,
JoonHo Lee [aut], Sophia Rabe-Hesketh [aut]| Title: | Infinite-Jackknife-Based Standard Errors for 'brms' Models |
|---|---|
| Description: | Provides a function to calculate infinite-jackknife-based standard errors for fixed effects parameters in 'brms' models, handling both clustered and independent data. References: Ji et al. (2024) <doi:10.48550/arXiv.2407.09772>; Giordano et al. (2024) <doi:10.48550/arXiv.2305.06466>. |
| Authors: | Feng Ji [aut, cre] ,
JoonHo Lee [aut], Sophia Rabe-Hesketh [aut] |
| Maintainer: | Feng Ji <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.1 |
| Built: | 2025-02-24 05:34:42 UTC |
| Source: | https://github.com/cran/IJSE |
Computes infinite-jackknife-based standard errors for fixed effects parameters from a 'brmsfit' model object. The function handles both clustered and independent data.
IJ_se(fit, cluster_var = NULL)IJ_se(fit, cluster_var = NULL)
fit |
A 'brmsfit' object resulting from fitting a model using the 'brms' package. |
cluster_var |
An optional vector indicating the cluster membership for each observation. If 'NULL', the function treats the data as independent. |
A named vector of standard errors for the fixed effects parameters.
# Load libraries library(brms) # Set a seed for reproducibility set.seed(42) ### Model 1: Linear Regression using brms # Simulate data n <- 300 age <- rnorm(n, mean = 40, sd = 10) income <- rnorm(n, mean = 50000, sd = 10000) education_years <- rnorm(n, mean = 12, sd = 2) # True coefficients beta_0 <- 50000 # Intercept beta_age <- -1000 # Age effect beta_income <- 0.5 # Income effect beta_edu <- 2000 # Education effect sigma <- 10000 # Residual standard deviation # Simulate house prices house_price <- beta_0 + beta_age * age + beta_income * income + beta_edu * education_years + rnorm(n, mean = 0, sd = sigma) # Create data frame data_linear <- data.frame(house_price, age, income, education_years) # Fit the model fit_linear <- brm( formula = house_price ~ age + income + education_years, data = data_linear, family = gaussian(), seed = 42 ) # Summary summary(fit_linear) # Obtain IJ-based SE IJ_se(fit_linear) ### Model 2: Linear Regression for Clustered Data using brms # Simulate data n_schools <- 30 students_per_school <- 100 n <- n_schools * students_per_school # School IDs and types school_id <- rep(1:n_schools, each = students_per_school) school_type <- rep(sample(c("Public", "Private"), n_schools, replace = TRUE), each = students_per_school) school_type_num <- ifelse(school_type == "Public", 0, 1) # Random intercepts for schools sigma_school <- 6 u_school <- rnorm(n_schools, mean = 0, sd = sigma_school) u_school_long <- rep(u_school, each = students_per_school) # Student-level predictors student_age <- rnorm(n, mean = 15, sd = 1) math_score <- rnorm(n, mean = 50, sd = 10) # True coefficients beta_0 <- 50 # Fixed intercept beta_age <- 1.5 # Age effect beta_math <- 1 # Math score effect beta_school_type <- 5 # School type effect sigma_student <- 3 # Residual standard deviation # Simulate reading scores reading_score <- beta_0 + beta_age * student_age + beta_math * math_score + beta_school_type * school_type_num + u_school_long + rnorm(n, mean = 0, sd = sigma_student) # Create data frame data_clustered <- data.frame( reading_score, student_age, math_score, school_id = factor(school_id), school_type, student_id = 1:n ) # Fit the model fit_clustered <- brm( formula = reading_score ~ student_age + math_score + school_type, data = data_clustered, family = gaussian(), seed = 42 ) # Summary summary(fit_clustered) # Obtain IJ-based SE, taking the clustering into account IJ_se(fit_clustered, cluster_var = data_clustered$school_id) ### Example 3: Quantile Regression using brms # Independent data for quantile regression N <- 100 x <- runif(N) eps <- 1 * x^2 + sin(rchisq(N, 8)) + sin(rnorm(x, 3)) # some random DGP y <- 2 * x + runif(N) + eps^2 # Create data frame data_quantile <- data.frame(y, x) # Fit quantile regression model fit_quantile <- brm( formula = bf(y ~ x, quantile = .3), # Quantile regression with 30th percentile data = data_quantile, family = asym_laplace(link_quantile = "identity"), seed = 42 ) # Summary of quantile regression model summary(fit_quantile) # Obtain IJ-based SE IJ_se(fit_quantile) ### Example 4: Quantile Regression for Clustered Data using brms # Clustered data for quantile regression J <- 30 # Number of clusters I <- 50 # Cluster size subj <- rep(1:J, each = I) rho <- 0.8 # Random effect and error terms U <- rnorm(J * I, sd = sqrt(1 / 3)) Z <- rep(rnorm(J, sd = 5), each = I) E <- rnorm(J * I) # Covariates and response