重回帰モデルのフィルター処理された推定値のドットアンドウィスカープロット

4つの異なる回帰モデルの信頼区間のドットウィスカープロットをプロットしようとしています。

データはこちらから入手できます。

#first importing data 
Q1<-read.table("~/Q1.txt", header=T)

# Optionally, read in data directly from figshare.
# Q1 <- read.table("https://ndownloader.figshare.com/files/13283882?private_link=ace5b44bc12394a7c46d", header=TRUE)

library(dplyr)

#splitting into female and male
female<-Q1 %>% 
  filter(sex=="F") 
male<-Q1 %>% 
  filter(sex=="M") 

library(lme4)

#Female models
#poisson regression
ab_f_LBS= lmer(LBS ~ ft + grid + (1|byear), data = subset(female))

#negative binomial regression
ab_f_surv= glmer.nb(age ~ ft + grid + (1|byear), data = subset(female), control=glmerControl(tol=1e-6,optimizer="bobyqa",optCtrl=list(maxfun=1e19)))

#Male models
#poisson regression
ab_m_LBS= lmer(LBS ~ ft + grid + (1|byear), data = subset(male))

#negative binomial regression
ab_m_surv= glmer.nb(age ~ ft + grid + (1|byear), data = subset(male), control=glmerControl(tol=1e-6,optimizer="bobyqa",optCtrl=list(maxfun=1e19)))

次に、各モデルから2つの変数（ft2およびgridSU）のみをプロットしたいと思います。

ab_f_LBS <- tidy(ab_f_LBS)  %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_m_LBS <- tidy(ab_m_LBS)  %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_f_surv <- tidy(ab_f_surv) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_m_surv <- tidy(ab_m_surv) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))

これで、ドットウィスカープロットを作成する準備が整いました。

#required packages
library(dotwhisker)
library(broom)

dwplot(list(ab_f_LBS, ab_m_LBS, ab_f_surv, ab_m_surv), 
    vline = geom_vline(xintercept = 0, colour = "black", linetype = 2),             
    dodge_size=0.2,
    style="dotwhisker") %>% # plot line at zero _behind_ coefs
relabel_predictors(c(ft2= "Immigrants",                       
                     gridSU = "Grid (SU)")) +
theme_classic() + 
xlab("Coefficient estimate (+/- CI)") + 
ylab("") +
scale_color_manual(values=c("#000000", "#666666", "#999999", "#CCCCCC"), 
labels = c("Female LRS", "Male LRS", "Female survival", "Male survival"), 
name = "First generation models") +
theme(axis.title=element_text(size=10),
    axis.text.x = element_text(size=10),
    axis.text.y = element_text(size=12, angle=90, hjust=.5),
    legend.position = c(0.7, 0.8),
    legend.justification = c(0, 0), 
    legend.title=element_text(size=12),
    legend.text=element_text(size=10),
    legend.key = element_rect(size = 0.1),
    legend.key.size = unit(0.5, "cm"))

私はこの問題に直面しています：

1. エラーメッセージ：Error in psych::describe(x, ...) : unused arguments (conf.int = TRUE, conf.int = TRUE)。1つのモデルだけで試してみると（つまりdwplot(ab_f_LBS)、機能しますが、別のモデルを追加するとすぐに、このエラーメッセージが表示されます。

同じドットウィスカープロットに4つの回帰モデルをプロットするにはどうすればよいですか？

更新

結果traceback()：

> traceback()
14: stop(gettextf("cannot coerce class \"%s\" to a data.frame",     deparse(class(x))), 
        domain = NA)
13: as.data.frame.default(x)
12: as.data.frame(x)
11: tidy.default(x, conf.int = TRUE, ...)
10: broom::tidy(x, conf.int = TRUE, ...)
9: .f(.x[[i]], ...)
8: .Call(map_impl, environment(), ".x", ".f", "list")
7: map(.x, .f, ...)
6: purrr::map_dfr(x, .id = "model", function(x) {
       broom::tidy(x, conf.int = TRUE, ...)
   })
5: eval(lhs, parent, parent)
4: eval(lhs, parent, parent)
3: purrr::map_dfr(x, .id = "model", function(x) {
       broom::tidy(x, conf.int = TRUE, ...)
   }) %>% mutate(model = if_else(!is.na(suppressWarnings(as.numeric(model))), 
       paste("Model", model), model))
2: dw_tidy(x, by_2sd, ...)
1: dwplot(list(ab_f_LBS, ab_m_LBS, ab_f_surv, ab_m_surv), effects = "fixed", 
       by_2sd = FALSE)

これが私のセッション情報です：

> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6

Matrix products: default
BLAS:     /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK:     /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] dotwhisker_0.5.0  broom_0.5.0       broom.mixed_0.2.2
 [4] glmmTMB_0.2.2.0   lme4_1.1-18-1     Matrix_1.2-14    
 [7] bindrcpp_0.2.2    forcats_0.3.0     stringr_1.3.1    
[10] dplyr_0.7.6       purrr_0.2.5       readr_1.1.1      
[13] tidyr_0.8.1       tibble_1.4.2      ggplot2_3.0.0    
[16] tidyverse_1.2.1   lubridate_1.7.4   devtools_1.13.6  

loaded via a namespace (and not attached):
 [1] ggstance_0.3.1   tidyselect_0.2.5 TMB_1.7.14       reshape2_1.4.3  
 [5] splines_3.5.1    haven_1.1.2      lattice_0.20-35  colorspace_1.3-2
 [9] rlang_0.2.2      pillar_1.3.0     nloptr_1.2.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     bindr_0.1.1     
[17] plyr_1.8.4       munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0
[21] rvest_0.3.2      coda_0.19-2      memoise_1.1.0    Rcpp_0.12.19    
[25] scales_1.0.0     backports_1.1.2  jsonlite_1.5     hms_0.4.2       
[29] digest_0.6.18    stringi_1.2.4    grid_3.5.1       cli_1.0.1       
[33] tools_3.5.1      magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4    
[37] pkgconfig_2.0.2  MASS_7.3-50      xml2_1.2.0       assertthat_0.2.0
[41] minqa_1.2.4      httr_1.3.1       rstudioapi_0.8   R6_2.3.0        
[45] nlme_3.1-137     compiler_3.5.1