Hi R users, I am trying to plot two dimensional posterior likelihood surface. I have a data like para1 para2 likehood ....... ........ ........... ....... ........ ........... I looked at contour plot but it needs a shorted values of parameters and a matrix of likelihood values. Is there any way to get the plot? or how can I change my likelihood values to a matrix for the function "contour"? Any suggestions are appreciated. GP University of Guelph Guelph, ON
Two dimensional likelihood surface plot
7 messages · David Winsemius, Duncan Murdoch, Bert Gunter +1 more
On Nov 11, 2014, at 11:17 AM, Gyanendra Pokharel wrote:
Hi R users, I am trying to plot two dimensional posterior likelihood surface. I have a data like para1 para2 likehood ....... ........ ........... ....... ........ ........... I looked at contour plot but it needs a shorted values of parameters and a matrix of likelihood values. Is there any way to get the plot? or how can I change my likelihood values to a matrix for the function "contour"?
If the data are organized in a regular manner, then this might succeed:
with( df, contour( x=unique(para1), y=unique(para2)
z= matrix( likehood, length(unique(para1), length(unique(para2) )
) )
Any suggestions are appreciated. GP University of Guelph Guelph, ON [[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
David Winsemius Alameda, CA, USA
On 11/11/2014, 2:17 PM, Gyanendra Pokharel wrote:
Hi R users, I am trying to plot two dimensional posterior likelihood surface. I have a data like para1 para2 likehood ....... ........ ........... ....... ........ ........... I looked at contour plot but it needs a shorted values of parameters and a matrix of likelihood values. Is there any way to get the plot? or how can I change my likelihood values to a matrix for the function "contour"?
The interp() function in the akima package should be able to do what you want. Duncan Murdoch
... as would the loess predict() method do from a loess() fit in the base package. I have used loess() for this purpose primarily to take advantage of its "robustness" capabilities. I hasten to add that the algorithm is not infallible in this regard, as it may not recover from a sufficiently bad set of (least squares)starting values, as ?loess explicitly says. It can also be slow. Cheers, Bert Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 "Data is not information. Information is not knowledge. And knowledge is certainly not wisdom." Clifford Stoll On Tue, Nov 11, 2014 at 12:56 PM, Duncan Murdoch
<murdoch.duncan at gmail.com> wrote:
On 11/11/2014, 2:17 PM, Gyanendra Pokharel wrote:
Hi R users, I am trying to plot two dimensional posterior likelihood surface. I have a data like para1 para2 likehood ....... ........ ........... ....... ........ ........... I looked at contour plot but it needs a shorted values of parameters and a matrix of likelihood values. Is there any way to get the plot? or how can I change my likelihood values to a matrix for the function "contour"?
The interp() function in the akima package should be able to do what you want. Duncan Murdoch
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Thanks David, what do you mean by organized data in regular manner? Gyanendra Pokharel University of Guelph Guelph, ON On Tue, Nov 11, 2014 at 3:53 PM, David Winsemius <dwinsemius at comcast.net> wrote:
On Nov 11, 2014, at 11:17 AM, Gyanendra Pokharel wrote:
Hi R users, I am trying to plot two dimensional posterior likelihood surface. I have
a
data like para1 para2 likehood ....... ........ ........... ....... ........ ........... I looked at contour plot but it needs a shorted values of parameters and
a
matrix of likelihood values. Is there any way to get the plot? or how
can I
change my likelihood values to a matrix for the function "contour"?
If the data are organized in a regular manner, then this might succeed:
with( df, contour( x=unique(para1), y=unique(para2)
z= matrix( likehood, length(unique(para1),
length(unique(para2) )
) )
Any suggestions are appreciated.
GP
University of Guelph
Guelph, ON
[[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius Alameda, CA, USA
On Nov 11, 2014, at 2:53 PM, Gyanendra Pokharel wrote:
Thanks David, what do you mean by organized data in regular manner?
This is what I meant by a regular manner:
matrix( c( rep( seq(0,1, by=.1), 11), rep( seq(0,1, by=.1),each=11) , runif(121) ), 121,3)
[,1] [,2] [,3]
[1,] 0.0 0.0 0.048946906
[2,] 0.1 0.0 0.332529489
[3,] 0.2 0.0 0.967099462
[4,] 0.3 0.0 0.565349269
[5,] 0.4 0.0 0.024230243
[6,] 0.5 0.0 0.421633329
[7,] 0.6 0.0 0.965847357
[8,] 0.7 0.0 0.719618276
[9,] 0.8 0.0 0.948675911
[10,] 0.9 0.0 0.180241643
[11,] 1.0 0.0 0.804828468
[12,] 0.0 0.1 0.713698501
[13,] 0.1 0.1 0.991003966
[14,] 0.2 0.1 0.936413540
[15,] 0.3 0.1 0.941731063
[16,] 0.4 0.1 0.373998953
[17,] 0.5 0.1 0.988915380
[18,] 0.6 0.1 0.500791201
[19,] 0.7 0.1 0.070137099
[20,] 0.8 0.1 0.968422057
[21,] 0.9 0.1 0.827396746
snipped the remain 100 lines
with( df, contour( x=unique(V1), y=unique(V2),
z= matrix( V3, length(unique(V1)), length(unique(V2) )
) ))
df <- as.data.frame( matrix( c( rep( seq(0,1, by=.1), 11), rep( seq(0,1, by=.1),each=11) , runif(121) ), 121,3))
str(df)
'data.frame': 121 obs. of 3 variables:
$ V1: num 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 ...
