R version 2.8.0 (2008-10-20) Copyright (C) 2008 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. Natural language support but running in an English locale 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(1 + ,7 + ,7 + ,6 + ,1 + ,5 + ,7 + ,7 + ,1 + ,5 + ,6 + ,4 + ,1 + ,4 + ,5 + ,5 + ,1 + ,6 + ,6 + ,6 + ,2 + ,5 + ,5 + ,6 + ,2 + ,4 + ,5 + ,4 + ,2 + ,4 + ,5 + ,6 + ,1 + ,5 + ,6 + ,2 + ,2 + ,4 + ,5 + ,6 + ,1 + ,6 + ,7 + ,5 + ,1 + ,6 + ,7 + ,5 + ,2 + ,7 + ,7 + ,1 + ,1 + ,5 + ,7 + ,7 + ,1 + ,6 + ,7 + ,6 + ,1 + ,3 + ,5 + ,6 + ,1 + ,6 + ,7 + ,3 + ,1 + ,4 + ,3 + ,7 + ,1 + ,6 + ,6 + ,4 + ,1 + ,4 + ,6 + ,6 + ,2 + ,5 + ,4 + ,3 + ,1 + ,2 + ,7 + ,7 + ,2 + ,5 + ,6 + ,2 + ,1 + ,5 + ,6 + ,7 + ,1 + ,4 + ,6 + ,4 + ,1 + ,3 + ,5 + ,6 + ,1 + ,6 + ,7 + ,3 + ,1 + ,5 + ,3 + ,6 + ,1 + ,6 + ,6 + ,5 + ,1 + ,6 + ,7 + ,7 + ,2 + ,5 + ,6 + ,3 + ,2 + ,4 + ,5 + ,6 + ,1 + ,3 + ,4 + ,3 + ,1 + ,3 + ,7 + ,7 + ,2 + ,7 + ,7 + ,6 + ,1 + ,6 + ,7 + ,7 + ,2 + ,3 + ,7 + ,1 + ,1 + ,7 + ,7 + ,7 + ,1 + ,5 + ,6 + ,1 + ,2 + ,2 + ,6 + ,7 + ,2 + ,3 + ,3 + ,1 + ,1 + ,6 + ,5 + ,5 + ,1 + ,5 + ,7 + ,5 + ,1 + ,4 + ,7 + ,7 + ,1 + ,2 + ,5 + ,2 + ,1 + ,1 + ,4 + ,5 + ,2 + ,6 + ,7 + ,3 + ,1 + ,4 + ,7 + ,6 + ,1 + ,3 + ,6 + ,3 + ,1 + ,3 + ,7 + ,7 + ,1 + ,6 + ,5 + ,5 + ,1 + ,6 + ,7 + ,6 + ,1 + ,6 + ,5 + ,5 + ,1 + ,6 + ,7 + ,6 + ,1 + ,5 + ,6 + ,1 + ,1 + ,3 + ,3 + ,6 + ,2 + ,5 + ,5 + ,2 + ,1 + ,6 + ,7 + ,6 + ,1 + ,7 + ,6 + ,3 + ,1 + ,4 + ,5 + ,6 + ,2 + ,6 + ,6 + ,5 + ,1 + ,7 + ,7 + ,6 + ,1 + ,5 + ,5 + ,3 + ,1 + ,4 + ,5 + ,5 + ,1 + ,5 + ,4 + ,5 + ,4 + ,4 + ,5 + ,3 + ,2 + ,4 + ,5 + ,3 + ,3 + ,3 + ,4 + ,3 + ,2 + ,4 + ,4 + ,2 + ,1 + ,4 + ,5 + ,5 + ,2 + ,6 + ,6 + ,4 + ,2 + ,6 + ,6 + ,6 + ,1 + ,5 + ,6 + ,3 + ,1 + ,3 + ,7 + ,7 + ,2 + ,5 + ,7 + ,3 + ,1 + ,2 + ,5 + ,7 + ,2 + ,7 + ,7 + ,6 + ,1 + ,7 + ,7 + ,7 + ,1 + ,5 + ,7 + ,6 + ,1 + ,7 + ,7 + ,6 + ,1 + ,5 + ,7 + ,3 + ,1 + ,5 + ,6 + ,7 + ,1 + ,6 + ,5 + ,3 + ,1 + ,5 + ,6 + ,7 + ,1 + ,5 + ,6 + ,4 + ,2 + ,6 + ,7 + ,6 + ,2 + ,6 + ,6 + ,4 + ,1 + ,7 + ,7 + ,6 + ,2 + ,7 + ,3 + ,2 + ,1 + ,6 + ,6 + ,5 + ,1 + ,5 + ,6 + ,2 + ,4 + ,3 + ,6 + ,6 + ,2 + ,5 + ,5 + ,3 + ,1 + ,4 + ,4 + ,6 + ,2 + ,5 + ,4 + ,2 + ,3 + ,4 + ,7 + ,7 + ,1 + ,6 + ,6 + ,5 + ,2 + ,4 + ,5 + ,6 + ,2 + ,2 + ,6 + ,5 + ,3 + ,2 + ,6 + ,7 + ,1 + ,4 + ,6 + ,6 + ,2 + ,4 + ,5 + ,6 + ,1 + ,4 + ,5 + ,3 + ,1 + ,3 + ,3 + ,5 + ,1 + ,6 + ,6 + ,3 + ,2 + ,5 + ,7 + ,7 + ,1 + ,3 + ,5 + ,2 + ,1 + ,3 + ,6 + ,4 + ,2 + ,6 + ,7 + ,5 + ,1 + ,5 + ,6 + ,7 + ,1 + ,6 + ,6 + ,2 + ,1 + ,5 + ,5 + ,5 + ,1 + ,5 + ,6 + ,4 + ,1 + ,4 + ,5 + ,6 + ,1 + ,6 + ,7 + ,4 + ,1 + ,5 + ,7 + ,7 + ,2 + ,1 + ,4 + ,1 + ,1 + ,5 + ,7 + ,7 + ,2 + ,5 + ,3 + ,6 + ,2 + ,6 + ,7 + ,7 + ,1 + ,7 + ,4 + ,2 + ,1 + ,4 + ,6 + ,7 + ,1 + ,4 + ,4 + ,3 + ,3 + ,4 + ,6 + ,6 + ,1 + ,5 + ,5 + ,4 + ,1 + ,7 + ,7 + ,6 + ,2 + ,6 + ,4 + ,3 + ,1 + ,6 + ,7 + ,6 + ,1 + ,4 + ,6 + ,4 + ,4 + ,5 + ,5 + ,4 + ,2 + ,6 + ,7 + ,3 + ,1 + ,6 + ,7 + ,6 + ,2 + ,6 + ,6 + ,6 + ,1 + ,6 + ,6 + ,6 + ,2 + ,5 + ,6 + ,4 + ,1 + ,5 + ,5 + ,7 + ,1 + ,5 + ,6 + ,5 + ,1 + ,4 + ,6 + ,7 + ,1 + ,3 + ,6 + ,3 + ,1 + ,2 + ,5 + ,7 + ,1 + ,5 + ,7 + ,4 + ,1 + ,7 + ,5 + ,7 + ,2 + ,6 + ,6 + ,3 + ,1 + ,5 + ,6 + ,7 + ,2 + ,5 + ,6 + ,3 + ,1 + ,5 + ,6 + ,6 + ,1 + ,6 + ,6 + ,6 + ,1 + ,6 + ,6 + ,5 + ,1 + ,6 + ,7 + ,6 + ,1 + ,6 + ,7 + ,7 + ,1 + ,4 + ,5 + ,2 + ,2 + ,4 + ,6 + ,5 + ,1 + ,4 + ,4 + ,2 + ,2 + ,4 + ,5 + ,5 + ,2 + ,6 + ,7 + ,6 + ,1 + ,6 + ,7 + ,7 + ,2 + ,7 + ,7 + ,5 + ,1 + ,6 + ,7 + ,7 + ,2 + ,4 + ,6 + ,1 + ,1 + ,5 + ,6 + ,2 + ,2 + ,5 + ,7 + ,2 + ,1 + ,7 + ,7 + ,6 + ,1 + ,6 + ,6 + ,5 + ,1 + ,3 + ,6 + ,6 + ,1 + ,6 + ,5 + ,3 + ,1 + ,6 + ,6 + ,6 + ,1 + ,5 + ,7 + ,3 + ,1 + ,5 + ,7 + ,6 + ,2 + ,3 + ,6 + ,4 + ,2 + ,6 + ,6 + ,5 + ,2 + ,7 + ,5 + ,6 + ,1 + ,7 + ,7 + ,6 + ,1 + ,6 + ,6 + ,4 + ,1 + ,4 + ,7 + ,7 + ,1 + ,4 + ,5 + ,2 + ,4 + ,2 + ,5 + ,5 + ,1 + ,4 + ,7 + ,4 + ,3 + ,3 + ,3 + ,7 + ,1 + ,5 + ,6 + ,4 + ,2 + ,5 + ,6 + ,6 + ,1 + ,3 + ,2 + ,2 + ,1 + ,5 + ,6 + ,5 + ,2 + ,7 + ,5 + ,4 + ,1 + ,6 + ,5 + ,6 + ,1 + ,6 + ,7 + ,3 + ,1 + ,3 + ,6 + ,7 + ,1 + ,6 + ,7 + ,6 + ,1 + ,3 + ,6 + ,7 + ,1 + ,4 + ,7 + ,4 + ,2 + ,5 + ,6 + ,6 + ,1 + ,5 + ,7 + ,4 + ,1 + ,5 + ,7 + ,7 + ,2 + ,6 + ,6 + ,3 + ,1 + ,3 + ,6 + ,6 + ,1 + ,5 + ,5 + ,3 + ,2 + ,4 + ,6 + ,5 + ,2 + ,6 + ,6 + ,3 + ,1 + ,1 + ,6 + ,5 + ,2 + ,6 + ,6 + ,4 + ,3 + ,7 + ,7 + ,6 + ,1 + ,4 + ,5 + ,2 + ,1 + ,4 + ,6 + ,6 + ,2 + ,5 + ,7 + ,2 + ,1 + ,7 + ,5 + ,6 + ,1 + ,6 + ,5 + ,3 + ,2 + ,4 + ,5 + ,6 + ,1 + ,5 + ,6 + ,3 + ,1 + ,5 + ,6 + ,6 + ,2 + ,5 + ,5 + ,4 + ,1 + ,5 + ,6 + ,5 + ,1 + ,4 + ,5 + ,4 + ,2 + ,6 + ,6 + ,5 + ,2 + ,4 + ,5 + ,2 + ,2 + ,4 + ,5 + ,5 + ,1 + ,6 + ,5 + ,5 + ,1 + ,4 + ,6 + ,7 + ,1 + ,5 + ,7 + ,5 + ,1 + ,4 + ,4 + ,7 + ,1 + ,6 + ,6 + ,4 + ,1 + ,6 + ,6 + ,6 + ,1 + ,5 + ,7 + ,4 + ,1 + ,4 + ,7 + ,7 + ,1 + ,6 + ,6 + ,3 + ,1 + ,3 + ,7 + ,7 + ,1 + ,5 + ,5 + ,4 + ,1 + ,3 + ,5 + ,4 + ,1 + ,4 + ,5 + ,4 + ,2 + ,5 + ,5 + ,5 + ,1 + ,6 + ,7 + ,5 + ,1 + ,5 + ,7 + ,7 + ,2 + ,4 + ,6 + ,4 + ,1 + ,3 + ,3 + ,7 + ,1 + ,5 + ,5 + ,5 + ,2 + ,4 + ,7 + ,7 + ,1 + ,5 + ,7 + ,3 + ,2 + ,5 + ,5 + ,6 + ,2 + ,6 + ,4 + ,2 + ,1 + ,5 + ,7 + ,5 + ,1 + ,3 + ,3 + ,1 + ,2 + ,3 + ,5 + ,7 + ,1 + ,5 + ,7 + ,5 + ,2 + ,3 + ,3 + ,1 + ,4 + ,5 + ,5 + ,2 + ,5 + ,6 + ,6 + ,1 + ,5 + ,6 + ,3 + ,2 + ,3 + ,5 + ,6 + ,1 + ,5 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,1 + ,7 + ,7 + ,4 + ,1 + ,7 + ,7 + ,7 + ,2 + ,5 + ,7 + ,5 + ,2 + ,6 + ,6 + ,6 + ,1 + ,7 + ,5 + ,5 + ,1 + ,5 + ,7 + ,7 + ,1 + ,5 + ,7 + ,3 + ,1 + ,2 + ,2 + ,6 + ,2 + ,4 + ,3 + ,4 + ,1 + ,4 + ,5 + ,5 + ,2 + ,6 + ,6 + ,6 + ,1 + ,6 + ,6 + ,6 + ,1 + ,4 + ,5 + ,4 + ,3 + ,6 + ,6 + ,6 + ,2 + ,4 + ,5 + ,5 + ,2 + ,5 + ,6 + ,7 + ,2 + ,4 + ,6 + ,4 + ,1 + ,4 + ,2 + ,6 + ,2 + ,4 + ,5 + ,4 + ,1 + ,4 + ,6 + ,7 + ,2 + ,6 + ,6 + ,5 + ,1 + ,5 + ,7 + ,6 + ,1 + ,6 + ,6 + ,4 + ,2 + ,3 + ,4 + ,6 + ,1 + ,5 + ,7 + ,3 + ,5 + ,5 + ,7 + ,7 + ,2 + ,3 + ,5 + ,3 + ,1 + ,5 + ,7 + ,7 + ,2 + ,6 + ,7 + ,6 + ,1 + ,5 + ,6 + ,7 + ,1 + ,5 + ,6 + ,5 + ,2 + ,6 + ,6 + ,7 + ,1 + ,4 + ,6 + ,2 + ,1 + ,4 + ,2 + ,6 + ,1 + ,5 + ,7 + ,4 + ,2 + ,3 + ,7 + ,7 + ,1 + ,2 + ,7 + ,3 + ,1 + ,3 + ,7 + ,7 + ,2 + ,5 + ,5 + ,3 + ,1 + ,4 + ,5 + ,6 + ,1 + ,7 + ,7 + ,5 + ,1 + ,5 + ,5 + ,7 + ,1 + ,4 + ,5 + ,3 + ,1 + ,5 + ,6 + ,6 + ,2 + ,4 + ,6 + ,3 + ,2 + ,3 + ,5 + ,7 + ,2 + ,7 + ,7 + ,4 + ,1 + ,6 + ,6 + ,6 + ,2 + ,6 + ,6 + ,5 + ,1 + ,5 + ,6 + ,5 + ,2 + ,5 + ,5 + ,5 + ,2 + ,4 + ,6 + ,4 + ,1 + ,5 + ,6 + ,5 + ,1 + ,5 + ,5 + ,7 + ,1 + ,5 + ,7 + ,5 + ,1 + ,4 + ,6 + ,7 + ,1 + ,7 + ,6 + ,3 + ,1 + ,7 + ,7 + ,5 + ,2 + ,6 + ,7 + ,4 + ,1 + ,5 + ,7 + ,7 + ,2 + ,6 + ,7 + ,4 + ,1 + ,3 + ,6 + ,6 + ,2 + ,5 + ,6 + ,3 + ,2 + ,5 + ,6 + ,5 + ,1 + ,2 + ,6 + ,4 + ,2 + ,2 + ,6 + ,6 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,7 + ,7 + ,1 + ,6 + ,7 + ,3 + ,1 + ,3 + ,6 + ,7 + ,1 + ,5 + ,6 + ,2 + ,1 + ,4 + ,5 + ,6 + ,2 + ,5 + ,4 + ,4 + ,1 + ,5 + ,5 + ,5 + ,1 + ,5 + ,5 + ,4 + ,1 + ,4 + ,5 + ,6 + ,1 + ,4 + ,6 + ,3 + ,1 + ,4 + ,4 + ,5 + ,1 + ,4 + ,5 + ,3 + ,5 + ,2 + ,4 + ,6) + ,dim=c(8 + ,164) + ,dimnames=list(c('Gender' + ,'1' + ,'2' + ,'3' + ,'4' + ,'5' + ,'6' + ,'7') + ,1:164)) > y <- array(NA,dim=c(8,164),dimnames=list(c('Gender','1','2','3','4','5','6','7'),1:164)) > 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 = 'Do not include Seasonal Dummies' > par1 = '3' > #'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 2 Gender 1 3 4 5 6 7 1 7 1 7 6 1 5 7 7 2 6 1 5 4 1 4 5 5 3 6 1 6 6 2 5 5 6 4 5 2 4 4 2 4 5 6 5 6 1 5 2 2 4 5 6 6 7 1 6 5 1 6 7 5 7 7 2 7 1 1 5 7 7 8 7 1 6 6 1 3 5 6 9 7 1 6 3 1 4 3 7 10 6 1 6 4 1 4 6 6 11 4 2 5 3 1 2 7 7 12 6 2 5 2 1 5 6 7 13 6 1 4 4 1 3 5 6 14 7 1 6 3 1 5 3 6 15 6 1 6 5 1 6 7 7 16 6 2 5 3 2 4 5 6 17 4 1 3 3 1 3 7 7 18 7 2 7 6 1 6 7 7 19 7 2 3 1 1 7 7 7 20 6 1 5 1 2 2 6 7 21 3 2 3 1 1 6 5 5 22 7 1 5 5 1 4 7 7 23 5 1 2 2 1 1 4 5 24 7 2 6 3 1 4 7 6 25 6 1 3 3 1 3 7 7 26 5 1 6 5 1 6 7 6 27 5 1 6 5 1 6 7 6 28 6 1 5 1 1 3 3 6 29 5 2 5 2 1 6 7 6 30 6 1 7 3 1 4 5 6 31 6 2 6 5 1 7 7 6 32 5 1 5 3 1 4 5 5 33 4 1 5 5 4 4 5 3 34 5 2 4 3 3 3 4 3 35 4 2 4 2 1 4 5 5 36 6 2 6 4 2 6 6 6 37 6 1 5 3 1 3 7 7 38 7 2 5 3 1 2 5 7 39 7 2 7 6 1 7 7 7 40 7 1 5 6 1 7 7 6 41 7 1 5 3 1 5 6 7 42 5 1 6 3 1 5 6 7 43 6 1 5 4 2 6 7 6 44 6 2 6 4 1 7 7 6 45 3 2 7 2 1 6 6 5 46 6 1 5 2 4 3 6 6 47 5 2 5 3 1 4 4 6 48 4 2 5 2 3 4 7 7 49 6 1 6 5 2 4 5 6 50 6 2 2 5 3 2 6 7 51 6 1 4 6 2 4 5 6 52 5 1 4 3 1 3 3 5 53 6 1 6 3 2 5 7 7 54 5 1 3 2 1 3 6 4 55 7 2 6 5 1 5 6 7 56 6 1 6 2 1 5 5 5 57 6 1 5 4 1 4 5 6 58 7 1 6 4 1 5 7 7 59 4 2 1 1 1 5 7 7 60 3 2 5 6 2 6 7 7 61 4 1 7 2 1 4 6 7 62 4 1 4 3 3 4 6 6 63 5 1 5 4 1 7 7 6 64 4 2 6 3 1 6 7 6 65 6 1 4 4 4 5 5 4 66 7 2 6 3 1 6 7 6 67 6 2 6 6 1 6 6 6 68 6 2 5 4 1 5 5 7 69 6 1 5 5 1 4 6 7 70 6 1 3 3 1 2 5 7 71 7 1 5 4 1 7 5 7 72 6 2 6 3 1 5 6 7 73 6 2 5 3 1 5 6 6 74 6 1 6 6 1 6 6 5 75 7 1 6 6 1 6 7 7 76 5 1 4 2 2 4 6 5 77 4 1 4 2 2 4 5 5 78 7 2 6 6 1 6 7 7 79 7 2 7 5 1 6 7 7 80 6 2 4 1 1 5 6 2 81 7 2 5 2 1 7 7 6 82 6 1 6 5 1 3 6 6 83 5 1 6 3 1 6 6 6 84 7 1 5 3 1 5 7 6 85 6 2 3 4 2 6 6 5 86 5 2 7 6 1 7 7 6 87 6 1 6 4 1 4 7 7 88 5 1 4 2 4 2 5 5 89 7 1 4 4 3 3 3 7 90 6 1 5 4 2 5 6 6 91 2 1 3 2 1 5 6 5 92 5 2 7 4 1 6 5 6 93 7 1 6 3 1 3 6 7 94 7 1 6 6 1 3 6 7 95 7 1 4 4 2 5 6 6 96 7 