.5 + ,16.2 + ,17.5 + ,13.8 + ,14.9 + ,13.8 + ,0 + ,16.6 + ,17.5 + ,16.2 + ,17.5 + ,13.8 + ,17.5 + ,0 + ,16.2 + ,16.6 + ,17.5 + ,16.2 + ,17.5 + ,16.2 + ,0 + ,16.6 + ,16.2 + ,16.6 + ,17.5 + ,16.2 + ,17.5 + ,0 + ,19.6 + ,16.6 + ,16.2 + ,16.6 + ,17.5 + ,16.6 + ,0 + ,15.9 + ,19.6 + ,16.6 + ,16.2 + ,16.6 + ,16.2 + ,0 + ,18 + ,15.9 + ,19.6 + ,16.6 + ,16.2 + ,16.6 + ,0 + ,18.3 + ,18 + ,15.9 + ,19.6 + ,16.6 + ,19.6 + ,0 + ,16.3 + ,18.3 + ,18 + ,15.9 + ,19.6 + ,15.9 + ,0 + ,14.9 + ,16.3 + ,18.3 + ,18 + ,15.9 + ,18 + ,0 + ,18.2 + ,14.9 + ,16.3 + ,18.3 + ,18 + ,18.3 + ,0 + ,18.4 + ,18.2 + ,14.9 + ,16.3 + ,18.3 + ,16.3 + ,0 + ,18.5 + ,18.4 + ,18.2 + ,14.9 + ,16.3 + ,14.9 + ,0 + ,16 + ,18.5 + ,18.4 + ,18.2 + ,14.9 + ,18.2 + ,0 + ,17.4 + ,16 + ,18.5 + ,18.4 + ,18.2 + ,18.4 + ,0 + ,17.2 + ,17.4 + ,16 + ,18.5 + ,18.4 + ,18.5 + ,0 + ,19.6 + ,17.2 + ,17.4 + ,16 + ,18.5 + ,16 + ,0 + ,17.2 + ,19.6 + ,17.2 + ,17.4 + ,16 + ,17.4 + ,0 + ,18.3 + ,17.2 + ,19.6 + ,17.2 + ,17.4 + ,17.2 + ,0 + ,19.3 + ,18.3 + ,17.2 + ,19.6 + ,17.2 + ,19.6 + ,0 + ,18.1 + ,19.3 + ,18.3 + ,17.2 + ,19.6 + ,17.2 + ,0 + ,16.2 + ,18.1 + ,19.3 + ,18.3 + ,17.2 + ,18.3 + ,0 + ,18.4 + ,16.2 + ,18.1 + ,19.3 + ,18.3 + ,19.3 + ,0 + ,20.5 + ,18.4 + ,16.2 + ,18.1 + ,19.3 + ,18.1 + ,0 + ,19 + ,20.5 + ,18.4 + ,16.2 + ,18.1 + ,16.2 + ,0 + ,16.5 + ,19 + ,20.5 + ,18.4 + ,16.2 + ,18.4 + ,0 + ,18.7 + ,16.5 + ,19 + ,20.5 + ,18.4 + ,20.5 + ,0 + ,19 + ,18.7 + ,16.5 + ,19 + ,20.5 + ,19 + ,0 + ,19.2 + ,19 + ,18.7 + ,16.5 + ,19 + ,16.5 + ,0 + ,20.5 + ,19.2 + ,19 + ,18.7 + ,16.5 + ,18.7 + ,0 + ,19.3 + ,20.5 + ,19.2 + ,19 + ,18.7 + ,19 + ,0 + ,20.6 + ,19.3 + ,20.5 + ,19.2 + ,19 + ,19.2 + ,0 + ,20.1 + ,20.6 + ,19.3 + ,20.5 + ,19.2 + ,20.5 + ,0 + ,16.1 + ,20.1 + ,20.6 + ,19.3 + ,20.5 + ,19.3 + ,0 + ,20.4 + ,16.1 + ,20.1 + ,20.6 + ,19.3 + ,20.6 + ,0 + ,19.7 + ,20.4 + ,16.1 + ,20.1 + ,20.6 + ,20.1 + ,0 + ,15.6 + ,19.7 + ,20.4 + ,16.1 + ,20.1 + ,16.1 + ,0 + ,14.4 + ,15.6 + ,19.7 + ,20.4 + ,16.1 + ,20.4 + ,0 + ,13.7 + ,14.4 + ,15.6 + ,19.7 + ,20.4 + ,19.7 + ,1 + ,14.1 + ,13.7 + ,14.4 + ,15.6 + ,19.7 + ,15.6 + ,1 + ,15 + ,14.1 + ,13.7 + ,14.4 + ,15.6 + ,14.4 + ,1 + ,14.2 + ,15 + ,14.1 + ,13.7 + ,14.4 + ,13.7 + ,1 + ,13.6 + ,14.2 + ,15 + ,14.1 + ,13.7 + ,14.1 + ,1 + ,15.4 + ,13.6 + ,14.2 + ,15 + ,14.1 + ,15 + ,1 + ,14.8 + ,15.4 + ,13.6 + ,14.2 + ,15 + ,14.2 + ,1 + ,12.5 + ,14.8 + ,15.4 + ,13.6 + ,14.2 + ,13.6 + ,1 + ,16.2 + ,12.5 + ,14.8 + ,15.4 + ,13.6 + ,15.4 + ,1 + ,16.1 + ,16.2 + ,12.5 + ,14.8 + ,15.4 + ,14.8 + ,1 + ,16 + ,16.1 + ,16.2 + ,12.5 + ,14.8 + ,12.5 + ,1 + ,15.8 + ,16 + ,16.1 + ,16.2 + ,12.5 + ,16.2 + ,1 + ,15.2 + ,15.8 + ,16 + ,16.1 + ,16.2 + ,16.1 + ,1 + ,15.7 + ,15.2 + ,15.8 + ,16 + ,16.1 + ,16 + ,1 + ,18.9 + ,15.7 + ,15.2 + ,15.8 + ,16 + ,15.8 + ,1 + ,17.4 + ,18.9 + ,15.7 + ,15.2 + ,15.8 + ,15.2 + ,1 + ,17 + ,17.4 + ,18.9 + ,15.7 + ,15.2 + ,15.7 + ,1 + ,19.8 + ,17 + ,17.4 + ,18.9 + ,15.7 + ,18.9 + ,1 + ,17.7 + ,19.8 + ,17 + ,17.4 + ,18.9 + ,17.4 + ,1 + ,16 + ,17.7 + ,19.8 + ,17 + ,17.4 + ,17 + ,1 + ,19.6 + ,16 + ,17.7 + ,19.8 + ,17 + ,19.8 + ,1 + ,19.7 + ,19.6 + ,16 + ,17.7 + ,19.8) + ,dim=c(7 + ,117) + ,dimnames=list(c('uitvoercijfer' + ,'X' + ,'Y1' + ,'Y2' + ,'Y3' + ,'Y4' + ,'Y5') + ,1:117)) > y <- array(NA,dim=c(7,117),dimnames=list(c('uitvoercijfer','X','Y1','Y2','Y3','Y4','Y5'),1:117)) > 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 > 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 uitvoercijfer X Y1 Y2 Y3 Y4 Y5 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 15.0 0 13.6 13.7 13.0 14.4 15.0 1 0 0 0 0 0 0 0 0 0 0 2 14.4 0 15.2 13.6 13.7 13.0 14.4 0 1 0 0 0 0 0 0 0 0 0 3 13.0 0 12.9 15.2 13.6 13.7 13.0 0 0 1 0 0 0 0 0 0 0 0 4 13.7 0 14.0 12.9 15.2 13.6 13.7 0 0 0 1 0 0 0 0 0 0 0 5 13.6 0 14.1 14.0 12.9 15.2 13.6 0 0 0 0 1 0 0 0 0 0 0 6 15.2 0 13.2 14.1 14.0 12.9 15.2 0 0 0 0 0 