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Type 'q()' to quit R. > x <- array(list(98.3 + ,0 + ,91.6 + ,104.6 + ,111.6 + ,97.7 + ,0 + ,98.3 + ,91.6 + ,104.6 + ,106.3 + ,0 + ,97.7 + ,98.3 + ,91.6 + ,102.3 + ,0 + ,106.3 + ,97.7 + ,98.3 + ,106.6 + ,0 + ,102.3 + ,106.3 + ,97.7 + ,108.1 + ,0 + ,106.6 + ,102.3 + ,106.3 + ,93.8 + ,0 + ,108.1 + ,106.6 + ,102.3 + ,88.2 + ,0 + ,93.8 + ,108.1 + ,106.6 + ,108.9 + ,0 + ,88.2 + ,93.8 + ,108.1 + ,114.2 + ,0 + ,108.9 + ,88.2 + ,93.8 + ,102.5 + ,0 + ,114.2 + ,108.9 + ,88.2 + ,94.2 + ,0 + ,102.5 + ,114.2 + ,108.9 + ,97.4 + ,0 + ,94.2 + ,102.5 + ,114.2 + ,98.5 + ,0 + ,97.4 + ,94.2 + ,102.5 + ,106.5 + ,0 + ,98.5 + ,97.4 + ,94.2 + ,102.9 + ,0 + ,106.5 + ,98.5 + ,97.4 + ,97.1 + ,0 + ,102.9 + ,106.5 + ,98.5 + ,103.7 + ,0 + ,97.1 + ,102.9 + ,106.5 + ,93.4 + ,0 + ,103.7 + ,97.1 + ,102.9 + ,85.8 + ,0 + ,93.4 + ,103.7 + ,97.1 + ,108.6 + ,0 + ,85.8 + ,93.4 + ,103.7 + ,110.2 + ,0 + ,108.6 + ,85.8 + ,93.4 + ,101.2 + ,0 + ,110.2 + ,108.6 + ,85.8 + ,101.2 + ,0 + ,101.2 + ,110.2 + ,108.6 + ,96.9 + ,0 + ,101.2 + ,101.2 + ,110.2 + ,99.4 + ,0 + ,96.9 + ,101.2 + ,101.2 + ,118.7 + ,0 + ,99.4 + ,96.9 + ,101.2 + ,108.0 + ,0 + ,118.7 + ,99.4 + ,96.9 + ,101.2 + ,0 + ,108.0 + ,118.7 + ,99.4 + ,119.9 + ,0 + ,101.2 + ,108.0 + ,118.7 + ,94.8 + ,0 + ,119.9 + ,101.2 + ,108.0 + ,95.3 + ,0 + ,94.8 + ,119.9 + ,101.2 + ,118.0 + ,0 + ,95.3 + ,94.8 + ,119.9 + ,115.9 + ,0 + ,118.0 + ,95.3 + ,94.8 + ,111.4 + ,0 + ,115.9 + ,118.0 + ,95.3 + ,108.2 + ,0 + ,111.4 + ,115.9 + ,118.0 + ,108.8 + ,0 + ,108.2 + ,111.4 + ,115.9 + ,109.5 + ,0 + ,108.8 + ,108.2 + ,111.4 + ,124.8 + ,0 + ,109.5 + ,108.8 + ,108.2 + ,115.3 + ,0 + ,124.8 + ,109.5 + ,108.8 + ,109.5 + ,0 + ,115.3 + ,124.8 + ,109.5 + ,124.2 + ,0 + ,109.5 + ,115.3 + ,124.8 + ,92.9 + ,0 + ,124.2 + ,109.5 + ,115.3 + ,98.4 + ,0 + ,92.9 + ,124.2 + ,109.5 + ,120.9 + ,0 + ,98.4 + ,92.9 + ,124.2 + ,111.7 + ,0 + ,120.9 + ,98.4 + ,92.9 + ,116.1 + ,0 + ,111.7 + ,120.9 + ,98.4 + ,109.4 + ,0 + ,116.1 + ,111.7 + ,120.9 + ,111.7 + ,0 + ,109.4 + ,116.1 + ,111.7 + ,114.3 + ,0 + ,111.7 + ,109.4 + ,116.1 + ,133.7 + ,0 + ,114.3 + ,111.7 + ,109.4 + ,114.3 + ,0 + ,133.7 + ,114.3 + ,111.7 + ,126.5 + ,0 + ,114.3 + ,133.7 + ,114.3 + ,131.0 + ,0 + ,126.5 + ,114.3 + ,133.7 + ,104.0 + ,0 + ,131.0 + ,126.5 + ,114.3 + ,108.9 + ,0 + ,104.0 + ,131.0 + ,126.5 + ,128.5 + ,0 + ,108.9 + ,104.0 + ,131.0 + ,132.4 + ,0 + ,128.5 + ,108.9 + ,104.0 + ,128.0 + ,0 + ,132.4 + ,128.5 + ,108.9 + ,116.4 + ,0 + ,128.0 + ,132.4 + ,128.5 + ,120.9 + ,0 + ,116.4 + ,128.0 + ,132.4 + ,118.6 + ,0 + ,120.9 + ,116.4 + ,128.0 + ,133.1 + ,0 + ,118.6 + ,120.9 + ,116.4 + ,121.1 + ,0 + ,133.1 + ,118.6 + ,120.9 + ,127.6 + ,0 + ,121.1 + ,133.1 + ,118.6 + ,135.4 + ,0 + ,127.6 + ,121.1 + ,133.1 + ,114.9 + ,0 + ,135.4 + ,127.6 + ,121.1 + ,114.3 + ,0 + ,114.9 + ,135.4 + ,127.6 + ,128.9 + ,0 + ,114.3 + ,114.9 + ,135.4 + ,138.9 + ,0 + ,128.9 + ,114.3 + ,114.9 + ,129.4 + ,0 + ,138.9 + ,128.9 + ,114.3 + ,115.0 + ,0 + ,129.4 + ,138.9 + ,128.9 + ,128.0 + ,0 + ,115.0 + ,129.4 + ,138.9 + ,127.0 + ,0 + ,128.0 + ,115.0 + ,129.4 + ,128.8 + ,0 + ,127.0 + ,128.0 + ,115.0 + ,137.9 + ,0 + ,128.8 + ,127.0 + ,128.0 + ,128.4 + ,0 + ,137.9 + ,128.8 + ,127.0 + ,135.9 + ,0 + ,128.4 + ,137.9 + ,128.8 + ,122.2 + ,0 + ,135.9 + ,128.4 + ,137.9 + ,113.1 + ,0 + ,122.2 + ,135.9 + ,128.4 + ,136.2 + ,1 + ,113.1 + ,122.2 + ,135.9 + ,138.0 + ,1 + ,136.2 + ,113.1 + ,122.2 + ,115.2 + ,1 + ,138.0 + ,136.2 + ,113.1 + ,111.0 + ,1 + ,115.2 + ,138.0 + ,136.2 + ,99.2 + ,1 + ,111.0 + ,115.2 + ,138.0 + ,102.4 + ,1 + ,99.2 + ,111.0 + ,115.2 + ,112.7 + ,1 + ,102.4 + ,99.2 + ,111.0 + ,105.5 + ,1 + ,112.7 + ,102.4 + ,99.2 + ,98.3 + ,1 + ,105.5 + ,112.7 + ,102.4 + ,116.4 + ,1 + ,98.3 + ,105.5 + ,112.7 + ,97.4 + ,1 + ,116.4 + ,98.3 + ,105.5 + ,93.3 + ,1 + ,97.4 + ,116.4 + ,98.3 + ,117.4 + ,1 + ,93.3 + ,97.4 + ,116.4) + ,dim=c(5 + ,93) + ,dimnames=list(c('y' + ,'dummy' + ,'y1' + ,'y2' + ,'y3') + ,1:93)) > y <- array(NA,dim=c(5,93),dimnames=list(c('y','dummy','y1','y2','y3'),1:93)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo 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 y dummy y1 y2 y3 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 98.3 0 91.6 104.6 111.6 1 0 0 0 0 0 0 0 0 0 0 1 2 97.7 0 98.3 91.6 104.6 0 1 0 0 0 0 0 0 0 0 0 2 3 106.3 0 97.7 98.3 91.6 0 0 1 0 0 0 0 0 0 0 0 3 4 102.3 0 106.3 97.7 98.3 0 0 0 1 0 0 0 0 0 0 0 4 5 106.6 0 102.3 106.3 97.7 0 0 0 0 1 0 0 0 0 0 0 5 6 108.1 0 106.6 102.3 106.3 0 0 0 0 0 1 0 0 0 0 0 6 7 93.8 0 108.1 106.6 102.3 0 0 0 0 0 0 1 0 0 0 0 7 8 88.2 0 93.8 108.1 106.6 0 0 0 0 0 0 0 1 0 0 0 8 9 108.9 0 88.2 93.8 108.1 0 0 0 0 0 0 0 0 1 0 0 9 10 114.2 0 108.9 88.2 93.8 0 0 0 0 0 0 0 0 0 1 0 10 11 102.5 0 114.2 108.9 88.2 0 0 0 0 0 0 0 0 0 0 1 11 12 94.2 0 102.5 114.2 108.9 0 0 0 0 0 0 0 0 0 0 0 12 13 97.4 0 94.2 102.5 114.2 1 0 0 0 0 0 0 0 0 0 0 13 14 98.5 0 97.4 94.2 102.5 0 1 0 0 0 0 0 0 0 0 0 14 15 106.5 0 98.5 97.4 94.2 0 0 1 0 0 0 0 0 0 0 0 15 16 102.9 0 106.5 98.5 97.4 0 0 0 1 0 0 0 0 0 0 0 16 17 97.1 0 102.9 106.5 98.5 0 0 0 0 1 0 0 0 0 0 0 17 18 103.7 0 97.1 102.9 106.5 0 0 0 0 0 1 0 0 0 0 0 18 19 93.4 0 103.7 97.1 102.9 0 0 0 0 0 0 1 0 0 0 0 19 20 85.8 0 93.4 103.7 97.1 0 0 0 0 0 0 0 1 0 0 0 20 21 108.6 0 85.8 93.4 103.7 0 0 0 0 0 0 0 0 1 0 0 21 22 110.2 0 108.6 85.8 93.4 0 0 0 0 0 0 0 0 0 1 0 22 23 101.2 0 110.2 108.6 85.8 0 0 0 0 0 0 0 0 0 0 1 23 24 101.2 0 101.2 110.2 108.6 0 0 0 0 0 0 0 0 0 0 0 24 25 96.9 0 101.2 101.2 110.2 1 0 0 0 0 0 0 0 0 0 0 25 26 99.4 0 96.9 101.2 101.2 0 1 0 0 0 0 0 0 0 0 0 26 27 118.7 0 99.4 96.9 101.2 0 0 1 0 0 0 0 0 0 0 0 27 28 108.0 0 118.7 99.4 96.9 0 0 0 1 0 0 0 0 0 0 0 28 29 101.2 0 108.0 118.7 99.4 0 0 0 0 1 0 0 0 0 0 0 29 30 119.9 0 101.2 108.0 118.7 0 0 0 0 0 1 0 0 0 0 0 30 31 94.8 0 119.9 101.2 108.0 0 0 0 0 0 0 1 0 0 0 0 31 32 95.3 0 94.8 119.9 101.2 0 0 0 0 0 0 0 1 0 0 0 32 33 118.0 0 95.3 94.8 119.9 0 0 0 0 0 0 0 0 1 0 0 33 34 115.9 0 