Package 'ecoCopula' reference manual

Title:	Graphical Modelling and Ordination using Copulas
Description:	Creates 'graphs' of species associations (interactions) and ordination biplots from co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in Popovic, GC., Hui, FKC., Warton, DI., (2018) <doi:10.1016/j.jmva.2017.12.002>.
Authors:	Gordana Popovic [aut, cre], David Warton [ctb], Francis K.C. Hui [ctb], Michelle Lim [ctb]
Maintainer:	Gordana Popovic <[email protected]>
License:	LGPL (>= 2.1)
Version:	1.0.2
Built:	2024-10-28 07:02:36 UTC
Source:	CRAN

Fitting Gaussian copula graphical lasso to co-occurrence data

Description

cgr is used to fit a Gaussian copula graphical model to multivariate discrete data, like species co-occurrence data in ecology. This function fits the model and estimates the shrinkage parameter using BIC. Use plot.cgr to plot the resulting graph.

Usage

cgr(
  obj,
  lambda = NULL,
  n.lambda = 100,
  n.samp = 500,
  method = "BIC",
  seed = NULL
)
cgr(
  obj,
  lambda = NULL,
  n.lambda = 100,
  n.samp = 500,
  method = "BIC",
  seed = NULL
)

Arguments

`obj`	object of either class `manyglm`, or `manyany` with ordinal models `clm`
`lambda`	vector, values of shrinkage parameter lambda for model selection (optional, see detail)
`n.lambda`	integer, number of lambda values for model selection (default = 100), ignored if lambda supplied
`n.samp`	integer (default = 500), number of sets residuals used for importance sampling (optional, see detail)
`method`	method for selecting shrinkage parameter lambda, either "BIC" (default) or "AIC"
`seed`	integer (default = 1), seed for random number generation (optional, see detail)

Value

Three objects are returned; best_graph is a list with parameters for the 'best' graphical model, chosen by the chosen method; all_graphs is a list with likelihood, BIC and AIC for all models along lambda path; obj is the input object.

Details

cgr is used to fit a Gaussian copula graphical model to multivariate discrete data, such as co-occurrence (multi species) data in ecology. The model is estimated using importance sampling with n.samp sets of randomised quantile or "Dunn-Smyth" residuals (Dunn & Smyth 1996), and the glasso package for fitting Gaussian graphical models. Models are fit for a path of values of the shrinkage parameter lambda chosen so that both completely dense and sparse models are fit. The lambda value for the best_graph is chosen by BIC (default) or AIC. The seed is controlled so that models with the same data and different predictors can be compared.

Author(s)

Gordana Popovic <[email protected]>.

References

Dunn, P.K., & Smyth, G.K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 236-244.

Popovic, G. C., Hui, F. K., & Warton, D. I. (2018). A general algorithm for covariance modeling of discrete data. Journal of Multivariate Analysis, 165, 86-100.

Examples

abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
plot(spid_graph,pad=1)
abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
plot(spid_graph,pad=1)

Model based ordination with Gaussian copulas

Description

Model based ordination with Gaussian copulas

Usage

cord(obj, nlv = 2, n.samp = 500, seed = NULL)
cord(obj, nlv = 2, n.samp = 500, seed = NULL)

Arguments

`obj`	object of either class `manyglm`, or `manyany` with ordinal models `clm`
`nlv`	number of latent variables (default = 2, for plotting on a scatterplot)
`n.samp`	integer (default = 500), number of sets residuals used for importance sampling (optional, see detail)
`seed`	integer (default = NULL), seed for random number generation (optional)

Value

loadings latent factor loadings scores latent factor scores sigma covariance matrix estimated with nlv latent variables theta precision matrix estimated with nlv latent variables BIC BIC of estimated model logL log-likelihood of estimated model

Details

cord is used to fit a Gaussian copula factor analytic model to multivariate discrete data, such as co-occurrence (multi species) data in ecology. The model is estimated using importance sampling with n.samp sets of randomised quantile or "Dunn-Smyth" residuals (Dunn & Smyth 1996), and the factanal function. The seed is controlled so that models with the same data and different predictors can be compared.