variable X <- sqrt(rho) * Z + sqrt(1 - rho) * E X2 <- X^2 Y <- 0.1 * U + X + X2 * U # Create data frame data_cluster_quantile <- data.frame(Y, X, X2, subj = factor(subj)) # Fit quantile regression model fit_quantile_cluster <- brm( formula = bf(Y ~ X + X2, quantile = .33), # Quantile regression with 33rd percentile data = data_cluster_quantile, family = asym_laplace(link_quantile = "identity"), seed = 42 ) # Summary of quantile regression model summary(fit_quantile_cluster) # Obtain IJ-based SE, taking clustering into account IJ_se(fit_quantile_cluster, cluster_var = data_cluster_quantile$subj)# Load libraries library(brms) # Set a seed for reproducibility set.seed(42) ### Model 1: Linear Regression using brms # Simulate data n <- 300 age <- rnorm(n, mean = 40, sd = 10) income <- rnorm(n, mean = 50000, sd = 10000) education_years <- rnorm(n, mean = 12, sd = 2) # True coefficients beta_0 <- 50000 # Intercept beta_age <- -1000 # Age effect beta_income <- 0.5 # Income effect beta_edu <- 2000 # Education effect sigma <- 10000 # Residual standard deviation # Simulate house prices house_price <- beta_0 + beta_age * age + beta_income * income + beta_edu * education_years + rnorm(n, mean = 0, sd = sigma) # Create data frame data_linear <- data.frame(house_price, age, income, education_years) # Fit the model fit_linear <- brm( formula = house_price ~ age + income + education_years, data = data_linear, family = gaussian(), seed = 42 ) # Summary summary(fit_linear) # Obtain IJ-based SE IJ_se(fit_linear) ### Model 2: Linear Regression for Clustered Data using brms # Simulate data n_schools <- 30 students_per_school <- 100 n <- n_schools * students_per_school # School IDs and types school_id <- rep(1:n_schools, each = students_per_school) school_type <- rep(sample(c("Public", "Private"), n_schools, replace = TRUE), each = students_per_school) school_type_num <- ifelse(school_type == "Public", 0, 1) # Random intercepts for schools sigma_school <- 6 u_school <- rnorm(n_schools, mean = 0, sd = sigma_school) u_school_long <- rep(u_school, each = students_per_school) # Student-level predictors student_age <- rnorm(n, mean = 15, sd = 1) math_score <- rnorm(n, mean = 50, sd = 10) # True coefficients beta_0 <- 50 # Fixed intercept beta_age <- 1.5 # Age effect beta_math <- 1 # Math score effect beta_school_type <- 5 # School type effect sigma_student <- 3 # Residual standard deviation # Simulate reading scores reading_score <- beta_0 + beta_age * student_age + beta_math * math_score + beta_school_type * school_type_num + u_school_long + rnorm(n, mean = 0, sd = sigma_student) # Create data frame data_clustered <- data.frame( reading_score, student_age, math_score, school_id = factor(school_id), school_type, student_id = 1:n ) # Fit the model fit_clustered <- brm( formula = reading_score ~ student_age + math_score + school_type, data = data_clustered, family = gaussian(), seed = 42 ) # Summary summary(fit_clustered) # Obtain IJ-based SE, taking the clustering into account IJ_se(fit_clustered, cluster_var = data_clustered$school_id) ### Example 3: Quantile Regression using brms # Independent data for quantile regression N <- 100 x <- runif(N) eps <- 1 * x^2 + sin(rchisq(N, 8)) + sin(rnorm(x, 3)) # some random DGP y <- 2 * x + runif(N) + eps^2 # Create data frame data_quantile <- data.frame(y, x) # Fit quantile regression model fit_quantile <- brm( formula = bf(y ~ x, quantile = .3), # Quantile regression with 30th percentile data = data_quantile, family = asym_laplace(link_quantile = "identity"), seed = 42 ) # Summary of quantile regression model summary(fit_quantile) # Obtain IJ-based SE IJ_se(fit_quantile) ### Example 4: Quantile Regression for Clustered Data using brms # Clustered data for quantile regression J <- 30 # Number of clusters I <- 50 # Cluster size subj <- rep(1:J, each = I) rho <- 0.8 # Random effect and error terms U <- rnorm(J * I, sd = sqrt(1 / 3)) Z <- rep(rnorm(J, sd = 5), each = I) E <- rnorm(J * I) # Covariates and response variable X <- sqrt(rho) * Z + sqrt(1 - rho) * E X2 <- X^2 Y <- 0.1 * U + X + X2 * U # Create data frame data_cluster_quantile <- data.frame(Y, X, X2, subj = factor(subj)) # Fit quantile regression model fit_quantile_cluster <- brm( formula = bf(Y ~ X + X2, quantile = .33), # Quantile regression with 33rd percentile data = data_cluster_quantile, family = asym_laplace(link_quantile = "identity"), seed = 42 ) # Summary of quantile regression model summary(fit_quantile_cluster) # Obtain IJ-based SE, taking clustering into account IJ_se(fit_quantile_cluster, cluster_var = data_cluster_quantile$subj)