<<<<<<< HEAD
title: “MRFs and CRFs for Bird.parasites data” output: rmarkdown::html_vignette vignette: > % % % —
We can explore the model’s primary functions using a test dataset
that is available with the package. Load the Bird.parasites
dataset, which contains binary occurrences of four avian blood parasites
in New Caledonian Zosterops species (available in its original
form at Dryad; Clark et al 2016). A single continuous
covariate is also included (scale.prop.zos), which reflects
the relative abundance of Zosterops species (compared to other
avian species) among different sample sites
The Bird.parasites dataset is already in the appropriate
structure for running MRFcov models; however, it is useful
to understand how this was done. Using the raw dataset (downloaded from
Dryad at the above link), we created a scaled continuous
covariate to represent Zosterops spp. proportional captures in
each sample site (as an estimate of relative abundance)
#Not run
#install.packages(dplyr)
data.paras = data.frame(data.paras) %>%
dplyr::group_by(Capturesession,Genus) %>%
dplyr::summarise(count = dlyr::n()) %>%
dplyr::mutate(prop.zos = count / sum(count)) %>%
dplyr::left_join(data.paras) %>%
dplyr::ungroup() %>% dplyr::filter(Genus == 'Zosterops') %>%
dplyr::mutate(scale.prop.zos = as.vector(scale(prop.zos)))
data.paras <- data.paras[, c(12:15, 23)]You can visualise the dataset to see how analysis data needs to be
structured. In short, for (family = "binomial") models,
node variable (i.e. species) occurrences should be included as binary
variables (1s and 0s) as the
n_nodes left-most variables in data. Note that
Gaussian continuous variables (family = "gaussian") and
Poisson non-negative counts (family = "poisson") are also
supported in MRFcov. Any covariates can be included as the
right-most variables. It is recommended that these covariates all be on
a similar scale, ideally using the scale function for
continuous covariates (or similar) so that covariates have roughly
mean = 0 and sd = 1, as this makes LASSO
variable selection and comparisons of effect sizes very
straightforward
Running MRFs and visualising interaction coefficients
We first test for interactions using a Markov Random Fields (MRF)
model without covariates. We set n_nodes as the number of
species to be represented in the graphical model (4). We
also need to specify the family for the model (binomial in
this case). We do not specify the level of penalisation used by the
LASSO algorithm. Instead, this is optimised separately for each species
through cross validation (using function cv.glmnet in the
glmnet package). This ensures the log-likelihood of each
species is properly maximised before unifying them into an undirected
graph. Finally, it is important to note that for MRF model functions, we
can specify the number of processing cores to split the job across
(i.e. n_cores). In this case we will only use 1 core, but
higher numbers may increase speed (if more cores are
available). Check available cores using the detectCores()
function in the parallel package
## Leave-one-out cv used for the following low-occurrence (rare) nodes:
## Microfilaria ...## Warning: `funs()` was deprecated in dplyr 0.8.0.
## ℹ Please use a list of either functions or lambdas:
##
## # Simple named list: list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`: tibble::lst(mean, median)
##
## # Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## ℹ The deprecated feature was likely used in the MRFcov package.
## Please report the issue to the authors.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Fitting MRF models in sequence using 1 core ...The message above can be useful for identifying nodes (i.e. species)
that are very rare or very common, as these can be difficult to
properly model. In this case, the Microfilaria node is
fairly rare, and so the function automatically reverts to using
leave-one-out cross-validation to optimise parameters. This can be slow,
so be aware when attempting to use large datasets that contain many very
rare or very common species. Now that the model has converged, we can
plot the estimated interaction coefficients as a heatmap using
plotMRF_hm. Note that we can specify the names of nodes
should we want to change them
plotMRF_hm(MRF_mod = MRF_fit, main = 'MRF (no covariates)',
node_names = c('H. zosteropis', 'H. killangoi',
'Plasmodium', 'Microfilaria'))
Note that other plotting methods an be used as desired. For instance,
to plot these as a network instead, we can simply extract the adjacency
matrix and plot it using standard igraph functions
## Warning: `graph.adjacency()` was deprecated in igraph 2.0.0.
## ℹ Please use `graph_from_adjacency_matrix()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.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'))
Running CRFs using additional covariates
We can now run a Conditional Random Fields (CRF) model using the
provided continuous covariate (scale.prop.zos). Again, each
species’ regression is optimised separately using LASSO regularization.
