根据这篇文章,我试图获得边际效应:http://andrewgelman.com/2016/01/14/rstanarm-and-more/
td <- readRDS("some data")
CHAINS <- 1
CORES <- 1
SEED <- 42
ITERATIONS <- 2000
MAX_TREEDEPTH <- 9
md <- td[,.(y,x1,x2)] # selection the columns i need. y is binary
glm1 <- stan_glm(y~x1+x2,
data = md,
family = binomial(link="logit"),
prior = NULL,
prior_intercept = NULL,
chains = CHAINS,
cores = CORES,
seed = SEED,
iter = ITERATIONS,
control=list(max_treedepth=MAX_TREEDEPTH)
)
# launch_shinystan(glm1)
tmp <- posterior_predict(glm1,newdata=md[,.(x1,x2)])
问题
运行此代码后,我收到以下错误:
我收到y
未找到的错误,这实际上意味着我还需要在y
中传递newdata
,根据?posterior_predict
情况不应该这样。
推理
我需要tmp <- posterior_predict(glm1,newdata=md[,.(x1,x2)])
因为根据上面的帖子(据我所知),为了计算x1的边际效应(如果我假设x1是二进制的)将是
temp <- md
temp[,x1:=0]
temp[,x2:=mean(x2)]
number_0 <- posterior_predict(glm1,newdata=temp)
temp <- md
temp[,x1:=1]
temp[,x2:=mean(x2)]
number_1 <- posterior_predict(glm1,newdata=temp)
marginal_effect_x1 <- number_1 - number_0
答案 0 :(得分:3)
对于二元logit模型,连续变量的边际效应是相对于该变量的成功概率的导数,其中链规则是逻辑密度(在预测变量的某些值处评估,通常是预测值的观测值乘以所讨论变量的系数。在你的情况下,那将是
df <- as.data.frame(glm1)
ME <- df$x2 * dlogis(posterior_linpred(glm1))
由于这取决于预测变量的观测值,因此通常对数据进行平均
AME <- rowMeans(ME)
对于二元预测变量,只需x1 = 0
从x1 = 1
成功的概率中减去成功概率
nd <- md
nd$x1 <- 0
p0 <- posterior_linpred(glm1, newdata = nd, transform = TRUE)
nd$x1 <- 1
p1 <- posterior_linpred(glm1, newdata = nd, transform = TRUE)
ME <- p1 - p0
AME <- rowMeans(ME)