In xytangtang/ProcData: Process Data Analysis

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ProcData: An R Package for Process Data Analysis

ProcData provides tools for exploratory process data analysis. It contains an example dataset and functions for

• reading responses from a csv file
• process manipulation
• action sequence generators
• feature extraction methods
• fitting and making prediction from sequence models

Installation

Download the package from the download page and execute the following command in R

``` {r installation, eval = FALSE} install.packages(FILENAME, repos = NULL, dependencies = TRUE)

where `FILENAME` should be replaced by the name of the package file downloaded including its path. The development version can be installed from [GitHub](https://github.com/) with:
``` {r github_install, eval = FALSE}
devtools::install_github("xytangtang/ProcData")

ProcData depends on packages Rcpp and keras. A C compiler and python are needed. Some functions in ProcData calls functions in keras to fit neural networks. To make sure these functions run properly, execute the following command in R. ``` {r keras_install, eval = FALSE} library(keras) install_keras(tensorflow = "1.13.1")

Note that if this step is skipped, `ProcData` can still be installed and loaded, but calling the functions that depends on `keras` will give an error. 


## Contents

### Data Structure
`ProcData` organizes response processes as an object of class `proc` which is a list containing the action sequences and the timestamp sequences. Functions are provided to summarize and manipulate `proc` objects.

### Dataset

`ProcData` includes a dataset `cc_data` of the action sequences and binary item responses of 16920 respondents of item CP025Q01 in PISA 2012. The item interface can be found [here](http://www.oecd.org/pisa/test-2012/testquestions/question3/). To load the dataset, run
``` {r dataset, eval=FALSE}
data(cc_data)

cc_data is a list of two elements:

• seqs is a `proc' object.
• responses is a numeric vector containing the binary responses outcomes.

For data stored in csv files, read.seqs can be used to read response processes into R and to organize them into a proc object. In the input csv file, each process can be stored in a single line or multiple lines. The sample files for the two styles are example_single.csv and example_multiple.csv. The processes in the two files can be read by running ``` {r read, eval=FALSE} seqs1 <- read.seqs(file="example_single.csv", style="single", id_var="ID", action_var="Action", time_var="Time", seq_sep=", ") seqs2 <- read.seqs(file="example_multiple.csv", style="multiple", id_var="ID", action_var="Action", time_var="Time")

`write.seqs` can be used to write `proc` objects in csv files.

### Data Generators

`ProcData` also provides three action sequences generators:

- `seq_gen` generates action sequences of an imaginary simulation-experiment-based item;
- `seq_gen2` generates action sequences according to a given probability transition matrix;
- `seq_gen3` generates action sequences from a recurrent neural network. It depends on `keras`.

### Feature Extraction Methods

`ProcData` implements three feature extraction methods that compress varying length response processes into fixed dimension numeric vectors.
The first method extract n-gram features from response processes. The other two methods are based on multidimensional scaling (MDS) and sequence-to-sequence autoencoders (seq2seq AE). Details of the methods can be found [here](http://www.scientifichpc.com/processdata/method.html).

#### N-Gram
Function `seq2feature_ngram` extracts ngram features from response processes.
```r
seqs <- seq_gen(100)
theta <- seq2feature_ngram(seqs)

MDS

The following functions implement the MDS methods.

• seq2feature_mds extracts K features from a given set of response processes or their dissimilarity matrix.
• chooseK_mds selects the number of features to be extracted by cross-validation. ``` {r mds, eval=FALSE} seqs <- seq_gen(100) K_res <- chooseK_mds(seqs, K_cand=5:10, return_dist=TRUE) theta <- seq2feature_mds(K_res$dist_mat, K_res$K)$theta

#### seq2seq AE

Similar to MDS, the seq2seq AE method is implemented by two functions. Both functions depend on `keras`.

- `seq2feature_seq2seq` extracts `K` features from a given set of response processes.
- `chooseK_seq2seq` selects the number of features to be extracted by cross-validation.

```r
seqs <- seq_gen(100)
K_res <- chooseK_seq2seq(seqs, K_cand=c(5, 10), valid_prop=0.2)
seq2seq_res <- seq2feature_seq2seq(seqs, K_res$K, samples_train=1:80, samples_valid=81:100)
theta <- seq2seq_res$theta

Note that if the number of candidates of K is large and a large number of epochs is needed for training the seq2seq AE, chooseK_seq2seq can be slow. One can parallel the selection procedure via multiple independent calls of seq2feature_seq2seq with properly specified training, validation, and test sets.

Sequence Models

A sequence model relates response processes and covariates with a response variable. The model combines a recurrent neural network and a fully connected neural network.

• seqm fits a sequence model. It returns an object of class `seqm'.
• predict.seqm predicts the response variable with a given fitted sequence model. Both seqm and predict.seqm depends on keras.

``` {r seqm, eval=FALSE} n <- 100 seqs <- seq_gen(n) y1 <- sapply(seqs$action_seqs, function(x) "CHECK_A" %in% x) y2 <- sapply(seqs$action_seqs, function(x) log10(length(x)))

index_test <- sample(1:n, 10) index_train <- setdiff(1:n, index_test) seqs_train <- sub_seqs(seqs, index_train) seqs_test <- sub_seqs(seqs, index_test)

actions <- unique(unlist(seqs))

a simple sequence model for a binary response variable

seqm_res1 <- seqm(seqs = seqs_train, response = y1, response_type = "binary", actions=actions, K_emb = 5, K_rnn = 5, n_epoch = 5) pred_res1 <- predict(seqm_res1, new_seqs = seqs_test)

a simple sequence model for a numeric response variable

seqm_res2 <- seqm(seqs = seqs_test, response = y2, response_type = "scale", actions=actions, K_emb = 5, K_rnn = 5, n_epoch = 5) pred_res2 <- predict(seqm_res2, new_seqs = seqs_test) ```

xytangtang/ProcData documentation built on May 30, 2021, 1:21 a.m.