| Title: | Markov Random Fields with Additional Covariates |
|---|---|
| Description: | Approximate node interaction parameters of Markov Random Fields graphical networks. Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) <doi:10.1002/ecy.2221>. |
| Authors: | Nicholas J Clark [aut, cre], Konstans Wells [aut], Oscar Lindberg [aut] |
| Maintainer: | Nicholas J Clark <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.39 |
| Built: | 2024-11-22 03:22:01 UTC |
| Source: | https://github.com/nicholasjclark/mrfcov |
A dataset containing binary occurrences of four blood parasite species in New Caledonian birds. The first four variables represent the parasite occurrences and the last variable is a scaled continuous covariate representing host relative abundance.
Bird.parasitesBird.parasites
A data frame with 449 rows and 5 variables:
binary occurrence of Haemoproteus zosteropis
binary occurrence of Haemoproteus killangoi
binary occurrence of Plasmdodium species
binary occurrence of Microfilaria species
scaled numeric variable representing relative abundance of Zosterops host species
Clark, N.J., Wells, K., Dimitrov, D. & Clegg, S.M. (2016) Co-infections and environmental conditions drive the distributions of blood parasites in wild birds. Journal of Animal Ecology, 85, 1461-1470.
This function runs MRFcov models multiple times to capture uncertainty
in parameter esimates. The dataset is shuffled and missing
values (if found) are imputed in each bootstrap iteration.
bootstrap_MRF( data, n_bootstraps, sample_seed, symmetrise, n_nodes, n_cores, n_covariates, family, sample_prop, spatial = FALSE, coords = NULL )bootstrap_MRF( data, n_bootstraps, sample_seed, symmetrise, n_nodes, n_cores, n_covariates, family, sample_prop, spatial = FALSE, coords = NULL )
data |
Dataframe. The input data where the |
n_bootstraps |
Positive integer. Represents the total number of bootstrap samples
to test. Default is |
sample_seed |
Numeric. Used as the seed value for generating bootstrap replicates, allowing users to generate replicated datasets on different systems. Default is a random seed |
symmetrise |
The method to use for symmetrising corresponding parameter estimates
(which are taken from separate regressions). Options are |
n_nodes |
Positive integer. The index of the last column in |
n_cores |
Integer. The number of cores to spread the job across using
|
n_covariates |
Positive integer. The number of covariates in |
family |
The response type. Responses can be quantitative continuous ( |
sample_prop |
Positive probability value indicating the proportion of rows to sample from
|
spatial |
Logical. If |
coords |
A two-column |
MRFcov models are fit via cross-validation using
cv.glmnet. For each model, the data is bootstrapped
by shuffling row observations and fitting models to a subset of observations
to account for uncertainty in parameter estimates.
Parameter estimates from the set of bootstrapped models are summarised
to present means and confidence intervals (as 95 percent quantiles).
A list containing:
direct_coef_means: dataframe containing mean coefficient values taken from all
bootstrapped models across the iterations
direct_coef_upper90 and direct_coef_lower90: dataframes
containing coefficient 95 percent and 5 percent quantiles taken from all
bootstrapped models across the iterations
indirect_coef_mean: list of symmetric matrices
(one matrix for each covariate) containing mean effects of covariates
on pairwise interactions
mean_key_coefs: list of matrices of length n_nodes
containing mean covariate coefficient values and their relative importances
(using the formula x^2 / sum (x^2)
taken from all bootstrapped models across iterations. Only coefficients
with mean relative importances >0.01 are returned. Note, relative importance are only
useful if all covariates are on a similar scale.
