R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. 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 + ,10 + ,12 + ,16 + ,6 + ,2 + ,0 + ,0 + ,9 + ,12 + ,9 + ,7 + ,12 + ,6 + ,1 + ,1 + ,2 + ,9 + ,9 + ,12 + ,11 + ,11 + ,4 + ,1 + ,2 + ,1 + ,9 + ,10 + ,12 + ,11 + ,12 + ,6 + ,0 + ,0 + ,0 + ,9 + ,13 + ,9 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,9 + ,16 + ,11 + ,16 + ,16 + ,7 + ,1 + ,0 + ,0 + ,9 + ,14 + ,12 + ,13 + ,13 + ,6 + ,0 + ,0 + ,0 + ,9 + ,16 + ,11 + ,13 + ,14 + ,7 + ,1 + ,1 + ,0 + ,9 + ,10 + ,12 + ,5 + ,13 + ,6 + ,0 + ,0 + ,0 + ,9 + ,8 + ,12 + ,8 + ,13 + ,4 + ,2 + ,0 + ,1 + ,10 + ,12 + ,11 + ,14 + ,13 + ,5 + ,1 + ,0 + ,0 + ,10 + ,15 + ,11 + ,15 + ,15 + ,8 + ,0 + ,0 + ,0 + ,10 + ,14 + ,12 + ,8 + ,14 + ,4 + ,0 + ,1 + ,0 + ,10 + ,14 + ,6 + ,13 + ,12 + ,6 + ,1 + ,1 + ,2 + ,10 + ,12 + ,13 + ,12 + ,12 + ,6 + ,1 + ,2 + ,1 + ,10 + ,12 + ,11 + ,11 + ,12 + ,5 + ,0 + ,0 + ,0 + ,10 + ,10 + ,12 + ,8 + ,11 + ,4 + ,0 + ,0 + ,0 + ,10 + ,4 + ,10 + ,4 + ,10 + ,2 + ,0 + ,0 + ,0 + ,10 + ,14 + ,11 + ,15 + ,15 + ,8 + ,0 + ,1 + ,0 + ,10 + ,15 + ,12 + ,12 + ,16 + ,7 + ,0 + ,0 + ,0 + ,10 + ,16 + ,12 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,10 + ,12 + ,12 + ,9 + ,13 + ,4 + ,0 + ,1 + ,0 + ,10 + ,12 + ,11 + ,16 + ,13 + ,4 + ,0 + ,0 + ,0 + ,10 + ,12 + ,12 + ,10 + ,13 + ,4 + ,0 + ,0 + ,1 + ,10 + ,12 + ,12 + ,8 + ,13 + ,5 + ,1 + ,0 + ,1 + ,9 + ,12 + ,12 + ,14 + ,14 + ,4 + ,0 + ,0 + ,0 + ,9 + ,11 + ,6 + ,6 + ,9 + ,4 + ,3 + ,2 + ,1 + ,9 + ,11 + ,5 + ,16 + ,14 + ,6 + ,1 + ,0 + ,0 + ,9 + ,11 + ,12 + ,11 + ,12 + ,6 + ,1 + ,1 + ,0 + ,9 + ,11 + ,14 + ,7 + ,13 + ,6 + ,1 + ,1 + ,0 + ,9 + ,11 + ,12 + ,13 + ,11 + ,4 + ,3 + ,1 + ,1 + ,9 + ,11 + ,9 + ,7 + ,13 + ,2 + ,0 + ,0 + ,0 + ,9 + ,15 + ,11 + ,14 + ,15 + ,7 + ,0 + ,0 + ,0 + ,9 + ,15 + ,11 + ,17 + ,16 + ,6 + ,0 + ,0 + ,0 + ,9 + ,9 + ,11 + ,15 + ,15 + ,7 + ,0 + ,0 + ,0 + ,9 + ,16 + ,12 + ,8 + ,14 + ,4 + ,0 + ,0 + ,0 + ,9 + ,13 + ,10 + ,8 + ,8 + ,4 + ,0 + ,2 + ,1 + ,9 + ,9 + ,12 + ,11 + ,11 + ,4 + ,1 + ,0 + ,0 + ,9 + ,16 + ,11 + ,16 + ,15 + ,6 + ,0 + ,0 + ,0 + ,9 + ,12 + ,12 + ,10 + ,15 + ,6 + ,0 + ,0 + ,0 + ,9 + ,15 + ,9 + ,5 + ,11 + ,3 + ,0 + ,0 + ,2 + ,9 + ,5 + ,15 + ,8 + ,12 + ,3 + ,0 + ,0 + ,0 + ,9 + ,11 + ,11 + ,8 + ,12 + ,6 + ,2 + ,2 + ,0 + ,9 + ,17 + ,11 + ,15 + ,14 + ,5 + ,2 + ,2 + ,0 + ,9 + ,9 + ,15 + ,6 + ,8 + ,4 + ,0 + ,1 + ,1 + ,9 + ,13 + ,12 + ,16 + ,16 + ,6 + ,0 + ,0 + ,0 + ,9 + ,16 + ,9 + ,16 + ,16 + ,6 + ,0 + ,0 + ,0 + ,10 + ,16 + ,12 + ,16 + ,14 + ,6 + ,0 + ,0 + ,0 + ,10 + ,14 + ,9 + ,19 + ,12 + ,6 + ,2 + ,0 + ,2 + ,10 + ,16 + ,11 + ,14 + ,15 + ,6 + ,1 + ,0 + ,0 + ,10 + ,11 + ,12 + ,15 + ,12 + ,6 + ,0 + ,0 + ,0 + ,10 + ,11 + ,11 + ,11 + ,14 + ,5 + ,0 + ,0 + ,0 + ,10 + ,11 + ,6 + ,14 + ,17 + ,6 + ,0 + ,0 + ,0 + ,10 + ,12 + ,10 + ,12 + ,13 + ,6 + ,0 + ,0 + ,0 + ,10 + ,12 + ,12 + ,15 + ,13 + ,6 + ,1 + ,1 + ,1 + ,10 + ,12 + ,13 + ,14 + ,12 + ,5 + ,0 + ,0 + ,0 + ,10 + ,14 + ,11 + ,13 + ,16 + ,6 + ,0 + ,0 + ,0 + ,10 + ,10 + ,10 + ,11 + ,12 + ,5 + ,2 + ,0 + ,0 + ,10 + ,9 + ,11 + ,8 + ,10 + ,4 + ,0 + ,2 + ,0 + ,10 + ,12 + ,7 + ,11 + ,15 + ,5 + ,0 + ,0 + ,1 + ,10 + ,10 + ,11 + ,9 + ,12 + ,4 + ,0 + ,0 + ,0 + ,10 + ,14 + ,11 + ,10 + ,16 + ,6 + ,0 + ,0 + ,0 + ,10 + ,8 + ,7 + ,4 + ,13 + ,6 + ,0 + ,0 + ,0 + ,10 + ,16 + ,12 + ,15 + ,15 + ,7 + ,1 + ,0 + ,0 + ,10 + ,14 + ,14 + ,17 + ,18 + ,6 + ,1 + ,0 + ,0 + ,10 + ,14 + ,11 + ,12 + ,12 + ,4 + ,0 + ,0 + ,0 + ,10 + ,12 + ,12 + ,12 + ,13 + ,4 + ,0 + ,0 + ,0 + ,10 + ,14 + ,11 + ,15 + ,14 + ,6 + ,1 + ,0 + ,0 + ,10 + ,7 + ,12 + ,13 + ,12 + ,3 + ,1 + ,1 + ,1 + ,10 + ,19 + ,12 + ,15 + ,15 + ,6 + ,0 + ,0 + ,0 + ,10 + ,15 + ,12 + ,14 + ,16 + ,4 + ,0 + ,0 + ,0 + ,10 + ,8 + ,12 + ,8 + ,14 + ,5 + ,0 + ,0 + ,0 + ,10 + ,10 + ,15 + ,15 + ,15 + ,6 + ,0 + ,0 + ,0 + ,10 + ,13 + ,11 + ,12 + ,13 + ,7 + ,0 + ,0 + ,0 + ,10 + ,13 + ,13 + ,14 + ,13 + ,3 + ,0 + ,0 + ,0 + ,9 + ,10 + ,10 + ,10 + ,11 + ,5 + ,0 + ,0 + ,0 + ,9 + ,12 + ,12 + ,7 + ,12 + ,3 + ,0 + ,0 + ,0 + ,9 + ,15 + ,13 + ,16 + ,18 + ,8 + ,0 + ,1 + ,1 + ,9 + ,7 + ,14 + ,12 + ,12 + ,4 + ,1 + ,0 + ,0 + ,9 + ,14 + ,11 + ,15 + ,16 + ,6 + ,0 + ,0 + ,0 + ,9 + ,10 + ,11 + ,7 + ,9 + ,4 + ,0 + ,0 + ,0 + ,9 + ,6 + ,7 + ,9 + ,11 + ,4 + ,0 + ,3 + ,0 + ,9 + ,11 + ,11 + ,15 + ,10 + ,5 + ,2 + ,0 + ,0 + ,9 + ,12 + ,12 + ,7 + ,11 + ,4 + ,0 + ,0 + ,0 + ,9 + ,14 + ,12 + ,15 + ,13 + ,6 + ,0 + ,0 + ,2 + ,9 + ,12 + ,10 + ,14 + ,13 + ,7 + ,0 + ,0 + ,0 + ,9 + ,14 + ,12 + ,14 + ,15 + ,7 + ,0 + ,0 + ,0 + ,9 + ,11 + ,8 + ,8 + ,13 + ,4 + ,2 + ,2 + ,0 + ,9 + ,10 + ,7 + ,8 + ,9 + ,5 + ,1 + ,0 + ,1 + ,9 + ,13 + ,11 + ,14 + ,13 + ,6 + ,0 + ,0 + ,1 + ,9 + ,8 + ,11 + ,10 + ,12 + ,4 + ,0 + ,0 + ,0 + ,9 + ,9 + ,11 + ,12 + ,13 + ,5 + ,0 + ,0 + ,0 + ,9 + ,6 + ,9 + ,15 + ,11 + ,6 + ,0 + ,0 + ,0 + ,10 + ,12 + ,12 + ,12 + ,14 + ,5 + ,1 + ,0 + ,2 + ,10 + ,14 + ,13 + ,13 + ,13 + ,5 + ,0 + ,0 + ,0 + ,10 + ,11 + ,9 + ,12 + ,12 + ,4 + ,0 + ,0 + ,0 + ,10 + ,8 + ,11 + ,10 + ,15 + ,2 + ,1 + ,0 + ,1 + ,10 + ,7 + ,12 + ,8 + ,12 + ,3 + ,0 + ,0 + ,0 + ,10 + ,9 + ,9 + ,6 + ,12 + ,5 + ,0 + ,2 + ,1 + ,10 + ,14 + ,12 + ,13 + ,13 + ,5 + ,2 + ,1 + ,0 + ,10 + ,13 + ,12 + ,7 + ,12 + ,5 + ,0 + ,0 + ,0 + ,10 + ,15 + ,12 + ,13 + ,13 + ,6 + ,0 + ,0 + ,0 + ,10 + ,5 + ,14 + ,4 + ,5 + ,2 + ,0 + ,0 + ,0 + ,10 + ,15 + ,11 + ,14 + ,13 + ,5 + ,3 + ,1 + ,0 + ,10 + ,13 + ,12 + ,13 + ,13 + ,5 + ,0 + ,1 + ,0 + ,10 + ,12 + ,8 + ,13 + ,13 + ,5 + ,0 + ,0 + ,0 + ,10 + ,6 + ,12 + ,6 + ,11 + ,2 + ,1 + ,0 + ,0 + ,10 + ,7 + ,12 + ,7 + ,12 + ,4 + ,0 + ,0 + ,0 + ,10 + ,13 + ,12 + ,5 + ,12 + ,3 + ,0 + ,0 + ,0 + ,10 + ,16 + ,11 + ,14 + ,15 + ,8 + ,1 + ,1 + ,0 + ,10 + ,10 + ,11 + ,13 + ,15 + ,6 + ,0 + ,0 + ,0 + ,10 + ,16 + ,12 + ,16 + ,16 + ,7 + ,0 + ,0 + ,0 + ,10 + ,15 + ,10 + ,16 + ,13 + ,6 + ,0 + ,0 + ,0 + ,10 + ,8 + ,13 + ,7 + ,10 + ,3 + ,0 + ,0 + ,0 + ,10 + ,11 + ,8 + ,14 + ,15 + ,5 + ,0 + ,0 + ,0 + ,10 + ,13 + ,12 + ,11 + ,13 + ,6 + ,0 + ,3 + ,1 + ,10 + ,16 + ,11 + ,17 + ,16 + ,7 + ,1 + ,0 + ,0 + ,10 + ,11 + ,10 + ,5 + ,13 + ,3 + ,0 + ,0 + ,0 + ,10 + ,14 + ,13 + ,10 + ,16 + ,8 + ,0 + ,0 + ,0 + ,10 + ,9 + ,10 + ,11 + ,13 + ,3 + ,2 + ,1 + ,0 + ,10 + ,8 + ,10 + ,10 + ,14 + ,3 + ,0 + ,0 + ,0 + ,10 + ,8 + ,7 + ,9 + ,15 + ,4 + ,1 + ,0 + ,1 + ,10 + ,11 + ,10 + ,12 + ,14 + ,5 + ,2 + ,0 + ,0 + ,10 + ,12 + ,8 + ,15 + ,13 + ,7 + ,0 + ,0 + ,0 + ,10 + ,11 + ,12 + ,7 + ,13 + ,6 + ,4 + ,0 + ,0 + ,10 + ,14 + ,12 + ,13 + ,15 + ,6 + ,0 + ,1 + ,2 + ,10 + ,11 + ,12 + ,8 + ,16 + ,6 + ,2 + ,1 + ,0 + ,10 + ,14 + ,11 + ,16 + ,12 + ,5 + ,0 + ,0 + ,0 + ,10 + ,13 + ,13 + ,15 + ,14 + ,6 + ,2 + ,1 + ,2 + ,10 + ,12 + ,12 + ,6 + ,14 + ,5 + ,0 + ,0 + ,0 + ,10 + ,4 + ,8 + ,6 + ,4 + ,4 + ,0 + ,0 + ,0 + ,10 + ,15 + ,11 + ,12 + ,13 + ,6 + ,2 + ,1 + ,1 + ,10 + ,10 + ,12 + ,8 + ,16 + ,4 + ,0 + ,0 + ,0 + ,10 + ,13 + ,13 + ,11 + ,15 + ,6 + ,1 + ,2 + ,1 + ,10 + ,15 + ,12 + ,13 + ,14 + ,6 + ,1 + ,1 + ,2 + ,10 + ,12 + ,10 + ,14 + ,14 + ,5 + ,1 + ,2 + ,1 + ,10 + ,13 + ,12 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,10 + ,8 + ,10 + ,10 + ,6 + ,4 + ,0 + ,0 + ,0 + ,10 + ,10 + ,13 + ,4 + ,13 + ,6 + ,2 + ,0 + ,0 + ,10 + ,15 + ,11 + ,16 + ,14 + ,6 + ,0 + ,0 + ,0 + ,10 + ,16 + ,12 + ,12 + ,15 + ,8 + ,0 + ,0 + ,0 + ,10 + ,16 + ,12 + ,15 + ,16 + ,7 + ,0 + ,0 + ,0 + ,10 + ,14 + ,10 + ,12 + ,15 + ,6 + ,0 + ,0 + ,0 + ,10 + ,14 + ,11 + ,14 + ,12 + ,6 + ,1 + ,1 + ,1 + ,10 + ,12 + ,11 + ,11 + ,14 + ,2 + ,1 + ,1 + ,1 + ,10 + ,15 + ,11 + ,16 + ,11 + ,5 + ,0 + ,1 + ,2 + ,9 + ,13 + ,8 + ,14 + ,14 + ,5 + ,1 + ,1 + ,1 + ,9 + ,16 + ,11 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,10 + ,14 + ,12 + ,15 + ,14 + ,6 + ,0 + ,0 + ,0 + ,10 + ,8 + ,11 + ,9 + ,12 + ,4 + ,0 + ,0 + ,0 + ,10 + ,16 + ,12 + ,15 + ,14 + ,6 + ,0 + ,1 + ,0 + ,10 + ,16 + ,12 + ,14 + ,16 + ,8 + ,1 + ,1 + ,1 + ,10 + ,12 + ,12 + ,15 + ,13 + ,6 + ,0 + ,0 + ,0 + ,10 + ,11 + ,8 + ,10 + ,14 + ,5 + ,0 + ,3 + ,1 + ,10 + ,16 + ,12 + ,14 + ,16 + ,8 + ,1 + ,1 + ,1 + ,10 + ,9 + ,11 + ,9 + ,12 + ,4 + ,0 + ,0 + ,0 + ,10) + ,dim=c(9 + ,156) + ,dimnames=list(c('Popularity' + ,'FindingFriends' + ,'KnowingPeople' + ,'Liked' + ,'Celebrity' + ,'B' + ,'2B' + ,'3B' + ,'Month') + ,1:156)) > y <- array(NA,dim=c(9,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','B','2B','3B','Month'),1:156)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > ylab = '' > xlab = '' > main = '' > #'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 Popularity FindingFriends KnowingPeople Liked Celebrity B 2B 3B Month 1 15 10 12 16 6 2 0 0 9 2 12 9 7 12 6 1 1 2 9 3 9 12 11 11 4 1 2 1 9 4 10 12 11 12 6 0 0 0 9 5 13 9 14 14 6 0 0 0 9 6 16 11 16 16 7 1 0 0 9 7 14 12 13 13 6 0 0 0 9 8 16 11 13 14 7 1 1 0 9 9 10 12 5 13 6 0 0 0 9 10 8 12 8 13 4 2 0 1 10 11 12 11 14 13 5 1 0 0 10 12 15 11 15 15 8 0 0 0 10 13 14 12 8 14 4 0 1 0 10 14 14 6 13 12 6 1 1 2 10 15 12 13 12 12 6 1 2 1 10 16 12 11 11 12 5 0 0 0 10 17 10 12 8 11 4 0 0 0 10 18 4 10 4 10 2 0 0 0 10 19 14 11 15 15 8 0 1 0 10 20 15 12 12 16 7 0 0 0 10 21 16 12 14 14 6 0 0 0 10 22 12 12 9 13 4 0 1 0 10 23 12 11 16 13 4 0 0 0 10 24 12 12 10 13 4 0 0 1 10 25 12 12 8 13 5 1 0 1 9 26 12 12 14 14 4 0 0 0 9 27 11 6 6 9 4 3 2 1 9 28 11 5 16 14 6 1 0 0 9 29 11 12 11 12 6 1 1 0 9 30 11 14 7 13 6 1 1 0 9 31 11 12 13 11 4 3 1 1 9 32 11 9 7 13 2 0 0 0 9 33 15 11 14 15 7 0 0 0 9 34 15 11 17 16 6 0 0 0 9 35 9 11 15 15 7 0 0 0 9 36 16 12 8 14 4 0 0 0 9 37 13 10 8 8 4 0 2 1 9 38 9 12 11 11 4 1 0 0 9 39 16 11 16 15 6 0 0 0 9 40 12 12 10 15 6 0 0 0 9 41 15 9 5 11 3 0 0 2 9 42 5 15 8 12 3 0 0 0 