Note that any columns in data to the right of column
n_nodes will be presumed to represent covariates if we
don’t specify an n_covariates argument
## Leave-one-out cv used for the following low-occurrence (rare) nodes:
## Microfilaria ...
## Fitting MRF models in sequence using 1 core ...Visualise the estimated species interaction coefficients as a
heatmap. These represent predicted interactions when the covariate is
set to its mean (i.e. in this case, when
scale.prop.zos = 0). If we had included other covariates,
then this graph would represent interactions predicted when all
covariates were set at their means
Regression coefficients and their relative importances can be
accessed as well. This call returns a matrix of the raw coefficient, as
well as standardised coefficients (standardised by the sd
of the covariate). Standardisation in this way helps to compare the
relative influences of each parameter on the target species’ occurrence
probability, but in general the two coefficients will be identical
(unless users have not pre-scaled their covariates). The list also
contains contains each variable’s relative importance (calculated using
the formula B^2 / sum(B^2), where the vector of
Bs represents regression coefficients for predictor
variables). Variables with an underscore (_) indicate an
interaction between a covariate and another node, suggesting that
conditional dependencies of the two nodes vary across environmental
gradients. Because of this, it is recommended to avoid using column
names with _ in them
## Variable Rel_importance Standardised_coef Raw_coef
## 1 Hkillangoi 0.63211417 -3.2240547 -3.2240547
## 4 scale.prop.zos_Hkillangoi 0.17835261 1.7125543 1.7125543
## 5 scale.prop.zos_Microfilaria 0.07529091 -1.1126944 -1.1126944
## 3 scale.prop.zos 0.05613122 -0.9607422 -0.9607422
## 2 Microfilaria 0.04970728 0.9040962 0.9040962Finally, a useful capability is to generate some fake data and test predictions. For instance, say we want to know how frequently malaria parasite infections are predicted to occur in sites with high occurrence of microfilaria
fake.dat <- Bird.parasites
fake.dat$Microfilaria <- rbinom(nrow(Bird.parasites), 1, 0.8)
fake.preds <- predict_MRF(data = fake.dat, MRF_mod = MRF_mod)The returned object from predict_MRF depends on the
family of the model. For family = "binomial", we get a list
including both linear prediction probabilities and binary predictions
(where a linear prediction probability > 0.5 equates to
a binary prediction of 1). These binary predictions can be
used to estimate parasite prevalence
H.zos.pred.prev <- sum(fake.preds$Binary_predictions[, 'Hzosteropis']) / nrow(fake.preds$Binary_predictions)
Plas.pred.prev <- sum(fake.preds$Binary_predictions[, 'Plas']) / nrow(fake.preds$Binary_predictions)
Plas.pred.prev## [1] 0.3853007Comparing fits of MRF and CRF models
A key step in the process of model exploration is to determine
whether inclusion of covariates actually improves model fit and is
warranted when estimating interaction parameters. This is
straightforward for binomial models, as we can compare classification
accuracy quickly and easily. The cv_diag functions will fit
models and then determine their predictive performances against the
supplied data. We can also compare MRF and CRF models directly to
determine whether covariates are warranted. For this dataset,
considering that parasite infections are quite rare, we are primarily
interested in maximising model Sensitivity (capacity to successfully
predict positive infections)
