ZinbModel-class {zinbwave} | R Documentation |
Objects of this class store all the values needed to work with a zero-inflated negative binomial (ZINB) 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 'ZinbModel' show(object) ## S4 method for signature 'ZinbModel' nSamples(x) ## S4 method for signature 'ZinbModel' nFeatures(x) ## S4 method for signature 'ZinbModel' nFactors(x) ## S4 method for signature 'ZinbModel' getX_mu(object, intercept = TRUE) ## S4 method for signature 'ZinbModel' getX_pi(object, intercept = TRUE) ## S4 method for signature 'ZinbModel' getV_mu(object, intercept = TRUE) ## S4 method for signature 'ZinbModel' getV_pi(object, intercept = TRUE) ## S4 method for signature 'ZinbModel' getLogMu(object) ## S4 method for signature 'ZinbModel' getMu(object) ## S4 method for signature 'ZinbModel' getLogitPi(object) ## S4 method for signature 'ZinbModel' getPi(object) ## S4 method for signature 'ZinbModel' getZeta(object) ## S4 method for signature 'ZinbModel' getPhi(object) ## S4 method for signature 'ZinbModel' getTheta(object) ## S4 method for signature 'ZinbModel' getEpsilon_beta_mu(object) ## S4 method for signature 'ZinbModel' getEpsilon_gamma_mu(object) ## S4 method for signature 'ZinbModel' getEpsilon_beta_pi(object) ## S4 method for signature 'ZinbModel' getEpsilon_gamma_pi(object) ## S4 method for signature 'ZinbModel' getEpsilon_W(object) ## S4 method for signature 'ZinbModel' getEpsilon_alpha(object) ## S4 method for signature 'ZinbModel' getEpsilon_zeta(object) ## S4 method for signature 'ZinbModel' getW(object) ## S4 method for signature 'ZinbModel' getBeta_mu(object) ## S4 method for signature 'ZinbModel' getBeta_pi(object) ## S4 method for signature 'ZinbModel' getGamma_mu(object) ## S4 method for signature 'ZinbModel' getGamma_pi(object) ## S4 method for signature 'ZinbModel' getAlpha_mu(object) ## S4 method for signature 'ZinbModel' getAlpha_pi(object)
object |
an object of class |
x |
an object of class |
intercept |
logical. Whether to return the intercept (ignored if the
design matrix has no intercept). Default |
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_mu
, O_pi
,
and W
need to have n
rows, V
needs to have J
rows, zeta
must be of length J
.
nSamples
returns the number of samples; nFeatures
returns the number of features; nFactors
returns the number of latent
factors.
show
: show useful info on the object.
nSamples
: returns the number of samples.
nFeatures
: returns the number of features.
nFactors
: returns the number of latent factors.
getX_mu
: returns the sample-level design matrix for mu.
getX_pi
: returns the sample-level design matrix for pi.
getV_mu
: returns the gene-level design matrix for mu.
getV_pi
: returns the sample-level design matrix for pi.
getLogMu
: returns the logarithm of the mean of the non-zero
component.
getMu
: returns the mean of the non-zero component.
getLogitPi
: returns the logit-probability of zero.
getPi
: returns the probability of zero.
getZeta
: returns the log of the inverse of the dispersion
parameter.
getPhi
: returns the dispersion parameter.
getTheta
: returns the inverse of the dispersion parameter.
getEpsilon_beta_mu
: returns the regularization parameters for
beta_mu
.
getEpsilon_gamma_mu
: returns the regularization parameters for
gamma_mu
.
getEpsilon_beta_pi
: returns the regularization parameters for
beta_pi
.
getEpsilon_gamma_pi
: returns the regularization parameters for
gamma_pi
.
getEpsilon_W
: returns the regularization parameters for
W
.
getEpsilon_alpha
: returns the regularization parameters for
alpha
.
getEpsilon_zeta
: returns the regularization parameters for
zeta
.
getW
: returns the matrix W of inferred sample-level
covariates.
getBeta_mu
: returns the matrix beta_mu of inferred parameters.
getBeta_pi
: returns the matrix beta_pi of inferred parameters.
getGamma_mu
: returns the matrix gamma_mu of inferred parameters.
getGamma_pi
: returns the matrix gamma_pi of inferred parameters.
getAlpha_mu
: returns the matrix alpha_mu of inferred parameters.
getAlpha_pi
: returns the matrix alpha_pi 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.
O_mu
matrix. The offset matrix for mu.
O_pi
matrix. The offset matrix for pi.
which_X_mu
integer. Indeces of which columns of X to use in the regression of mu.
which_V_mu
integer. Indeces of which columns of V to use in the regression of mu.
which_X_pi
integer. Indeces of which columns of X to use in the regression of pi.
which_V_pi
integer. Indeces of which columns of V to use in the regression of pi.
X_mu_intercept
logical. TRUE if X_mu contains an intercept.
X_pi_intercept
logical. TRUE if X_pi contains an intercept.
V_mu_intercept
logical. TRUE if V_mu contains an intercept.
V_pi_intercept
logical. TRUE if V_pi contains an intercept.
W
matrix. The factors of sample-level latent factors.
beta_mu
matrix or NULL. The coefficients of X in the regression of mu.
gamma_mu
matrix or NULL. The coefficients of V in the regression of mu.
alpha_mu
matrix or NULL. The coefficients of W in the regression of mu.
beta_pi
matrix or NULL. The coefficients of X in the regression of pi.
gamma_pi
matrix or NULL. The coefficients of V in the regression of pi.
alpha_pi
matrix or NULL. The coefficients of W in the regression of pi.
zeta
numeric. A vector of log of inverse dispersion parameters.
epsilon_beta_mu
nonnegative scalar. Regularization parameter for beta_mu
epsilon_gamma_mu
nonnegative scalar. Regularization parameter for gamma_mu
epsilon_beta_pi
nonnegative scalar. Regularization parameter for beta_pi
epsilon_gamma_pi
nonnegative scalar. Regularization parameter for gamma_pi
epsilon_W
nonnegative scalar. Regularization parameter for W
epsilon_alpha
nonnegative scalar. Regularization parameter for alpha (both alpha_mu and alpha_pi)
epsilon_zeta
nonnegative scalar. Regularization parameter for zeta
epsilon_min_logit
scalar. Minimum regularization parameter for parameters of the logit model, including the intercept.