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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_density.wasp
Title produced by softwareKernel Density Estimation
Date of computationWed, 31 Jan 2018 19:43:06 +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/2018/Jan/31/t1517424256qgn8ndw2q3459oo.htm/, Retrieved Mon, 06 May 2024 20:08:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=313102, Retrieved Mon, 06 May 2024 20:08:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kernel Density Estimation] [ook vraag 8] [2018-01-31 18:43:06] [76161aa76684ab75eda7753df0aa1ca0] [Current]
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Dataseries X:
-7.34658641160781
-3.83393270509477
0.601079165693089
10.1708123315415
-19.3839869794365
76.9922425740189
5.53070349660016
-40.6642925799497
7.89237275190129
17.2902637570394
12.6760697817719
19.6464948843585
-22.8607017650403
24.2145566602915
-102.787405880591
-22.146815361688
19.5158080760987
-27.5133483228974
32.8225102202477
-23.6235769736771
9.83643314161893
4.60698806707138
70.5427891086114
50.2208190276499
-0.61017208092774
109.512600045334
38.3844237614283
-17.1329178076636
9.89146516204617
8.93506884166256
-50.6089193933982
6.2483062499274
-15.4577854355838
17.4048085530475
-39.2380533193169
-41.6349321334386
5.69033922999009
31.4039155628394
20.6034212187875
4.54632743294801
-32.8317960857246
5.29772239563853
18.8811727936388
21.0417879652906
-34.8082213857578
32.3312855186524
-31.5422133188891
-6.96524384146905
-51.1887131351437
-19.6210349753855
16.3293278006868
-54.3249050913563
11.0791037716048
51.4231162089253
-17.4375959401962
-28.8303943807569
23.0112582539784
-6.31347225533265
-53.84077421835
-12.2495235162429
13.1904099411244
8.39642203599642
-42.7811420681513
59.8084957547755
36.2483062499274
-29.8633361331903
38.3824605416578
76.0113792797905
45.9664338814935
-1.17612597762016
21.4146723322159
-26.7205970597839
-36.2752826830959
-1.78601535890883
-12.2530318706035
2.56265100567706
1.16142391932059
9.31058249798769
-27.9481685557842
-35.9069347340566
-16.4696314381811
7.28921972470831
-19.3587966655329
1.19328162042652
22.2468389243388
0.58809659244866
-19.1512627690297
-32.8384189427771
-19.9199397354674
28.5582080595397
-27.2895444904127
20.3171435437495
39.2813121694082
-9.73130430948931
-0.942375677488168
9.60548926754501
18.1714456892322
-4.9384020042528
-7.56232751201247
12.8399739909504
-30.4867554511851
-3.63900096482931
-17.8985321242085
30.4073651037041
-5.7272065943191
-9.33170040228831
-5.54115129593736
-14.796117433813
-55.8251391362126
-7.45031070418756
7.46339499060843
3.41546646113855
-77.9853494545382
-70.9245602064598
25.0086309727327
29.2440128930041
10.2707157618025
-13.3739967262292
60.4465269458381
27.8033040934051
28.783673988851
-65.8301244767567
18.4628713819454
-89.4987213214184
-19.623252462839
-11.2502101051705
9.82813335318713
-20.7816766235179
40.6664595968392
-28.5945915143623
-10.6826220633439
26.0439497578208
-9.82613598785402
-7.37666572389216
-50.7149932897269
-40.8176172241267
-1.70087235956341
-99.8017389500321
14.137527638575
-11.3700983552581
-11.5851921361629
43.8639647275284
32.0648275157806
1.17526571711362
15.5921920420822
5.30726906493565
0.309518089321937
-25.6242515771811
0.855347084597154
45.8259602848201
34.6050928277554
-22.2974114549406
25.724966525478
38.2553520465631
-20.4191182276973
5.13598732690238
36.9150764336932
-22.4415575475537
17.9049156953637
-14.7084067380213
20.6994781446868
4.38462512248503
35.8516667812385
-124.97712092823
64.3234036524464
-31.3808056049636
22.9818734549562
9.2251214183814
7.1626781219956
-6.1401584954662
10.5281938816587
19.0310230212635
40.8664924012808
38.6731382905774
38.6731382905774
28.9974598982497
-6.40376507741122
23.1365593461027
56.3239790494825
-81.4622605682184
-6.58429493929693
26.0776190049686
19.861114006955
5.20928321135139
57.0212696008941
9.45682352090343
7.45682352090343
12.4568235209034
16.306500717659
-34.7239638045267
-27.969678691805
33.4925597898478
-10.2838683863397
22.2460928279007
-13.1050628287336
-18.4535558221218
9.35706193361562
3.31777871462933
-9.6207490651828
18.7281864038017
89.783673988851
-23.6846828893207
13.0052033197453
21.7361067500842
-3.67581318460959
-28.9608756213021
-76.9932529907464
-32.6912391121651
49.37539265306
17.3415441214018
-49.6868480326369
25.4616632245656
-21.8286948794475
-12.784343749941
14.6582683760197
-21.787055413815
9.07381431646217
48.8846677481931
-33.938148589021
10.7993040682602
13.2288911666416
-33.8784514839943
-23.4321103218372
7.38081929087432
39.519507078168
-41.8079987923958
9.4401045191812
1.73576760864493
-11.368712656493
14.4103106601748
36.7926918156455
11.4415062241488
3.58294561440227
42.2508217717385
-29.784849680975
-79.348398518174
-47.2453527162769
22.0010904512135
-55.9351724842194
-1.49485326878916
-25.0235049393696
-28.114665908017
15.240449060843
64.3234036524464
-1.89631364815309
29.7923853622617
31.3960625611379
-13.8095321274413
-7.75535772813713
10.663145044613
12.0411114253833
-54.9164187819607
5.60624912728185
-11.4532615656104
36.1205139843701
14.201605571223
-21.5211895057792
-47.5902501757404
11.8037424584265
25.0071469888737
-31.3808056049636
-9.64807162553254
7.74461357567001
15.240449060843
8.14127045873425
-17.663610396475
-23.8619452129315
22.111100502846
24.4276514774178
-35.2637422077927
25.4358785498882
-11.1428696590303
9.01713310814046
12.6078628048154
19.1899681873745
-22.1745442232816
21.1920012076042
-67.4087619189756