9 43 11 11 8 12 6 2 2 0 9 44 17 11 15 14 5 2 2 0 9 45 9 15 6 8 4 0 1 1 9 46 13 12 16 16 6 0 0 0 9 47 16 9 16 16 6 0 0 0 10 48 16 12 16 14 6 0 0 0 10 49 14 9 19 12 6 2 0 2 10 50 16 11 14 15 6 1 0 0 10 51 11 12 15 12 6 0 0 0 10 52 11 11 11 14 5 0 0 0 10 53 11 6 14 17 6 0 0 0 10 54 12 10 12 13 6 0 0 0 10 55 12 12 15 13 6 1 1 1 10 56 12 13 14 12 5 0 0 0 10 57 14 11 13 16 6 0 0 0 10 58 10 10 11 12 5 2 0 0 10 59 9 11 8 10 4 0 2 0 10 60 12 7 11 15 5 0 0 1 10 61 10 11 9 12 4 0 0 0 10 62 14 11 10 16 6 0 0 0 10 63 8 7 4 13 6 0 0 0 10 64 16 12 15 15 7 1 0 0 10 65 14 14 17 18 6 1 0 0 10 66 14 11 12 12 4 0 0 0 10 67 12 12 12 13 4 0 0 0 10 68 14 11 15 14 6 1 0 0 10 69 7 12 13 12 3 1 1 1 10 70 19 12 15 15 6 0 0 0 10 71 15 12 14 16 4 0 0 0 10 72 8 12 8 14 5 0 0 0 10 73 10 15 15 15 6 0 0 0 10 74 13 11 12 13 7 0 0 0 10 75 13 13 14 13 3 0 0 0 9 76 10 10 10 11 5 0 0 0 9 77 12 12 7 12 3 0 0 0 9 78 15 13 16 18 8 0 1 1 9 79 7 14 12 12 4 1 0 0 9 80 14 11 15 16 6 0 0 0 9 81 10 11 7 9 4 0 0 0 9 82 6 7 9 11 4 0 3 0 9 83 11 11 15 10 5 2 0 0 9 84 12 12 7 11 4 0 0 0 9 85 14 12 15 13 6 0 0 2 9 86 12 10 14 13 7 0 0 0 9 87 14 12 14 15 7 0 0 0 9 88 11 8 8 13 4 2 2 0 9 89 10 7 8 9 5 1 0 1 9 90 13 11 14 13 6 0 0 1 9 91 8 11 10 12 4 0 0 0 9 92 9 11 12 13 5 0 0 0 9 93 6 9 15 11 6 0 0 0 10 94 12 12 12 14 5 1 0 2 10 95 14 13 13 13 5 0 0 0 10 96 11 9 12 12 4 0 0 0 10 97 8 11 10 15 2 1 0 1 10 98 7 12 8 12 3 0 0 0 10 99 9 9 6 12 5 0 2 1 10 100 14 12 13 13 5 2 1 0 10 101 13 12 7 12 5 0 0 0 10 102 15 12 13 13 6 0 0 0 10 103 5 14 4 5 2 0 0 0 10 104 15 11 14 13 5 3 1 0 10 105 13 12 13 13 5 0 1 0 10 106 12 8 13 13 5 0 0 0 10 107 6 12 6 11 2 1 0 0 10 108 7 12 7 12 4 0 0 0 10 109 13 12 5 12 3 0 0 0 10 110 16 11 14 15 8 1 1 0 10 111 10 11 13 15 6 0 0 0 10 112 16 12 16 16 7 0 0 0 10 113 15 10 16 13 6 0 0 0 10 114 8 13 7 10 3 0 0 0 10 115 11 8 14 15 5 0 0 0 10 116 13 12 11 13 6 0 3 1 10 117 16 11 17 16 7 1 0 0 10 118 11 10 5 13 3 0 0 0 10 119 14 13 10 16 8 0 0 0 10 120 9 10 11 13 3 2 1 0 10 121 8 10 10 14 3 0 0 0 10 122 8 7 9 15 4 1 0 1 10 123 11 10 12 14 5 2 0 0 10 124 12 8 15 13 7 0 0 0 10 125 11 12 7 13 6 4 0 0 10 126 14 12 13 15 6 0 1 2 10 127 11 12 8 16 6 2 1 0 10 128 14 11 16 12 5 0 0 0 10 129 13 13 15 14 6 2 1 2 10 130 12 12 6 14 5 0 0 0 10 131 4 8 6 4 4 0 0 0 10 132 15 11 12 13 6 2 1 1 10 133 10 12 8 16 4 0 0 0 10 134 13 13 11 15 6 1 2 1 10 135 15 12 13 14 6 1 1 2 10 136 12 10 14 14 5 1 2 1 10 137 13 12 14 14 6 0 0 0 10 138 8 10 10 6 4 0 0 0 10 139 10 13 4 13 6 2 0 0 10 140 15 11 16 14 6 0 0 0 10 141 16 12 12 15 8 0 0 0 10 142 16 12 15 16 7 0 0 0 10 143 14 10 12 15 6 0 0 0 10 144 14 11 14 12 6 1 1 1 10 145 12 11 11 14 2 1 1 1 10 146 15 11 16 11 5 0 1 2 9 147 13 8 14 14 5 1 1 1 9 148 16 11 14 14 6 0 0 0 10 149 14 12 15 14 6 0 0 0 10 150 8 11 9 12 4 0 0 0 10 151 16 12 15 14 6 0 1 0 10 152 16 12 14 16 8 1 1 1 10 153 12 12 15 13 6 0 0 0 10 154 11 8 10 14 5 0 3 1 10 155 16 12 14 16 8 1 1 1 10 156 9 11 9 12 4 0 0 0 10 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) FindingFriends KnowingPeople Liked Celebrity 1.65208 0.11888 0.24088 0.37903 0.60780 B `2B` `3B` Month -0.04689 0.16531 0.49935 -0.21449 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.00635 -1.21908 -0.05281 1.34553 6.01261 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.65208 3.68147 0.449 0.654270 FindingFriends 0.11888 0.09660 1.231 0.220453 KnowingPeople 0.24088 0.06174 3.901 0.000145 *** Liked 0.37903 0.09778 3.876 0.000159 *** Celebrity 0.60780 0.15677 3.877 0.000159 *** B -0.04689 0.22369 -0.210 0.834249 `2B` 0.16531 0.26936 0.614 0.540366 `3B` 0.49935 0.31713 1.575 0.117504 Month -0.21449 0.36336 -0.590 0.555906 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.102 on 147 degrees of freedom Multiple R-squared: 0.5141, Adjusted R-squared: 0.4876 F-statistic: 19.44 on 8 and 147 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.32896021 0.6579204273 0.6710397864 [2,] 0.43034706 0.8606941276 0.5696529362 [3,] 0.30232161 0.6046432208 0.6976783896 [4,] 0.19596787 0.3919357349 0.8040321326 [5,] 0.14943343 0.2988668506 0.8505665747 [6,] 0.10926450 0.2185290016 0.8907354992 [7,] 0.16913597 0.3382719445 0.8308640277 [8,] 0.28109488 0.5621897686 0.7189051157 [9,] 0.20706898 0.4141379597 0.7929310201 [10,] 0.23321765 0.4664353099 0.7667823451 [11,] 0.17436899 0.3487379800 0.8256310100 [12,] 0.13544819 0.2708963839 0.8645518081 [13,] 0.09517416 0.1903483154 0.9048258423 [14,] 0.07063288 0.1412657519 0.9293671241 [15,] 0.05663832 0.1132766461 0.9433616770 [16,] 0.08060007 0.1612001367 0.9193999317 [17,] 0.14841135 0.2968227003 0.8515886498 [18,] 0.11703534 0.2340706883 0.8829646559 [19,] 0.09033849 0.1806769757 0.9096615122 [20,] 0.06605632 