Initialize an Ultimate Deconvolution model fit. See
ud_fit for background and model definition.
ud_init(
dat,
V,
n_rank1,
n_unconstrained,
U_scaled,
U_rank1,
U_unconstrained,
control = list()
)An n x m data matrix, in which each row of the matrix is
an m-dimensional data point, or a “mash” object, for example
created by mash_set_data. When X is a
matrix it should have at least 2 rows and at least 2 columns.
Either an m x m matrix giving the residual covariance matrix for all n data points, or a list of m x m covariance matrices of length n.
A non-negative integer specifying the number of
rank-1 covariance matrices included in the mixture prior. Initial
estimates of the m x m rank-1 covariance matrices are generated at
random. At most one of n_rank1 and U_rank1 should be
provided. If neither are specified, rank-1 matrices will not be
included.
A non-negative integer specifying the number
of unconstrained covariance matrices included in the mixture
prior. Initial estimates of the m x m covariance matrices are
generated at random. At most one of n_unconstrained and
U_unconstrained should be provided. If neither are
specified, 4 random unconstrained matrices will be included.
A list specifying initial estimates of the scaled
covariance matrices in the mixture prior. The default setting
specifies two commonly used covariance matrices. In the case of a
single scaled matrix, U_scaled may be a matrix instead of a
list.
A list specifying initial estimates of the rank-1
matrices in the mixture prior. At most one of n_rank1 and
U_rank1 should be provided. If U_rank1 is not given,
the rank-1 covariates are initialized at random. In the case of a
single rank-1 matrix, U_rank1 may be a matrix instead of
list.
A list specifying initial estimates of the
unconstrained matrices in the mixture prior. At most one of
n_unconstrained and U_unconstrained should be
provided. If U_unconstrained is not given, the matrices are
initialized at random. In the case of a single unconstrained
matrix, U_unconstrained may be a matrix instead of a list.
A list of parameters controlling the behaviour of
the model initialization. See ud_fit for details.
# Here we illustrate the different ways to initialize a UD model fit,
# with and without mashr. In the very simplest invocation, we supply a
# data matrix X.
set.seed(1)
X <- matrix(rnorm(2000),200,10)
fit <- ud_init(X)
# For more sophisticated usage, first create a mash data object with
# mash_set_data, then call ud_init. By setting alpha = 1, we invoke
# the EZ ("exchangeable z-scores") model, and with the model the
# residual covariance matrix is the same for all effects (rows of
# Bhat), and is the same as the V in the mash object.
library(mashr)
#> Loading required package: ashr
V <- matrix(0.1,10,10)
diag(V) <- 1
Shat <- sqrt(matrix(rexp(2000),200,10))
out <- simple_sims(200,10,Shat)
dat <- mash_set_data(out$Bhat,out$Shat,V = V,alpha = 1)
fit <- ud_init(dat)
all.equal(dat$V,fit$V,check.attributes = FALSE)
#> [1] TRUE
# By setting alpha = 0, we instead use the EE ("exchangeable effects")
# model. Now the residual covariance is different for each effect, and
# these covariances are now stored in a list with one element for
# every row of Bhat (or X).
dat <- mash_set_data(out$Bhat,out$Shat,V = V,alpha = 0)
fit <- ud_init(dat)
class(fit$V)
#> [1] "list"
# Again we use the EE model, but since the standard errors are the
# same for all the effects (and the s.e.'s are equal to 1), the
# residual covariance matrix is now the same for all effects.
dat <- mash_set_data(out$Bhat,Shat = 1,V = V,alpha = 0)
fit <- ud_init(dat)
all.equal(dat$V,fit$V,check.attributes = FALSE)
#> [1] TRUE
# In this final example, we customize the mixture prior to include
# different numbers of unconstrained and rank-1 matrices.
fit <- ud_init(dat,n_rank1 = 2,n_unconstrained = 4)
names(fit$U)
#> [1] "indep" "equal" "rank1_1" "rank1_2"
#> [5] "unconstrained1" "unconstrained2" "unconstrained3" "unconstrained4"