View source: R/fitOnSinglePlat.R
fitOnSinglePlat | R Documentation |
This function fits the Item Response Model for one platform. It assumes the user has already dichotomized the data.
fitOnSinglePlat(data, model = 2, guessing = FALSE, sampleIndices = 1:ncol(data), geneIndices = 1:nrow(data), ...)
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. |
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 |
Pan Tong (nickytong@gmail.com), Kevin R Coombes (krc@silicovore.com)
Rizopoulos, D. (2006) ltm: An R package for latent variable modelling and item response theory analyses. Journal of Statistical Software, 17(5), 1-25.
computeAbility, intIRTeasyRun, calculatePermutedScoreByGeneSampling
# 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.