In nicholasjclark/mvgam: Multivariate (Dynamic) Generalized Additive Models
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
message = FALSE,
warning = FALSE,
eval = if (isTRUE(exists("params"))) params$EVAL else FALSE
)
knitr::opts_chunk$set(
echo = TRUE,
dpi = 100,
fig.asp = 0.8,
fig.width = 6,
out.width = "60%",
fig.align = "center"
)
library(mvgam)
library(ggplot2)
library(dplyr)
# A custom ggplot2 theme
theme_set(theme_classic(base_size = 12, base_family = "serif") +
theme(
axis.line.x.bottom = element_line(
colour = "black",
size = 1
),
axis.line.y.left = element_line(
colour = "black",
size = 1
)
))
options(
ggplot2.discrete.colour = c(
"#A25050",
"#00008b",
"darkred",
"#010048"
),
ggplot2.discrete.fill = c(
"#A25050",
"#00008b",
"darkred",
"#010048"
)
)
The purpose of this vignette is to show how the mvgam package can be used to fit and interrogate N-mixture models for population abundance counts made with imperfect detection.
N-mixture models
An N-mixture model is a fairly recent addition to the ecological modeller's toolkit that is designed to make inferences about variation in the abundance of species when observations are imperfect (Royle 2004{target="blank"}). Briefly, assume $\boldsymbol{Y{i,r}}$ is the number of individuals recorded at site $i$ during replicate sampling observation $r$ (recorded as a non-negative integer). If multiple replicate surveys are done within a short enough period to satisfy the assumption that the population remained closed (i.e. there was no substantial change in true population size between replicate surveys), we can account for the fact that observations aren't perfect. This is done by assuming that these replicate observations are Binomial random variables that are parameterized by the true "latent" abundance $N$ and a detection probability $p$:
\begin{align}
\boldsymbol{Y_{i,r}} & \sim \text{Binomial}(N_i, p_r) \
N_{i} & \sim \text{Poisson}(\lambda_i) \end{align}
Using a set of linear predictors, we can estimate effects of covariates $\boldsymbol{X}$ on the expected latent abundance (with a log link for $\lambda$) and, jointly, effects of possibly different covariates (call them $\boldsymbol{Q}$) on detection probability (with a logit link for $p$):
\begin{align}
log(\lambda) & = \beta \boldsymbol{X} \
logit(p) & = \gamma \boldsymbol{Q}\end{align}
mvgam can handle this type of model because it is designed to propagate unobserved temporal processes that evolve independently of the observation process in a State-space format. This setup adapts well to N-mixture models because they can be thought of as State-space models in which the latent state is a discrete variable representing the "true" but unknown population size. This is very convenient because we can incorporate any of the package's diverse effect types (i.e. multidimensional splines, time-varying effects, monotonic effects, random effects etc...) into the linear predictors. All that is required for this to work is a marginalization trick that allows Stan's sampling algorithms to handle discrete parameters (see more about how this method of "integrating out" discrete parameters works in this nice blog post by Maxwell Joseph{target="_blank"}).
The family nmix() is used to set up N-mixture models in mvgam, but we still need to do a little bit of data wrangling to ensure the data are set up in the correct format (this is especially true when we have more than one replicate survey per time period). The most important aspects are: (1) how we set up the observation series and trend_map arguments to ensure replicate surveys are mapped to the correct latent abundance model and (2) the inclusion of a cap variable that defines the maximum possible integer value to use for each observation when estimating latent abundance. The two examples below give a reasonable overview of how this can be done.
Example 1: a two-species system with nonlinear trends
First we will use a simple simulation in which multiple replicate observations are taken at each timepoint for two different species. The simulation produces observations at a single site over six years, with five replicate surveys per year. Each species is simulated to have different nonlinear temporal trends and different detection probabilities. For now, detection probability is fixed (i.e. it does not change over time or in association with any covariates). Notice that we add the cap variable, which does not need to be static, to define the maximum possible value that we think the latent abundance could be for each timepoint. This simply needs to be large enough that we get a reasonable idea of which latent N values are most likely, without adding too much computational cost:
set.seed(999)
# Simulate observations for species 1, which shows a declining trend and 0.7 detection probability
data.frame(
site = 1,
# five replicates per year; six years
replicate = rep(1:5, 6),
time = sort(rep(1:6, 5)),
species = "sp_1",
# true abundance declines nonlinearly
truth = c(
rep(28, 5),
rep(26, 5),
rep(23, 5),
rep(16, 5),
rep(14, 5),
rep(14, 5)
),
# observations are taken with detection prob = 0.7
obs = c(
rbinom(5, 28, 0.7),
rbinom(5, 26, 0.7),
rbinom(5, 23, 0.7),
rbinom(5, 15, 0.7),
rbinom(5, 14, 0.7),
rbinom(5, 14, 0.7)
)
