调整使用 lmerTest::lmer() 获得的 p 值以进行多重比较
Adjust p-values obtained with lmerTest::lmer() for multiple comparisons
我想使用 lmerTest::lmer() 拟合线性混合模型并逐渐添加随机和固定效应（见下面的代码）。
稍后我的目标是编译一个回归 table，包括所有具有 jtools::export_summs() 或 huxtable::huxreg() 的模型。
在此步骤之前，我想使用 Bonferroni-Holm (BH) 方法调整回归中获得的 p 值以进行多重比较。
我将每个调整后的模型存储在一个列表中，并编写了一个函数来将 BH 应用于我的模型，如下所示：
summary(glht(model), test = adjusted('holm'))
但是，当我通过 huxreg(list_lm_models_adj) 或 export_summs(list_lm_models_adj) 编译带有调整模型的列表的回归 table 时，我收到以下错误消息：
"Error in fix.by(by.x, x) : 'by' must specify a uniquely valid column"
检查调整后和未调整模型的摘要表明，在应用 summary(glht(model), test = adjusted('holm')) 时结构似乎发生了变化。
比较 summary(model_lm2) 和 summary(model2_adjusted) 的输出，随机效应似乎在转换中丢失了。
# Define models
# ------------------------------------------------------------------
# base model: fixed effect: cat1
model_lm0 <- lm(likertscore ~ cat1, data = df_long)
# + random effect: subject => (1 | subject)
model_lm <- lmer(likertscore ~ cat1 + (1 | subject), data = df_long)
# + fixed effect: index => + index
model_lm1 <- lmer(likertscore ~ cat1 + index + (1 | subject), data = df_long)
# full model
# + random effect: group => (1 | group)
model_lm2 <- lmer(likertscore ~ cat1 + index + (1 | subject) + (1 | group), data = df_long)
# 1) unadjusted models => regression table
# ------------------------------------------------------------------
# Store models in list and output regression table
list_lm_models <- list()
list_lm_models[["model_lm"]] <- model_lm
list_lm_models[["model_lm1"]] <- model_lm1
list_lm_models[["model_lm2"]] <- model_lm2
huxreg(list_lm_models)
# ==> provides regression table with unadjusted p-values
# 2) adjusted models => regression table
# ------------------------------------------------------------------
# Function to adjust p-values
adjMC <- function( model_name ) {
model_glht <- glht(model_name)
model_MCadj <- summary(model_glht, test = adjusted('holm')) # Bonferroni-Holm
return(model_MCadj)
}
# Apply function to models
model_lm_adj <- adjMC( model_name = model_lm )
model_lm1_adj <- adjMC( model_name = model_lm1 )
model_lm2_adj <- adjMC( model_name = model_lm2 )
# Store adjusted models in list and output regression table
list_lm_models_adj <- list()
list_lm_models_adj[["model_lm"]] <- model_adjusted
list_lm_models_adj[["model_lm1"]] <- model_lm1_adj
list_lm_models_adj[["model_lm2"]] <- model_lm2_adj
huxreg(list_lm_models_adj)
非常感谢任何帮助！
AddOn1:
分别调用huxreg(list_lm_models_adj)或export_summs(list_lm_models_adj)时出现错误
df_long 看起来如下：
'data.frame': 1715 obs. of 5 variables:
$ subject : Factor w/ 245 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
$ task : Factor w/ 7 levels "Q1_Level1",..: 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "contrasts")= num [1:7, 1:6] 0.25 0.25 0.25 0.25 -0.333 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr "Q1_Level1" "Q1_Level2" "Q1_Level3" "Q1_Level4" ...
.. .. ..$ : chr "c1_CogMem_vs_MechFun" "c2_Cog_vs_Mem" "c3_CogOnly_Math_vs_Words" "c4_MemOnly_Codes_vs_Encrypt" ...
$ likertscore : int 4 3 4 7 4 7 4 7 7 2 ...
$ index : int 7 7 7 7 7 7 7 7 7 7 ...
$ session_code: Factor w/ 24 levels "1t75nw8b","2wwkn7pm",..: 15 15 15 15 15 15 15 15 15 15 ...