1 5 4 1 5 7 7 97 6 2 6 3 1 3 6 6 98 5 1 5 3 2 4 6 5 99 6 2 6 3 1 1 6 5 100 6 2 6 4 3 7 7 6 101 5 1 4 2 1 4 6 6 102 7 2 5 2 1 7 5 6 103 5 1 6 3 2 4 5 6 104 6 1 5 3 1 5 6 6 105 5 2 5 4 1 5 6 5 106 5 1 4 4 2 6 6 5 107 5 2 4 2 2 4 5 5 108 5 1 6 5 1 4 6 7 109 7 1 5 5 1 4 4 7 110 6 1 6 4 1 6 6 6 111 7 1 5 4 1 4 7 7 112 6 1 6 3 1 3 7 7 113 5 1 5 4 1 3 5 4 114 5 1 4 4 2 5 5 5 115 7 1 6 5 1 5 7 7 116 6 2 4 4 1 3 3 7 117 5 1 5 5 2 4 7 7 118 7 1 5 3 2 5 5 6 119 4 2 6 2 1 5 7 5 120 3 1 3 1 2 3 5 7 121 7 1 5 5 2 3 3 1 122 5 4 5 2 5 6 6 1 123 3 5 6 2 3 5 6 1 124 4 5 4 4 4 4 3 1 125 4 7 7 1 7 7 7 2 126 5 5 7 2 6 6 6 1 127 5 7 5 1 5 7 7 1 128 3 5 7 1 2 2 6 2 129 4 4 3 1 4 5 5 2 130 6 6 6 1 6 6 6 1 131 4 4 5 3 6 6 6 2 132 5 4 5 2 5 6 7 2 133 4 4 6 1 4 2 6 2 134 4 4 5 1 4 6 7 2 135 5 6 6 1 5 7 6 1 136 4 6 6 2 3 4 6 1 137 3 5 7 5 5 7 7 2 138 3 3 5 1 5 7 7 2 139 6 6 7 1 5 6 7 1 140 5 5 6 2 6 6 7 1 141 2 4 6 1 4 2 6 1 142 4 5 7 2 3 7 7 1 143 3 2 7 1 3 7 7 2 144 3 5 5 1 4 5 6 1 145 5 7 7 1 5 5 7 1 146 3 4 5 1 5 6 6 2 147 3 4 6 2 3 5 7 2 148 4 7 7 1 6 6 6 2 149 5 6 6 1 5 6 5 2 150 5 5 5 2 4 6 4 1 151 5 5 6 1 5 5 7 1 152 5 5 7 1 4 6 7 1 153 3 7 6 1 7 7 5 2 154 4 6 7 1 5 7 7 2 155 4 6 7 1 3 6 6 2 156 3 5 6 2 5 6 5 1 157 4 2 6 2 2 6 6 1 158 4 4 4 4 4 7 7 1 159 3 6 7 1 3 6 7 1 160 2 5 6 1 4 5 6 2 161 4 5 4 1 5 5 5 1 162 4 5 5 1 4 5 6 1 163 3 4 6 1 4 4 5 1 164 3 4 5 5 2 4 6 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Gender `1` `3` `4` `5` 3.11942 -0.02483 0.14914 0.17480 0.01064 0.06767 `6` `7` -0.15826 0.30595 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3.32549 -0.71441 0.09762 0.67876 2.23020 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.11942 0.68506 4.554 1.06e-05 *** Gender -0.02483 0.10288 -0.241 0.80963 `1` 0.14914 0.07963 1.873 0.06295 . `3` 0.17480 0.06571 2.660 0.00862 ** `4` 0.01064 0.09364 0.114 0.90969 `5` 0.06767 0.06667 1.015 0.31168 `6` -0.15826 0.08681 -1.823 0.07023 . `7` 0.30595 0.07044 4.344 2.51e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.034 on 156 degrees of freedom Multiple R-squared: 0.426, Adjusted R-squared: 0.4002 F-statistic: 16.54 on 7 and 156 DF, p-value: 3.315e-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,] 0.26033000 0.52066000 0.7396700 [2,] 0.14108011 0.28216021 0.8589199 [3,] 0.11445915 0.22891830 0.8855409 [4,] 0.07575850 0.15151699 0.9242415 [5,] 0.06240387 0.12480774 0.9375961 [6,] 0.03942737 0.07885475 0.9605726 [7,] 0.02059620 0.04119239 0.9794038 [8,] 0.01028643 0.02057285 0.9897136 [9,] 0.02528418 0.05056835 0.9747158 [10,] 0.04029539 0.08059078 0.9597046 [11,] 0.31975255 0.63950511 0.6802474 [12,] 0.31572891 0.63145782 0.6842711 [13,] 0.33039776 0.66079552 0.6696022 [14,] 0.36138260 0.72276520 0.6386174 [15,] 0.33115881 0.66231762 0.6688412 [16,] 0.42150202 0.84300404 0.5784980 [17,] 0.45914732 0.91829464 0.5408527 [18,] 0.39985346 0.79970692 0.6001465 [19,] 0.35145393 0.70290786 0.6485461 [20,] 0.31601669 0.63203338 0.6839833 [21,] 0.25885546 0.51771091 0.7411445 [22,] 0.21842899 0.43685797 0.7815710 [23,] 0.18298075 0.36596150 0.8170193 [24,] 0.17756100 0.35512201 0.8224390 [25,] 0.17560102 0.35120203 0.8243990 [26,] 0.13819099 0.27638199 0.8618090 [27,] 0.10785207 0.21570413 0.8921479 [28,] 0.10312589 0.20625178 0.8968741 [29,] 0.07930826 0.15861651 0.9206917 [30,] 0.08466370 0.16932740 0.9153363 [31,] 0.07760537 0.15521074 0.9223946 [32,] 0.10674745 0.21349490 0.8932525 [33,] 0.08516348 0.17032696 0.9148365 [34,] 0.06523870 0.13047739 0.9347613 [35,] 0.18223966 0.36447932 0.8177603 [36,] 0.15518194 0.31036388 0.8448181 [37,] 0.14521147 0.29042294 0.8547885 [38,] 0.20089758 0.40179517 0.7991024 [39,] 0.16930130 0.33860261 0.8306987 [40,] 0.14062215 0.28124430 0.8593778 [41,] 0.11495221 0.22990443 0.8850478 [42,] 0.09615543 0.19231087 0.9038446 [43,] 0.07645896 0.15291792 0.9235410 [44,] 0.07220547 0.14441093 0.9277945 [45,] 0.06085401 0.12170801 0.9391460 [46,] 0.05210281 0.10420561 0.9478972 [47,] 0.03993248 0.07986496 0.9600675 [48,] 0.03500939 0.07001877 0.9649906 [49,] 0.03124662 0.06249324 0.9687534 [50,] 0.22255528 0.44511056 0.7774447 [51,] 0.37166257 0.74332514 0.6283374 [52,] 0.39692517 0.79385033 0.6030748 [53,] 0.37421482 0.74842964 0.6257852 [54,] 0.42183603 0.84367205 0.5781640 [55,] 0.43289198 0.86578396 0.5671080 [56,] 0.48600488 0.97200977 0.5139951 [57,] 0.44146242 0.88292484 0.5585376 [58,] 0.39639576 0.79279151 0.6036042 [59,] 0.35587606 0.71175213 0.6441239 [60,] 0.31532295 0.63064590 0.6846771 [61,] 0.28541077 0.57082153 0.7145892 [62,] 0.24669667 0.49339335 0.7533033 [63,] 0.21991903 0.43983806 0.7800810 [64,] 0.18666353 0.37332707 0.8133365 [65,] 0.16067091 0.32134182 0.8393291 [66,] 0.13390876 0.26781752 0.8660912 [67,] 0.13358201 0.26716401 0.8664180 [68,] 0.11665243 0.23330486 0.8833476 [69,] 0.10225267 0.20450535 0.8977473 [70,] 0.19818353 0.39636707 0.8018165 [71,] 0.25524683 0.51049365 0.7447532 [72,] 0.21967799 0.43935598 0.7803220 [73,] 0.20648488 0.41296976 0.7935151 [74,] 0.24575101 0.49150202 0.7542490 [75,] 0.23601489 0.47202978 0.7639851 [76,] 0.26170298 0.52340595 0.7382970 [77,] 0.22548977 0.45097954 0.7745102 [78,] 0.19322738 0.38645476 0.8067726 [79,] 0.18085069 0.36170138 0.8191493 [80,] 0.15283871 0.30567742 0.8471613 [81,] 0.37370172 0.74740344 0.6262983 [82,] 0.39144267 0.78288534 0.6085573 [83,] 0.39242407 0.78484814 0.6075759 [84,] 0.35768114 0.71536228 0.6423189 [85,] 0.38908187 0.77816374 0.6109181 [86,] 0.39344310 0.78688619 0.6065569 [87,] 0.36161841 0.72323683 0.6383816 [88,] 0.31956527 0.63913053 0.6804347 [89,] 0.32747544 0.65495088 0.6725246 [90,] 0.28863093 0.57726186 0.7113691 [91,] 0.25153089 0.50306177 0.7484691 [92,] 0.28085777 0.56171555 0.7191422 [93,] 0.25897469 0.51794938 0.7410253 [94,] 0.23322317 0.46644635 0.7667768 [95,] 0.20060520 0.40121039 0.7993948 [96,] 0.17059677 0.34119354 0.8294032 [97,] 0.14267549 0.28535099 0.8573245 [98,] 0.15274560 0.30549120 0.8472544 [99,] 0.13341346 0.26682691 0.8665865 [100,] 0.10910339 0.21820678 0.8908966 [101,] 0.13094818 0.26189636 0.8690518 [102,] 0.12421611 0.24843222 0.8757839 [103,] 0.10196775 0.20393551 0.8980322 [104,] 0.08242496 0.16484992 0.9175750 [105,] 0.10205667 0.20411335 0.8979433 [106,] 0.09140267 0.18280534 0.9085973 [107,] 0.08292824 0.16585647 0.9170718 [108,] 0.23367398 0.46734795 0.7663260 [109,] 0.25394774 0.50789548 0.7460523 [110,] 0.38640530 0.77281060 0.6135947 [111,] 0.83933507 0.32132987 0.1606649 [112,] 0.82915193 0.34169614 0.1708481 [113,] 0.82596452 0.34807095 0.1740355 [114,] 0.79663492 0.40673016 0.2033651 [115,] 0.77217696 0.45564607 0.2278230 [116,] 0.74778352 0.50443296 0.2522165 [117,] 0.71943432 0.56113137 0.2805657 [118,] 0.69228713 0.61542575 0.3077129 [119,] 0.66203013 0.67593975 0.3379699 [120,] 0.71866032 0.56267935 0.2813397 [121,] 0.68143031 0.63713939 0.3185697 [122,] 0.74174033 0.51651934 0.2582597 [123,] 0.84442070 0.31115859 0.1555793 [124,] 0.83240558 0.33518884 0.1675944 [125,] 0.78862035 0.42275930 0.2113796 [126,] 0.74041922 0.51916155 0.2595808 [127,] 0.72931989 0.54136022 0.2706801 [128,] 0.68243622 0.63512757 0.3175638 [129,] 0.75097625 0.49804751 0.2490238 [130,] 0.72714005 0.54571991 0.2728600 [131,] 0.71885142 0.56229715 0.2811486 [132,] 0.65801236 0.68397528 0.3419876 [133,] 0.59566473 0.80867055 0.4043353 [134,] 0.58533722 0.82932556 0.4146628 [135,] 0.54498828 0.91002345 0.4550117 [136,] 0.47987829 0.95975657 0.5201217 [137,] 0.39320200 0.78640400 0.6067980 [138,] 0.32152057 0.64304114 0.6784794 [139,] 0.45000635 0.90001269 0.5499937 [140,] 0.59599258 0.80801485 0.4040074 [141,] 0.54466206 0.91067588 0.4553379 [142,] 0.64778005 0.70443990 0.3522199 [143,] 0.50459066 0.99081867 0.4954093 > postscript(file="/var/www/html/freestat/rcomp/tmp/1hb4i1290499179.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/29kll1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/freestat/rcomp/tmp/39kll1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4kbko1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5kbko1290499179.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 = 164 Frequency = 1 1 2 3 4 5 6 0.429711646 0.440669760 -0.442345119 -0.701953920 0.473684111 1.297892017 7 8 9 10 11 12 1.328553808 0.703639999 0.537907471 0.143830679 -1.519747076 0.293778460 13 14 15 16 17 18 0.351532008 0.776187954 -0.314015751 0.323706625 -1.313960806 0.386864032 19 20 21 22 23 24 1.789777924 0.636137838 -1.847155971 0.970473782 0.282467172 1.501717274 25 26 27 28 29 30 0.686039194 -1.008061867 -1.008061867 0.410284037 -0.309683525 0.011233691 31 32 33 34 35 36 -0.050909481 -0.384526965 -1.154140689 0.289488562 -1.035755172 0.022671349 37 38 39 40 41 42 0.387753734 1.163737859 0.319190631 0.898604186 1.094149397 -1.054993333 43 44 45 46 47 48 0.305245824 0.123893794 -2.460272633 0.678338417 -0.823912594 -1.501567233 49 50 51 52 53 54 -0.199868443 0.398540403 -0.076386258 -0.484225898 0.092625886 0.620446588 55 56 57 58 59 60 0.620225906 0.573460178 0.134715876 0.928460925 -0.776589812 -3.325488822 61 62 63 64 