---
output: 
    md_document:
        variant: gfm
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  fig.path = "README_figs/README-"
)
```

# ubms: Unmarked Bayesian Models with Stan

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`ubms` is an `R` package for fitting Bayesian hierarchical models of animal occurrence and abundance.
The package has a formula-based interface compatible with [unmarked](https://cran.r-project.org/web/packages/unmarked/index.html), but the model is fit using MCMC with [Stan](https://mc-stan.org/) instead of using maximum likelihood.
Currently there are Stan versions of unmarked functions `occu`, `occuRN`, `colext`, `occuTTD`, `pcount`, `distsamp`, and `multinomPois`.
These functions follow the `stan_` prefix naming format established by [rstanarm](https://cran.r-project.org/web/packages/rstanarm/index.html). For example, the Stan version of the `unmarked` function `occu` is `stan_occu`. 

Advantages compared to `unmarked`:

  1. Obtain posterior distributions of parameters and derived parameters
  2. Include random effects in parameter formulas (same syntax as `lme4`)
  3. Assess model fit using WAIC and LOO via the [loo](https://cran.r-project.org/web/packages/loo/index.html) package

Disadvantages compared to `unmarked`:

  1. MCMC is slower than maximum likelihood
  2. Not all model types are supported
  3. Potential for convergence issues

## Installation

`ubms` is on [CRAN](https://cran.r-project.org/web/packages/ubms/index.html):

```{r, eval=FALSE}
install.packages("ubms")
```

Alternatively, the latest development version can be installed from Github:

```{r, eval=FALSE}
# install.packages("devtools")
devtools::install_github("kenkellner/ubms")
```

## Example

Simulate occupancy data including a random effect on occupancy:

```{r,message=FALSE}
library(ubms)

set.seed(123)
dat_occ <- data.frame(x1=rnorm(500))
dat_p <- data.frame(x2=rnorm(500*5))

y <- matrix(NA, 500, 5)
z <- rep(NA, 500)

b <- c(0.4, -0.5, 0.3, 0.5)

re_fac <- factor(sample(letters[1:26], 500, replace=T))
dat_occ$group <- re_fac
re <- rnorm(26, 0, 1.2)
re_idx <- as.numeric(re_fac)

idx <- 1
for (i in 1:500){
  z[i] <- rbinom(1,1, plogis(b[1] + b[2]*dat_occ$x1[i] + re[re_idx[i]]))
  for (j in 1:5){
    y[i,j] <- z[i]*rbinom(1,1, 
                    plogis(b[3] + b[4]*dat_p$x2[idx]))
    idx <- idx + 1
  }
}
```

Create `unmarked` frame:

```{r}
umf <- unmarkedFrameOccu(y=y, siteCovs=dat_occ, obsCovs=dat_p)
```

Fit a model with a random intercept, using 3 parallel cores:

```{r, eval=FALSE}
(fm <- stan_occu(~x2 ~x1 + (1|group), umf, chains=3, cores=3, seed=123))
```

```{r, echo=FALSE, warning=FALSE}
(fm <- stan_occu(~x2 ~x1 + (1|group), umf, chains=3, cores=3, refresh=0, seed=123))
```


Examine residuals for occupancy and detection submodels (following [Wright et al. 2019](https://doi.org/10.1002/ecy.2703)).
Each panel represents a draw from the posterior distribution.

```{r resids, fig.height=7}
plot(fm)
```

Assess model goodness-of-fit with a posterior predictive check, using the MacKenzie-Bailey chi-square test:

```{r, eval=FALSE}
fm_fit <- gof(fm, draws=500)
plot(fm_fit)
```

```{r gof, echo=FALSE}
fm_fit <- gof(fm, draws=500, quiet=TRUE)
plot(fm_fit)
```

Look at the marginal effect of `x2` on detection:

```{r marginal}
plot_effects(fm, "det")
```

A more detailed vignette for the package is available [here](https://kenkellner.com/ubms/articles/ubms.html).
