R中用fitdist()拟合负二项分布时标准误差与size参数显示NA求助

Hey there! Let's break down why you're seeing NA values for the size (theta/dispersion) parameter and its standard error when fitting a negative binomial distribution using fitdistrplus on your Mac (10.11.6) with R 3.4.3. I've dealt with similar fitting hiccups before, so here are actionable checks and fixes:

Common Causes & Solutions #

1. Your Data May Be Too Close to a Poisson Distribution #

Negative binomial models are designed for overdispersed data (variance > mean). If your data has variance nearly equal to its mean (low or no overdispersion), the size parameter will tend toward infinity—this makes the model effectively Poisson, and the fitting algorithm can't reliably estimate a finite size value, leading to NA.

First, check your data's mean and variance to confirm:

data_mean <- mean(your_count_data)
data_var <- var(your_count_data)
cat("Mean:", data_mean, "\nVariance:", data_var)

If data_var is very close to data_mean, consider switching to a Poisson distribution instead—it's a better fit here, and you'll avoid the unstable size parameter issue.

2. Missing or Poor Initial Values #

fitdistrplus relies on initial guesses to converge to maximum likelihood estimates. The default initial values might not work well for your data, especially if overdispersion is low or the distribution is skewed.

Calculate a reasonable initial size value using the method of moments, then pass it to fitdist:

# Moment estimate for the size parameter
init_size <- data_mean^2 / (data_var - data_mean)
# Fit with custom initial values
nb_fit <- fitdist(your_count_data, "nbinom", start = list(mu = data_mean, size = init_size))

This gives the algorithm a solid starting point to converge to valid estimates.

3. Suboptimal Optimization Algorithm #

By default, fitdist uses the Nelder-Mead optimization method, which can struggle with certain data distributions. Try switching to a more robust method like BFGS:

nb_fit <- fitdist(your_count_data, "nbinom", 
                  start = list(mu = data_mean, size = init_size),
                  optim.method = "BFGS")

Different optimization algorithms handle convergence differently—BFGS often performs better for problems with smooth likelihood surfaces.

4. Version Compatibility Issues #

R 3.4.3 (released in 2017) and macOS 10.11.6 are quite old, and the version of fitdistrplus you're using might have bugs that were fixed in later releases.

• Check if you're running the latest fitdistrplus version compatible with R 3.4.3 (archived versions are available on CRAN).
• As a cross-check, use the MASS package's fitdistr function to see if it can estimate the parameters:

  library(MASS)
  mass_fit <- fitdistr(your_count_data, "negative binomial")
  print(mass_fit)
  

If MASS::fitdistr returns valid estimates, the issue is likely with fitdistrplus's default settings, not your data.

Final Notes #

If none of these fixes work, share a small sample of your data (or a simulated dataset that replicates the issue) and the exact code you're running—this will help narrow down the problem further.

内容的提问来源于stack exchange，提问作者user