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Type 'q()' to quit R. > x <- array(list(3030.29 + ,25.64 + ,2803.47 + ,27.97 + ,2767.63 + ,27.62 + ,2882.6 + ,23.31 + ,2863.36 + ,29.07 + ,2897.06 + ,29.58 + ,3012.61 + ,28.63 + ,3142.95 + ,29.92 + ,3032.93 + ,32.68 + ,3045.78 + ,31.54 + ,3110.52 + ,32.43 + ,3013.24 + ,26.54 + ,2987.1 + ,25.85 + ,2995.55 + ,27.6 + ,2833.18 + ,25.71 + ,2848.96 + ,25.38 + ,2794.83 + ,28.57 + ,2845.26 + ,27.64 + ,2915.03 + ,25.36 + ,2892.63 + ,25.9 + ,2604.42 + ,26.29 + ,2641.65 + ,21.74 + ,2659.81 + ,19.2 + ,2638.53 + ,19.32 + ,2720.25 + ,19.82 + ,2745.88 + ,20.36 + ,2735.7 + ,24.31 + ,2811.7 + ,25.97 + ,2799.43 + ,25.61 + ,2555.28 + ,24.67 + ,2304.98 + ,25.59 + ,2214.95 + ,26.09 + ,2065.81 + ,28.37 + ,1940.49 + ,27.34 + ,2042 + ,24.46 + ,1995.37 + ,27.46 + ,1946.81 + ,30.23 + ,1765.9 + ,32.33 + ,1635.25 + ,29.87 + ,1833.42 + ,24.87 + ,1910.43 + ,25.48 + ,1959.67 + ,27.28 + ,1969.6 + ,28.24 + ,2061.41 + ,29.58 + ,2093.48 + ,26.95 + ,2120.88 + ,29.08 + ,2174.56 + ,28.76 + ,2196.72 + ,29.59 + ,2350.44 + ,30.7 + ,2440.25 + ,30.52 + ,2408.64 + ,32.67 + ,2472.81 + ,33.19 + ,2407.6 + ,37.13 + ,2454.62 + ,35.54 + ,2448.05 + ,37.75 + ,2497.84 + ,41.84 + ,2645.64 + ,42.94 + ,2756.76 + ,49.14 + ,2849.27 + ,44.61 + ,2921.44 + ,40.22 + ,2981.85 + ,44.23 + ,3080.58 + ,45.85 + ,3106.22 + ,53.38 + ,3119.31 + ,53.26 + ,3061.26 + ,51.8 + ,3097.31 + ,55.3 + ,3161.69 + ,57.81 + ,3257.16 + ,63.96 + ,3277.01 + ,63.77 + ,3295.32 + ,59.15 + ,3363.99 + ,56.12 + ,3494.17 + ,57.42 + ,3667.03 + ,63.52 + ,3813.06 + ,61.71 + ,3917.96 + ,63.01 + ,3895.51 + ,68.18 + ,3801.06 + ,72.03 + ,3570.12 + ,69.75 + ,3701.61 + ,74.41 + ,3862.27 + ,74.33 + ,3970.1 + ,64.24 + ,4138.52 + ,60.03 + ,4199.75 + ,59.44 + ,4290.89 + ,62.5 + ,4443.91 + ,55.04 + ,4502.64 + ,58.34 + ,4356.98 + ,61.92 + ,4591.27 + ,67.65 + ,4696.96 + ,67.68 + ,4621.4 + ,70.3 + ,4562.84 + ,75.26 + ,4202.52 + ,71.44 + ,4296.49 + ,76.36 + ,4435.23 + ,81.71 + ,4105.18 + ,92.6 + ,4116.68 + ,90.6 + ,3844.49 + ,92.23 + ,3720.98 + ,94.09 + ,3674.4 + ,102.79 + ,3857.62 + ,109.65 + ,3801.06 + ,124.05 + ,3504.37 + ,132.69 + ,3032.6 + ,135.81 + ,3047.03 + ,116.07 + ,2962.34 + ,101.42 + ,2197.82 + ,75.73 + ,2014.45 + ,55.48 + ,1862.83 + ,43.8 + ,1905.41 + ,45.29) + ,dim=c(2 + ,109) + ,dimnames=list(c('Bel20' + ,'Olie') + ,1:109)) > y <- array(NA,dim=c(2,109),dimnames=list(c('Bel20','Olie'),1:109)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'no' > par3 = '' > par2 = 'none' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, 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: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Bel20" > x[,par1] [1] 3030.29 2803.47 2767.63 2882.60 2863.36 2897.06 3012.61 3142.95 3032.93 [10] 3045.78 3110.52 3013.24 2987.10 2995.55 2833.18 2848.96 2794.83 2845.26 [19] 2915.03 2892.63 2604.42 2641.65 2659.81 2638.53 2720.25 2745.88 2735.70 [28] 2811.70 2799.43 2555.28 2304.98 2214.95 2065.81 1940.49 2042.00 1995.37 [37] 1946.81 1765.90 1635.25 1833.42 1910.43 1959.67 1969.60 2061.41 2093.48 [46] 2120.88 2174.56 2196.72 2350.44 2440.25 2408.64 2472.81 2407.60 2454.62 [55] 2448.05 2497.84 2645.64 2756.76 2849.27 2921.44 2981.85 3080.58 3106.22 [64] 3119.31 3061.26 3097.31 3161.69 3257.16 3277.01 3295.32 3363.99 3494.17 [73] 3667.03 3813.06 3917.96 3895.51 3801.06 3570.12 3701.61 3862.27 3970.10 [82] 4138.52 4199.75 4290.89 4443.91 4502.64 4356.98 4591.27 4696.96 4621.40 [91] 4562.84 4202.52 4296.49 4435.23 4105.18 4116.68 3844.49 3720.98 3674.40 [100] 3857.62 3801.06 3504.37 3032.60 3047.03 2962.34 2197.82 2014.45 1862.83 [109] 1905.41 