> headTail(df_long,8,8)
subject task likertscore index session_code
1 1 Q1_Level1 4 7 lo0h31ts
2 2 Q1_Level1 3 7 lo0h31ts
3 3 Q1_Level1 4 7 lo0h31ts
4 4 Q1_Level1 7 7 lo0h31ts
5 5 Q1_Level1 4 7 lo0h31ts
6 6 Q1_Level1 7 7 lo0h31ts
7 7 Q1_Level1 4 7 lo0h31ts
8 8 Q1_Level1 7 7 lo0h31ts
... <NA> <NA> ... ... <NA>
1708 238 Q1_Level7 1 2 5tc0tw92
1709 239 Q1_Level7 3 2 5tc0tw92
1710 240 Q1_Level7 3 5 v9z7sllr
1711 241 Q1_Level7 4 5 v9z7sllr
1712 242 Q1_Level7 2 5 v9z7sllr
1713 243 Q1_Level7 1 5 v9z7sllr
1714 244 Q1_Level7 4 5 v9z7sllr
1715 245 Q1_Level7 3 5 v9z7sllr
AddOn2：最小工作示例
# MWE
# ------------------------------------------------------------------
library("tidyverse")
library("lmerTest")
library("multcomp")
library("huxtable") # or alternatively
# library("jtools")
states <- as.data.frame(state.x77)
df_wide <- states[, c("Frost", "Area")]
colnames(df_wide) <- c("cat1_level1", "cat1_level2")
# add column with "SubjectIDs":
df_wide$subject <- c(paste0("S", 1:(nrow(df_wide))))
df_long <- df_wide %>%
gather(cat1, likertscore, -subject)
# Define models
# ------------------------------------------------------------------
# base model: fixed effect: cat1
model_lm0 <- lm(likertscore ~ cat1, data = df_long)
# + random effect: subject => (1 | subject)
model_lm <- lmer(likertscore ~ cat1 + (1 | subject), data = df_long)
# 1) unadjusted models => regression table
# ------------------------------------------------------------------
# Store models in list and output regression table
list_lm_models <- list()
list_lm_models[["model_lm0"]] <- model_lm0
list_lm_models[["model_lm"]] <- model_lm
huxreg(list_lm_models)
# ==> provides regression table with unadjusted p-values
# 2) adjusted models => regression table
# ------------------------------------------------------------------
# Function to adjust p-values
adjMC <- function( model_name ) {
model_glht <- glht(model_name)
model_MCadj <- summary(model_glht, test = adjusted('holm')) # Bonferroni-Holm
return(model_MCadj)
}
# Apply function to models
model_lm0_adj <- adjMC( model_name = model_lm0 )
model_lm_adj <- adjMC( model_name = model_lm )
# Store adjusted models in list and output regression table
list_lm_models_adj <- list()
list_lm_models_adj[["model_lm0"]] <- model_lm0_adj
list_lm_models_adj[["model_lm"]] <- model_lm_adj
huxreg(list_lm_models_adj) # huxtable
# export_summs(list_lm_models_adj) # jtools wrapper for huxtable::huxreg
# ==> Error in fix.by(by.x, x) : 'by' must specify a uniquely valid column
AddOn3: REPREX
library("tidyverse")
library("lmerTest")
#> Loading required package: lme4
#> Loading required package: Matrix
#>
#> Attaching package: 'Matrix'
#> The following object is masked from 'package:tidyr':
#>
#> expand
#>
#> Attaching package: 'lmerTest'
#> The following object is masked from 'package:lme4':
#>
#> lmer
#> The following object is masked from 'package:stats':
#>
#> step
library("multcomp")
#> Loading required package: mvtnorm
#> Loading required package: survival
#> Loading required package: TH.data
#> Loading required package: MASS
#>
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#>
#> select
#>
#> Attaching package: 'TH.data'
#> The following object is masked from 'package:MASS':
#>
#> geyser
library("huxtable") # or alternatively
#>
#> Attaching package: 'huxtable'
#> The following object is masked from 'package:dplyr':
#>
#> add_rownames
#> The following object is masked from 'package:purrr':
#>
#> every
#> The following object is masked from 'package:ggplot2':
#>
#> theme_grey
# library("jtools")
states <- as.data.frame(state.x77)
df_wide <- states[, c("Frost", "Area")]
colnames(df_wide) <- c("cat1_level1", "cat1_level2")
# add column with "SubjectIDs":
df_wide$subject <- c(paste0("S", 1:(nrow(df_wide))))
df_long <- df_wide %>%
gather(cat1, likertscore,-subject)
# Define models
# base model: fixed effect: cat1
model_lm0 <- lm(likertscore ~ cat1, data = df_long)
# + random effect: subject => (1 | subject)
model_lm <- lmer(likertscore ~ cat1 + (1 | subject), data = df_long)
# 1) unadjusted models => regression table
# Store models in list and output regression table
list_lm_models <- list()
list_lm_models[["model_lm0"]] <- model_lm0
list_lm_models[["model_lm"]] <- model_lm
huxreg(list_lm_models)
#> Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
#> TMB was built with Matrix version 1.2.15
#> Current Matrix version is 1.2.17
#> Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
#> Warning in knit_print.huxtable(x, ...): Unrecognized output format "markdown". Using `to_screen` to print huxtables.
