magic.eval | R Documentation |
This function takes various input arguments from user and simulates the desired MAGIC design. The MAGIC population can be designed by either providing a pedigree or other input arguments. Depending on the input arguments, a full, partial, basic or NP2 MAGIC design can be created. For the partial or NP2 designs, the funnels can be generated in either a balanced or unbalanced (random) way. For further information, please refer to the vignette.
magic.eval( ped = NULL, n = NULL, m = NULL, reps = NULL, self = NULL, inbred = TRUE, balanced = FALSE, minimize = FALSE, n.try = 1000, addx = NULL, repx = 1, selfx = 3, marker.dist = 0.01, chr.len = c(1, 2), n.sim = 1, hap.int = 0.05, n.hap = 1, keep = FALSE )
ped |
a pedigree with 4 columns: individual ID, parent 1 ID, parent 2 ID, generation number, in the format of either matrix or data.frame. |
n |
an integer of number of founders. |
m |
an integer of number of funnels ( |
reps |
a vector of replicates in each crossing generation. |
self |
a vector of number of generations to self after crossing. |
inbred |
a logical indicator of whether the founders are inbred. |
balanced |
a logical indicator of whether a balanced partial design is desired. |
minimize |
a logical indicator of whether to minimize crossing. |
n.try |
an integer of number of attempts to find balanced partial design (ignored if |
addx |
an integer of either 1 or 2 indicating the type of additional crosses. |
repx |
an integer of number of replicates in the additional crossing. |
selfx |
an integer of number of generations to self after additional crossing. |
marker.dist |
a numerical value of marker distance in Morgan. |
chr.len |
a vector of chromosome lengths in Morgan. |
n.sim |
an integer of number of simulations. |
hap.int |
a numerical value of marker interval for evaluating haplotypes. |
n.hap |
an integer of 1 or 2 haploid marker data of each RIL are used. |
keep |
a logical indicator of whether to export the marker data. |
a list of simulation summary.
mpop <- magic.eval(n=8, m=1, reps=c(1,1,2), self=c(0,0,3), balanced=TRUE)
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