mod_fits <- cv_MRF_diag_rep(data = Bird.parasites, n_nodes = 4,
n_cores = 1, family = 'binomial', plot = F,
compare_null = T,
n_folds = 10)## Generating node-optimised Conditional Random Fields model
##
## Generating Markov Random Fields model (no covariates)
##
## Calculating model predictions of the supplied data
## Generating CRF predictions ...
## Generating null MRF predictions ...
##
## Calculating predictive performance across test folds
## Processing cross-validation run 1 of 10 ...
## Processing cross-validation run 2 of 10 ...
## Processing cross-validation run 3 of 10 ...
## Processing cross-validation run 4 of 10 ...
## Processing cross-validation run 5 of 10 ...
## Processing cross-validation run 6 of 10 ...
## Processing cross-validation run 7 of 10 ...
## Processing cross-validation run 8 of 10 ...
## Processing cross-validation run 9 of 10 ...
## Processing cross-validation run 10 of 10 ...# CRF (with covariates) model sensitivity
quantile(mod_fits$mean_sensitivity[mod_fits$model == 'CRF'], probs = c(0.05, 0.95))## 5% 95%
## 0.2629630 0.5561111# MRF (no covariates) model sensitivity
quantile(mod_fits$mean_sensitivity[mod_fits$model != 'CRF'], probs = c(0.05, 0.95))## 5% 95%
## 0.1562500 0.3615741Bootstrapping the data and running models
We now may want to fit models to bootstrapped subsets of the data to
account for parameter uncertainty. Users can change the proportion of
observations to include in each bootstrap run with the
sample_prop option.
booted_MRF <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4, family = 'binomial', n_bootstraps = 10, n_cores = 1, sample_prop = 0.9)## Fitting bootstrap_MRF models in sequence using 1 core...## Warning: from glmnet C++ code (error code -91); Convergence for 91th lambda
## value not reached after maxit=25000 iterations; solutions for larger lambdas
## returnedExploring regression coefficients and interpreting results
Finally, we can explore regression coefficients to get a better
understanding of just how important interactions are for predicting
species’ occurrence probabilities (in comparison to other covariates).
This is perhaps the strongest property of CRFs, as comparing the
relative importances of interactions and fixed covariates using
competing methods (such as Joint Species Distribution Models) is
difficult. The bootstrap_MRF function conveniently returns
a matrix of important coefficients for each node in the graph, as well
as their relative importances
## Variable Rel_importance Mean_coef
## 4 scale.prop.zos_Hkillangoi 0.64372452 6.0905924
## 1 Hkillangoi 0.28579128 -4.0582052
## 2 Microfilaria 0.03650473 1.4503875
## 3 scale.prop.zos 0.01392971 -0.8959434
## 5 scale.prop.zos_Microfilaria 0.01255024 -0.8504240## Variable Rel_importance Mean_coef
## 4 scale.prop.zos_Hzosteropis 0.63011708 6.0905924
## 1 Hzosteropis 0.27975005 -4.0582052
## 2 Microfilaria 0.05901906 -1.8639964
## 5 scale.prop.zos_Microfilaria 0.01581572 -0.9649247
## 3 scale.prop.zos 0.01341025 -0.8885204## Variable Rel_importance Mean_coef
## 3 Microfilaria 0.51013619 1.3714637
## 4 scale.prop.zos 0.32503647 -1.0947307
## 1 Hzosteropis 0.09836595 -0.6022317
## 5 scale.prop.zos_Hzosteropis 0.01884551 -0.2635999
## 6 scale.prop.zos_Hkillangoi 0.01870796 -0.2626361
## 7 scale.prop.zos_Microfilaria 0.01747352 0.2538233
## 2 Hkillangoi 0.01143440 -0.2053278## Variable Rel_importance Mean_coef
## 2 Hkillangoi 0.33678315 -1.8639964
## 1 Hzosteropis 0.20390520 1.4503875
## 3 Plas 0.18231770 1.3714637
## 4 scale.prop.zos 0.11039698 -1.0672066
## 6 scale.prop.zos_Hkillangoi 0.09024997 -0.9649247
## 5 scale.prop.zos_Hzosteropis 0.07010213 -0.8504240Users can also use the predict_MRFnetworks function to
calculate network statistics for each node in each observation or to
generate adjacency matrices for each observation. By default, this
function generates a list of igraph adjacency matrices, one
for each row in data, which can be used to make network
plots using a host of other packages. Note, both this function and the
predict_MRF function rely on data that has a
structure exactly matching to the data that was used to fit the model.
