Mastering SdmTMB Residuals: The Updated Checking Guide

Dec 9, 2025 by Admin 55 views

Hey there, data enthusiasts and modeling pros! If you're deep into spatial and spatio-temporal modeling using the awesome sdmTMB package in R, you know how crucial it is to trust your model's outputs. And how do we build that trust? By rigorously checking our model's assumptions, especially through residual analysis. We're talking about making sure our model isn't just spitting out numbers, but meaningful numbers that truly reflect the underlying process. For a while, many of us relied on the dharma_residuals function, often found in sdmTMBextra, to get our diagnostic plots using the DHARMa package. But, hold up, guys! Things have changed a bit in the R ecosystem, and this function has faced some hiccups. We've seen updates to DHARMa itself, leading to dharma_residuals no longer playing nice, particularly with the return_DHARMa = TRUE argument becoming deprecated. This means our old workflows might not be working as expected, leaving us scratching our heads when trying to get those vital diagnostic plots. But don't you worry, because the sdmTMB developers have been on it, and there's a new, robust, and recommended way to perform your residual checking directly within sdmTMB using residuals.sdmTMB with type = "mle-mcmc". This article is your go-to guide to navigate these changes, get you up to speed with the latest best practices, and ensure your sdmTMB models are as bulletproof as possible. We're going to dive deep into why these updates were necessary, what the new approach entails, and how to implement it step-by-step, making your model validation process smooth and effective. So, buckle up, because we're about to make your sdmTMB residual checks top-notch!

Why Residuals Matter (and Why Updates Are Key!)

Understanding why residuals matter is the bedrock of any solid statistical analysis, especially when you're dealing with complex models like those built with sdmTMB. Simply put, residuals are the differences between your observed data and the values predicted by your model. Think of them as the leftovers or errors your model couldn't explain. If your model is doing a great job, these leftovers should look like random noise – no patterns, no trends, just a jumble. When we talk about sdmTMB models, which often incorporate spatial or spatio-temporal random effects, checking these residuals becomes even more critical. Why? Because these models are designed to capture complex spatial autocorrelation or temporal dynamics, and if your residuals still show patterns, it means your model isn't fully capturing these complexities. This could lead to biased parameter estimates, incorrect standard errors, and ultimately, misleading scientific conclusions. Nobody wants that, right? That's why residual checking isn't just a suggestion; it's a fundamental step in ensuring the validity and reliability of your sdmTMB model. It allows us to scrutinize the assumptions underlying our statistical tests and confirm that our model is indeed a good fit for the data. Ignoring residual diagnostics is like building a house without checking the foundation – it might stand for a bit, but it's bound to crumble under pressure!

Now, about why updates are key: the world of R packages is dynamic. Developers are constantly refining code, improving efficiency, fixing bugs, and enhancing functionality. The DHARMa package, which is incredibly powerful for randomized quantile residuals, has also evolved. These evolutions, while beneficial for the broader R ecosystem, can sometimes create compatibility issues with packages that rely on older versions or specific function arguments. This is precisely what happened with the dharma_residuals function, which was once a go-to tool for sdmTMB users. The change in DHARMa regarding the return_DHARMa = TRUE argument meant that the older dharma_residuals function could no longer reliably produce the DHARMa objects we needed for comprehensive diagnostics. This kind of update highlights the continuous need for vigilance and adaptation in our analytical workflows. Relying on outdated methods, even if they worked perfectly yesterday, can lead to frustration and, more importantly, inaccurate model assessments. So, staying current with the recommended tools, like the new residuals.sdmTMB(type = "mle-mcmc") method, isn't just about keeping up with the latest tech; it's about maintaining the integrity and robustness of our research. It ensures that our sdmTMB model diagnostics are always based on the most accurate and up-to-date statistical principles, giving us the confidence to present our findings without reservation.