) %>%
# add 'series' information, which is an identifier of site, replicate and species
dplyr::mutate(
series = paste0(
"site_", site,
"_", species,
"_rep_", replicate
),
time = as.numeric(time),
# add a 'cap' variable that defines the maximum latent N to
# marginalize over when estimating latent abundance; in other words
# how large do we realistically think the true abundance could be?
cap = 100
) %>%
dplyr::select(-replicate) -> testdat
# Now add another species that has a different temporal trend and a smaller
# detection probability (0.45 for this species)
testdat <- testdat %>%
dplyr::bind_rows(data.frame(
site = 1,
replicate = rep(1:5, 6),
time = sort(rep(1:6, 5)),
species = "sp_2",
truth = c(
rep(4, 5),
rep(7, 5),
rep(15, 5),
rep(16, 5),
rep(19, 5),
rep(18, 5)
),
obs = c(
rbinom(5, 4, 0.45),
rbinom(5, 7, 0.45),
rbinom(5, 15, 0.45),
rbinom(5, 16, 0.45),
rbinom(5, 19, 0.45),
rbinom(5, 18, 0.45)
)
) %>%
dplyr::mutate(
series = paste0(
"site_", site,
"_", species,
"_rep_", replicate
),
time = as.numeric(time),
cap = 50
) %>%
dplyr::select(-replicate))
This data format isn't too difficult to set up, but it does differ from the traditional multidimensional array setup that is commonly used for fitting N-mixture models in other software packages. Next we ensure that species and series IDs are included as factor variables, in case we'd like to allow certain effects to vary by species
testdat$species <- factor(testdat$species,
levels = unique(testdat$species)
)
testdat$series <- factor(testdat$series,
levels = unique(testdat$series)
)
Preview the dataset to get an idea of how it is structured:
dplyr::glimpse(testdat)
head(testdat, 12)
Setting up the trend_map
Finally, we need to set up the trend_map object. This is crucial for allowing multiple observations to be linked to the same latent process model (see more information about this argument in the Shared latent states vignette{target="_blank"}). In this case, the mapping operates by species and site to state that each set of replicate observations from the same time point should all share the exact same latent abundance model:
testdat %>%
# each unique combination of site*species is a separate process
dplyr::mutate(trend = as.numeric(factor(paste0(site, species)))) %>%
dplyr::select(trend, series) %>%
dplyr::distinct() -> trend_map
trend_map
Notice how all of the replicates for species 1 in site 1 share the same process (i.e. the same trend). This will ensure that all replicates are Binomial draws of the same latent N.
Modelling with the nmix() family
Now we are ready to fit a model using mvgam(). This model will allow each species to have different detection probabilities and different temporal trends. We will use Cmdstan as the backend, which by default will use Hamiltonian Monte Carlo for full Bayesian inference
mod <- mvgam(
# the observation formula sets up linear predictors for
# detection probability on the logit scale
formula = obs ~ species - 1,
# the trend_formula sets up the linear predictors for
# the latent abundance processes on the log scale
trend_formula = ~ s(time, by = trend, k = 4) + species,
# the trend_map takes care of the mapping
trend_map = trend_map,
# nmix() family and data
family = nmix(),
data = testdat,
# priors can be set in the usual way
priors = c(
prior(std_normal(), class = b),
prior(normal(1, 1.5), class = Intercept_trend)
),
samples = 1000
)
mod <- mvgam(
# the observation formula sets up linear predictors for
# detection probability on the logit scale
formula = obs ~ species - 1,
# the trend_formula sets up the linear predictors for
# the latent abundance processes on the log scale
trend_formula = ~ s(time, by = trend, k = 4) + species,
# the trend_map takes care of the mapping
trend_map = trend_map,
# nmix() family and data
family = nmix(),
data = testdat,
# priors can be set in the usual way
priors = c(
prior(std_normal(), class = b),
prior(normal(1, 1.5), class = Intercept_trend)
),
samples = 1000
)
View the automatically-generated Stan code to get a sense of how the marginalization over latent N works
code(mod)
The posterior summary of this model shows that it has converged nicely
summary(mod)
loo() functionality works just as it does for all mvgam models to aid in model comparison / selection (though note that Pareto K values often give warnings for mixture models so these may not be too helpful)
loo(mod)
Plot the estimated smooths of time from each species' latent abundance process (on the log scale)
plot(mod, type = "smooths", trend_effects = TRUE)
marginaleffects support allows for more useful prediction-based interrogations on different scales (though note that at the time of writing this Vignette, you must have the development version of marginaleffects installed for nmix() models to be supported; use remotes::install_github('vincentarelbundock/marginaleffects') to install). Objects that use family nmix() have a few additional prediction scales that can be used (i.e. link, response, detection or latent_N). For example, here are the estimated detection probabilities per species, which show that the model has done a nice job of estimating these parameters:
marginaleffects::plot_predictions(mod,
condition = "species",
type = "detection"
) +
