pedigree_ll_terms  R Documentation 
Constructs an object needed for eval_pedigree_ll
and
eval_pedigree_grad
.
pedigree_ll_terms(data, max_threads = 1L, n_sequences = 8L) pedigree_ll_terms_loadings(data, max_threads = 1L, n_sequences = 8L)
data 

max_threads 
maximum number of threads to use. 
n_sequences 
number of randomized quasiMonte Carlo sequences to use. More samples yields a better estimate of the error but a worse approximation. Eight is used in the original Fortran code. If one is used then the error will be set to zero because it cannot be estimated. 
An intercept column is not added to the X
matrices
like what lm.fit
and glm.fit
do.
Thus, it is often important that the user adds an intercept column
to these matrices as it is hardly ever justified to not include the
intercept (the exceptions being e.g. when splines are used which include
the intercept and with certain dummy designs). This equally holds for
the Z
matrices with pedigree_ll_terms_loadings
.
pedigree_ll_terms_loadings
relax the assumption that the scale
parameter is the same for all individuals. pedigree_ll_terms_loadings
and pedigree_ll_terms
yield the same model if "Z"
is an
intercept column for all families but with a different parameterization.
In this case, pedigree_ll_terms
will be
faster. See vignette("pedmod", "pedmod")
for examples of using
pedigree_ll_terms_loadings
.
# three families as an example fam_dat < list( list( y = c(FALSE, TRUE, FALSE, FALSE), X = structure(c( 1, 1, 1, 1, 1.2922654151273, 0.358134905909256, 0.734963997107464, 0.855235473516044, 1.16189500386223, 0.387298334620742, 0.387298334620742, 1.16189500386223), .Dim = 4:3, .Dimnames = list( NULL, c("(Intercept)", "X1", ""))), rel_mat = structure(c( 1, 0.5, 0.5, 0.125, 0.5, 1, 0.5, 0.125, 0.5, 0.5, 1, 0.125, 0.125, 0.125, 0.125, 1), .Dim = c(4L, 4L)), met_mat = structure(c(1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1), .Dim = c(4L, 4L))), list( y = c(FALSE, FALSE, FALSE), X = structure(c( 1, 1, 1, 0.0388728997202442, 0.0913782435233639, 0.0801619722392612, 1, 0, 1), .Dim = c(3L, 3L)), rel_mat = structure(c( 1, 0.5, 0.125, 0.5, 1, 0.125, 0.125, 0.125, 1), .Dim = c(3L, 3L)), met_mat = structure(c( 1, 1, 0, 1, 1, 0, 0, 0, 1), .Dim = c(3L, 3L))), list( y = c(TRUE, FALSE), X = structure(c( 1, 1, 0.305275750370738, 1.49482995913648, 0.707106781186547, 0.707106781186547), .Dim = 2:3, .Dimnames = list( NULL, c("(Intercept)", "X1", ""))), rel_mat = structure(c(1, 0.5, 0.5, 1), .Dim = c(2L, 2L)), met_mat = structure(c(1, 1, 1, 1), .Dim = c(2L, 2L)))) # get the data into the format needed for the package dat_arg < lapply(fam_dat, function(x){ # we need the following for each family: # y: the zeroone outcomes. # X: the design matrix for the fixed effects. # scale_mats: list with the scale matrices for each type of effect. list(y = as.numeric(x$y), X = x$X, scale_mats = list(x$rel_mat, x$met_mat)) }) # get a pointer to the C++ object ptr < pedigree_ll_terms(dat_arg, max_threads = 1L) # get the argument for a the version with loadings dat_arg_loadings < lapply(fam_dat, function(x){ list(y = as.numeric(x$y), X = x$X, Z = x$X[, 1:2], scale_mats = list(x$rel_mat, x$met_mat)) }) ptr < pedigree_ll_terms_loadings(dat_arg_loadings, max_threads = 1L)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.