In other words, the column names and column order need to be identical.
The cutoff argument is important for binary problems, as
this specifies the probability threshold for stating whether or not a
species should be considered present at a site (and thus, whether their
interactions will be present). Here, we state that a predicted
occurrence above 0.33 is sufficient
adj_mats <- predict_MRFnetworks(data = Bird.parasites,
MRF_mod = booted_MRF,
metric = 'eigencentrality',
cutoff = 0.33)
colnames(adj_mats) <- colnames(Bird.parasites[, 1:4])
apply(adj_mats, 2, summary)## Hzosteropis Hkillangoi Plas Microfilaria
## Min. 0.0000000 0.0000000 0.0000000 0.0000000
## 1st Qu. 0.0000000 0.0000000 0.0000000 0.0000000
## Median 0.0000000 0.0000000 0.0000000 0.0000000
## Mean 0.1599096 0.1625835 0.1091844 0.1024499
## 3rd Qu. 0.3725811 0.0000000 0.0985056 0.0000000
## Max. 1.0000000 1.0000000 1.0000000 1.0000000Accounting for possible spatial autocorrelation
Lastly, MRFcov has the capability to account for
possible spatial autocorrelation when estimating interaction parameters.
To do this, we incorporate functions from the mgcv package
to include smoothed Gaussian process spatial regression splines in each
node-wise regression. The user must supply a two-column
data.frame called coords, which will ideally
contain Latitude and Longitude values for each row in data.
We don’t have these coordinates for the Bird.parasites
dataset, so we will instead create some fake coordinates to showcase the
model. Note, these commands were not run here, but feel free to move
through them as you did for the above examples
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)The syntax for the MRFcov_spatial function is nearly
identical to MRFcov, with the exception that
coords must be supplied
CRFmod_spatial <- MRFcov_spatial(data = Bird.parasites, n_nodes = 4,
family = 'binomial', coords = coords)Interpretation is also identical to MRFcov objects.
Here, key coefficients are those that are retained after
accounting for spatial influences
Finally, we can compare fits of spatial and non-spatial models just as we did for MRFs and CRFs above
=======
title: “MRFs and CRFs for Bird.parasites data” output: rmarkdown::html_vignette vignette: > % % % —
We can explore the model’s primary functions using a test dataset
that is available with the package. Load the Bird.parasites
dataset, which contains binary occurrences of four avian blood parasites
in New Caledonian Zosterops species (available in its original
form at Dryad; Clark et al 2016). A single continuous
covariate is also included (scale.prop.zos), which reflects
the relative abundance of Zosterops species (compared to other
avian species) among different sample sites
The Bird.parasites dataset is already in the appropriate
structure for running MRFcov models; however, it is useful
to understand how this was done. Using the raw dataset (downloaded from
Dryad at the above link), we created a scaled continuous
covariate to represent Zosterops spp. proportional captures in
each sample site (as an estimate of relative abundance)
#Not run
#install.packages(dplyr)
data.paras = data.frame(data.paras) %>%
dplyr::group_by(Capturesession,Genus) %>%
dplyr::summarise(count = dlyr::n()) %>%
dplyr::mutate(prop.zos = count / sum(count)) %>%
dplyr::left_join(data.paras) %>%
dplyr::ungroup() %>% dplyr::filter(Genus == 'Zosterops') %>%
dplyr::mutate(scale.prop.zos = as.vector(scale(prop.zos)))
data.paras <- data.paras[, c(12:15, 23)]You can visualise the dataset to see how analysis data needs to be
structured. In short, for (family = "binomial") models,
node variable (i.e. species) occurrences should be included as binary
variables (1s and 0s) as the
n_nodes left-most variables in data. Note that
Gaussian continuous variables (family = "gaussian") and
Poisson non-negative counts (family = "poisson") are also
supported in MRFcov. Any covariates can be included as the
right-most variables. It is recommended that these covariates all be on
a similar scale, ideally using the scale function for
continuous covariates (or similar) so that covariates have roughly
mean = 0 and sd = 1, as this makes LASSO
variable selection and comparisons of effect sizes very
straightforward
Running MRFs and visualising interaction coefficients
We first test for interactions using a Markov Random Fields (MRF)
model without covariates. We set n_nodes as the number of
species to be represented in the graphical model (4). We
also need to specify the family for the model (binomial in
this case). We do not specify the level of penalisation used by the
LASSO algorithm. Instead, this is optimised separately for each species
through cross validation (using function cv.glmnet in the
glmnet package). This ensures the log-likelihood of each
species is properly maximised before unifying them into an undirected
graph. Finally, it is important to note that for MRF model functions, we
can specify the number of processing cores to split the job across
(i.e. n_cores). In this case we will only use 1 core, but
higher numbers may increase speed (if more cores are
available). Check available cores using the detectCores()
