使用itsadug包check_resid()可视化mgcv包bam模型结果报错

Hey there, let's work through this error you're hitting when using check_resid() from the itsadug package with your bam-fitted GAMM model. Here are some targeted steps to diagnose and fix the issue:

1. Verify Package Versions & Dependencies #

First, version mismatches between mgcv (which powers bam()) and itsadug are a common culprit. Let's check and update:

• Run these commands to check your current versions:

  packageVersion("mgcv")
  packageVersion("itsadug")
  

• If either is outdated, update them (you might need to restart R afterward):

  update.packages(c("mgcv", "itsadug"))
  

2. Validate Your split_pred Variables #

The split_pred argument relies on variables that exist in your data and are formatted correctly:

• Double-check that Subject and Trial are present in simdat and are appropriate types (factors work best for grouping variables):

  str(simdat[, c("Subject", "Trial")])
  

• If they're character strings, convert them to factors before refitting your model:

  simdat$Subject <- factor(simdat$Subject)
  simdat$Trial <- factor(simdat$Trial)
  # Refit the model
  m1 <- bam(Y ~ Group + s(Time, by=Group) + s(Condition, by=Group, k=5) + ti(Time, Condition, by=Group) + s(Time, Subject, bs='fs', m=1) + s(Trial, Subject, bs='fs', m=1), data=simdat)
  

3. Simplify the Model to Isolate the Issue #

Your model has several complex components (tensor interactions, factor smooths) that might be tripping up check_resid(). Let's narrow down the problem:

• Fit a stripped-down version of the model first, for example:

  m_simple <- bam(Y ~ Group + s(Time, by=Group) + s(Time, Subject, bs='fs', m=1), data=simdat)
  

• Run check_resid(m_simple, split_pred=c("Subject", "Trial")) on this simplified model. If it works, gradually add back the other terms (like the tensor interaction or Trial smooth) until the error reappears—this will tell you exactly which component is causing the conflict.

4. Check for Residual Anomalies #

Sometimes missing values or extreme residuals can break the function:

• Extract and inspect the residuals manually:

  resids <- residuals(m1)
  summary(resids)
  sum(is.na(resids)) # Check for missing residuals
  

• If there are NA residuals, refit your model with na.action=na.exclude to handle missing data more gracefully:

  m1 <- bam(Y ~ Group + s(Time, by=Group) + s(Condition, by=Group, k=5) + ti(Time, Condition, by=Group) + s(Time, Subject, bs='fs', m=1) + s(Trial, Subject, bs='fs', m=1), data=simdat, na.action=na.exclude)
  

5. Test Single split_pred Variables First #

Try using just one variable in split_pred to rule out issues with combining two grouping variables:

check_resid(m1, split_pred="Subject")
check_resid(m1, split_pred="Trial")

If either of these works, the problem might be that some Subject-Trial combinations have no data points, which can cause the function to fail when trying to split residuals across both.

内容的提问来源于stack exchange，提问作者user8270077