Arguments to run Maxent in R

[Daisy]

Hello,

Is there a list of the Arguments that can be used for Maxent in R?

Using google I came across one very ugly source:

http://code.google.com/p/api-maxent-programming/source/browse/trunk/src/edu/berkeley/mvz/amp/MaxEntRun.java?spec=svn15&r=15

This is from 2009 and I know Maxent has more features now.

Thanks,

Daisy

[Marwa Waseem]

Hi Daisy,,

Attached is tutorial for species distribution modelling with R, in that account for the use of Maxent in R. It is not so detailed but I hope it would work for you.

Best,,,,

 

Marwa Waseem

---

From: Daisy <daisy....@gmail.com>
To: Maxent <max...@googlegroups.com>
Sent: Sunday, April 29, 2012 10:24 PM
Subject: Arguments to run Maxent in R

--
You received this message because you are subscribed to the Google Groups "Maxent" group.
To post to this group, send email to max...@googlegroups.com.
To unsubscribe from this group, send email to maxent+unsub...@googlegroups.com.
For more options, visit this group at http://groups.google.com/group/maxent?hl=en.

[John B]

You can either use Dismo, which is described in Marwa's attachment, or you can use the system() function to run Maxent from the command line, to have access to all the command line arguments (which is essentially what Dismo is doing anyway... Not sure but you may have more control over it doing it yourself though). Output won't automatically be returned to R, so you'd need to write a few lines to read it in.

[cicada]

Thanks for the info.....

I should note that our manuscript is not a methods paper and isn't
trying to do anything cutting-edge; we're just trying to figure out
what happened to some populations that went extinct recently. We
built all our models by including the extinct populations in our
distributional dataset, and then we look at where those populations
fall in the resulting models (bad places, mostly). In the end the
models suggest certain things, but they also suggest that this way of
approaching our question is limited.

I've seen discussions about Dismo, but then I found the system()
function in R and, after a bit of fiddling, got everything working
fine (except that I could not allocate as much memory to Maxent as I
usually do). But that's where I'm stuck-- I can read the results into
R, but I'm not entirely sure how to read both those results and the
results of my BIOMOD analyses in together and in a way that would
allow me to do a giant ensemble forecast of everything. Maybe I'm
over thinking this, but I'm also a little concerned that even if I
could do such a thing, I'd be having an apple/oranges problem-- Maxent
is not building or scoring its models in the same ways that the
methods within BIOMOD are. I'm not sure exactly what I'd be getting
by combining everything (assuming that I could find a way to do so),
and I'm not convinced that any such end result would be any better
than laying the Maxent and the BIOMOD ensemble prediction maps down on
top of each other, whipping up some sort of weighted (by what?)
average and calling it "good".

If it was so easy or so legitimate to combine everything and come up
with an ensemble, then everybody would be doing it. But recent
papers, such as this one....

Ballesteros-Mejia, L., Kitching, I.J. & Beck, J. (2011) Projecting the
potential invasion of the Pink Spotted Hawkmoth (Agrius cingulata)
across Africa. International Journal of Pest Management, 57, 153-159.

....are just dealing with Maxent and BIOMOD separately, which is what
we're doing.

So I'm curious about how other people wrestle with these issues......

[Daisy]

Hello,

Thanks for the replies but I am actually looking for a complete list
of "things" that can be used when you use the args argument to run
Maxent in R. This include things like the nohinge, nothreshold,
replicates, betamultiplier....

Here is an example of my current maxent run but I would like to
include things like Mess Analysis but I do not know the correct
"thing" to call it so I can add it to args. I would also like to know
the correct name for these "things". Is it the arguments for the
argument args?

maxent(x=ev,p=pres,path=paste(new.out.dir),args=c("betamultiplier=1","nohinge","nothreshold","replicates=5","nooutputgrids","projectionlayer=..............

On Apr 30, 3:24 pm, Daisy <daisy.duur...@gmail.com> wrote:
> Hello,
>
> Is there a list of the Arguments that can be used for Maxent in R?
>
> Using google I came across one very ugly source:
>

> http://code.google.com/p/api-maxent-programming/source/browse/trunk/s...

