BINET {exametrika} | R Documentation |
Bicluster Network Model
Description
Bicluster Network Model: BINET is a model that combines the Bayesian network model and Biclustering. BINET is very similar to LDB and LDR. The most significant difference is that in LDB, the nodes represent the fields, whereas in BINET, they represent the class. BINET explores the local dependency structure among latent classes at each latent field, where each field is a locus.
Usage
BINET(
U,
Z = NULL,
w = NULL,
na = NULL,
conf = NULL,
ncls = NULL,
nfld = NULL,
g_list = NULL,
adj_list = NULL,
adj_file = NULL,
verbose = FALSE
)
Arguments
U |
U is either a data class of exametrika, or raw data. When raw data is given, it is converted to the exametrika class with the dataFormat function. |
Z |
Z is a missing indicator matrix of the type matrix or data.frame |
w |
w is item weight vector |
na |
na argument specifies the numbers or characters to be treated as missing values. |
conf |
For the confirmatory parameter, you can input either a vector with items and corresponding fields in sequence, or a field membership profile matrix. In the case of the former, the field membership profile matrix will be generated internally. When providing a membership profile matrix, it needs to be either matrix or data.frame. The number of fields(nfld) will be overwrite to the number of columns of this matrix. |
ncls |
number of classes |
nfld |
number of fields |
g_list |
A list compiling graph-type objects for each rank/class. |
adj_list |
A list compiling matrix-type adjacency matrices for each rank/class. |
adj_file |
A file detailing the relationships of the graph for each rank/class, listed in the order of starting point, ending point, and rank(class). |
verbose |
verbose output Flag. default is TRUE |
Value
- nobs
Sample size. The number of rows in the dataset.
- testlength
Length of the test. The number of items included in the test.
- Nclass
Optimal number of classes.
- Nfield
Optimal number of fields.
- crr
Correct Response Rate
- ItemLabel
Label of Items
- FieldLabel
Label of Fields
- all_adj
Integrated Adjacency matrix used to plot graph.
- all_g
Integrated graph object used to plot graph.see also plot.exametrika
- adj_list
List of Adjacency matrix used in the model
- params
A list of the estimated conditional probabilities. It indicates which path was obtained from which parent node(class) to which child node(class), held by
parent
,child
, andfield
. The item Items contained in the field is infld
. Namedchap
includes the conditional correct response answer rate of the child node, whilepap
contains the pass rate of the parent node.- PSRP
Response pattern by the students belonging to the parent classes of Class c. A more comprehensible arrangement of
params.
- LCD
Latent Class Distribution. see also plot.exametrika
- LFD
Latent Field Distribution. see also plot.exametrika
- CMD
Class Membership Distribution.
- FRP
Marginal bicluster reference matrix.
- FRPIndex
Index of FFP includes the item location parameters B and Beta, the slope parameters A and Alpha, and the monotonicity indices C and Gamma.
- TRP
Test Reference Profile
- LDPSR
A rearranged set of parameters for output. It includes the field the items contained within that field, and the conditional correct response rate of parent nodes(class) and child node(class).
- FieldEstimated
Given vector which correspondence between items and the fields.
- Students
Rank Membership Profile matrix.The s-th row vector of
\hat{M}_R
,\hat{m}_R
, is the rank membership profile of Student s, namely the posterior probability distribution representing the student's belonging to the respective latent classes.- NextStage
The next class that easiest for students to move to, its membership probability, class-up odds, and the field required for more.
- MG_FitIndices
Multigroup as Null model.See also TestFit
- SM_FitIndices
Saturated Model as Null model.See also TestFit
Examples
# Example: Bicluster Network Model (BINET)
# BINET combines Bayesian network model and Biclustering to explore
# local dependency structure among latent classes at each field
# Create field configuration vector based on field assignments
conf <- c(
1, 5, 5, 5, 9, 9, 6, 6, 6, 6, 2, 7, 7, 11, 11, 7, 7,
12, 12, 12, 2, 2, 3, 3, 4, 4, 4, 8, 8, 12, 1, 1, 6, 10, 10
)
# Create edge data for network structure between classes
edges_data <- data.frame(
"From Class (Parent) >>>" = c(
1, 2, 3, 4, 5, 7, 2, 4, 6, 8, 10, 6, 6, 11, 8, 9, 12
),
">>> To Class (Child)" = c(
2, 4, 5, 5, 6, 11, 3, 7, 9, 12, 12, 10, 8, 12, 12, 11, 13
),
"At Field (Locus)" = c(
1, 2, 2, 3, 4, 4, 5, 5, 5, 5, 5, 7, 8, 8, 9, 9, 12
)
)
# Save edge data to temporary CSV file
tmp_file <- tempfile(fileext = ".csv")
write.csv(edges_data, file = tmp_file, row.names = FALSE)
# Fit Bicluster Network Model
result.BINET <- BINET(
J35S515,
ncls = 13, # Maximum class number from edges (13)
nfld = 12, # Maximum field number from conf (12)
conf = conf, # Field configuration vector
adj_file = tmp_file # Path to the CSV file
)
# Clean up temporary file
unlink(tmp_file)
# Display model results
print(result.BINET)
# Visualize different aspects of the model
plot(result.BINET, type = "Array") # Show bicluster structure
plot(result.BINET, type = "TRP") # Test Response Profile
plot(result.BINET, type = "LRD") # Latent Rank Distribution
plot(result.BINET,
type = "RMP", # Rank Membership Profiles
students = 1:9, nc = 3, nr = 3
)
plot(result.BINET,
type = "FRP", # Field Reference Profiles
nc = 3, nr = 2
)
plot(result.BINET,
type = "LDPSR", # Locally Dependent Passing Student Rates
nc = 3, nr = 2
)