0.1321126349 0.9339436826 [21,] 0.05830228 0.1166045542 0.9416977229 [22,] 0.04158942 0.0831788316 0.9584105842 [23,] 0.02952781 0.0590556176 0.9704721912 [24,] 0.20571181 0.4114236239 0.7942881880 [25,] 0.40780322 0.8156064476 0.5921967762 [26,] 0.54800860 0.9039827958 0.4519913979 [27,] 0.49407883 0.9881576507 0.5059211746 [28,] 0.47252039 0.9450407885 0.5274796057 [29,] 0.43757234 0.8751446792 0.5624276604 [30,] 0.68208196 0.6358360812 0.3179180406 [31,] 0.84322893 0.3135421465 0.1567710733 [32,] 0.80874999 0.3825000271 0.1912500136 [33,] 0.84905099 0.3018980136 0.1509490068 [34,] 0.82341105 0.3531779061 0.1765889530 [35,] 0.81853920 0.3629215939 0.1814607970 [36,] 0.79460512 0.4107897512 0.2053948756 [37,] 0.81453497 0.3709300612 0.1854650306 [38,] 0.77789255 0.4442148949 0.2221074475 [39,] 0.79435209 0.4112958221 0.2056479110 [40,] 0.77109940 0.4578011997 0.2289005999 [41,] 0.74658668 0.5068266411 0.2534133205 [42,] 0.84737431 0.3052513874 0.1526256937 [43,] 0.81543884 0.3691223254 0.1845611627 [44,] 0.80942395 0.3811520932 0.1905760466 [45,] 0.77754580 0.4449084011 0.2224542005 [46,] 0.73846433 0.5230713405 0.2615356703 [47,] 0.69856582 0.6028683600 0.3014341800 [48,] 0.66406023 0.6718795435 0.3359397717 [49,] 0.64211395 0.7157721037 0.3578860519 [50,] 0.59460436 0.8107912846 0.4053956423 [51,] 0.55508459 0.8898308155 0.4449154077 [52,] 0.53628681 0.9274263894 0.4637131947 [53,] 0.52809086 0.9438182874 0.4719091437 [54,] 0.52498156 0.9500368815 0.4750184408 [55,] 0.59410849 0.8117830124 0.4058915062 [56,] 0.55078843 0.8984231417 0.4492115708 [57,] 0.51038500 0.9792299962 0.4896149981 [58,] 0.67420160 0.6515968057 0.3257984029 [59,] 0.84438406 0.3112318883 0.1556159441 [60,] 0.84103738 0.3179252348 0.1589626174 [61,] 0.87445007 0.2510998565 0.1255499282 [62,] 0.94019263 0.1196147437 0.0598073719 [63,] 0.92518591 0.1496281852 0.0748140926 [64,] 0.91597966 0.1680406854 0.0840203427 [65,] 0.89654683 0.2069063382 0.1034531691 [66,] 0.91883278 0.1623344354 0.0811672177 [67,] 0.92611896 0.1477620781 0.0738810391 [68,] 0.96865962 0.0626807575 0.0313403788 [69,] 0.95957054 0.0808589240 0.0404294620 [70,] 0.95523284 0.0895343288 0.0447671644 [71,] 0.97844966 0.0431006764 0.0215503382 [72,] 0.97253841 0.0549231819 0.0274615910 [73,] 0.97671802 0.0465639577 0.0232819789 [74,] 0.96932449 0.0613510274 0.0306755137 [75,] 0.96422176 0.0715564824 0.0357782412 [76,] 0.95503714 0.0899257212 0.0449628606 [77,] 0.94648233 0.1070353383 0.0535176692 [78,] 0.94774201 0.1045159704 0.0522579852 [79,] 0.93351037 0.1329792573 0.0664896287 [80,] 0.93626909 0.1274618202 0.0637309101 [81,] 0.96230119 0.0753976178 0.0376988089 [82,] 0.99743386 0.0051322847 0.0025661423 [83,] 0.99646791 0.0070641896 0.0035320948 [84,] 0.99568077 0.0086384642 0.0043192321 [85,] 0.99414552 0.0117089515 0.0058544757 [86,] 0.99457717 0.0108456562 0.0054228281 [87,] 0.99530549 0.0093890148 0.0046945074 [88,] 0.99357055 0.0128588912 0.0064294456 [89,] 0.99229401 0.0154119764 0.0077059882 [90,] 0.99487215 0.0102557011 0.0051278505 [91,] 0.99485559 0.0102888249 0.0051444124 [92,] 0.99280637 0.0143872653 0.0071936326 [93,] 0.99455652 0.0108869611 0.0054434806 [94,] 0.99215145 0.0156970953 0.0078485477 [95,] 0.98963734 0.0207253190 0.0103626595 [96,] 0.98863297 0.0227340625 0.0113670312 [97,] 0.99271523 0.0145695492 0.0072847746 [98,] 0.99919691 0.0016061786 0.0008030893 [99,] 0.99882608 0.0023478423 0.0011739212 [100,] 0.99972454 0.0005509171 0.0002754586 [101,] 0.99953394 0.0009321131 0.0004660566 [102,] 0.99946413 0.0010717382 0.0005358691 [103,] 0.99916567 0.0016686653 0.0008343326 [104,] 0.99880939 0.0023812242 0.0011906121 [105,] 0.99802880 0.0039423906 0.0019711953 [106,] 0.99683504 0.0063299151 0.0031649575 [107,] 0.99935071 0.0012985704 0.0006492852 [108,] 0.99901788 0.0019642386 0.0009821193 [109,] 0.99840307 0.0031938564 0.0015969282 [110,] 0.99825395 0.0034920902 0.0017460451 [111,] 0.99782131 0.0043573804 0.0021786902 [112,] 0.99666166 0.0066766838 0.0033383419 [113,] 0.99680629 0.0063874222 0.0031937111 [114,] 0.99462830 0.0107434065 0.0053717032 [115,] 0.99186020 0.0162796098 0.0081398049 [116,] 0.98917380 0.0216523944 0.0108261972 [117,] 0.98437436 0.0312512781 0.0156256390 [118,] 0.99141921 0.0171615846 0.0085807923 [119,] 0.99410043 0.0117991444 0.0058995722 [120,] 0.99085939 0.0182812128 0.0091406064 [121,] 0.98962238 0.0207552447 0.0103776224 [122,] 0.98444955 0.0311009081 0.0155504540 [123,] 0.97396368 0.0520726305 0.0260363152 [124,] 0.95584815 0.0883037059 0.0441518529 [125,] 0.94854447 0.1029110648 0.0514555324 [126,] 0.93099843 0.1380031308 0.0690015654 [127,] 0.90122627 0.1975474638 0.0987737319 [128,] 0.87453952 0.2509209622 0.1254604811 [129,] 0.80523817 0.3895236595 0.1947618298 [130,] 0.83173674 0.3365265177 0.1682632588 [131,] 0.73332821 0.5333435814 0.2666717907 [132,] 0.61468672 0.7706265680 0.3853132840 [133,] 0.68537284 0.6292543131 0.3146271566 > postscript(file="/var/www/html/rcomp/tmp/19ru81293206094.