118.0 95.3 94.8 0 0 0 0 0 0 0 0 0 1 0 34 35 111.4 0 115.9 118.0 95.3 0 0 0 0 0 0 0 0 0 0 1 35 36 108.2 0 111.4 115.9 118.0 0 0 0 0 0 0 0 0 0 0 0 36 37 108.8 0 108.2 111.4 115.9 1 0 0 0 0 0 0 0 0 0 0 37 38 109.5 0 108.8 108.2 111.4 0 1 0 0 0 0 0 0 0 0 0 38 39 124.8 0 109.5 108.8 108.2 0 0 1 0 0 0 0 0 0 0 0 39 40 115.3 0 124.8 109.5 108.8 0 0 0 1 0 0 0 0 0 0 0 40 41 109.5 0 115.3 124.8 109.5 0 0 0 0 1 0 0 0 0 0 0 41 42 124.2 0 109.5 115.3 124.8 0 0 0 0 0 1 0 0 0 0 0 42 43 92.9 0 124.2 109.5 115.3 0 0 0 0 0 0 1 0 0 0 0 43 44 98.4 0 92.9 124.2 109.5 0 0 0 0 0 0 0 1 0 0 0 44 45 120.9 0 98.4 92.9 124.2 0 0 0 0 0 0 0 0 1 0 0 45 46 111.7 0 120.9 98.4 92.9 0 0 0 0 0 0 0 0 0 1 0 46 47 116.1 0 111.7 120.9 98.4 0 0 0 0 0 0 0 0 0 0 1 47 48 109.4 0 116.1 111.7 120.9 0 0 0 0 0 0 0 0 0 0 0 48 49 111.7 0 109.4 116.1 111.7 1 0 0 0 0 0 0 0 0 0 0 49 50 114.3 0 111.7 109.4 116.1 0 1 0 0 0 0 0 0 0 0 0 50 51 133.7 0 114.3 111.7 109.4 0 0 1 0 0 0 0 0 0 0 0 51 52 114.3 0 133.7 114.3 111.7 0 0 0 1 0 0 0 0 0 0 0 52 53 126.5 0 114.3 133.7 114.3 0 0 0 0 1 0 0 0 0 0 0 53 54 131.0 0 126.5 114.3 133.7 0 0 0 0 0 1 0 0 0 0 0 54 55 104.0 0 131.0 126.5 114.3 0 0 0 0 0 0 1 0 0 0 0 55 56 108.9 0 104.0 131.0 126.5 0 0 0 0 0 0 0 1 0 0 0 56 57 128.5 0 108.9 104.0 131.0 0 0 0 0 0 0 0 0 1 0 0 57 58 132.4 0 128.5 108.9 104.0 0 0 0 0 0 0 0 0 0 1 0 58 59 128.0 0 132.4 128.5 108.9 0 0 0 0 0 0 0 0 0 0 1 59 60 116.4 0 128.0 132.4 128.5 0 0 0 0 0 0 0 0 0 0 0 60 61 120.9 0 116.4 128.0 132.4 1 0 0 0 0 0 0 0 0 0 0 61 62 118.6 0 120.9 116.4 128.0 0 1 0 0 0 0 0 0 0 0 0 62 63 133.1 0 118.6 120.9 116.4 0 0 1 0 0 0 0 0 0 0 0 63 64 121.1 0 133.1 118.6 120.9 0 0 0 1 0 0 0 0 0 0 0 64 65 127.6 0 121.1 133.1 118.6 0 0 0 0 1 0 0 0 0 0 0 65 66 135.4 0 127.6 121.1 133.1 0 0 0 0 0 1 0 0 0 0 0 66 67 114.9 0 135.4 127.6 121.1 0 0 0 0 0 0 1 0 0 0 0 67 68 114.3 0 114.9 135.4 127.6 0 0 0 0 0 0 0 1 0 0 0 68 69 128.9 0 114.3 114.9 135.4 0 0 0 0 0 0 0 0 1 0 0 69 70 138.9 0 128.9 114.3 114.9 0 0 0 0 0 0 0 0 0 1 0 70 71 129.4 0 138.9 128.9 114.3 0 0 0 0 0 0 0 0 0 0 1 71 72 115.0 0 129.4 138.9 128.9 0 0 0 0 0 0 0 0 0 0 0 72 73 128.0 0 115.0 129.4 138.9 1 0 0 0 0 0 0 0 0 0 0 73 74 127.0 0 128.0 115.0 129.4 0 1 0 0 0 0 0 0 0 0 0 74 75 128.8 0 127.0 128.0 115.0 0 0 1 0 0 0 0 0 0 0 0 75 76 137.9 0 128.8 127.0 128.0 0 0 0 1 0 0 0 0 0 0 0 76 77 128.4 0 137.9 128.8 127.0 0 0 0 0 1 0 0 0 0 0 0 77 78 135.9 0 128.4 137.9 128.8 0 0 0 0 0 1 0 0 0 0 0 78 79 122.2 0 135.9 128.4 137.9 0 0 0 0 0 0 1 0 0 0 0 79 80 113.1 0 122.2 135.9 128.4 0 0 0 0 0 0 0 1 0 0 0 80 81 136.2 1 113.1 122.2 135.9 0 0 0 0 0 0 0 0 1 0 0 81 82 138.0 1 136.2 113.1 122.2 0 0 0 0 0 0 0 0 0 1 0 82 83 115.2 1 138.0 136.2 113.1 0 0 0 0 0 0 0 0 0 0 1 83 84 111.0 1 115.2 138.0 136.2 0 0 0 0 0 0 0 0 0 0 0 84 85 99.2 1 111.0 115.2 138.0 1 0 0 0 0 0 0 0 0 0 0 85 86 102.4 1 99.2 111.0 115.2 0 1 0 0 0 0 0 0 0 0 0 86 87 112.7 1 102.4 99.2 111.0 0 0 1 0 0 0 0 0 0 0 0 87 88 105.5 1 112.7 102.4 99.2 0 0 0 1 0 0 0 0 0 0 0 88 