使用mblogit拟合多项混合模型时遭遇str2lang错误的求助及替代方案咨询

Hey there! Let's work through this problem together. First, let's break down why you're getting that str2lang error, then look at fixes for mblogit and alternative tools that might be more reliable for multinomial mixed models.

错误原因分析 #

The str2lang error you're seeing is almost certainly caused by the space in your variable name Hunting season. When mblogit tries to parse the random effect formula, the space gets interpreted as a break in the expression—hence the "unexpected symbol" message pointing to "Hunting season". Your earlier model (without the random effect) worked because you wrapped the spaced variable in backticks in the main formula, but the random effect parsing logic in mblogit doesn't handle this correctly.

针对mblogit的解决方案 #

方法1：重命名变量（推荐） #

The simplest fix is to rename your variable to remove the space, which avoids parsing issues entirely:

# Rename the variable to use underscores instead of spaces
modeldata$Hunting_season <- modeldata$`Hunting season`

# Fit the model with the new variable name
library(mblogit)
mblogit_model <- mblogit(
  formula = `Age class` ~ Sex*group_d,
  random = ~ 1 | Hunting_season,
  data = modeldata
)

方法2：尝试在随机效应公式中包裹变量 #

While less reliable (some formula parsers don't support backticks in random effect terms), you can try wrapping Hunting season in backticks in the random formula:

mblogit_model <- mblogit(
  formula = `Age class` ~ Sex*group_d,
  random = ~ 1 | `Hunting season`,
  data = modeldata
)

Note that this might not work depending on how mblogit processes the random effect syntax, so method 1 is safer.

替代拟合工具推荐 #

If mblogit continues to give you trouble, there are more robust packages for multinomial mixed models that are widely used in the R community:

1. glmmTMB（频率论框架） #

glmmTMB is a versatile package for fitting generalized linear mixed models, including multinomial mixed models. It handles spaced variable names well and has syntax similar to lme4:

library(glmmTMB)
library(emmeans)

# Fit the model
glmm_model <- glmmTMB(
  `Age class` ~ Sex*group_d + (1 | `Hunting season`),
  data = modeldata,
  family = multinomial()
)

# View model summary
summary(glmm_model)

# Calculate estimated marginal means and pairwise comparisons
emmeans(glmm_model, pairwise ~ group_d | Sex)

2. brms（贝叶斯框架） #

If you're open to a Bayesian approach, brms is an excellent choice. It uses Stan under the hood, provides flexible model specification, and makes it easy to get uncertainty estimates for your marginal means:

library(brms)
library(emmeans)

# Fit the Bayesian multinomial mixed model
brm_model <- brm(
  formula = `Age class` ~ Sex*group_d + (1 | `Hunting season`),
  data = modeldata,
  family = categorical(),
  chains = 4,  # Use at least 4 chains for reliable inference
  iter = 2000  # Adjust iterations based on convergence checks
)

# View model summary
summary(brm_model)

# Calculate estimated marginal means and pairwise comparisons
emmeans(brm_model, pairwise ~ group_d | Sex)

Both of these packages will let you address your core research question—comparing age structure across different group types—and work seamlessly with emmeans for marginal mean comparisons, just as you planned.

备注：内容来源于stack exchange，提问作者S_vdp