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(9.1 + ,4.5 + ,1.0 + ,-1.0 + ,1989.3 + ,9.0 + ,4.3 + ,1.0 + ,3.0 + ,2097.8 + ,9.0 + ,4.3 + ,1.3 + ,2.0 + ,2154.9 + ,8.9 + ,4.2 + ,1.1 + ,3.0 + ,2152.2 + ,8.8 + ,4.0 + ,0.8 + ,5.0 + ,2250.3 + ,8.7 + ,3.8 + ,0.7 + ,5.0 + ,2346.9 + ,8.5 + ,4.1 + ,0.7 + ,3.0 + ,2525.6 + ,8.3 + ,4.2 + ,0.9 + ,2.0 + ,2409.4 + ,8.1 + ,4.0 + ,1.3 + ,1.0 + ,2394.4 + ,7.9 + ,4.3 + ,1.4 + ,-4.0 + ,2401.3 + ,7.8 + ,4.7 + ,1.6 + ,1.0 + ,2354.3 + ,7.6 + ,5.0 + ,2.1 + ,1.0 + ,2450.4 + ,7.4 + ,5.1 + ,0.3 + ,6.0 + ,2504.7 + ,7.2 + ,5.4 + ,2.1 + ,3.0 + ,2661.4 + ,7.0 + ,5.4 + ,2.5 + ,2.0 + ,2880.4 + ,7.0 + ,5.4 + ,2.3 + ,2.0 + ,3064.4 + ,6.8 + ,5.5 + ,2.4 + ,2.0 + ,3141.1 + ,6.8 + ,5.8 + ,3.0 + ,-8.0 + ,3327.7 + ,6.7 + ,5.7 + ,1.7 + ,0.0 + ,3565.0 + ,6.8 + ,5.5 + ,3.5 + ,-2.0 + ,3403.1 + ,6.7 + ,5.6 + ,4.0 + ,3.0 + ,3149.9 + ,6.7 + ,5.6 + ,3.7 + ,5.0 + ,3006.8 + ,6.7 + ,5.5 + ,3.7 + ,8.0 + ,3230.7 + ,6.5 + ,5.5 + ,3.0 + ,8.0 + ,3361.1 + ,6.3 + ,5.7 + ,2.7 + ,9.0 + ,3484.7 + ,6.3 + ,5.6 + ,2.5 + ,11.0 + ,3411.1 + ,6.3 + ,5.6 + ,2.2 + ,13.0 + ,3288.2 + ,6.5 + ,5.4 + ,2.9 + ,12.0 + ,3280.4 + ,6.6 + ,5.2 + ,3.1 + ,13.0 + ,3174.0 + ,6.5 + ,5.1 + ,3.0 + ,15.0 + ,3165.3 + ,6.3 + ,5.1 + ,2.8 + ,13.0 + ,3092.7 + ,6.3 + ,5.0 + ,2.5 + ,16.0 + ,3053.1 + ,6.5 + ,5.3 + ,1.9 + ,10.0 + ,3182.0 + ,7.0 + ,5.4 + ,1.9 + ,14.0 + ,2999.9 + ,7.1 + ,5.3 + ,1.8 + ,14.0 + ,3249.6 + ,7.3 + ,5.1 + ,2.0 + ,15.0 + ,3210.5 + ,7.3 + ,5.0 + ,2.6 + ,13.0 + ,3030.3 + ,7.4 + ,5.0 + ,2.5 + ,8.0 + ,2803.5 + ,7.4 + ,4.6 + ,2.5 + ,7.0 + ,2767.6 + ,7.3 + ,4.8 + ,1.6 + ,3.0 + ,2882.6 + ,7.4 + ,5.1 + ,1.4 + ,3.0 + ,2863.4 + ,7.5 + ,5.1 + ,0.8 + ,4.0 + ,2897.1 + ,7.7 + ,5.1 + ,1.1 + ,4.0 + ,3012.6 + ,7.7 + ,5.4 + ,1.3 + ,0.0 + ,3143.0 + ,7.7 + ,5.3 + ,1.2 + ,-4.0 + ,3032.9 + ,7.7 + ,5.3 + ,1.3 + ,-14.0 + ,3045.8 + ,7.7 + ,5.1 + ,1.1 + ,-18.0 + ,3110.5 + ,7.8 + ,4.9 + ,1.3 + ,-8.0 + ,3013.2 + ,8.0 + ,4.7 + ,1.2 + ,-1.0 + ,2987.1 + ,8.1 + ,4.4 + ,1.6 + ,1.0 + ,2995.6 + ,8.1 + ,4.6 + ,1.7 + ,2.0 + ,2833.2 + ,8.2 + ,4.5 + ,1.5 + ,0.0 + ,2849.0 + ,8.2 + ,4.2 + ,0.9 + ,1.0 + ,2794.8 + ,8.2 + ,4.0 + ,1.5 + ,0.0 + ,2845.3 + ,8.1 + ,3.9 + ,1.4 + ,-1.0 + ,2915.0 + ,8.1 + ,4.1 + ,1.6 + ,-3.0 + ,2892.6 + ,8.2 + ,4.1 + ,1.7 + ,-3.0 + ,2604.4 + ,8.3 + ,3.7 + ,1.4 + ,-3.0 + ,2641.7 + ,8.3 + ,3.8 + ,1.8 + ,-4.0 + ,2659.8 + ,8.4 + ,4.1 + ,1.7 + ,-8.0 + ,2638.5 + ,8.5 + ,4.1 + ,1.4 + ,-9.0 + ,2720.3 + ,8.5 + ,4.0 + ,1.2 + ,-13.0 + ,2745.9 + ,8.4 + ,4.3 + ,1.0 + ,-18.0 + ,2735.7 + ,8.0 + ,4.4 + ,1.7 + ,-11.0 + ,2811.7 + ,7.9 + ,4.2 + ,2.4 + ,-9.0 + ,2799.4 + ,8.1 + ,4.2 + ,2.0 + ,-10.0 + ,2555.3 + ,8.5 + ,4.0 + ,2.1 + ,-13.0 + ,2305.0 + ,8.8 + ,4.0 + ,2.0 + ,-11.0 + ,2215.0 + ,8.8 + ,4.3 + ,1.8 + ,-5.0 + ,2065.8 + ,8.6 + ,4.4 + ,2.7 + ,-15.0 + ,1940.5 + ,8.3 + ,4.4 + ,2.3 + ,-6.0 + ,2042.0 + ,8.3 + ,4.3 + ,1.9 + ,-6.0 + ,1995.4 + ,8.3 + ,4.1 + ,2.0 + ,-3.0 + ,1946.8 + ,8.4 + ,4.1 + ,2.3 + ,-1.0 + ,1765.9 + ,8.4 + ,3.9 + ,2.8 + ,-3.0 + ,1635.3 + ,8.5 + ,3.8 + ,2.4 + ,-4.0 + ,1833.4 + ,8.6 + ,3.7 + ,2.3 + ,-6.0 + ,1910.4 + ,8.6 + ,3.5 + ,2.7 + ,0.0 + ,1959.7 + ,8.6 + ,3.7 + ,2.7 + ,-4.0 + ,1969.6 + ,8.6 + ,3.7 + ,2.9 + ,-2.0 + ,2061.4 + ,8.6 + ,3.5 + ,3.0 + ,-2.0 + ,2093.5 + ,8.5 + ,3.3 + ,2.2 + ,-6.0 + ,2120.9 + ,8.4 + ,3.2 + ,2.3 + ,-7.0 + ,2174.6 + ,8.4 + ,3.3 + ,2.8 + ,-6.0 + ,2196.7 + ,8.4 + ,3.1 + ,2.8 + ,-6.0 + ,2350.4 + ,8.5 + ,3.2 + ,2.8 + ,-3.0 + ,2440.3 + ,8.5 + ,3.4 + ,2.2 + ,-2.0 + ,2408.6 + ,8.6 + ,3.5 + ,2.6 + ,-5.0 + ,2472.8 + ,8.6 + ,3.3 + ,2.8 + ,-11.0 + ,2407.6 + ,8.4 + ,3.5 + ,2.5 + ,-11.0 + ,2454.6 + ,8.2 + ,3.5 + ,2.4 + ,-11.0 + ,2448.1 + ,8.0 + ,3.8 + ,2.3 + ,-10.0 + ,2497.8 + ,8.0 + ,4.0 + ,1.9 + ,-14.0 + ,2645.6 + ,8.0 + ,4.0 + ,1.7 + ,-8.0 + ,2756.8 + ,8.0 + ,4.1 + ,2.0 + ,-9.0 + ,2849.3 + ,7.9 + ,4.0 + ,2.1 + ,-5.0 + ,2921.4 + ,7.9 + ,3.8 + ,1.7 + ,-1.0 + ,2981.9 + ,7.8 + ,3.7 + ,1.8 + ,-2.0 + ,3080.6 + ,7.8 + ,3.8 + ,1.8 + ,-5.0 + ,3106.2 + ,8.0 + ,3.7 + ,1.8 + ,-4.0 + ,3119.3 + ,7.8 + ,4.0 + ,1.3 + ,-6.0 + ,3061.3 + ,7.4 + ,4.2 + ,1.3 + ,-2.0 + ,3097.3 + ,7.2 + ,4.0 + ,1.3 + ,-2.0 + ,3161.7 + ,7.0 + ,4.1 + ,1.2 + ,-2.0 + ,3257.2 + ,7.0 + ,4.2 + ,1.4 + ,-2.0 + ,3277.0 + ,7.2 + ,4.5 + ,2.2 + ,2.0 + ,3295.3 + ,7.2 + ,4.6 + ,2.9 + ,1.0 + ,3364.0 + ,7.2 + ,4.5 + ,3.1 + ,-8.0 + ,3494.2 + ,7.0 + ,4.5 + ,3.5 + ,-1.0 + ,3667.0 + ,6.9 + ,4.5 + ,3.6 + ,1.0 + ,3813.1 + ,6.8 + ,4.4 + ,4.4 + ,-1.0 + ,3918.0 + ,6.8 + ,4.3 + ,4.1 + ,2.0 + ,3895.5 + ,6.8 + ,4.5 + ,5.1 + ,2.0 + ,3801.1 + ,6.9 + ,4.1 + ,5.8 + ,1.0 + ,3570.1 + ,7.2 + ,4.1 + ,5.9 + ,-1.0 + ,3701.6 + ,7.2 + ,4.3 + ,5.4 + ,-2.0 + ,3862.3 + ,7.2 + ,4.4 + ,5.5 + ,-2.0 + ,3970.1 + ,7.1 + ,4.7 + ,4.8 + ,-1.0 + ,4138.5 + ,7.2 + ,5.0 + ,3.2 + ,-8.0 + ,4199.8 + ,7.3 + ,4.7 + ,2.7 + ,-4.0 + ,4290.9 + ,7.5 + ,4.5 + ,2.1 + ,-6.0 + ,4443.9 + ,7.6 + ,4.5 + ,1.9 + ,-3.0 + ,4502.6 + ,7.7 + ,4.5 + ,0.6 + ,-3.0 + ,4357.0 + ,7.7 + ,5.5 + ,0.7 + ,-7.0 + ,4591.3 + ,7.7 + ,4.5 + ,-0.2 + ,-9.0 + ,4697.0 + ,7.8 + ,4.4 + ,-1.0 + ,-11.0 + ,4621.4 + ,8.0 + ,4.2 + ,-1.7 + ,-13.0 + ,4562.8 + ,8.1 + ,3.9 + ,-0.7 + ,-11.0 + ,4202.5 + ,8.1 + ,3.9 + ,-1.0 + ,-9.0 + ,4296.5 + ,8.0 + ,4.2 + ,-0.9 + ,-17.0 + ,4435.2 + ,8.1 + ,4.0 + ,0.0 + ,-22.0 + ,4105.2 + ,8.2 + ,3.8 + ,0.3 + ,-25.0 + ,4116.7 + ,8.3 + ,3.7 + ,0.8 + ,-20.0 + ,3844.5 + ,8.4 + ,3.7 + ,0.8 + ,-24.0 + ,3721.0 + ,8.4 + ,3.7 + ,1.9 + ,-24.0 + ,3674.4 + ,8.4 + ,3.7 + ,2.1 + ,-22.0 + ,3857.6 + ,8.5 + ,3.7 + ,2.5 + ,-19.0 + ,3801.1 + ,8.5 + ,3.8 + ,2.7 + ,-18.0 + ,3504.4 + ,8.6 + ,3.7 + ,2.4 + ,-17.0 + ,3032.6 + ,8.6 + ,3.5 + ,2.4 + ,-11.0 + ,3047.0 + ,8.5 + ,3.5 + ,2.9 + ,-11.0 + ,2962.3 + ,8.5 + ,3.1 + ,3.1 + ,-12.0 + ,2197.8) + ,dim=c(5 + ,142) + ,dimnames=list(c('Werkloosheid' + ,'rente' + ,'inflatie' + ,'consumer' + ,'Bel20') + ,1:142)) > y <- array(NA,dim=c(5,142),dimnames=list(c('Werkloosheid','rente','inflatie','consumer','Bel20'),1:142)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Werkloosheid rente inflatie consumer Bel20 t 1 9.1 4.5 1.0 -1 1989.3 1 2 9.0 4.3 1.0 3 2097.8 2 3 9.0 4.3 1.3 2 2154.9 3 4 8.9 4.2 1.1 3 2152.2 4 5 8.8 4.0 0.8 5 2250.3 5 6 8.7 3.8 0.7 5 2346.9 6 7 8.5 4.1 0.7 3 2525.6 7 8 8.3 4.2 0.9 2 2409.4 8 9 8.1 4.0 1.3 1 2394.4 9 10 7.9 4.3 1.4 -4 2401.3 10 11 7.8 4.7 1.6 1 2354.3 11 12 7.6 5.0 2.1 1 2450.4 12 13 7.4 5.1 0.3 6 2504.7 13 14 7.2 5.4 2.1 3 2661.4 14 15 7.0 5.4 2.5 2 2880.4 15 16 7.0 5.4 2.3 2 3064.4 16 17 6.8 5.5 2.4 2 3141.1 17 18 6.8 5.8 3.0 -8 3327.7 18 19 6.7 5.7 1.7 0 3565.0 19 20 6.8 5.5 3.5 -2 3403.1 20 21 6.7 5.6 4.0 3 3149.9 21 22 6.7 5.6 3.7 5 3006.8 22 23 6.7 5.5 3.7 8 3230.7 23 24 6.5 5.5 3.0 8 3361.1 24 25 6.3 5.7 2.7 9 3484.7 25 26 6.3 5.6 2.5 11 3411.1 26 27 6.3 5.6 2.2 13 3288.2 27 28 6.5 5.4 2.9 12 3280.4 28 29 6.6 5.2 3.1 13 3174.0 29 30 6.5 5.1 3.0 15 3165.3 30 31 6.3 5.1 2.8 13 3092.7 31 32 6.3 5.0 2.5 16 3053.1 32 33 6.5 5.3 1.9 10 3182.0 33 34 7.0 5.4 1.9 14 2999.9 34 35 7.1 5.3 1.8 14 3249.6 35 36 7.3 5.1 2.0 15 3210.5 36 37 7.3 5.0 2.6 13 3030.3 37 38 7.4 5.0 2.5 8 2803.5 38 39 7.4 4.6 2.5 7 2767.6 39 40 7.3 4.8 1.6 3 2882.6 40 41 7.4 5.1 1.4 3 2863.4 41 42 7.5 5.1 0.8 4 2897.1 42 43 7.7 5.1 1.1 4 3012.6 43 44 7.7 5.4 1.3 0 3143.0 44 45 7.7 5.3 1.2 -4 3032.9 45 46 7.7 5.3 1.3 -14 3045.8 46 47 7.7 5.1 1.1 -18 3110.5 47 48 7.8 4.9 1.3 -8 3013.2 48 49 8.0 4.7 1.2 -1 2987.1 49 50 8.1 4.4 1.6 1 2995.6 50 51 8.1 4.6 1.7 2 2833.2 51 52 8.2 4.5 1.5 0 2849.0 52 53 8.2 4.2 0.9 1 2794.8 53 54 8.2 4.0 1.5 0 2845.3 54 55 8.1 3.9 1.4 -1 2915.0 55 56 8.1 4.1 1.6 -3 2892.6 56 57 8.2 4.1 1.7 -3 2604.4 57 58 8.3 3.7 1.4 -3 2641.7 58 59 8.3 3.8 1.8 -4 2659.8 59 60 8.4 4.1 1.7 -8 2638.5 60 61 8.5 4.1 1.4 -9 2720.3 61 62 8.5 4.0 1.2 -13 2745.9 62 63 8.4 4.3 1.0 -18 2735.7 63 64 8.0 4.4 1.7 -11 2811.7 64 65 7.9 4.2 2.4 -9 2799.4 65 66 8.1 4.2 2.0 -10 2555.3 66 67 8.5 4.0 2.1 -13 2305.0 67 68 8.8 4.0 2.0 -11 2215.0 68 69 8.8 4.3 1.8 -5 2065.8 69 70 8.6 4.4 2.7 -15 1940.5 70 71 8.3 4.4 2.3 -6 2042.0 71 72 8.3 4.3 1.9 -6 1995.4 72 73 8.3 4.1 2.0 -3 1946.8 73 74 8.4 4.1 2.3 -1 1765.9 74 75 8.4 3.9 2.8 -3 1635.3 75 76 8.5 3.8 2.4 -4 1833.4 76 77 8.6 3.7 2.3 -6 1910.4 77 78 8.6 3.5 2.7 0 1959.7 78 79 8.6 3.7 2.7 -4 1969.6 79 80 8.6 3.7 2.9 -2 2061.4 80 81 8.6 3.5 3.0 -2 2093.5 81 82 8.5 3.3 2.2 -6 2120.9 82 83 8.4 3.2 2.3 -7 2174.6 83 84 8.4 3.3 2.8 -6 2196.7 84 85 8.4 3.1 2.8 -6 2350.4 85 86 8.5 3.2 2.8 -3 2440.3 86 87 8.5 3.4 2.2 -2 2408.6 87 88 8.6 3.5 2.6 -5 2472.8 88 89 8.6 3.3 