newmodel-class: Class newmodel
In NewWave: Negative binomial model for scRNA-seq

Description Usage Arguments Details Value Methods (by generic) Slots

Objects of this class store all the values needed to work with a negative binomial model, as described in the vignette. They contain all information to fit a model by penalized maximum likelihood or simulate data from a model.

## S4 method for signature 'newmodel'
show(object)

## S4 method for signature 'newmodel'
numberSamples(x)

## S4 method for signature 'newmodel'
numberFeatures(x)

## S4 method for signature 'newmodel'
numberFactors(x)

## S4 method for signature 'newmodel'
newX(object)

## S4 method for signature 'newmodel'
newV(object)

## S4 method for signature 'newmodel'
newLogMu(object)

## S4 method for signature 'newmodel'
newMu(object)

## S4 method for signature 'newmodel'
newZeta(object)

## S4 method for signature 'newmodel'
newPhi(object)

## S4 method for signature 'newmodel'
newTheta(object)

## S4 method for signature 'newmodel'
newEpsilon_beta(object)

## S4 method for signature 'newmodel'
newEpsilon_gamma(object)

## S4 method for signature 'newmodel'
newEpsilon_W(object)

## S4 method for signature 'newmodel'
newEpsilon_alpha(object)

## S4 method for signature 'newmodel'
newEpsilon_zeta(object)

## S4 method for signature 'newmodel'
newW(object)

## S4 method for signature 'newmodel'
newBeta(object)

## S4 method for signature 'newmodel'
newGamma(object)

## S4 method for signature 'newmodel'
newAlpha(object)

`object`	an object of class `newmodel`.
`x`	an object of class `newmodel`.

For the full description of the model see the model vignette. Internally, the slots are checked so that the matrices are of the appropriate dimensions: in particular, X, O and W need to have n rows, V needs to have J rows, zeta must be of length J.

numberSamples returns the number of samples; numberFeaturesreturns the number of features; numberFactors returns the number of latent factors.

show: show useful info on the object.
numberSamples: returns the number of samples.
numberFeatures: returns the number of features.
numberFactors: returns the number of latent factors.
newX: returns the sample-level design matrix for mu.
newV: returns the gene-level design matrix for mu.
newLogMu: returns the logarithm of the mean of the non-zero component.
newMu: returns the mean of the non-zero component.
newZeta: returns the log of the inverse of the dispersion parameter.
newPhi: returns the dispersion parameter.
newTheta: returns the inverse of the dispersion parameter.
newEpsilon_beta: returns the regularization parameters for beta.
newEpsilon_gamma: returns the regularization parameters for gamma.
newEpsilon_W: returns the regularization parameters for W.
newEpsilon_alpha: returns the regularization parameters for alpha.
newEpsilon_zeta: returns the regularization parameters for zeta.
newW: returns the matrix W of inferred sample-level covariates.
newBeta: returns the matrix beta of inferred parameters.
newGamma: returns the matrix gamma of inferred parameters.
newAlpha: returns the matrix alpha of inferred parameters.

X: matrix. The design matrix containing sample-level covariates, one sample per row.
V: matrix. The design matrix containing gene-level covariates, one gene per row.
X_intercept: logical. TRUE if X contains an intercept.
V_intercept: logical. TRUE if V contains an intercept.
W: matrix. The factors of sample-level latent factors.
beta: matrix or NULL. The coefficients of X in the regression.
gamma: matrix or NULL. The coefficients of V in the regression.
alpha: matrix. The weight of sample-level latent factors.
zeta: numeric. A vector of log of inverse dispersion parameters.
epsilon_beta: nonnegative scalar. Regularization parameter for beta
epsilon_gamma: nonnegative scalar. Regularization parameter for gamma
epsilon_W: nonnegative scalar. Regularization parameter for W
epsilon_alpha: nonnegative scalar. Regularization parameter for alpha
epsilon_zeta: nonnegative scalar. Regularization parameter for zeta