Description Usage Arguments Value Examples

Performs multiple–typical–steps in a memgene analysis of genetic distance data. Gracefully handles potential errors. Steps are as follows:

1. Find MEM eigenvectors given
coordinates (`coords`

)

2. Perform separate forward selections of positive and
negative MEM eigenvectors against genetic distance (`genD`

),
to identify a significant
subset, using parameters `forwardPerm`

as the number of
permutations and `forwardAlpha`

as the alpha level
for a significant eigenvector. NOTE: The number of permutations
`forwardPerm`

is set at 100 by default to reduce analysis time for
exploratory analyses. This number should be increased for final analyses.

3. Find the fit of the selected eigenvectors to the
genetic distance data (using RDA).

4. Optionally run a permutation test (`finalPerm`

) for
the fit of the selected eigenvectors to the genetic distance
data.

5. Produce MEMGENE variables using the fitted values from the RDA analysis. MEMGENE variables are the eigenvectors from a PCA of the fitted values. These are the product of memgene and can be used for visualization and subsequent analyses.

6. Optionally produce plots of the scores for the
first `n`

MEMGENE variables if `doPlot = n`

.

1 | ```
mgQuicker(geoD, genD)
``` |

`genD` |
A symmetrical distance matrix giving the genetic distances among individual genotypes or populations |

`geoD` |
A symmetrical distance matrix giving the geographical distances among individual. |

A list

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## Not run:
## Prepare the radial data for analysis
radialData <- read.csv(system.file("extdata/radial.csv", package="memgene"))
radialGen <- radialData[, -c(1,2)]
radialXY <- radialData[, 1:2]
if (require(adegenet)) {
radialDM <- codomToPropShared(radialGen)
} else {
stop("adegenent package required to produce genetic distance matrix in example.")
}
## Run the MEMGENE analysis
radialAnalysis <- mgQuicker(radialDM, radialXY)
## Extract the scores on the first 3 MEMGENE variables
## for subsequent analysis
radialMEMGENE1 <- radialAnalysis$memgene[, 1]
radialMEMGENE2 <- radialAnalysis$memgene[, 2]
radialMEMGENE3 <- radialAnalysis$memgene[, 3]
## Find the proportion of variation explained by all MEMGENE variables
propVariation <- radialAnalysis$sdev/sum(radialAnalysis$sdev)
## End(Not run)
``` |

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