Author(s)

Gordana Popovic <[email protected]>.

References

Dunn, P.K., & Smyth, G.K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 236-244.

Popovic, G. C., Hui, F. K., & Warton, D. I. (2018). A general algorithm for covariance modeling of discrete data. Journal of Multivariate Analysis, 165, 86-100.

Examples

abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_lv=cord(spider_mod)
plot(spid_lv,biplot = TRUE)
abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_lv=cord(spider_mod)
plot(spid_lv,biplot = TRUE)

Fitted values from a stackedsdm object

Description

Fitted values from a stackedsdm object

Usage

## S3 method for class 'stackedsdm'
fitted(object, ...)
## S3 method for class 'stackedsdm'
fitted(object, ...)

Arguments

`object`	An object of class `stackedsdm`
`...`	Not used

Value

A matrix of fitted values.

Details

Extracts the fitted values from stackedsdm object.

Author(s)

Francis K.C. Hui <[email protected]>.

Examples

library(mvabund)
data(spider)
X <- spider$x
abund <- spider$abund

# Example 1: Simple example
myfamily <- "negative.binomial"
# Example 1: Funkier example where Species are assumed to have different distributions
# Fit models including all covariates are linear terms, but exclude for bare sand
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores=2)
fitted(fit0)

# Example 2: Funkier example where Species are assumed to have different distributions
abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence
myfamily <- c(rep(c("binomial"), 3),
              rep(c("negative.binomial"), (ncol(abund)-3)))
fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores=2)
fitted(fit0)
library(mvabund)
data(spider)
X <- spider$x
abund <- spider$abund

# Example 1: Simple example
myfamily <- "negative.binomial"
# Example 1: Funkier example where Species are assumed to have different distributions
# Fit models including all covariates are linear terms, but exclude for bare sand
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores=2)
fitted(fit0)

# Example 2: Funkier example where Species are assumed to have different distributions
abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence
myfamily <- c(rep(c("binomial"), 3),
              rep(c("negative.binomial"), (ncol(abund)-3)))
fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores=2)
fitted(fit0)

Plot graph of direct species associations.

Description

Plot graph of direct species associations.

Usage

## S3 method for class 'cgr'
plot(
  x,
  P = NULL,
  vary.edge.lwd = FALSE,
  edge.col = c("light blue", "pink"),
  label = colnames(x$obj$fitted),
  vertex.col = "blue",
  label.cex = 0.8,
  edge.lwd = ifelse(vary.edge.lwd, 10, 4),
  edge.lty = c(1, 1),
  ...
)
## S3 method for class 'cgr'
plot(
  x,
  P = NULL,
  vary.edge.lwd = FALSE,
  edge.col = c("light blue", "pink"),
  label = colnames(x$obj$fitted),
  vertex.col = "blue",
  label.cex = 0.8,
  edge.lwd = ifelse(vary.edge.lwd, 10, 4),
  edge.lty = c(1, 1),
  ...
)

Arguments

`x`	is a cgr object, e.g. from output of `cgr`.
`P`	locations of graph nodes, if NULL (default) these are generated with a Fruchterman Reingold algorithm.
`vary.edge.lwd`	is logical, TRUE will vary line width according to the strength of partial correlation, default (FALSE) uses fixed line width.
`edge.col`	takes two colours as arguments - the first is the colour used for positive partial correlations, the second is the colour of negative partial correlations.
`label`	is a vector of labels to apply to each variable, defaulting to the column names supplied in the data.
`vertex.col`	the colour of graph nodes.
`label.cex`	is the size of labels.
`edge.lwd`	is line width, defaulting to 10*partial correlation when varying edge width, and 4 otherwise.
`edge.lty`	is a vector of two integers specifying the line types for positive and negative partial correlations, respectively. Both default to solid lines.
`...`	other parameters to be passed through to plotting gplot, in particular `pad`, the amount to pad the plotting range is useful if labels are being clipped. For details see the `gplot` help file.

