Visualize random terms of communityPGLMMs

Plot variance-cov matrix of random terms; also it is optional to simulate and visualize data based on these var-cov matrices. The input can be a communityPGLMM model (by setting argument x). If no model has been fitted, you can also specify data, formula, and family, etc. without actually fitting the model, which will save time.

pglmm_plot_ranef(
  formula = NULL,
  data = NULL,
  family = "gaussian",
  sp.var = "sp",
  site.var = "site",
  tree = NULL,
  tree_site = NULL,
  repulsion = FALSE,
  x = NULL,
  show.image = TRUE,
  show.sim.image = FALSE,
  random.effects = NULL,
  add.tree.sp = TRUE,
  add.tree.site = FALSE,
  cov_ranef = NULL,
  tree.panel.space = 0.5,
  title.space = 5,
  tree.size = 3,
  ...
)

communityPGLMM.show.re(
  formula = NULL,
  data = NULL,
  family = "gaussian",
  sp.var = "sp",
  site.var = "site",
  tree = NULL,
  tree_site = NULL,
  repulsion = FALSE,
  x = NULL,
  show.image = TRUE,
  show.sim.image = FALSE,
  random.effects = NULL,
  add.tree.sp = TRUE,
  add.tree.site = FALSE,
  cov_ranef = NULL,
  tree.panel.space = 0.5,
  title.space = 5,
  tree.size = 3,
  ...
)

pglmm_plot_re(
  formula = NULL,
  data = NULL,
  family = "gaussian",
  sp.var = "sp",
  site.var = "site",
  tree = NULL,
  tree_site = NULL,
  repulsion = FALSE,
  x = NULL,
  show.image = TRUE,
  show.sim.image = FALSE,
  random.effects = NULL,
  add.tree.sp = TRUE,
  add.tree.site = FALSE,
  cov_ranef = NULL,
  tree.panel.space = 0.5,
  title.space = 5,
  tree.size = 3,
  ...
)

communityPGLMM.plot.re(
  formula = NULL,
  data = NULL,
  family = "gaussian",
  sp.var = "sp",
  site.var = "site",
  tree = NULL,
  tree_site = NULL,
  repulsion = FALSE,
  x = NULL,
  show.image = TRUE,
  show.sim.image = FALSE,
  random.effects = NULL,
  add.tree.sp = TRUE,
  add.tree.site = FALSE,
  cov_ranef = NULL,
  tree.panel.space = 0.5,
  title.space = 5,
  tree.size = 3,
  ...
)

Arguments

formula

A two-sided linear formula object describing the mixed effects of the model.

To specify that a random term should have phylogenetic covariance matrix along with non-phylogenetic one, add __ (two underscores) at the end of the group variable; e.g., + (1 | sp__) will construct two random terms, one with phylogenetic covariance matrix and another with non-phylogenetic (identity) matrix. In contrast, __ in the nested terms (below) will only create a phylogenetic covariance matrix. Nested random terms have the general form (1|sp__@site__) which represents phylogenetically related species nested within correlated sites. This form can be used for bipartite questions. For example, species could be phylogenetically related pollinators and sites could be phylogenetically related plants, leading to the random effect (1|insects__@plants__). If more than one phylogeny is used, remember to add all to the argument cov_ranef = list(insects = insect_phylo, plants = plant_phylo). Phylogenetic correlations can be dropped by removing the __ underscores. Thus, the form (1|sp@site__) excludes the phylogenetic correlations among species, while the form (1|sp__@site) excludes the correlations among sites.

Note that correlated random terms are not allowed. For example, (x|g) will be the same as (0 + x|g) in the lme4::lmer syntax. However, (x1 + x2|g) won't work, so instead use (x1|g) + (x2|g).

data

A data.frame containing the variables named in formula.

family

Either "gaussian" for a Linear Mixed Model, or "binomial" or "poisson" for Generalized Linear Mixed Models. "family" should be specified as a character string (i.e., quoted). For binomial and Poisson data, we use the canonical logit and log link functions, respectively. Binomial data can be either presence/absence, or a two-column array of 'successes' and 'failures'. For both binomial and Poisson data, we add an observation-level random term by default via add.obs.re = TRUE. If bayes = TRUE there are two additional families available: "zeroinflated.binomial", and "zeroinflated.poisson", which add a zero inflation parameter; this parameter gives the probability that the response is a zero. The rest of the parameters of the model then reflect the "non-zero" part part of the model. Note that "zeroinflated.binomial" only makes sense for success/failure response data.