mod_type: A character stating the type of model that was fit
(used in other functions)
mod_family: A character stating the family of model that was fit
(used in other functions)
poiss_sc_factors: A vector of the square-root mean scaling factors
used to standardise poisson variables (only returned if family = "poisson")
MRFcov, MRFcov_spatial,
cv.glmnet
data("Bird.parasites") # Perform 2 quick bootstrap replicates using 70% of observations bootedCRF <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4, family = 'binomial', sample_prop = 0.7, n_bootstraps = 2) # Small example of using spatial coordinates for a spatial CRF Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE) Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE) coords <- data.frame(Latitude = Latitude, Longitude = Longitude) bootedSpatial <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4, family = 'binomial', spatial = TRUE, coords = coords, sample_prop = 0.5, n_bootstraps = 2)data("Bird.parasites") # Perform 2 quick bootstrap replicates using 70% of observations bootedCRF <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4, family = 'binomial', sample_prop = 0.7, n_bootstraps = 2) # Small example of using spatial coordinates for a spatial CRF Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE) Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE) coords <- data.frame(Latitude = Latitude, Longitude = Longitude) bootedSpatial <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4, family = 'binomial', spatial = TRUE, coords = coords, sample_prop = 0.5, n_bootstraps = 2)
cv_MRF_diag runs cross validation of MRFcov models and tests predictive
performance.
cv_MRF_diag_rep fits a single node-optimised model
and test's this model's predictive performance across multiple test subsets of the data.
cv_MRF_diag_rep_spatial fits a single node-optimised spatial model
and test's this model's predictive performance across multiple test subsets of the data.
All cv_MRF functions assess model predictive performance and produce
either diagnostic plots or matrices of predictive metrics.
cv_MRF_diag( data, symmetrise, n_nodes, n_cores, sample_seed, n_folds, n_fold_runs, n_covariates, compare_null, family, plot = TRUE, cached_model, cached_predictions, mod_labels = NULL ) cv_MRF_diag_rep( data, symmetrise, n_nodes, n_cores, sample_seed, n_folds, n_fold_runs, n_covariates, compare_null, family, plot = TRUE ) cv_MRF_diag_rep_spatial( data, coords, symmetrise, n_nodes, n_cores, sample_seed, n_folds, n_fold_runs, n_covariates, compare_null, family, plot = TRUE )cv_MRF_diag( data, symmetrise, n_nodes, n_cores, sample_seed, n_folds, n_fold_runs, n_covariates, compare_null, family, plot = TRUE, cached_model, cached_predictions, mod_labels = NULL ) cv_MRF_diag_rep( data, symmetrise, n_nodes, n_cores, sample_seed, n_folds, n_fold_runs, n_covariates, compare_null, family, plot = TRUE ) cv_MRF_diag_rep_spatial( data, coords, symmetrise, n_nodes, n_cores, sample_seed, n_folds, n_fold_runs, n_covariates, compare_null, family, plot = TRUE )
data |
Dataframe. The input data where the |
symmetrise |
The method to use for symmetrising corresponding parameter estimates
(which are taken from separate regressions). Options are |
n_nodes |
Positive integer. The index of the last column in |
n_cores |
Positive integer. The number of cores to spread the job across using
|
sample_seed |
Numeric. This seed will be used as the basis for dividing data into folds. Default is a random seed between 1 and 100000 |
n_folds |
Integer. The number of folds for cross-validation. Default is 10 |
n_fold_runs |
Integer. The number of total training runs to perform. During
each run, the data will be split into |
n_covariates |
Positive integer. The number of covariates in |
compare_null |
Logical. If |
family |
The response type. Responses can be quantitative continuous ( |
plot |
Logical. If |
cached_model |
Used by function |
cached_predictions |
Used by function |
mod_labels |
Optional character string of labels for the two models being compared
(if |
coords |
A two-column |
Node-optimised models are fitted using cv.glmnet,
and these models is used to predict data test subsets.
Test and training data subsets are created using createFolds.
To account for uncertainty in parameter estimates and in random fold generation, it is recommended
to perform cross-validation multiple times (by controlling the n_fold_runs argument) using
cv_MRF_diag_rep to supply a single cached model and that model's predictions.
This is useful for optimising a single model (using cv.glmnet) and testing
this model's predictive performance across many test subsets. Alternatively, one can run
cv_MRF_diag many times to fit different models in each iteration. This will be slower but
technically more sound
If plot = TRUE, a ggplot2 object is returned. This will be
a plot containing boxplots of predictive metrics across test sets using the
optimised model (see cv.glmnet for further details of lambda1
optimisation). If plot = FALSE, a matrix of prediction metrics is returned.
