Getting Started with bayesTPC

Learn to fit thermal performance curves in minutes

Welcome to bayesTPC

This guide will get you fitting thermal performance curves (TPCs) with bayesTPC quickly and efficiently. Whether you’re new to Bayesian methods or an experienced user, we have tutorials designed for your skill level.

Quick Start (5 minutes)

Installation

First, install the required packages:

# Install nimble first
install.packages("nimble")

# Install bayesTPC from GitHub
remotes::install_github("johnwilliamsmithjr/bayesTPC")

Your First TPC Fit

library(nimble)

nimble version 1.3.0 is loaded.
For more information on NIMBLE and a User Manual,
please visit https://R-nimble.org.

Note for advanced users who have written their own MCMC samplers:
  As of version 0.13.0, NIMBLE's protocol for handling posterior
  predictive nodes has changed in a way that could affect user-defined
  samplers in some situations. Please see Section 15.5.1 of the User Manual.


Attaching package: 'nimble'

The following object is masked from 'package:stats':

    simulate

The following object is masked from 'package:base':

    declare

library(bayesTPC)

bayesTPC requires loading the package NIMBLE to function.
Please run 'library(nimble)` before using.

# Load example data
data <- read.csv("data/AeaegyptiTraitData.csv")
data_list <- list(Trait = data$trait, Temp = data$Temp)

# Quick plot
plot(data_list$Trait ~ data_list$Temp, ylab = "Development Rate", xlab = "Temperature (°C)", pch = 16)

# Choose a model and fit
set.seed(1)
fit <- b_TPC(data = data_list, model = "briere")

Warning in check_data(data): Unusual (Temp>50) temperature values detected (are
Temps given in Celcius?)

Creating NIMBLE model:
 - Configuring model.

  [Note] safeDeparse: truncating deparse output to 1 line

 - Compiling model.

Creating MCMC:
 - Configuring MCMC.
 - Compiling MCMC.
 - Running MCMC.

Progress:
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|

Configuring Output:
 - Finding Max. a Post. parameters.

# Check results
print(fit)

bayesTPC MCMC of Type:
  briere

Formula:
  m[i] <- ( q * Temp * (Temp - T_min) * sqrt((T_max > Temp) * abs(T_max - Temp))
* (T_max > Temp) * (Temp > T_min) )

Distribution:
  Trait[i] ~ T(dnorm(mean = m[i], tau = 1/sigma.sq), 0, )

Parameters:

Chain 1:
            MAP   Mean Median        Priors
T_max    25.016 28.948 28.192 dunif(25, 60)
T_min    19.977 12.828 14.216  dunif(0, 20)
q         0.057  0.010  0.005       dexp(1)
sigma.sq 60.460 61.131 61.175       dexp(1)

plot(fit, temp_interval = seq(0, 35, length.out = 1000))

Warning in plot.btpc_MCMC(fit, temp_interval = seq(0, 35, length.out = 1000)):
Using default title for plots. Either one title or individual titles for each
plot may be provided.

That’s it! You’ve successfully fit a thermal performance curve.

Tutorial Series

For Beginners: Complete Workflow

EEID 2024 Basic Tutorial - Perfect for new users - Dataset: Aedes aegypti mosquito traits (development rate, adult longevity, juvenile survival) - What you’ll learn: Data formatting, model fitting, diagnostics, model comparison - Time: 30-45 minutes - Jump to: Packages | Data setup | Examples | Practice

For Advanced Users: Custom Models

EEID 2024 Advanced Tutorial - For experienced users - Dataset: E. coli growth under antibiotics - What you’ll learn: Custom model creation, posterior transformations, advanced analysis - Time: 45-60 minutes - Jump to: Custom models | Fitting | Transformations

Hands-on Practice

VectorByte Workshop - Interactive activity - Focus: Mosquito trait modeling - Format: Self-contained exercise - Time: 20-30 minutes

Learning Paths

New to bayesTPC?

Quick Start (above) - 5 minutes
Basic Tutorial - Complete workflow
VectorByte Workshop - Hands-on practice
Advanced Tutorial - Custom models

Experienced user?

Jump directly to Advanced Tutorial for custom model creation
Check Model comparison for WAIC analysis
Explore Posterior transformations for advanced analysis

Available Datasets

All tutorials use real biological datasets:

VecTraits Database: Access thousands of vector trait datasets
Antibiotic resistance data: E. coli growth under various antibiotics
Mosquito trait data: Development, survival, and longevity data

Common Issues & Solutions

Poor convergence?

Try these adjustments:

# Adjust priors
priors = list(T_min = "dunif(-5,10)", T_max = "dunif(30,40)")

# Use different sampler
samplerType = "AF_slice"

# Add initial values
inits = list(T_min = 10, T_max = 32)

Model selection?

Compare models using WAIC:

fit1 <- b_TPC(data_list, "briere")
fit2 <- b_TPC(data_list, "quadratic")
bayesTPC::get_WAIC(fit1)
bayesTPC::get_WAIC(fit2)

Custom models?

Create your own TPC function:

my_model <- specify_normal_model("my_model", 
                                parameters = c(q = "dunif(0,1)", T_max = "dunif(30,50)"),
                                formula = expression(q * Temp * (T_max - Temp)))

Need Help?

Diagnostics: See troubleshooting section in Basic Tutorial
Convergence: Check convergence tips in Advanced Tutorial
Package issues: Ensure you’re using nimble version 0.13.2 (see README)

What’s Next?

Once you’re comfortable with the basics, explore: - Different TPC models: get_models() shows all available options - Custom likelihoods: Beyond normal distributions - Model comparison: Using WAIC and other criteria - Real data: Apply to your own research questions

Ready to dive deeper? Start with the Basic Tutorial for a comprehensive workflow!