newAIC | Compute the AIC of a model given some data |
newAIC-method | Compute the AIC of a model given some data |
newAlpha | Returns the matrix of paramters alpha |
newAlpha-method | Class newmodel |
newBeta | Returns the matrix of paramters beta |
newBeta-method | Class newmodel |
newBIC | Compute the BIC of a model given some data |
newBIC-method | Compute the BIC of a model given some data |
newEpsilon_alpha | Returns the vector of regularization parameter for alpha |
newEpsilon_alpha-method | Class newmodel |
newEpsilon_beta | Returns the vector of regularization parameter for beta |
newEpsilon_beta-method | Class newmodel |
newEpsilon_gamma | Returns the vector of regularization parameter for gamma |
newEpsilon_gamma-method | Class newmodel |
newEpsilon_W | Returns the vector of regularization parameter for W |
newEpsilon_W-method | Class newmodel |
newEpsilon_zeta | Returns the regularization parameter for the dispersion parameter |
newEpsilon_zeta-method | Class newmodel |
newFit | Fit a nb regression model |
newFit-method | Fit a nb regression model |
newGamma | Returns the matrix of paramters gamma |
newGamma-method | Class newmodel |
newloglik | Compute the log-likelihood of a model given some data |
newloglik-method | Compute the log-likelihood of a model given some data |
newLogMu | Returns the matrix of logarithm of mean parameters |
newLogMu-method | Class newmodel |
newmodel | Initialize an object of class newmodel |
newmodel-class | Class newmodel |
newMu | Returns the matrix of mean parameters |
newMu-method | Class newmodel |
newpenalty | Compute the penalty of a model |
newpenalty-method | Compute the penalty of a model |
newPhi | Returns the vector of dispersion parameters |
newPhi-method | Class newmodel |
newSim | Simulate counts from a negative binomial model |
newSim-method | Simulate counts from a negative binomial model |
newTheta | Returns the vector of inverse dispersion parameters |
newTheta-method | Class newmodel |
newV | Returns the gene-level design matrix for mu |
newV-method | Class newmodel |
newW | Returns the low-dimensional matrix of inferred sample-level covariates W |
newW-method | Class newmodel |
newWave | Perform dimensionality reduction using a nb regression model with gene and cell-level covariates. |
newWave-method | Perform dimensionality reduction using a nb regression model with gene and cell-level covariates. |
newX | Returns the sample-level design matrix for mu |
newX-method | Class newmodel |
newZeta | Returns the vector of log of inverse dispersion parameters |
newZeta-method | Class newmodel |
numberFactors | Generic function that returns the number of latent factors |
numberFactors-method | Class newmodel |
numberFeatures | Generic function that returns the number of features |
numberFeatures-method | Class newmodel |
numberParams | Generic function that returns the total number of parameters of the model |
numberParams-method | Generic function that returns the total number of parameters of the model |
numberSamples | Generic function that returns the number of samples |
numberSamples-method | Class newmodel |
show-method | Class newmodel |