ylab("Pr(detection)") +
ylim(c(0, 1)) +
theme_classic() +
theme(legend.position = "none")
A common goal in N-mixture modelling is to estimate the true latent abundance. The model has automatically generated predictions for the unknown latent abundance that are conditional on the observations. We can extract these and produce decent plots using a small function
hc <- hindcast(mod, type = "latent_N")
# Function to plot latent abundance estimates vs truth
plot_latentN <- function(hindcasts, data, species = "sp_1") {
all_series <- unique(data %>%
dplyr::filter(species == !!species) %>%
dplyr::pull(series))
# Grab the first replicate that represents this series
# so we can get the true simulated values
series <- as.numeric(all_series[1])
truths <- data %>%
dplyr::arrange(time, series) %>%
dplyr::filter(series == !!levels(data$series)[series]) %>%
dplyr::pull(truth)
# In case some replicates have missing observations,
# pull out predictions for ALL replicates and average over them
hcs <- do.call(rbind, lapply(all_series, function(x) {
ind <- which(names(hindcasts$hindcasts) %in% as.character(x))
hindcasts$hindcasts[[ind]]
}))
# Calculate posterior empirical quantiles of predictions
pred_quantiles <- data.frame(t(apply(hcs, 2, function(x) {
quantile(x, probs = c(
0.05, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.95
))
})))
pred_quantiles$time <- 1:NROW(pred_quantiles)
pred_quantiles$truth <- truths
# Grab observations
data %>%
dplyr::filter(series %in% all_series) %>%
dplyr::select(time, obs) -> observations
# Plot
ggplot(pred_quantiles, aes(x = time, group = 1)) +
geom_ribbon(aes(ymin = X5., ymax = X95.), fill = "#DCBCBC") +
geom_ribbon(aes(ymin = X30., ymax = X70.), fill = "#B97C7C") +
geom_line(aes(x = time, y = truth),
colour = "black", linewidth = 1
) +
geom_point(aes(x = time, y = truth),
shape = 21, colour = "white", fill = "black",
size = 2.5
) +
geom_jitter(
data = observations, aes(x = time, y = obs),
width = 0.06,
shape = 21, fill = "darkred", colour = "white", size = 2.5
) +
labs(
y = "Latent abundance (N)",
x = "Time",
title = species
)
}
Latent abundance plots vs the simulated truths for each species are shown below. Here, the red points show the imperfect observations, the black line shows the true latent abundance, and the ribbons show credible intervals of our estimates:
plot_latentN(hc, testdat, species = "sp_1")
plot_latentN(hc, testdat, species = "sp_2")
We can see that estimates for both species have correctly captured the true temporal variation and magnitudes in abundance
Example 2: a larger survey with possible nonlinear effects
Now for another example with a larger dataset. We will use data from Jeff Doser's simulation example from the wonderful spAbundance package{target="_blank"}. The simulated data include one continuous site-level covariate, one factor site-level covariate and two continuous sample-level covariates. This example will allow us to examine how we can include possibly nonlinear effects in the latent process and detection probability models.
Download the data and grab observations / covariate measurements for one species
# Date link
load(url("https://github.com/doserjef/spAbundance/raw/main/data/dataNMixSim.rda"))
data.one.sp <- dataNMixSim
# Pull out observations for one species
data.one.sp$y <- data.one.sp$y[1, , ]
# Abundance covariates that don't change across repeat sampling observations
abund.cov <- dataNMixSim$abund.covs[, 1]
abund.factor <- as.factor(dataNMixSim$abund.covs[, 2])
# Detection covariates that can change across repeat sampling observations
# Note that `NA`s are not allowed for covariates in mvgam, so we randomly
# impute them here
det.cov <- dataNMixSim$det.covs$det.cov.1[, ]
det.cov[is.na(det.cov)] <- rnorm(length(which(is.na(det.cov))))
det.cov2 <- dataNMixSim$det.covs$det.cov.2
det.cov2[is.na(det.cov2)] <- rnorm(length(which(is.na(det.cov2))))
Next we wrangle into the appropriate 'long' data format, adding indicators of time and series for working in mvgam. We also add the cap variable to represent the maximum latent N to marginalize over for each observation
mod_data <- do.call(
rbind,
lapply(1:NROW(data.one.sp$y), function(x) {
data.frame(
y = data.one.sp$y[x, ],
abund_cov = abund.cov[x],
abund_fac = abund.factor[x],
det_cov = det.cov[x, ],
det_cov2 = det.cov2[x, ],
replicate = 1:NCOL(data.one.sp$y),
site = paste0("site", x)
)
})
) %>%
dplyr::mutate(
species = "sp_1",
series = as.factor(paste0(site, "_", species, "_", replicate))
) %>%
dplyr::mutate(
site = factor(site, levels = unique(site)),
species = factor(species, levels = unique(species)),
time = 1,
cap = max(data.one.sp$y, na.rm = TRUE) + 20
)
The data include observations for 225 sites with three replicates per site, though some observations are missing
NROW(mod_data)
dplyr::glimpse(mod_data)
head(mod_data)
The final step for data preparation is of course the trend_map, which sets up the mapping between observation replicates and the latent abundance models. This is done in the same way as in the example above
mod_data %>%
# each unique combination of site*species is a separate process
dplyr::mutate(trend = as.numeric(factor(paste0(site, species)))) %>%
dplyr::select(trend, series) %>%
dplyr::distinct() -> trend_map