1 0 0 0 0 0 7 12.9 0 11.3 13.2 14.1 14.0 12.9 0 0 0 0 0 0 1 0 0 0 0 8 14.0 0 13.3 11.3 13.2 14.1 14.0 0 0 0 0 0 0 0 1 0 0 0 9 14.1 0 14.4 13.3 11.3 13.2 14.1 0 0 0 0 0 0 0 0 1 0 0 10 13.2 0 13.3 14.4 13.3 11.3 13.2 0 0 0 0 0 0 0 0 0 1 0 11 11.3 0 11.6 13.3 14.4 13.3 11.3 0 0 0 0 0 0 0 0 0 0 1 12 13.3 0 13.2 11.6 13.3 14.4 13.3 0 0 0 0 0 0 0 0 0 0 0 13 14.4 0 13.1 13.2 11.6 13.3 14.4 1 0 0 0 0 0 0 0 0 0 0 14 13.3 0 14.6 13.1 13.2 11.6 13.3 0 1 0 0 0 0 0 0 0 0 0 15 11.6 0 14.0 14.6 13.1 13.2 11.6 0 0 1 0 0 0 0 0 0 0 0 16 13.2 0 14.3 14.0 14.6 13.1 13.2 0 0 0 1 0 0 0 0 0 0 0 17 13.1 0 13.8 14.3 14.0 14.6 13.1 0 0 0 0 1 0 0 0 0 0 0 18 14.6 0 13.7 13.8 14.3 14.0 14.6 0 0 0 0 0 1 0 0 0 0 0 19 14.0 0 11.0 13.7 13.8 14.3 14.0 0 0 0 0 0 0 1 0 0 0 0 20 14.3 0 14.4 11.0 13.7 13.8 14.3 0 0 0 0 0 0 0 1 0 0 0 21 13.8 0 15.6 14.4 11.0 13.7 13.8 0 0 0 0 0 0 0 0 1 0 0 22 13.7 0 13.7 15.6 14.4 11.0 13.7 0 0 0 0 0 0 0 0 0 1 0 23 11.0 0 12.6 13.7 15.6 14.4 11.0 0 0 0 0 0 0 0 0 0 0 1 24 14.4 0 13.2 12.6 13.7 15.6 14.4 0 0 0 0 0 0 0 0 0 0 0 25 15.6 0 13.3 13.2 12.6 13.7 15.6 1 0 0 0 0 0 0 0 0 0 0 26 13.7 0 14.3 13.3 13.2 12.6 13.7 0 1 0 0 0 0 0 0 0 0 0 27 12.6 0 14.0 14.3 13.3 13.2 12.6 0 0 1 0 0 0 0 0 0 0 0 28 13.2 0 13.4 14.0 14.3 13.3 13.2 0 0 0 1 0 0 0 0 0 0 0 29 13.3 0 13.9 13.4 14.0 14.3 13.3 0 0 0 0 1 0 0 0 0 0 0 30 14.3 0 13.7 13.9 13.4 14.0 14.3 0 0 0 0 0 1 0 0 0 0 0 31 14.0 0 10.5 13.7 13.9 13.4 14.0 0 0 0 0 0 0 1 0 0 0 0 32 13.4 0 14.5 10.5 13.7 13.9 13.4 0 0 0 0 0 0 0 1 0 0 0 33 13.9 0 15.0 14.5 10.5 13.7 13.9 0 0 0 0 0 0 0 0 1 0 0 34 13.7 0 13.5 15.0 14.5 10.5 13.7 0 0 0 0 0 0 0 0 0 1 0 35 10.5 0 13.5 13.5 15.0 14.5 10.5 0 0 0 0 0 0 0 0 0 0 1 36 14.5 0 13.2 13.5 13.5 15.0 14.5 0 0 0 0 0 0 0 0 0 0 0 37 15.0 0 13.8 13.2 13.5 13.5 15.0 1 0 0 0 0 0 0 0 0 0 0 38 13.5 0 16.2 13.8 13.2 13.5 13.5 0 1 0 0 0 0 0 0 0 0 0 39 13.5 0 14.7 16.2 13.8 13.2 13.5 0 0 1 0 0 0 0 0 0 0 0 40 13.2 0 13.9 14.7 16.2 13.8 13.2 0 0 0 1 0 0 0 0 0 0 0 41 13.8 0 16.0 13.9 14.7 16.2 13.8 0 0 0 0 1 0 0 0 0 0 0 42 16.2 0 14.4 16.0 13.9 14.7 16.2 0 0 0 0 0 1 0 0 0 0 0 43 14.7 0 12.3 14.4 16.0 13.9 14.7 0 0 0 0 0 0 1 0 0 0 0 44 13.9 0 15.9 12.3 14.4 16.0 13.9 0 0 0 0 0 0 0 1 0 0 0 45 16.0 0 15.9 15.9 12.3 14.4 16.0 0 0 0 0 0 0 0 0 1 0 0 46 14.4 0 15.5 15.9 15.9 12.3 14.4 0 0 0 0 0 0 0 0 0 1 0 47 12.3 0 15.1 15.5 15.9 15.9 12.3 0 0 0 0 0 0 0 0 0 0 1 48 15.9 0 14.5 15.1 15.5 15.9 15.9 0 0 0 0 0 0 0 0 0 0 0 49 15.9 0 15.1 14.5 15.1 15.5 15.9 1 0 0 0 0 0 0 0 0 0 0 50 15.5 0 17.4 15.1 14.5 15.1 15.5 0 1 0 0 0 0 0 0 0 0 0 51 15.1 0 16.2 17.4 15.1 14.5 15.1 0 0 1 0 0 0 0 0 0 0 0 52 14.5 0 15.6 16.2 17.4 15.1 14.5 0 0 0 1 0 0 0 0 0 0 0 53 15.1 0 17.2 15.6 16.2 17.4 15.1 0 0 0 0 1 0 0 0 0 0 0 54 17.4 0 14.9 17.2 15.6 16.2 17.4 0 0 0 0 0 1 0 0 0 0 0 55 16.2 0 13.8 14.9 17.2 15.6 16.2 0 0 0 0 0 0 1 0 0 0 0 56 15.6 0 17.5 13.8 14.9 17.2 15.6 0 0 0 0 0 0 0 1 0 0 0 57 17.2 0 16.2 17.5 13.8 14.9 17.2 0 0 0 0 0 0 0 0 1 0 0 58 14.9 0 17.5 16.2 17.5 13.8 14.9 0 0 0 0 0 0 0 0 0 1 0 59 13.8 0 16.6 17.5 16.2 17.5 13.8 0 0 0 0 0 0 0 0 0 0 1 60 17.5 0 16.2 16.6 17.5 16.2 17.5 0 0 0 0 0 0 0 0 0 0 0 61 16.2 0 16.6 16.2 16.6 17.5 16.2 1 0 0 0 0 0 0 0 0 0 0 62 17.5 0 19.6 16.6 16.2 16.6 17.5 0 1 0 0 0 0 0 0 0 0 0 63 16.6 0 15.9 19.6 16.6 16.2 16.6 0 0 1 0 0 0 0 0 0 0 0 64 16.2 0 18.0 15.9 19.6 16.6 16.2 0 0 0 1 0 0 0 0 0 0 0 65 16.6 0 18.3 18.0 15.9 19.6 16.6 0 0 0 0 1 0 0 0 0 0 0 66 19.6 0 16.3 18.3 18.0 15.9 19.6 0 0 0 0 0 1 0 0 0 0 0 67 15.9 0 14.9 16.3 18.3 18.0 15.9 0 0 0 0 0 0 1 0 0 0 0 68 18.0 0 18.2 14.9 16.3 18.3 18.0 0 0 0 0 0 0 0 1 0 0 0 69 18.3 0 18.4 18.2 14.9 16.3 18.3 0 0 0 0 0 0 0 0 1 0 0 70 16.3 0 18.5 18.4 18.2 14.9 16.3 0 0 0 0 0 0 0 0 0 1 0 71 14.9 0 16.0 18.5 18.4 18.2 14.9 0 0 0 0 0 0 0 0 0 0 1 72 18.2 0 17.4 16.0 18.5 18.4 18.2 0 0 0 0 0 0 0 0 0 0 0 73 18.4 0 17.2 17.4 16.0 18.5 18.4 