你如何获得广义线性混合模型的上下置信区间？

问题描述

我正在尝试将置信区间放入一个小标题中，以便我可以绘制它们；但是，我不断收到一条错误消息，指出维度数不正确。我已经粘贴了我的模型以及我用来尝试提取置信上限和下限的代码。请告诉我解决这个问题。

    public static void main(String[] args) {
        System.out.println(atanInvInt(5,99));
        // 0.197395559849880758370049765194790293447585103787852101517688940241033969978243785732697828037288045
    }

    public static BigDecimal atanInvInt(int x,int scale) {
        BigDecimal one = new BigDecimal("1");
        BigDecimal two = new BigDecimal("2");
        BigDecimal xVal = new BigDecimal(x);
        BigDecimal xSquare = xVal.multiply(xVal);
        BigDecimal divisor = new BigDecimal(1);

        BigDecimal result = one.divide(xVal,scale,RoundingMode.FLOOR);
        BigDecimal term = one.divide(xVal,RoundingMode.FLOOR);
        BigDecimal midResult;

        while (term.compareto(new BigDecimal(0)) > 0) {
            term = term.divide(xSquare,RoundingMode.FLOOR);
            divisor = divisor.add(two);
            midResult = result.subtract(term.divide(divisor,RoundingMode.FLOOR));
            term = term.divide(xSquare,RoundingMode.FLOOR);
            divisor = divisor.add(two);
            result = midResult.add(term.divide(divisor,RoundingMode.FLOOR));

            if (divisor.compareto(new BigDecimal(2101)) >= 0) {
                return result.add(midResult).divide(two,RoundingMode.FLOOR);
            }
        }
        return result;
    }

exp((fixef(mod6b)))[,1] 中的错误：维数不正确

解决方法

如何使用 def is_a_valid_date(date): month_names = ["January","February","March","April","May","June","July","August","September","October","November","December"] days_in_month = [31,28,31,30,31] clean_date = date.split() clean_date[1:] = ["".join(clean_date[1:])] a = False b = False if clean_date[0] in month_names: a = True x = month_names.find(clean_date[0]) else: a = a if clean_date[1].isdigit() == True and int(clean_date[1]) <= int(days_in_month[x]): b = True else: b = b if a == True and b == True: return True else: return False ？ （提示，broom.mixed::tidy() 返回一个向量，而不是具有多列的对象：您是如何想到示例中显示的代码的？）

fixef()

？结果：

library(lme4)
library(broom.mixed)
library(dplyr)
## built-in example from `?glmer`
m1 <- glmer(cbind(incidence,size - incidence) ~ period + (1 | herd),family = binomial,data = cbpp)
tidy(m1,effects="fixed",conf.int=TRUE,conf.method="profile",exponentiate=TRUE) %>% select(term,estimate,conf.low,conf.high)

如果省略 term estimate conf.low conf.high <chr> <dbl> <dbl> <dbl> 1 (Intercept) 0.247 0.149 0.388 2 period2 0.371 0.199 0.665 3 period3 0.324 0.165 0.600 4 period4 0.206 0.0820 0.449 ，您将获得更快但准确度较低的 Wald 置信区间。

您可能还对 conf.method="profile" 软件包感兴趣。