Description Usage Arguments Details Value References
Create an mbm model
1 2 3 4 |
y |
Square dissimilarity or distance matrix, can be complete or lower triangular
only. Row and column names are required and must match the site names in the
rows of |
x |
Matrix giving a series of covariates (in columns) for all sites (in rows). Row names are required. All variables will be included in the model. |
y_name |
A name to give to the y variable |
link |
Link function to use |
likelihood |
Likelihood function to use |
lengthscale |
Either NULL (in which case all lengthscales will be optimized) or
a numeric vector of length |
sparse |
Should we use the stochastic variational GP (see 'details'). |
force_increasing |
Boolean; if true, beta diversity will be constrained to increase with environmental distance |
sparse_inducing |
Number of inducing inputs to use if 'sparse = TRUE' |
sparse_batch |
Batch size to use if 'sparse = TRUE' |
sparse_iter |
Maximum number of optimizer iterations if 'sparse = TRUE' |
exact_thresh |
integer; threshold at which mbm will refuse to run an exact gp. |
verbose |
Should messages during model fitting be printed? |
For larger datasets (more than ~100 sites), it is recommended to use 'sparse=TRUE'. This will use a sparse approximation to the default method, following the stochastical variational GP (Hensman et al 2013). Note that if a link function is selected, it will be applied as a transformation of the y data–i.e., for link function L() we fit a SVGP to describe the expectation E(L(y))–rather than as a true link function– fitting L(E(y))–as is done when 'svgp=FALSE'. This is due to a limitation in the underlying GP library.
An S3 object of class mbm, containing the following components: * 'x': the original (untransformed) site by covariate matrix * 'y': the original (untransformed) site by site diversity data * 'covariates': Transformed x-variables supplied to mbm * 'response': Transformed response variable; this is the data supplied to mbm * 'covar_sites': Site names to match the covariate matrix * 'y_transform': transformation applied to y-data before modelling * 'y_rev_transform': reverse transformation to get y-data back on the original scale * 'link': a character string identifying the link function * 'inv_link': inverse of the link function * 'pyobj': A list of python objects used by the model; this is not meant for user interaction
Hensman J, Fusi N, and Lawrence ND. 2013. Gaussian Processes for Big Data. In: In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence.
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