65 66 -1.961659386 -1.404357215 -0.751789264 -1.633629529 0.796178030 1.366370471 67 68 69 70 71 72 -0.316296886 -0.214085622 -0.187783750 0.437197530 0.625741786 -0.030167544 73 74 75 76 77 78 0.424929070 -0.035168790 0.511180974 0.087038258 -1.071219274 0.536006762 79 80 81 82 83 84 0.561667307 2.147493886 1.622643074 0.036700806 -0.816712850 1.558360814 85 86 87 88 89 90 0.776053423 -1.374855485 -0.003865673 0.042850901 0.707786430 0.214661693 91 92 93 94 95 96 -2.820854100 -1.274090599 1.080353471 0.555943647 1.363804423 1.077603655 97 98 99 100 101 102 0.411133144 -0.236907747 0.852433831 0.102617166 -0.208277312 1.306128008 103 104 105 106 107 108 -0.850261893 0.400103282 -0.443920321 -0.397915095 -0.046393486 -1.336926480 109 110 111 112 113 114 0.495701185 0.008483875 1.145277057 0.238611004 -0.185702954 -0.488499226 115 116 117 118 119 120 0.753657651 -0.246111153 -1.040164532 1.231207435 -1.085198969 -2.291507636 121 122 123 124 125 126 2.230202045 1.068926684 -0.966440228 -0.435498828 -0.216724519 0.784828698 127 128 129 130 131 132 1.408791454 -1.033075048 0.156115717 2.133600491 -0.422468790 0.921230332 133 134 135 136 137 138 0.069965266 0.106671921 1.076565403 0.126058962 -1.944312566 -0.996465583 139 140 141 142 143 144 2.153353607 1.092228960 -1.624080850 -0.092672229 -1.298300203 -0.653132537 145 146 147 148 149 150 1.245852798 -1.062223926 -1.138962368 -0.296670335 0.680027388 0.787875721 151 152 153 154 155 156 1.345343951 1.139166133 -1.384096854 -0.220273679 -0.289581181 -1.213647791 157 158 159 160 161 162 -0.097952680 -0.030314691 -0.825369764 -2.108229152 0.327114346 0.346867463 163 164 -0.917685186 -1.288221394 > postscript(file="/var/www/html/freestat/rcomp/tmp/6kbko1290499179.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 = 164 Frequency = 1 lag(myerror, k = 1) myerror 0 0.429711646 NA 1 0.440669760 0.429711646 2 -0.442345119 0.440669760 3 -0.701953920 -0.442345119 4 0.473684111 -0.701953920 5 1.297892017 0.473684111 6 1.328553808 1.297892017 7 0.703639999 1.328553808 8 0.537907471 0.703639999 9 0.143830679 0.537907471 10 -1.519747076 0.143830679 11 0.293778460 -1.519747076 12 0.351532008 0.293778460 13 0.776187954 0.351532008 14 -0.314015751 0.776187954 15 0.323706625 -0.314015751 16 -1.313960806 0.323706625 17 0.386864032 -1.313960806 18 1.789777924 0.386864032 19 0.636137838 1.789777924 20 -1.847155971 0.636137838 21 0.970473782 -1.847155971 22 0.282467172 0.970473782 23 1.501717274 0.282467172 24 0.686039194 1.501717274 25 -1.008061867 0.686039194 26 -1.008061867 -1.008061867 27 0.410284037 -1.008061867 28 -0.309683525 0.410284037 29 0.011233691 -0.309683525 30 -0.050909481 0.011233691 31 -0.384526965 -0.050909481 32 -1.154140689 -0.384526965 33 0.289488562 -1.154140689 34 -1.035755172 0.289488562 35 0.022671349 -1.035755172 36 0.387753734 0.022671349 37 1.163737859 0.387753734 38 0.319190631 1.163737859 39 0.898604186 0.319190631 40 1.094149397 0.898604186 41 -1.054993333 1.094149397 42 0.305245824 -1.054993333 43 0.123893794 0.305245824 44 -2.460272633 0.123893794 45 0.678338417 -2.460272633 46 -0.823912594 0.678338417 47 -1.501567233 -0.823912594 48 -0.199868443 -1.501567233 49 0.398540403 -0.199868443 50 -0.076386258 0.398540403 51 -0.484225898 -0.076386258 52 0.092625886 -0.484225898 53 0.620446588 0.092625886 54 0.620225906 0.620446588 55 0.573460178 0.620225906 56 0.134715876 0.573460178 57 0.928460925 0.134715876 58 -0.776589812 0.928460925 59 -3.325488822 -0.776589812 60 -1.961659386 -3.325488822 61 -1.404357215 -1.961659386 62 -0.751789264 -1.404357215 63 -1.633629529 -0.751789264 64 0.796178030 -1.633629529 65 1.366370471 0.796178030 66 -0.316296886 1.366370471 67 -0.214085622 -0.316296886 68 -0.187783750 -0.214085622 69 0.437197530 -0.187783750 70 0.625741786 0.437197530 71 -0.030167544 0.625741786 72 0.424929070 -0.030167544 73 -0.035168790 0.424929070 74 0.511180974 -0.035168790 75 0.087038258 0.511180974 76 -1.071219274 0.087038258 77 0.536006762 -1.071219274 78 0.561667307 0.536006762 79 2.147493886 0.561667307 80 1.622643074 2.147493886 81 0.036700806 1.622643074 82 -0.816712850 0.036700806 83 1.558360814 -0.816712850 84 0.776053423 1.558360814 85 -1.374855485 0.776053423 86 -0.003865673 -1.374855485 87 0.042850901 -0.003865673 88 0.707786430 0.042850901 89 0.214661693 0.707786430 90 -2.820854100 0.214661693 91 -1.274090599 -2.820854100 92 1.080353471 -1.274090599 93 0.555943647 1.080353471 94 1.363804423 0.555943647 95 1.077603655 1.363804423 96 0.411133144 