function in the parallel package
## Leave-one-out cv used for the following low-occurrence (rare) nodes:
## Microfilaria ...
## Fitting MRF models in sequence using 1 core ...The message above can be useful for identifying nodes (i.e. species)
that are very rare or very common, as these can be difficult to
properly model. In this case, the Microfilaria node is
fairly rare, and so the function automatically reverts to using
leave-one-out cross-validation to optimise parameters. This can be slow,
so be aware when attempting to use large datasets that contain many very
rare or very common species. Now that the model has converged, we can
plot the estimated interaction coefficients as a heatmap using
plotMRF_hm. Note that we can specify the names of nodes
should we want to change them
plotMRF_hm(MRF_mod = MRF_fit, main = 'MRF (no covariates)',
node_names = c('H. zosteropis', 'H. killangoi',
'Plasmodium', 'Microfilaria'))
Note that other plotting methods an be used as desired. For instance,
to plot these as a network instead, we can simply extract the adjacency
matrix and plot it using standard igraph functions
net <- igraph::graph.adjacency(MRF_fit$graph, weighted = T, 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'))
Running CRFs using additional covariates
We can now run a Conditional Random Fields (CRF) model using the
provided continuous covariate (scale.prop.zos). Again, each
species’ regression is optimised separately using LASSO regularization.
Note that any columns in data to the right of column
n_nodes will be presumed to represent covariates if we
don’t specify an n_covariates argument
## Leave-one-out cv used for the following low-occurrence (rare) nodes:
## Microfilaria ...
## Fitting MRF models in sequence using 1 core ...Visualise the estimated species interaction coefficients as a
heatmap. These represent predicted interactions when the covariate is
set to its mean (i.e. in this case, when
scale.prop.zos = 0). If we had included other covariates,
then this graph would represent interactions predicted when all
covariates were set at their means
Regression coefficients and their relative importances can be
accessed as well. This call returns a matrix of the raw coefficient, as
well as standardised coefficients (standardised by the sd
of the covariate). Standardisation in this way helps to compare the
relative influences of each parameter on the target species’ occurrence
probability, but in general the two coefficients will be identical
(unless users have not pre-scaled their covariates). The list also
contains contains each variable’s relative importance (calculated using
the formula B^2 / sum(B^2), where the vector of
Bs represents regression coefficients for predictor
variables). Variables with an underscore (_) indicate an
interaction between a covariate and another node, suggesting that
conditional dependencies of the two nodes vary across environmental
gradients. Because of this, it is recommended to avoid using column
names with _ in them
## Variable Rel_importance Standardised_coef Raw_coef
## 1 Hkillangoi 0.67721605 -2.4671873 -2.4671873
## 5 scale.prop.zos_Microfilaria 0.12231755 -1.0485349 -1.0485349
## 3 Microfilaria 0.09273410 0.9129736 0.9129736
## 4 scale.prop.zos 0.09250795 -0.9118597 -0.9118597
## 2 Plas 0.01195042 -0.3277405 -0.3277405Finally, a useful capability is to generate some fake data and test predictions. For instance, say we want to know how frequently malaria parasite infections are predicted to occur in sites with high occurrence of microfilaria
fake.dat <- Bird.parasites
