gsummary.dmm {dmm}

Make summary tables of genetic parameters for a dmm object

Description

Extracts the genetic parameters from an object of class dmm, for the specified set of traits and set of components. Makes tables of parameters ordered either by trait or by component. Tables include parameter estimate, its standard error, and its 95 percent confidence limits. Parameters are grouped intp class-specific classes, if there are any class-specific components fitted.

Usage

## S3 method for class 'dmm'
gsummary(dmmobj, traitset = "all", componentset = "all", bytrait = T,
                    gls = F, digits = 3, ...)

Arguments

dmmobj

An object of class dmm. Genetic parameters are obtained from this object.

traitset

A vector containing the names of the subset of traits for which tables of genetic parameters are to be constructed. Default is "all" which means all traits present in object dmmobj.

componentset

A vector containing the names of the subset of (co)variance components for which tables of genetic parmeters are to be constructed. Default is "all" which means all (co)variance components present in object dmmobj.

bytrait

Logical flag: should the tables of genetic parameters be constructed with trait varying least rapidly from line to line? If TRUE each subtable contains parameters for one trait or traitpair and for all components. If FALSE each subtable contains parameters for one component and for all traits or traitpairs.

gls

Logical flag: should the parameter estimates by GLS-b method be tabled in addition to the parameter estimates by OLS-b method? Default is gls=FALSE. The GLS-b parameters can only be tabled if object dmmobj contains the attribute gls, that is if dmmobj was constructed by a dmm() call with argument gls=TRUE.

digits

Number of digits for output. This is returned as part of the return value for use by the S3 print function print.gsummary.dmm().

...

Ellipsis argument.

Details

This is a long printout with estimates, standard errors and confidence limits, arranged in tables with one estimate per line. It is the analog of csummary() for a dmm object, but with genetic parameters instead of variance components. In the case of class-specific parameters, the parameters are listed in class groups, so that the proportions of variance sum to unity within each group. In the case where all parameters are non-specific, there is just one class group.

Value

An object of class gsummary.dmm which is a list containing the following items:

ftables

A list of dataframe objects each containing one subtable of estimates of the proportion of variance attributable to components, along with the appropriate standard errors and confidence limits. Based on OLS-b component estimates.

rtables

A list of dataframe objects each containing one subtable of estimates of the correlation coefficient attributable to components, along with the appropriate standard errors and confidence limits. Based on OLS-b component estimates.

ptables

A single dataframe object containing estimates of the phenotypic (co)variance. Note that these are based on all components, even if a subset of components is specified in argument componentset. Based on OLS-b component estimates.

gftables

A list of dataframe objects each containing one subtable of estimates of the proportion of variance attributable to components, along with the appropriate standard errors and confidence limits. Based on GLS-b component estimates. Only present if argument gls=TRUE.

grtables

A list of dataframe objects each containing one subtable of estimates of the correlation coefficient attributable to components, along with the appropriate standard errors and confidence limits. Based on GLS-b component estimates. Only present if argument gls=TRUE.

gptables

A single dataframe object containing estimates of the phenotypic (co)variance. Note that these are based on all components, even if a subset of components is specified in argument componentset. Based on GLS-b component estimates. Only present if argument gls=TRUE.

traits

A vector of traitnames as specified in argument traitset.

components

A vector of component names as specified in argument componentset.

bytrait

Logical flag: as specified in argument bytrait.

gls

Logical flag: as specified in argument gls.

digits

A numeric value, as specified in argument digits.

call

The function call

Note

There is no provision to constrain the 95 percent confidence limits for parameter estimates. Hence for small samples, these may vary outside the bounds for the parameter.

Author(s)

Nevillle Jackson

See Also

Function print.gsummary.dmm().

Examples

# get some data
data(sheep.df)
# prepare it - only need "E" and "A" relationship matrices
sheep.mdf <- mdf(sheep.df,pedcols=c(1:3),factorcols=c(4:6),ycols=c(7:9),
             sexcode=c("M","F"),relmat=c("E","A"))
# estimate genetic parameters - individual and maternal
sheep.fit5 <- dmm(sheep.mdf, Ymat ~ 1 + Year + Tb + Sex,
              components=c("VarE(I)","VarG(Ia)","VarE(M)","VarG(Ma)",
              "CovG(Ia,Ma)","CovG(Ma,Ia)"))
# look just at parameter "VarG(Ma)" across all traits
gsummary(sheep.fit5,componentset="VarG(Ma)",bytrait=FALSE)
# look just at trait "Cww"
gsummary(sheep.fit5,traitset="Cww")
# cleanup
rm(sheep.df)
rm(sheep.mdf)
rm(sheep.fit5)