我正在使用本教程来了解 JAGS 代码。在“具有附加分类预测变量的相同模型”部分中,它指出“该模型包括性别和体长之间的相互作用”。如何删除它以便没有交互?
这是 R 和 JAGS 中的完整设置和模型。
先上数据:
set.seed(42)
samplesize <- 50 # Larger sample size because we're fitting a more complex model
b_length <- sort(rnorm(samplesize)) # Body length
sex <- sample(c(0, 1), size = samplesize, replace = T) # Sex (0: female, 1: male)
int_true_f <- 30 # Intercept of females
int_true_m_diff <- 5 # Difference between intercepts of males and females
slope_true_f <- 10 # Slope of females
slope_true_m_diff <- -3 # Difference between slopes of males and females
mu <- int_true_f + sex * int_true_m_diff + (slope_true_f + sex * slope_true_m_diff) * b_length # True means
sigma <- 5 # True standard deviation of normal distributions
b_mass <- rnorm(samplesize, mean = mu, sd = sigma) # Body mass (response variable)
# Combine into a data frame:
snakes2 <- data.frame(b_length = b_length, b_mass = b_mass, sex = sex)
head(snakes2)
jagsdata_s2 <- with(snakes2, list(b_mass = b_mass, b_length = b_length, sex = sex, N = length(b_mass)))
JAGS代码:
lm2_jags <- function(){
# Likelihood:
for (i in 1:N){
b_mass[i] ~ dnorm(mu[i], tau) # tau is precision (1 / variance)
mu[i] <- alpha[1] + sex[i] * alpha[2] + (beta[1] + beta[2] * sex[i]) * b_length[i]
}
# Priors:
for (i in 1:2){
alpha[i] ~ dnorm(0, 0.01)
beta[i] ~ dnorm(0, 0.01)
}
sigma ~ dunif(0, 100)
tau <- 1 / (sigma * sigma)
}
初始值并运行:
init_values <- function(){
list(alpha = rnorm(2), beta = rnorm(2), sigma = runif(1))
}
params <- c("alpha", "beta", "sigma")
fit_lm2 <- jags(data = jagsdata_s2, inits = init_values, parameters.to.save = params, model.file = lm2_jags,
n.chains = 3, n.iter = 12000, n.burnin = 2000, n.thin = 10, DIC = F)