Value

a plot of species associations after accounting for the effect of all other species, positive/negative are blue/pink. The matrix of node positions (P) is returned silently.

Examples

abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
plot(spid_graph, edge.col=c("forestgreen","darkorchid4"), 
                 vertex.col = "black",vary.edge.lwd=TRUE)
                 

library(tidyr)
library(tidygraph)
library(ggraph)

igraph_out<-spid_graph$best_graph$igraph_out

igraph_out %>% ggraph('fr') + # see ?layout_tbl_graph_igraph
   geom_edge_fan0(aes( colour = partcor, width=partcor)) +
   scale_edge_width(range = c(0.5, 3))+
   scale_edge_color_gradient2(low="#b2182b",mid="white",high="#2166ac")+
   geom_node_text(aes(label=name), repel = TRUE)+
   geom_node_point(aes(size=1.3))+
   theme_void() +
   theme(legend.position = 'none')

abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
plot(spid_graph, edge.col=c("forestgreen","darkorchid4"), 
                 vertex.col = "black",vary.edge.lwd=TRUE)
                 

library(tidyr)
library(tidygraph)
library(ggraph)

igraph_out<-spid_graph$best_graph$igraph_out

igraph_out %>% ggraph('fr') + # see ?layout_tbl_graph_igraph
   geom_edge_fan0(aes( colour = partcor, width=partcor)) +
   scale_edge_width(range = c(0.5, 3))+
   scale_edge_color_gradient2(low="#b2182b",mid="white",high="#2166ac")+
   geom_node_text(aes(label=name), repel = TRUE)+
   geom_node_point(aes(size=1.3))+
   theme_void() +
   theme(legend.position = 'none')

Plots an ordination of latent variables and their corresponding coefficients (biplot).

Description

Plots an ordination of latent variables and their corresponding coefficients (biplot).

Usage

## S3 method for class 'cord'
plot(
  x,
  biplot = FALSE,
  site.col = "black",
  sp.col = "blue",
  alpha = 0.7,
  arrow = TRUE,
  site.text = FALSE,
  labels = dimnames(x$obj$fitted),
  ...
)
## S3 method for class 'cord'
plot(
  x,
  biplot = FALSE,
  site.col = "black",
  sp.col = "blue",
  alpha = 0.7,
  arrow = TRUE,
  site.text = FALSE,
  labels = dimnames(x$obj$fitted),
  ...
)

Arguments

`x`	is a cord object, e.g. from output of `cord`
`biplot`	`TRUE` if both latent variables and their coefficients are plotted, `FALSE` if only latent variables
`site.col`	site number colour (default is black), vector of length equal to the number of sites
`sp.col`	species name colour (default is blue), vector of length equal to the number of sites (if arrow=TRUE)
`alpha`	scaling factor for ratio of scores to loadings (default is 0.7)
`arrow`	should arrows be plotted for species loadings (default is TRUE)
`site.text`	should sites be labeled by row names of data (default is FALSE, points are drawn)
`labels`	the labels for sites and species (for biplots only) (default is data labels)
`...`	other parameters to be passed through to plotting functions.

Value

an ordination plot.

Examples

X <- spider$x
abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = X, ncores=2) 
spid_lv=cord(spider_mod)
#colour sites according to second column of x (bare sand)
cols=ifelse(spider$x[,2]>0,"black","red")
plot(spid_lv,biplot = FALSE,site.col=cols, site.text = TRUE)


library(ggplot2)
library(RColorBrewer)
alpha= 2.5
site_res <- data.frame(spid_lv$scores,X)
sp_res <- data.frame(spid_lv$loadings,species=colnames(abund))
ggplot()+
 geom_point(aes(x=Factor1,y=Factor2,color = reflection ),site_res)+
 geom_text(aes(x = Factor1*alpha, y = Factor2*alpha,label = species),data=sp_res)+
 scale_color_gradientn(colours = brewer.pal(n = 10, name = "PuOr"))+
 theme_classic()