89 98.3 1 105.5 112.7 102.4 0 0 0 0 1 0 0 0 0 0 0 89 90 116.4 1 98.3 105.5 112.7 0 0 0 0 0 1 0 0 0 0 0 90 91 97.4 1 116.4 98.3 105.5 0 0 0 0 0 0 1 0 0 0 0 91 92 93.3 1 97.4 116.4 98.3 0 0 0 0 0 0 0 1 0 0 0 92 93 117.4 1 93.3 97.4 116.4 0 0 0 0 0 0 0 0 1 0 0 93 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) dummy y1 y2 y3 M1 19.98699 -11.43923 -0.01536 0.25279 0.43672 2.46240 M2 M3 M4 M5 M6 M7 8.57600 23.49119 15.39443 10.21225 16.46864 -0.16839 M8 M9 M10 M11 t -4.48865 18.78221 29.57012 16.70707 0.14983 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -12.2288 -2.4300 0.1207 2.4045 8.3231 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 19.98699 9.29762 2.150 0.034762 * dummy -11.43923 2.35897 -4.849 6.43e-06 *** y1 -0.01536 0.09979 -0.154 0.878057 y2 0.25279 0.08924 2.833 0.005908 ** y3 0.43672 0.09292 4.700 1.14e-05 *** M1 2.46240 2.41992 1.018 0.312118 M2 8.57600 2.50023 3.430 0.000978 *** M3 23.49119 2.54025 9.248 4.49e-14 *** M4 15.39443 2.89210 5.323 1.00e-06 *** M5 10.21225 2.45990 4.151 8.55e-05 *** M6 16.46864 2.35478 6.994 9.02e-10 *** M7 -0.16839 2.68884 -0.063 0.950229 M8 -4.48865 2.53853 -1.768 0.081039 . M9 18.78221 3.02333 6.212 2.56e-08 *** M10 29.57012 3.45674 8.554 9.54e-13 *** M11 16.70707 3.30087 5.061 2.82e-06 *** t 0.14983 0.04277 3.503 0.000774 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.272 on 76 degrees of freedom Multiple R-squared: 0.9123, Adjusted R-squared: 0.8938 F-statistic: 49.41 on 16 and 76 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,] 0.406527314 0.813054627 0.5934727 [2,] 0.256146499 0.512292998 0.7438535 [3,] 0.160772739 0.321545478 0.8392273 [4,] 0.088220611 0.176441223 0.9117794 [5,] 0.173344134 0.346688268 0.8266559 [6,] 0.105172898 0.210345795 0.8948271 [7,] 0.081597542 0.163195084 0.9184025 [8,] 0.198340680 0.396681359 0.8016593 [9,] 0.145770417 0.291540834 0.8542296 [10,] 0.119025806 0.238051611 0.8809742 [11,] 0.104719837 0.209439674 0.8952802 [12,] 0.103862716 0.207725431 0.8961373 [13,] 0.107370677 0.214741354 0.8926293 [14,] 0.073547010 0.147094019 0.9264530 [15,] 0.049451490 0.098902981 0.9505485 [16,] 0.031527665 0.063055330 0.9684723 [17,] 0.022841678 0.045683356 0.9771583 [18,] 0.020921556 0.041843111 0.9790784 [19,] 0.012616694 0.025233388 0.9873833 [20,] 0.007874712 0.015749424 0.9921253 [21,] 0.004898958 0.009797915 0.9951010 [22,] 0.004902188 0.009804375 0.9950978 [23,] 0.002728085 0.005456169 0.9972719 [24,] 0.025724558 0.051449117 0.9742754 [25,] 0.017034393 0.034068785 0.9829656 [26,] 0.010847317 0.021694635 0.9891527 [27,] 0.030343664 0.060687328 0.9696563 [28,] 0.022130744 0.044261488 0.9778693 [29,] 0.015394963 0.030789927 0.9846050 [30,] 0.024512060 0.049024121 0.9754879 [31,] 0.016273579 0.032547159 0.9837264 [32,] 0.051829076 0.103658153 0.9481709 [33,] 0.050861013 0.101722027 0.9491390 [34,] 0.070595488 0.141190976 0.9294045 [35,] 0.049754465 0.099508929 0.9502455 [36,] 0.052928186 0.105856371 