2.8 -11 2407.6 89 90 8.4 3.5 2.5 -11 2454.6 90 91 8.2 3.5 2.4 -11 2448.1 91 92 8.0 3.8 2.3 -10 2497.8 92 93 8.0 4.0 1.9 -14 2645.6 93 94 8.0 4.0 1.7 -8 2756.8 94 95 8.0 4.1 2.0 -9 2849.3 95 96 7.9 4.0 2.1 -5 2921.4 96 97 7.9 3.8 1.7 -1 2981.9 97 98 7.8 3.7 1.8 -2 3080.6 98 99 7.8 3.8 1.8 -5 3106.2 99 100 8.0 3.7 1.8 -4 3119.3 100 101 7.8 4.0 1.3 -6 3061.3 101 102 7.4 4.2 1.3 -2 3097.3 102 103 7.2 4.0 1.3 -2 3161.7 103 104 7.0 4.1 1.2 -2 3257.2 104 105 7.0 4.2 1.4 -2 3277.0 105 106 7.2 4.5 2.2 2 3295.3 106 107 7.2 4.6 2.9 1 3364.0 107 108 7.2 4.5 3.1 -8 3494.2 108 109 7.0 4.5 3.5 -1 3667.0 109 110 6.9 4.5 3.6 1 3813.1 110 111 6.8 4.4 4.4 -1 3918.0 111 112 6.8 4.3 4.1 2 3895.5 112 113 6.8 4.5 5.1 2 3801.1 113 114 6.9 4.1 5.8 1 3570.1 114 115 7.2 4.1 5.9 -1 3701.6 115 116 7.2 4.3 5.4 -2 3862.3 116 117 7.2 4.4 5.5 -2 3970.1 117 118 7.1 4.7 4.8 -1 4138.5 118 119 7.2 5.0 3.2 -8 4199.8 119 120 7.3 4.7 2.7 -4 4290.9 120 121 7.5 4.5 2.1 -6 4443.9 121 122 7.6 4.5 1.9 -3 4502.6 122 123 7.7 4.5 0.6 -3 4357.0 123 124 7.7 5.5 0.7 -7 4591.3 124 125 7.7 4.5 -0.2 -9 4697.0 125 126 7.8 4.4 -1.0 -11 4621.4 126 127 8.0 4.2 -1.7 -13 4562.8 127 128 8.1 3.9 -0.7 -11 4202.5 128 129 8.1 3.9 -1.0 -9 4296.5 129 130 8.0 4.2 -0.9 -17 4435.2 130 131 8.1 4.0 0.0 -22 4105.2 131 132 8.2 3.8 0.3 -25 4116.7 132 133 8.3 3.7 0.8 -20 3844.5 133 134 8.4 3.7 0.8 -24 3721.0 134 135 8.4 3.7 1.9 -24 3674.4 135 136 8.4 3.7 2.1 -22 3857.6 136 137 8.5 3.7 2.5 -19 3801.1 137 138 8.5 3.8 2.7 -18 3504.4 138 139 8.6 3.7 2.4 -17 3032.6 139 140 8.6 3.5 2.4 -11 3047.0 140 141 8.5 3.5 2.9 -11 2962.3 141 142 8.5 3.1 3.1 -12 2197.8 142 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) rente inflatie consumer Bel20 t 11.6359824 -0.5593203 -0.1438996 -0.0296060 -0.0003337 -0.0026812 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.86356 -0.19670 -0.03018 0.20871 0.89830 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.164e+01 2.674e-01 43.520 < 2e-16 *** rente -5.593e-01 7.558e-02 -7.400 1.27e-11 *** inflatie -1.439e-01 2.429e-02 -5.923 2.45e-08 *** consumer -2.961e-02 4.442e-03 -6.665 6.07e-10 *** Bel20 -3.337e-04 6.961e-05 -4.794 4.23e-06 *** t -2.681e-03 1.503e-03 -1.784 0.0767 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3072 on 136 degrees of freedom Multiple R-squared: 0.8214, Adjusted R-squared: 0.8148 F-statistic: 125.1 on 5 and 136 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,] 3.154002e-02 6.308005e-02 9.684600e-01 [2,] 8.811649e-03 1.762330e-02 9.911884e-01 [3,] 1.945980e-03 3.891961e-03 9.980540e-01 [4,] 4.380720e-04 8.761440e-04 9.995619e-01 [5,] 1.232592e-04 2.465184e-04 9.998767e-01 [6,] 4.753799e-05 9.507598e-05 9.999525e-01 [7,] 1.692712e-05 3.385425e-05 9.999831e-01 [8,] 7.903131e-06 1.580626e-05 9.999921e-01 [9,] 1.692677e-06 3.385355e-06 9.999983e-01 [10,] 1.181677e-05 2.363355e-05 9.999882e-01 [11,] 3.617337e-06 7.234673e-06 9.999964e-01 [12,] 4.045914e-05 8.091828e-05 9.999595e-01 [13,] 9.913315e-05 1.982663e-04 9.999009e-01 [14,] 3.394398e-04 6.788796e-04 9.996606e-01 [15,] 3.004867e-04 6.009733e-04 9.996995e-01 [16,] 2.070559e-04 4.141118e-04 9.997929e-01 [17,] 1.228634e-04 2.457268e-04 9.998771e-01 [18,] 9.855927e-05 1.971185e-04 9.999014e-01 [19,] 1.422926e-04 2.845851e-04 9.998577e-01 [20,] 4.625667e-04 9.251335e-04 9.995374e-01 [21,] 8.955483e-04 1.791097e-03 9.991045e-01 [22,] 6.426895e-04 1.285379e-03 9.993573e-01 [23,] 5.620171e-04 1.124034e-03 9.994380e-01 [24,] 5.247807e-04 1.049561e-03 9.994752e-01 [25,] 1.467238e-02 2.934476e-02 9.853276e-01 [26,] 2.168784e-01 4.337568e-01 7.831216e-01 [27,] 5.708046e-01 8.583908e-01 4.291954e-01 [28,] 8.114731e-01 3.770538e-01 1.885269e-01 [29,] 8.653630e-01 2.692741e-01 1.346370e-01 [30,] 8.502557e-01 2.994885e-01 1.497443e-01 [31,] 8.181955e-01 3.636089e-01 1.818045e-01 [32,] 7.999442e-01 4.001116e-01 2.000558e-01 [33,] 7.702302e-01 4.595396e-01 2.297698e-01 [34,] 7.380656e-01 5.238688e-01 2.619344e-01 [35,] 7.645644e-01 4.708712e-01 2.354356e-01 [36,] 8.176976e-01 3.646049e-01 1.823024e-01 [37,] 7.881645e-01 4.236711e-01 2.118355e-01 [38,] 7.615699e-01 4.768602e-01 2.384301e-01 [39,] 7.824079e-01 4.351842e-01 2.175921e-01 [40,] 7.502223e-01 