R如何获得摘要功能以在censReg包模型上工作答案

【问题标题】：R how to get summary function to work on censReg package modelsR如何获得摘要功能以在censReg包模型上工作
【发布时间】：2020-03-14 03:28:23
【问题描述】：

我正在尝试使用 R 中的 censReg 函数，它是 censReg 包的一部分。我正在尝试对本质上连续且包含零的鱼类生物量数据进行建模。零是可能的最低值，因为没有负生物量之类的东西。我决定使用 censReg 包来处理 total_biomass 响应变量的高度零膨胀、连续分布。我已成功运行下面提供的模型，但是当我尝试在模型上运行 summary() 函数时，我收到以下错误：

Error in printCoefmat(coef(x, logSigma = logSigma), digits = digits) : 
  'x' must be coefficient matrix/data frame

查看此错误消息，我不明白这意味着什么，也不明白我的代码或数据库有什么问题。谁能提供我需要做的任何额外代码或调整才能成功获得模型摘要？

数据库

library(tidyverse)
library(censReg)

mean_fish_totals <- structure(list(Date = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 
7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 
10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 
16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 
18L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 
21L, 21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 25L, 
25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L), .Label = c("11/28/17", 
"12/10/19", "12/14/19", "12/3/17", "12/4/18", "12/8/18", "2/25/17", 
"3/19/19", "3/22/19", "4/18/17", "5/15/18", "5/20/18", "5/25/17", 
"6/3/19", "6/4/19", "6/6/17", "8/28/18", "9/1/18", "9/10/19", 
"9/15/19", "9/20/16", "9/22/16", "9/25/16", "9/27/16", "9/5/17", 
"9/7/17"), class = "factor"), `Module #` = c(211L, 212L, 213L, 
214L, 215L, 216L, 211L, 212L, 213L, 214L, 215L, 216L, 111L, 112L, 
113L, 114L, 115L, 116L, 111L, 112L, 113L, 114L, 115L, 116L, 211L, 
212L, 213L, 214L, 215L, 216L, 111L, 112L, 113L, 114L, 115L, 116L, 
111L, 112L, 113L, 114L, 115L, 116L, 211L, 212L, 213L, 214L, 215L, 
216L, 111L, 112L, 113L, 114L, 115L, 116L, 211L, 212L, 213L, 214L, 
215L, 216L, 211L, 212L, 213L, 214L, 215L, 216L, 111L, 112L, 113L, 
114L, 115L, 116L, 111L, 112L, 113L, 114L, 115L, 116L, 111L, 112L, 
113L, 114L, 115L, 116L, 211L, 212L, 213L, 214L, 215L, 216L, 211L, 
212L, 213L, 214L, 215L, 216L, 211L, 212L, 213L, 214L, 215L, 216L, 
111L, 112L, 113L, 114L, 115L, 116L, 211L, 212L, 213L, 214L, 215L, 
216L, 111L, 112L, 113L, 114L, 115L, 116L, 213L, 214L, 215L, 216L, 
114L, 115L, 116L, 111L, 112L, 113L, 211L, 212L, 211L, 212L, 213L, 
214L, 215L, 216L, 111L, 112L, 113L, 114L, 115L, 116L), Site_long = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Hanauma Bay", 
"Waikiki"), class = "factor"), Treatment_long = structure(c(2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("Closed", 
"Open"), class = "factor"), Shelter = structure(c(1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("High", 
"Low"), class = "factor"), TimeStep = c(5, 5, 5, 5, 5, 5, 15, 
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 5, 5, 5, 5, 5, 5, 
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 0.75, 0.75, 0.75, 
0.75, 0.75, 0.75, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 
12, 0.75, 2, 0.75, 0.75, 0.75, 2, 7, 7, 7, 7, 7, 7, 7, 7, 7, 
7, 7, 7, 1, 1, 1, 1, 1, 1, 13, 13, 13, 13, 13, 13, 13, 13, 13, 
13, 13, 13, 1, 1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 
0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4), total_biomass = c(0.0526394784788566, 
0.00650991088549517, 0.180596698411345, 0, 0.526717015131238, 
0, 0.206894204748519, 0.0164498212264971, 0, 0, 0, 0, 0.201603325166693, 
0, 0.283172330030785, 0, 0, 0, 0.209199851062596, 0, 0.283172330030785, 
0, 0.394899281159044, 0.176129136061979, 0.169586003898729, 0, 
0.120034320302602, 0, 0.996727938559748, 0, 0, 0, 0, 0, 0.257380940140258, 
0.443609909402316, 0.308392987176445, 0, 0.748305018557033, 0, 
0.169586003898729, 0, 0.120034320302602, 0, 0.120034320302602, 
0, 1.08474493759439, 0, 0, 0.0745413774887557, 0, 0.0467403151010407, 
0.233352036920048, 0, 0.0899664423257818, 0, 0.308392987176445, 
0, 0, 0, 10.6461511880577, 0, 26.4504921170652, 0, 0.526717015131238, 
0, 0, 0, 0.403061371653634, 0.209695276260751, 0.120034320302602, 
0.0206199419933497, 0.078489026854395, 0, 0.165344302422082, 
0, 0.0317487117533543, 0, 0, 0, 0, 0, 0.94950027744447, 0, 0, 
0, 0, 0, 1.03519325399826, 0, 0.169586003898729, 0.125258604363503, 
0.310810458426215, 0, 10.6461511880577, 0, 0, 0, 0.976669817356845, 
0, 0.996727938559748, 0, 0, 0.0202327772014947, 0.0403651893214743, 
0, 0.168776380464422, 0, 0.133454408606973, 0, 0, 0.621724957549784, 
0.0164498212264971, 0, 0.110237738607749, 0, 0.116136901985565, 
0, 0, 0.0135959326713389, 0.00889824575015321, 0.078489026854395, 
0.16627064788403, 0, 0.028053154008064, 0.0526394784788566, 0.0419621766803908, 
0, 0, 0, 0, 0, 0.308392987176445, 0.0520989800170256, 0.222619650542138, 
0, 21.2935111117403, 15.7227719241434, 0.232861857335555, 0, 
