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Data:
2443.6 2460.2 2448.2 2470.4 2484.7 2466.8 2487.9 2508.4 2510.5 2497.4 2532.5 2556.8 2561 2547.3 2541.5 2558.5 2587.9 2580.5 2579.6 2589.3 2595 2595.6 2588.8 2591.7 2601.7 2585.4 2573.3 2597.4 2600.6 2570.6 2569.4 2584.9 2608.8 2617.2 2621 2540.5 2554.5 2601.9 2623 2640.7 2640.7 2619.8 2624.2 2638.2 2645.7 2679.6 2669 2664.6 2663.3 2667.4 2653.2 2630.8 2626.6 2641.9 2625.8 2606 2594.4 2583.6 2588.7 2600.3 2579.5 2576.6 2597.8 2595.6 2599 2621.7 2645.6 2644.2 2625.6 2624.6 2596.2 2599.5 2584.1 2570.8 2555 2574.5 2576.7 2579 2588.7 2601.1 2575.7 2559.5 2561.1 2528.3 2514.7 2558.5 2553.3 2577.1 2566 2549.5 2527.8 2540.9 2534.2 2538 2559 2554.9 2575.5 2546.5 2561.6 2546.6 2502.9 2463.1 2472.6 2463.5 2446.3 2456.2 2471.5 2447.5 2428.6 2420.2 2414.9 2420.2 2423.8 2407 2388.7 2409.6 2392 2380.2 2423.3 2451.6 2440.8 2432.9 2413.6 2391.6 2358.1 2345.4 2384.4 2384.4 2384.4 2418.7 2420 2493.1 2493.1 2492.8
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R Code
library(Hmisc) m <- mean(x) e <- median(x) bitmap(file='test1.png') op <- par(mfrow=c(2,1)) mydensity1 <- density(x,kernel='gaussian',na.rm=TRUE) plot(mydensity1,main='Density Plot - Gaussian Kernel',xlab='Median (0 -> full line) | Mean (0 -> dashed line)',ylab='density') abline(v=e,lty=1) abline(v=m,lty=5) grid() myseq <- seq(0.01, 0.99, 0.01) hd <- hdquantile(x, probs = myseq, se = TRUE, na.rm = FALSE, names = TRUE, weights=FALSE) plot(myseq,hd,col=2,main='Harrell-Davis Quantiles',xlab='quantiles',ylab='Median (0 -> full) | Mean (0 -> dashed)') abline(h=m,lty=5) abline(h=e,lty=1) grid() par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Median versus Mean',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,mean(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) a<-table.element(a,median(x)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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