dmm-internal: Functions internal to dmm
In dmm: Dyadic Mixed Model for Pedigree Data

dmm-internal

R Documentation

Functions internal to dmm

Description

Internal functions not documented and not visible to user.

Usage

am.arel(df)
am.mcrel(df)
am.pyrel(df)
am.zandrel(mdf, df, k, l, x, y, cohortparts, components,
     specific.components, relmat, ctable)
combpaste(x, y)
comtopar(v, l, siga, vara, vsiga, sesiga, ctable)
comtopar.specific(v, l, thisvc, vara, vthisvc, sethisvc, ctable,
     ic, phencovclass, longrownames, siga)
countnotna(x)
covcit(v, l, iv, vsiga, il, jl)
covlyz(cyz, y, z)
covtopar(covg, covt)
covt(v, l, vsiga, il, jl, kl)
crossclasscovtopar(covx, covt, var1, var2)
crosseffectcovtopar(covx, covt, varcomp, var1, var2)
csummary_specific(object, traitset, componentset, bytrait, gls, digits, ...)
dae.nonspecific.I(zpre, zpost, mmat, componentname, cnames, cnamesie,
     emat, vmat, icol, iecol, gls)
dae.nonspecific(zpre, rel, zpost, mmat, componentname, cnames, cnamesie,
     emat, vmat, icol, iecol, gls)
dae.nonspecific.S(zpre1, zpost1, zpre2, zpost2, zop, mmat,
     componentname, cnames, cnamesie, emat, vmat, icol, iecol, gls)
dae.specific.I(zpre, zpost, mmat, factorno, componentname,
     effnames, effcodes, effnandc, comcodes, varcodes, cnames, cnamesie,
     emat, vmat, icol, iecol, gls, ctable)
dae.specific(zpre, rel, zpost, mmat, factorno, componentname,
     effnames, effcodes, effnandc, comcodes, varcodes, cnames, cnamesie,
     emat, vmat, icol, iecol, gls, ctable)
dae.specific.S(zpre1, zpost1, zpre2, zpost2, zop, mmat, factorno,
     componentname, effnames, effcodes, effnandc, comcodes, varcodes,
     cnames, cnamesie, emat, vmat, icol, iecol, gls, ctable)
dmesolve(mdf, fixform = Ymat ~ 1, components = c("VarE(I)", "VarG(Ia)"),
     specific.components = NULL, cohortform = NULL, posdef = T, gls = F,
     glsopt = list(maxiter = 200, bdamp = 0.8, stoptol = 0.01),
     dmeopt = "qr", ncomp.pcr = "rank", relmat = "inline",
     dmekeep = F, dmekeepfit = F)
dmm_array(mdf, fixform, components, specific.components, cohortform,
     posdef, gls, glsopt, dmeopt, ncomp.pcr, relmat, dmekeep, dmekeepfit)
dmm_blockarray(mdf, fixform, components, specific.components, cohortform,
     posdef, gls, glsopt, dmeopt, ncomp.pcr, relmat, dmekeep, dmekeepfit, ...)
duplicated_first(x)
dyad.am.expect(am, gls, dmeopt)
expect.v(am, siga, dme.explist)
fixpaste(x, fix)
genericvarvcnames(x)
genericvcnames(x)
gls.b.gmat(am, vinv, dme.explist, vmatblock)
gls.iter.b(am, start.b, start.siga, dyad.explist, glsopt, dmeopt, ctable,
     ncomp.pcr, dmekeepfit)
gsummary_specific(dmmobj, traitset, componentset, bytrait, gls, digits, ...)
is.cecov(x, allcovs)
is.crossclass(x)
is.specific(x)
is.var(x)
is.withinclass(x)
make.cohort(df, ce)
make.csummarytables(object, traitset = "all",
     componentset = "all", bytrait = T, gls = F, digits = 3)
make.gsummarytables(dmmobj, traitset = "all",
     componentset = "all", bytrait = T, gls = F, digits = 3)
make.matcline(df)
make.patyline(df)
match.cecov.nonspecific(longrowname)
match.cecov.specific(longrowname)
match.var.specific(longrowname)
part.add(x, p, prow, pcol)
partocov(par, covt)
permpaste(x)
phenclasses(comcodes)
selfpaste(x)
separ(siga, vsiga, v, l, fracta, correa)
siga.posdef(siga, am, ctable, varopt = "both", covopt = "bound")
siga.posdef.specific(siga, am, ctable, varopt = "both", covopt = "bound")
sigatoie(cnames,cnamesie,siga,vsiga,sesiga,am,nsf)
sigatovc(siga, vsiga, sesiga, am, nsf)
traitpairstotraits(tp)
varlz(vz, z)
varz(vlz, z)
vt(v, l, vsiga, il, jl)