The Old Way: Diving into `dharma_residuals` and Its Hiccups

Alright, let's take a quick trip down memory lane and talk about the old way we used to handle sdmTMB residual checking, specifically with the dharma_residuals function. For a good while, this function, often accessed through the sdmTMBextra package, was a super convenient tool for many of us. The DHARMa package itself is brilliant; it provides a unified framework for residual diagnostics for any regression model fitted with maximum likelihood by converting raw residuals into randomized quantile residuals. These are awesome because they should be uniformly distributed between 0 and 1 if your model is a perfect fit, making it incredibly easy to spot deviations using standard diagnostic plots like QQ plots against a uniform distribution, or residual-vs-predicted plots. The dharma_residuals function in sdmTMBextra was specifically designed to bridge the gap between your fitted sdmTMB model and DHARMa's powerful diagnostic capabilities. It aimed to streamline the process of getting those standardized residuals, allowing us to quickly assess things like overdispersion, zero-inflation, and overall model fit for our sdmTMB models.

However, as many of you might have discovered, this once-reliable pathway started hitting some hiccups. The primary issue stemmed from an update in the core DHARMa package. You see, software evolves, and sometimes that evolution means certain function arguments change or get deprecated. In this case, the dharma_residuals function relied on an argument, return_DHARMa = TRUE, to directly output a DHARMa object, which was essential for subsequent plotting and analysis using DHARMa's built-in functions. But here's the kicker: DHARMa was updated, and this return_DHARMa argument was either removed or its behavior changed significantly, making the dharma_residuals function no longer able to return a proper DHARMa object. This created a frustrating situation for users. You'd run your code, expecting those beautiful diagnostic plots, and instead, you'd get errors or an output that wasn't compatible with the DHARMa plotting functions you've come to rely on. It essentially broke the connection between sdmTMB and the DHARMa diagnostics we cherished.

These legacy issues highlight a common challenge in data science: dependency management and package compatibility. While the intention behind dharma_residuals was fantastic – providing an easy, integrated way to check residuals – its reliance on external package specifics meant it was vulnerable to upstream changes. For users, this translated into wasted time debugging, searching for workarounds, and a general loss of confidence in their sdmTMB model diagnostics. It's a classic example of why staying informed about package updates and recommended practices is so vital. But fear not, because these challenges often pave the way for even better, more integrated solutions, which brings us perfectly to the new recommended approach directly within sdmTMB itself. We're talking about a more robust, less dependent way to ensure your sdmTMB models are thoroughly checked, avoiding these frustrating compatibility woes entirely. This evolution is all about making your life easier and your model diagnostics more reliable, moving beyond the dharma_residuals function's limitations towards a native and powerful solution.

Embracing the New Standard: `residuals.sdmTMB` with `type = "mle-mcmc"`

Alright, guys, let's get excited about the new standard for sdmTMB residual checking! Out with the old, in with the robust and integrated residuals.sdmTMB function, specifically when you set type = "mle-mcmc". This isn't just a minor tweak; it's a significant improvement that brings the powerful concept of randomized quantile residuals directly into the sdmTMB package's workflow, making your model assessment process smoother and more reliable. So, what exactly is this residuals.sdmTMB function, and why is type = "mle-mcmc" the magic sauce? Let's break it down.

First off, residuals.sdmTMB is a generic function that, when applied to an sdmTMB object, extracts the residuals. The key here is the type argument. While you might be familiar with traditional types of residuals (like 'response' or 'pearson'), "mle-mcmc" is where the true power lies for complex models like sdmTMB. This type of residual leverages a simulation-based approach, drawing samples from the estimated posterior distribution of your model's predictions to generate true randomized quantile residuals. What does that mean in plain English? Imagine your model making a prediction for a certain data point. Instead of just giving you a single number, it essentially tells you, "Given everything I've learned, I think the true value could be anywhere in this range, with these probabilities." The mle-mcmc residuals then compare your observed data point to this distribution of predictions. If your observed data point falls, say, at the 75th percentile of the predicted distribution, your randomized quantile residual for that point will be 0.75. If your model is perfectly specified, these residuals, across all your data points, should be uniformly distributed between 0 and 1. This is a super powerful concept because it allows us to check all sorts of model assumptions (like the distribution family, link function, and overall fit) in a single, standardized way. It’s akin to what the DHARMa package aims to do, but now it's natively implemented and optimized for sdmTMB's unique structure, including its spatial and spatio-temporal random effects.