X <- spider$x
abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = X, ncores=2) 
spid_lv=cord(spider_mod)
#colour sites according to second column of x (bare sand)
cols=ifelse(spider$x[,2]>0,"black","red")
plot(spid_lv,biplot = FALSE,site.col=cols, site.text = TRUE)


library(ggplot2)
library(RColorBrewer)
alpha= 2.5
site_res <- data.frame(spid_lv$scores,X)
sp_res <- data.frame(spid_lv$loadings,species=colnames(abund))
ggplot()+
 geom_point(aes(x=Factor1,y=Factor2,color = reflection ),site_res)+
 geom_text(aes(x = Factor1*alpha, y = Factor2*alpha,label = species),data=sp_res)+
 scale_color_gradientn(colours = brewer.pal(n = 10, name = "PuOr"))+
 theme_classic()

Plot residuals of stackedsdm.

Description

Plot residuals of stackedsdm.

Usage

## S3 method for class 'stackedsdm'
plot(x, ...)
## S3 method for class 'stackedsdm'
plot(x, ...)

Arguments

`x`	is a stackedsdm object.
`...`	not used

Examples

abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = spider$x) 
plot(spider_mod)
abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = spider$x) 
plot(spider_mod)

Predictions from a stackedsdm object

Description

Predictions from a stackedsdm object

Usage

## S3 method for class 'stackedsdm'
predict(
  object,
  newdata = NULL,
  type = "link",
  se.fit = FALSE,
  na.action = na.pass,
  ...
)
## S3 method for class 'stackedsdm'
predict(
  object,
  newdata = NULL,
  type = "link",
  se.fit = FALSE,
  na.action = na.pass,
  ...
)

Arguments

`object`	An object of class `stackedsdm`
`newdata`	Optionally, a data frame in which to look for variables with which to predict. If omitted, the covariates from the existing dataset are used.
`type`	The type of prediction required. This can be supplied as either a single character string, when is applied to all species, or a vector of character strings of the same length as `ncol(object$y)` specifying the type of predictions desired for each species. The exact type of prediction allowed depends precisely on the distribution, but for many there is at least `"link"` which is on the scale of the linear predictors, and `"response"` which is on the scale of the response variable. The values of this argument can be abbreviated.
`se.fit`	Logical switch indicating if standard errors are required.
`na.action`	Function determining what should be done with missing values in `newdata`. The default is to predict `NA`..
`...`	not used

Value

A list where the k-th element is the result of applying the predict method to the k-th fitted model in object$fits.

Details

This function simply applies a for loop, cycling through each fitted model from the stackedsdm object and then attempting to construct the relevant predictions by applying the relevant predict method. Please keep in mind no formatting is done to the predictions.

Author(s)

Francis K.C. Hui <[email protected]>.

Examples

X <- spider$x
abund <- spider$abund

# Example 1: Simple example
myfamily <- "negative.binomial"
# Fit models including all covariates are linear terms, but exclude for bare sand
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores=2) 
predict(fit0, type = "response")


# Example 2: Funkier example where Species are assumed to have different distributions
abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence
myfamily <- c(rep(c("binomial"), 3),
       rep(c("negative.binomial"), 5),
       rep(c("tweedie"), 4)
       )
fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores=2)
predict(fit0, type = "response")

X <- spider$x
abund <- spider$abund

# Example 1: Simple example
myfamily <- "negative.binomial"
# Fit models including all covariates are linear terms, but exclude for bare sand
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores=2) 
predict(fit0, type = "response")


# Example 2: Funkier example where Species are assumed to have different distributions
abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence
myfamily <- c(rep(c("binomial"), 3),
       rep(c("negative.binomial"), 5),
       rep(c("tweedie"), 4)
       )
fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores=2)
predict(fit0, type = "response")

Print function for cgr object

Description

Print function for cgr object

Usage

## S3 method for class 'cgr'
print(x, ...)
## S3 method for class 'cgr'
print(x, ...)

Arguments

`x`	is a cgr object, e.g. from output of `cgr`.
`...`	not used

Examples

abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
print(spid_graph)
abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
print(spid_graph)

