Package 'LKT'

buildLKTModel

Description

Forward and backwards stepwise search for a set of features and components

with tracking of nonlinear parameters.

Usage

buildLKTModel(
  data,
  usefolds = NA,
  allcomponents,
  allfeatures,
  currentcomponents = c(),
  specialcomponents = c(),
  specialfeatures = c(),
  forv,
  bacv,
  preset = NA,
  presetint = T,
  currentfeatures = c(),
  verbose = FALSE,
  currentfixedpars = c(),
  maxitv = 10,
  interc = FALSE,
  forward = TRUE,
  backward = TRUE,
  metric = "BIC",
  removefeat = c(),
  removecomp = c()
)

Arguments

data	is a dataset with Anon.Student.Id and CF..ansbin.
usefolds	Numeric Vector | Specifies the folds for model fitting in LKT; the features are still calculated across all folds to compute test fold fit externally
allcomponents	is search space for LKT components
allfeatures	is search space for LKT features
currentcomponents	components to start search from
specialcomponents	add special components (not crossed with features, only paired with special features 1 for 1)
specialfeatures	features for each special component (not crossed during search)
forv	the minimuum amount of improvement needed for the addition of a new term
bacv	the maximuum amount of loss for a term to be removed
preset	One of "static","AFM","PFA","advanced","AFMLLTM","PFALLTM","advancedLLTM"
presetint	should the intercepts be included for preset components
currentfeatures	features to start search from
verbose	passed to LKT
currentfixedpars	used for current features as an option to start
maxitv	passed to LKT
interc	passed to LKT
forward	TRUE or FALSE
backward	TRUE or FALSE
metric	One of "BIC","AUC","AIC", and "RMSE"
removefeat	Character Vector | Excludes specified features from the test list.
removecomp	Character Vector | Excludes specified components from the test list.

Value

list of values "tracetable" and "currentfit"

---

computefeatures

Description

Compute feature describing prior practice effect.

Usage

computefeatures(data, feat, par1, par2, index, index2, par3, par4, par5, fcomp)

Arguments

data	copy of main data frame.
feat	is the feature to be computed.
par1	nonlinear parameters used for nonlinear features.
par2	nonlinear parameters used for nonlinear features.
index	a student by component levels index
index2	a component levels index
par3	nonlinear parameters used for nonlinear features.
par4	nonlinear parameters used for nonlinear features.
par5	nonlinear parameters used for nonlinear features.
fcomp	the component name.

Value

a vector suitable for regression input.

---

computeSpacingPredictors

Description

Compute repetition spacing time based features from input data CF..Time. and/or CF..reltime.

which will be automatically computed from Duration..sec. if not present themselves.

Usage

computeSpacingPredictors(data, KCs)

Arguments

data	is a dataset with Anon.Student.Id and CF..ansbin.
KCs	are the components for which spaced features will be specified in LKT

Value

data which is the same frame with the added spacing relevant columns.

---

countOutcome

Description

Compute the prior sum of the response appearing in the outcome column for the index

Usage

countOutcomeold(data, index, response)

Arguments

data	the dataset to compute an outcome vector for
index	the subsets to count over
response	the actually response value being counted

Value

the vector of the lagged cumulative sum.

---

Trial sequences for practice participants.

Description

A dataset containing a raw sample from the Memphis Datashop.

Usage

largerawsample

Format

A data frame please see the DataShop for more info.

It has many columns.

Source

https://pslcdatashop.web.cmu.edu/Export?datasetId=5513

---

LASSOLKTData

Description

Forward and backwards stepwise search for a set of features and components

with tracking of nonlinear parameters.

Usage

LASSOLKTData(
  data,
  gridpars,
  allcomponents,
  allfeatures,
  preset = NA,
  presetint = T,
  specialcomponents = c(),
  specialfeatures = c(),
  specialpars = c(),
  removefeat = c(),
  removecomp = c()
)

Arguments

data	is a dataset with Anon.Student.Id and CF..ansbin.
gridpars	a vector of parameters to create each feature at
allcomponents	is search space for LKT components
allfeatures	is search space for LKT features
preset	One of "static","AFM","PFA","advanced","AFMLLTM","PFALLTM","advancedLLTM"
presetint	should the intercepts be included for preset components
specialcomponents	add special components (not crossed with features, only paired with special features 1 for 1)
specialfeatures	features for each special component (not crossed during search)
specialpars	parameters for the special features (if needed)
removefeat	Character Vector | Excludes specified features from the test list.
removecomp	Character Vector | Excludes specified components from the test list.

Value

data which is the same frame with the added spacing relevant columns.

list of values "tracetable" and "currentfit"

---

LASSOLKTModel

Description

runs LASSO search on the data

Usage

LASSOLKTModel(
  data,
  gridpars,
  allcomponents,
  preset = NA,
  presetint = T,
  allfeatures,
  specialcomponents = c(),
  specialfeatures = c(),
  specialpars = c(),
  target_n,
  removefeat = c(),
  removecomp = c(),
  test_fold = 1
)

Arguments

data	is a dataset with Anon.Student.Id and CF..ansbin.
gridpars	a vector of parameters to create each feature at
allcomponents	is search space for LKT components
preset	One of "static","AFM","PFA","advanced","AFMLLTM","PFALLTM","advancedLLTM"
presetint	should the intercepts be included for preset components
allfeatures	is search space for LKT features
specialcomponents	add special components (not crossed with features, only paired with special features 1 for 1)
specialfeatures	features for each special component (not crossed during search)
specialpars	parameters for the special features (if needed)
target_n	chosen number of features in model
removefeat	Character Vector | Excludes specified features from the test list.
removecomp	Character Vector | Excludes specified components from the test list.
test_fold	the fold that the chosen LASSO model will be tested on

Value

list of matrices and values "train_x","train_y","test_x","test_y","fit","target_auc","target_rmse","n_features","auc_lambda","rmse_lambda","BIC_lambda","target_idx", "preds"

---

LKT

Description

Compute a logistic regression model of learning for input data.