0.9470718 [37,] 0.042810755 0.085621510 0.9571892 [38,] 0.030005392 0.060010784 0.9699946 [39,] 0.030989283 0.061978566 0.9690107 [40,] 0.025479440 0.050958880 0.9745206 [41,] 0.018167168 0.036334336 0.9818328 [42,] 0.013546425 0.027092850 0.9864536 [43,] 0.010007218 0.020014435 0.9899928 [44,] 0.008299495 0.016598991 0.9917005 [45,] 0.010850939 0.021701878 0.9891491 [46,] 0.011511611 0.023023222 0.9884884 [47,] 0.006887982 0.013775964 0.9931120 [48,] 0.003985865 0.007971729 0.9960141 [49,] 0.001976861 0.003953721 0.9980231 [50,] 0.022016931 0.044033862 0.9779831 [51,] 0.048401276 0.096802551 0.9515987 [52,] 0.033858183 0.067716366 0.9661418 [53,] 0.373504642 0.747009284 0.6264954 [54,] 0.246324882 0.492649765 0.7536751 > postscript(file="/var/www/html/rcomp/tmp/1p9jb1262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2c47v1262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/37p091262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/486zn1262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5ug2f1262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 93 Frequency = 1 1 2 3 4 5 6 1.92892775 1.51166192 -0.97891013 0.32579703 7.68475878 0.09996870 7 8 9 10 11 12 2.97009871 -0.93622977 -0.78319838 1.55773319 -0.13467385 -2.43700806 13 14 15 16 17 18 -1.33376415 0.75967997 -3.47259825 -0.67833256 -4.00397435 -6.48301361 19 20 21 22 23 24 2.84391679 0.12068174 -0.89541647 -3.46352218 -2.17018671 3.88715915 25 26 27 28 29 30 -1.44875065 -1.34778853 4.01257748 2.80192723 -5.10067571 1.36479066 31 32 33 34 35 36 -0.56890571 1.95849755 -0.57621598 -2.43000019 -0.20564277 3.69981549 37 38 39 40 41 42 3.69306392 0.91298902 2.40454817 0.64754367 -4.43938860 -0.51503125 43 44 45 46 47 48 -9.48702642 -1.48044783 -0.82420376 -8.33733948 0.54490958 2.96922058 49 50 51 52 53 54 5.45960122 1.60360557 8.32313055 -4.49359917 6.40119010 1.11411670 55 56 57 58 59 60 -3.94121893 -1.75108419 -0.63651571 3.17958122 4.45819404 -0.19770118 61 62 63 64 65 66 0.92091360 -2.71951695 0.60851795 -4.60561873 4.08142360 2.27608360 67 68 69 70 71 72 1.98061330 0.42570509 -6.62849880 1.76244002 1.70064332 -5.19200741 73 74 75 76 77 78 3.00882046 3.73403555 -6.54382735 5.10620440 0.76005098 -1.37856069 79 80 81 82 83 84 -0.04881168 -2.93593219 8.23058520 7.73110744 -4.19324361 -2.72947857 85 86 87 88 89 90 -12.22881214 -4.45466656 -4.35343842 0.89607813 -5.38338480 3.52164589 91 92 93 6.25133394 4.59880961 2.11346391 > postscript(file="/var/www/html/rcomp/tmp/6dah71262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 93 Frequency = 1 lag(myerror, k = 1) myerror 0 1.92892775 NA 1 1.51166192 1.92892775 2 -0.97891013 1.51166192 3 0.32579703 -0.97891013 4 7.68475878 0.32579703 5 0.09996870 7.68475878 6 2.97009871 0.09996870 7 -0.93622977 2.97009871 8 -0.78319838 -0.93622977 9 1.55773319 -0.78319838 10 -0.13467385 1.55773319 11 -2.43700806 -0.13467385 12 -1.33376415 -2.43700806 13 0.75967997 -1.33376415 