1.077603655 97 -0.236907747 0.411133144 98 0.852433831 -0.236907747 99 0.102617166 0.852433831 100 -0.208277312 0.102617166 101 1.306128008 -0.208277312 102 -0.850261893 1.306128008 103 0.400103282 -0.850261893 104 -0.443920321 0.400103282 105 -0.397915095 -0.443920321 106 -0.046393486 -0.397915095 107 -1.336926480 -0.046393486 108 0.495701185 -1.336926480 109 0.008483875 0.495701185 110 1.145277057 0.008483875 111 0.238611004 1.145277057 112 -0.185702954 0.238611004 113 -0.488499226 -0.185702954 114 0.753657651 -0.488499226 115 -0.246111153 0.753657651 116 -1.040164532 -0.246111153 117 1.231207435 -1.040164532 118 -1.085198969 1.231207435 119 -2.291507636 -1.085198969 120 2.230202045 -2.291507636 121 1.068926684 2.230202045 122 -0.966440228 1.068926684 123 -0.435498828 -0.966440228 124 -0.216724519 -0.435498828 125 0.784828698 -0.216724519 126 1.408791454 0.784828698 127 -1.033075048 1.408791454 128 0.156115717 -1.033075048 129 2.133600491 0.156115717 130 -0.422468790 2.133600491 131 0.921230332 -0.422468790 132 0.069965266 0.921230332 133 0.106671921 0.069965266 134 1.076565403 0.106671921 135 0.126058962 1.076565403 136 -1.944312566 0.126058962 137 -0.996465583 -1.944312566 138 2.153353607 -0.996465583 139 1.092228960 2.153353607 140 -1.624080850 1.092228960 141 -0.092672229 -1.624080850 142 -1.298300203 -0.092672229 143 -0.653132537 -1.298300203 144 1.245852798 -0.653132537 145 -1.062223926 1.245852798 146 -1.138962368 -1.062223926 147 -0.296670335 -1.138962368 148 0.680027388 -0.296670335 149 0.787875721 0.680027388 150 1.345343951 0.787875721 151 1.139166133 1.345343951 152 -1.384096854 1.139166133 153 -0.220273679 -1.384096854 154 -0.289581181 -0.220273679 155 -1.213647791 -0.289581181 156 -0.097952680 -1.213647791 157 -0.030314691 -0.097952680 158 -0.825369764 -0.030314691 159 -2.108229152 -0.825369764 160 0.327114346 -2.108229152 161 0.346867463 0.327114346 162 -0.917685186 0.346867463 163 -1.288221394 -0.917685186 164 NA -1.288221394 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.440669760 0.429711646 [2,] -0.442345119 0.440669760 [3,] -0.701953920 -0.442345119 [4,] 0.473684111 -0.701953920 [5,] 1.297892017 0.473684111 [6,] 1.328553808 1.297892017 [7,] 0.703639999 1.328553808 [8,] 0.537907471 0.703639999 [9,] 0.143830679 0.537907471 [10,] -1.519747076 0.143830679 [11,] 0.293778460 -1.519747076 [12,] 0.351532008 0.293778460 [13,] 0.776187954 0.351532008 [14,] -0.314015751 0.776187954 [15,] 0.323706625 -0.314015751 [16,] -1.313960806 0.323706625 [17,] 0.386864032 -1.313960806 [18,] 1.789777924 0.386864032 [19,] 0.636137838 1.789777924 [20,] -1.847155971 0.636137838 [21,] 0.970473782 -1.847155971 [22,] 0.282467172 0.970473782 [23,] 1.501717274 0.282467172 [24,] 0.686039194 1.501717274 [25,] -1.008061867 0.686039194 [26,] -1.008061867 -1.008061867 [27,] 0.410284037 -1.008061867 [28,] -0.309683525 0.410284037 [29,] 0.011233691 -0.309683525 [30,] -0.050909481 0.011233691 [31,] -0.384526965 -0.050909481 [32,] -1.154140689 -0.384526965 [33,] 0.289488562 -1.154140689 [34,] -1.035755172 0.289488562 [35,] 0.022671349 -1.035755172 [36,] 0.387753734 0.022671349 [37,] 1.163737859 0.387753734 [38,] 0.319190631 1.163737859 [39,] 0.898604186 0.319190631 [40,] 1.094149397 0.898604186 [41,] -1.054993333 1.094149397 [42,] 0.305245824 -1.054993333 [43,] 0.123893794 0.305245824 [44,] -2.460272633 0.123893794 [45,] 0.678338417 -2.460272633 [46,] -0.823912594 0.678338417 [47,] -1.501567233 -0.823912594 [48,] -0.199868443 -1.501567233 [49,] 0.398540403 -0.199868443 [50,] -0.076386258 0.398540403 [51,] -0.484225898 -0.076386258 [52,] 0.092625886 -0.484225898 [53,] 0.620446588 0.092625886 [54,] 0.620225906 0.620446588 [55,] 0.573460178 0.620225906 [56,] 0.134715876 0.573460178 [57,] 0.928460925 0.134715876 [58,] -0.776589812 0.928460925 [59,] -3.325488822 -0.776589812 [60,] -1.961659386 -3.325488822 [61,] -1.404357215 -1.961659386 [62,] -0.751789264 -1.404357215 [63,] -1.633629529 -0.751789264 [64,] 0.796178030 -1.633629529 [65,] 1.366370471 0.796178030 [66,] -0.316296886 1.366370471 [67,] -0.214085622 -0.316296886 [68,] -0.187783750 -0.214085622 [69,] 0.437197530 -0.187783750 [70,] 0.625741786 0.437197530 [71,] -0.030167544 0.625741786 [72,] 0.424929070 -0.030167544 [73,] -0.035168790 0.424929070 [74,] 0.511180974 -0.035168790 [75,] 0.087038258 0.511180974 [76,] -1.071219274 0.087038258 [77,] 0.536006762 -1.071219274 [78,] 0.561667307 0.536006762 [79,] 2.147493886 0.561667307 [80,] 1.622643074 2.147493886 [81,] 0.036700806 