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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 computationThu, 29 Oct 2009 10:26:54 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Oct/29/t1256833682piiand19uyn65oy.htm/, Retrieved Mon, 29 Apr 2024 05:16:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52017, Retrieved Mon, 29 Apr 2024 05:16:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Trivariate Scatterplots] [SHW_WS5] [2009-10-29 16:26:54] [f0f26816ac6124f58333f11f6c174000] [Current]
- RMP     [Partial Correlation] [WS5_Partialcorrel...] [2009-10-29 17:22:10] [8b1aef4e7013bd33fbc2a5833375c5f5]
-  M D      [Partial Correlation] [ws 5] [2009-11-01 17:03:16] [b5908418e3090fddbd22f5f0f774653d]
-  M D      [Partial Correlation] [] [2009-11-04 12:17:47] [08fc5c07292c885b941f0cb515ce13f3]
-  M D      [Partial Correlation] [WS5(4)] [2009-11-04 18:37:19] [7d268329e554b8694908ba13e6e6f258]
-  M D      [Partial Correlation] [] [2009-11-29 16:15:43] [8b1aef4e7013bd33fbc2a5833375c5f5]
-    D        [Partial Correlation] [partiele corr paper] [2009-11-29 16:25:46] [8b1aef4e7013bd33fbc2a5833375c5f5]
- RMPD    [Bivariate Explorative Data Analysis] [SHW_WS5] [2009-10-29 17:44:27] [8b1aef4e7013bd33fbc2a5833375c5f5]
-  M D      [Bivariate Explorative Data Analysis] [ws 5] [2009-11-01 17:19:37] [b5908418e3090fddbd22f5f0f774653d]
-  M        [Bivariate Explorative Data Analysis] [] [2009-11-04 11:57:05] [08fc5c07292c885b941f0cb515ce13f3]
-  M D      [Bivariate Explorative Data Analysis] [] [2009-11-04 17:11:54] [44aba10e04b8829f2a97df951213e00a]
-  M D      [Bivariate Explorative Data Analysis] [WS5(1)] [2009-11-04 17:11:54] [7d268329e554b8694908ba13e6e6f258]
-    D        [Bivariate Explorative Data Analysis] [WS5(2)] [2009-11-04 17:41:47] [7d268329e554b8694908ba13e6e6f258]
- RM D      [Bivariate Explorative Data Analysis] [Y=f(Z)] [2009-12-28 14:37:02] [2663058f2a5dda519058ac6b2228468f]
-    D        [Bivariate Explorative Data Analysis] [X=f(Z)] [2009-12-28 14:49:17] [2663058f2a5dda519058ac6b2228468f]
- RM D      [Bivariate Explorative Data Analysis] [X=f(Z)] [2009-12-28 14:41:24] [2663058f2a5dda519058ac6b2228468f]
- RM D      [Bivariate Explorative Data Analysis] [model_3a] [2009-12-29 09:34:10] [2663058f2a5dda519058ac6b2228468f]
- RM D      [Bivariate Explorative Data Analysis] [model_3b] [2009-12-29 09:39:08] [2663058f2a5dda519058ac6b2228468f]
- RM D      [Bivariate Explorative Data Analysis] [model_3] [2009-12-29 09:45:53] [2663058f2a5dda519058ac6b2228468f]
-  M      [Trivariate Scatterplots] [] [2009-11-04 12:20:05] [08fc5c07292c885b941f0cb515ce13f3]
-  MPD    [Trivariate Scatterplots] [WS5(5)] [2009-11-04 18:44:04] [7d268329e554b8694908ba13e6e6f258]
- RMPD    [Trivariate Scatterplots] [Trivariate] [2009-12-28 15:06:45] [2663058f2a5dda519058ac6b2228468f]
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Dataseries X:
153.3
154.5
155.2
156.9
157
157.4
157.2
157.5
158
158.5
159
159.3
160
160.8
161.9
162.5
162.7
162.8
162.9
163
164
164.7
164.8
164.9
165
165.8
166.1
167.2
167.7
168.3
168.6
168.9
169.1
169.5
169.6
169.7
169.8
170.4
170.9
171.9
171.9
172
172
172.4
173
173.7
173.8
173.8
173.9
174.6
175
175.9
176
175.1
175.6
175.9
176.7
176.1
176.1
176.2
176.3
177.8
178.5
179.4
179.5