4.995554e-01 2.497777e-01 [41,] 7.314583e-01 5.370833e-01 2.685417e-01 [42,] 7.301496e-01 5.397008e-01 2.698504e-01 [43,] 7.334871e-01 5.330257e-01 2.665129e-01 [44,] 7.302842e-01 5.394317e-01 2.697158e-01 [45,] 7.049051e-01 5.901898e-01 2.950949e-01 [46,] 6.770642e-01 6.458717e-01 3.229358e-01 [47,] 6.481017e-01 7.037966e-01 3.518983e-01 [48,] 6.084541e-01 7.830918e-01 3.915459e-01 [49,] 5.835888e-01 8.328225e-01 4.164112e-01 [50,] 5.729700e-01 8.540599e-01 4.270300e-01 [51,] 5.396117e-01 9.207766e-01 4.603883e-01 [52,] 5.011106e-01 9.977789e-01 4.988894e-01 [53,] 4.750777e-01 9.501554e-01 5.249223e-01 [54,] 4.352705e-01 8.705409e-01 5.647295e-01 [55,] 4.072865e-01 8.145731e-01 5.927135e-01 [56,] 3.757333e-01 7.514665e-01 6.242667e-01 [57,] 3.465929e-01 6.931858e-01 6.534071e-01 [58,] 3.300870e-01 6.601739e-01 6.699130e-01 [59,] 2.938881e-01 5.877762e-01 7.061119e-01 [60,] 2.776442e-01 5.552885e-01 7.223558e-01 [61,] 3.280499e-01 6.560997e-01 6.719501e-01 [62,] 2.903829e-01 5.807658e-01 7.096171e-01 [63,] 2.648998e-01 5.297996e-01 7.351002e-01 [64,] 2.543529e-01 5.087058e-01 7.456471e-01 [65,] 2.438452e-01 4.876903e-01 7.561548e-01 [66,] 2.299287e-01 4.598574e-01 7.700713e-01 [67,] 2.124994e-01 4.249988e-01 7.875006e-01 [68,] 1.953415e-01 3.906830e-01 8.046585e-01 [69,] 1.804150e-01 3.608299e-01 8.195850e-01 [70,] 1.912643e-01 3.825285e-01 8.087357e-01 [71,] 2.048815e-01 4.097630e-01 7.951185e-01 [72,] 2.770802e-01 5.541603e-01 7.229198e-01 [73,] 3.420398e-01 6.840796e-01 6.579602e-01 [74,] 3.433767e-01 6.867533e-01 6.566233e-01 [75,] 3.489067e-01 6.978134e-01 6.510933e-01 [76,] 3.210959e-01 6.421918e-01 6.789041e-01 [77,] 2.868243e-01 5.736487e-01 7.131757e-01 [78,] 3.057684e-01 6.115369e-01 6.942316e-01 [79,] 3.740788e-01 7.481576e-01 6.259212e-01 [80,] 5.678820e-01 8.642360e-01 4.321180e-01 [81,] 6.361632e-01 7.276735e-01 3.638368e-01 [82,] 6.674533e-01 6.650934e-01 3.325467e-01 [83,] 6.690645e-01 6.618709e-01 3.309355e-01 [84,] 6.562187e-01 6.875625e-01 3.437813e-01 [85,] 6.329680e-01 7.340640e-01 3.670320e-01 [86,] 6.299722e-01 7.400556e-01 3.700278e-01 [87,] 6.630862e-01 6.738276e-01 3.369138e-01 [88,] 7.228348e-01 5.543305e-01 2.771652e-01 [89,] 8.039505e-01 3.920989e-01 1.960495e-01 [90,] 8.535386e-01 2.929227e-01 1.464614e-01 [91,] 9.061892e-01 1.876215e-01 9.381076e-02 [92,] 9.961249e-01 7.750111e-03 3.875055e-03 [93,] 9.999435e-01 1.129478e-04 5.647392e-05 [94,] 9.999898e-01 2.042426e-05 1.021213e-05 [95,] 9.999934e-01 1.311355e-05 6.556776e-06 [96,] 9.999933e-01 1.338954e-05 6.694770e-06 [97,] 9.999920e-01 1.592645e-05 7.963226e-06 [98,] 9.999928e-01 1.441272e-05 7.206360e-06 [99,] 9.999985e-01 2.921678e-06 1.460839e-06 [100,] 9.999998e-01 3.356903e-07 1.678452e-07 [101,] 1.000000e+00 4.635008e-08 2.317504e-08 [102,] 1.000000e+00 2.444034e-08 1.222017e-08 [103,] 1.000000e+00 6.043044e-08 3.021522e-08 [104,] 9.999999e-01 1.224465e-07 6.122325e-08 [105,] 9.999999e-01 1.646543e-07 8.232714e-08 [106,] 9.999999e-01 2.517377e-07 1.258688e-07 [107,] 9.999999e-01 2.389855e-07 1.194927e-07 [108,] 9.999999e-01 2.460618e-07 1.230309e-07 [109,] 9.999999e-01 2.832596e-07 1.416298e-07 [110,] 9.999997e-01 5.167315e-07 2.583657e-07 [111,] 9.999996e-01 8.900795e-07 4.450398e-07 [112,] 9.999995e-01 9.782517e-07 4.891258e-07 [113,] 9.999989e-01 2.245320e-06 1.122660e-06 [114,] 9.999972e-01 5.625854e-06 2.812927e-06 [115,] 9.999917e-01 1.667353e-05 8.336763e-06 [116,] 9.999920e-01 1.608365e-05 8.041825e-06 [117,] 9.999793e-01 4.147580e-05 2.073790e-05 [118,] 9.999766e-01 4.683778e-05 2.341889e-05 [119,] 9.999083e-01 1.834698e-04 9.173490e-05 [120,] 9.996674e-01 6.652633e-04 3.326317e-04 [121,] 9.996036e-01 7.928690e-04 3.964345e-04 [122,] 9.987189e-01 2.562223e-03 1.281111e-03 [123,] 9.982183e-01 3.563385e-03 1.781692e-03 [124,] 9.975957e-01 4.808663e-03 2.404331e-03 [125,] 9.898618e-01 2.027644e-02 1.013822e-02 > postscript(file="/var/www/html/rcomp/tmp/17dzq1293187912.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/27dzq1293187912.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3inyt1293187912.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4inyt1293187912.