1.622643074 [82,] -0.816712850 0.036700806 [83,] 1.558360814 -0.816712850 [84,] 0.776053423 1.558360814 [85,] -1.374855485 0.776053423 [86,] -0.003865673 -1.374855485 [87,] 0.042850901 -0.003865673 [88,] 0.707786430 0.042850901 [89,] 0.214661693 0.707786430 [90,] -2.820854100 0.214661693 [91,] -1.274090599 -2.820854100 [92,] 1.080353471 -1.274090599 [93,] 0.555943647 1.080353471 [94,] 1.363804423 0.555943647 [95,] 1.077603655 1.363804423 [96,] 0.411133144 1.077603655 [97,] -0.236907747 0.411133144 [98,] 0.852433831 -0.236907747 [99,] 0.102617166 0.852433831 [100,] -0.208277312 0.102617166 [101,] 1.306128008 -0.208277312 [102,] -0.850261893 1.306128008 [103,] 0.400103282 -0.850261893 [104,] -0.443920321 0.400103282 [105,] -0.397915095 -0.443920321 [106,] -0.046393486 -0.397915095 [107,] -1.336926480 -0.046393486 [108,] 0.495701185 -1.336926480 [109,] 0.008483875 0.495701185 [110,] 1.145277057 0.008483875 [111,] 0.238611004 1.145277057 [112,] -0.185702954 0.238611004 [113,] -0.488499226 -0.185702954 [114,] 0.753657651 -0.488499226 [115,] -0.246111153 0.753657651 [116,] -1.040164532 -0.246111153 [117,] 1.231207435 -1.040164532 [118,] -1.085198969 1.231207435 [119,] -2.291507636 -1.085198969 [120,] 2.230202045 -2.291507636 [121,] 1.068926684 2.230202045 [122,] -0.966440228 1.068926684 [123,] -0.435498828 -0.966440228 [124,] -0.216724519 -0.435498828 [125,] 0.784828698 -0.216724519 [126,] 1.408791454 0.784828698 [127,] -1.033075048 1.408791454 [128,] 0.156115717 -1.033075048 [129,] 2.133600491 0.156115717 [130,] -0.422468790 2.133600491 [131,] 0.921230332 -0.422468790 [132,] 0.069965266 0.921230332 [133,] 0.106671921 0.069965266 [134,] 1.076565403 0.106671921 [135,] 0.126058962 1.076565403 [136,] -1.944312566 0.126058962 [137,] -0.996465583 -1.944312566 [138,] 2.153353607 -0.996465583 [139,] 1.092228960 2.153353607 [140,] -1.624080850 1.092228960 [141,] -0.092672229 -1.624080850 [142,] -1.298300203 -0.092672229 [143,] -0.653132537 -1.298300203 [144,] 1.245852798 -0.653132537 [145,] -1.062223926 1.245852798 [146,] -1.138962368 -1.062223926 [147,] -0.296670335 -1.138962368 [148,] 0.680027388 -0.296670335 [149,] 0.787875721 0.680027388 [150,] 1.345343951 0.787875721 [151,] 1.139166133 1.345343951 [152,] -1.384096854 1.139166133 [153,] -0.220273679 -1.384096854 [154,] -0.289581181 -0.220273679 [155,] -1.213647791 -0.289581181 [156,] -0.097952680 -1.213647791 [157,] -0.030314691 -0.097952680 [158,] -0.825369764 -0.030314691 [159,] -2.108229152 -0.825369764 [160,] 0.327114346 -2.108229152 [161,] 0.346867463 0.327114346 [162,] -0.917685186 0.346867463 [163,] -1.288221394 -0.917685186 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.440669760 0.429711646 2 -0.442345119 0.440669760 3 -0.701953920 -0.442345119 4 0.473684111 -0.701953920 5 1.297892017 0.473684111 6 1.328553808 1.297892017 7 0.703639999 1.328553808 8 0.537907471 0.703639999 9 0.143830679 0.537907471 10 -1.519747076 0.143830679 11 0.293778460 -1.519747076 12 0.351532008 0.293778460 13 0.776187954 0.351532008 14 -0.314015751 0.776187954 15 0.323706625 -0.314015751 16 -1.313960806 0.323706625 17 0.386864032 -1.313960806 18 1.789777924 0.386864032 19 0.636137838 1.789777924 20 -1.847155971 0.636137838 21 0.970473782 -1.847155971 22 0.282467172 0.970473782 23 1.501717274 0.282467172 24 0.686039194 1.501717274 25 -1.008061867 0.686039194 26 -1.008061867 -1.008061867 27 0.410284037 -1.008061867 28 -0.309683525 0.410284037 29 0.011233691 -0.309683525 30 -0.050909481 0.011233691 31 -0.384526965 -0.050909481 32 -1.154140689 -0.384526965 33 0.289488562 -1.154140689 34 -1.035755172 0.289488562 35 0.022671349 -1.035755172 36 0.387753734 0.022671349 37 1.163737859 0.387753734 38 0.319190631 1.163737859 39 0.898604186 0.319190631 40 1.094149397 0.898604186 41 -1.054993333 1.094149397 42 0.305245824 -1.054993333 43 0.123893794 0.305245824 44 -2.460272633 0.123893794 45 0.678338417 -2.460272633 46 -0.823912594 0.678338417 47 -1.501567233 -0.823912594 48 -0.199868443 -1.501567233 49 0.398540403 -0.199868443 50 -0.076386258 0.398540403 51 -0.484225898 -0.076386258 52 0.092625886 -0.484225898 53 0.620446588 0.092625886 54 0.620225906 0.620446588 55 0.573460178 0.620225906 56 0.134715876 0.573460178 57 0.928460925 0.134715876 58 -0.776589812 0.928460925 59 -3.325488822 -0.776589812 60 -1.961659386 -3.325488822 61 -1.404357215 -1.961659386 62 -0.751789264 -1.404357215 63 -1.633629529 -0.751789264 64 0.796178030 -1.633629529 65 1.366370471 0.796178030 66 -0.316296886 1.366370471 