an S4 class containing the analytics for a classified set of documents.

Description

An S4 class containing the analytics for a classified set of documents. This includes a label summary and a document summary. This class is returned if virgin=TRUE in create_container.

Objects from the Class

Objects could in principle be created by calls of the form new("analytics_virgin", ...). The preferred form is to have them created via a call to create_analytics.

Slots

label_summary

Object of class "data.frame": stores the analytics for each label, including how many documents were classified with each label

document_summary

Object of class "data.frame": stores the analytics for each document, including all available raw data associated with the learning process

Author(s)

Timothy P. Jurka <tpjurka@ucdavis.edu>

Examples

library(RTextTools)
data(NYTimes)
data <- NYTimes[sample(1:3100,size=100,replace=FALSE),]
matrix <- create_matrix(cbind(data["Title"],data["Subject"]), language="english", 
removeNumbers=TRUE, stemWords=FALSE, weighting=tm::weightTfIdf)
container <- create_container(matrix,data$Topic.Code,trainSize=1:75, testSize=76:100, 
virgin=TRUE)
models <- train_models(container, algorithms=c("MAXENT","SVM"))
results <- classify_models(container, models)
analytics <- create_analytics(container, results)

summary(analytics)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(RTextTools)
Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RTextTools/analytics_virgin-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: analytics_virgin-class
> ### Title: an S4 class containing the analytics for a classified set of
> ###   documents.
> ### Aliases: analytics_virgin-class
> ### Keywords: classes
> 
> ### ** Examples
> 
> library(RTextTools)
> data(NYTimes)
> data <- NYTimes[sample(1:3100,size=100,replace=FALSE),]
> matrix <- create_matrix(cbind(data["Title"],data["Subject"]), language="english", 
+ removeNumbers=TRUE, stemWords=FALSE, weighting=tm::weightTfIdf)
> container <- create_container(matrix,data$Topic.Code,trainSize=1:75, testSize=76:100, 
+ virgin=TRUE)
> models <- train_models(container, algorithms=c("MAXENT","SVM"))
> results <- classify_models(container, models)
> analytics <- create_analytics(container, results)
> 
> summary(analytics)
SUMMARY OF LABEL CLASSIFICATION

   NUM_CONSENSUS_CODED NUM_PROBABILITY_CODED PROP_CONSENSUS_CODED
1                    0                     0                    0
2                    0                     0                    0
3                    0                     0                    0
4                    0                     0                    0
5                    0                     0                    0
6                    0                     0                    0
8                    0                     0                    0
10                   0                     0                    0
12                   0                     0                    0
13                   1                     1                    4
15                   0                     0                    0
16                   0                     0                    0
17                   1                     1                    4
18                   0                     0                    0
19                  23                    23                   92
20                   0                     0                    0
24                   0                     0                    0
26                   0                     0                    0
28                   0                     0                    0
29                   0                     0                    0
30                   0                     0                    0
31                   0                     0                    0
   PROP_PROBABILITY_CODED
1                       0
2                       0
3                       0
4                       0
5                       0
6                       0
8                       0
10                      0
12                      0
13                      4
15                      0
16                      0
17                      4
18                      0
19                     92
20                      0
24                      0
26                      0
28                      0
29                      0
30                      0
31                      0
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>