trend_map %>%
dplyr::arrange(trend) %>%
head(12)
Now we are ready to fit a model using mvgam(). Here we will use penalized splines for each of the continuous covariate effects to detect possible nonlinear associations. We also showcase how mvgam can make use of the different approximation algorithms available in Stan by using the meanfield variational Bayes approximator (this reduces computation time from around 90 seconds to around 12 seconds for this example)
mod <- mvgam(
# effects of covariates on detection probability;
# here we use penalized splines for both continuous covariates
formula = y ~ s(det_cov, k = 3) + s(det_cov2, k = 3),
# effects of the covariates on latent abundance;
# here we use a penalized spline for the continuous covariate and
# hierarchical intercepts for the factor covariate
trend_formula = ~ s(abund_cov, k = 3) +
s(abund_fac, bs = "re"),
# link multiple observations to each site
trend_map = trend_map,
# nmix() family and supplied data
family = nmix(),
data = mod_data,
# standard normal priors on key regression parameters
priors = c(
prior(std_normal(), class = "b"),
prior(std_normal(), class = "Intercept"),
prior(std_normal(), class = "Intercept_trend"),
prior(std_normal(), class = "sigma_raw_trend")
),
# use Stan's variational inference for quicker results
algorithm = "meanfield",
# no need to compute "series-level" residuals
residuals = FALSE,
samples = 1000
)
mod <- mvgam(
# effects of covariates on detection probability;
# here we use penalized splines for both continuous covariates
formula = y ~ s(det_cov, k = 4) + s(det_cov2, k = 4),
# effects of the covariates on latent abundance;
# here we use a penalized spline for the continuous covariate and
# hierarchical intercepts for the factor covariate
trend_formula = ~ s(abund_cov, k = 4) +
s(abund_fac, bs = "re"),
# link multiple observations to each site
trend_map = trend_map,
# nmix() family and supplied data
family = nmix(),
data = mod_data,
# standard normal priors on key regression parameters
priors = c(
prior(std_normal(), class = "b"),
prior(std_normal(), class = "Intercept"),
prior(std_normal(), class = "Intercept_trend"),
prior(std_normal(), class = "sigma_raw_trend")
),
# use Stan's variational inference for quicker results
algorithm = "meanfield",
# no need to compute "series-level" residuals
residuals = FALSE,
samples = 1000
)
Inspect the model summary but don't bother looking at estimates for all individual spline coefficients. Notice how we no longer receive information on convergence because we did not use MCMC sampling for this model
summary(mod, include_betas = FALSE)
Again we can make use of marginaleffects support for interrogating the model through targeted predictions. First, we can inspect the estimated average detection probability
marginaleffects::avg_predictions(mod, type = "detection")
Next investigate estimated effects of covariates on latent abundance using the conditional_effects() function and specifying type = 'link'; this will return plots on the expectation scale
abund_plots <- plot(
conditional_effects(mod,
type = "link",
effects = c(
"abund_cov",
"abund_fac"
)
),
plot = FALSE
)
The effect of the continuous covariate on expected latent abundance
abund_plots[[1]] +
ylab("Expected latent abundance")
The effect of the factor covariate on expected latent abundance, estimated as a hierarchical random effect
abund_plots[[2]] +
ylab("Expected latent abundance")
Now we can investigate estimated effects of covariates on detection probability using type = 'detection'
det_plots <- plot(
conditional_effects(mod,
type = "detection",
effects = c(
"det_cov",
"det_cov2"
)
),
plot = FALSE
)
The covariate smooths were estimated to be somewhat nonlinear on the logit scale according to the model summary (based on their approximate significances). But inspecting conditional effects of each covariate on the probability scale is more intuitive and useful
det_plots[[1]] +
ylab("Pr(detection)")
det_plots[[2]] +
ylab("Pr(detection)")
More targeted predictions are also easy with marginaleffects support. For example, we can ask: How does detection probability change as we change both detection covariates?
fivenum_round <- function(x) round(fivenum(x, na.rm = TRUE), 2)
marginaleffects::plot_predictions(mod,
newdata = marginaleffects::datagrid(
det_cov = unique,
det_cov2 = fivenum_round
),
by = c("det_cov", "det_cov2"),
type = "detection"
) +
theme_classic() +
ylab("Pr(detection)")
The model has found support for some important covariate effects, but of course we'd want to interrogate how well the model predicts and think about possible spatial effects to capture unmodelled variation in latent abundance (which can easily be incorporated into both linear predictors using spatial smooths).
Further reading
The following papers and resources offer useful material about N-mixture models for ecological population dynamics investigations:
Guélat, Jérôme, and Kéry, Marc. “Effects of Spatial Autocorrelation and Imperfect Detection on Species Distribution Models.” Methods in Ecology and Evolution 9 (2018): 1614–25.
Kéry, Marc, and Royle Andrew J. "Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 2: Dynamic and advanced models". London, UK: Academic Press (2020).