0.0634974235067086, 0, 0.0492074004365164, 0), new_date = structure(c(17498, 
17498, 17498, 17498, 17498, 17498, 18240, 18240, 18240, 18240, 
18240, 18240, 18244, 18244, 18244, 18244, 18244, 18244, 17503, 
17503, 17503, 17503, 17503, 17503, 17869, 17869, 17869, 17869, 
17869, 17869, 17873, 17873, 17873, 17873, 17873, 17873, 17222, 
17222, 17222, 17222, 17222, 17222, 17974, 17974, 17974, 17974, 
17974, 17974, 17977, 17977, 17977, 17977, 17977, 17977, 17274, 
17274, 17274, 17274, 17274, 17274, 17666, 17666, 17666, 17666, 
17666, 17666, 17671, 17671, 17671, 17671, 17671, 17671, 17311, 
17311, 17311, 17311, 17311, 17311, 18050, 18050, 18050, 18050, 
18050, 18050, 18051, 18051, 18051, 18051, 18051, 18051, 17323, 
17323, 17323, 17323, 17323, 17323, 17771, 17771, 17771, 17771, 
17771, 17771, 17775, 17775, 17775, 17775, 17775, 17775, 18149, 
18149, 18149, 18149, 18149, 18149, 18154, 18154, 18154, 18154, 
18154, 18154, 17064, 17064, 17064, 17064, 17066, 17066, 17066, 
17069, 17069, 17069, 17071, 17071, 17414, 17414, 17414, 17414, 
17414, 17414, 17416, 17416, 17416, 17416, 17416, 17416), class = "Date")), row.names = c(NA, 
-144L), groups = structure(list(Date = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 
4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 
6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 
13L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 
16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 
18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 
20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 
23L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 
26L, 26L), .Label = c("11/28/17", "12/10/19", "12/14/19", "12/3/17", 
"12/4/18", "12/8/18", "2/25/17", "3/19/19", "3/22/19", "4/18/17", 
"5/15/18", "5/20/18", "5/25/17", "6/3/19", "6/4/19", "6/6/17", 
"8/28/18", "9/1/18", "9/10/19", "9/15/19", "9/20/16", "9/22/16", 
"9/25/16", "9/27/16", "9/5/17", "9/7/17"), class = "factor"), 
    `Module #` = c(211L, 212L, 213L, 214L, 215L, 216L, 211L, 
    212L, 213L, 214L, 215L, 216L, 111L, 112L, 113L, 114L, 115L, 
    116L, 111L, 112L, 113L, 114L, 115L, 116L, 211L, 212L, 213L, 
    214L, 215L, 216L, 111L, 112L, 113L, 114L, 115L, 116L, 111L, 
    112L, 113L, 114L, 115L, 116L, 211L, 212L, 213L, 214L, 215L, 
    216L, 111L, 112L, 113L, 114L, 115L, 116L, 211L, 212L, 213L, 
    214L, 215L, 216L, 211L, 212L, 213L, 214L, 215L, 216L, 111L, 
    112L, 113L, 114L, 115L, 116L, 111L, 112L, 113L, 114L, 115L, 
    116L, 111L, 112L, 113L, 114L, 115L, 116L, 211L, 212L, 213L, 
    214L, 215L, 216L, 211L, 212L, 213L, 214L, 215L, 216L, 211L, 
    212L, 213L, 214L, 215L, 216L, 111L, 112L, 113L, 114L, 115L, 
    116L, 211L, 212L, 213L, 214L, 215L, 216L, 111L, 112L, 113L, 
    114L, 115L, 116L, 213L, 214L, 215L, 216L, 114L, 115L, 116L, 
    111L, 112L, 113L, 211L, 212L, 211L, 212L, 213L, 214L, 215L, 
    216L, 111L, 112L, 113L, 114L, 115L, 116L), Site_long = c("Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Waikiki", "Waikiki", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", 
    "Hanauma Bay", "Hanauma Bay", "Hanauma Bay", "Waikiki", "Waikiki", 
    "Waikiki", "Waikiki", "Waikiki", "Waikiki"), Treatment_long = c("Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Open", "Closed", "Open", "Closed", 
    "Open", "Closed", "Open", "Closed", "Open", "Closed", "Open", 
    "Closed", "Open", "Closed"), Shelter = c("High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "Low", "High", "Low", "High", "Low", "High", "High", 
    "Low", "High", "Low", "High", "Low", "High", "Low", "High", 
    "Low", "High", "Low", "High", "Low"), .rows = list(1L, 2L, 
        3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
        15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 
        26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 
        37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 
        48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 
        59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 
        70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 
        81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 
        92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 
        103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 
        112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 
        121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 
        130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 
        139L, 140L, 141L, 142L, 143L, 144L)), row.names = c(NA, 
-144L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