179.6
179.7
179.7
179.8
179.9
180.2
180.4
180.4
181.3
181.9
182.5
182.7
183.1
183.6
183.7
183.8
183.9
184.1
184.4
184.5
185.9
186.6
187.6
187.8
187.9
188
188.3
188.4
188.5
188.5
188.6
188.6
189.4
190
191.9
192.5
193
193.5
193.9
194.2
194.9
194.9
194.9
194.9
195.5
196
196.2
196.2
196.2
196.2
197
197.7
198
198.2
198.5
198.6
199.5
200
201.3
202.2
202.9
203.5
203.5
204
204.1
204.3
204.5
204.8
205.1
205.7
206.5
206.9
207.1
207.8
208
208.5
208.6
209
209.1
209.7
209.8
209.9
210
210.8
211.4
211.7
212
212.2
212.4
212.9
213.4
213.7
214
214.3
214.8
215
215.9
216.4
216.9
217.2
217.5
217.9
218.1
218.6
218.9
219.3
220.4
220.9
221
221.8
222
222.2
222.5
222.9
223.1
223.4
224
225.1
225.5
225.9
226.3
226.5
227
227.3
227.8
228.1
228.4
228.5
228.8
229
229.1
229.3
229.6
229.9
230
230.2
230.8
231
231.7
231.9
233
235.1
236
236.9
237.1
237.5
238.2
238.9
239.1
240
240.2
240.5
240.7
241.1
241.4
242.2
242.9
243.2
243.9
Dataseries Y:
3.88
3.98
3.29
2.88
3.22
3.62
3.82
3.54
2.53
2.22
2.85
2.78
2.28
2.26
2.71
2.77
2.77
2.64
2.56
2.07
2.32
2.16
2.23
2.4
2.84
2.77
2.93
2.91
2.69
2.38
2.58
3.19
2.82
2.72
2.53
2.7
2.42
2.5
2.31
2.41
2.56
2.76
2.71
2.44
2.46
2.12
1.99
1.86
1.88
1.82
1.74
1.71
1.38
1.27
1.19
1.28
1.19
1.22
1.47
1.46
1.96
1.88
2.03
2.04
1.9
1.8
1.92
1.92
1.97
2.46
2.36
2.53
2.31
1.98
1.46
1.26
1.58
1.74
1.89
1.85
1.62
1.3
1.42
1.15
0.42
0.74
1.02
1.51
1.86
1.59
1.03
0.44
0.82
0.86
0.58
0.59
0.95
0.98
1.23
1.17
0.84
0.74
0.65
0.91
1.19
1.3
1.53
1.94
1.79
1.95
2.26
2.04
2.16
2.75
2.79
2.88
3.36
2.97
3.1
2.49
2.2
2.25
2.09
2.79
3.14
2.93
2.65
2.67
2.26
2.35
2.13
2.18
2.9
2.63
2.67
1.81
1.33
0.88
1.28
1.26
1.26
1.29
1.1
1.37
1.21
1.74
1.76
1.48
1.04
1.62
1.49
1.79
1.8
1.58
1.86
1.74
1.59
1.26
1.13
1.92
2.61
2.26
2.41
2.26
2.03
2.86
2.55
2.27
2.26
2.57
3.07
2.76
2.51
2.87
3.14
3.11
3.16
2.47
2.57
2.89
2.63
2.38
1.69
1.96
2.19
1.87
1.6
1.63
1.22
1.21
1.49
1.64
1.66
1.77
1.82
1.78
1.28
1.29
1.37
1.12
1.51
2.24
2.94
3.09
3.46
3.64
4.39
4.15
5.21
5.8
5.91
5.39
5.46
4.72
3.14
2.63
2.32
1.93
0.62
0.6
-0.37
-1.1
-1.68
-0.78
Dataseries Z:
4.1
4.1
4
3.9
3.8
3.8
4
4.4
4.6
4.6
4.6
4.7
4.8
4.8
4.7
4.7
4.7
4.6
5
5.4
5.5
5.6
5.6
5.8
6
6.1
6.1
6
6
6.1
6.5
7.1
7.4
7.4
7.5
7.6
7.8
7.8
7.7
7.6
7.5
7.3
7.6
8
8
7.9
7.8
7.7
7.8
7.7
7.5
7.3
7.1
7
7.3
7.8
7.9
7.9
7.8
7.8
7.9
7.8
7.6
7.4
7.2
6.9
7.1
7.5
7.6
7.4
7.3
7.2
7.3
7.2
7.1
7
6.9
6.8
7.2
7.6
7.7
7.6
7.5
7.5
7.6
7.6
7.6
7.5
7.3
7.2
7.4
8
8.2
8
7.7
7.7
7.8
7.8
7.7
7.5
7.3
7.1
7.1
7.2
6.8
6.6
6.4
6.4
6.5
6.3
5.9
5.5
5.2
4.9
5.4
5.8
5.7
5.6
5.5
5.4
5.4
5.4
5.5
5.8
5.7
5.4
5.6
5.8
6.2
6.8
6.7
6.7
6.4
6.3
6.3
6.4
6.3
6
6.3
6.3
6.6
7.5
7.8
7.9
7.8
7.6
7.5
7.6
7.5
7.3
7.6
7.5
7.6
7.9
7.9
8.1
8.2
8
7.5
6.8
6.5
6.6
7.6
8
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8
8.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52017&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52017&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52017&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135



Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Uurlonen ; par6 = Inflatie ; par7 = Werkloosheidsgraad mannen ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Uurlonen ; par6 = Inflatie ; par7 = Werkloosheidsgraad mannen ;
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()