Usage

LKT(
  data,
  usefolds = NA,
  components,
  features,
  fixedpars = NA,
  seedpars = NA,
  interacts = NA,
  curvefeats = NA,
  dualfit = FALSE,
  interc = FALSE,
  verbose = TRUE,
  epsilon = 1e-04,
  cost = 512,
  lowb = 1e-05,
  highb = 0.99999,
  type = 0,
  maketimes = FALSE,
  bias = 0,
  maxitv = 100,
  factrv = 1e+12,
  nosolve = FALSE,
  autoKC = rep(0, length(components)),
  autoKCcont = rep("NA", length(components)),
  connectors = rep("+", max(1, length(components) - 1))
)

Arguments

data	A dataset with Anon.Student.Id and CF..ansbin.
usefolds	Numeric Vector | Specifies the folds for model fitting in LKT; the features are still calculated across all folds to compute test fold fit externally
components	A vector of factors that can be used to compute each features for each subject.
features	a vector methods to use to compute a feature for the component.
fixedpars	a vector of parameters for all features+components.
seedpars	a vector of parameters for all features+components to seed non-linear parameter search.
interacts	A list of components that interacts with component by feature in the main specification.
curvefeats	vector of columns to use with "diff" functions
dualfit	TRUE or FALSE, fit a simple latency using logit. Requires Duration..sec. column in data.
interc	TRUE or FALSE, include a global intercept.
verbose	provides more output in some cases.
epsilon	passed to LiblineaR
cost	passed to LiblineaR
lowb	lower bound for non-linear optimizations
highb	upper bound for non-linear optimizations
type	passed to LiblineaR
maketimes	Boolean indicating whether to create time based features (or may be precomputed)
bias	passed to LiblineaR
maxitv	passed to nonlinear optimization a maxit control
factrv	controls the optim() function
nosolve	causes the function to return a sparse data matrix of the features, rather than a solution
autoKC	a vector to indicate whether to use autoKC for the component (0) or the k for the numebr of clusters
autoKCcont	a vector of text strings set to "rand" for component to make autoKC assignment to cluster is randomized (for comaprison)
connectors	a vector if linear equation R operators including +, * and :

Value

list of values "model", "coefs", "r2", "prediction", "nullmodel", "latencymodel", "optimizedpars","subjectrmse", "newdata", and "automat"

---

LKT_HDI

Description

Bootstrap credibility intervals to aid in interpreting coefficients.

Usage

LKT_HDI(
  dat,
  n_boot,
  n_students,
  comps,
  feats,
  conns = rep("+", max(1, length(comps) - 1)),
  ints = NA,
  fixeds,
  get_hdi = TRUE,
  cred_mass = 0.95
)

Arguments

dat	Dataframe
n_boot	Number of subsamples to fit
n_students	Number of students per subsample
comps	Components in model
feats	Features in model
conns	R notation for linear equation connectors in model
ints	Interacts in model
fixeds	Fixed parameters in model
get_hdi	Boolean to decide if generating HDI per coefficient
cred_mass	Credibility mass parameter to decide width of HDI

Value

List of values "par_reps", "mod_full", "coef_hdi"

---

Predict for LKT Models

Description

Generates predictions and evaluates logistic regression models tailored for learning data, specifically designed for Logistic Knowledge Tracing (LKT) models. This function provides flexibility in returning either just the predicted probabilities or both the predictions and key evaluation statistics.

Usage

predict_lkt(
  modelob,
  data,
  fold = NULL,
  return_stats = FALSE,
  min_pred_limit = 1e-05,
  max_pred_limit = 0.99999
)

Arguments

modelob	An LKT model object containing necessary model coefficients and predictors for generating predictions.
data	A dataset including predictor variables, the outcome variable CF..ansbin., and fold information.
fold	Optional. Numeric vector specifying which folds to include for prediction. If NULL or empty, uses all data.
return_stats	Logical. If TRUE, returns both predictions and evaluation statistics (Log-Likelihood, AUC, RMSE, R^2). If FALSE, returns only the predictions.
min_pred_limit	Minimum prediction limit. Default is 0.00001.
max_pred_limit	Maximum prediction limit. Default is 0.99999.

Value

If return_stats is FALSE, returns a list containing:

• predictions: The predicted probabilities for each observation in the specified fold(s).

If return_stats is TRUE, returns a list containing:

• predictions: The predicted probabilities for each observation in the specified fold(s).

• LL: Log-Likelihood of the model given the actual outcomes.

• AUC: Area Under the ROC Curve.

• RMSE: Root Mean Squared Error.

• R2: R-squared value, indicating the proportion of variance explained by the model.

---

Trial sequences for practice participants.

Description

A dataset containing a small sample of participants in a memory experiment.

Usage

samplelkt

Format

A data frame with 2074 rows and many variables:

Anon.Student.Id

unique identifier for each student

Duration..sec.

unique identifier for each student

KC..Default.

unique identifier for each student

Outcome

unique identifier for each student

...

Source

https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=5508

---

smallSet

Description

smallSet

Usage

smallSet(data, nSub)

Arguments

data	Dataframe of student data
nSub	Number of students

---

ViewExcel

Description

ViewExcel

Usage

ViewExcel(df = .Last.value, file = tempfile(fileext = ".csv"))

Arguments

df	Dataframe
file	name of the Excel file

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