fake.dat$Microfilaria <- rbinom(nrow(Bird.parasites), 1, 0.8)
fake.preds <- predict_MRF(data = fake.dat, MRF_mod = MRF_mod)The returned object from predict_MRF depends on the
family of the model. For family = "binomial", we get a list
including both linear prediction probabilities and binary predictions
(where a linear prediction probability > 0.5 equates to
a binary prediction of 1). These binary predictions can be
used to estimate parasite prevalence
H.zos.pred.prev <- sum(fake.preds$Binary_predictions[, 'Hzosteropis']) / nrow(fake.preds$Binary_predictions)
Plas.pred.prev <- sum(fake.preds$Binary_predictions[, 'Plas']) / nrow(fake.preds$Binary_predictions)
Plas.pred.prev## [1] 0.4498886Comparing fits of MRF and CRF models
A key step in the process of model exploration is to determine
whether inclusion of covariates actually improves model fit and is
warranted when estimating interaction parameters. This is
straightforward for binomial models, as we can compare classification
accuracy quickly and easily. The cv_diag functions will fit
models and then determine their predictive performances against the
supplied data. We can also compare MRF and CRF models directly to
determine whether covariates are warranted. For this dataset,
considering that parasite infections are quite rare, we are primarily
interested in maximising model Sensitivity (capacity to successfully
predict positive infections)
mod_fits <- cv_MRF_diag_rep(data = Bird.parasites, n_nodes = 4,
n_cores = 1, family = 'binomial', plot = F,
compare_null = T,
n_folds = 10)## Generating node-optimised Conditional Random Fields model
##
## Generating Markov Random Fields model (no covariates)
##
## Calculating model predictions of the supplied data
## Generating CRF predictions ...
## Generating null MRF predictions ...
##
## Calculating predictive performance across test folds
## Processing cross-validation run 1 of 10 ...
## Processing cross-validation run 2 of 10 ...
## Processing cross-validation run 3 of 10 ...
## Processing cross-validation run 4 of 10 ...
## Processing cross-validation run 5 of 10 ...
## Processing cross-validation run 6 of 10 ...
## Processing cross-validation run 7 of 10 ...
## Processing cross-validation run 8 of 10 ...
## Processing cross-validation run 9 of 10 ...
## Processing cross-validation run 10 of 10 ...# CRF (with covariates) model sensitivity
quantile(mod_fits$mean_sensitivity[mod_fits$model == 'CRF'], probs = c(0.05, 0.95))## 5% 95%
## 0.2397647 0.5153199# MRF (no covariates) model sensitivity
quantile(mod_fits$mean_sensitivity[mod_fits$model != 'CRF'], probs = c(0.05, 0.95))## 5% 95%
## 0.1664729 0.4009677Bootstrapping the data and running models
We now may want to fit models to bootstrapped subsets of the data to
account for parameter uncertainty. Users can change the proportion of
observations to include in each bootstrap run with the
sample_prop option.
booted_MRF <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4, family = 'binomial', n_bootstraps = 10, n_cores = 1, sample_prop = 0.9)## Fitting bootstrap_MRF models in sequence using 1 core...## Warning: from glmnet C++ code (error code -88); Convergence for 88th lambda
## value not reached after maxit=25000 iterations; solutions for larger lambdas
## returnedExploring regression coefficients and interpreting results
Finally, we can explore regression coefficients to get a better
understanding of just how important interactions are for predicting
species’ occurrence probabilities (in comparison to other covariates).