14 -3.47259825 0.75967997 15 -0.67833256 -3.47259825 16 -4.00397435 -0.67833256 17 -6.48301361 -4.00397435 18 2.84391679 -6.48301361 19 0.12068174 2.84391679 20 -0.89541647 0.12068174 21 -3.46352218 -0.89541647 22 -2.17018671 -3.46352218 23 3.88715915 -2.17018671 24 -1.44875065 3.88715915 25 -1.34778853 -1.44875065 26 4.01257748 -1.34778853 27 2.80192723 4.01257748 28 -5.10067571 2.80192723 29 1.36479066 -5.10067571 30 -0.56890571 1.36479066 31 1.95849755 -0.56890571 32 -0.57621598 1.95849755 33 -2.43000019 -0.57621598 34 -0.20564277 -2.43000019 35 3.69981549 -0.20564277 36 3.69306392 3.69981549 37 0.91298902 3.69306392 38 2.40454817 0.91298902 39 0.64754367 2.40454817 40 -4.43938860 0.64754367 41 -0.51503125 -4.43938860 42 -9.48702642 -0.51503125 43 -1.48044783 -9.48702642 44 -0.82420376 -1.48044783 45 -8.33733948 -0.82420376 46 0.54490958 -8.33733948 47 2.96922058 0.54490958 48 5.45960122 2.96922058 49 1.60360557 5.45960122 50 8.32313055 1.60360557 51 -4.49359917 8.32313055 52 6.40119010 -4.49359917 53 1.11411670 6.40119010 54 -3.94121893 1.11411670 55 -1.75108419 -3.94121893 56 -0.63651571 -1.75108419 57 3.17958122 -0.63651571 58 4.45819404 3.17958122 59 -0.19770118 4.45819404 60 0.92091360 -0.19770118 61 -2.71951695 0.92091360 62 0.60851795 -2.71951695 63 -4.60561873 0.60851795 64 4.08142360 -4.60561873 65 2.27608360 4.08142360 66 1.98061330 2.27608360 67 0.42570509 1.98061330 68 -6.62849880 0.42570509 69 1.76244002 -6.62849880 70 1.70064332 1.76244002 71 -5.19200741 1.70064332 72 3.00882046 -5.19200741 73 3.73403555 3.00882046 74 -6.54382735 3.73403555 75 5.10620440 -6.54382735 76 0.76005098 5.10620440 77 -1.37856069 0.76005098 78 -0.04881168 -1.37856069 79 -2.93593219 -0.04881168 80 8.23058520 -2.93593219 81 7.73110744 8.23058520 82 -4.19324361 7.73110744 83 -2.72947857 -4.19324361 84 -12.22881214 -2.72947857 85 -4.45466656 -12.22881214 86 -4.35343842 -4.45466656 87 0.89607813 -4.35343842 88 -5.38338480 0.89607813 89 3.52164589 -5.38338480 90 6.25133394 3.52164589 91 4.59880961 6.25133394 92 2.11346391 4.59880961 93 NA 2.11346391 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.51166192 1.92892775 [2,] -0.97891013 1.51166192 [3,] 0.32579703 -0.97891013 [4,] 7.68475878 0.32579703 [5,] 0.09996870 7.68475878 [6,] 2.97009871 0.09996870 [7,] -0.93622977 2.97009871 [8,] -0.78319838 -0.93622977 [9,] 1.55773319 -0.78319838 [10,] -0.13467385 1.55773319 [11,] -2.43700806 -0.13467385 [12,] -1.33376415 -2.43700806 [13,] 0.75967997 -1.33376415 [14,] -3.47259825 0.75967997 [15,] -0.67833256 -3.47259825 [16,] -4.00397435 -0.67833256 [17,] -6.48301361 -4.00397435 [18,] 2.84391679 -6.48301361 [19,] 0.12068174 2.84391679 [20,] -0.89541647 0.12068174 [21,] -3.46352218 -0.89541647 [22,] -2.17018671 -3.46352218 [23,] 3.88715915 -2.17018671 [24,] -1.44875065 3.88715915 [25,] -1.34778853 -1.44875065 [26,] 4.01257748 -1.34778853 [27,] 2.80192723 4.01257748 [28,] -5.10067571 2.80192723 [29,] 1.36479066 -5.10067571 [30,] -0.56890571 1.36479066 [31,] 1.95849755 -0.56890571 [32,] -0.57621598 1.95849755 [33,] -2.43000019 -0.57621598 [34,] -0.20564277 -2.43000019 [35,] 3.69981549 -0.20564277 [36,] 3.69306392 3.69981549 [37,] 0.91298902 3.69306392 [38,] 2.40454817 0.91298902 [39,] 0.64754367 2.40454817 [40,] -4.43938860 0.64754367 [41,] -0.51503125 -4.43938860 [42,] -9.48702642 -0.51503125 [43,] -1.48044783 -9.48702642 [44,] -0.82420376 -1.48044783 [45,] -8.33733948 -0.82420376 [46,] 0.54490958 -8.33733948 [47,] 2.96922058 0.54490958 [48,] 5.45960122 2.96922058 [49,] 1.60360557 5.45960122 [50,] 8.32313055 1.60360557 [51,] -4.49359917 8.32313055 [52,] 6.40119010 -4.49359917 [53,] 1.11411670 6.40119010 [54,] -3.94121893 1.11411670 [55,] -1.75108419 -3.94121893 [56,] -0.63651571 -1.75108419 [57,] 3.17958122 -0.63651571 [58,] 4.45819404 3.17958122 [59,] -0.19770118 4.45819404 [60,] 0.92091360 -0.19770118 [61,] -2.71951695 0.92091360 [62,] 0.60851795 -2.71951695 [63,] -4.60561873 0.60851795 [64,] 4.08142360 -4.60561873 [65,] 2.27608360 4.08142360 [66,] 1.98061330 2.27608360 [67,] 0.42570509 1.98061330 [68,] -6.62849880 0.42570509 [69,] 1.76244002 -6.62849880 [70,] 1.70064332 1.76244002 [71,] -5.19200741 1.70064332 [72,] 3.00882046 -5.19200741 [73,] 3.73403555 3.00882046 [74,] -6.54382735 3.73403555 [75,] 5.10620440 -6.54382735 [76,] 0.76005098 5.10620440 [77,] -1.37856069 0.76005098 [78,] -0.04881168 -1.37856069 [79,] -2.93593219 -0.04881168 [80,] 8.23058520 -2.93593219 [81,] 7.73110744 8.23058520 [82,] -4.19324361 7.73110744 [83,] -2.72947857 -4.19324361 [84,] -12.22881214 -2.72947857 [85,] -4.45466656 -12.22881214 [86,] -4.35343842 -4.45466656 [87,] 0.89607813 -4.35343842 [88,] -5.38338480 0.89607813 [89,] 3.52164589 -5.38338480 [90,] 6.25133394 3.52164589 [91,] 4.59880961 6.25133394 [92,] 2.11346391 4.59880961 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.51166192 1.92892775 2 -0.97891013 1.51166192 3 0.32579703 -0.97891013 4 7.68475878 0.32579703 5 0.09996870 7.68475878 6 2.97009871 0.09996870 7 -0.93622977 2.97009871 8 -0.78319838 -0.93622977 9 1.55773319 -0.78319838 10 -0.13467385 1.55773319 11 -2.43700806 -0.13467385 12 -1.33376415 -2.43700806 13 0.75967997 -1.33376415 14 -3.47259825 0.75967997 15 -0.67833256 -3.47259825 16 -4.00397435 -0.67833256 17 -6.48301361 -4.00397435 18 2.84391679 -6.48301361 19 0.12068174 2.84391679 20 -0.89541647 0.12068174 21 -3.46352218 -0.89541647 22 -2.17018671 -3.46352218 23 3.88715915 -2.17018671 24 -1.44875065 3.88715915 25 -1.34778853 -1.44875065 26 4.01257748 -1.34778853 27 2.80192723 4.01257748 28 -5.10067571 2.80192723 29 1.36479066 -5.10067571 30 -0.56890571 1.36479066 31 1.95849755 -0.56890571 32 -0.57621598 1.95849755 33 -2.43000019 -0.57621598 34 -0.20564277 -2.43000019 35 3.69981549 -0.20564277 36 3.69306392 3.69981549 37 0.91298902 3.69306392 38 2.40454817 0.91298902 39 0.64754367 2.40454817 40 -4.43938860 0.64754367 41 -0.51503125 -4.43938860 