Description Usage Arguments Details Value Author(s) References Examples
For a given sequence of ordered markers, computes the multipoint likelihood of alternative orders, by shuffling subsets (windows) of markers within the sequence. For each position of the window, all possible (ws)! orders are compared.
1 2 3 | ripple_2pops(markers_names, data_P1, data_P2, twopts_POP1, twopts_POP2,
LOD = 3, max.rf = 0.5, log10.mintol = -6, max_it = 50,
window = 3)
|
data_P1 |
an onemap object class with the information of the first population. |
data_P2 |
an onemap object class with the information of the second population. |
twopts_POP1 |
an rf_2pts object with the information of rf from the first population. |
twopts_POP2 |
an rf_2pts object with the information of rf from the second population. |
LOD |
minimum LOD Score to declare linkage (defaults to |
max.rf |
maximum recombination fraction to declare linkage (defaults
to |
log10.mintol |
log base 10 of the minimum tolerance to converge the EM (defaults to |
max_it |
maximum number of iteration to converge the EM (defaults to |
window |
an integer specifying the length of the window size (defaults to 4). |
marker_names |
an vector of strings with the marker order. |
Large values for the window size make computations very slow, specially if there are many partially informative markers.
A list with two objects. The first is a matrix with the tested ordered markers, the second is a vector with the respectivally log-likelihood
Rodrigo R Amadeu rramadeu@gmail.com and Marianella Quezada marianellaquezada@gmail.com
Quezada et al. 2018
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | notrun{
data(onemap2pop)
#Extracting the marker names:
order_LG1POP1 <- colnames(POP1.geno$geno)[LG1_POP1_final$seq.num]
#RIPPLE algorithm:
LG1_POP1order <- ripple_2pops(markers_names = order_LG1POP1,
data_P1 = POP1_geno,
data_P2 = POP2_geno,
rftwopoints_P1 = twopts_POP1,
rftwopoints_P2 = twopts_POP2,
LOD = 3,
max.rf = 0.5,
log10.mintol = -6,
max_it = 50,
window = 4)
}
|
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