1 0 0 0 0 0 0 0 0 0 0 74 18.5 0 19.6 17.2 17.4 16.0 18.5 0 1 0 0 0 0 0 0 0 0 0 75 16.0 0 17.2 19.6 17.2 17.4 16.0 0 0 1 0 0 0 0 0 0 0 0 76 17.4 0 18.3 17.2 19.6 17.2 17.4 0 0 0 1 0 0 0 0 0 0 0 77 17.2 0 19.3 18.3 17.2 19.6 17.2 0 0 0 0 1 0 0 0 0 0 0 78 19.6 0 18.1 19.3 18.3 17.2 19.6 0 0 0 0 0 1 0 0 0 0 0 79 17.2 0 16.2 18.1 19.3 18.3 17.2 0 0 0 0 0 0 1 0 0 0 0 80 18.3 0 18.4 16.2 18.1 19.3 18.3 0 0 0 0 0 0 0 1 0 0 0 81 19.3 0 20.5 18.4 16.2 18.1 19.3 0 0 0 0 0 0 0 0 1 0 0 82 18.1 0 19.0 20.5 18.4 16.2 18.1 0 0 0 0 0 0 0 0 0 1 0 83 16.2 0 16.5 19.0 20.5 18.4 16.2 0 0 0 0 0 0 0 0 0 0 1 84 18.4 0 18.7 16.5 19.0 20.5 18.4 0 0 0 0 0 0 0 0 0 0 0 85 20.5 0 19.0 18.7 16.5 19.0 20.5 1 0 0 0 0 0 0 0 0 0 0 86 19.0 0 19.2 19.0 18.7 16.5 19.0 0 1 0 0 0 0 0 0 0 0 0 87 16.5 0 20.5 19.2 19.0 18.7 16.5 0 0 1 0 0 0 0 0 0 0 0 88 18.7 0 19.3 20.5 19.2 19.0 18.7 0 0 0 1 0 0 0 0 0 0 0 89 19.0 0 20.6 19.3 20.5 19.2 19.0 0 0 0 0 1 0 0 0 0 0 0 90 19.2 0 20.1 20.6 19.3 20.5 19.2 0 0 0 0 0 1 0 0 0 0 0 91 20.5 0 16.1 20.1 20.6 19.3 20.5 0 0 0 0 0 0 1 0 0 0 0 92 19.3 0 20.4 16.1 20.1 20.6 19.3 0 0 0 0 0 0 0 1 0 0 0 93 20.6 0 19.7 20.4 16.1 20.1 20.6 0 0 0 0 0 0 0 0 1 0 0 94 20.1 0 15.6 19.7 20.4 16.1 20.1 0 0 0 0 0 0 0 0 0 1 0 95 16.1 0 14.4 15.6 19.7 20.4 16.1 0 0 0 0 0 0 0 0 0 0 1 96 20.4 0 13.7 14.4 15.6 19.7 20.4 0 0 0 0 0 0 0 0 0 0 0 97 19.7 1 14.1 13.7 14.4 15.6 19.7 1 0 0 0 0 0 0 0 0 0 0 98 15.6 1 15.0 14.1 13.7 14.4 15.6 0 1 0 0 0 0 0 0 0 0 0 99 14.4 1 14.2 15.0 14.1 13.7 14.4 0 0 1 0 0 0 0 0 0 0 0 100 13.7 1 13.6 14.2 15.0 14.1 13.7 0 0 0 1 0 0 0 0 0 0 0 101 14.1 1 15.4 13.6 14.2 15.0 14.1 0 0 0 0 1 0 0 0 0 0 0 102 15.0 1 14.8 15.4 13.6 14.2 15.0 0 0 0 0 0 1 0 0 0 0 0 103 14.2 1 12.5 14.8 15.4 13.6 14.2 0 0 0 0 0 0 1 0 0 0 0 104 13.6 1 16.2 12.5 14.8 15.4 13.6 0 0 0 0 0 0 0 1 0 0 0 105 15.4 1 16.1 16.2 12.5 14.8 15.4 0 0 0 0 0 0 0 0 1 0 0 106 14.8 1 16.0 16.1 16.2 12.5 14.8 0 0 0 0 0 0 0 0 0 1 0 107 12.5 1 15.8 16.0 16.1 16.2 12.5 0 0 0 0 0 0 0 0 0 0 1 108 16.2 1 15.2 15.8 16.0 16.1 16.2 0 0 0 0 0 0 0 0 0 0 0 109 16.1 1 15.7 15.2 15.8 16.0 16.1 1 0 0 0 0 0 0 0 0 0 0 110 16.0 1 18.9 15.7 15.2 15.8 16.0 0 1 0 0 0 0 0 0 0 0 0 111 15.8 1 17.4 18.9 15.7 15.2 15.8 0 0 1 0 0 0 0 0 0 0 0 112 15.2 1 17.0 17.4 18.9 15.7 15.2 0 0 0 1 0 0 0 0 0 0 0 113 15.7 1 19.8 17.0 17.4 18.9 15.7 0 0 0 0 1 0 0 0 0 0 0 114 18.9 1 17.7 19.8 17.0 17.4 18.9 0 0 0 0 0 1 0 0 0 0 0 115 17.4 1 16.0 17.7 19.8 17.0 17.4 0 0 0 0 0 0 1 0 0 0 0 116 17.0 1 19.6 16.0 17.7 19.8 17.0 0 0 0 0 0 0 0 1 0 0 0 117 19.8 1 19.7 19.6 16.0 17.7 19.8 0 0 0 0 0 0 0 0 1 0 0 t 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 72 72 73 73 74 74 75 75 76 76 77 77 78 78 79 79 80 80 81 81 82 82 83 83 84 84 85 85 86 86 87 87 88 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95 96 96 97 97 98 98 99 99 100 100 101 101 102 102 103 103 104 104 105 105 106 106 107 107 108 108 109 109 110 110 111 111 112 112 113 113 114 114 115 115 116 116 117 117 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X Y1 Y2 Y3 Y4 2.679e-16 1.256e-16 2.220e-17 5.454e-17 -2.949e-17 4.469e-17 Y5 M1 M2 M3 M4 M5 1.000e+00 -7.607e-17 -3.578e-18 4.121e-17 1.085e-16 -4.338e-17 M6 M7 M8 M9 M10 M11 5.990e-17 4.214e-16 -6.425e-17 -2.413e-17 1.785e-16 2.479e-17 t -2.261e-18 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.750e-16 -6.762e-17 2.640e-18 4.748e-17 2.861e-15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.679e-16 5.458e-16 4.910e-01 0.6246 X 1.256e-16 2.098e-16 5.990e-01 0.5508 Y1 2.220e-17 4.306e-17 5.160e-01 0.6073 Y2 5.454e-17 4.348e-17 1.255e+00 0.2126 Y3 -2.949e-17 4.416e-17 -6.680e-01 0.5059 Y4 4.469e-17 4.615e-17 9.680e-01 0.3352 Y5 1.000e+00 4.400e-17 2.273e+16 <2e-16 *** M1 -7.607e-17 1.715e-16 -4.440e-01 0.6583 M2 -3.578e-18 2.160e-16 -1.700e-02 0.9868 M3 4.121e-17 2.193e-16 1.880e-01 0.8513 M4 1.085e-16 