Summary
The discrepancy in standard errors for predicted probabilities between the ggeffects and marginaleffects packages in R is a significant issue. This discrepancy arises when using these packages to estimate the relationship between depression symptoms and protest participation in the European Social Survey (ESS). The weighted survey logistic regression model is estimated using svyglm with a quadratic term for depression symptoms.
Root Cause
The root cause of this discrepancy lies in the different methods used by ggeffects and marginaleffects to calculate the standard errors of the predicted probabilities. The key factors contributing to this discrepancy are:
- Calculation of standard errors: The two packages use different formulas to calculate the standard errors, leading to slightly different results.
- Rounding errors: The packages may also introduce rounding errors during the calculation process, which can contribute to the discrepancy.
- Numerical instability: The numerical instability of the calculations can also lead to small differences in the results.
Why This Happens in Real Systems
This discrepancy can occur in real systems due to:
- Complexity of the models: The use of complex models, such as weighted survey logistic regression, can lead to numerical instability and rounding errors.
- Large datasets: The analysis of large datasets, like the European Social Survey, can also contribute to the discrepancy due to the sheer volume of calculations involved.
- Package implementation: The implementation of the ggeffects and marginaleffects packages can also introduce small differences in the results.
Real-World Impact
The real-world impact of this discrepancy is:
- Inaccurate conclusions: The use of different standard errors can lead to inaccurate conclusions about the relationship between depression symptoms and protest participation.
- Overestimation or underestimation: The discrepancy can result in overestimation or underestimation of the predicted probabilities, which can have significant consequences in policy-making and decision-making.
- Lack of reproducibility: The discrepancy can also lead to a lack of reproducibility of the results, making it difficult to verify and validate the findings.
Example or Code
# Load necessary libraries
library(survey)
library(ggeffects)
library(marginaleffects)
library(dplyr)
library(ggplot2)
# Estimate the model
m2_es <- svyglm(protest ~ depmeans_rs + I(depmeans_rs^2) + essround,
design = df_design_es,
family = quasibinomial())
# Calculate predicted probabilities using ggeffects
gge <- predict_response(m2_es,
terms = c("depmeans_rs [0:1 by=.10]", "essround"))
# Calculate predicted probabilities using marginaleffects
dep_seq <- seq(0, 1, by = 0.10)
grid_typical <- datagrid(model = m2_es,
depmeans_rs = dep_seq,
essround = levels(model.frame(m2_es)$essround))
me <- predictions(m2_es,
newdata = grid_typical,
type = "response")How Senior Engineers Fix It
Senior engineers can fix this issue by:
- Verifying the calculations: Carefully verifying the calculations used by both packages to ensure that they are correct and consistent.
- Using alternative methods: Using alternative methods, such as bootstrapping, to estimate the standard errors and validate the results.
- Checking for numerical instability: Checking for numerical instability and rounding errors in the calculations and addressing them accordingly.
Why Juniors Miss It
Junior engineers may miss this issue due to:
- Lack of experience: Limited experience with complex models and large datasets can make it difficult to anticipate and identify the discrepancy.
- Insufficient knowledge: Insufficient knowledge of the underlying calculations and numerical methods used by the packages can make it challenging to detect and address the issue.
- Overreliance on packages: Overreliance on packages without verifying and validating the results can lead to inaccurate conclusions and unreliable findings.