分析

## Herbivorous fish with interaction
# variables
module_fish <- mean_fish_totals$`Module #`

# Data distribution #
plotNormalHistogram(mean_fish_totals$total_biomass)

## Generalized Mixed Effects Model Fishes ##
# glmer new database #
mean_fish_totals$Shelter <- factor(mean_fish_totals$Shelter, levels = c("Low", "High"), ordered = TRUE)
mean_fish_totals$Site_long <- as.factor(mean_fish_totals$Site_long)

# Censreg Model #
fish_mixed_effects_censreg <- censReg(total_biomass ~ Site_long*Shelter + (1|module_fish), left = 0, right = Inf, data = mean_fish_totals)
summary(fish_mixed_effects_censreg)

提前感谢您的意见！

【问题讨论】：

标签： r dplyr summary

【解决方案1】：

问题在于“(1|module_fish)”部分，因为 censReg() 不“知道”这种指定回归公式的方式。如果您有面板数据，则 cenReg() 可以考虑截距中的随机效应（参见 censReg 包的“小插图”），但不能考虑斜率参数中的随机效应。您可以通过以下方式估计没有随机效应的模型：

mean_fish_totals$module_fish <- mean_fish_totals$`Module #`

fish_censreg <- censReg(
  total_biomass ~ Site_long*Shelter + module_fish,
  left = 0, right = Inf, data = mean_fish_totals )

summary( fish_censreg )

您还可以使用“AER”包的函数 tobit() 估计此规范：

library( "AER" )
fish_tobit <- tobit(total_biomass ~ Site_long*Shelter + module_fish,
  data = mean_fish_totals)
summary( fish_tobit )

这给出了相同的结果：

toMatrix <- function(x){ class( x ) <- "matrix"; x }
all.equal( toMatrix( coef( summary( fish_tobit ) ) ), 
  coef( summary( fish_censreg ) ), check.attributes = FALSE )

残差可以从tobit()估计的模型中获得：

qqnorm( resid( fish_tobit ) ) 
qqline( resid( fish_tobit ) )

请注意，有不同的方法来定义（并因此计算）删失回归模型的残差（例如，参见“survival”包中的residuals.survreg() 文档，在获取时内部使用该文档由 tobit()) 估计的模型的残差。

如果您（或其他任何人）有兴趣在 censReg 包中实现当前缺少的功能，请随时与我联系： https://r-forge.r-project.org/projects/sampleselection/

【讨论】：

(1|module) 项是我在所有模型中运行的随机效应项，以说明实验单元（模块）随时间的重复测量。 censReg 模型中不可能有随机效应吗？我需要在我的模型中考虑这种重复测量设计。
当我尝试绘制 censReg 模型的 qqplot 以查看模型与这些数据的拟合程度时，我收到了错误。您是否必须使用不同的功能或者此代码是否应该工作？ {r} qqnorm(resid(fish_mixed_effects_censreg)) qqline(resid(fish_mixed_effects_censreg))
您能否澄清我需要做些什么才能将随机效应纳入 censReg 模型？还是目前不可能？我绝不是编码员，所以在为 censReg 包添加内容时我不会有太多帮助。
为了在 censReg 中实现具有类似“(1|module)”的模型方程，需要找出该规范的对数似然函数（即，在文献中找到或导出它oneself)，扩展 censReg() 中的当前内部函数，该函数计算对数似然值，以便它也可以计算具有“(1|module)”的模型的对数似然值，实现 censReg() 'understands' “( 1|module)”，相应地修改文档，也许做一些小的调整。