Royle, Andrew J. "N‐mixture models for estimating population size from spatially replicated counts." Biometrics 60.1 (2004): 108-115.
Interested in contributing?
I'm actively seeking PhD students and other researchers to work in the areas of ecological forecasting, multivariate model evaluation and development of mvgam. Please see this small list of opportunities on my website and do reach out if you are interested (n.clark'at'uq.edu.au)
nicholasjclark/mvgam documentation built on April 17, 2025, 9:39 p.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE, eval = if (isTRUE(exists("params"))) params$EVAL else FALSE )
knitr::opts_chunk$set( echo = TRUE, dpi = 100, fig.asp = 0.8, fig.width = 6, out.width = "60%", fig.align = "center" ) library(mvgam) library(ggplot2) library(dplyr) # A custom ggplot2 theme theme_set(theme_classic(base_size = 12, base_family = "serif") + theme( axis.line.x.bottom = element_line( colour = "black", size = 1 ), axis.line.y.left = element_line( colour = "black", size = 1 ) )) options( ggplot2.discrete.colour = c( "#A25050", "#00008b", "darkred", "#010048" ), ggplot2.discrete.fill = c( "#A25050", "#00008b", "darkred", "#010048" ) )
The purpose of this vignette is to show how the mvgam package can be used to fit and interrogate N-mixture models for population abundance counts made with imperfect detection.
N-mixture models
An N-mixture model is a fairly recent addition to the ecological modeller's toolkit that is designed to make inferences about variation in the abundance of species when observations are imperfect (Royle 2004{target="blank"}). Briefly, assume $\boldsymbol{Y{i,r}}$ is the number of individuals recorded at site $i$ during replicate sampling observation $r$ (recorded as a non-negative integer). If multiple replicate surveys are done within a short enough period to satisfy the assumption that the population remained closed (i.e. there was no substantial change in true population size between replicate surveys), we can account for the fact that observations aren't perfect. This is done by assuming that these replicate observations are Binomial random variables that are parameterized by the true "latent" abundance $N$ and a detection probability $p$:
\begin{align} \boldsymbol{Y_{i,r}} & \sim \text{Binomial}(N_i, p_r) \ N_{i} & \sim \text{Poisson}(\lambda_i) \end{align}
Using a set of linear predictors, we can estimate effects of covariates $\boldsymbol{X}$ on the expected latent abundance (with a log link for $\lambda$) and, jointly, effects of possibly different covariates (call them $\boldsymbol{Q}$) on detection probability (with a logit link for $p$):
\begin{align} log(\lambda) & = \beta \boldsymbol{X} \ logit(p) & = \gamma \boldsymbol{Q}\end{align}
mvgam can handle this type of model because it is designed to propagate unobserved temporal processes that evolve independently of the observation process in a State-space format. This setup adapts well to N-mixture models because they can be thought of as State-space models in which the latent state is a discrete variable representing the "true" but unknown population size. This is very convenient because we can incorporate any of the package's diverse effect types (i.e. multidimensional splines, time-varying effects, monotonic effects, random effects etc...) into the linear predictors. All that is required for this to work is a marginalization trick that allows Stan's sampling algorithms to handle discrete parameters (see more about how this method of "integrating out" discrete parameters works in this nice blog post by Maxwell Joseph{target="_blank"}).
The family nmix() is used to set up N-mixture models in mvgam, but we still need to do a little bit of data wrangling to ensure the data are set up in the correct format (this is especially true when we have more than one replicate survey per time period). The most important aspects are: (1) how we set up the observation series and trend_map arguments to ensure replicate surveys are mapped to the correct latent abundance model and (2) the inclusion of a cap variable that defines the maximum possible integer value to use for each observation when estimating latent abundance. The two examples below give a reasonable overview of how this can be done.