#> Set options("huxtable.knitr_output_format") manually to "latex", "html", "rtf", "docx", "pptx", "md" or "screen".
───────────────────────────────────────────────────── model_lm0 model_lm
─────────────────────────────────── (Intercept) 104.460 104.460
(8532.732) (8532.732)
cat1cat1_level2 70631.420 *** 70631.420 ***
(12067.105) (12064.550)
sd__(Intercept) 1241.576
(NA)
sd__Observation 60322.752
(NA)
─────────────────────────────────── N 100 100
R2 0.259
logLik -1241.651 -1221.720
AIC 2489.303 2451.441
───────────────────────────────────────────────────── *** p < 0.001; ** p < 0.01; * p < 0.05.
Column names: names, model_lm0, model_lm
# ==> provides regression table with unadjusted p-values
# 2) adjusted models => regression table
# Function to adjust p-values
adjMC <- function( model_name ) {
model_glht <- glht(model_name)
model_MCadj <- summary(model_glht, test = adjusted('holm')) # Bonferroni-Holm
return(model_MCadj)
}
# Apply function to models
model_lm0_adj <- adjMC( model_name = model_lm0 )
model_lm_adj <- adjMC( model_name = model_lm )
# Store adjusted models in list and output regression table
list_lm_models_adj <- list()
list_lm_models_adj[["model_lm0"]] <- model_lm0_adj
list_lm_models_adj[["model_lm"]] <- model_lm_adj
huxreg(list_lm_models_adj) # huxtable
#> Warning: Unknown or uninitialised column: 'term'.
#> Warning: Unknown or uninitialised column: 'term'.
#> Error in fix.by(by.x, x): 'by' must specify a uniquely valid column
# export_summs(list_lm_models_adj) # jtools wrapper for huxtable::huxreg
# ==> Error in fix.by(by.x, x) : 'by' must specify a uniquely valid column
Created on 2019-08-24 by the reprex package (v0.3.0)
你的问题可以被
看到
tidy(model_lm_adj)
# A tibble: 2 x 6
lhs rhs estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 0 104. 8533. 0.0122 0.990
2 cat1cat1_level2 0 70631. 12067. 5.85 0.00000000963
来自?huxreg：
Models must have a generics::tidy() method defined, which should
return "term", "estimate", "std.error", "statistic" and "p.value".
summary.glht class 有一个 tidy 方法，但它没有 return 一个 "term" 列。所以 huxreg 感到困惑。在对使用它的统计数据包执行标准时，broom 数据包介于 "anything goes" 和 "whips and chains" 之间。
我会尝试改进报错代码。同时，您可能想使用 tidy_override:
adj_override <- function( model ) {
model_glht <- glht(model)
model_mc_adj <- summary(model_glht, test = adjusted('holm')) # Bonferroni-Holm
pvals <- tidy(model_mc_adj)$p.value
return(tidy_override(model, p.value = pvals))
}
overridden_models <- lapply(list_lm_models, adj_override)
huxreg(overridden_models)
顺便说一句，您在这里可能不需要 glht 的所有机器。你可以只使用 stats::p.adjust.
