fitOnSinglePlat: Fit IRT model on a single platform

In integIRTy: Integrating Multiple Modalities of High Throughput Assays Using Item Response Theory

Fit IRT model on a single platform

Description

This function fits the Item Response Model for one platform. It assumes the user has already dichotomized the data.

Usage

fitOnSinglePlat(data, model = 2, guessing = FALSE,
    sampleIndices = 1:ncol(data), geneIndices = 1:nrow(data), ...)

Arguments

data	A matrix of 0's and 1's with rows being genes (treated as examinees) and columns being samples (treated as items).
model	IRT model. 1-Rasch model where all item discrination are set to 1; 2-all item discrimation are set to be equal but not necessarily as 1; 3-the 2PL model where no constraint is put on the item difficulty and discrimination parameter.
guessing	A logical variable indicating whether to include guessing parameter in the model.
sampleIndices	Indices of the samples to be feeded into the model. Default is set to use all samples.
geneIndices	Indices of the genes to be feeded into the model. Default is to use all genes.
...	Additional options available in ltm package. Currently not used in intIRT package.

Value

A list giving the estimated IRT model and related information

fit	An object returned by calling ltm package. Item parameters and other auxillary inforamtion (i.e. loglikelihood, convergence, Hessian) can be accessed from this object. For more details, please refer to ltm package
model	The model type
guessing	The guessing parameter
sampleIndices	The sample indices used in the model
geneIndices	The gene indices used in the model

Author(s)

Pan Tong ([e-mail]), Kevin R Coombes ([e-mail])

References

Rizopoulos, D. (2006) ltm: An R package for latent variable modelling and item response theory analyses. Journal of Statistical Software, 17(5), 1-25.

See Also

computeAbility, intIRTeasyRun, calculatePermutedScoreByGeneSampling

Examples

# number of items and number of genes
nSample <- 10
nGene <- 2000
set.seed(1000)
a <- rgamma(nSample, shape=1, scale=1)
b <- rgamma(nSample, shape=1, scale=1)
# true latent traits
theta <- rnorm(nGene, mean=0)

# probability of correct response (P_ij) for gene i in sample j
P <- matrix(NA, nrow=nGene, ncol=nSample)
for(i in 1:nSample){
	P[, i] <- exp(a[i]*(theta-b[i]))/(1+exp(a[i]*(theta-b[i])))
}
# binary matrix
X <- matrix(NA, nrow=nGene, ncol=nSample)
for(i in 1:nSample){
	X[, i] <- rbinom(nGene, size=1, prob=P[, i])
}
# IRT fitting
fit2PL <- fitOnSinglePlat(X, model=3)

integIRTy documentation built on May 3, 2022, 9:08 a.m.