Clark, NJ, Wells, K and Lindberg, O. Unravelling changing interspecific interactions across environmental gradients using Markov random fields. (2018). Ecology doi: 10.1002/ecy.2221 Full text here.
MRFcov,
predict_MRF,
cv.glmnet
data("Bird.parasites") # Generate boxplots of model predictive metrics cv_MRF_diag(data = Bird.parasites, n_nodes = 4, n_cores = 1, family = 'binomial') # Generate boxplots comparing the CRF to an MRF model (no covariates) cv_MRF_diag(data = Bird.parasites, n_nodes = 4, n_cores = 1, family = 'binomial', compare_null = TRUE) # Replicate 10-fold cross-validation 10 times cv.preds <- cv_MRF_diag_rep(data = Bird.parasites, n_nodes = 4, n_cores = 1, family = 'binomial', compare_null = TRUE, plot = FALSE, n_fold_runs = 10) # Plot model sensitivity and % true predictions library(ggplot2) gridExtra::grid.arrange( ggplot(data = cv.preds, aes(y = mean_sensitivity, x = model)) + geom_boxplot() + theme(axis.text.x = ggplot2::element_blank()) + labs(x = ''), ggplot(data = cv.preds, aes(y = mean_tot_pred, x = model)) + geom_boxplot(), ncol = 1, heights = c(1, 1)) # Create some sample Poisson data with strong correlations cov <- rnorm(500, 0.2) cov2 <- rnorm(500, 1) sp.2 <- rpois(500, lambda = exp(1.5 + (cov * 0.9))) poiss.dat <- data.frame(sp.1 = rpois(500, lambda = exp(0.5 + (cov * 0.3))), sp.2 = sp.2, sp.3 = rpois(500, lambda = exp(log(sp.2 + 1) + (cov * -0.5))), cov = cov, cov2 = cov2) # A CRF should produce a better fit (lower deviance, lower MSE) cvMRF.poiss <- cv_MRF_diag(data = poiss.dat, n_nodes = 3, n_folds = 10, family = 'poisson', compare_null = TRUE, plot = TRUE)data("Bird.parasites") # Generate boxplots of model predictive metrics cv_MRF_diag(data = Bird.parasites, n_nodes = 4, n_cores = 1, family = 'binomial') # Generate boxplots comparing the CRF to an MRF model (no covariates) cv_MRF_diag(data = Bird.parasites, n_nodes = 4, n_cores = 1, family = 'binomial', compare_null = TRUE) # Replicate 10-fold cross-validation 10 times cv.preds <- cv_MRF_diag_rep(data = Bird.parasites, n_nodes = 4, n_cores = 1, family = 'binomial', compare_null = TRUE, plot = FALSE, n_fold_runs = 10) # Plot model sensitivity and % true predictions library(ggplot2) gridExtra::grid.arrange( ggplot(data = cv.preds, aes(y = mean_sensitivity, x = model)) + geom_boxplot() + theme(axis.text.x = ggplot2::element_blank()) + labs(x = ''), ggplot(data = cv.preds, aes(y = mean_tot_pred, x = model)) + geom_boxplot(), ncol = 1, heights = c(1, 1)) # Create some sample Poisson data with strong correlations cov <- rnorm(500, 0.2) cov2 <- rnorm(500, 1) sp.2 <- rpois(500, lambda = exp(1.5 + (cov * 0.9))) poiss.dat <- data.frame(sp.1 = rpois(500, lambda = exp(0.5 + (cov * 0.3))), sp.2 = sp.2, sp.3 = rpois(500, lambda = exp(log(sp.2 + 1) + (cov * -0.5))), cov = cov, cov2 = cov2) # A CRF should produce a better fit (lower deviance, lower MSE) cvMRF.poiss <- cv_MRF_diag(data = poiss.dat, n_nodes = 3, n_folds = 10, family = 'poisson', compare_null = TRUE, plot = TRUE)
This function is the workhorse of the MRFcov package, running
separate penalized regressions for each node to estimate parameters of
Markov Random Fields (MRF) graphs. Covariates can be included
(a class of models known as Conditional Random Fields; CRF), to estimate
how interactions between nodes vary across covariate magnitudes.