我想使用 lmerTest::lmer() 拟合线性混合模型并逐渐添加随机和固定效应（见下面的代码）。
稍后我的目标是编译一个回归 table，包括所有具有 jtools::export_summs() 或 huxtable::huxreg() 的模型。
在此步骤之前，我想使用 Bonferroni-Holm (BH) 方法调整回归中获得的 p 值以进行多重比较。
我将每个调整后的模型存储在一个列表中，并编写了一个函数来将 BH 应用于我的模型，如下所示：
summary(glht(model), test = adjusted('holm'))
但是，当我通过 huxreg(list_lm_models_adj) 或 export_summs(list_lm_models_adj) 编译带有调整模型的列表的回归 table 时，我收到以下错误消息：
"Error in fix.by(by.x, x) : 'by' must specify a uniquely valid column"
检查调整后和未调整模型的摘要表明，在应用 summary(glht(model), test = adjusted('holm')) 时结构似乎发生了变化。
比较 summary(model_lm2) 和 summary(model2_adjusted) 的输出，随机效应似乎在转换中丢失了。
# Define models
# ------------------------------------------------------------------
# base model: fixed effect: cat1
model_lm0 <- lm(likertscore ~ cat1, data = df_long)
# + random effect: subject => (1 | subject)
model_lm <- lmer(likertscore ~ cat1 + (1 | subject), data = df_long)
# + fixed effect: index => + index
model_lm1 <- lmer(likertscore ~ cat1 + index + (1 | subject), data = df_long)
# full model
# + random effect: group => (1 | group)
model_lm2 <- lmer(likertscore ~ cat1 + index + (1 | subject) + (1 | group), data = df_long)
# 1) unadjusted models => regression table
# ------------------------------------------------------------------
# Store models in list and output regression table
list_lm_models <- list()
list_lm_models[["model_lm"]] <- model_lm
list_lm_models[["model_lm1"]] <- model_lm1
list_lm_models[["model_lm2"]] <- model_lm2
huxreg(list_lm_models)
# ==> provides regression table with unadjusted p-values
# 2) adjusted models => regression table
# ------------------------------------------------------------------
# Function to adjust p-values
adjMC <- function( model_name ) {
model_glht <- glht(model_name)
model_MCadj <- summary(model_glht, test = adjusted('holm')) # Bonferroni-Holm
return(model_MCadj)
}
# Apply function to models
model_lm_adj <- adjMC( model_name = model_lm )
model_lm1_adj <- adjMC( model_name = model_lm1 )
model_lm2_adj <- adjMC( model_name = model_lm2 )
# Store adjusted models in list and output regression table
list_lm_models_adj <- list()
list_lm_models_adj[["model_lm"]] <- model_adjusted
list_lm_models_adj[["model_lm1"]] <- model_lm1_adj
list_lm_models_adj[["model_lm2"]] <- model_lm2_adj
huxreg(list_lm_models_adj)
非常感谢任何帮助！
AddOn1:
分别调用huxreg(list_lm_models_adj)或export_summs(list_lm_models_adj)时出现错误
df_long 看起来如下：
'data.frame': 1715 obs. of 5 variables:
$ subject : Factor w/ 245 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
$ task : Factor w/ 7 levels "Q1_Level1",..: 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "contrasts")= num [1:7, 1:6] 0.25 0.25 0.25 0.25 -0.333 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr "Q1_Level1" "Q1_Level2" "Q1_Level3" "Q1_Level4" ...
.. .. ..$ : chr "c1_CogMem_vs_MechFun" "c2_Cog_vs_Mem" "c3_CogOnly_Math_vs_Words" "c4_MemOnly_Codes_vs_Encrypt" ...
$ likertscore : int 4 3 4 7 4 7 4 7 7 2 ...
$ index : int 7 7 7 7 7 7 7 7 7 7 ...
$ session_code: Factor w/ 24 levels "1t75nw8b","2wwkn7pm",..: 15 15 15 15 15 15 15 15 15 15 ...