This is perhaps the strongest property of CRFs, as comparing the
relative importances of interactions and fixed covariates using
competing methods (such as Joint Species Distribution Models) is
difficult. The bootstrap_MRF function conveniently returns
a matrix of important coefficients for each node in the graph, as well
as their relative importances
## Variable Rel_importance Mean_coef
## 1 Hkillangoi 0.61561261 -3.2531837
## 5 scale.prop.zos_Hkillangoi 0.19696397 1.8401288
## 6 scale.prop.zos_Microfilaria 0.07594532 -1.1426293
## 4 scale.prop.zos 0.04977971 -0.9250837
## 3 Microfilaria 0.04332809 0.8630572
## 2 Plas 0.01591192 -0.5230174## Variable Rel_importance Mean_coef
## 1 Hzosteropis 0.68720871 -3.2531837
## 4 scale.prop.zos_Hzosteropis 0.21987099 1.8401288
## 2 Microfilaria 0.04485208 -0.8311044
## 3 scale.prop.zos 0.03747536 -0.7596914
## 5 scale.prop.zos_Plas 0.01028894 0.3980610## Variable Rel_importance Mean_coef
## 2 Microfilaria 0.48897744 1.3081790
## 3 scale.prop.zos 0.34441579 -1.0979038
## 1 Hzosteropis 0.07816040 -0.5230174
## 5 scale.prop.zos_Hkillangoi 0.04527450 0.3980610
## 6 scale.prop.zos_Microfilaria 0.03102618 0.3295239
## 4 scale.prop.zos_Hzosteropis 0.01207567 -0.2055789## Variable Rel_importance Mean_coef
## 3 Plas 0.29734396 1.3081790
## 5 scale.prop.zos_Hzosteropis 0.22684827 -1.1426293
## 4 scale.prop.zos 0.20673443 -1.0907972
## 1 Hzosteropis 0.12942077 0.8630572
## 2 Hkillangoi 0.12001511 -0.8311044
## 6 scale.prop.zos_Plas 0.01886682 0.3295239Users can also use the predict_MRFnetworks function to
calculate network statistics for each node in each observation or to
generate adjacency matrices for each observation. By default, this
function generates a list of igraph adjacency matrices, one
for each row in data, which can be used to make network
plots using a host of other packages. Note, both this function and the
predict_MRF function rely on data that has a
structure exactly matching to the data that was used to fit the model.
In other words, the column names and column order need to be identical.
The cutoff argument is important for binary problems, as
this specifies the probability threshold for stating whether or not a
species should be considered present at a site (and thus, whether their
interactions will be present). Here, we state that a predicted
occurrence above 0.33 is sufficient
adj_mats <- predict_MRFnetworks(data = Bird.parasites,
MRF_mod = booted_MRF,
metric = 'eigencentrality',
cutoff = 0.33)
colnames(adj_mats) <- colnames(Bird.parasites[, 1:4])
apply(adj_mats, 2, summary)## Hzosteropis Hkillangoi Plas Microfilaria
## Min. 0.0000000 0.00000000 0.0000000 0.0000000
## 1st Qu. 0.0000000 0.00000000 0.0000000 0.0000000
## Median 0.0000000 0.00000000 0.0000000 0.0000000
## Mean 0.1497115 0.03563474 0.1947795 0.1002227
## 3rd Qu. 0.3660254 0.00000000 0.1078446 0.0000000
## Max. 1.0000000 1.00000000 1.0000000 1.0000000Accounting for possible spatial autocorrelation
Lastly, MRFcov has the capability to account for
possible spatial autocorrelation when estimating interaction parameters.
To do this, we incorporate functions from the mgcv package
to include smoothed Gaussian process spatial regression splines in each
node-wise regression. The user must supply a two-column
data.frame called coords, which will ideally
contain Latitude and Longitude values for each row in data.
We don’t have these coordinates for the Bird.parasites
dataset, so we will instead create some fake coordinates to showcase the
model. Note, these commands were not run here, but feel free to move
through them as you did for the above examples
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)The syntax for the MRFcov_spatial function is nearly
identical to MRFcov, with the exception that
coords must be supplied
CRFmod_spatial <- MRFcov_spatial(data = Bird.parasites, n_nodes = 4,
family = 'binomial', coords = coords)Interpretation is also identical to MRFcov objects.
Here, key coefficients are those that are retained after
accounting for spatial influences
Finally, we can compare fits of spatial and non-spatial models just as we did for MRFs and CRFs above
cv_MRF_diag_rep_spatial(data = Bird.parasites, n_nodes = 4,
n_cores = 3, family = 'binomial', plot = T, compare_null = T,
coords = coords)d4590f253a4e9a4e75e82d27d6259f5c053d2a3c