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/29ru81293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/html/rcomp/tmp/320tt1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/420tt1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/520tt1293206094.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 = 156 Frequency = 1 1 2 3 4 5 6 1.581542850 0.210014404 -2.181437127 -1.992992911 -0.117053547 0.844475216 7 8 9 10 11 12 1.146225776 2.159856031 -0.926772649 -2.624881085 -0.106595233 0.024177424 13 14 15 16 17 18 3.236350627 1.335877357 -0.921330559 0.948169557 0.538749999 -2.665368040 19 20 21 22 23 24 -1.141129191 0.856696027 2.740807001 1.374506350 -0.027437817 0.799585082 25 26 27 28 29 30 0.505940325 -0.258081281 2.588037722 -2.076409830 -1.111407805 -0.764689132 31 32 33 34 35 36 -0.404097466 2.379301869 0.658364809 0.164507829 -5.582510388 5.187169901 37 38 39 40 41 42 3.869140697 -1.351471213 1.784413945 -0.889210472 6.012609201 -4.803595524 43 44 45 46 47 48 -0.388321512 3.775289802 -0.078180283 -1.713492572 1.857621562 2.259056607 49 50 51 52 53 54 -0.253802283 2.527543399 -1.742006357 -0.809892281 -2.683032170 -0.160660491 55 56 57 58 59 60 -1.738804856 -0.012207229 0.342495957 -0.839171405 -0.293956713 -0.212773495 61 62 63 64 65 66 0.037719481 1.065121548 -1.877032123 1.559993075 -1.688801742 3.315093890 67 68 69 70 71 72 0.817187373 0.665699122 -4.054624955 5.120900885 2.198344222 -3.206142288 73 74 75 76 77 78 -4.235725908 0.112664382 1.609873570 -0.527536070 2.793906465 -2.470688367 79 80 81 82 83 84 -4.209128524 -0.353741778 1.442075291 -3.818154393 -0.377973293 2.565137855 85 86 87 88 89 90 -0.334229988 -1.464697755 -0.460510789 0.804873421 0.616441990 -0.475126508 91 92 93 94 95 96 -2.417643057 -2.886223900 -6.006348645 -1.121456727 1.849637049 0.552845086 97 98 99 100 101 102 -2.577110380 -2.232481390 -1.439669177 1.896989472 2.792794747 2.360713117 103 104 105 106 107 108 -0.245715834 2.821881593 0.803206033 0.444015037 -1.717008828 -2.599405723 109 110 111 112 113 114 4.490144200 1.146637726 -3.278473124 0.893195239 1.875838722 -0.352419953 115 116 117 118 119 120 -1.554921998 -0.152809017 0.818087360 2.348864476 -0.388228707 -1.167909879 121 122 123 124 125 126 -2.234542427 -3.076331852 -0.838108440 -1.253334415 -0.006468822 -0.561360706 127 128 129 130 131 132 -1.643526831 1.743793573 -1.689172339 1.275608106 -1.850780782 2.149588052 133 134 135 136 137 138 -1.356404597 -0.817548119 0.864561933 -1.196716468 -0.259192999 0.189905397 139 140 141 142 143 144 -0.496502269 1.377932205 1.627927416 1.134070436 1.081277671 0.999976857 145 146 147 148 149 150 1.395738728 1.744325166 -0.008146000 2.859682599 0.499931804 -1.962280519 151 152 153 154 155 156 2.334625190 0.149378524 -1.121037277 -1.207662819 0.149378524 -0.962280519 > postscript(file="/var/www/html/rcomp/tmp/6uabe1293206094.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 = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 1.581542850 NA 1 0.210014404 1.581542850 2 -2.181437127 0.210014404 3 -1.992992911 -2.181437127 4 -0.117053547 -1.992992911 5 0.844475216 -0.117053547 6 1.146225776 0.844475216 7 2.159856031 1.146225776 8 -0.926772649 2.159856031 9 -2.624881085 -0.926772649 10 -0.106595233 -2.624881085 11 0.024177424 -0.106595233 12 3.236350627 0.024177424 13 1.335877357 3.236350627 14 -0.921330559 1.335877357 15 0.948169557 -0.921330559 16 0.538749999 0.948169557 17 -2.665368040 0.538749999 18 -1.141129191 -2.665368040 19 0.856696027 -1.141129191 20 2.740807001 0.856696027 21 1.374506350 2.740807001 22 -0.027437817 1.374506350 23 0.799585082 -0.027437817 24 0.505940325 0.799585082 25 -0.258081281 0.505940325 26 2.588037722 -0.258081281 27 -2.076409830 2.588037722 28 -1.111407805 -2.076409830 29 -0.764689132 -1.111407805 30 -0.404097466 -0.764689132 31 2.379301869 -0.404097466 32 0.658364809 2.379301869 33 0.164507829 0.658364809 34 -5.582510388 0.164507829 35 5.187169901 -5.582510388 36 3.869140697 5.187169901 37 -1.351471213 3.869140697 38 1.784413945 -1.351471213 39 -0.889210472 1.784413945 40 6.012609201 -0.889210472 41 -4.803595524 6.012609201 42 -0.388321512 -4.803595524 43 3.775289802 -0.388321512 44 -0.078180283 3.775289802 45 -1.713492572 -0.078180283 46 1.857621562 -1.713492572 47 2.259056607 1.857621562 48 -0.253802283 2.259056607 49 2.527543399 -0.253802283 50 -1.742006357 2.527543399 51 -0.809892281 -1.742006357 52 -2.683032170 -0.809892281 53 -0.160660491 -2.683032170 54 -1.738804856 -0.160660491 55 -0.012207229 -1.738804856 56 0.342495957 -0.012207229 57 -0.839171405 0.342495957 58 -0.293956713 -0.839171405 59 -0.212773495 -0.293956713 60 0.037719481 -0.212773495 61 1.065121548 0.037719481 62 -1.877032123 1.065121548 63 1.559993075 -1.877032123 64 -1.688801742 1.559993075 65 3.315093890 -1.688801742 66 0.817187373 3.315093890 67 0.665699122 0.817187373 68 -4.054624955 0.665699122 69 5.120900885 -4.054624955 70 2.198344222 5.120900885 71 -3.206142288 2.198344222 72 -4.235725908 -3.206142288 73 0.112664382 -4.235725908 74 1.609873570 0.112664382 75 -0.527536070 1.609873570 76 2.793906465 -0.527536070 77 -2.470688367 2.793906465 78 -4.209128524 -2.470688367 79 -0.353741778 -4.209128524 80 1.442075291 -0.353741778 81 -3.818154393 1.442075291 82 -0.377973293 -3.818154393 83 2.565137855 -0.377973293 84 -0.334229988 2.565137855 85 -1.464697755 -0.334229988 86 -0.460510789 -1.464697755 87 0.804873421 -0.460510789 88 0.616441990 0.804873421 89 -0.475126508 0.616441990 90 -2.417643057 -0.475126508 91 -2.886223900 -2.417643057 92 -6.006348645 -2.886223900 93 -1.121456727 -6.006348645 94 1.849637049 -1.121456727 95 0.552845086 1.849637049 96 -2.577110380 0.552845086 97 -2.232481390 -2.577110380 98 -1.439669177 -2.232481390 99 1.896989472 -1.439669177 100 2.792794747 1.896989472 101 2.360713117 2.792794747 102 -0.245715834 2.360713117 103 2.821881593 -0.245715834 104 0.803206033 2.821881593 105 0.444015037 0.803206033 106 -1.717008828 0.444015037 107 -2.599405723 -1.717008828 108 4.490144200 -2.599405723 109 1.146637726 4.490144200 110 -3.278473124 1.146637726 111 0.893195239 -3.278473124 112 1.875838722 0.893195239 113 -0.352419953 1.875838722 114 -1.554921998 -0.352419953 115 -0.152809017 -1.554921998 116 0.818087360 -0.152809017 117 2.348864476 0.818087360 118 -0.388228707 2.348864476 119 -1.167909879 -0.388228707 120 -2.234542427 -1.167909879 121 -3.076331852 -2.234542427 122 -0.838108440 -3.076331852 123 -1.253334415 -0.838108440 124 -0.006468822 -1.253334415 125 -0.561360706 -0.006468822 126 -1.643526831 -0.561360706 127 1.743793573 -1.643526831 128 -1.689172339 1.743793573 129 1.275608106 -1.689172339 130 -1.850780782 1.275608106 131 2.149588052 -1.850780782 132 -1.356404597 2.149588052 133 -0.817548119 -1.356404597 134 0.864561933 -0.817548119 135 -1.196716468 0.864561933 136 -0.259192999 -1.196716468 137 0.189905397 -0.259192999 138 -0.496502269 0.189905397 139 1.377932205 -0.496502269 140 1.627927416 1.377932205 141 1.134070436 1.627927416 142 1.081277671 1.134070436 143 0.999976857 1.081277671 144 1.395738728 0.999976857 145 1.744325166 1.395738728 146 -0.008146000 1.744325166 147 2.859682599 -0.008146000 148 0.499931804 2.859682599 149 -1.962280519 0.499931804 150 2.334625190 -1.962280519 151 0.149378524 2.334625190 152 -1.121037277 0.149378524 153 -1.207662819 -1.121037277 154 0.149378524 -1.207662819 155 -0.962280519 0.149378524 156 NA -0.962280519 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.210014404 1.581542850 [2,] -2.181437127 0.210014404 [3,] -1.992992911 -2.181437127 [4,] -0.117053547 -1.992992911 [5,] 0.844475216 -0.117053547 [6,] 1.146225776 0.844475216 [7,] 2.159856031 1.146225776 [8,] -0.926772649 2.159856031 [9,] -2.624881085 -0.926772649 [10,] -0.106595233 -2.624881085 [11,] 0.024177424 -0.106595233 [12,] 3.236350627 0.024177424 [13,] 1.335877357 3.236350627 [14,] -0.921330559 1.335877357 [15,] 0.948169557 -0.921330559 [16,] 0.538749999 0.948169557 [17,] -2.665368040 0.538749999 [18,] -1.141129191 -2.665368040 [19,] 0.856696027 -1.141129191 [20,] 2.740807001 0.856696027 [21,] 1.374506350 2.740807001 [22,] -0.027437817 1.374506350 [23,] 0.799585082 -0.027437817 [24,] 0.505940325 0.799585082 [25,] -0.258081281 0.505940325 [26,] 2.588037722 -0.258081281 [27,] -2.076409830 2.588037722 [28,] -1.111407805 -2.076409830 [29,] -0.764689132 -1.111407805 [30,] -0.404097466 -0.764689132 [31,] 2.379301869 -0.404097466 [32,] 0.658364809 2.379301869 [33,] 0.164507829 0.658364809 [34,] -5.582510388 0.164507829 [35,] 5.187169901 -5.582510388 [36,] 3.869140697 5.187169901 [37,] -1.351471213 3.869140697 [38,] 1.784413945 -1.351471213 [39,] -0.889210472 1.784413945 [40,] 6.012609201 -0.889210472 [41,] -4.803595524 6.012609201 [42,] -0.388321512 -4.803595524 [43,] 3.775289802 -0.388321512 [44,] -0.078180283 3.775289802 [45,] -1.713492572 -0.078180283 [46,] 1.857621562 -1.713492572 [47,] 2.259056607 1.857621562 [48,] -0.253802283 2.259056607 [49,] 2.527543399 -0.253802283 [50,] -1.742006357 2.527543399 [51,] -0.809892281 -1.742006357 [52,] -2.683032170 -0.809892281 [53,] -0.160660491 -2.683032170 [54,] -1.738804856 -0.160660491 [55,] -0.012207229 -1.738804856 [56,] 0.342495957 -0.012207229 [57,] -0.839171405 0.342495957 [58,] -0.293956713 -0.839171405 [59,] -0.212773495 -0.293956713 [60,] 0.037719481 -0.212773495 [61,] 1.065121548 0.037719481 [62,] -1.877032123 1.065121548 [63,] 1.559993075 -1.877032123 [64,] -1.688801742 1.559993075 [65,] 3.315093890 -1.688801742 [66,] 0.817187373 3.315093890 [67,] 0.665699122 0.817187373 [68,] -4.054624955 0.665699122 [69,] 5.120900885 -4.054624955 [70,] 2.198344222 5.120900885 [71,] -3.206142288 2.198344222 [72,] -4.235725908 -3.206142288 [73,] 0.112664382 -4.235725908 [74,] 1.609873570 0.112664382 [75,] -0.527536070 1.609873570 [76,] 2.793906465 -0.527536070 [77,] -2.470688367 2.793906465 [78,] -4.209128524 -2.470688367 [79,] -0.353741778 -4.209128524 [80,] 1.442075291 -0.353741778 [81,] -3.818154393 1.442075291 [82,] -0.377973293 -3.818154393 [83,] 2.565137855 -0.377973293 [84,] -0.334229988 2.565137855 [85,] -1.464697755 -0.334229988 [86,] -0.460510789 -1.464697755 [87,] 0.804873421 -0.460510789 [88,] 0.616441990 0.804873421 [89,] -0.475126508 0.616441990 [90,] -2.417643057 -0.475126508 [91,] -2.886223900 -2.417643057 [92,] -6.006348645 -2.886223900 [93,] -1.121456727 -6.006348645 [94,] 1.849637049 -1.121456727 [95,] 0.552845086 1.849637049 [96,] -2.577110380 0.552845086 [97,] -2.232481390 -2.577110380 [98,] -1.439669177 -2.232481390 [99,] 1.896989472 -1.439669177 [100,] 2.792794747 1.896989472 [101,] 2.360713117 2.792794747 [102,] -0.245715834 2.360713117 [103,] 2.821881593 -0.245715834 [104,] 0.803206033 2.821881593 [105,] 0.444015037 0.803206033 [106,] -1.717008828 0.444015037 [107,] -2.599405723 -1.717008828 [108,] 4.490144200 -2.599405723 [109,] 1.146637726 4.490144200 [110,] -3.278473124 1.146637726 [111,] 0.893195239 -3.278473124 [112,] 1.875838722 0.893195239 [113,] -0.352419953 1.875838722 [114,] -1.554921998 -0.352419953 [115,] -0.152809017 -1.554921998 [116,] 0.818087360 -0.152809017 [117,] 2.348864476 0.818087360 [118,] -0.388228707 2.348864476 [119,] -1.167909879 -0.388228707 [120,] -2.234542427 -1.167909879 [121,] -3.076331852 -2.234542427 [122,] -0.838108440 -3.076331852 [123,] -1.253334415 -0.838108440 [124,] -0.006468822 -1.253334415 [125,] -0.561360706 -0.006468822 [126,] -1.643526831 -0.561360706 [127,] 1.743793573 -1.643526831 [128,] -1.689172339 1.743793573 [129,] 1.275608106 -1.689172339 [130,] -1.850780782 1.275608106 [131,] 2.149588052 -1.850780782 [132,] -1.356404597 2.149588052 [133,] -0.817548119 -1.356404597 [134,] 0.864561933 -0.817548119 [135,] -1.196716468 0.864561933 [136,] -0.259192999 -1.196716468 [137,] 0.189905397 -0.259192999 [138,] -0.496502269 0.189905397 [139,] 1.377932205 -0.496502269 [140,] 1.627927416 1.377932205 [141,] 1.134070436 1.627927416 [142,] 1.081277671 1.134070436 [143,] 0.999976857 1.081277671 [144,] 1.395738728 0.999976857 [145,] 1.744325166 1.395738728 [146,] -0.008146000 1.744325166 [147,] 2.859682599 -0.008146000 [148,] 0.499931804 2.859682599 [149,] -1.962280519 0.499931804 [150,] 2.334625190 -1.962280519 [151,] 0.149378524 2.334625190 [152,] -1.121037277 0.149378524 [153,] -1.207662819 -1.121037277 [154,] 0.149378524 -1.207662819 [155,] -0.962280519 0.149378524 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.210014404 1.581542850 2 -2.181437127 0.210014404 3 -1.992992911 -2.181437127 4 -0.117053547 -1.992992911 5 0.844475216 -0.117053547 6 1.146225776 0.844475216 7 2.159856031 1.146225776 8 -0.926772649 2.159856031 9 -2.624881085 -0.926772649 10 -0.106595233 -2.624881085 11 0.024177424 -0.106595233 12 3.236350627 0.024177424 13 1.335877357 3.236350627 14 -0.921330559 1.335877357 15 0.948169557 -0.921330559 16 0.538749999 0.948169557 17 -2.665368040 0.538749999 18 -1.141129191 -2.665368040 19 0.856696027 -1.141129191 20 2.740807001 0.856696027 21 1.374506350 2.740807001 22 -0.027437817 1.374506350 23 0.799585082 -0.027437817 24 0.505940325 0.799585082 25 -0.258081281 0.505940325 26 2.588037722 -0.258081281 27 -2.076409830 2.588037722 28 -1.111407805 -2.076409830 29 -0.764689132 -1.111407805 30 -0.404097466 -0.764689132 31 2.379301869 -0.404097466 32 0.658364809 2.379301869 33 0.164507829 0.658364809 34 -5.582510388 0.164507829 35 5.187169901 -5.582510388 36 3.869140697 5.187169901 37 -1.351471213 3.869140697 38 1.784413945 -1.351471213 39 -0.889210472 1.784413945 40 6.012609201 -0.889210472 41 -4.803595524 6.012609201 42 -0.388321512 -4.803595524 43 3.775289802 -0.388321512 44 -0.078180283 3.775289802 45 -1.713492572 -0.078180283 46 1.857621562 -1.713492572 47 2.259056607 1.857621562 48 -0.253802283 2.259056607 