danlwarren/enmtools

ENMTools R Package

enmtools's Issues

Display empirical overlaps in ecospat summaries

They're in the output under $test.results$obs, but aren't given in the summaries.

Should visualize.enm default to PCA?

Just a thought - if passed an env with more than two layers, can we bash things together so that visualize.enm does a PCA on those layers and plots model predictions in that PC space? It could definitely be quite complicated to do - we're passing in a model that makes predictions onto the raw predictor values as well as some test data that we need to associate with predictor values (although that's already being done). It would be easy to just make the PCA heatmap of model predictions, but getting the background density and points into that same PC space could be quite challenging.

Model outputs should contain species.name

Maxent with multiple cores

Suggested by Andrey Lissovsky:

Maxent uses parameter of its temp directory (path="") and writes a number of files with identical names there. Maxent will work on parallel cores only if "path" parameter will be unique for each core. Otherwise it will overwrite its own files.

Ecospat tests should default to doing PCA when more than two layers provided

Seems like the reasonable thing to do.

checks should accept NULL or NA

Ecospat tests (and maybe others) not handling NAs well

Ecospat tests are currently dying if any occurrence points return NA from the environmental layers. That's due to this line:

sp1.bg.env <- cbind(rep(paste0(species.1$species.name, ".bg"), nrow(species.1$background.points)),
species.1$background.points,
sp1.bg.env[complete.cases(sp1.bg.env),])

And the same for sp2. Basically the "complete.cases" bit is throwing away the NAs, which results in a mismatch of column lengths. Should probably just cbind everything together and then do complete.cases after. Not sure how widespread this issue is, but could be affecting other tests as well.

Look at incorporating McCormack et al. tests

Eh?

Get some testthat code working and integrate travis CI

Hook in ENMEval

Get some of that spatially structured crossvalidation realness UIH

See if we can figure out env.breadth and env.overlap for BC/DM models

For some reason the predict function isn't liking these models in environment space. The workaround we've got in place is just to not calculate it if the results look weird, but that's not ideal.

Need to auto-recognize lat and long columns

Ideally we should vectorize RTS code

And maybe add some data/predictor bootstrapping options too?

Generic interactive plots function

It could be nice to have a single function called "interactive.plots" or something like that that takes models, species, and test results (when appropriate) and calls a dedicated interactive plot function based on that class.

enmtools.aoc is returning screwy p values

See here:

https://groups.google.com/forum/#!topic/maxent/BTuEaa-IRfs

Make a function to drop species from clade

Drop from list and tree

Need to implement Raes and ter Steege test for significance

Get enmtools.aoc working with overlap matrix

The argument is already there, but the function isn't really set up to handle it. Shouldn't take long.

Add `ranger` based random forest method

Though the RandomForest package is still the most popular by a wide margin, and still works pretty good, it is getting a little long in the tooth. There are several newer packages for random forest that are much faster and more feature rich, including the ranger package (https://github.com/imbs-hl/ranger). I think it would make sense to implement a new random forest-based model with ranger (this would help with the phylogenetic eigenvector stuff I want to do too). I'm happy to implement this if you are not against it. I won't replace enmtools.rf just yet, but rather add an enmtools.rf.ranger or something like that.

Get better demo data and code

check.species should cast lat/lon matrices to df

See above

Niche Overlap

Dear ENMtools users/developers

As a part of my research, I want to calculate “niche overlap” for two species (one carnivore and one herbivore species). I have used MaxEnt to produce ENM for each one in the raw format (MaxEnt 3.4.1). I found that there are some functions in ENMtool which can be used for this purpose, but my problem is that these suitability layers have resulted from different background points (10000 points) and also the number of presence points which are used in MaxEnt is not same for these species. Could you please tell me your point of view in this way?

Best regards

Normalisation code in enmtools.ppmlasso isn't working

Travis CI is barfing an error based on this bit:

if(normalise) {
getValues(suitability) <- getValues(suitability) / sum(getValues(suitability), na.rm = TRUE)
}

It seems like the issue is that you can't use getValues to assign values to the raster. There's actually a function in ENMTools for this which I think we could use, raster.standardize. You can just do:

if(normalise) {
suitability <- raster.standardize(suitability)
}

I went ahead and did that on the cranify branch, but we'll need to do some checking before merging it in to master.