MRFcov( data, symmetrise, prep_covariates, n_nodes, n_cores, n_covariates, family, bootstrap = FALSE, progress_bar = FALSE )MRFcov( data, symmetrise, prep_covariates, n_nodes, n_cores, n_covariates, family, bootstrap = FALSE, progress_bar = FALSE )
data |
A |
symmetrise |
The method to use for symmetrising corresponding parameter estimates
(which are taken from separate regressions). Options are |
prep_covariates |
Logical. If |
n_nodes |
Positive integer. The index of the last column in |
n_cores |
Positive integer. The number of cores to spread the job across using
|
n_covariates |
Positive integer. The number of covariates in |
family |
The response type. Responses can be quantitative continuous ( |
bootstrap |
Logical. Used by |
progress_bar |
Logical. Progress bar in pbapply is used if |
Separate penalized regressions are used to approximate
MRF parameters, where the regression for node j includes an
intercept and coefficients for the abundance (families gaussian or poisson)
or presence-absence (family binomial) of all other
nodes (/j) in data. If covariates are included, coefficients
are also estimated for the effect of the covariate on j, and for the
effects of the covariate on interactions between j and all other nodes
(/j). Note that interaction coefficients must be estimated between variables that
are on roughly the same scale, as the resulting parameter estimates are
unified into a Markov Random Field using the specified symmetrise function.
Counts for poisson variables, which are often not on the same scale,
will therefore be normalised with a nonparanormal transformation
x = qnorm(rank(log2(x + 0.01)) / (length(x) + 1)). These transformed counts
will be used in a (family = "gaussian")
model and their respective raw distribution parameters returned so that coefficients
can be back-transformed for interpretation (this back-transformation is
performed automatatically by other functions including predict_MRF
and cv_MRF_diag). Gaussian variables are not automatically transformed, so
if they cover quite different ranges and scales, then it is recommended to scale them prior to fitting
models. For more information on this process, use
vignette("Gaussian_Poisson_CRFs")
Note that since the number of parameters to estimate in each node-wise regression
quickly increases with increasing numbers of nodes and covariates,
LASSO penalization is used to regularize
regressions. This is done by minimising the cross-validated
mean error for each node separately using cv.glmnet. In this way,
we maximise the log-likelihood of each node
separately before unifying the nodes into a graph.
A list containing:
graph: Estimated parameter matrix of pairwise interaction effects
intercepts: Estimated parameter vector of node intercepts
indirect_coefs: list containing matrices representing
indirect effects of each covariate on pairwise node interactions
direct_coefs: matrix of direct effects of each parameter on
each outcome node. For family = 'binomial' models, all coefficients are
estimated on the logit scale.
param_names: Character string of covariate parameter names
mod_type: A character stating the type of model that was fit
(used in other functions)
mod_family: A character stating the family of model that was fit
(used in other functions)
poiss_sc_factors: A matrix of the estimated negative binomial or
poisson parameters for each raw node variable (only returned if family = "poisson").
These are needed for converting coefficients back to their original distribution, and are
used for prediction purposes only
Ising, E. (1925). Beitrag zur Theorie des Ferromagnetismus.
Zeitschrift für Physik A Hadrons and Nuclei, 31, 253-258.
Cheng, J., Levina, E., Wang, P. & Zhu, J. (2014).
A sparse Ising model with covariates. (2012). Biometrics, 70, 943-953.
Clark, NJ, Wells, K and Lindberg, O.
Unravelling changing interspecific interactions across environmental gradients
using Markov random fields. (2018). Ecology doi: 10.1002/ecy.2221
Full text here.
Sutton C, McCallum A. An introduction to conditional random fields.
Foundations and Trends in Machine Learning 4, 267-373.