67 -0.214085622 -0.316296886 68 -0.187783750 -0.214085622 69 0.437197530 -0.187783750 70 0.625741786 0.437197530 71 -0.030167544 0.625741786 72 0.424929070 -0.030167544 73 -0.035168790 0.424929070 74 0.511180974 -0.035168790 75 0.087038258 0.511180974 76 -1.071219274 0.087038258 77 0.536006762 -1.071219274 78 0.561667307 0.536006762 79 2.147493886 0.561667307 80 1.622643074 2.147493886 81 0.036700806 1.622643074 82 -0.816712850 0.036700806 83 1.558360814 -0.816712850 84 0.776053423 1.558360814 85 -1.374855485 0.776053423 86 -0.003865673 -1.374855485 87 0.042850901 -0.003865673 88 0.707786430 0.042850901 89 0.214661693 0.707786430 90 -2.820854100 0.214661693 91 -1.274090599 -2.820854100 92 1.080353471 -1.274090599 93 0.555943647 1.080353471 94 1.363804423 0.555943647 95 1.077603655 1.363804423 96 0.411133144 1.077603655 97 -0.236907747 0.411133144 98 0.852433831 -0.236907747 99 0.102617166 0.852433831 100 -0.208277312 0.102617166 101 1.306128008 -0.208277312 102 -0.850261893 1.306128008 103 0.400103282 -0.850261893 104 -0.443920321 0.400103282 105 -0.397915095 -0.443920321 106 -0.046393486 -0.397915095 107 -1.336926480 -0.046393486 108 0.495701185 -1.336926480 109 0.008483875 0.495701185 110 1.145277057 0.008483875 111 0.238611004 1.145277057 112 -0.185702954 0.238611004 113 -0.488499226 -0.185702954 114 0.753657651 -0.488499226 115 -0.246111153 0.753657651 116 -1.040164532 -0.246111153 117 1.231207435 -1.040164532 118 -1.085198969 1.231207435 119 -2.291507636 -1.085198969 120 2.230202045 -2.291507636 121 1.068926684 2.230202045 122 -0.966440228 1.068926684 123 -0.435498828 -0.966440228 124 -0.216724519 -0.435498828 125 0.784828698 -0.216724519 126 1.408791454 0.784828698 127 -1.033075048 1.408791454 128 0.156115717 -1.033075048 129 2.133600491 0.156115717 130 -0.422468790 2.133600491 131 0.921230332 -0.422468790 132 0.069965266 0.921230332 133 0.106671921 0.069965266 134 1.076565403 0.106671921 135 0.126058962 1.076565403 136 -1.944312566 0.126058962 137 -0.996465583 -1.944312566 138 2.153353607 -0.996465583 139 1.092228960 2.153353607 140 -1.624080850 1.092228960 141 -0.092672229 -1.624080850 142 -1.298300203 -0.092672229 143 -0.653132537 -1.298300203 144 1.245852798 -0.653132537 145 -1.062223926 1.245852798 146 -1.138962368 -1.062223926 147 -0.296670335 -1.138962368 148 0.680027388 -0.296670335 149 0.787875721 0.680027388 150 1.345343951 0.787875721 151 1.139166133 1.345343951 152 -1.384096854 1.139166133 153 -0.220273679 -1.384096854 154 -0.289581181 -0.220273679 155 -1.213647791 -0.289581181 156 -0.097952680 -1.213647791 157 -0.030314691 -0.097952680 158 -0.825369764 -0.030314691 159 -2.108229152 -0.825369764 160 0.327114346 -2.108229152 161 0.346867463 0.327114346 162 -0.917685186 0.346867463 163 -1.288221394 -0.917685186 > 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/freestat/rcomp/tmp/7dl2r1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8nujb1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9nujb1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/10nujb1290499179.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/freestat/rcomp/tmp/11k4yk1290499179.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/freestat/rcomp/tmp/12nmfq1290499179.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/freestat/rcomp/tmp/131wvz1290499179.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/freestat/rcomp/tmp/14u5uk1290499179.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/freestat/rcomp/tmp/15fob81290499179.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/freestat/rcomp/tmp/16tg8h1290499179.tab") + } > > try(system("convert tmp/1hb4i1290499179.ps tmp/1hb4i1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/29kll1290499179.ps tmp/29kll1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/39kll1290499179.ps tmp/39kll1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/4kbko1290499179.ps tmp/4kbko1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/5kbko1290499179.ps tmp/5kbko1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/6kbko1290499179.ps tmp/6kbko1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/7dl2r1290499179.ps tmp/7dl2r1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/8nujb1290499179.ps tmp/8nujb1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/9nujb1290499179.ps tmp/9nujb1290499179.png",intern=TRUE)) character(0) > try(system("convert tmp/10nujb1290499179.ps tmp/10nujb1290499179.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.126 2.733 7.337