> headTail(df_long,8,8)
subject task likertscore index session_code
1 1 Q1_Level1 4 7 lo0h31ts
2 2 Q1_Level1 3 7 lo0h31ts
3 3 Q1_Level1 4 7 lo0h31ts
4 4 Q1_Level1 7 7 lo0h31ts
5 5 Q1_Level1 4 7 lo0h31ts
6 6 Q1_Level1 7 7 lo0h31ts
7 7 Q1_Level1 4 7 lo0h31ts
8 8 Q1_Level1 7 7 lo0h31ts
... <NA> <NA> ... ... <NA>
1708 238 Q1_Level7 1 2 5tc0tw92
1709 239 Q1_Level7 3 2 5tc0tw92
1710 240 Q1_Level7 3 5 v9z7sllr
1711 241 Q1_Level7 4 5 v9z7sllr
1712 242 Q1_Level7 2 5 v9z7sllr
1713 243 Q1_Level7 1 5 v9z7sllr
1714 244 Q1_Level7 4 5 v9z7sllr
1715 245 Q1_Level7 3 5 v9z7sllr
AddOn2：最小工作示例
# MWE
# ------------------------------------------------------------------
library("tidyverse")
library("lmerTest")
library("multcomp")
library("huxtable") # or alternatively
# library("jtools")
states <- as.data.frame(state.x77)
df_wide <- states[, c("Frost", "Area")]
colnames(df_wide) <- c("cat1_level1", "cat1_level2")
# add column with "SubjectIDs":
df_wide$subject <- c(paste0("S", 1:(nrow(df_wide))))
df_long <- df_wide %>%
gather(cat1, likertscore, -subject)
# Define models
# ------------------------------------------------------------------
# base model: fixed effect: cat1
model_lm0 <- lm(likertscore ~ cat1, data = df_long)
# + random effect: subject => (1 | subject)
model_lm <- lmer(likertscore ~ cat1 + (1 | subject), data = df_long)
# 1) unadjusted models => regression table
# ------------------------------------------------------------------
# Store models in list and output regression table
list_lm_models <- list()
list_lm_models[["model_lm0"]] <- model_lm0
list_lm_models[["model_lm"]] <- model_lm
huxreg(list_lm_models)
# ==> provides regression table with unadjusted p-values
# 2) adjusted models => regression table
# ------------------------------------------------------------------
# Function to adjust p-values
adjMC <- function( model_name ) {
model_glht <- glht(model_name)
model_MCadj <- summary(model_glht, test = adjusted('holm')) # Bonferroni-Holm
return(model_MCadj)
}
# Apply function to models
model_lm0_adj <- adjMC( model_name = model_lm0 )
model_lm_adj <- adjMC( model_name = model_lm )
# Store adjusted models in list and output regression table
list_lm_models_adj <- list()
list_lm_models_adj[["model_lm0"]] <- model_lm0_adj
list_lm_models_adj[["model_lm"]] <- model_lm_adj
huxreg(list_lm_models_adj) # huxtable
# export_summs(list_lm_models_adj) # jtools wrapper for huxtable::huxreg
# ==> Error in fix.by(by.x, x) : 'by' must specify a uniquely valid column
AddOn3: REPREX
library("tidyverse")
library("lmerTest")
#> Loading required package: lme4
#> Loading required package: Matrix
#>
#> Attaching package: 'Matrix'
#> The following object is masked from 'package:tidyr':
#>
#> expand
#>
#> Attaching package: 'lmerTest'
#> The following object is masked from 'package:lme4':
#>
#> lmer
#> The following object is masked from 'package:stats':
#>
#> step
library("multcomp")
#> Loading required package: mvtnorm
#> Loading required package: survival
#> Loading required package: TH.data
#> Loading required package: MASS
#>
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#>
#> select
#>
#> Attaching package: 'TH.data'
#> The following object is masked from 'package:MASS':
#>
#> geyser
library("huxtable") # or alternatively
#>
#> Attaching package: 'huxtable'
#> The following object is masked from 'package:dplyr':
#>
#> add_rownames
#> The following object is masked from 'package:purrr':
#>
#> every
#> The following object is masked from 'package:ggplot2':
#>
#> theme_grey
# library("jtools")
states <- as.data.frame(state.x77)
df_wide <- states[, c("Frost", "Area")]
colnames(df_wide) <- c("cat1_level1", "cat1_level2")
# add column with "SubjectIDs":
df_wide$subject <- c(paste0("S", 1:(nrow(df_wide))))
df_long <- df_wide %>%
gather(cat1, likertscore,-subject)
# Define models
# base model: fixed effect: cat1
model_lm0 <- lm(likertscore ~ cat1, data = df_long)
# + random effect: subject => (1 | subject)
model_lm <- lmer(likertscore ~ cat1 + (1 | subject), data = df_long)
# 1) unadjusted models => regression table
# Store models in list and output regression table
list_lm_models <- list()
list_lm_models[["model_lm0"]] <- model_lm0
list_lm_models[["model_lm"]] <- model_lm
huxreg(list_lm_models)
#> Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
#> TMB was built with Matrix version 1.2.15
#> Current Matrix version is 1.2.17
#> Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
#> Warning in knit_print.huxtable(x, ...): Unrecognized output format "markdown". Using `to_screen` to print huxtables.
