AnalyzeCrossesMM: Multimodel Analysis of Line Cross Data

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/AnalyzeCrossesMM.R

Description

Analyze all possible genetic architecture models based on mean phenotypes from line cross data.

Usage

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AnalyzeCrossesMM(data, Cmatrix = "XY", model.sum = .95,
                 max.models = 300000, even.sex = F, graph=F, 
                 cex.axis=1, cex.names=1, cex.main=1, max.pars = NULL)

Arguments

data

a data frame with the first three columns: 1) id of the cohort this must mach the coefficient row of the c-matrix 2) mean phenotype measure of the cohort 3) Standard error of the cohort's mean phenotype

Cmatrix

A text string used to select the mid-parent scaled c-matrix to be used in the analysis. Included options are "XY", "XO", "ZW", "ZO", or "esd". If you need a different matrix you can supply it here as well.

model.sum

This is the sum of the probability of the models to be included

max.models

The maximum number of fitted models to return from the function. This is included as an option to allow analysis of large model space on computers with limited RAM.

even.sex

A logical by default it is false. It should be set as true if either sexed cohorts are included or if mixed sex cohorts are included but have equal numbers of males and females.

max.pars

Optional parameter limiting the size of the equations evaluated.

graph

Logical indicating whether a plot of results should be produced

cex.axis

expansion factor for numeric axis labels.

cex.names

expansion factor for CGE labels.

cex.main

expansion factor for main title.

Details

Provides model averaged estimates of the contribution of composite genetic effects to the line means in line cross analysis experiments. Using AICc models are given weights and these are used to construct a confidence model set that allows for parameter estimates and errors to include model selection uncertainty. (see Burnham and Anderson 2002). The vignette contains a lengthy discussion of

Value

Returns a "genarch" object which is a list with the following elements:

models:

a list containing the weighted least squares solution for all models tested.

estimates:

a data frame containing Model Weighted Average for each parameter and its unconditional standard error.

daicc:

a vector of the delta AICc scores for all models tested.

varimp:

a data frame containing the variable importance scores for composite effects.

Author(s)

Heath Blackmon and Jeffery P. Demuth

References

Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: a practical information-theoretic approach. Springer.

See Also

VisModelSpace

Examples

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data(PH)
results <- AnalyzeCrossesMM(PH)

coleoguy/SAGA documentation built on June 12, 2017, 6:35 a.m.