Print function for cord object

Description

Print function for cord object

Usage

## S3 method for class 'cord'
print(x, ...)
## S3 method for class 'cord'
print(x, ...)

Arguments

`x`	is a cord object, e.g. from output of `cord`.
`...`	not used

Examples

abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_lv=cord(spider_mod)
print(spid_lv)
abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_lv=cord(spider_mod)
print(spid_lv)

Calculate residuals from a stackedsdm object

Description

Calculate residuals from a stackedsdm object

Usage

## S3 method for class 'stackedsdm'
residuals(object, type = "dunnsmyth", seed = NULL, ...)
## S3 method for class 'stackedsdm'
residuals(object, type = "dunnsmyth", seed = NULL, ...)

Arguments

`object`	An object of class `stackedsdm`;
`type`	Determined what type of residuals to calculate. The current options include Dunn-Smyth residuals (default; "dunnsmyth"), raw response residuals ("response") or probability integral transform residuals ("PIT");
`seed`	For Dunn-Smyth and PIT residuals applied to discrete responses, random jittering is added, and the seed can be used to seed to jittering.
`...`	not used

Value

A matrix of residuals

Details

Calculated the residuals from stackedsdm object.

Author(s)

Francis K.C. Hui <[email protected]>.

Examples

X <- spider$x
abund <- spider$abund

# Example 1: Simple example
myfamily <- "negative.binomial"
# Example 1: Funkier example where Species are assumed to have different distributions
# Fit models including all covariates are linear terms, but exclude for bare sand
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores=2) 
residuals(fit0)

# Example 2: Funkier example where Species are assumed to have different distributions
abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence
myfamily <- c(rep(c("binomial"), 3),
              rep(c("negative.binomial"), (ncol(abund)-3)))
fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores=2)
residuals(fit0)
X <- spider$x
abund <- spider$abund

# Example 1: Simple example
myfamily <- "negative.binomial"
# Example 1: Funkier example where Species are assumed to have different distributions
# Fit models including all covariates are linear terms, but exclude for bare sand
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores=2) 
residuals(fit0)

# Example 2: Funkier example where Species are assumed to have different distributions
abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence
myfamily <- c(rep(c("binomial"), 3),
              rep(c("negative.binomial"), (ncol(abund)-3)))
fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores=2)
residuals(fit0)

Simulates new data from a given cord object

Description

Simulates new data from a given cord object

Usage

## S3 method for class 'cord'
simulate(object, nsim = 1, seed = NULL, newdata = object$obj$data, ...)
## S3 method for class 'cord'
simulate(object, nsim = 1, seed = NULL, newdata = object$obj$data, ...)

Arguments

`object`	is a cord object, e.g. from output of `cord`
`nsim`	Number of simulations, defaults to 1. If nsim > 1, the simulated data will be appended.
`seed`	Random number seed, defaults to a random seed number.
`newdata`	A data frame in which to look for X covariates with which to simulate.
`...`	not used Defaults to the X covariates in the fitted model.

Examples

abund = spider$abund

spider_mod_ssdm = stackedsdm(abund,~1, data = spider$x, ncores=2)
spid_lv_ssdm = cord(spider_mod_ssdm)
simulate(spid_lv_ssdm, nsim=2)


# using mvabund
library(mvabund) #for manyglm
abund=mvabund(abund)
spider_mod = manyglm(abund~1)
spid_lv = cord(spider_mod)
simulate(spid_lv)

spider_mod_X = manyglm(abund ~ soil.dry + bare.sand, data=spider$x)
spid_lv_X = cord(spider_mod_X)
Xnew = spider$x[1:10,]
simulate(spid_lv_X, newdata = Xnew)
simulate(spid_lv_X, nsim=2, newdata = Xnew)

spider_mod_X_ssdm = stackedsdm(abund, formula_X = ~. -bare.sand, data = spider$x, ncores=2)
spid_lv_X_ssdm = cord(spider_mod_X_ssdm)
simulate(spid_lv_X_ssdm, newdata = Xnew)

abund = spider$abund

spider_mod_ssdm = stackedsdm(abund,~1, data = spider$x, ncores=2)
spid_lv_ssdm = cord(spider_mod_ssdm)
simulate(spid_lv_ssdm, nsim=2)


