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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 computationMon, 02 Nov 2009 04:33:01 -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/2009/Nov/02/t1257161814piq6jg6vfa82fj1.htm/, Retrieved Fri, 03 May 2024 21:59:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52496, Retrieved Fri, 03 May 2024 21:59:31 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [De Belgische uitv...] [2009-10-13 01:35:54] [df6326eec97a6ca984a853b142930499]
F   PD  [Univariate Data Series] [Belgische uitvoer...] [2009-10-13 07:43:28] [74be16979710d4c4e7c6647856088456]
- RMPD      [Trivariate Scatterplots] [WS4-Trivariate Sc...] [2009-11-02 11:33:01] [0cc924834281808eda7297686c82928f] [Current]
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Dataseries X:
11881.4
10374.2
13828
13490.5
13092.2
13184.4
12398.4
13882.3
15861.5
13286.1
15634.9
14211
13646.8
12224.6
15916.4
16535.9
15796
14418.6
15044.5
14944.2
16754.8
14254
15454.9
15644.8
14568.3
12520.2
14803
15873.2
14755.3
12875.1
14291.1
14205.3
15859.4
15258.9
15498.6
15106.5
15023.6
12083
15761.3
16943
15070.3
13659.6
14768.9
14725.1
15998.1
15370.6
14956.9
15469.7
15101.8
11703.7
16283.6
16726.5
14968.9
14861
14583.3
15305.8
17903.9
16379.4
15420.3
17870.5
15912.8
13866.5
17823.2
17872
17420.4
16704.4
15991.2
16583.6
19123.5
17838.7
17209.4
18586.5
16258.1
15141.6
19202.1
17746.5
19090.1
18040.3
17515.5
17751.8
21072.4
17170
19439.5
19795.4
17574.9
16165.4
19464.6
19932.1
19961.2
17343.4
18924.2
18574.1
21350.6
18594.6
19823.1
20844.4
19640.2
17735.4
19813.6
22160
20664.3
17877.4
20906.5
21164.1
21374.4
22952.3
21343.5
23899.3
22392.9
18274.1
22786.7
22321.5
17842.2
16373.5
15993.8
16446.1
17729
16643
16196.7
18252.1
Dataseries Y:
423.4
404.1
500
472.6
496.1
562
434.8
538.2
577.6
518.1
625.2
561.2
523.3
536.1
607.3
637.3
606.9
652.9
617.2
670.4
729.9
677.2
710
844.3
748.2
653.9
742.6
854.2
808.4
1819
1936.5
1966.1
2083.1
1620.1
1527.6
1795
1685.1
1851.8
2164.4
1981.8
1726.5
2144.6
1758.2
1672.9
1837.3
1596.1
1446
1898.4
1964.1
1755.9
2255.3
1881.2
2117.9
1656.5
1544.1
2098.9
2133.3
1963.5
1801.2
2365.4
1936.5
1667.6
1983.5
2058.6
2448.3
1858.1
1625.4
2130.6
2515.7
2230.2
2086.9
2235
2100.2
2288.6
2490
2573.7
2543.8
2004.7
2390
2338.4
2724.5
2292.5
2386
2477.9
2337
2605.1
2560.8
2839.3
2407.2
2085.2
2735.6
2798.7
3053.2
2405
2471.9
2727.3
2790.7
2385.4
3206.6
2705.6
3518.4
1954.9
2584.3
2535.8
2685.9
2866
2236.6
2934.9
2668.6
2371.2
3165.9
2887.2
3112.2
2671.2
2432.6
2812.3
3095.7
2862.9
2607.3
2862.5
Dataseries Z:
286.1
307
358.1
341.8
378.8
375.2
295.6
362.7
409.6
336.8
389.1
389.3
355.9
542
648.4
452
582.4
506.5
555.5
530.4
609.4
543.9
616.2
634.6
541.7
549.8
627.6
797.4
689.8
1576.6
1572.1
1626.4
1972.4
1509.6
1584.9
1880
1324
1777.7
2172.4
1780.3
2134.9
1838.4
1557
1755.2
1702
1577.5
1485.9
2179.1
1740.9
1724.5
2328.1
1774.1
2224.2
1536.3
1521.2
2051.8
2483.1
1929.8
1808.6
2584.9
1997.9
1639.9
2379.1
1715
2750.9
1865.4
1647.4
2180.4
2593
2057.2
2635.8
2315.4
1863.6
2038
2235.8
2222.1
2636.9
2076.8
1935.5
2086.3
2470.9
1854.6
2041.3
2170.8
1905.5
2130.2
2791.2
2539.7
2661.3
1764.9
2176.9
2458.5
2179
2242.5
2089.6
2661.6
2112
2367.3
2543
2603.9
3146.7
1789.2
2114.8
2236.3
2288.1
2173.2
1877.7
2807.4
2357.4
2107.7
2856.8
2510.8
2875
2229.7
2055.1
2545.4
2775.1
2252.2
2091.7
2433




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52496&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]5 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=52496&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52496&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk



Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Totale export van België ; par6 = Export van farmaceutische producten uit België ; par7 = Import van farmaceutische producten uit België ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Totale export van België ; par6 = Export van farmaceutische producten uit België ; par7 = Import van farmaceutische producten uit België ;
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()