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5inyt1293187912.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 = 142 Frequency = 1 1 2 3 4 5 6 0.761741960 0.707188259 0.742487017 0.589161281 0.428755404 0.237416967 7 8 9 10 11 12 0.208312605 0.027325088 -0.258909262 -0.419769394 -0.132233696 -0.057739073 13 14 15 16 17 18 -0.291995874 -0.099028158 -0.195315148 -0.160015031 -0.261417872 -0.238394105 19 20 21 22 23 24 -0.262682146 -0.126082035 -0.031979300 -0.061006965 0.049273142 -0.205262267 25 26 27 28 29 30 -0.263036903 -0.310415240 -0.332702382 -0.173364300 -0.159665824 -0.270997771 31 32 33 34 35 36 -0.580534263 -0.601351151 -0.451836853 0.164435440 0.280116866 0.416272630 37 38 39 40 41 42 0.330018787 0.194599456 -0.068032892 -0.263046838 -0.027756283 0.029436480 43 44 45 46 47 48 0.313828662 0.438175082 0.215371226 -0.059312718 -0.294109677 -0.010920905 49 50 51 52 53 54 0.264038768 0.318531991 0.422882045 0.386911652 0.146977026 0.111379236 55 56 57 58 59 60 -0.062609378 0.014029224 0.034931129 -0.116839062 -0.024232196 0.106323691 61 62 63 64 65 66 0.163524838 -0.028387338 -0.138123426 -0.146178327 -0.199523939 -0.165462086 67 68 69 70 71 72 -0.032595332 0.284875816 0.554422580 0.204674460 0.150119023 0.023758445 73 74 75 76 77 78 0.001566144 0.146264761 0.006239917 0.031927138 0.030768485 0.173232142 79 80 81 82 83 84 0.172657057 0.293962785 0.209881302 -0.223702004 -0.374249735 -0.206706217 85 86 87 88 89 90 -0.264601040 0.012828769 0.060062320 0.208840323 -0.070954987 -0.183896191 91 92 93 94 95 96 -0.397773926 -0.395496254 -0.407615411 -0.218972057 -0.115928665 -0.112306666 97 98 99 100 101 102 -0.140437278 -0.275968991 -0.297631218 -0.116904752 -0.296943147 -0.451961188 103 104 105 106 107 108 -0.739654464 -0.863563858 -0.769563663 -0.159436321 -0.006774908 -0.254253207 109 110 111 112 113 114 -0.129108888 -0.104073787 -0.166412903 -0.181523705 0.045420857 -0.081584577 115 116 117 118 119 120 0.220154782 0.286768169 0.395743065 0.451289919 0.304741290 0.316499581 121 122 123 124 125 126 0.312819500 0.495126258 0.362152869 0.898303885 0.188214163 0.035404833 127 128 129 130 131 132 -0.053273856 -0.035505505 0.014584559 -0.091113233 -0.228933804 -0.279927283 133 134 135 136 137 138 -0.104028711 -0.160982014 -0.015561216 0.136243713 0.366449203 0.384442708 139 140 141 142 0.260193372 0.333451448 0.279818947 -0.197159501 > postscript(file="/var/www/html/rcomp/tmp/6awxe1293187912.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 = 142 Frequency = 1 lag(myerror, k = 1) myerror 0 0.761741960 NA 1 0.707188259 0.761741960 2 0.742487017 0.707188259 3 0.589161281 0.742487017 4 0.428755404 0.589161281 5 0.237416967 0.428755404 6 0.208312605 0.237416967 7 0.027325088 0.208312605 8 -0.258909262 0.027325088 9 -0.419769394 -0.258909262 10 -0.132233696 -0.419769394 11 -0.057739073 -0.132233696 12 -0.291995874 -0.057739073 13 -0.099028158 -0.291995874 14 -0.195315148 -0.099028158 15 -0.160015031 -0.195315148 16 -0.261417872 -0.160015031 17 -0.238394105 -0.261417872 18 -0.262682146 -0.238394105 19 -0.126082035 -0.262682146 20 -0.031979300 -0.126082035 21 -0.061006965 -0.031979300 22 0.049273142 -0.061006965 23 -0.205262267 0.049273142 24 -0.263036903 -0.205262267 25 -0.310415240 -0.263036903 26 -0.332702382 -0.310415240 27 -0.173364300 -0.332702382 28 -0.159665824 -0.173364300 29 -0.270997771 -0.159665824 30 -0.580534263 -0.270997771 31 -0.601351151 -0.580534263 32 -0.451836853 -0.601351151 33 0.164435440 -0.451836853 34 0.280116866 0.164435440 35 0.416272630 0.280116866 36 0.330018787 0.416272630 37 0.194599456 0.330018787 38 -0.068032892 0.194599456 39 -0.263046838 -0.068032892 40 -0.027756283 -0.263046838 41 0.029436480 -0.027756283 42 0.313828662 0.029436480 43 0.438175082 0.313828662 44 0.215371226 0.438175082 45 -0.059312718 0.215371226 46 -0.294109677 -0.059312718 47 -0.010920905 -0.294109677 48 0.264038768 -0.010920905 49 0.318531991 0.264038768 50 0.422882045 0.318531991 51 0.386911652 0.422882045 52 0.146977026 0.386911652 53 0.111379236 0.146977026 54 -0.062609378 0.111379236 55 0.014029224 -0.062609378 56 0.034931129 0.014029224 57 -0.116839062 0.034931129 58 -0.024232196 -0.116839062 59 