sm.density.compare()绘图无显示问题技术求助

Let’s break down why your density plot isn’t showing up and how to fix it—this is a common issue with a few straightforward checks:

1. First, Verify Your Data Structure & Quality #

The sm.density.compare() function relies on specific data formats to work correctly:

• Ensure your grouping variable Y is a factor: If Y is stored as numeric, the function won’t recognize it as distinct categories. Convert it with:

  df$Y <- as.factor(df$Y)
  

• Check for missing values in X: A large number of NA values can break density estimation. Run sum(is.na(df$X)) to count missing entries—if there are many, you’ll need to impute or remove them first.
• Validate X has a meaningful distribution: Use summary(df$X) and hist(df$X) to check if X has a range of values. If all entries are identical or the range is tiny, there’s no density to plot.
• Check category sample sizes: Run table(df$Y) to confirm each of the 13 categories has enough samples (ideally 5+ per group). A category with 0 or 1 sample will throw off the density calculation.

2. Adjust Function Parameters #

Sometimes the default settings don’t play well with your data:

• Manually set the bandwidth (h): The default bandwidth might be too small (resulting in noisy/non-visible curves) or too large (over-smoothing). Try using the histogram-based bandwidth as a starting point:

  optimal_h <- sm.hist(df$X)$h
  sm.density.compare(df$X, df$Y, col=rainbow(13), h=optimal_h)
  

• Update the sm package: Old versions can have bugs. Run packageVersion("sm") to check, and update with install.packages("sm") if needed.

3. Test with a Subset of Categories #

To rule out issues with 13 groups overwhelming the plot, try plotting just 2-3 categories first:

# Filter to first 3 categories
subset_df <- df[df$Y %in% levels(df$Y)[1:3], ]
sm.density.compare(subset_df$X, subset_df$Y, col=c("red", "blue", "green"))

If this works, the problem might be related to overlapping distributions across all 13 groups (more on that below).

4. Check if X Actually Has Discriminative Power #

If all the above checks pass but the plot still looks empty or fully overlapping, X may not distinguish between your 13 categories. You can confirm this statistically with an ANOVA:

anova_result <- anova(lm(X ~ Y, data=df))
print(anova_result)

A high p-value (e.g., >0.05) indicates no significant difference in X values across Y categories—meaning X doesn’t have the discriminative power you’re looking for.

Full Troubleshooting Code Example #

Here’s a consolidated script to walk through all these checks:

library(sm)
load("~/df.Rdata")

# Prep grouping variable
df$Y <- as.factor(df$Y)

# Data quality checks
cat("Missing values in X:", sum(is.na(df$X)), "\n")
cat("Summary of X:\n")
print(summary(df$X))
cat("Sample count per category:\n")
print(table(df$Y))

# Visualize X's base distribution
hist(df$X, main="Distribution of Variable X", xlab="X Value")

# Plot density with optimized bandwidth
optimal_h <- sm.hist(df$X)$h
sm.density.compare(df$X, df$Y, col=rainbow(nlevels(df$Y)), h=optimal_h)
# Add legend for clarity
legend("topright", legend=levels(df$Y), fill=rainbow(nlevels(df$Y)), cex=0.7)

内容的提问来源于stack exchange，提问作者Chris T.