FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationMon, 04 Apr 2011 12:27:55 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Apr/04/t13019198939439t5whngkeqkz.htm/, Retrieved Wed, 08 May 2024 18:22:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=120096, Retrieved Wed, 08 May 2024 18:22:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Mean Plot] [KDGP1W52] [2011-04-04 12:27:55] [75b7fe93b57f9e359de9d9cae642ffd9] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
 77.1
 70.9
 64.8
 70.1
 77.3
 79.5
100.6
100.7
107.1
 95.9
 82.8
 83.3
   80
 80.4
 67.5
 75.7
 71.1
 89.3
101.1
105.2
114.1
 96.3
 84.4
 91.2
 81.9
 80.5
 70.4
 74.8
 75.9
 86.3
 98.7
100.9
113.8
 89.8
 84.4
 87.2
 85.6
   72
 69.2
 77.5
 78.1
 94.3
 97.7
100.2
116.4
 97.1
   93
   96
 80.5
 76.1
 69.9
 73.6
 92.6
 94.2
 93.5
108.5
109.4
105.1
 92.5
 97.1
 81.4
 79.1
 72.1
 78.7
 87.1
 91.4
109.9
116.3
  113
  100
 84.8
 94.3
 87.1
 90.3
 72.4
 84.9
 92.7
 92.2
114.9
112.5
118.3
  106
 91.2
 96.6
 96.3
 88.2
 70.2
 86.5
 88.2
102.8
119.1
119.2
125.1
106.1
102.1
105.2
  101
 84.3
 87.5
 92.7
 94.4
  113
113.9
122.9
132.7
106.9
 96.6
127.3
 98.2
100.2
 89.4
 95.3
104.2
106.4
116.2
135.9
  134
104.6
107.1
123.5
 98.8
 98.6
 90.6
 89.1
105.2
  114
122.1
  138
142.2
116.4
112.6
123.8
103.6
113.9
 98.6
   95
  116
113.9
127.5
131.4
145.9
131.5
  131
130.5
118.9
114.3
 85.7
104.6
105.1
117.3
142.5
  140
159.8
131.2
125.4
126.5
119.4
113.5
 98.7
114.5
113.8
133.1
143.4
137.3
165.2
126.9
  124
135.7
  130
109.4
117.8
120.3
  121
132.3
142.9
147.4
175.9
132.6
123.7
153.3
  134
119.6
116.2
118.6
130.7
129.3
144.4
163.2
179.4
128.1
138.4
152.7
  120
140.5
116.2
121.4
127.8
143.6
157.6
166.2
182.3
153.1
147.6
157.7
137.2
151.5
 98.7
145.8
151.7
129.4
174.1
  197
193.9
164.1
142.8
157.9
159.2
162.2
123.1
  130
150.1
169.4
179.7
182.1
194.3
161.4
169.4
168.8
158.1
158.5
135.3
149.3
143.4
142.2
188.4
166.2
199.2
182.7
145.2
182.1
158.7
141.6
132.6
139.6
  147
166.6
  157
180.4
210.2
159.8
157.8
168.2
158.4
  152
142.2
137.2
152.6
166.8
165.6
198.6
201.5
170.7
164.4
179.7
  157
  168
139.3
138.6
153.4
138.9
172.1
198.4
217.8
173.7
153.8
175.6
147.1
160.3
135.2
148.8
  151
148.2
182.2
189.2
183.1
  170
158.4
176.1
156.2
153.2
117.9
149.8
156.6
166.7
156.8
158.6
210.8
203.6
175.2
168.7
155.9
147.3
  137
141.1
167.4
160.2
191.9
174.4
208.2
159.4
161.1
172.1
158.4
114.6
159.6
159.7
159.4
160.7
165.5
  205
205.2
141.6
148.1
184.9
132.5
137.3
135.5
121.7
166.1
146.8
162.8
186.8
185.5
151.5
158.1
  143
151.2
147.6
130.7
137.5
146.1
133.6
167.9
181.9
  202
166.5
151.3
146.2
148.3
144.7
123.6
151.6
133.9
137.4
181.6
  182
  190
161.2
155.5
141.9
164.6
136.2
126.8
152.5
126.6
150.1
186.3
147.5
200.4
177.2
127.4
177.1
154.4
135.2
126.4
147.3
140.6
152.3
151.2
172.2
215.3
154.1
159.3
160.4
151.9
148.4
139.6
148.2
153.5
145.1
183.7
210.5
203.3
153.3
144.3
169.6
143.7
160.1
135.6
141.8
159.9
145.7
183.5
198.2
186.8
  172
150.6
163.3
153.7
152.9
135.5
148.5
148.4
133.6
194.1
208.6
197.3
164.4
148.1
  152
144.1
  155
124.5
  153
  146
  138
  190
  192
  192
  147
  133
  163
  150
  129
  131
  145
  137
  138
  168
  176
  188
  139
  143
  150
  154
  137
  129
  128
  140
  143
  151
  177
  184
  151
  134
  164
  126
  131
  125
  127
  143
  143
  160
  190
  182
  138
  136
  152
  127
  151
  130
  119
  153

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120096&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120096&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120096&T=0

As an alternative you can also use a QR Code:  
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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
(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()