0.106323691 -0.024232196 60 0.163524838 0.106323691 61 -0.028387338 0.163524838 62 -0.138123426 -0.028387338 63 -0.146178327 -0.138123426 64 -0.199523939 -0.146178327 65 -0.165462086 -0.199523939 66 -0.032595332 -0.165462086 67 0.284875816 -0.032595332 68 0.554422580 0.284875816 69 0.204674460 0.554422580 70 0.150119023 0.204674460 71 0.023758445 0.150119023 72 0.001566144 0.023758445 73 0.146264761 0.001566144 74 0.006239917 0.146264761 75 0.031927138 0.006239917 76 0.030768485 0.031927138 77 0.173232142 0.030768485 78 0.172657057 0.173232142 79 0.293962785 0.172657057 80 0.209881302 0.293962785 81 -0.223702004 0.209881302 82 -0.374249735 -0.223702004 83 -0.206706217 -0.374249735 84 -0.264601040 -0.206706217 85 0.012828769 -0.264601040 86 0.060062320 0.012828769 87 0.208840323 0.060062320 88 -0.070954987 0.208840323 89 -0.183896191 -0.070954987 90 -0.397773926 -0.183896191 91 -0.395496254 -0.397773926 92 -0.407615411 -0.395496254 93 -0.218972057 -0.407615411 94 -0.115928665 -0.218972057 95 -0.112306666 -0.115928665 96 -0.140437278 -0.112306666 97 -0.275968991 -0.140437278 98 -0.297631218 -0.275968991 99 -0.116904752 -0.297631218 100 -0.296943147 -0.116904752 101 -0.451961188 -0.296943147 102 -0.739654464 -0.451961188 103 -0.863563858 -0.739654464 104 -0.769563663 -0.863563858 105 -0.159436321 -0.769563663 106 -0.006774908 -0.159436321 107 -0.254253207 -0.006774908 108 -0.129108888 -0.254253207 109 -0.104073787 -0.129108888 110 -0.166412903 -0.104073787 111 -0.181523705 -0.166412903 112 0.045420857 -0.181523705 113 -0.081584577 0.045420857 114 0.220154782 -0.081584577 115 0.286768169 0.220154782 116 0.395743065 0.286768169 117 0.451289919 0.395743065 118 0.304741290 0.451289919 119 0.316499581 0.304741290 120 0.312819500 0.316499581 121 0.495126258 0.312819500 122 0.362152869 0.495126258 123 0.898303885 0.362152869 124 0.188214163 0.898303885 125 0.035404833 0.188214163 126 -0.053273856 0.035404833 127 -0.035505505 -0.053273856 128 0.014584559 -0.035505505 129 -0.091113233 0.014584559 130 -0.228933804 -0.091113233 131 -0.279927283 -0.228933804 132 -0.104028711 -0.279927283 133 -0.160982014 -0.104028711 134 -0.015561216 -0.160982014 135 0.136243713 -0.015561216 136 0.366449203 0.136243713 137 0.384442708 0.366449203 138 0.260193372 0.384442708 139 0.333451448 0.260193372 140 0.279818947 0.333451448 141 -0.197159501 0.279818947 142 NA -0.197159501 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.707188259 0.761741960 [2,] 0.742487017 0.707188259 [3,] 0.589161281 0.742487017 [4,] 0.428755404 0.589161281 [5,] 0.237416967 0.428755404 [6,] 0.208312605 0.237416967 [7,] 0.027325088 0.208312605 [8,] -0.258909262 0.027325088 [9,] -0.419769394 -0.258909262 [10,] -0.132233696 -0.419769394 [11,] -0.057739073 -0.132233696 [12,] -0.291995874 -0.057739073 [13,] -0.099028158 -0.291995874 [14,] -0.195315148 -0.099028158 [15,] -0.160015031 -0.195315148 [16,] -0.261417872 -0.160015031 [17,] -0.238394105 -0.261417872 [18,] -0.262682146 -0.238394105 [19,] -0.126082035 -0.262682146 [20,] -0.031979300 -0.126082035 [21,] -0.061006965 -0.031979300 [22,] 0.049273142 -0.061006965 [23,] -0.205262267 0.049273142 [24,] -0.263036903 -0.205262267 [25,] -0.310415240 -0.263036903 [26,] -0.332702382 -0.310415240 [27,] -0.173364300 -0.332702382 [28,] -0.159665824 -0.173364300 [29,] -0.270997771 -0.159665824 [30,] -0.580534263 -0.270997771 [31,] -0.601351151 -0.580534263 [32,] -0.451836853 -0.601351151 [33,] 0.164435440 -0.451836853 [34,] 0.280116866 0.164435440 [35,] 0.416272630 0.280116866 [36,] 0.330018787 0.416272630 [37,] 0.194599456 0.330018787 [38,] -0.068032892 0.194599456 [39,] -0.263046838 -0.068032892 [40,] -0.027756283 -0.263046838 [41,] 0.029436480 -0.027756283 [42,] 0.313828662 0.029436480 [43,] 0.438175082 0.313828662 [44,] 0.215371226 0.438175082 [45,] -0.059312718 0.215371226 [46,] -0.294109677 -0.059312718 [47,] -0.010920905 -0.294109677 [48,] 0.264038768 -0.010920905 [49,] 0.318531991 0.264038768 [50,] 0.422882045 0.318531991 [51,] 0.386911652 0.422882045 [52,] 0.146977026 0.386911652 [53,] 0.111379236 0.146977026 [54,] -0.062609378 0.111379236 [55,] 0.014029224 -0.062609378 [56,] 0.034931129 0.014029224 [57,] -0.116839062 0.034931129 [58,] -0.024232196 -0.116839062 [59,] 0.106323691 -0.024232196 [60,] 0.163524838 0.106323691 [61,] -0.028387338 0.163524838 [62,] -0.138123426 -0.028387338 [63,] -0.146178327 -0.138123426 [64,] -0.199523939 -0.146178327 [65,] -0.165462086 -0.199523939 [66,] -0.032595332 -0.165462086 [67,] 0.284875816 -0.032595332 [68,] 0.554422580 0.284875816 [69,] 0.204674460 0.554422580 [70,] 0.150119023 0.204674460 [71,] 0.023758445 0.150119023 [72,] 0.001566144 0.023758445 [73,] 0.146264761 0.001566144 [74,] 0.006239917 0.146264761 [75,] 0.031927138 0.006239917 [76,] 0.030768485 0.031927138 [77,] 0.173232142 0.030768485 [78,] 0.172657057 0.173232142 [79,] 0.293962785 0.172657057 [80,] 0.209881302 0.293962785 [81,] -0.223702004 0.209881302 [82,] -0.374249735 -0.223702004 [83,] -0.206706217 -0.374249735 [84,] -0.264601040 -0.206706217 [85,] 0.012828769 -0.264601040 [86,] 0.060062320 0.012828769 [87,] 0.208840323 0.060062320 [88,] -0.070954987 0.208840323 [89,] -0.183896191 -0.070954987 [90,] -0.397773926 -0.183896191 [91,] -0.395496254 -0.397773926 [92,] -0.407615411 -0.395496254 [93,] -0.218972057 -0.407615411 [94,] -0.115928665 -0.218972057 [95,] -0.112306666 -0.115928665 [96,] -0.140437278 -0.112306666 [97,] -0.275968991 -0.140437278 [98,] -0.297631218 -0.275968991 [99,] -0.116904752 -0.297631218 [100,] -0.296943147 -0.116904752 [101,] -0.451961188 -0.296943147 [102,] -0.739654464 -0.451961188 [103,] -0.863563858 -0.739654464 [104,] -0.769563663 -0.863563858 [105,] -0.159436321 -0.769563663 [106,] -0.006774908 -0.159436321 [107,] -0.254253207 -0.006774908 [108,] -0.129108888 -0.254253207 [109,] -0.104073787 -0.129108888 [110,] -0.166412903 -0.104073787 [111,] -0.181523705 -0.166412903 [112,] 0.045420857 -0.181523705 [113,] -0.081584577 0.045420857 [114,] 0.220154782 -0.081584577 [115,] 0.286768169 0.220154782 [116,] 0.395743065 0.286768169 [117,] 0.451289919 0.395743065 [118,] 0.304741290 0.451289919 [119,] 0.316499581 0.304741290 [120,] 0.312819500 0.316499581 [121,] 0.495126258 0.312819500 [122,] 0.362152869 0.495126258 [123,] 0.898303885 0.362152869 [124,] 0.188214163 0.898303885 [125,] 0.035404833 0.188214163 [126,] -0.053273856 0.035404833 [127,] -0.035505505 -0.053273856 [128,] 0.014584559 -0.035505505 [129,] -0.091113233 0.014584559 [130,] -0.228933804 -0.091113233 [131,] -0.279927283 -0.228933804 [132,] -0.104028711 -0.279927283 [133,] -0.160982014 -0.104028711 [134,] -0.015561216 -0.160982014 [135,] 0.136243713 -0.015561216 [136,] 0.366449203 0.136243713 [137,] 0.384442708 0.366449203 [138,] 0.260193372 0.384442708 [139,] 0.333451448 0.260193372 [140,] 0.279818947 0.333451448 [141,] -0.197159501 0.279818947 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.707188259 0.761741960 2 0.742487017 0.707188259 3 0.589161281 0.742487017 4 0.428755404 0.589161281 5 0.237416967 0.428755404 6 0.208312605 0.237416967 7 0.027325088 0.208312605 8 -0.258909262 0.027325088 9 -0.419769394 -0.258909262 10 -0.132233696 -0.419769394 11 -0.057739073 -0.132233696 12 -0.291995874 -0.057739073 13 -0.099028158 -0.291995874 14 -0.195315148 -0.099028158 15 -0.160015031 -0.195315148 16 -0.261417872 -0.160015031 17 -0.238394105 -0.261417872 18 -0.262682146 -0.238394105 19 -0.126082035 -0.262682146 20 -0.031979300 -0.126082035 21 -0.061006965 -0.031979300 22 0.049273142 -0.061006965 23 -0.205262267 0.049273142 24 -0.263036903 -0.205262267 25 -0.310415240 -0.263036903 26 -0.332702382 -0.310415240 27 -0.173364300 -0.332702382 28 -0.159665824 -0.173364300 29 -0.270997771 -0.159665824 30 -0.580534263 -0.270997771 31 -0.601351151 -0.580534263 32 -0.451836853 -0.601351151 33 0.164435440 -0.451836853 34 0.280116866 0.164435440 35 0.416272630 0.280116866 36 0.330018787 0.416272630 37 0.194599456 0.330018787 38 -0.068032892 0.194599456 39 -0.263046838 -0.068032892 40 -0.027756283 -0.263046838 41 0.029436480 -0.027756283 42 0.313828662 0.029436480 43 0.438175082 0.313828662 44 0.215371226 0.438175082 45 -0.059312718 0.215371226 46 -0.294109677 -0.059312718 47 -0.010920905 -0.294109677 48 0.264038768 -0.010920905 49 0.318531991 0.264038768 50 0.422882045 0.318531991 51 0.386911652 0.422882045 52 0.146977026 