[corymerow]

hi daisy,
these args appear to be the same syntax as calling maxent from the r
using system as described in post above. you can figure out what to
call each 'thing' by first doing a trial run through maxent's gui with
all the settings you want and then checking the java command at the
bottom of the html summary file. that gives the command for each
thing, which you can then use for all subsequent analyses without
having to deal with the gui. i've found this useful for finding a few
quirks that aren't mentioned in maxent's helpfile (e.g. related to
bias surfaces) or to work around flags that i thought should work, but
didn't.
cory

On May 3, 10:30 pm, Daisy <daisy.duur...@gmail.com> wrote:
> Hello,
>

> Thanks for the replies but I am actually looking for a complete list
> of "things" that can be used when you use the args argument to run
> Maxent in R. This include things like the nohinge, nothreshold,
> replicates, betamultiplier....
>
> Here is an example of my current maxent run but I would like to
> include things like Mess Analysis but I do not know the correct
> "thing" to call it so I can add it to args. I would also like to know
> the correct name for these "things". Is it the arguments for the
> argument args?
>

> maxent(x=ev,p=pres,path=paste(new.out.dir),args=c("betamultiplier=1","nohin ge","nothreshold","replicates=5","nooutputgrids","projectionlayer=......... .....

[Gerardo de la Vega]

as said Fred at the bottom from the HELP file there is a complete list:

the list!:

Flag	Abbrv	Type	Default	Meaning
responsecurves	P	boolean	false	Create graphs showing how predicted relative probability of occurrence depends on the value of each environmental variable
pictures		boolean	true	Create a .png image for each output grid
jackknife	J	boolean	false	Measure importance of each environmental variable by training with each environmental variable first omitted, then used in isolation
outputformat		string	logistic	Representation of probabilities used in writing output grids. See Help for details
outputfiletype		string	asc	File format used for writing output grids
outputdirectory	o	directory		Directory where outputs will be written. This should be different from the environmental layers directory.
projectionlayers	j	file/directory		Location of an alternate set of environmental variables. Maxent models will be projected onto these variables. Can be a .csv file (in SWD format) or a directory containing one file per variable. Multiple projection files/directories can be separated by commas.
samplesfile	s	file		Please enter the name of a file containing presence locations for one or more species.
environmentallayers	e	file/directory		Environmental variables can be in a directory containing one file per variable, or all together in a .csv file in SWD format. Please enter a directory name or file name.
randomseed		boolean	false	If selected, a different random seed will be used for each run, so a different random test/train partition will be made and a different random subset of the background will be used, if applicable.
logscale		boolean	true	If selected, all pictures of models will use a logarithmic scale for color-coding.
warnings		boolean	true	Pop up windows to warn about potential problems with input data. Regardless of this setting, warnings are always printed to the log file.
tooltips		boolean	true	Show messages that explain various parts of the interface, like this message
askoverwrite	r	boolean	true	If output files already exist for a species being modeled, pop up a window asking whether to overwrite or skip. Default is to overwrite.
skipifexists	S	boolean	false	If output files already exist for a species being modeled, skip the species without remaking the model.
removeduplicates		boolean	true	Remove duplicate presence records. If environmental data are in grids, duplicates are records in the same grid cell. Otherwise, duplicates are records with identical coordinates.
writeclampgrid		boolean	true	Write a grid that shows the spatial distribution of clamping. At each point, the value is the absolute difference between prediction values with and without clamping.
writemess		boolean	true	A multidimensional environmental similarity surface (MESS) shows where novel climate conditions exist in the projection layers. The analysis shows both the degree of novelness and the variable that is most out of range at each point.
randomtestpoints	X	integer	0	Percentage of presence localities to be randomly set aside as test points, used to compute AUC, omission etc.
betamultiplier	b	double	1.0	Multiply all automatic regularization parameters by this number. A higher number gives a more spread-out distribution.
maximumbackground	MB	integer	10000	If the number of background points / grid cells is larger than this number, then this number of cells is chosen randomly for background points
biasfile		file		Sampling is assumed to be biased according to the sampling distribution given in this grid file. Values in this file must not be zero or negative. MaxEnt will factor out the bias. Requires environmental data to be in grids, rather than a SWD format file
testsamplesfile	T	file		Use the presence localities in this file to compute statistics (AUC, omission etc.) The file can contain different localities for different species. It takes precedence over the random test percentage.
replicates		integer	1	Number of replicate runs to do when cross-validating, bootstrapping or doing sampling with replacement runs
replicatetype		string	crossvalidate	If replicates > 1, do multiple runs of this type: Crossvalidate: samples divided into replicates folds; each fold in turn used for test data. Bootstrap: replicate sample sets chosen by sampling with replacement. Subsample: replicate sample sets chosen by removing random test percentage without replacement to be used for evaluation.
perspeciesresults		boolean	false	Write separate maxentResults file for each species
writebackgroundpredictions		boolean	false	Write .csv file with predictions at background points
responsecurvesexponent		boolean	false	Instead of showing the logistic value for the y axis in response curves, show the exponent (a linear combination of features)
linear	l	boolean	true	Allow linear features to be used
quadratic	q	boolean	true	Allow quadratic features to be used
product	p	boolean	true	Allow product features to be used
threshold		boolean	true	Allow threshold features to be used
hinge	h	boolean	true	Allow hinge features to be used
addsamplestobackground	d	boolean	true	Add to the background any sample for which has a combination of environmental values that isn't already present in the background
addallsamplestobackground		boolean	false	Add all samples to the background, even if they have combinations of environmental values that are already present in the background
autorun	a	boolean	false	Start running as soon as the the program starts up
writeplotdata		boolean	false	Write output files containing the data used to make response curves, for import into external plotting software
fadebyclamping		boolean	false	Reduce prediction at each point in projections by the difference between clamped and non-clamped output at that point
extrapolate		boolean	true	Predict to regions of environmental space outside the limits encountered during training
visible	z	boolean	true	Make the Maxent user interface visible
autofeature	A	boolean	true	Automatically select which feature classes to use, based on number of training samples
doclamp		boolean	true	Apply clamping when projecting
outputgrids	x	boolean	true	Write output grids. Turning this off when doing replicate runs causes only the summary grids (average, std deviation etc.) to be written, not those for the individual runs.
plots		boolean	true	Write various plots for inclusion in .html output
appendtoresultsfile		boolean	false	If false, maxentResults.csv file is reinitialized before each run
maximumiterations	m	integer	500	Stop training after this many iterations of the optimization algorithm
convergencethreshold	c	double	1.0E-5	Stop training when the drop in log loss per iteration drops below this number
adjustsampleradius		integer	0	Add this number of pixels to the radius of white/purple dots for samples on pictures of predictions. Negative values reduce size of dots.
threads		integer	1	Number of processor threads to use. Matching this number to the number of cores on your computer speeds up some operations, especially variable jackknifing.
lq2lqptthreshold		integer	80	Number of samples at which product and threshold features start being used
l2lqthreshold		integer	10	Number of samples at which quadratic features start being used
hingethreshold		integer	15	Number of samples at which hinge features start being used
beta_threshold		double	-1.0	Regularization parameter to be applied to all threshold features; negative value enables automatic setting
beta_categorical		double	-1.0	Regularization parameter to be applied to all categorical features; negative value enables automatic setting
beta_lqp		double	-1.0	Regularization parameter to be applied to all linear, quadratic and product features; negative value enables automatic setting
beta_hinge		double	-1.0	Regularization parameter to be applied to all hinge features; negative value enables automatic setting
logfile		string	maxent.log	File name to be used for writing debugging information about a run in output directory
cache		boolean	true	Make a .mxe cached version of ascii files, for faster access
defaultprevalence		double	0.5	Default prevalence of the species: probability of presence at ordinary occurrence points. See Elith et al., Diversity and Distributions, 2011 for details.
applythresholdrule		string		Apply a threshold rule, generating a binary output grid in addition to the regular prediction grid. Use the full name of the threshold rule in Maxent's html output as the argument. For example, 'applyThresholdRule=Fixed cumulative value 1'.
togglelayertype	t	string		Toggle continuous/categorical for environmental layers whose names begin with this prefix (default: all continuous)
togglespeciesselected	E	string		Toggle selection of species whose names begin with this prefix (default: all selected)
togglelayerselected	N	string		Toggle selection of environmental layers whose names begin with this prefix (default: all selected)
verbose	v	boolean	false	Gived detailed diagnostics for debugging
allowpartialdata		boolean	false	During model training, allow use of samples that have nodata values for one or more environmental variables.
prefixes		boolean	true	When toggling samples or layers selected or layer types, allow toggle string to be a prefix rather than an exact match.
nodata	n	integer	-9999	Value to be interpreted as nodata values in SWD sample data

El jueves, 23 de mayo de 2013 05:09:32 UTC-3, Fred Feuerstein escribió:

Hey Daisy,

you probably already came across it--run the jar file and hit the help button. There is a complete list of all arguments that maxent accepts and how to pass them to maxent. You use R--check examples in dismo::maxent doc for the syntax arg=c("arg1 arg2 ...")

cheers