ProcData provides tools for exploratory process data analysis. It contains an example dataset and functions for
Download the package from the download page and execute the following command in
install.packages(FILENAME, repos = NULL, dependencies = TRUE)
FILENAME should be replaced by the name of the package file downloaded including its path. The development version can be installed from GitHub with:
ProcData depends on packages
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
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.
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
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. To load the dataset, run
cc_data is a list of two elements:
seqsis a `proc' object.
responsesis 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
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.
ProcData also provides three action sequences generators:
seq_gengenerates action sequences of an imaginary simulation-experiment-based item;
seq_gen2generates action sequences according to a given probability transition matrix;
seq_gen3generates action sequences from a recurrent neural network. It depends on
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.
seq2feature_ngram extracts ngram features from response processes.
seqs <- seq_gen(100) theta <- seq2feature_ngram(seqs)
The following functions implement the MDS methods.
Kfeatures from a given set of response processes or their dissimilarity matrix.
chooseK_mdsselects the number of features to be extracted by cross-validation.
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)$theta
Similar to MDS, the seq2seq AE method is implemented by two functions. Both functions depend on
Kfeatures from a given set of response processes.
chooseK_seq2seqselects the number of features to be extracted by cross-validation.
seqs <- seq_gen(100) K_res <- chooseK_seq2seq(seqs, K_cand=c(5, 10), valid_prop=0.2) seq2seq_res <- seq2feature_seq2seq(seqs, K_res$K, samples_train=1:80, samples_valid=81:100) theta <- seq2seq_res$theta
Note that if the number of candidates of
K is large and a large number of epochs is needed for training the seq2seq AE,
chooseK_seq2seq can be slow. One can parallel the selection procedure via multiple independent calls of
seq2feature_seq2seq with properly specified training, validation, and test sets.
A sequence model relates response processes and covariates with a response variable. The model combines a recurrent neural network and a fully connected neural network.
seqmfits a sequence model. It returns an object of class `seqm'.
predict.seqmpredicts the response variable with a given fitted sequence model. Both
n <- 100 seqs <- seq_gen(n) y1 <- sapply(seqs$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)
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