49 2.527543399 -0.253802283 50 -1.742006357 2.527543399 51 -0.809892281 -1.742006357 52 -2.683032170 -0.809892281 53 -0.160660491 -2.683032170 54 -1.738804856 -0.160660491 55 -0.012207229 -1.738804856 56 0.342495957 -0.012207229 57 -0.839171405 0.342495957 58 -0.293956713 -0.839171405 59 -0.212773495 -0.293956713 60 0.037719481 -0.212773495 61 1.065121548 0.037719481 62 -1.877032123 1.065121548 63 1.559993075 -1.877032123 64 -1.688801742 1.559993075 65 3.315093890 -1.688801742 66 0.817187373 3.315093890 67 0.665699122 0.817187373 68 -4.054624955 0.665699122 69 5.120900885 -4.054624955 70 2.198344222 5.120900885 71 -3.206142288 2.198344222 72 -4.235725908 -3.206142288 73 0.112664382 -4.235725908 74 1.609873570 0.112664382 75 -0.527536070 1.609873570 76 2.793906465 -0.527536070 77 -2.470688367 2.793906465 78 -4.209128524 -2.470688367 79 -0.353741778 -4.209128524 80 1.442075291 -0.353741778 81 -3.818154393 1.442075291 82 -0.377973293 -3.818154393 83 2.565137855 -0.377973293 84 -0.334229988 2.565137855 85 -1.464697755 -0.334229988 86 -0.460510789 -1.464697755 87 0.804873421 -0.460510789 88 0.616441990 0.804873421 89 -0.475126508 0.616441990 90 -2.417643057 -0.475126508 91 -2.886223900 -2.417643057 92 -6.006348645 -2.886223900 93 -1.121456727 -6.006348645 94 1.849637049 -1.121456727 95 0.552845086 1.849637049 96 -2.577110380 0.552845086 97 -2.232481390 -2.577110380 98 -1.439669177 -2.232481390 99 1.896989472 -1.439669177 100 2.792794747 1.896989472 101 2.360713117 2.792794747 102 -0.245715834 2.360713117 103 2.821881593 -0.245715834 104 0.803206033 2.821881593 105 0.444015037 0.803206033 106 -1.717008828 0.444015037 107 -2.599405723 -1.717008828 108 4.490144200 -2.599405723 109 1.146637726 4.490144200 110 -3.278473124 1.146637726 111 0.893195239 -3.278473124 112 1.875838722 0.893195239 113 -0.352419953 1.875838722 114 -1.554921998 -0.352419953 115 -0.152809017 -1.554921998 116 0.818087360 -0.152809017 117 2.348864476 0.818087360 118 -0.388228707 2.348864476 119 -1.167909879 -0.388228707 120 -2.234542427 -1.167909879 121 -3.076331852 -2.234542427 122 -0.838108440 -3.076331852 123 -1.253334415 -0.838108440 124 -0.006468822 -1.253334415 125 -0.561360706 -0.006468822 126 -1.643526831 -0.561360706 127 1.743793573 -1.643526831 128 -1.689172339 1.743793573 129 1.275608106 -1.689172339 130 -1.850780782 1.275608106 131 2.149588052 -1.850780782 132 -1.356404597 2.149588052 133 -0.817548119 -1.356404597 134 0.864561933 -0.817548119 135 -1.196716468 0.864561933 136 -0.259192999 -1.196716468 137 0.189905397 -0.259192999 138 -0.496502269 0.189905397 139 1.377932205 -0.496502269 140 1.627927416 1.377932205 141 1.134070436 1.627927416 142 1.081277671 1.134070436 143 0.999976857 1.081277671 144 1.395738728 0.999976857 145 1.744325166 1.395738728 146 -0.008146000 1.744325166 147 2.859682599 -0.008146000 148 0.499931804 2.859682599 149 -1.962280519 0.499931804 150 2.334625190 -1.962280519 151 0.149378524 2.334625190 152 -1.121037277 0.149378524 153 -1.207662819 -1.121037277 154 0.149378524 -1.207662819 155 -0.962280519 0.149378524 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7uabe1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8njsz1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9njsz1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10gar21293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11jb8p1293206094.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/12u27a1293206094.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/131lmm1293206094.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/14tc3p1293206094.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/15fdkv1293206094.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/16tmz41293206094.tab") + } > > try(system("convert tmp/19ru81293206094.ps tmp/19ru81293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/29ru81293206094.ps tmp/29ru81293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/320tt1293206094.ps tmp/320tt1293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/420tt1293206094.ps tmp/420tt1293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/520tt1293206094.ps tmp/520tt1293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/6uabe1293206094.ps tmp/6uabe1293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/7uabe1293206094.ps tmp/7uabe1293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/8njsz1293206094.ps tmp/8njsz1293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/9njsz1293206094.ps tmp/9njsz1293206094.png",intern=TRUE)) character(0) > try(system("convert tmp/10gar21293206094.ps tmp/10gar21293206094.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.374 1.868 9.881