# using mvabund
library(mvabund) #for manyglm
abund=mvabund(abund)
spider_mod = manyglm(abund~1)
spid_lv = cord(spider_mod)
simulate(spid_lv)

spider_mod_X = manyglm(abund ~ soil.dry + bare.sand, data=spider$x)
spid_lv_X = cord(spider_mod_X)
Xnew = spider$x[1:10,]
simulate(spid_lv_X, newdata = Xnew)
simulate(spid_lv_X, nsim=2, newdata = Xnew)

spider_mod_X_ssdm = stackedsdm(abund, formula_X = ~. -bare.sand, data = spider$x, ncores=2)
spid_lv_X_ssdm = cord(spider_mod_X_ssdm)
simulate(spid_lv_X_ssdm, newdata = Xnew)

Spider data

Description

Abundance of hunting spiders and associated environmental variables

Usage

spider
spider

Format

A list containing the elements

abund: A matrix with 28 observations of abundance of 12 hunting spider species.
x: A data frame of six (transformed) environmental variables at each of the 28 sites.
trait: A data frame of three species trait variables for each of the 12 species.

Details

The matrix abund has the following species abundances (column name abbreviation in brackets)

Alopecosa accentuata (Alopacce)
Alopecosa cuneata (Alopcune)
Alopecosa fabrilis (Alopfabr)
Arctosa lutetiana (Arctlute)
Arctosa perita(Arctperi)
Aulonia albimana (Auloalbi)
Pardosa lugubris (Pardlugu)
Pardosa monticola (Pardmont)
Pardosa nigriceps (Pardnigr)
Pardosa pullata (Pardpull)
Trochosa terricola (Trocterr)
Zora spinimana (Zoraspin)

The data frame x has the following log(x+1)-transformed environmental variables

soil.dry - Soil dry mass
bare.sand - Cover bare sand
fallen.leaves - Cover fallen leaves / twigs
moss - Cover moss
herb.layer - Cover herb layer
reflection - Reflection of the soil surface with a cloudless sky

The data frame trait has the following variables

length (numeric) - Length (log-transformed), averaged across typical lengths (in centimetres) for male and females
(factor) - Predominant colour, "yellow" or "dark"
(factor) - Whether the spider typically has markings on it: "none", "spots" or "stripes"

Source

Data attributed to van der Aart & Smeenk-Enserink (1975), obtained from the spider2 directory, CANOCO FORTRAN package, with trait data added by David Warton, exported from mvabund R package.

Stacked species regression models, possibly fitted in parallel

Description

Stacked species regression models, possibly fitted in parallel

Usage

stackedsdm(
  y,
  formula_X = ~1,
  data = NULL,
  family = "negative.binomial",
  trial_size = 1,
  do_parallel = FALSE,
  ncores = NULL,
  trace = FALSE
)
stackedsdm(
  y,
  formula_X = ~1,
  data = NULL,
  family = "negative.binomial",
  trial_size = 1,
  do_parallel = FALSE,
  ncores = NULL,
  trace = FALSE
)

Arguments

`y`	A matrix of species responses
`formula_X`	An object of class `formula` representing the relationship to the covariates to be fitted. There should be nothing to the left hand side of the "~" sign.
`data`	Data frame of the covariates
`family`	Either a single character vector, in which case all responses are assumed to be from this family, or a vector of character strings of the same length as the number of columns of y. Families as strings and not actual `family` class objects. This could be changed though if desired in the future e.g., for custom link functions. Currently, the following families are supported (hopefully properly!): "gaussian", "negative.binomial" (with quadratic mean-variance relationship), "poisson", "binomial" (with logit link), "tweedie", "Gamma" (with log link), "exponential", "beta" (with logit link), "ordinal" (cumulative logit model), "ztpoisson", "ztnegative.binomial", "zipoisson", "zinegative.binomial".
`trial_size`	The trial size if any of the responses are binomial. Is either a single number or a matrix with the same dimension as y. If the latter, then all columns that do not correspond to binomial responses are ignored.
`do_parallel`	Do the separate species model fits in parallel? Defaults to `TRUE`
`ncores`	The number of cores to use if separate the species model fits are done in parallel. If `do_parallel = TRUE`, then it defaults to `detectCores() - 2`
`trace`	Print information. This is not actually used currently

