Description Format Details Source

The `pwrFDR`

package currently incorporates 3 distribution types,
normal, t and F. The first two of these are strictly for statistics formed
from two group comparison while the third is for statistics formed from the
omnibus test of any difference among an arbitrary number of groups >=2. The
structure is general and user expandable. One must specify the density,
CDF and quantile function for a given distribution and its parameters under
the null and under the alternative. These parameters must be expressions
to be evaluated inside the kernel of the power program, functions of the
arguments `n.sample`

, `groups`

and `effect.size`

. This is
not used directly by the user at all unless she (he) wants to add a
distribution type.

A data frame with 3 observations on the following 6 variables.

`pars0`

a list vector having components 'c(nd, p1, p2, ...)' where 'nd' is the distribution number starting with 0, and p1, p2, ..., are paramters of the distribution, which are functions of 'n.sample', 'groups' and 'effect.size' as mentioned above. These must be expressed as a call e.g. as.call(expression(c, nd, p1, p2, ...)) etc. 'pars0' are the parameters under the null.

`pars1`

a list vector. See directly above. Parameters under the alternative.

`minv`

a list vector with components given the values -Inf or 0, which will be used to decide if the two sided corrections are used or not.

`ddist`

a list vector with components set to functions, each one computing the probability density function corresponding to the particular distribution. A function of arguments 'x' and 'par'. See details below.

`pdist`

a list vector with components set to the functions, each one computing the cumulative distribution function corresponding to the particular distribution. A function of arguments 'x' and 'par'. See details below.

`qdist`

a list vector with components set to the functions, each one computing the quantile function (inverse cumulative distribution function) corresponding to the particular distribution. A function of arguments 'x' and 'par'. See details below.

The instance shipped with the current version of the package was created using
the following commands. Read this and you'll figure out how to add a distribution
type yourself.

"dists" <-

as.data.frame(rbind(

`### Normal with 2 groups ###`

c(pars0=as.call(expression(c,0,ncp=0,sd=1)),

pars1=as.call(expression(c,0,ncp=(n.sample/2)^0.5*effect.size, sd=1)),

minv=-Inf,

ddist=function(x, par) dnorm(x, mean=par[2], sd=par[3]),

pdist=function(x, par) pnorm(x, mean=par[2], sd=par[3]),

qdist=function(x, par) qnorm(x, mean=par[2], sd=par[3])),

`### t with 2 groups ###`

c(pars0=as.call(expression(c,1,ncp=0, ndf=2*n.sample - 2)),

pars1=as.call(expression(c,1,ncp=(n.sample/2)^0.5*effect.size, ndf=2*n.sample - 2)),

minv=-Inf,

ddist=function(x, par) dt(x, ncp=par[2], df=par[3]),

pdist=function(x, par) pt(x, ncp=par[2], df=par[3]),

qdist=function(x, par) qt(x, ncp=par[2], df=par[3])),

`### F with 'groups' groups, effect.size=theta*c(0, 0.5, 0.5, ..., 0.5, 1) ###`

c(pars0=as.call(expression(c,2,ncp=0, ndf1=groups-1, ndf2=groups*(n.sample-1))),

pars1=as.call(expression(c,2,ncp=n.sample*effect.size^2/2, ndf1=groups-1, ndf2=groups*(n.sample-1))),

minv=0,

ddist=function(x, par) df(x, ncp=par[2], df1=par[3], df2=par[4]),

pdist=function(x, par) pf(x, ncp=par[2], df1=par[3], df2=par[4]),

qdist=function(x, par) qf(x, ncp=par[2], df1=par[3], df2=par[4]))))

This isn't 'data' data, its a kind of a 'family' object.

Izmirlian G. (2017) Average Power and *λ*-power in
Multiple Testing Scenarios when the Benjamini-Hochberg False
Discovery Rate Procedure is Used. arXiv:1801.03989

pwrFDR documentation built on May 2, 2019, 7:53 a.m.

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