ptycho.all.
createData(X, y, omega = NULL, beta = NULL)
createDataBayesModel(mode = c("exchange","pleiotropy","gene"), n, p, q, nreps, tau.min, tau.max, G)
createPubData(mode = c("tinysim","ptychoIn", "exchange","pleiotropy","gene", "actualGeno","actualPheno","corTest", "fixedOmega","uniformEffects"), X=NULL, y=NULL, var.detail=NULL, variants=NULL)createPubData, X is ignored
unless mode is “actualGeno”, “actualPheno”, or
corTest.nreps
q
sd
In createPubData, y is ignored unless mode is
“actualPheno”.
omega; see Details for its meaning. If this
is a list, the first entry is a function name and the second is a list of
arguments to the function, which will be prepended by the number of rows in
output X and the number of columns in output y. Only used if
y is a list.y$sd. Only used if y is a list.tinysimpleiotropy except that the dataset is tiny,
with n=100, p=10, q=5, and nreps=10
ptychoIngene except that the dataset is tiny,
with n=3000, p=10, q=1, and nreps=1
exchangeX and exchangeable
variants; n=5000, p=50, q=5, and nreps=100
pleiotropyX, and several variants
have nonzero effects on multiple responses; n=5000, p=50,
q=5, and nreps=100
geneX, and each group of variants
typically has either several or no variants that effect a response;
n=5000, p=50, q=5, and nreps=100
actualGenoX
corTestq=2 responses for input X.
There will be 10 replicates with the first variant in argument
variants causal for both responses, 10 with the second variant
causal, and 20 with variant i causal for response i. No
other variant will be causal.
actualPhenoX and y into data
object
fixedOmegaX, and each variant has
a certain probability of a nonzero effect size
uniformEffectsfixedOmega except that
effect sizes are uniformly rather than normally distributed
For createDataBayesModel, mode must be one of
“exchange”, “pleiotropy”, or “gene”.
taumode is not
“gene”X;
must have columns “MAF” and “GENE”. Ignored unless
mode is “actualGeno”.X;
ignored unless mode is “corTest”.NULL if
input y is numericomegabetay$nreps (length 1 if y is
numeric), each entry of which is a list with the following components:
omegacolnames(X) and column
names are colnames(y); NULL if input y is numericindic.varomega;
NULL if input y is numericbetaomega; NULL if input y is numericyeta2colnames(X) and
column names equal to colnames(y)createDataBayesModel with mode that uses a second level of
indicator variables, each entry in the replicate list also has
components omega.grp and indic.grp containing the intermediate
steps of drawing the second-level indicator variable before drawing
omega. If the argument beta to createData is
“createBetaNormal” (which it is when called by
createDataBayesModel), then each replicate will also have a component
tau giving the value drawn by a call to
runif(1, tau.min, tau.max).
createData and then describe its wrappers
createDataBayesModel and createPubData. Although createData can form the data object required by
ptycho.all when X and y are input, it primarily exists to
simplify simulating data from $Y=X*beta+epsilon$, where
$\epsilon$ is normal with mean zero and specified standard deviation and
$\beta$ is sparse with entries simulated as specified.
The function generates a specified number of replicates, all of which use the same design matrix $X$. If this matrix is not input, then its argument must specify a function call to generate it. In either case, suppose $X$ has $n$ rows and $p$ columns.
If the input y is numeric, then it will be used for the lone replicate.
If it is a matrix, it must have $n$ rows; let $q$ be its number of
columns. If input y is a numeric vector, it must have $n$ entries
and will be cast as a matrix with $q=1$ column. Otherwise, input y
is a list specifying, along with the arguments omega and beta,
how to simulate the response(s). Because it is useful in analysis of the
estimation of the marginal posterior distribution, the returned object always
contains, regardless of how X and y are specified, a matrix
eta2 with $(j,k)$ entry equal to
$
If y is to be simulated, the first step is to choose the probability
that each covariate is associated with each reponse as specified by the input
argument omega. If this argument is a matrix, it must have size
$p$-by-$q$. If it is not a matrix but is numeric, it will be passed
to matrix to create a matrix of the correct size. Otherwise,
the matrix for each replicate will be generated by calling the function whose
name is given by omega[[1]] with argument list
(p, q, omega[[2]]). This function must return a list with component
omega set to a $p$-by-$q$ matrix; the list may also contain
additional components. The package contains several functions whose names
start with “createOmega” that might guide users in writing their own
functions.