Cheng et al. (2014), Sutton & McCallum (2012) and Clark et al. (2018)
for overviews of Conditional Random Fields. See cv.glmnet for
details of cross-validated optimization using LASSO penalty. Worked examples to showcase
this function can be found using vignette("Bird_Parasite_CRF") and
vignette("Gaussian_Poisson_CRFs")
data("Bird.parasites") CRFmod <- MRFcov(data = Bird.parasites, n_nodes = 4, family = 'binomial')data("Bird.parasites") CRFmod <- MRFcov(data = Bird.parasites, n_nodes = 4, family = 'binomial')
This function calls the MRFcov function to fit
separate penalized regressions for each node and approximate parameters of
Markov Random Fields (MRF) graphs. Supplied GPS coordinates are used to
account for spatial autocorrelation via Gaussian Process spatial regression
splines.
MRFcov_spatial( data, symmetrise, prep_covariates, n_nodes, n_cores, n_covariates, family, coords, prep_splines = TRUE, bootstrap = FALSE, progress_bar = FALSE )MRFcov_spatial( data, symmetrise, prep_covariates, n_nodes, n_cores, n_covariates, family, coords, prep_splines = TRUE, bootstrap = FALSE, progress_bar = FALSE )
data |
A |
symmetrise |
The method to use for symmetrising corresponding parameter estimates
(which are taken from separate regressions). Options are |
prep_covariates |
Logical. If |
n_nodes |
Positive integer. The index of the last column in |
n_cores |
Positive integer. The number of cores to spread the job across using
|
n_covariates |
Positive integer. The number of covariates in |
family |
The response type. Responses can be quantitative continuous ( |
coords |
A two-column |
prep_splines |
Logical. If spatial splines are already included in |
bootstrap |
Logical. Used by |
progress_bar |
Logical. Progress bar in pbapply is used if |
A list of all elements contained in a returned MRFcov object, with
the inclusion of a dataframe called mrf_data. This contains all prepped covariates
including the added spatial regression
splines, and should be used as data when generating predictions
via predict_MRF or predict_MRFnetworks
Kammann, E. E. and M.P. Wand (2003) Geoadditive Models. Applied Statistics 52(1):1-18.
See smooth.construct2 and smooth.construct.gp.smooth.spec
for details of Gaussian process spatial regression splines. Worked examples to showcase
this function can be found using vignette("Bird_Parasite_CRF")
data("Bird.parasites") Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE) Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE) coords <- data.frame(Latitude = Latitude, Longitude = Longitude) CRFmod_spatial <- MRFcov_spatial(data = Bird.parasites, n_nodes = 4, family = 'binomial', coords = coords)data("Bird.parasites") Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE) Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE) coords <- data.frame(Latitude = Latitude, Longitude = Longitude) CRFmod_spatial <- MRFcov_spatial(data = Bird.parasites, n_nodes = 4, family = 'binomial', coords = coords)
This function uses outputs from fitted MRFcov and
bootstrap_MRF models to plot a heatmap of node interaction coefficients.
plotMRF_hm(MRF_mod, node_names, main, plot_observed_vals, data)plotMRF_hm(MRF_mod, node_names, main, plot_observed_vals, data)
MRF_mod |
A fitted |
node_names |
A character vector of species names for axis labels. Default
is to use rownames from the |
main |
An optional character title for the plot |
plot_observed_vals |
Logical. If |
data |
Optional |
Interaction parameters from MRF_mod are plotted as a heatmap, where
red colours indicate positive interactions and blue indicate negative interactions. If
plot_observed_vals == TRUE, raw observed values of single occurrences (on the diagonal)
and co-occurrences for each species in data are overlaid on the plot
(only avaiable for family = 'binomial' models). Note, this option is not
available for bootstrap_MRF models
A ggplot2 object
data("Bird.parasites") CRFmod <- MRFcov(data = Bird.parasites, n_nodes = 4, family = 'binomial') plotMRF_hm(MRF_mod = CRFmod) plotMRF_hm(MRF_mod = CRFmod, plot_observed_vals = TRUE, data = Bird.parasites) #To plot as an igraph network instead, we can simply extract the adjacency matrix net <- igraph::graph.adjacency(CRFmod$graph, weighted = TRUE, mode = "undirected") igraph::plot.igraph(net, layout = igraph::layout.circle, edge.width = abs(igraph::E(net)$weight), edge.color = ifelse(igraph::E(net)$weight < 0, 'blue', 'red'))data("Bird.parasites") CRFmod <- MRFcov(data = Bird.parasites, n_nodes = 4, family = 'binomial') plotMRF_hm(MRF_mod = CRFmod) plotMRF_hm(MRF_mod = CRFmod, plot_observed_vals = TRUE, data = Bird.parasites) #To plot as an igraph network instead, we can simply extract the adjacency matrix net <- igraph::graph.adjacency(CRFmod$graph, weighted = TRUE, mode = "undirected") igraph::plot.igraph(net, layout = igraph::layout.circle, edge.width = abs(igraph::E(net)$weight), edge.color = ifelse(igraph::E(net)$weight < 0, 'blue', 'red'))
This function calculates linear predictors for node observations
using coefficients from an MRFcov or MRFcov_spatial object.