Species names on enmtools.model plots

See if I can hook in the hypervolume packages as in Blonder et al. 2014

How to run ENMTools using predefined regulirization multiplier, feature type and background points

Dear ENMTools users
I need to use this package to produce habitat suitability layers for some species. I have found the optimum regularization multiplier and also feature type before, but I can't find the relevant arguments for these settings in "enmtools.maxent". also I have the same problem to use predefined background points instead of random points. Any comments to solve the problem is highly appreciated.

Make env space metrics polymorphic

Should accept either a set of layers (as currently implemented) or a list of limits.

Env Breadth calculations of B1 are acting weird

B1 in env space seems to just be a perfectly smooth increasing function of the number of generations. That's obviously not right at all. Here's B1:

And here's B2, acting as it should:

Notice that there's a large difference in scales there.

I'm playing around with it, but I haven't yet figured out what the issue is.

Change density plots to histograms

Also consider doing the CIs like RWTY does.

Node overlap function craps out when node labels exist

Currently I've got it set up on the RF branch to just set node labels to NULL within the function. That's not ideal, though; need to chase down what's going on there and fix it.

Maxent suitabilities not plotting

Should be able to add shapefile as background area

Predict function for projecting to other times/places

We need something we can pass enmtools.model objects to to make projections to other times/places. Would be nice to wrap those prediction objects up with some sort of other plots comparing environment spaces, etc.

Need to change a lot of stuff to match.arg and a switch

marginal.plots explodes if there are any missing values

The issue is with density(). Any NAs causes it to fail. There is a na.rm parameter in density(), so the easiest would be to just add this in. At the moment this causes the entire modeling function to error out, so would be good to fix this, since NAs are not uncommon to get (e.g. if a few points fall off the coast, or in a lake, or something, where the env raster has no value).

This also made me think it might be worth adding some flags to the parameters of the main modelling function to turn on or off different parts of the function. In other words, I may not need marginal.plots, so I can turn them off to, 1) speed up function, or 2) if marginal.plot (or any other section) is causing an error, I can just turn it off, rather than having to post an issue and getting it fixed before I can proceed with my analysis.

Rangebreak summary maps

Make map summarizing blob reps
Store maps in rangebreak objects

plot.enmtools.species does not work if the enmtools.species object has no `range`

If the enmtools.species$range is empty, plot.enmtools.species does not work, because plotting the presence and background points is done with points(), which does not work if there is not already a plot on the graphics device (which only happens if the 'range' slot is not missing, currently.

AOC plots should have y intercept at age = 0

Parallelization

is a funny word in that I can never remember how many Ls go where

Add more advanced features of ppmlasso

Add options to enmtools.ppmlasso to do more of the advanced features of ppmlasso, such as point interaction models, which allows occurrence points to 'interact', e.g. attract or repulse each other to deal with clustering or overdispersion of sampling relative to a Poisson model. Also, automatically finding ideal number of background points, etc.

Plot test data in visualize.enm

Calls to evaluate() should be dismo::evaluate

Apparently it's causing some problems when Biomod is loaded.

AOC tests should be histograms, not density plots

Response plots is making plots for all variables in env

For models where a specific formula is passed, we need to make sure response.plots is only producing plots with those variables. As it is, it'll make a plot for everything in env, with just a flat line for variables that weren't used in the model.

Species from file

I really ought to write a function that goes through a .csv file and builds a species object for each unique value in the species column and runs it through check.species. Idea being you'd pass it a file with ten species' occurrences in it, it passes back a list of ten enmtools.species objects with species.name and presence.points fields automatically populated.

It'd be nice for a bunch of purposes, but even more so when people are doing analyses for entire clades.

Incorporate null model from Nunes and Pearson 2016?

What do you think about adding this null model for phylogenetic niche divergence?

See: http://onlinelibrary.wiley.com/doi/10.1111/jbi.12910/full

Prettier plots for enmtools.species and enmtools.clade objects

Can we set up rangebreak tests to only build models for unique splits?

Make sure there's some independence between pseudoreps and also reduce redundant processing time when you've already modeled a particular split.

Some of the modeling functions aren't recognizing RasterBrick objects

I should do something about that.

Identity test is sometimes not plotting all reps in histogram