The next step is to draw a $p$-by-$q$ matrix indic.var whose
$(j,k)$ entry is equal to one with probability omega[j,k] and zero
otherwise. This matrix will be drawn until all column sums are positive.
For each entry in indic.var that is equal to one, the effect size must
be drawn. This is done by calling the function whose name is given by
beta[[1]] with argument list (indic.var, y$sd, beta[[2]]). This
function must return a list with component beta set to a
$p$-by-$q$ matrix; the list may also contain additional components.
If indic.var[j,k] is zero, then beta[j,k] should be zero. The
package contains functions whose names start with “createBeta” that
might guide users in writing their own functions.
Finally, an $n$-by-$q$ matrix of noise is drawn from
$N(0,\sigma^2)$, where $\sigma$ is the input noise.sd, and
added to $X*beta$ to obtain y. The column names of each
response matrix generated will be y1, y2, and so forth.
The function createPubData generates the data sets used in Stell and
Sabatti (2015). For mode equal to “exchange”,
“pleiotropy”, or “geno”, it calls createData via
createDataBayesModel; otherwise, it calls createData directly.
These functions also serve as additional examples of the use of
createData. For reproducibility, createPubData first sets the
random seed to 1234, except that it is set to 4 when mode equals
“ptychoIn” and it does not set it when mode equals
“corTest”.
In createDataBayesModel, if mode is “exchange”, then
one $omega ~ Beta(12,48)$ is drawn
independently for each trait. If mode is “pleiotropy”, then one
probability of association for a trait is drawn from Beta(16,55) for each data
set, that probability is used to draw indic.grp for each variant, and
then the probability of nonzero indic.var[j,k] is drawn from
Beta(48,12) for each nonzero indic.grp[j]. Finally, if mode is
“gene”, the process is analogous to pleiotropy except that each trait
is simulated independently.
createOrthogonalX, createGroupsSim;
also Data describes tinysim in example below as well as another
object output by createData
### EXAMPLE 1
data(tinysim)
# Data generated with mode equal to pleiotropy, so indic.grp exists and
# has an entry for each column in X.
colnames(tinysim$X)
tinysim$replicates[[5]]$indic.grp
# X4, X6, and X9 are associated with some responses.
tinysim$replicates[[5]]$indic.var
### EXAMPLE 2
# Generate miniature data set with information shared across covariates.
set.seed(1234)
tiny1 <- createDataBayesModel(mode="gene", n=100, p=10, q=5, nreps=10,
tau.min=0.045, tau.max=0.063, G=2)
# A covariate can only have indic.var=1 if the group it belongs to has
# indic.grp=1. For example,indic.grp[1,4]=0 implies
# indic.var[groups$group2var[1],4]=0.
tiny1$replicates[[1]]$indic.grp
tiny1$omega[[2]]$groups$group2var[1]
tiny1$replicates[[1]]$indic.var
### EXAMPLE 3
# Alternatively, call createData directly
groups <- createGroupsSim(G=2, p=10)
omegaargs <- list(indic.grp.shape1=16, indic.grp.shape2=55,
shape1=48, shape2=12, groups=groups)
betaargs <- list(tau.min=0.045, tau.max=0.063)
set.seed(1234)
tiny2 <- createData(X=list("createOrthogonalX", list(n=100, p=10)),
y=list(nreps=10, q=5, sd=1),
omega=list("createOmegaCrossVars", omegaargs),
beta=list("createBetaNormal", betaargs))
identical(tiny1, tiny2)
### SEE THE CODE FOR createPubData FOR MORE EXAMPLES.
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