42 -9.48702642 -0.51503125 43 -1.48044783 -9.48702642 44 -0.82420376 -1.48044783 45 -8.33733948 -0.82420376 46 0.54490958 -8.33733948 47 2.96922058 0.54490958 48 5.45960122 2.96922058 49 1.60360557 5.45960122 50 8.32313055 1.60360557 51 -4.49359917 8.32313055 52 6.40119010 -4.49359917 53 1.11411670 6.40119010 54 -3.94121893 1.11411670 55 -1.75108419 -3.94121893 56 -0.63651571 -1.75108419 57 3.17958122 -0.63651571 58 4.45819404 3.17958122 59 -0.19770118 4.45819404 60 0.92091360 -0.19770118 61 -2.71951695 0.92091360 62 0.60851795 -2.71951695 63 -4.60561873 0.60851795 64 4.08142360 -4.60561873 65 2.27608360 4.08142360 66 1.98061330 2.27608360 67 0.42570509 1.98061330 68 -6.62849880 0.42570509 69 1.76244002 -6.62849880 70 1.70064332 1.76244002 71 -5.19200741 1.70064332 72 3.00882046 -5.19200741 73 3.73403555 3.00882046 74 -6.54382735 3.73403555 75 5.10620440 -6.54382735 76 0.76005098 5.10620440 77 -1.37856069 0.76005098 78 -0.04881168 -1.37856069 79 -2.93593219 -0.04881168 80 8.23058520 -2.93593219 81 7.73110744 8.23058520 82 -4.19324361 7.73110744 83 -2.72947857 -4.19324361 84 -12.22881214 -2.72947857 85 -4.45466656 -12.22881214 86 -4.35343842 -4.45466656 87 0.89607813 -4.35343842 88 -5.38338480 0.89607813 89 3.52164589 -5.38338480 90 6.25133394 3.52164589 91 4.59880961 6.25133394 92 2.11346391 4.59880961 > 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/7t4nl1262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8letw1262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/98ryp1262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/108zy11262014082.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11clhd1262014082.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/12ivpg1262014082.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/13ts901262014082.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/14kzw91262014082.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/15znin1262014083.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/16gqpl1262014083.tab") + } > > try(system("convert tmp/1p9jb1262014082.ps tmp/1p9jb1262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/2c47v1262014082.ps tmp/2c47v1262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/37p091262014082.ps tmp/37p091262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/486zn1262014082.ps tmp/486zn1262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/5ug2f1262014082.ps tmp/5ug2f1262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/6dah71262014082.ps tmp/6dah71262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/7t4nl1262014082.ps tmp/7t4nl1262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/8letw1262014082.ps tmp/8letw1262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/98ryp1262014082.ps tmp/98ryp1262014082.png",intern=TRUE)) character(0) > try(system("convert tmp/108zy11262014082.ps tmp/108zy11262014082.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.972 1.632 4.231