$ V2: num 0 0 0 0 0 0 0 0 0 0 ...
$ V3: num 0.628 0.661 0.163 0.57 0.527 ...
My original untested suggestion had some missing parentheses and a missing comma:
with( df, contour( x=unique(V1), y=unique(V2),
z= matrix( V3, length(unique(V1)), length(unique(V2) )
) ))
Gyanendra Pokharel University of Guelph Guelph, ON On Tue, Nov 11, 2014 at 3:53 PM, David Winsemius <dwinsemius at comcast.net> wrote: On Nov 11, 2014, at 11:17 AM, Gyanendra Pokharel wrote:
Hi R users, I am trying to plot two dimensional posterior likelihood surface. I have a data like para1 para2 likehood ....... ........ ........... ....... ........ ........... I looked at contour plot but it needs a shorted values of parameters and a matrix of likelihood values. Is there any way to get the plot? or how can I change my likelihood values to a matrix for the function "contour"?
If the data are organized in a regular manner, then this might succeed:
with( df, contour( x=unique(para1), y=unique(para2)
z= matrix( likehood, length(unique(para1), length(unique(para2) )
) )
Any suggestions are appreciated.
GP
University of Guelph
Guelph, ON
[[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
David Winsemius Alameda, CA, USA
David Winsemius Alameda, CA, USA
Thanks a lot, David ! Thats what I wanted. it is greatly helpful. GP University of Guelph Guelph, ON On Tue, Nov 11, 2014 at 7:02 PM, David Winsemius <dwinsemius at comcast.net> wrote:
On Nov 11, 2014, at 2:53 PM, Gyanendra Pokharel wrote:
Thanks David, what do you mean by organized data in regular manner?
This is what I meant by a regular manner:
matrix( c( rep( seq(0,1, by=.1), 11), rep( seq(0,1, by=.1),each=11) ,
runif(121) ), 121,3)
[,1] [,2] [,3]
[1,] 0.0 0.0 0.048946906
[2,] 0.1 0.0 0.332529489
[3,] 0.2 0.0 0.967099462
[4,] 0.3 0.0 0.565349269
[5,] 0.4 0.0 0.024230243
[6,] 0.5 0.0 0.421633329
[7,] 0.6 0.0 0.965847357
[8,] 0.7 0.0 0.719618276
[9,] 0.8 0.0 0.948675911
[10,] 0.9 0.0 0.180241643
[11,] 1.0 0.0 0.804828468
[12,] 0.0 0.1 0.713698501
[13,] 0.1 0.1 0.991003966
[14,] 0.2 0.1 0.936413540
[15,] 0.3 0.1 0.941731063
[16,] 0.4 0.1 0.373998953
[17,] 0.5 0.1 0.988915380
[18,] 0.6 0.1 0.500791201
[19,] 0.7 0.1 0.070137099
[20,] 0.8 0.1 0.968422057
[21,] 0.9 0.1 0.827396746
snipped the remain 100 lines
with( df, contour( x=unique(V1), y=unique(V2),
z= matrix( V3, length(unique(V1)), length(unique(V2) )
) ))
df <- as.data.frame( matrix( c( rep( seq(0,1, by=.1), 11), rep( seq(0,1,
by=.1),each=11) , runif(121) ), 121,3))
str(df)
'data.frame': 121 obs. of 3 variables:
$ V1: num 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 ...
$ V2: num 0 0 0 0 0 0 0 0 0 0 ...
$ V3: num 0.628 0.661 0.163 0.57 0.527 ...
My original untested suggestion had some missing parentheses and a missing
comma:
with( df, contour( x=unique(V1), y=unique(V2),
z= matrix( V3, length(unique(V1)), length(unique(V2) )
) ))
Gyanendra Pokharel University of Guelph Guelph, ON On Tue, Nov 11, 2014 at 3:53 PM, David Winsemius <dwinsemius at comcast.net>
wrote:
On Nov 11, 2014, at 11:17 AM, Gyanendra Pokharel wrote:
Hi R users, I am trying to plot two dimensional posterior likelihood surface. I
have a
data like para1 para2 likehood ....... ........ ........... ....... ........ ........... I looked at contour plot but it needs a shorted values of parameters
and a
matrix of likelihood values. Is there any way to get the plot? or how
can I
change my likelihood values to a matrix for the function "contour"?
If the data are organized in a regular manner, then this might succeed:
with( df, contour( x=unique(para1), y=unique(para2)
z= matrix( likehood, length(unique(para1),
length(unique(para2) )
) )
Any suggestions are appreciated.
GP
University of Guelph
Guelph, ON
[[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius Alameda, CA, USA David Winsemius Alameda, CA, USA