Example 1: a two-species system with nonlinear trends
First we will use a simple simulation in which multiple replicate observations are taken at each timepoint for two different species. The simulation produces observations at a single site over six years, with five replicate surveys per year. Each species is simulated to have different nonlinear temporal trends and different detection probabilities. For now, detection probability is fixed (i.e. it does not change over time or in association with any covariates). Notice that we add the cap variable, which does not need to be static, to define the maximum possible value that we think the latent abundance could be for each timepoint. This simply needs to be large enough that we get a reasonable idea of which latent N values are most likely, without adding too much computational cost:
set.seed(999) # Simulate observations for species 1, which shows a declining trend and 0.7 detection probability data.frame( site = 1, # five replicates per year; six years replicate = rep(1:5, 6), time = sort(rep(1:6, 5)), species = "sp_1", # true abundance declines nonlinearly truth = c( rep(28, 5), rep(26, 5), rep(23, 5), rep(16, 5), rep(14, 5), rep(14, 5) ), # observations are taken with detection prob = 0.7 obs = c( rbinom(5, 28, 0.7), rbinom(5, 26, 0.7), rbinom(5, 23, 0.7), rbinom(5, 15, 0.7), rbinom(5, 14, 0.7), rbinom(5, 14, 0.7) ) ) %>% # add 'series' information, which is an identifier of site, replicate and species dplyr::mutate( series = paste0( "site_", site, "_", species, "_rep_", replicate ), time = as.numeric(time), # add a 'cap' variable that defines the maximum latent N to # marginalize over when estimating latent abundance; in other words # how large do we realistically think the true abundance could be? cap = 100 ) %>% dplyr::select(-replicate) -> testdat # Now add another species that has a different temporal trend and a smaller # detection probability (0.45 for this species) testdat <- testdat %>% dplyr::bind_rows(data.frame( site = 1, replicate = rep(1:5, 6), time = sort(rep(1:6, 5)), species = "sp_2", truth = c( rep(4, 5), rep(7, 5), rep(15, 5), rep(16, 5), rep(19, 5), rep(18, 5) ), obs = c( rbinom(5, 4, 0.45), rbinom(5, 7, 0.45), rbinom(5, 15, 0.45), rbinom(5, 16, 0.45), rbinom(5, 19, 0.45), rbinom(5, 18, 0.45) ) ) %>% dplyr::mutate( series = paste0( "site_", site, "_", species, "_rep_", replicate ), time = as.numeric(time), cap = 50 ) %>% dplyr::select(-replicate))
This data format isn't too difficult to set up, but it does differ from the traditional multidimensional array setup that is commonly used for fitting N-mixture models in other software packages. Next we ensure that species and series IDs are included as factor variables, in case we'd like to allow certain effects to vary by species
testdat$species <- factor(testdat$species, levels = unique(testdat$species) ) testdat$series <- factor(testdat$series, levels = unique(testdat$series) )
Preview the dataset to get an idea of how it is structured:
dplyr::glimpse(testdat) head(testdat, 12)
Setting up the trend_map
Finally, we need to set up the trend_map object. This is crucial for allowing multiple observations to be linked to the same latent process model (see more information about this argument in the Shared latent states vignette{target="_blank"}). In this case, the mapping operates by species and site to state that each set of replicate observations from the same time point should all share the exact same latent abundance model:
testdat %>% # each unique combination of site*species is a separate process dplyr::mutate(trend = as.numeric(factor(paste0(site, species)))) %>% dplyr::select(trend, series) %>% dplyr::distinct() -> trend_map trend_map
Notice how all of the replicates for species 1 in site 1 share the same process (i.e. the same trend). This will ensure that all replicates are Binomial draws of the same latent N.
Modelling with the nmix() family
Now we are ready to fit a model using mvgam(). This model will allow each species to have different detection probabilities and different temporal trends. We will use Cmdstan as the backend, which by default will use Hamiltonian Monte Carlo for full Bayesian inference
mod <- mvgam( # the observation formula sets up linear predictors for # detection probability on the logit scale formula = obs ~ species - 1, # the trend_formula sets up the linear predictors for # the latent abundance processes on the log scale trend_formula = ~ s(time, by = trend, k = 4) + species, # the trend_map takes care of the mapping trend_map = trend_map, # nmix() family and data family = nmix(), data = testdat, # priors can be set in the usual way priors = c( prior(std_normal(), class = b), prior(normal(1, 1.5), class = Intercept_trend) ), samples = 1000 )
mod <- mvgam( # the observation formula sets up linear predictors for # detection probability on the logit scale formula = obs ~ species - 1, # the trend_formula sets up the linear predictors for # the latent abundance processes on the log scale trend_formula = ~ s(time, by = trend, k = 4) + species, # the trend_map takes care of the mapping trend_map = trend_map, # nmix() family and data family = nmix(), data = testdat, # priors can be set in the usual way priors = c( prior(std_normal(), class = b), prior(normal(1, 1.5), class = Intercept_trend) ), samples = 1000 )
View the automatically-generated Stan code to get a sense of how the marginalization over latent N works
code(mod)
The posterior summary of this model shows that it has converged nicely
summary(mod)
loo() functionality works just as it does for all mvgam models to aid in model comparison / selection (though note that Pareto K values often give warnings for mixture models so these may not be too helpful)
loo(mod)
Plot the estimated smooths of time from each species' latent abundance process (on the log scale)
