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Author's title

Author*Unverified author*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationMon, 08 Aug 2016 23:33:02 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Aug/08/t1470695625ll3wsp8sxobj8xe.htm/, Retrieved Mon, 29 Apr 2024 08:33:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=296107, Retrieved Mon, 29 Apr 2024 08:33:43 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [omzet lego technique] [2016-08-08 19:25:41] [74be16979710d4c4e7c6647856088456]
- RMPD  [Harrell-Davis Quantiles] [Harrel-Davis quan...] [2016-08-08 22:07:37] [4c392b130fccc63297597dd6ffb6df17]
- RM        [Mean Plot] [mean plot lego te...] [2016-08-08 22:33:02] [d7adcc7732e5b057da1b42af54844e1a] [Current]
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Dataseries X:
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296107&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=296107&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296107&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ yule.wessa.net



Parameters (Session):
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
x <- na.omit(x)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np+1))
darr <- array(NA,dim=c(par1,np+1))
ari <- array(0,dim=par1)
dx <- diff(x)
j <- 0
for (i in 1:n)
{
j = j + 1
ari[j] = ari[j] + 1
arr[j,ari[j]] <- x[i]
darr[j,ari[j]] <- dx[i]
if (j == par1) j = 0
}
ari
arr
darr
arr.mean <- array(NA,dim=par1)
arr.median <- array(NA,dim=par1)
arr.midrange <- array(NA,dim=par1)
for (j in 1:par1)
{
arr.mean[j] <- mean(arr[j,],na.rm=TRUE)
arr.median[j] <- median(arr[j,],na.rm=TRUE)
arr.midrange[j] <- (quantile(arr[j,],0.75,na.rm=TRUE) + quantile(arr[j,],0.25,na.rm=TRUE)) / 2
}
overall.mean <- mean(x)
overall.median <- median(x)
overall.midrange <- (quantile(x,0.75) + quantile(x,0.25)) / 2
bitmap(file='plot1.png')
plot(arr.mean,type='b',ylab='mean',main='Mean Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.mean,0)
dev.off()
bitmap(file='plot2.png')
plot(arr.median,type='b',ylab='median',main='Median Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.median,0)
dev.off()
bitmap(file='plot3.png')
plot(arr.midrange,type='b',ylab='midrange',main='Midrange Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.midrange,0)
dev.off()
bitmap(file='plot4.png')
z <- data.frame(t(arr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Periodic Subseries'))
dev.off()
bitmap(file='plot4b.png')
z <- data.frame(t(darr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Differenced Periodic Subseries'))
dev.off()
bitmap(file='plot5.png')
z <- data.frame(arr)
names(z) <- c(1:np)
(boxplot(z,notch=TRUE,col='grey',xlab='Block Index',ylab='Value',main='Notched Box Plots - Sequential Blocks'))
dev.off()
bitmap(file='plot6.png')
z <- data.frame(cbind(arr.mean,arr.median,arr.midrange))
names(z) <- list('mean','median','midrange')
(boxplot(z,notch=TRUE,col='grey',ylab='Overall Central Tendency',main='Notched Box Plots'))
dev.off()