sp.var

The variable name of "species"; y-axis of the image.

site.var

The variable name of "site"; x-axis of the image.

tree

A phylogeny for column sp, with "phylo" class, or a covariance matrix for sp. Make sure to have all species in the matrix; if the matrix is not standardized, (i.e., det(tree) != 1), pglmm will try to standardize it for you. No longer used: keep here for compatibility.

tree_site

A second phylogeny for "site". This is required only if the site column contains species instead of sites. This can be used for bipartitie questions; tree_site can also be a covariance matrix. Make sure to have all sites in the matrix; if the matrix is not standardized (i.e., det(tree_site) != 1), pglmm` will try to standardize it for you. No longer used: keep here for compatibility.

repulsion

When there are nested random terms specified, repulsion = FALSE tests for phylogenetic underdispersion while repulsion = FALSE tests for overdispersion. This argument is a logical vector of length either 1 or >1. If its length is 1, then all covariance matrices in nested terms will be either inverted (overdispersion) or not. If its length is >1, then you can select which covariance matrix in the nested terms to be inverted. Make sure to get the length right: for all the terms with @, count the number of "__" to determine the length of repulsion. For example, sp__@site and sp@site__ will each require one element of repulsion, while sp__@site__ will take two elements (repulsion for sp and repulsion for site). Therefore, if your nested terms are (1|sp__@site) + (1|sp@site__) + (1|sp__@site__), then you should set the repulsion to be something like c(TRUE, FALSE, TRUE, TRUE) (length of 4).

x

A fitted model with class communityPGLMM.

show.image

Whether to show the images of random effects.

show.sim.image

Whether to show the images of simulated site by sp matrix. This can be useful to see how the phylogenetic information were included.

random.effects

Optional pre-build list of random effects. If NULL (the default), the function prep_dat_pglmm will prepare the random effects for you from the information in formula, data, and cov_ranef. random.effect allows a list of pre-generated random effects terms to increase flexibility; for example, this makes it possible to construct models with both phylogenetic correlation and spatio-temporal autocorrelation. In preparing random.effect, make sure that the orders of rows and columns of covariance matrices in the list are the same as their corresponding group variables in the data. Also, this should be a list of lists, e.g. random.effects = list(re1 = list(matrix_a), re2 = list(1, sp = sp, covar = Vsp)).

add.tree.sp

Whether to add a phylogeny of species at the top of the simulated site by sp matrix plot, default is TRUE.

add.tree.site

Whether to add a phylogeny of sites at the right of the simulated site by sp matrix plot, default is FALSE.

cov_ranef

A named list of covariance matrices of random terms. The names should be the group variables that are used as random terms with specified covariance matrices (without the two underscores, e.g. list(sp = tree1, site = tree2)). The actual object can be either a phylogeny with class "phylo" or a prepared covariance matrix. If it is a phylogeny, pglmm will prune it and then convert it to a covariance matrix assuming Brownian motion evolution. pglmm will also standardize all covariance matrices to have determinant of one. Group variables will be converted to factors and all covariance matrices will be rearranged so that rows and columns are in the same order as the levels of their corresponding group variables.

tree.panel.space

The number of lines between the phylogeny and the matrix plot, if add.tree is TRUE.

title.space

The number of lines between the title and the matrix plot, if add.tree is TRUE.

tree.size

The height of the phylogeny to be plotted (number of lines), if add.tree is TRUE.

...

Additional arguments for Matrix::image() or lattice::levelplot(). Common ones are:

• useAbs whether to use absolute values of the matrix; if no negative values, this will be set to TRUE if not specified. When useAbs = TRUE the color scheme will be black-white, otherwise, it will be red/blue.

• colorkey whether to draw the scale legend at the right side of each plot?

Value

A hidden list, including the covariance matrices and simulated site by species matrices. Individual plots are saved as plt_re_list and plt_sim_list. If show.image or show.sim.image is TRUE, the corresponding final plot (plt_re_all_in_one or plt_sim_all_in_one) can be saved as external file using ggplot2::ggsave as it is a grid object.