Value

A object of class stackedsdm with the following components: call The function call; fits A list where the j-th element corresponds to the to the fitted model for species j i.e., the j-th column in y; linear_predictor A matrix of the fitted linear predictors fitted A matrix of the fitted values

Details

stackedsdm behaves somewhat like the manyglm or manyany function in the package mvabund, in the sense that it fits a separate regression to each species response i.e., column of y. The main difference is that different families can be permitted for each species, which thus allows for mixed responses types.

Author(s)

Francis K.C. Hui <[email protected]>.

Examples

data(spider)
X <- spider$x
abund <- spider$abund

# Example 1: Simple example
myfamily <- "negative.binomial"
# Example 1: Funkier example where Species are assumed to have different distributions
# Fit models including all covariates are linear terms, but exclude for bare sand
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores = 2) 

# Example 2: Funkier example where Species are assumed to have different distributions
abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence
myfamily <- c(rep(c("binomial"), 3),
              rep(c("negative.binomial"), (ncol(abund)-3)))
fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores = 2)
data(spider)
X <- spider$x
abund <- spider$abund

# Example 1: Simple example
myfamily <- "negative.binomial"
# Example 1: Funkier example where Species are assumed to have different distributions
# Fit models including all covariates are linear terms, but exclude for bare sand
fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores = 2) 

# Example 2: Funkier example where Species are assumed to have different distributions
abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence
myfamily <- c(rep(c("binomial"), 3),
              rep(c("negative.binomial"), (ncol(abund)-3)))
fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores = 2)

Summary function for cgr object

Description

Summary function for cgr object

Usage

## S3 method for class 'cgr'
summary(object, ...)
## S3 method for class 'cgr'
summary(object, ...)

Arguments

`object`	is a cgr object, e.g. from output of `cgr`.
`...`	not used

Examples

abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
summary(spid_graph)
abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
summary(spid_graph)

Summary function for cgr object

Description

Summary function for cgr object

Usage

## S3 method for class 'cord'
summary(object, ...)
## S3 method for class 'cord'
summary(object, ...)

Arguments

`object`	is a cord object, e.g. from output of `cgr`.
`...`	not used

Examples

abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_lv=cord(spider_mod)
summary(spid_lv)
abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_lv=cord(spider_mod)
summary(spid_lv)

Package 'ecoCopula'

Help Index

Fitting Gaussian copula graphical lasso to co-occurrence data

Description

Usage

Arguments

Value

Details

Author(s)

References

See also

Examples

Model based ordination with Gaussian copulas

Description

Usage

Arguments

Value

Details

Author(s)

References

See also

Examples

Fitted values from a stackedsdm object

Description

Usage

Arguments

Value

Details

Author(s)

Examples

Plot graph of direct species associations.

Description

Usage

Arguments

Value

See Also

Examples

Plots an ordination of latent variables and their corresponding coefficients (biplot).

Description

Usage

Arguments

Value

Examples

Plot residuals of stackedsdm.

Description

Usage

Arguments

Examples

Predictions from a stackedsdm object

Description

Usage

Arguments

Value

Details

Author(s)

Examples

Print function for cgr object

Description

Usage

Arguments

See Also

Examples

Print function for cord object

Description

Usage

Arguments

See Also

Examples

Calculate residuals from a stackedsdm object

Description

Usage

Arguments

Value

Details

Author(s)

Examples

Simulates new data from a given cord object

Description

Usage

Arguments