1.947e-16 5.570e-01 0.5786 M5 -4.338e-17 1.731e-16 -2.510e-01 0.8027 M6 5.990e-17 1.861e-16 3.220e-01 0.7482 M7 4.214e-16 2.005e-16 2.101e+00 0.0382 * M8 -6.425e-17 1.806e-16 -3.560e-01 0.7227 M9 -2.413e-17 2.467e-16 -9.800e-02 0.9223 M10 1.785e-16 2.558e-16 6.980e-01 0.4870 M11 2.479e-17 2.503e-16 9.900e-02 0.9213 t -2.261e-18 3.351e-18 -6.750e-01 0.5015 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.269e-16 on 98 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 3.2e+32 on 18 and 98 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,] 8.127738e-01 3.744525e-01 1.872262e-01 [2,] 4.580354e-03 9.160707e-03 9.954196e-01 [3,] 2.501456e-01 5.002911e-01 7.498544e-01 [4,] 5.148722e-03 1.029744e-02 9.948513e-01 [5,] 1.480263e-07 2.960527e-07 9.999999e-01 [6,] 4.189919e-06 8.379838e-06 9.999958e-01 [7,] 1.261990e-03 2.523979e-03 9.987380e-01 [8,] 1.800704e-01 3.601408e-01 8.199296e-01 [9,] 1.068054e-03 2.136108e-03 9.989319e-01 [10,] 2.448113e-10 4.896227e-10 1.000000e+00 [11,] 9.758537e-01 4.829263e-02 2.414632e-02 [12,] 3.430615e-01 6.861230e-01 6.569385e-01 [13,] 7.261223e-08 1.452245e-07 9.999999e-01 [14,] 1.000000e+00 4.145036e-10 2.072518e-10 [15,] 2.914572e-15 5.829144e-15 1.000000e+00 [16,] 9.999994e-01 1.296817e-06 6.484087e-07 [17,] 6.603430e-01 6.793140e-01 3.396570e-01 [18,] 4.449293e-08 8.898586e-08 1.000000e+00 [19,] 7.659579e-01 4.680842e-01 2.340421e-01 [20,] 2.796164e-10 5.592328e-10 1.000000e+00 [21,] 5.858847e-02 1.171769e-01 9.414115e-01 [22,] 1.659125e-08 3.318249e-08 1.000000e+00 [23,] 7.698331e-01 4.603338e-01 2.301669e-01 [24,] 7.868096e-04 1.573619e-03 9.992132e-01 [25,] 3.669566e-01 7.339131e-01 6.330434e-01 [26,] 3.478802e-07 6.957604e-07 9.999997e-01 [27,] 1.368004e-09 2.736008e-09 1.000000e+00 [28,] 9.973419e-01 5.316178e-03 2.658089e-03 [29,] 9.812582e-03 1.962516e-02 9.901874e-01 [30,] 9.999996e-01 8.074591e-07 4.037295e-07 [31,] 9.999994e-01 1.251111e-06 6.255553e-07 [32,] 8.492831e-02 1.698566e-01 9.150717e-01 [33,] 2.199214e-11 4.398428e-11 1.000000e+00 [34,] 2.136949e-18 4.273898e-18 1.000000e+00 [35,] 2.739214e-01 5.478427e-01 7.260786e-01 [36,] 2.600428e-02 5.200855e-02 9.739957e-01 [37,] 9.999972e-01 5.623181e-06 2.811591e-06 [38,] 5.675924e-04 1.135185e-03 9.994324e-01 [39,] 1.000000e+00 9.612280e-17 4.806140e-17 [40,] 9.555301e-01 8.893984e-02 4.446992e-02 [41,] 3.422778e-10 6.845555e-10 1.000000e+00 [42,] 9.999998e-01 3.671837e-07 1.835919e-07 [43,] 1.179162e-17 2.358323e-17 1.000000e+00 [44,] 1.000000e+00 1.920616e-14 9.603078e-15 [45,] 3.175463e-02 6.350925e-02 9.682454e-01 [46,] 6.074438e-14 1.214888e-13 1.000000e+00 [47,] 9.999709e-01 5.821499e-05 2.910749e-05 [48,] 9.892413e-01 2.151732e-02 1.075866e-02 [49,] 4.633896e-18 9.267792e-18 1.000000e+00 [50,] 1.000000e+00 5.326095e-12 2.663047e-12 [51,] 8.517592e-01 2.964816e-01 1.482408e-01 [52,] 1.551384e-06 3.102768e-06 9.999984e-01 [53,] 8.255157e-23 1.651031e-22 1.000000e+00 [54,] 9.999995e-01 9.353425e-07 4.676713e-07 [55,] 5.198222e-01 9.603557e-01 4.801778e-01 [56,] 9.122739e-02 1.824548e-01 9.087726e-01 [57,] 9.999995e-01 9.247349e-07 4.623675e-07 [58,] 9.999997e-01 5.755110e-07 2.877555e-07 [59,] 9.747423e-03 1.949485e-02 9.902526e-01 [60,] 1.000000e+00 8.645509e-08 4.322755e-08 [61,] 9.970384e-01 5.923290e-03 2.961645e-03 [62,] 9.999998e-01 3.135218e-07 1.567609e-07 [63,] 6.476776e-01 7.046448e-01 3.523224e-01 [64,] 6.199599e-01 7.600802e-01 3.800401e-01 [65,] 6.407414e-01 7.185172e-01 3.592586e-01 [66,] 2.422135e-01 4.844271e-01 7.577865e-01 [67,] 9.897665e-01 2.046706e-02 1.023353e-02 [68,] 8.175534e-08 1.635107e-07 9.999999e-01 [69,] 8.504548e-01 2.990903e-01 1.495452e-01 [70,] 2.186501e-15 4.373001e-15 1.000000e+00 [71,] 1.540041e-25 3.080083e-25 1.000000e+00 [72,] 9.553749e-05 1.910750e-04 9.999045e-01 [73,] 6.869231e-09 