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Prepare Input Files for MStrat

Source: R/prep_mstrat_input.R
prep_mstrat_input.Rd

Prepare input files for MStrat, a software for building germplasm core collections by maximizing allelic or phenotypic richness (Schoen and Brown 1993; Gouesnard et al. 2001; Gouesnard et al. 2002) .

Usage

prep_mstrat_input(
  data,
  genotype,
  qualitative,
  quantitative,
  active,
  target,
  center = TRUE,
  scale = TRUE,
  weights.qualitative = NULL,
  weights.quantitative = NULL,
  nclass.quantitative = NULL,
  always.selected = NULL,
  file.name = "MStrat_input",
  folder.path = getwd()
)

Arguments

data

The data as a data frame object. The data frame should possess columns with the genotype names and multiple quantitative and/or qualitative trait/variable data.

genotype

Name of column with the genotype names as a character string.

qualitative

Name of columns with the qualitative traits as a character vector.

quantitative

Name of columns with the quantitative traits as a character vector.

active

Name of traits/variables to be declared as active.

target

Name of traits/variables to be declared as target.

center

either a logical value or numeric-alike vector of length equal to the number of columns of x, where ‘numeric-alike’ means that as.numeric(.) will be applied successfully if is.numeric(.) is not true.

scale

either a logical value or a numeric-alike vector of length equal to the number of columns of x.

weights.qualitative

An vector of weight to be applied on a qualitative traits. Should be NULL or a numeric vector of the same length as the number of qualitative traits. If NULL, the default weight of 1 is given.

weights.quantitative

An vector of weight to be applied on a quantitative traits. Should be NULL or a numeric vector of the same length as the number of quantitative traits. If NULL, the default weight of 1 is given.

nclass.quantitative

The number of classes into which each quantitative trait data have to be divided into. Should be NULL or a integer vector of the same length as the number of quantitative traits. If NULL, the default of 5 is applied. MStat limits the maximum number of classes to 1000.

always.selected

A character vector with names of individuals in the genotype that should always be selected in the core collection. The maximum length accepted by MStrat is 500.

file.name

A character string of name of file where the data will be saved.

folder.path

The path to folder where the input files are to be saved.

References

Gouesnard B, Bataillon TM, Decoux G, Rozale C, Schoen DJ, David JL (2001). “MSTRAT: An algorithm for building germ plasm core collections by maximizing allelic or phenotypic richness.” Journal of Heredity, 92(1), 93–94.

Gouesnard B, Bataillon TM, Decoux G, Rozale C, Schoen DJ, David JL (2002). “MStrat Documentation 1.1.” Evolutionary genomics and population management (GE\(^{\textrm{2}}\)pop), Institut Amelioration Genetique et Adaptation des Plantes mediterraneennes et tropicales (agAp Institute), CIRAD, Montpellier, France.

Schoen DJ, Brown AH (1993). “Conservation of allelic richness in wild crop relatives is aided by assessment of genetic markers.” Proceedings of the National Academy of Sciences, 90(22), 10623–10627.

Examples


library(EvaluateCore)
#> 
#> --------------------------------------------------------------------------------
#> Welcome to EvaluateCore version 0.1.3
#> 
#> 
#> # To know whats new in this version type:
#>   news(package='EvaluateCore')
#>   for the NEWS file.
#> 
#> # To cite the methods in the package type:
#>   citation(package='EvaluateCore')
#> 
#> # To suppress this message use:
#>   suppressPackageStartupMessages(library(EvaluateCore))
#> --------------------------------------------------------------------------------

data(cassava_EC)
data <- cassava_EC

quant <- c("NMSR", "TTRN", "TFWSR", "TTSW", "TTPW", "AVPW",
           "ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "LFRT", "LBTEF", "CBTR", "NMLB",
          "ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
          "PSTR")

# Prepare genotype column
data$Accession <- rownames(data)
rownames(data) <- NULL
data$Accession <- as.factor(data$Accession)

# Convert qualitative data as factors
data[, qual] <- lapply(data[, qual],
                       function(x) factor(as.factor(x)))

active = c("LNGS", "LFRT", "LBTEF", "CBTR", "NMLB",
           "ANGB", "CUAL9M", "LVC9M", "TNPR9M",
           "TTRN", "TFWSR", "TTSW", "TTPW", "AVPW")
target = c("NMSR", "TTRN", "ARSR", "SRDM",
           "CUAL", "LNGS", "TNPR9M",
           "PL9M", "STRP", "STRC",
           "PSTR")

sel <- c("TMe-2906", "TMe-3412", "TMe-1374", "TMe-768", "TMe-14",
         "TMe-3284", "TMe-937", "TMe-1859", "TMe-3265", "TMe-1739",
         "TMe-972", "TMe-769", "TMe-3243", "TMe-3719", "TMe-1095",
         "TMe-893", "TMe-1262", "TMe-2083", "TMe-376", "TMe-3633",
         "TMe-1738", "TMe-2428", "TMe-259", "TMe-3457", "TMe-1406",
         "TMe-977", "TMe-3006", "TMe-925", "TMe-3671", "TMe-2779",
         "TMe-1293", "TMe-268", "TMe-266", "TMe-3562", "TMe-801")

prep_mstrat_input(data = data, genotype = "Accession",
                  qualitative = qual, quantitative = quant,
                  active = active, target = target,
                  center = TRUE, scale = TRUE,
                  weights.qualitative = NULL,
                  weights.quantitative = NULL,
                  nclass.quantitative = NULL, always.selected = sel,
                  file.name = "MStrat_input",
                  folder.path = tempdir())
#> The following MStrat input files created at /var/folders/2s/h6hvv9ps03xgz_krkkstvq_r0000gn/T//RtmpnQmFgO:
#> MStrat_input_data.dat
#> MStrat_input_variable.var
#> MStrat_input_kernel.ker