One of the biggest advantages of this approach is its integration. You don't need to juggle multiple packages or worry about compatibility issues arising from external updates, as was the case with dharma_residuals. This method is designed to work seamlessly with your sdmTMB objects, providing a more robust and stable way to get your sdmTMB model diagnostics. It inherently handles the complexities of sdmTMB's mixed-effects structure, giving you a more accurate assessment of your model's fit. Furthermore, because it's simulation-based, it's particularly well-suited for models with non-Gaussian response distributions (like Poisson, Negative Binomial, or binomial), where traditional residuals can be tricky to interpret. By generating these randomized quantile residuals, we can create standard diagnostic plots (like QQ plots against a uniform distribution, or residuals vs. fitted values) that are much easier to interpret than raw or Pearson residuals from non-Gaussian models. This means you can confidently check for issues like overdispersion, zero-inflation, and unmodeled patterns, ensuring your sdmTMB best practice is always followed. Embracing residuals.sdmTMB(type = "mle-mcmc") is a clear step forward, providing a cutting-edge and reliable way to validate your spatial and spatio-temporal models, making your research more credible and your findings more trustworthy.

A Step-by-Step Guide to Modern `sdmTMB` Residual Checking

Alright, guys, let's get our hands dirty and walk through a step-by-step guide to performing modern sdmTMB residual checking using the awesome residuals.sdmTMB with type = "mle-mcmc". This is where we turn theory into practice and ensure your sdmTMB model diagnostics are solid. Follow these practical steps, and you'll be a pro at model validation in no time!

Step 1: Load Your Packages and Prepare Your Data

First things first, you need to load the necessary packages. Of course, sdmTMB is paramount. We'll also need ggplot2 for visualization and dplyr for data manipulation, which are common allies in any R workflow. Make sure you have these installed! After that, you’ll need some data. For illustration, let's imagine we have some simulated data or a real dataset ready to go. This typically involves latitude, longitude, year, and your response_variable, along with any covariates.

# Load essential packages
library(sdmTMB)
library(ggplot2)
library(dplyr)

# (Optional: If you need to simulate data for testing)
set.seed(42)
data <- sdmTMB_sim(
  x = runif(300, -1, 1),
  y = runif(300, -1, 1),
  time = rep(1:5, each = 60),
  range = 0.5,
  phi = 0.8,
  sigma_O = 0.1,
  sigma_E = 0.1,
  beta_trend = 0.05,
  seed = 42
) %>%
  mutate(density_log = log(density), # A common transformation for log-link models
         fake_covariate = rnorm(n())) # Adding a dummy covariate

# Ensure your coordinates are projected (important for spatial models)
data$X <- data$x * 111 # rough conversion to km
data$Y <- data$y * 111 # rough conversion to km

# Create a mesh for spatial effects
mesh <- make_mesh(data, c("X", "Y"), cutoff = 10)

Step 2: Fit Your sdmTMB Model

Now that your data is ready, it's time to fit your sdmTMB model. Remember to specify your formula, family, spatial (spatial = "on") and/or spatiotemporal (spatiotemporal = "iid" or "ar1") components, and any offsets. This example assumes a log-link and Gaussian family for simplicity, but the residual checking method applies similarly to other families (like nbinom1, poisson, etc.).

# Fit a sample sdmTMB model
m <- sdmTMB(
  density_log ~ fake_covariate,
  data = data,
  mesh = mesh,
  time = "time",
  family = gaussian(link = "identity"), # Use identity link with log-transformed response
  spatial = "on",
  spatiotemporal = "iid",
  control = sdmTMBcontrol(newton_loops = 1)
)

# Optional: Check model summary (always a good idea)
# summary(m)

Step 3: Generate Randomized Quantile Residuals with type = "mle-mcmc"

This is the crucial step! We'll use residuals.sdmTMB to extract the residuals. The magic happens with type = "mle-mcmc". This will return a vector of randomized quantile residuals that, if your model is perfectly specified, should be uniformly distributed between 0 and 1. We'll also attach them back to our original data for easy plotting.

# Generate randomized quantile residuals
data$res <- residuals(m, type = "mle-mcmc")

# You can check the range of residuals - should be between 0 and 1
# range(data$res)

Step 4: Visualize Your Residuals (The Interpretation Part!)

Here's where we interpret the sdmTMB residual plots. We'll primarily look at two types of plots: a QQ plot against a uniform distribution and a plot of residuals versus fitted values. For spatial models, it's also good to check for spatial patterns.

QQ Plot Against a Uniform Distribution: If your residuals are truly uniformly distributed, they should fall along the 1:1 line. Deviations suggest issues with your model's distributional assumptions or fit.