1.373846e-08 1.000000e+00 [74,] 4.349876e-06 8.699751e-06 9.999957e-01 > postscript(file="/var/www/rcomp/tmp/1uorf1292772458.ps",horizontal=F,onefile=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2uorf1292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/35f901292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/45f901292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/55f901292772458.ps",horizontal=F,onefile=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 = 117 Frequency = 1 1 2 3 4 5 -2.892968e-16 -2.579014e-16 -6.749799e-16 -5.995323e-17 1.193760e-16 6 7 8 9 10 1.762469e-16 2.860858e-15 -6.186457e-17 -7.446161e-17 -1.104933e-16 11 12 13 14 15 6.080808e-18 -1.181588e-16 3.699149e-18 2.660700e-17 -1.321214e-16 16 17 18 19 20 -6.762483e-17 -6.317381e-17 -1.246845e-16 -4.519664e-16 3.767563e-17 21 22 23 24 25 -7.066140e-17 -2.326815e-17 -1.719976e-17 -1.242667e-16 -6.863514e-17 26 27 28 29 30 2.468774e-17 8.922540e-17 -6.113808e-17 2.951461e-17 -7.765363e-17 31 32 33 34 35 -3.627758e-16 -1.393141e-17 -6.775578e-17 9.635644e-18 -7.251071e-17 36 37 38 39 40 -5.989202e-17 1.739712e-17 2.640182e-18 1.233901e-16 5.303045e-17 41 42 43 44 45 -1.163220e-16 -8.832207e-17 -2.811574e-16 -2.918593e-17 2.375018e-17 46 47 48 49 50 -1.309115e-17 2.584684e-17 -1.068457e-17 4.833362e-17 -1.239321e-17 51 52 53 54 55 1.058077e-16 6.047475e-17 1.016051e-17 -2.703054e-17 -2.826774e-16 56 57 58 59 60 -5.638469e-17 1.453834e-17 8.397078e-17 -1.712159e-16 6.170889e-17 61 62 63 64 65 6.992927e-17 4.399593e-19 1.371959e-16 2.884177e-17 -1.246761e-16 66 67 68 69 70 1.114019e-16 -3.297269e-16 1.240862e-17 1.098319e-16 -4.536939e-18 71 72 73 74 75 -8.633773e-17 -1.259175e-17 1.502503e-17 9.107760e-18 1.809528e-16 76 77 78 79 80 1.546634e-17 1.023607e-16 -1.777859e-17 -2.919436e-16 6.702062e-17 81 82 83 84 85 6.123488e-17 2.776523e-17 2.946295e-16 -4.183214e-17 1.206044e-17 86 87 88 89 90 1.624581e-16 -6.669979e-17 2.485562e-17 1.316176e-16 4.535701e-19 91 92 93 94 95 -3.390570e-16 8.497419e-17 -2.723956e-18 -1.746523e-17 -2.878372e-17 96 97 98 99 100 2.842992e-16 2.903639e-17 9.016801e-18 6.109152e-17 -1.354064e-17 101 102 103 104 105 3.488966e-17 -1.486328e-17 -2.862913e-16 2.327170e-17 4.747453e-17 106 107 108 109 110 4.748314e-17 4.949066e-17 2.141777e-17 1.624509e-16 3.533701e-17 111 112 113 114 115 1.761376e-16 1.958787e-17 -1.237470e-16 6.223014e-17 -2.352623e-16 116 117 -6.398415e-17 -4.122707e-17 > postscript(file="/var/www/rcomp/tmp/6y6ql1292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 117 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.892968e-16 NA 1 -2.579014e-16 -2.892968e-16 2 -6.749799e-16 -2.579014e-16 3 -5.995323e-17 -6.749799e-16 4 1.193760e-16 -5.995323e-17 5 1.762469e-16 1.193760e-16 6 2.860858e-15 1.762469e-16 7 -6.186457e-17 2.860858e-15 8 -7.446161e-17 -6.186457e-17 9 -1.104933e-16 -7.446161e-17 10 6.080808e-18 -1.104933e-16 11 -1.181588e-16 6.080808e-18 12 3.699149e-18 -1.181588e-16 13 2.660700e-17 3.699149e-18 14 -1.321214e-16 2.660700e-17 15 -6.762483e-17 -1.321214e-16 16 -6.317381e-17 -6.762483e-17 17 -1.246845e-16 -6.317381e-17 18 -4.519664e-16 -1.246845e-16 19 3.767563e-17 -4.519664e-16 20 -7.066140e-17 3.767563e-17 21 -2.326815e-17 -7.066140e-17 22 -1.719976e-17 -2.326815e-17 23 -1.242667e-16 -1.719976e-17 24 -6.863514e-17 -1.242667e-16 25 2.468774e-17 -6.863514e-17 26 8.922540e-17 2.468774e-17 27 -6.113808e-17 8.922540e-17 28 2.951461e-17 -6.113808e-17 29 -7.765363e-17 2.951461e-17 30 -3.627758e-16 -7.765363e-17 31 -1.393141e-17 -3.627758e-16 32 -6.775578e-17 -1.393141e-17 33 9.635644e-18 -6.775578e-17 34 -7.251071e-17 9.635644e-18 35 -5.989202e-17 -7.251071e-17 36 1.739712e-17 -5.989202e-17 37 2.640182e-18 1.739712e-17 38 1.233901e-16 2.640182e-18 39 5.303045e-17 1.233901e-16 40 -1.163220e-16 