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$centers,1:par3),ncol=2) + clm <- clm[sort.list(clm[,1]),] + for (i in 1:par3) { + cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') + } + cl$cluster <- as.factor(cl$cluster) + print(cl$cluster) + x[,par1] <- cl$cluster + } > if (par2 == 'quantiles') { + x[,par1] <- cut2(x[,par1],g=par3) + } > if (par2 == 'hclust') { + hc <- hclust(dist(x[,par1])^2, 'cen') + print(hc) + memb <- cutree(hc, k = par3) + dum <- c(mean(x[memb==1,par1])) + for (i in 2:par3) { + dum <- c(dum, mean(x[memb==i,par1])) + } + hcm <- matrix(cbind(dum,1:par3),ncol=2) + hcm <- hcm[sort.list(hcm[,1]),] + for (i in 1:par3) { + memb[memb==hcm[i,2]] <- paste('C',i,sep='') + } + memb <- as.factor(memb) + print(memb) + x[,par1] <- memb + } > if (par2=='equal') { + ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) + x[,par1] <- as.factor(ed) + } > table(x[,par1]) 1635.25 1765.9 1833.42 1862.83 1905.41 1910.43 1940.49 1946.81 1959.67 1969.6 1 1 1 1 1 1 1 1 1 1 1995.37 2014.45 2042 2061.41 2065.81 2093.48 2120.88 2174.56 2196.72 2197.82 1 1 1 1 1 1 1 1 1 1 2214.95 2304.98 2350.44 2407.6 2408.64 2440.25 2448.05 2454.62 2472.81 2497.84 1 1 1 1 1 1 1 1 1 1 2555.28 2604.42 2638.53 2641.65 2645.64 2659.81 2720.25 2735.7 2745.88 2756.76 1 1 1 1 1 1 1 1 1 1 2767.63 2794.83 2799.43 2803.47 2811.7 2833.18 2845.26 2848.96 2849.27 2863.36 1 1 1 1 1 1 1 1 1 1 2882.6 2892.63 2897.06 2915.03 2921.44 2962.34 2981.85 2987.1 2995.55 3012.61 1 1 1 1 1 1 1 1 1 1 3013.24 3030.29 3032.6 3032.93 3045.78 3047.03 3061.26 3080.58 3097.31 3106.22 1 1 1 1 1 1 1 1 1 1 3110.52 3119.31 3142.95 3161.69 3257.16 3277.01 3295.32 3363.99 3494.17 3504.37 1 1 1 1 1 1 1 1 1 1 3570.12 3667.03 3674.4 3701.61 3720.98 3801.06 3813.06 3844.49 3857.62 3862.27 1 1 1 1 1 2 1 1 1 1 3895.51 3917.96 3970.1 4105.18 4116.68 4138.52 4199.75 4202.52 4290.89 4296.49 1 1 1 1 1 1 1 1 1 1 4356.98 4435.23 4443.91 4502.64 4562.84 4591.27 4621.4 4696.96 1 1 1 1 1 1 1 1 > colnames(x) [1] "Bel20" "Olie" > colnames(x)[par1] [1] "Bel20" > x[,par1] [1] 3030.29 2803.47 2767.63 2882.60 2863.36 2897.06 3012.61 3142.95 3032.93 [10] 3045.78 3110.52 3013.24 2987.10 2995.55 2833.18 2848.96 2794.83 2845.26 [19] 2915.03 2892.63 2604.42 2641.65 2659.81 2638.53 2720.25 2745.88 2735.70 [28] 2811.70 2799.43 2555.28 2304.98 2214.95 2065.81 1940.49 2042.00 1995.37 [37] 1946.81 1765.90 1635.25 1833.42 1910.43 1959.67 1969.60 2061.41 2093.48 [46] 2120.88 2174.56 2196.72 2350.44 2440.25 2408.64 2472.81 2407.60 2454.62 [55] 2448.05 2497.84 2645.64 2756.76 2849.27 2921.44 2981.85 3080.58 3106.22 [64] 3119.31 3061.26 3097.31 3161.69 3257.16 3277.01 3295.32 3363.99 3494.17 [73] 3667.03 3813.06 3917.96 3895.51 3801.06 3570.12 3701.61 3862.27 3970.10 [82] 4138.52 4199.75 4290.89 4443.91 4502.64 4356.98 4591.27 4696.96 4621.40 [91] 4562.84 4202.52 4296.49 4435.23 4105.18 4116.68 3844.49 3720.98 3674.40 [100] 3857.62 3801.06 3504.37 3032.60 3047.03 2962.34 2197.82 2014.45 1862.83 [109] 1905.41 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > if (par2 != 'none') { + m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) + if (par4=='yes') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + a<-table.element(a,'Prediction (training)',par3+1,TRUE) + a<-table.element(a,'Prediction (testing)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Actual',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + a<-table.row.end(a) + for (i in 1:10) { + ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) + m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) + if (i==1) { + m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) + m.ct.i.actu <- x[ind==1,par1] + m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) + m.ct.x.actu <- x[ind==2,par1] + } else { + m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) + m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) + m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) + m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) + } + } + print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) + numer <- numer + m.ct.i.tab[i,i] + } + print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) + print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) + numer <- numer + m.ct.x.tab[i,i] + } + print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) + a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) + for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) + a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) + a<-table.row.end(a) + } + a<-table.row.start(a) + a<-table.element(a,'Overall',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.i.cp,4)) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.x.cp,4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/1r12s1292937779.tab") + } + } > m Conditional inference tree with 2 terminal nodes Response: Bel20 Input: Olie Number of observations: 109 1) Olie <= 53.38; criterion = 1, statistic = 40.954 2)* weights = 67 1) Olie > 53.38 3)* weights = 42 > postscript(file="/var/www/html/freestat/rcomp/tmp/2jt1d1292937779.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/3jt1d1292937779.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') > dev.off() null device 1 > if (par2 == 'none') { + forec <- predict(m) + result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) + colnames(result) <- c('Actuals','Forecasts','Residuals') + print(result) + } Actuals Forecasts Residuals 1 3030.29 2561.644 468.645522 2 2803.47 2561.644 241.825522 3 2767.63 2561.644 205.985522 4 2882.60 2561.644 320.955522 5 2863.36 2561.644 301.715522 6 2897.06 2561.644 335.415522 7 3012.61 2561.644 450.965522 8 3142.95 2561.644 581.305522 9 3032.93 2561.644 471.285522 10 3045.78 2561.644 484.135522 11 3110.52 2561.644 548.875522 12 3013.24 2561.644 451.595522 13 2987.10 2561.644 425.455522 14 2995.55 2561.644 433.905522 15 2833.18 2561.644 271.535522 16 2848.96 2561.644 287.315522 17 2794.83 2561.644 233.185522 18 2845.26 2561.644 283.615522 19 2915.03 2561.644 353.385522 20 2892.63 2561.644 330.985522 21 2604.42 2561.644 42.775522 22 2641.65 2561.644 80.005522 23 2659.81 2561.644 98.165522 24 2638.53 2561.644 76.885522 25 2720.25 2561.644 158.605522 26 2745.88 2561.644 184.235522 27 2735.70 2561.644 174.055522 28 2811.70 2561.644 250.055522 29 2799.43 2561.644 237.785522 30 2555.28 2561.644 -6.364478 31 2304.98 2561.644 -256.664478 32 2214.95 2561.644 -346.694478 33 2065.81 2561.644 -495.834478 34 1940.49 2561.644 -621.154478 35 2042.00 2561.644 -519.644478 36 1995.37 2561.644 -566.274478 37 1946.81 2561.644 -614.834478 38 1765.90 2561.644 -795.744478 39 1635.25 2561.644 -926.394478 40 1833.42 2561.644 -728.224478 41 1910.43 2561.644 -651.214478 42 1959.67 2561.644 -601.974478 43 1969.60 2561.644 -592.044478 44 2061.41 2561.644 -500.234478 45 2093.48 2561.644 -468.164478 46 2120.88 2561.644 -440.764478 47 2174.56 2561.644 -387.084478 48 2196.72 2561.644 -364.924478 49 2350.44 2561.644 -211.204478 50 2440.25 2561.644 -121.394478 51 2408.64 2561.644 -153.004478 52 2472.81 2561.644 -88.834478 53 2407.60 2561.644 -154.044478 54 2454.62 2561.644 -107.024478 55 2448.05 2561.644 -113.594478 56 2497.84 2561.644 -63.804478 57 2645.64 2561.644 83.995522 58 2756.76 2561.644 195.115522 59 2849.27 2561.644 287.625522 60 2921.44 2561.644 