0.386911652 53 0.111379236 0.146977026 54 -0.062609378 0.111379236 55 0.014029224 -0.062609378 56 0.034931129 0.014029224 57 -0.116839062 0.034931129 58 -0.024232196 -0.116839062 59 0.106323691 -0.024232196 60 0.163524838 0.106323691 61 -0.028387338 0.163524838 62 -0.138123426 -0.028387338 63 -0.146178327 -0.138123426 64 -0.199523939 -0.146178327 65 -0.165462086 -0.199523939 66 -0.032595332 -0.165462086 67 0.284875816 -0.032595332 68 0.554422580 0.284875816 69 0.204674460 0.554422580 70 0.150119023 0.204674460 71 0.023758445 0.150119023 72 0.001566144 0.023758445 73 0.146264761 0.001566144 74 0.006239917 0.146264761 75 0.031927138 0.006239917 76 0.030768485 0.031927138 77 0.173232142 0.030768485 78 0.172657057 0.173232142 79 0.293962785 0.172657057 80 0.209881302 0.293962785 81 -0.223702004 0.209881302 82 -0.374249735 -0.223702004 83 -0.206706217 -0.374249735 84 -0.264601040 -0.206706217 85 0.012828769 -0.264601040 86 0.060062320 0.012828769 87 0.208840323 0.060062320 88 -0.070954987 0.208840323 89 -0.183896191 -0.070954987 90 -0.397773926 -0.183896191 91 -0.395496254 -0.397773926 92 -0.407615411 -0.395496254 93 -0.218972057 -0.407615411 94 -0.115928665 -0.218972057 95 -0.112306666 -0.115928665 96 -0.140437278 -0.112306666 97 -0.275968991 -0.140437278 98 -0.297631218 -0.275968991 99 -0.116904752 -0.297631218 100 -0.296943147 -0.116904752 101 -0.451961188 -0.296943147 102 -0.739654464 -0.451961188 103 -0.863563858 -0.739654464 104 -0.769563663 -0.863563858 105 -0.159436321 -0.769563663 106 -0.006774908 -0.159436321 107 -0.254253207 -0.006774908 108 -0.129108888 -0.254253207 109 -0.104073787 -0.129108888 110 -0.166412903 -0.104073787 111 -0.181523705 -0.166412903 112 0.045420857 -0.181523705 113 -0.081584577 0.045420857 114 0.220154782 -0.081584577 115 0.286768169 0.220154782 116 0.395743065 0.286768169 117 0.451289919 0.395743065 118 0.304741290 0.451289919 119 0.316499581 0.304741290 120 0.312819500 0.316499581 121 0.495126258 0.312819500 122 0.362152869 0.495126258 123 0.898303885 0.362152869 124 0.188214163 0.898303885 125 0.035404833 0.188214163 126 -0.053273856 0.035404833 127 -0.035505505 -0.053273856 128 0.014584559 -0.035505505 129 -0.091113233 0.014584559 130 -0.228933804 -0.091113233 131 -0.279927283 -0.228933804 132 -0.104028711 -0.279927283 133 -0.160982014 -0.104028711 134 -0.015561216 -0.160982014 135 0.136243713 -0.015561216 136 0.366449203 0.136243713 137 0.384442708 0.366449203 138 0.260193372 0.384442708 139 0.333451448 0.260193372 140 0.279818947 0.333451448 141 -0.197159501 0.279818947 > 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/7lnwz1293187912.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8lnwz1293187912.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9lnwz1293187912.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10eeek1293187912.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11zfc81293187912.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/12lgbw1293187912.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/13hp841293187912.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/142q7s1293187912.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/15n85g1293187912.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/16r9441293187912.tab") + } > > try(system("convert tmp/17dzq1293187912.ps tmp/17dzq1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/27dzq1293187912.ps tmp/27dzq1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/3inyt1293187912.ps tmp/3inyt1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/4inyt1293187912.ps tmp/4inyt1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/5inyt1293187912.ps tmp/5inyt1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/6awxe1293187912.ps tmp/6awxe1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/7lnwz1293187912.ps tmp/7lnwz1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/8lnwz1293187912.ps tmp/8lnwz1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/9lnwz1293187912.ps tmp/9lnwz1293187912.png",intern=TRUE)) character(0) > try(system("convert tmp/10eeek1293187912.ps tmp/10eeek1293187912.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.819 1.762 8.729