如何解决brms hurdle_lognormal模型:这是将后验值转换为y变量单位,并将障碍值转换为概率的正确方法吗?
能否请您确认我的解决方案,以将brms跨栏对数正态后验值转换为有意义的估计值,例如y变量单位(对数正态部分)或零概率(跨栏部分)。
模型
model = brm(br(y ~ (1 | county),hu ~ (1 | county),data = data,family = hurdle_lognormal())
后验变量
get_variables(model)
[1] "b_Intercept"
[2] "b_hu_Intercept"
[3] "sd_county__Intercept"
[4] "sd_county__hu_Intercept"
[5] "sigma"
[6] "r_county[A,Intercept]"
[7] "r_county[B,Intercept]"
[8] "r_county[C,Intercept]"
[9] "r_county[D,Intercept]"
[10] "r_county__hu[A,Intercept]"
[11] "r_county__hu[B,Intercept]"
[12] "r_county__hu[C,Intercept]"
[13] "r_county__hu[D,Intercept]"
[14] "lp__"
[15] "accept_stat__"
[16] "stepsize__"
[17] "treedepth__"
[18] "n_leapfrog__"
[19] "divergent__"
[20] "energy__"
每个县的平均y值计算解决方案
我必须添加“ b_Intercept”和“ r_county [,Intercept]”,并采用exp()?在代码中:
model %>%
spread_draws(b_Intercept,r_county[county,]) %>%
median_qi(condition_mean = exp(b_Intercept + r_county))
每个县计算零概率(y = 0)的解决方案
我必须添加“ b_hu__Intercept”和“ r_county__hu [,Intercept]”,并将其放入inv_logit_scaled()函数中吗?在代码中:
m %>%
spread_draws(b_hu_Intercept,r_county__hu[county,]) %>%
median_qi(condition_mean = inv_logit_scaled(b_hu_Intercept + r_county__hu))
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