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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cloud.wasp
Title produced by softwareTrivariate Scatterplots
Date of computationSun, 09 Nov 2008 11:03:24 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/09/t12262538311ksunht5rr7pysb.htm/, Retrieved Sun, 19 May 2024 09:21:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22801, Retrieved Sun, 19 May 2024 09:21:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Bivariate Kernel Density Estimation] [] [2008-11-09 17:47:04] [4c8dfb519edec2da3492d7e6be9a5685]
F RMPD  [Partial Correlation] [] [2008-11-09 17:56:39] [4c8dfb519edec2da3492d7e6be9a5685]
F RMPD      [Trivariate Scatterplots] [] [2008-11-09 18:03:24] [6d40a467de0f28bd2350f82ac9522c51] [Current]
Feedback Forum
2008-11-19 14:25:57 [2df1bcd103d52957f4a39bd4617794c8] [reply
De Trivariate Scatterplots methode werd hier correct aangewend.

De 3D kubussen vergelijken is erg moeilijk om inzicht in de samenhang van de datareeksen te krijgen. Daarom is het aangewezen de matrix en beter nog de verschillende gekleurde grafieken te bekijken.

De matrix geeft op de hoofddiagonaal de verschillende histogrammen weer. Link en rechts van de hoofddiagonaal noteren we verschillende scatterplots. Dit zijn in principe de projecties van de verschillende kubussen.

Wanneer we de gekleurde grafieken bekijken merken we ook hier dat de correlatie het grootst is tussen reeksen y en z. Maw de uitvoer van Producten van het plantenrijk en ‘Minerale producten.

De student trok dus de correcte conclusie.
2008-11-22 12:36:14 [Jeroen Michel] [reply
Hier is een correcte en volledige analyse gebeurd, maar ook de uitwerking is correct uitgevoerd.

Voorts worden er 3d- en 2d modellen weergegeven. Bij de 3d-modellen is het zeer moeilijk in te schatten hoe de verschillende van elkaar liggen. Afhankelijk van de dimmensie zijn de punten vaak niet achter elkaar waar te nemen. Dit is dus niet zo handig om te gebruiken.

Het 2d-model daarentegen is iets handiger. Hier kan je afleiden of er een normaalverdeling is af te leiden en hoe de correlatie is tussen de verschillende variabelen.

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Dataseries X:
299.63
305.945
382.252
348.846
335.367
373.617
312.612
312.232
337.161
331.476
350.103
345.127
297.256
295.979
361.007
321.803
354.937
349.432
290.979
349.576
327.625
349.377
336.777
339.134
323.321
318.86
373.583
333.03
408.556
414.646
291.514
348.857
349.368
375.765
364.136
349.53
348.167
332.856
360.551
346.969
392.815
372.02
371.027
342.672
367.343
390.786
343.785
362.6
349.468
340.624
369.536
407.782
392.239
404.824
373.669
344.902
396.7
398.911
366.009
392.484
Dataseries Y:
154.783
187.646
237.863
215.54
231.745
199.548
164.147
159.388
203.514
224.901
211.539
211.16
181.712
203.908
240.774
232.819
255.221
246.7
206.263
211.679
236.601
237.43
233.767
219.52
222.625
216.238
248.587
221.376
242.453
246.539
189.351
185.956
213.175
228.732
212.93
218.254
227.103
219.026
264.529
262.057
258.779
231.928
211.167
205.439
224.883
228.624
209.435
215.607
287.356
306.015
338.546
344.16
328.412
342.006
277.668
290.477
314.967
324.627
290.646
315.033
Dataseries Z:
301.606
268.225
362.082
310.984
350.907
365.759
357.504
432.236
394.335
404.182
371.721
387.012
280.042
357.111
359.451
341.206
349.156
430.298
354.447
400.785
358.974
352.853
374.229
364.568
352.411
376.47
357.475
299.497
361.805
343.188
335.597
330.985
336.723
348.076
317.518
345.737
342.568
352.951
400.269
428.121
475.804
392.732
388.22
410.643
428.044
530.799
463.074
477.686
440.586
424.757
511.061
511.421
454.39
498.403
516.143
463.642
498.391
533.752
404.341
435.645




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22801&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22801&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22801&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001



Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Variable X ; par6 = Variable Y ; par7 = Variable Z ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Variable X ; par6 = Variable Y ; par7 = Variable Z ;
R code (references can be found in the software module):
x <- array(x,dim=c(length(x),1))
colnames(x) <- par5
y <- array(y,dim=c(length(y),1))
colnames(y) <- par6
z <- array(z,dim=c(length(z),1))
colnames(z) <- par7
d <- data.frame(cbind(z,y,x))
colnames(d) <- list(par7,par6,par5)
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par1>500) par1 <- 500
if (par2>500) par2 <- 500
if (par1<10) par1 <- 10
if (par2<10) par2 <- 10
library(GenKern)
library(lattice)
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='black', ...)
}
bitmap(file='cloud1.png')
cloud(z~x*y, screen = list(x=-45, y=45, z=35),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='cloud2.png')
cloud(z~x*y, screen = list(x=35, y=45, z=25),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='cloud3.png')
cloud(z~x*y, screen = list(x=35, y=-25, z=90),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='pairs.png')
pairs(d,diag.panel=panel.hist)
dev.off()
x <- as.vector(x)
y <- as.vector(y)
z <- as.vector(z)
bitmap(file='bidensity1.png')
op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=cor(x,y), xbandwidth=dpik(x), ybandwidth=dpik(y))
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (x,y)',xlab=par5,ylab=par6)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(x,y)
(r<-lm(y ~ x))
abline(r)
box()
dev.off()
bitmap(file='bidensity2.png')
op <- KernSur(y,z, xgridsize=par1, ygridsize=par2, correlation=cor(y,z), xbandwidth=dpik(y), ybandwidth=dpik(z))
op
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (y,z)',xlab=par6,ylab=par7)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(y,z)
(r<-lm(z ~ y))
abline(r)
box()
dev.off()
bitmap(file='bidensity3.png')
op <- KernSur(x,z, xgridsize=par1, ygridsize=par2, correlation=cor(x,z), xbandwidth=dpik(x), ybandwidth=dpik(z))
op
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (x,z)',xlab=par5,ylab=par7)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(x,z)
(r<-lm(z ~ x))
abline(r)
box()
dev.off()