将多个地块分成4个不同的地块

时间:2020-06-19 10:14:43

标签: r

此代码段:

library(stm)
gadarian <- gadarian
K<-c(5,10,15)
temp<-textProcessor(documents=gadarian$open.ended.response,metadata=gadarian)
out <- prepDocuments(temp$documents, temp$vocab, temp$meta)
documents <- out$documents
vocab <- out$vocab
meta <- out$meta
set.seed(02138)
K<-c(5,10,15)
df1 <- searchK(documents, vocab, K, prevalence=~treatment + s(pid_rep), data=meta)

使用以下命令生成图:

plot(df1)

此图包含4个图洞见。

如何将它们接收到4个不同的地块中,它们在一个合并的地块中具有相同的标签?

2 个答案:

答案 0 :(得分:1)

您可以使用类似的

plot(df1$results$K,df1$results$heldout, type = "b", 
     xlab = "Number of Topics (K)", ylab = "Held-out Likelihood",
     main="Held-out Likelihood")

plot(df1$results$K,df1$results$semcoh, type = "b", 
     xlab = "Number of Topics (K)", ylab = "Semantic Coherence",
     main="Semantic Coherence")

plot(df1$results$K,df1$results$residual, type = "b", 
     xlab = "Number of Topics (K)", ylab = "Residual",
     main="Residual")

plot(df1$results$K,df1$results$lbound, type = "b", 
     xlab = "Number of Topics (K)", ylab = "Lower Bound",
     main="Lower Bound")

答案 1 :(得分:1)

您可以尝试以下方法:

plot(df1$results$K, df1$results$heldout, type = "p", main = "Held-Out Likelihood", 
    xlab = "Number of Topics (K)", ylab = "Held-Out Likelihood", ask = TRUE)
lines(df1$results$K, df1$results$heldout, lty = 1, col = 1)

enter image description here

plot(df1$results$K, df1$results$residual, type = "p", main = "Residuals", 
    xlab = "Number of Topics (K)", ylab = "Residuals", ask = TRUE)
lines(df1$results$K, df1$results$residual, lty = 1, col = 1)

enter image description here

plot(df1$results$K, df1$results$semcoh, type = "p", main = "Semantic Coherence", 
      xlab = "Number of Topics (K)", ylab = "Semantic Coherence", ask = TRUE)
lines(df1$results$K, df1$results$semcoh, lty = 1, col = 1)

enter image description here

plot(df1$results$K, df1$results$lbound, type = "p", main = "Lower Bound", 
     xlab = "Number of Topics (K)", ylab = "Lower Bound", ask = TRUE)
lines(df1$results$K, df1$results$lbound, lty = 1, col = 1)

enter image description here

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