Description Usage Arguments Details Value References See Also Examples

Load data, estimate a linkage map and plot diagnostics for the fit.

1 2 3 |

`fname` |
Character string containing the base file from which the data
should be read - should contain the complete file name excluding the suffix
which should be |

`p` |
Smoothing parameter. |

`n` |
Vector of integers or character strings containing the name or position in the input list of loci to be excluded from the analysis. |

`ispc` |
Logical determining the method to be used to estimate the map. By
default this is |

`ndim` |
Integer the number of dimensions to use if the Principal curves method is used. By default this is 2, but it can also be 3. |

`weightfn` |
Character string specifying the values to use for the weight
matrix in the MDS |

`mapfn` |
Character string specifying the map function to use on the
recombination fractions |

`D1lim` |
Numeric vector specifying the limits of the axis relating to dimension 1 of the wMDS used to estimate the map. |

`D2lim` |
Numeric vector specifying the limits of the axis relating to dimension 1 of the wMDS used to estimate the map. |

`D3lim` |
Numeric vector specifying the limits of the axis relating to dimension 1 of the wMDS used to estimate the map. |

`displaytext` |
Logical argument determining how markers should be labelled
in the wMDS configuration plot. If |

Data is read from a text file which should be of the form described below.
By default, `ispc=TRUE`

, in which case maps are estimated using unconstrained
weighted MDS followed by fitting a principal curve. Details can be found in
the description of the function `calc.maps.pc`

. If `ispc=FALSE`

maps are estimated using spherically constrained weighted MDS. Details can be
found in the description of the function `calc.maps.sphere`

.

`ndim`

is only relevant if `ispc=TRUE`

, in which case it specifies the number of
dimensions to be used, the default is 2 but it can also be 3 dimensions.

Diagnostic plots are then produced using `plot.pcmap`

for the method of
principal curves in 2 dimensions, `plot.pcmap3d`

for the method of principal
curves in 3 dimensions and `plot.spheremap`

for the method using spherically
constrained MDS.

`n`

specifies markers to be omitted from the analysis. It can be a vector of
character strings specifying makers to be omitted, or a vector of integers
specifying the markers to omit. The latter method is likely to be useful when
removing outliers after inspection of the diagnostic plot, because the output
contains a dataframe, locikey, which associates each marker with its
identifying number. By default this is NULL and all markers in the file will
be analysed.

`p`

is a smoothing parameter which operates quite differently depending on
whether map estimation is performed using Principal Curves or Constrained
MDS. If the PC method is used, `p`

determines the smoothing parameter spar in
the function `principal_curve`

from the package
princurve. If `NULL`

then the most appropriate value will be determined
using leave one out cross validation.
If Constrained MDS is used then `p`

must be set to a number which specifies the
penalty for deviations from the sphere in the function `smacofSphere`

from the
smacof package. Something between 50 and 100 is generally appropriate and this
penalty can be decreased if stress from the constrained analysis is more than
about 10
for details)

File names should be of the form `fname.txt`

and it is assumed that they are in
a tab or space separated file of the format displayed below. The first entry on
the first row is the number of markers to be analysed. Underneath this is a
table in which the first two columns contain marker names, the third column
contains the pairwise recombination fractions between the markers and the
fourth column the associated LOD score. Note that marker names in the first
column vary more slowly than in the second column. Missing recombination pairs
are acceptable. Recombination fractions greater than 0.499999 are set to that
value.

`nmarkers` | |||

`marker_1` | `marker_2` | `recombination fraction` | `LOD` |

`1` | `2` | `.` | `.` |

`1` | `3` | `.` | `.` |

`1` | `4` | `.` | `.` |

`.` | `.` | `.` | `.` |

`.` | `.` | `.` | `.` |

`.` | `.` | `.` | `.` |

`2` | `3` | `.` | `.` |

`2` | `4` | `.` | `.` |

`.` | `.` | `.` | `.` |

map (s3 class pcmap, pcmap3d or spheremap) from `calc.maps.pc`

if `ispc=TRUE`

or
`calc.maps.sphere`

if `ispc=FALSE`

.

de Leeuw J, Mair P (2009) Multidimensional scaling using majorization: SMACOF in R. J Stat Softw 31: 1-30 http://www.jstatsoft.org/v31/i03/

Hastie T, Weingessel A, Bengtsson H, Cannoodt R (1999) princurve: Fits a Principal Curve in Arbitrary Dimension. ) R package version 2.1.2. https://CRAN.R-project.org/package=princurve

`smacofSphere`

, `principal_curve`

, `calc.maps.pc`

, `calc.maps.sphere`

, `plot.pcmap`

, `plot.pcmap3d`

, `plot.spheremap`

1 2 | ```
estimate.map(system.file("extdata", "lgI.txt", package="MDSMap"),
ndim=3)
``` |

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