#> Set options("huxtable.knitr_output_format") manually to "latex", "html", "rtf", "docx", "pptx", "md" or "screen".
───────────────────────────────────────────────────── model_lm0 model_lm
─────────────────────────────────── (Intercept) 104.460 104.460
(8532.732) (8532.732)
cat1cat1_level2 70631.420 *** 70631.420 ***
(12067.105) (12064.550)
sd__(Intercept) 1241.576
(NA)
sd__Observation 60322.752
(NA)
─────────────────────────────────── N 100 100
R2 0.259
logLik -1241.651 -1221.720
AIC 2489.303 2451.441
───────────────────────────────────────────────────── *** p < 0.001; ** p < 0.01; * p < 0.05.
Column names: names, model_lm0, model_lm
# ==> provides regression table with unadjusted p-values
# 2) adjusted models => regression table
# Function to adjust p-values
adjMC <- function( model_name ) {
model_glht <- glht(model_name)
model_MCadj <- summary(model_glht, test = adjusted('holm')) # Bonferroni-Holm
return(model_MCadj)
}
# Apply function to models
model_lm0_adj <- adjMC( model_name = model_lm0 )
model_lm_adj <- adjMC( model_name = model_lm )
# Store adjusted models in list and output regression table
list_lm_models_adj <- list()
list_lm_models_adj[["model_lm0"]] <- model_lm0_adj
list_lm_models_adj[["model_lm"]] <- model_lm_adj
huxreg(list_lm_models_adj) # huxtable
#> Warning: Unknown or uninitialised column: 'term'.
#> Warning: Unknown or uninitialised column: 'term'.
#> Error in fix.by(by.x, x): 'by' must specify a uniquely valid column
# export_summs(list_lm_models_adj) # jtools wrapper for huxtable::huxreg
# ==> Error in fix.by(by.x, x) : 'by' must specify a uniquely valid column
Created on 2019-08-24 by the reprex package (v0.3.0)
你的问题可以被
看到tidy(model_lm_adj)
# A tibble: 2 x 6
lhs rhs estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 0 104. 8533. 0.0122 0.990
2 cat1cat1_level2 0 70631. 12067. 5.85 0.00000000963
来自?huxreg：
Models must have a generics::tidy() method defined, which should return "term", "estimate", "std.error", "statistic" and "p.value".
summary.glht class 有一个 tidy 方法，但它没有 return 一个 "term" 列。所以 huxreg 感到困惑。在对使用它的统计数据包执行标准时，broom 数据包介于 "anything goes" 和 "whips and chains" 之间。
我会尝试改进报错代码。同时，您可能想使用 tidy_override:
adj_override <- function( model ) {
model_glht <- glht(model)
model_mc_adj <- summary(model_glht, test = adjusted('holm')) # Bonferroni-Holm
pvals <- tidy(model_mc_adj)$p.value
return(tidy_override(model, p.value = pvals))
}
overridden_models <- lapply(list_lm_models, adj_override)
huxreg(overridden_models)
顺便说一句，您在这里可能不需要 glht 的所有机器。你可以只使用 stats::p.adjust.