# QQ Plot against a Uniform Distribution
qq_plot <- ggplot(data, aes(sample = res)) +
  stat_qq(distribution = qunif) +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "red") +
  labs(
    title = "QQ Plot of Randomized Quantile Residuals (Uniform)",
    x = "Theoretical Uniform Quantiles",
    y = "Sample Quantiles"
  ) +
  theme_minimal()
print(qq_plot)

Residuals vs. Fitted Values Plot: This plot helps detect non-linearity, heteroscedasticity (changing variance), or unmodeled patterns. Ideally, you should see a random cloud of points with no discernible trend or shape.

# Residuals vs. Fitted Values
# First, get the fitted values (response scale for clarity)
data$fitted_values <- predict(m)$est

res_vs_fitted_plot <- ggplot(data, aes(x = fitted_values, y = res)) +
  geom_point(alpha = 0.5) +
  geom_hline(yintercept = 0.5, linetype = "dashed", color = "blue") + # Midpoint of uniform dist
  labs(
    title = "Randomized Quantile Residuals vs. Fitted Values",
    x = "Fitted Values",
    y = "Randomized Quantile Residuals"
  ) +
  theme_minimal()
print(res_vs_fitted_plot)

Residuals vs. Predictors (Covariates) Plot: This can help identify if a specific covariate is poorly modeled or has non-linear effects not captured.

# Residuals vs. a Covariate (e.g., fake_covariate)
res_vs_cov_plot <- ggplot(data, aes(x = fake_covariate, y = res)) +
  geom_point(alpha = 0.5) +
  geom_hline(yintercept = 0.5, linetype = "dashed", color = "blue") +
  labs(
    title = "Randomized Quantile Residuals vs. Fake Covariate",
    x = "Fake Covariate Value",
    y = "Randomized Quantile Residuals"
  ) +
  theme_minimal()
print(res_vs_cov_plot)

Spatial Residual Plot: For sdmTMB models, checking for remaining spatial patterns in your residuals is paramount. If your spatial random effects have done their job, these residuals should ideally show no strong spatial autocorrelation.

# Spatial plot of residuals (for a single time step if time is included)
spatial_res_plot <- ggplot(filter(data, time == 1), aes(x = X, y = Y, color = res)) +
  geom_point(size = 2) +
  scale_color_viridis_c(option = "B", name = "Residual") +
  labs(
    title = "Spatial Plot of Randomized Quantile Residuals (Time = 1)",
    x = "Longitude (X)",
    y = "Latitude (Y)"
  ) +
  theme_minimal() +
  coord_fixed()
print(spatial_res_plot)

Step 5: Interpret and Refine

After generating these plots, you need to carefully interpret them. Are the points on the QQ plot largely hugging the line? Is the residuals-vs-fitted plot a shapeless blob? Are there any visible spatial patterns? If you see deviations, it's a signal to revisit your model specification. This might involve: trying a different family, adding more covariates, considering non-linear effects for covariates, or rethinking your spatial/spatiotemporal structure. This iterative process of sdmTMB model building and residual diagnostics is how we arrive at the best possible model. Remember, perfect residuals are rare, but significant patterns are red flags you can't ignore. This new residuals.sdmTMB(type = "mle-mcmc") approach gives us a clear, consistent, and robust way to check our assumptions, bringing confidence to our complex spatial models.

Advanced Tips, Common Pitfalls, and Staying Updated

Alright, you've got the basics down for checking your sdmTMB residuals, but let's dive into some advanced tips, discuss common pitfalls, and chat about the importance of staying updated in this fast-paced world of R packages. After all, building robust spatial models with sdmTMB is an art as much as it is a science, and a little extra knowledge can go a long way in honing your craft!

First, for advanced tips: what if your residuals still look a bit weird even after using residuals.sdmTMB(type = "mle-mcmc")? Don't panic! The DHARMa package itself, even if we're not using its wrapper function anymore, is still an incredible resource for understanding how to interpret randomized quantile residuals. Their vignettes and documentation offer fantastic guidance on typical patterns seen in QQ plots and residual-vs-fitted plots, and what these patterns might indicate (e.g., under/overdispersion, incorrect link function, missing covariates, zero-inflation). For example, a