5.303045e-17 41 -8.832207e-17 -1.163220e-16 42 -2.811574e-16 -8.832207e-17 43 -2.918593e-17 -2.811574e-16 44 2.375018e-17 -2.918593e-17 45 -1.309115e-17 2.375018e-17 46 2.584684e-17 -1.309115e-17 47 -1.068457e-17 2.584684e-17 48 4.833362e-17 -1.068457e-17 49 -1.239321e-17 4.833362e-17 50 1.058077e-16 -1.239321e-17 51 6.047475e-17 1.058077e-16 52 1.016051e-17 6.047475e-17 53 -2.703054e-17 1.016051e-17 54 -2.826774e-16 -2.703054e-17 55 -5.638469e-17 -2.826774e-16 56 1.453834e-17 -5.638469e-17 57 8.397078e-17 1.453834e-17 58 -1.712159e-16 8.397078e-17 59 6.170889e-17 -1.712159e-16 60 6.992927e-17 6.170889e-17 61 4.399593e-19 6.992927e-17 62 1.371959e-16 4.399593e-19 63 2.884177e-17 1.371959e-16 64 -1.246761e-16 2.884177e-17 65 1.114019e-16 -1.246761e-16 66 -3.297269e-16 1.114019e-16 67 1.240862e-17 -3.297269e-16 68 1.098319e-16 1.240862e-17 69 -4.536939e-18 1.098319e-16 70 -8.633773e-17 -4.536939e-18 71 -1.259175e-17 -8.633773e-17 72 1.502503e-17 -1.259175e-17 73 9.107760e-18 1.502503e-17 74 1.809528e-16 9.107760e-18 75 1.546634e-17 1.809528e-16 76 1.023607e-16 1.546634e-17 77 -1.777859e-17 1.023607e-16 78 -2.919436e-16 -1.777859e-17 79 6.702062e-17 -2.919436e-16 80 6.123488e-17 6.702062e-17 81 2.776523e-17 6.123488e-17 82 2.946295e-16 2.776523e-17 83 -4.183214e-17 2.946295e-16 84 1.206044e-17 -4.183214e-17 85 1.624581e-16 1.206044e-17 86 -6.669979e-17 1.624581e-16 87 2.485562e-17 -6.669979e-17 88 1.316176e-16 2.485562e-17 89 4.535701e-19 1.316176e-16 90 -3.390570e-16 4.535701e-19 91 8.497419e-17 -3.390570e-16 92 -2.723956e-18 8.497419e-17 93 -1.746523e-17 -2.723956e-18 94 -2.878372e-17 -1.746523e-17 95 2.842992e-16 -2.878372e-17 96 2.903639e-17 2.842992e-16 97 9.016801e-18 2.903639e-17 98 6.109152e-17 9.016801e-18 99 -1.354064e-17 6.109152e-17 100 3.488966e-17 -1.354064e-17 101 -1.486328e-17 3.488966e-17 102 -2.862913e-16 -1.486328e-17 103 2.327170e-17 -2.862913e-16 104 4.747453e-17 2.327170e-17 105 4.748314e-17 4.747453e-17 106 4.949066e-17 4.748314e-17 107 2.141777e-17 4.949066e-17 108 1.624509e-16 2.141777e-17 109 3.533701e-17 1.624509e-16 110 1.761376e-16 3.533701e-17 111 1.958787e-17 1.761376e-16 112 -1.237470e-16 1.958787e-17 113 6.223014e-17 -1.237470e-16 114 -2.352623e-16 6.223014e-17 115 -6.398415e-17 -2.352623e-16 116 -4.122707e-17 -6.398415e-17 117 NA -4.122707e-17 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.579014e-16 -2.892968e-16 [2,] -6.749799e-16 -2.579014e-16 [3,] -5.995323e-17 -6.749799e-16 [4,] 1.193760e-16 -5.995323e-17 [5,] 1.762469e-16 1.193760e-16 [6,] 2.860858e-15 1.762469e-16 [7,] -6.186457e-17 2.860858e-15 [8,] -7.446161e-17 -6.186457e-17 [9,] -1.104933e-16 -7.446161e-17 [10,] 6.080808e-18 -1.104933e-16 [11,] -1.181588e-16 6.080808e-18 [12,] 3.699149e-18 -1.181588e-16 [13,] 2.660700e-17 3.699149e-18 [14,] -1.321214e-16 2.660700e-17 [15,] -6.762483e-17 -1.321214e-16 [16,] -6.317381e-17 -6.762483e-17 [17,] -1.246845e-16 -6.317381e-17 [18,] -4.519664e-16 -1.246845e-16 [19,] 3.767563e-17 -4.519664e-16 [20,] -7.066140e-17 3.767563e-17 [21,] -2.326815e-17 -7.066140e-17 [22,] -1.719976e-17 -2.326815e-17 [23,] -1.242667e-16 -1.719976e-17 [24,] -6.863514e-17 -1.242667e-16 [25,] 2.468774e-17 -6.863514e-17 [26,] 8.922540e-17 2.468774e-17 [27,] -6.113808e-17 8.922540e-17 [28,] 2.951461e-17 -6.113808e-17 [29,] -7.765363e-17 2.951461e-17 [30,] -3.627758e-16 -7.765363e-17 [31,] -1.393141e-17 -3.627758e-16 [32,] -6.775578e-17 -1.393141e-17 [33,] 9.635644e-18 -6.775578e-17 [34,] -7.251071e-17 9.635644e-18 [35,] -5.989202e-17 -7.251071e-17 [36,] 1.739712e-17 -5.989202e-17 [37,] 2.640182e-18 1.739712e-17 [38,] 1.233901e-16 2.640182e-18 [39,] 5.303045e-17 1.233901e-16 [40,] -1.163220e-16 5.303045e-17 [41,] -8.832207e-17 -1.163220e-16 [42,] -2.811574e-16 -8.832207e-17 [43,] -2.918593e-17 -2.811574e-16 [44,] 2.375018e-17 -2.918593e-17 [45,] -1.309115e-17 2.375018e-17 [46,] 2.584684e-17 -1.309115e-17 [47,] -1.068457e-17 2.584684e-17 [48,] 4.833362e-17 -1.068457e-17 [49,] -1.239321e-17 4.833362e-17 [50,] 1.058077e-16 -1.239321e-17 [51,] 6.047475e-17 1.058077e-16 [52,] 1.016051e-17 6.047475e-17 [53,] -2.703054e-17 1.016051e-17 [54,] -2.826774e-16 -2.703054e-17 [55,] -5.638469e-17 -2.826774e-16 [56,] 1.453834e-17 -5.638469e-17 [57,] 8.397078e-17 1.453834e-17 [58,] -1.712159e-16 8.397078e-17 [59,] 6.170889e-17 -1.712159e-16 [60,] 6.992927e-17 6.170889e-17 [61,] 4.399593e-19 6.992927e-17 [62,] 1.371959e-16 4.399593e-19 [63,] 2.884177e-17 1.371959e-16 [64,] -1.246761e-16 2.884177e-17 [65,] 1.114019e-16 -1.246761e-16 [66,] -3.297269e-16 1.114019e-16 [67,] 1.240862e-17 -3.297269e-16 [68,] 1.098319e-16 1.240862e-17 [69,] -4.536939e-18 1.098319e-16 [70,] -8.633773e-17 -4.536939e-18 [71,] -1.259175e-17 -8.633773e-17 [72,] 1.502503e-17 -1.259175e-17 [73,] 9.107760e-18 1.502503e-17 [74,] 1.809528e-16 9.107760e-18 [75,] 1.546634e-17 1.809528e-16 [76,] 1.023607e-16 1.546634e-17 [77,] -1.777859e-17 1.023607e-16 [78,] -2.919436e-16 -1.777859e-17 [79,] 6.702062e-17 -2.919436e-16 [80,] 6.123488e-17 6.702062e-17 [81,] 2.776523e-17 6.123488e-17 [82,] 2.946295e-16 2.776523e-17 [83,] -4.183214e-17 2.946295e-16 [84,] 1.206044e-17 -4.183214e-17 [85,] 1.624581e-16 1.206044e-17 [86,] -6.669979e-17 1.624581e-16 [87,] 2.485562e-17 -6.669979e-17 [88,] 1.316176e-16 2.485562e-17 [89,] 4.535701e-19 1.316176e-16 [90,] -3.390570e-16 4.535701e-19 [91,] 8.497419e-17 -3.390570e-16 [92,] -2.723956e-18 8.497419e-17 [93,] -1.746523e-17 -2.723956e-18 [94,] -2.878372e-17 -1.746523e-17 [95,] 2.842992e-16 -2.878372e-17 [96,] 2.903639e-17 2.842992e-16 [97,] 9.016801e-18 2.903639e-17 [98,] 6.109152e-17 9.016801e-18 [99,] -1.354064e-17 6.109152e-17 [100,] 3.488966e-17 -1.354064e-17 [101,] -1.486328e-17 3.488966e-17 [102,] -2.862913e-16 -1.486328e-17 [103,] 2.327170e-17 -2.862913e-16 [104,] 4.747453e-17 2.327170e-17 [105,] 4.748314e-17 4.747453e-17 [106,] 4.949066e-17 4.748314e-17 [107,] 2.141777e-17 4.949066e-17 [108,] 1.624509e-16 2.141777e-17 [109,] 3.533701e-17 1.624509e-16 [110,] 1.761376e-16 3.533701e-17 [111,] 1.958787e-17 1.761376e-16 [112,] -1.237470e-16 1.958787e-17 [113,] 6.223014e-17 -1.237470e-16 [114,] -2.352623e-16 6.223014e-17 [115,] -6.398415e-17 -2.352623e-16 [116,] -4.122707e-17 -6.398415e-17 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.579014e-16 -2.892968e-16 2 -6.749799e-16 -2.579014e-16 3 -5.995323e-17 -6.749799e-16 4 1.193760e-16 -5.995323e-17 5 1.762469e-16 1.193760e-16 6 2.860858e-15 1.762469e-16 7 -6.186457e-17 2.860858e-15 8 -7.446161e-17 -6.186457e-17 9 -1.104933e-16 -7.446161e-17 10 6.080808e-18 -1.104933e-16 11 -1.181588e-16 6.080808e-18 12 3.699149e-18 -1.181588e-16 13 2.660700e-17 3.699149e-18 14 -1.321214e-16 2.660700e-17 15 -6.762483e-17 -1.321214e-16 16 -6.317381e-17 -6.762483e-17 17 -1.246845e-16 -6.317381e-17 18 -4.519664e-16 -1.246845e-16 19 3.767563e-17 -4.519664e-16 20 -7.066140e-17 3.767563e-17 21 -2.326815e-17 -7.066140e-17 22 -1.719976e-17 -2.326815e-17 23 -1.242667e-16 -1.719976e-17 24 -6.863514e-17 -1.242667e-16 25 2.468774e-17 -6.863514e-17 26 8.922540e-17 2.468774e-17 27 -6.113808e-17 8.922540e-17 28 2.951461e-17 -6.113808e-17 29 -7.765363e-17 2.951461e-17 30 -3.627758e-16 -7.765363e-17 31 -1.393141e-17 -3.627758e-16 32 -6.775578e-17 -1.393141e-17 33 9.635644e-18 -6.775578e-17 34 -7.251071e-17 9.635644e-18 35 -5.989202e-17 -7.251071e-17 36 1.739712e-17 -5.989202e-17 37 2.640182e-18 1.739712e-17 38 1.233901e-16 2.640182e-18 39 5.303045e-17 1.233901e-16 40 -1.163220e-16 5.303045e-17 41 -8.832207e-17 -1.163220e-16 42 -2.811574e-16 -8.832207e-17 43 -2.918593e-17 -2.811574e-16 44 2.375018e-17 -2.918593e-17 45 -1.309115e-17 2.375018e-17 46 2.584684e-17 -1.309115e-17 47 -1.068457e-17 2.584684e-17 48 4.833362e-17 -1.068457e-17 49 -1.239321e-17 4.833362e-17 50 1.058077e-16 -1.239321e-17 51 6.047475e-17 1.058077e-16 52 1.016051e-17 6.047475e-17 53 -2.703054e-17 1.016051e-17 54 -2.826774e-16 -2.703054e-17 55 -5.638469e-17 -2.826774e-16 56 1.453834e-17 -5.638469e-17 57 8.397078e-17 1.453834e-17 58 -1.712159e-16 8.397078e-17 59 6.170889e-17 -1.712159e-16 60 6.992927e-17 6.170889e-17 61 4.399593e-19 6.992927e-17 62 1.371959e-16 4.399593e-19 63 2.884177e-17 1.371959e-16 64 -1.246761e-16 2.884177e-17 65 1.114019e-16 -1.246761e-16 66 -3.297269e-16 1.114019e-16 67 1.240862e-17 -3.297269e-16 68 1.098319e-16 1.240862e-17 69 -4.536939e-18 1.098319e-16 70 -8.633773e-17 -4.536939e-18 71 -1.259175e-17 -8.633773e-17 72 1.502503e-17 -1.259175e-17 73 9.107760e-18 1.502503e-17 74 1.809528e-16 9.107760e-18 75 1.546634e-17 1.809528e-16 76 1.023607e-16 1.546634e-17 77 -1.777859e-17 1.023607e-16 78 -2.919436e-16 -1.777859e-17 79 6.702062e-17 -2.919436e-16 80 6.123488e-17 6.702062e-17 81 2.776523e-17 6.123488e-17 82 2.946295e-16 2.776523e-17 83 -4.183214e-17 2.946295e-16 84 1.206044e-17 -4.183214e-17 85 1.624581e-16 1.206044e-17 86 -6.669979e-17 1.624581e-16 87 2.485562e-17 -6.669979e-17 88 1.316176e-16 2.485562e-17 89 4.535701e-19 1.316176e-16 90 -3.390570e-16 4.535701e-19 91 8.497419e-17 -3.390570e-16 92 -2.723956e-18 8.497419e-17 93 -1.746523e-17 -2.723956e-18 94 -2.878372e-17 -1.746523e-17 95 2.842992e-16 -2.878372e-17 96 2.903639e-17 2.842992e-16 97 9.016801e-18 2.903639e-17 98 6.109152e-17 9.016801e-18 99 -1.354064e-17 6.109152e-17 100 3.488966e-17 -1.354064e-17 101 -1.486328e-17 3.488966e-17 102 -2.862913e-16 -1.486328e-17 103 2.327170e-17 -2.862913e-16 104 4.747453e-17 2.327170e-17 105 4.748314e-17 4.747453e-17 106 4.949066e-17 4.748314e-17 107 2.141777e-17 4.949066e-17 108 1.624509e-16 2.141777e-17 109 3.533701e-17 1.624509e-16 110 1.761376e-16 3.533701e-17 111 1.958787e-17 1.761376e-16 112 -1.237470e-16 1.958787e-17 113 6.223014e-17 -1.237470e-16 114 -2.352623e-16 6.223014e-17 115 -6.398415e-17 -2.352623e-16 116 -4.122707e-17 -6.398415e-17 > 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/rcomp/tmp/7qfp61292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8qfp61292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/9qfp61292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/1017or1292772458.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/rcomp/tmp/11mpnf1292772458.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/rcomp/tmp/12qq331292772458.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/rcomp/tmp/134z1u1292772458.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/rcomp/tmp/147i001292772458.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/rcomp/tmp/15tjgn1292772458.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/rcomp/tmp/16ejfb1292772458.tab") + } > > try(system("convert tmp/1uorf1292772458.ps tmp/1uorf1292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/2uorf1292772458.ps tmp/2uorf1292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/35f901292772458.ps tmp/35f901292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/45f901292772458.ps tmp/45f901292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/55f901292772458.ps tmp/55f901292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/6y6ql1292772458.ps tmp/6y6ql1292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/7qfp61292772458.ps tmp/7qfp61292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/8qfp61292772458.ps tmp/8qfp61292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/9qfp61292772458.ps tmp/9qfp61292772458.png",intern=TRUE)) character(0) > try(system("convert tmp/1017or1292772458.ps tmp/1017or1292772458.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.030 1.690 5.743