sparrowZ | R Documentation |
This function wraps variable bays spike regression of a genes expression across a matrix of genes expressed in the same samples.
sparrowZ( y, x, ordering_mat = NULL, eps = 1e-06, exclude = NULL, add.intercept = TRUE, maxit = 10000, n_orderings = 10, family = "normal", scaling = TRUE, return_kl = TRUE, estimation_type = "BMA", bma_approximation = TRUE, screen = 1, post = 0.95, already_screened = 1, kl = 0.99, l0_path = NULL, cleanSolution = FALSE )
y |
Required. response variable. Normally distributed errors for family="normal". For family="binomial" should be coded as a vector of 0's and 1's. |
x |
Required. Design matrix, an n x m matrix, with rows as observations. |
ordering_mat |
Optional. Optionally specified coordinate update ordering matrix. Must be in matrix form with columns as permutation vectors of length m, and there must be n_orderings columns. (Default = NULL) |
eps |
Optional. Tolerance used to determine convergence of the algorithm based on the lower bound. (Default = 1e-6) |
exclude |
Optional. An optional indicator vector of length m of 0's and 1's indicating whether to penalize a particular variable or not (0=penalize, 1=unpenalized) (Default = NULL) |
add.intercept |
Optional. A boolean variable indicating whether or not to include an unpenalized intercept variable. (Default = TRUE) |
maxit |
Optional. The maximum number of iterations to run the algorithm for a given solution to a penalized regression problem. (Default = 1e4) |
n_orderings |
Optional. The number of random starts used. (Default = 10) |
family |
Optional. The type of error model used. Currently supported modes are family="normal" and family="binomial". (Default = "normal") |
scaling |
Optional. The type of error model used. Currently supported modes are family="normal" and family="binomial" (Default = TRUE) |
return_kl |
Optional. A boolean variable indicating whether or not to return an analysis of the null distributed features in the data-set as a function of the penalty parameter. (Default = TRUE) |
estimation_type |
Optional. The type of estimation to perform based on the number of unique solution identified to the penalized regression problem. Valid values are estimation_type="BMA" and estimation_type="MAXIMAL" (Default = "BMA") |
bma_approximation |
Optional. A boolean variable indicating whether to compute a full correction to the z statistic. WARNING can make the algorithm very computationally intensive for highly multi-modal posterior surfaces. (Default = TRUE) |
screen |
Optional. P-value to do marginal screening. Default is to not do marginal prescreening (e.g marginal p-value of 1.0) (Default = 1.0) |
post |
Optional. Choice of penalty parameter such that a feature will have a posterior probability of 0.95 if it passes a Bonferroni correction in the multivariate model. Default is post=.95. More conservative approach would be post=0.5(Default =0.95) |
already_screened |
Optional. If features are already screened, the marginal p-value used for screening. (Default = 1.0) |
kl |
Optional. If features are already screened, the marginal p-value used for screening. (Default = 0.99) |
l0_path |
Optional. The path of penalty parameters to solve the spike regression problem. If post is specified, this is computed automatically. (Default =NULL) |
cleanSolution |
Optional. This parameter determines whether a given solution is further filtered using an unpenalized model. If cleanSolution=TRUE, then the features that are significant after a Bonferroni correction given the p-values from the vbsr regression model are then tested in an unpenalized linear regression model. The p-values and z-statistics are updated using the Wald test from the unpenalized linear regression model for the features that were selected.(Default =FALSE) |
A coexpression value
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