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=313102&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=313102&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=313102&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center







Properties of Density Trace
Bandwidth8.72809346321312
#Observations278

\begin{tabular}{lllllllll}
\hline
Properties of Density Trace \tabularnewline
Bandwidth & 8.72809346321312 \tabularnewline
#Observations & 278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=313102&T=1

[TABLE]
[ROW][C]Properties of Density Trace[/C][/ROW]
[ROW][C]Bandwidth[/C][C]8.72809346321312[/C][/ROW]
[ROW][C]#Observations[/C][C]278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=313102&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=313102&T=1

As an alternative you can also use a QR Code:  

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

Properties of Density Trace
Bandwidth8.72809346321312
#Observations278







Maximum Density Values
Kernelx-valuemax. density
Gaussian10.5121508246330.0139305407358149
Epanechnikov10.5121508246330.0135767509491992
Rectangular7.143951821664190.0134832856796534
Triangular9.389417823643380.0138752981504045
Biweight9.950784324138170.0136839114608466
Cosine9.950784324138170.0137258830096594
Optcosine10.5121508246330.0136067670673888

\begin{tabular}{lllllllll}
\hline
Maximum Density Values \tabularnewline
Kernel & x-value & max. density \tabularnewline
Gaussian & 10.512150824633 & 0.0139305407358149 \tabularnewline
Epanechnikov & 10.512150824633 & 0.0135767509491992 \tabularnewline
Rectangular & 7.14395182166419 & 0.0134832856796534 \tabularnewline
Triangular & 9.38941782364338 & 0.0138752981504045 \tabularnewline
Biweight & 9.95078432413817 & 0.0136839114608466 \tabularnewline
Cosine & 9.95078432413817 & 0.0137258830096594 \tabularnewline
Optcosine & 10.512150824633 & 0.0136067670673888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=313102&T=2

[TABLE]
[ROW][C]Maximum Density Values[/C][/ROW]
[ROW][C]Kernel[/C][C]x-value[/C][C]max. density[/C][/ROW]
[ROW][C]Gaussian[/C][C]10.512150824633[/C][C]0.0139305407358149[/C][/ROW]
[ROW][C]Epanechnikov[/C][C]10.512150824633[/C][C]0.0135767509491992[/C][/ROW]
[ROW][C]Rectangular[/C][C]7.14395182166419[/C][C]0.0134832856796534[/C][/ROW]
[ROW][C]Triangular[/C][C]9.38941782364338[/C][C]0.0138752981504045[/C][/ROW]
[ROW][C]Biweight[/C][C]9.95078432413817[/C][C]0.0136839114608466[/C][/ROW]
[ROW][C]Cosine[/C][C]9.95078432413817[/C][C]0.0137258830096594[/C][/ROW]
[ROW][C]Optcosine[/C][C]10.512150824633[/C][C]0.0136067670673888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=313102&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=313102&T=2