predict_MRF( data, MRF_mod, prep_covariates = TRUE, n_cores, progress_bar = FALSE )predict_MRF( data, MRF_mod, prep_covariates = TRUE, n_cores, progress_bar = FALSE )
data |
Dataframe. The input data to be predicted, where the |
MRF_mod |
A fitted |
prep_covariates |
Logical flag stating whether to prep the dataset
by cross-multiplication ( |
n_cores |
Positive integer stating the number of processing cores to split the job across.
Default is |
progress_bar |
Logical. Progress bar in pbapply is used if |
Observations for nodes in data are predicted using linear predictions
from MRF_mod. If family = "binomial", a second element containing binary
predictions for nodes is returned. Note that predicting values for unobserved locations using a
spatial MRF is not currently supported
A matrix containing predictions for each observation in data. If
family = "binomial", a second element containing binary
predictions for nodes is returned.
Clark, NJ, Wells, K and Lindberg, O. Unravelling changing interspecific interactions across environmental gradients using Markov random fields. (2018). Ecology doi: 10.1002/ecy.2221 Full text here.
data("Bird.parasites") # Fit a model to a subset of the data (training set) CRFmod <- MRFcov(data = Bird.parasites[1:300, ], n_nodes = 4, family = "binomial") # If covariates are included, prep the dataset for gathering predictions prepped_pred <- prep_MRF_covariates(Bird.parasites[301:nrow(Bird.parasites), ], n_nodes = 4) # Predict occurrences for the remaining subset (test set) predictions <- predict_MRF(data = prepped_pred, MRF_mod = CRFmod) # Visualise predicted occurrences for nodes in the test set predictions$Binary_predictions # Predicting spatial MRFs requires the user to supply the spatially augmented dataset data("Bird.parasites") Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE) Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE) coords <- data.frame(Latitude = Latitude, Longitude = Longitude) CRFmod_spatial <- MRFcov_spatial(data = Bird.parasites, n_nodes = 4, family = 'binomial', coords = coords) predictions <- predict_MRF(data = CRFmod_spatial$mrf_data, prep_covariates = FALSE, MRF_mod = CRFmod_spatial)data("Bird.parasites") # Fit a model to a subset of the data (training set) CRFmod <- MRFcov(data = Bird.parasites[1:300, ], n_nodes = 4, family = "binomial") # If covariates are included, prep the dataset for gathering predictions prepped_pred <- prep_MRF_covariates(Bird.parasites[301:nrow(Bird.parasites), ], n_nodes = 4) # Predict occurrences for the remaining subset (test set) predictions <- predict_MRF(data = prepped_pred, MRF_mod = CRFmod) # Visualise predicted occurrences for nodes in the test set predictions$Binary_predictions # Predicting spatial MRFs requires the user to supply the spatially augmented dataset data("Bird.parasites") Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE) Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE) coords <- data.frame(Latitude = Latitude, Longitude = Longitude) CRFmod_spatial <- MRFcov_spatial(data = Bird.parasites, n_nodes = 4, family = 'binomial', coords = coords) predictions <- predict_MRF(data = CRFmod_spatial$mrf_data, prep_covariates = FALSE, MRF_mod = CRFmod_spatial)
MRFcov objectThis function uses outputs from fitted MRFcov
and bootstrap_MRF models to
generate linear predictions for each observation in data and
calculate probabilistic network metrics from weighted adjacency matrices.
predict_MRFnetworks( data, MRF_mod, cutoff, omit_zeros, metric, cached_predictions = NULL, prep_covariates, n_cores, progress_bar = FALSE )predict_MRFnetworks( data, MRF_mod, cutoff, omit_zeros, metric, cached_predictions = NULL, prep_covariates, n_cores, progress_bar = FALSE )
data |
Dataframe. The sample data where the
left-most variables are variables that are represented by nodes in the graph.