plot(mod, type = "smooths", trend_effects = TRUE)
marginaleffects support allows for more useful prediction-based interrogations on different scales (though note that at the time of writing this Vignette, you must have the development version of marginaleffects installed for nmix() models to be supported; use remotes::install_github('vincentarelbundock/marginaleffects') to install). Objects that use family nmix() have a few additional prediction scales that can be used (i.e. link, response, detection or latent_N). For example, here are the estimated detection probabilities per species, which show that the model has done a nice job of estimating these parameters:
marginaleffects::plot_predictions(mod, condition = "species", type = "detection" ) + ylab("Pr(detection)") + ylim(c(0, 1)) + theme_classic() + theme(legend.position = "none")
A common goal in N-mixture modelling is to estimate the true latent abundance. The model has automatically generated predictions for the unknown latent abundance that are conditional on the observations. We can extract these and produce decent plots using a small function
hc <- hindcast(mod, type = "latent_N") # Function to plot latent abundance estimates vs truth plot_latentN <- function(hindcasts, data, species = "sp_1") { all_series <- unique(data %>% dplyr::filter(species == !!species) %>% dplyr::pull(series)) # Grab the first replicate that represents this series # so we can get the true simulated values series <- as.numeric(all_series[1]) truths <- data %>% dplyr::arrange(time, series) %>% dplyr::filter(series == !!levels(data$series)[series]) %>% dplyr::pull(truth) # In case some replicates have missing observations, # pull out predictions for ALL replicates and average over them hcs <- do.call(rbind, lapply(all_series, function(x) { ind <- which(names(hindcasts$hindcasts) %in% as.character(x)) hindcasts$hindcasts[[ind]] })) # Calculate posterior empirical quantiles of predictions pred_quantiles <- data.frame(t(apply(hcs, 2, function(x) { quantile(x, probs = c( 0.05, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.95 )) }))) pred_quantiles$time <- 1:NROW(pred_quantiles) pred_quantiles$truth <- truths # Grab observations data %>% dplyr::filter(series %in% all_series) %>% dplyr::select(time, obs) -> observations # Plot ggplot(pred_quantiles, aes(x = time, group = 1)) + geom_ribbon(aes(ymin = X5., ymax = X95.), fill = "#DCBCBC") + geom_ribbon(aes(ymin = X30., ymax = X70.), fill = "#B97C7C") + geom_line(aes(x = time, y = truth), colour = "black", linewidth = 1 ) + geom_point(aes(x = time, y = truth), shape = 21, colour = "white", fill = "black", size = 2.5 ) + geom_jitter( data = observations, aes(x = time, y = obs), width = 0.06, shape = 21, fill = "darkred", colour = "white", size = 2.5 ) + labs( y = "Latent abundance (N)", x = "Time", title = species ) }
Latent abundance plots vs the simulated truths for each species are shown below. Here, the red points show the imperfect observations, the black line shows the true latent abundance, and the ribbons show credible intervals of our estimates:
plot_latentN(hc, testdat, species = "sp_1") plot_latentN(hc, testdat, species = "sp_2")
We can see that estimates for both species have correctly captured the true temporal variation and magnitudes in abundance
Example 2: a larger survey with possible nonlinear effects
Now for another example with a larger dataset. We will use data from Jeff Doser's simulation example from the wonderful spAbundance package{target="_blank"}. The simulated data include one continuous site-level covariate, one factor site-level covariate and two continuous sample-level covariates. This example will allow us to examine how we can include possibly nonlinear effects in the latent process and detection probability models.
Download the data and grab observations / covariate measurements for one species
# Date link load(url("https://github.com/doserjef/spAbundance/raw/main/data/dataNMixSim.rda")) data.one.sp <- dataNMixSim # Pull out observations for one species data.one.sp$y <- data.one.sp$y[1, , ] # Abundance covariates that don't change across repeat sampling observations abund.cov <- dataNMixSim$abund.covs[, 1] abund.factor <- as.factor(dataNMixSim$abund.covs[, 2]) # Detection covariates that can change across repeat sampling observations # Note that `NA`s are not allowed for covariates in mvgam, so we randomly # impute them here det.cov <- dataNMixSim$det.covs$det.cov.1[, ] det.cov[is.na(det.cov)] <- rnorm(length(which(is.na(det.cov)))) det.cov2 <- dataNMixSim$det.covs$det.cov.2 det.cov2[is.na(det.cov2)] <- rnorm(length(which(is.na(det.cov2))))
Next we wrangle into the appropriate 'long' data format, adding indicators of time and series for working in mvgam. We also add the cap variable to represent the maximum latent N to marginalize over for each observation
mod_data <- do.call( rbind, lapply(1:NROW(data.one.sp$y), function(x) { data.frame( y = data.one.sp$y[x, ], abund_cov = abund.cov[x], abund_fac = abund.factor[x], det_cov = det.cov[x, ], det_cov2 = det.cov2[x, ], replicate = 1:NCOL(data.one.sp$y), site = paste0("site", x) ) }) ) %>% dplyr::mutate( species = "sp_1", series = as.factor(paste0(site, "_", species, "_", replicate)) ) %>% dplyr::mutate( site = factor(site, levels = unique(site)), species = factor(species, levels = unique(species)), time = 1, cap = max(data.one.sp$y, na.rm = TRUE) + 20 )
The data include observations for 225 sites with three replicates per site, though some observations are missing
NROW(mod_data) dplyr::glimpse(mod_data) head(mod_data)
The final step for data preparation is of course the trend_map, which sets up the mapping between observation replicates and the latent abundance models. This is done in the same way as in the example above