359.795522 61 2981.85 2561.644 420.205522 62 3080.58 2561.644 518.935522 63 3106.22 2561.644 544.575522 64 3119.31 2561.644 557.665522 65 3061.26 2561.644 499.615522 66 3097.31 3770.566 -673.256429 67 3161.69 3770.566 -608.876429 68 3257.16 3770.566 -513.406429 69 3277.01 3770.566 -493.556429 70 3295.32 3770.566 -475.246429 71 3363.99 3770.566 -406.576429 72 3494.17 3770.566 -276.396429 73 3667.03 3770.566 -103.536429 74 3813.06 3770.566 42.493571 75 3917.96 3770.566 147.393571 76 3895.51 3770.566 124.943571 77 3801.06 3770.566 30.493571 78 3570.12 3770.566 -200.446429 79 3701.61 3770.566 -68.956429 80 3862.27 3770.566 91.703571 81 3970.10 3770.566 199.533571 82 4138.52 3770.566 367.953571 83 4199.75 3770.566 429.183571 84 4290.89 3770.566 520.323571 85 4443.91 3770.566 673.343571 86 4502.64 3770.566 732.073571 87 4356.98 3770.566 586.413571 88 4591.27 3770.566 820.703571 89 4696.96 3770.566 926.393571 90 4621.40 3770.566 850.833571 91 4562.84 3770.566 792.273571 92 4202.52 3770.566 431.953571 93 4296.49 3770.566 525.923571 94 4435.23 3770.566 664.663571 95 4105.18 3770.566 334.613571 96 4116.68 3770.566 346.113571 97 3844.49 3770.566 73.923571 98 3720.98 3770.566 -49.586429 99 3674.40 3770.566 -96.166429 100 3857.62 3770.566 87.053571 101 3801.06 3770.566 30.493571 102 3504.37 3770.566 -266.196429 103 3032.60 3770.566 -737.966429 104 3047.03 3770.566 -723.536429 105 2962.34 3770.566 -808.226429 106 2197.82 3770.566 -1572.746429 107 2014.45 3770.566 -1756.116429 108 1862.83 2561.644 -698.814478 109 1905.41 2561.644 -656.234478 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/www/html/freestat/rcomp/tmp/4uk1g1292937779.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > if(par2=='none') { + op <- par(mfrow=c(2,2)) + plot(density(result$Actuals),main='Kernel Density Plot of Actuals') + plot(density(result$Residuals),main='Kernel Density Plot of Residuals') + plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') + plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') + par(op) + } > if(par2!='none') { + plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') + } > dev.off() null device 1 > if (par2 == 'none') { + detcoef <- cor(result$Forecasts,result$Actuals) + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goodness of Fit',2,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Correlation',1,TRUE) + a<-table.element(a,round(detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'R-squared',1,TRUE) + a<-table.element(a,round(detcoef*detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'RMSE',1,TRUE) + a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/5xlh41292937779.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$Actuals)) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,result$Actuals[i]) + a<-table.element(a,result$Forecasts[i]) + a<-table.element(a,result$Residuals[i]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/61lfa1292937779.tab") + } > if (par2 != 'none') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + for (i in 1:par3) { + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + } + a<-table.row.end(a) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (j in 1:par3) { + a<-table.element(a,myt[i,j]) + } + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/7ucfv1292937779.tab") + } > > try(system("convert tmp/2jt1d1292937779.ps tmp/2jt1d1292937779.png",intern=TRUE)) character(0) > try(system("convert tmp/3jt1d1292937779.ps tmp/3jt1d1292937779.png",intern=TRUE)) character(0) > try(system("convert tmp/4uk1g1292937779.ps tmp/4uk1g1292937779.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.968 0.755 4.181