As an alternative you can also use a QR Code:  

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

Maximum Density Values
Kernelx-valuemax. density
Gaussian10.5121508246330.0139305407358149
Epanechnikov10.5121508246330.0135767509491992
Rectangular7.143951821664190.0134832856796534
Triangular9.389417823643380.0138752981504045
Biweight9.950784324138170.0136839114608466
Cosine9.950784324138170.0137258830096594
Optcosine10.5121508246330.0136067670673888







Kernel Density Values
x-valueGaussianEpanechnikovRectangularTriangularBiweightCosineOptcosine
Kernel Density Values are not shown

\begin{tabular}{lllllllll}
\hline
Kernel Density Values \tabularnewline
x-value & Gaussian & Epanechnikov & Rectangular & Triangular & Biweight & Cosine & Optcosine \tabularnewline
Kernel Density Values are not shown \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=313102&T=3

[TABLE]
[ROW][C]Kernel Density Values[/C][/ROW]
[ROW][C]x-value[/C][C]Gaussian[/C][C]Epanechnikov[/C][C]Rectangular[/C][C]Triangular[/C][C]Biweight[/C][C]Cosine[/C][C]Optcosine[/C][/ROW]
[ROW][C]Kernel Density Values are not shown[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=313102&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=313102&T=3

As an alternative you can also use a QR Code:  

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

Kernel Density Values
x-valueGaussianEpanechnikovRectangularTriangularBiweightCosineOptcosine
Kernel Density Values are not shown



