使用整洁的文本和扫帚，但找不到LDA_VEM的整洁度 [英] Using tidytext and broom but not finding tidier for LDA_VEM

问题描述

tidytext本书中的示例带有主题模型的修饰符：

The tidytext book has examples with a tidier for topicmodels:

library(tidyverse)
library(tidytext)
library(topicmodels)
library(broom)

year_word_counts <- tibble(year = c("2007", "2008", "2009"),
+                            word = c("dog", "cat", "chicken"),
+                            n = c(1753L, 1157L, 1057L))

animal_dtm <- cast_dtm(data = year_word_counts, document = year, term = word, value = n)

animal_lda <- LDA(animal_dtm, k = 5, control = list( seed = 1234))

animal_lda <- tidy(animal_lda, matrix = "beta")

# Console output
Error in as.data.frame.default(x) : 
  cannot coerce class "structure("LDA_VEM", package = "topicmodels")" to a data.frame
In addition: Warning message:
In tidy.default(animal_lda, matrix = "beta") :
  No method for tidying an S3 object of class LDA_VEM , using as.data.frame

来整理LDA_VEM类的S3对象的方法解决此处也看到的错误但是在这种情况下， library（tidytext）存在
。

Replicating the error which is also seen here but in this instance library(tidytext) is present.

下面是所有软件包的列表是它们的相应版本：

Below is a list of all packages are their corresponding version:

 packageVersion("tidyverse")
 ‘1.2.1’

 packageVersion("tidytext")
 ‘0.1.6’   

 packageVersion("topicmodels")
 ‘0.2.7’  

 packageVersion("broom")
 ‘0.4.3’

函数调用的输出 sessionInfo（）：

R version 3.4.3 (2017-11-30)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

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

other attached packages:
 [1] broom_0.4.3       tidytext_0.1.6    forcats_0.2.0     stringr_1.2.0     dplyr_0.7.4       purrr_0.2.4       readr_1.1.1       tidyr_0.8.0      
 [9] tibble_1.4.2      ggplot2_2.2.1     tidyverse_1.2.1   topicmodels_0.2-7

loaded via a namespace (and not attached):
 [1] modeltools_0.2-21 slam_0.1-42       NLP_0.1-11        reshape2_1.4.3    haven_1.1.1       lattice_0.20-35   colorspace_1.3-2  SnowballC_0.5.1  
 [9] stats4_3.4.3      yaml_2.1.16       rlang_0.1.6       pillar_1.1.0      foreign_0.8-69    glue_1.2.0        modelr_0.1.1      readxl_1.0.0     
[17] bindrcpp_0.2      bindr_0.1         plyr_1.8.4        munsell_0.4.3     gtable_0.2.0      cellranger_1.1.0  rvest_0.3.2       psych_1.7.8      
[25] tm_0.7-3          parallel_3.4.3    tokenizers_0.1.4  Rcpp_0.12.15      scales_0.5.0      jsonlite_1.5      mnormt_1.5-5      hms_0.4.1        
[33] stringi_1.1.6     grid_3.4.3        cli_1.0.0         tools_3.4.3       magrittr_1.5      lazyeval_0.2.1    janeaustenr_0.1.5 crayon_1.3.4     
[41] pkgconfig_2.0.1   Matrix_1.2-12     xml2_1.2.0        lubridate_1.7.2   assertthat_0.2.0  httr_1.3.1        rstudioapi_0.7    R6_2.2.2         
[49] nlme_3.1-131      compiler_3.4.3   

推荐答案

删除.Rhistory和.RData纠正行为。

Deleting .Rhistory and .RData led to correct behaviour.

这篇关于使用整洁的文本和扫帚，但找不到LDA_VEM的整洁度的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！