mod_data %>% # each unique combination of site*species is a separate process dplyr::mutate(trend = as.numeric(factor(paste0(site, species)))) %>% dplyr::select(trend, series) %>% dplyr::distinct() -> trend_map trend_map %>% dplyr::arrange(trend) %>% head(12)
Now we are ready to fit a model using mvgam(). Here we will use penalized splines for each of the continuous covariate effects to detect possible nonlinear associations. We also showcase how mvgam can make use of the different approximation algorithms available in Stan by using the meanfield variational Bayes approximator (this reduces computation time from around 90 seconds to around 12 seconds for this example)
mod <- mvgam( # effects of covariates on detection probability; # here we use penalized splines for both continuous covariates formula = y ~ s(det_cov, k = 3) + s(det_cov2, k = 3), # effects of the covariates on latent abundance; # here we use a penalized spline for the continuous covariate and # hierarchical intercepts for the factor covariate trend_formula = ~ s(abund_cov, k = 3) + s(abund_fac, bs = "re"), # link multiple observations to each site trend_map = trend_map, # nmix() family and supplied data family = nmix(), data = mod_data, # standard normal priors on key regression parameters priors = c( prior(std_normal(), class = "b"), prior(std_normal(), class = "Intercept"), prior(std_normal(), class = "Intercept_trend"), prior(std_normal(), class = "sigma_raw_trend") ), # use Stan's variational inference for quicker results algorithm = "meanfield", # no need to compute "series-level" residuals residuals = FALSE, samples = 1000 )
mod <- mvgam( # effects of covariates on detection probability; # here we use penalized splines for both continuous covariates formula = y ~ s(det_cov, k = 4) + s(det_cov2, k = 4), # effects of the covariates on latent abundance; # here we use a penalized spline for the continuous covariate and # hierarchical intercepts for the factor covariate trend_formula = ~ s(abund_cov, k = 4) + s(abund_fac, bs = "re"), # link multiple observations to each site trend_map = trend_map, # nmix() family and supplied data family = nmix(), data = mod_data, # standard normal priors on key regression parameters priors = c( prior(std_normal(), class = "b"), prior(std_normal(), class = "Intercept"), prior(std_normal(), class = "Intercept_trend"), prior(std_normal(), class = "sigma_raw_trend") ), # use Stan's variational inference for quicker results algorithm = "meanfield", # no need to compute "series-level" residuals residuals = FALSE, samples = 1000 )
Inspect the model summary but don't bother looking at estimates for all individual spline coefficients. Notice how we no longer receive information on convergence because we did not use MCMC sampling for this model
summary(mod, include_betas = FALSE)
Again we can make use of marginaleffects support for interrogating the model through targeted predictions. First, we can inspect the estimated average detection probability
marginaleffects::avg_predictions(mod, type = "detection")
Next investigate estimated effects of covariates on latent abundance using the conditional_effects() function and specifying type = 'link'; this will return plots on the expectation scale
abund_plots <- plot( conditional_effects(mod, type = "link", effects = c( "abund_cov", "abund_fac" ) ), plot = FALSE )
The effect of the continuous covariate on expected latent abundance
abund_plots[[1]] + ylab("Expected latent abundance")
The effect of the factor covariate on expected latent abundance, estimated as a hierarchical random effect
abund_plots[[2]] + ylab("Expected latent abundance")
Now we can investigate estimated effects of covariates on detection probability using type = 'detection'
det_plots <- plot( conditional_effects(mod, type = "detection", effects = c( "det_cov", "det_cov2" ) ), plot = FALSE )
The covariate smooths were estimated to be somewhat nonlinear on the logit scale according to the model summary (based on their approximate significances). But inspecting conditional effects of each covariate on the probability scale is more intuitive and useful
det_plots[[1]] + ylab("Pr(detection)") det_plots[[2]] + ylab("Pr(detection)")
More targeted predictions are also easy with marginaleffects support. For example, we can ask: How does detection probability change as we change both detection covariates?
fivenum_round <- function(x) round(fivenum(x, na.rm = TRUE), 2) marginaleffects::plot_predictions(mod, newdata = marginaleffects::datagrid( det_cov = unique, det_cov2 = fivenum_round ), by = c("det_cov", "det_cov2"), type = "detection" ) + theme_classic() + ylab("Pr(detection)")
The model has found support for some important covariate effects, but of course we'd want to interrogate how well the model predicts and think about possible spatial effects to capture unmodelled variation in latent abundance (which can easily be incorporated into both linear predictors using spatial smooths).
Further reading
The following papers and resources offer useful material about N-mixture models for ecological population dynamics investigations:
Guélat, Jérôme, and Kéry, Marc. “Effects of Spatial Autocorrelation and Imperfect Detection on Species Distribution Models.” Methods in Ecology and Evolution 9 (2018): 1614–25.
Kéry, Marc, and Royle Andrew J. "Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 2: Dynamic and advanced models". London, UK: Academic Press (2020).
Royle, Andrew J. "N‐mixture models for estimating population size from spatially replicated counts." Biometrics 60.1 (2004): 108-115.
Interested in contributing?
I'm actively seeking PhD students and other researchers to work in the areas of ecological forecasting, multivariate model evaluation and development of mvgam. Please see this small list of opportunities on my website and do reach out if you are interested (n.clark'at'uq.edu.au)
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