Parameters (Session):
Parameters (R input):
par1 = 0 ; par2 = no ; par3 = 512 ;
R code (references can be found in the software module):
if (par1 == '0') bw <- 'nrd0'
if (par1 != '0') bw <- as.numeric(par1)
par3 <- as.numeric(par3)
mydensity <- array(NA, dim=c(par3,8))
bitmap(file='density1.png')
mydensity1<-density(x,bw=bw,kernel='gaussian',na.rm=TRUE)
mydensity[,8] = signif(mydensity1$x,3)
mydensity[,1] = signif(mydensity1$y,3)
plot(mydensity1,main='Gaussian Kernel',xlab=xlab,ylab=ylab)
grid()
dev.off()
mydensity1
bitmap(file='density2.png')
mydensity2<-density(x,bw=bw,kernel='epanechnikov',na.rm=TRUE)
mydensity[,2] = signif(mydensity2$y,3)
plot(mydensity2,main='Epanechnikov Kernel',xlab=xlab,ylab=ylab)
grid()
dev.off()
bitmap(file='density3.png')
mydensity3<-density(x,bw=bw,kernel='rectangular',na.rm=TRUE)
mydensity[,3] = signif(mydensity3$y,3)
plot(mydensity3,main='Rectangular Kernel',xlab=xlab,ylab=ylab)
grid()
dev.off()
bitmap(file='density4.png')
mydensity4<-density(x,bw=bw,kernel='triangular',na.rm=TRUE)
mydensity[,4] = signif(mydensity4$y,3)
plot(mydensity4,main='Triangular Kernel',xlab=xlab,ylab=ylab)
grid()
dev.off()
bitmap(file='density5.png')
mydensity5<-density(x,bw=bw,kernel='biweight',na.rm=TRUE)
mydensity[,5] = signif(mydensity5$y,3)
plot(mydensity5,main='Biweight Kernel',xlab=xlab,ylab=ylab)
grid()
dev.off()
bitmap(file='density6.png')
mydensity6<-density(x,bw=bw,kernel='cosine',na.rm=TRUE)
mydensity[,6] = signif(mydensity6$y,3)
plot(mydensity6,main='Cosine Kernel',xlab=xlab,ylab=ylab)
grid()
dev.off()
bitmap(file='density7.png')
mydensity7<-density(x,bw=bw,kernel='optcosine',na.rm=TRUE)
mydensity[,7] = signif(mydensity7$y,3)
plot(mydensity7,main='Optcosine Kernel',xlab=xlab,ylab=ylab)
grid()
dev.off()
load(file='createtable')
ab<-table.start()
ab<-table.row.start(ab)
ab<-table.element(ab,'Properties of Density Trace',2,TRUE)
ab<-table.row.end(ab)
ab<-table.row.start(ab)
ab<-table.element(ab,'Bandwidth',header=TRUE)
ab<-table.element(ab,mydensity1$bw)
ab<-table.row.end(ab)
ab<-table.row.start(ab)
ab<-table.element(ab,'#Observations',header=TRUE)
ab<-table.element(ab,mydensity1$n)
ab<-table.row.end(ab)
ab<-table.end(ab)
a <- ab
table.save(ab,file='mytable123.tab')
b<-table.start()
b<-table.row.start(b)
b<-table.element(b,'Maximum Density Values',3,TRUE)
b<-table.row.end(b)
b<-table.row.start(b)
b<-table.element(b,'Kernel',1,TRUE)
b<-table.element(b,'x-value',1,TRUE)
b<-table.element(b,'max. density',1,TRUE)
b<-table.row.end(b)
b<-table.row.start(b)
b<-table.element(b,'Gaussian',1,TRUE)
b<-table.element(b,mydensity1$x[mydensity1$y==max(mydensity1$y)],1)
b<-table.element(b,mydensity1$y[mydensity1$y==max(mydensity1$y)],1)
b<-table.row.end(b)
b<-table.row.start(b)
b<-table.element(b,'Epanechnikov',1,TRUE)
b<-table.element(b,mydensity2$x[mydensity2$y==max(mydensity2$y)],1)
b<-table.element(b,mydensity2$y[mydensity2$y==max(mydensity2$y)],1)
b<-table.row.end(b)
b<-table.row.start(b)
b<-table.element(b,'Rectangular',1,TRUE)
b<-table.element(b,mydensity3$x[mydensity3$y==max(mydensity3$y)],1)
b<-table.element(b,mydensity3$y[mydensity3$y==max(mydensity3$y)],1)
b<-table.row.end(b)
b<-table.row.start(b)
b<-table.element(b,'Triangular',1,TRUE)
b<-table.element(b,mydensity4$x[mydensity4$y==max(mydensity4$y)],1)
b<-table.element(b,mydensity4$y[mydensity4$y==max(mydensity4$y)],1)
b<-table.row.end(b)
b<-table.row.start(b)
b<-table.element(b,'Biweight',1,TRUE)
b<-table.element(b,mydensity5$x[mydensity5$y==max(mydensity5$y)],1)
b<-table.element(b,mydensity5$y[mydensity5$y==max(mydensity5$y)],1)
b<-table.row.end(b)
b<-table.row.start(b)
b<-table.element(b,'Cosine',1,TRUE)
b<-table.element(b,mydensity6$x[mydensity6$y==max(mydensity6$y)],1)
b<-table.element(b,mydensity6$y[mydensity6$y==max(mydensity6$y)],1)
b<-table.row.end(b)
b<-table.row.start(b)
b<-table.element(b,'Optcosine',1,TRUE)
b<-table.element(b,mydensity7$x[mydensity7$y==max(mydensity7$y)],1)
b<-table.element(b,mydensity7$y[mydensity7$y==max(mydensity7$y)],1)
b<-table.row.end(b)
b<-table.end(b)
a <- b[1]
table.save(b,file='mytable2a.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Kernel Density Values',8,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'x-value',1,TRUE)
a<-table.element(a,'Gaussian',1,TRUE)
a<-table.element(a,'Epanechnikov',1,TRUE)
a<-table.element(a,'Rectangular',1,TRUE)
a<-table.element(a,'Triangular',1,TRUE)
a<-table.element(a,'Biweight',1,TRUE)
a<-table.element(a,'Cosine',1,TRUE)
a<-table.element(a,'Optcosine',1,TRUE)
a<-table.row.end(a)
if (par2=='yes') {
for(i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,mydensity[i,8],1,TRUE)
for(j in 1:7) {
a<-table.element(a,mydensity[i,j],1)
}
a<-table.row.end(a)
}
} else {
a<-table.row.start(a)
a<-table.element(a,'Kernel Density Values are not shown',8)
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')