Colnames from this sample dataset must exactly match the colnames in the dataset that
was used to fit the |
MRF_mod |
A fitted |
cutoff |
Single numeric value specifying the linear prediction threshold. Species whose
linear prediction is below this level for a given observation in |
omit_zeros |
Logical. If |
metric |
The network metric to be calculated for each observation in |
cached_predictions |
Use if providing stored predictions from |
prep_covariates |
Logical flag stating whether to prep the dataset
by cross-multiplication ( |
n_cores |
Positive integer stating the number of processing cores to split the job across.
Default is |
progress_bar |
Logical. Progress bar in pbapply is used if |
Interaction parameters are predicted for each observation in data
and then converted into a weighted, undirected adjacency matrix
using graph.adjacency. Note that the network is probabilistic,
as node occurrences/abundances are predicted using fitted model equations from
MRF_mod. If a linear prediction for a given observation falls below the
user-specified cutoff, the node is considered absent from the community and cannot
participate in the network. After correcting for the linear predictions,
the specified network metric (degree centrality,
eigencentrality, or betweenness) for each observation in data
is then calculated and returned in a matrix. If metric is not
supplied, the weighted, undirected adjacency matrices are returned in a list
Either a matrix with nrow = nrow(data),
containing each species' predicted network metric at each observation in data, or
a list with length = nrow(data) containing the weighted, undirected
adjacency matrix predicted at each observation in data
MRFcov, bootstrap_MRF, degree,
eigen_centrality, betweenness
data("Bird.parasites") CRFmod <- MRFcov(data = Bird.parasites, n_nodes = 4, family = "binomial") predict_MRFnetworks(data = Bird.parasites[1:200, ], MRF_mod = CRFmod, metric = "degree", cutoff = 0.25)data("Bird.parasites") CRFmod <- MRFcov(data = Bird.parasites, n_nodes = 4, family = "binomial") predict_MRFnetworks(data = Bird.parasites[1:200, ], MRF_mod = CRFmod, metric = "degree", cutoff = 0.25)
This function performs the cross-multiplication necessary
for prepping datasets to be used in MRFcov models. This
function is called by several of the functions within the package.
prep_MRF_covariates(data, n_nodes)prep_MRF_covariates(data, n_nodes)
data |
Dataframe. The input data where the |
n_nodes |
Integer. The index of the last column in data which is represented by a node in the final graph. Columns with index greater than n_nodes are taken as covariates. Default is the number of columns in data, corresponding to no additional covariates |
Observations of nodes (species) in data are prepped for
MRFcov analysis by multiplication. This function is not designed to be called directly,
but is used by other functions in the package (namely MRFcov,
MRFcov_spatial,
cv_MRF_diag, and
bootstrap_MRF)
Dataframe of the prepped response and covariate variables necessary for
input in MRFcov models
This function performs the cross-multiplication necessary
for prepping datasets to be used in MRFcov_spatial models.
prep_MRF_covariates_spatial(data, n_nodes, coords)prep_MRF_covariates_spatial(data, n_nodes, coords)
data |
Dataframe. The input data where the |
n_nodes |
Integer. The index of the last column in data which is represented by a node in the final graph. Columns with index greater than n_nodes are taken as covariates. Default is the number of columns in data, corresponding to no additional covariates |
coords |
A two-column |
Observations of nodes (species) in data are prepped for
MRFcov_spatial analysis by multiplication. This function is useful if
users wish to prep the spatial splines beforehand and split the
data manually for out-of-sample cross-validation. To do so,
prep the splines here and set prep_splines = FALSE in MRFcov_spatial
Dataframe of the prepped response and covariate variables necessary for
input in MRFcov_spatial models