Help for package plantmix

Title:

Genetic Study of Plant Mixtures

Version:

1.0.2

Maintainer:

Timothee Flutre <timothee.flutre@inrae.fr>

Description:

Fit linear mixed models dedicated to the genetic study of plant mixtures, such as those based on general and specific mixing abilities (GMA-SMA) as well as direct and social breeding values (DBV-SBV), also known as direct and indirect genetic effects (DGE-IGE). More details in Forst et al (2019, <doi:10.1016/j.fcr.2019.107571>) for GMA-SMA models, and Salomon et al (2026, <doi:10.64898/2026.03.27.714849>) for DBV-SBV models. The package also provides functions to optimize experimental designs, simulate data sets and compute interaction indices.

Depends:

R (≥ 4.0.0), ggplot2, lme4

Imports:

graphics, igraph, MASS, Matrix, methods, Rcpp (≥ 1.0.13), stats, TMB (≥ 1.9.17), utils

License:

AGPL-3

INRAE

Encoding:

UTF-8

RoxygenNote:

7.3.3

Suggests:

breedR, INLA, emmeans, knitr, MM4LMM, rmarkdown, testthat

Additional_repositories:

https://famuvie.github.io/breedR, https://inla.r-inla-download.org/R/stable

VignetteBuilder:

knitr

LinkingTo:

TMB, Rcpp, RcppEigen

NeedsCompilation:

yes

Packaged:

2026-06-04 19:04:01 UTC; tflutre

Author:

Timothee Flutre

[aut, ctb, cre], INRAE [cph, fnd], Jerome Enjalbert

[ctb], Emma Forst

[ctb], Maxence Remerand

[ctb], Jemay Salomon

[ctb]

Repository:

CRAN

Date/Publication:

2026-06-09 15:50:07 UTC

The plantmix package

Description

plantmix is a package to study plant mixtures.

Introduction

The main goal of the plantmix package is to provide a set of functions for pain-free analysis of both varietal (intraspecific) and crop (interspecific) mixtures, such as optimizing experimental designs, simulating data sets, fitting GMA-SMA and DBV-SBV models, computing diversity indices, etc. Work is ongoing on DBV-SBV models for varietal mixtures, spatial heterogeneity, etc.

Author(s)

Maintainer: Timothee Flutre timothee.flutre@inrae.fr (ORCID) [contributor]

Other contributors:

INRAE [copyright holder, funder]
Jerome Enjalbert jerome.enjalbert@inrae.fr (ORCID) [contributor]
Emma Forst emma.forst@inra.fr (ORCID) [contributor]
Maxence Remerand maxence.remerand@ens-paris-saclay.fr (ORCID) [contributor]
Jemay Salomon jemaysalomon@gmail.com (ORCID) [contributor]

Change in contribution

Description

Computes the change in contribution (CC) proposed by Williams and McCarthy (2001, doi:10.1046/j.1440-1703.2001.00368.x) (this function implements the equation for mixtures with any number of components as written in appendix V of the paper).

Usage

CC(
  dat,
  colIDstand = "ID",
  colIDfocal = "focal",
  colProp = "prop",
  colY = "yield"
)

Arguments

dat

data frame with one yield value per row and with at least four columns: the identifier of each stand, the identifier of the focal genotype or species in each stand, the sowing (plant) proportion of the focal in each stand, and the focal yield in each stand (see the example below); a stand with a single proportion equals to 1 will be interpreted as a monovarietal culture ("pure stand"), a mixture otherwise (and its proportions should sum to 1)

colIDstand

column name for the stand identifiers

colIDfocal

column name for the focal identifiers

colProp

column name for the proportions

colY

column name for the yield values

Value

input data.frame with an extra column named "CC"

Author(s)

Timothee Flutre

Examples

(dat <- data.frame(ID=c("geno1", "geno2", "mixg1g2", "mixg1g2"),
                   focal=c("geno1", "geno2", "geno1", "geno2"),
                   prop=c(1, 1, 0.5, 0.5),
                   yield=c(50, 40, 25, 22)))
CC(dat)

Land equivalent ratio (LER)

Description

Computes the land equivalent ratio (LER) of Willey and Osiru (1972, doi:10.1017/S0021859600025909) interpreted as the relative land area required under sole cropping to produce the same yield as under intercropping. It corresponds to equation 28 in Weigelt and Jolliffe (2003, doi:10.1046/j.1365-2745.2003.00805.x), and is equivalent to the index called "relative yield total" (RYT) corresponding to equation 26 of the same paper. Missing values are forbidden.

Usage

LER(x)

Arguments

x

data frame with species in rows (with species names as row names) and at least two columns, the first being the performance under sole-cropping and the second under inter-cropping

Value

list with partial and total LERs

Author(s)

Timothee Flutre

Examples

(dat <- data.frame(solecrop=c(5, 15),
                   intercrop=c(4, 9),
                   row.names=c("grain", "fruit")))
LER(dat)

Relative interaction index (RII)

Description

Computes the relative interaction index (RII) as defined in Armas et al (2004, doi:10.1890/03-0650): RII_i = (Y_i(j) - Y_i) / (Y_i(j) + Y_i) where Y_i(j) is the yield per plant of i when mixed with j, and Y_i is the yield per plant of i when growned in isolation. Missing values are forbidden.

Usage

RII(
  dat,
  colIDstand = "ID",
  colIDfocal = "focal",
  colProp = "prop",
  colY = "yield",
  avgIfReps = FALSE
)

Arguments

dat

data.frame with one yield value per row and with at least four columns: the identifier of each stand, the identifier of the focal genotype or species in each stand, the sowing (plant) proportion of the focal in each stand, and the focal yield in each stand (see the example below); a stand with a single proportion equals to 1 will be interpreted as a monovarietal culture ("pure stand"), a mixture otherwise (and its proportions should sum to 1)

colIDstand

column name for the stand identifiers

colIDfocal

column name for the focal identifiers

colProp

column name for the proportions

colY

column name for the yield values

avgIfReps

if TRUE, replicates of monovarietals will be averaged; if FALSE, the presence of replicates will return an error

Value

input data.frame with an extra column named "RII"

Author(s)

Timothee Flutre

Examples

sow_density <- 200
(dat <- data.frame(ID=c("geno1", "geno2", "mixg1g2", "mixg1g2"),
                   focal=c("geno1", "geno2", "geno1", "geno2"),
                   prop=c(1, 1, 0.5, 0.5),
                   yield_qt_ha=c(50, 40, 25, 22)))
dat$yield_g_m2 <- (dat$yield_qt_ha / 10^4) * 10^6
dat$yield_g_plant <- dat$yield_g_m2 / (sow_density * dat$prop)
RII(dat, colY = "yield_g_plant")

Net Relative interaction index (RIInet)

Description

Computes the net relative interaction index (RII) as defined in Stefan et al (2022): RIInet = sum_i prop_i RII_i. Missing values are forbidden.

Usage

RIInet(
  dat,
  colIDstand = "ID",
  colIDfocal = "focal",
  colProp = "prop",
  colRII = "RII"
)

Arguments

dat

colIDstand

column name for the stand identifiers

colIDfocal

column name for the focal identifiers

colProp

column name for the proportions

colRII

column name for the RII values

Value

data.frame with columns colIDstand and "RIInet"

Author(s)

Timothee Flutre

Examples

sow_density <- 200
(dat <- data.frame(ID=c("geno1", "geno2", "mixg1g2", "mixg1g2"),
                   focal=c("geno1", "geno2", "geno1", "geno2"),
                   prop=c(1, 1, 0.5, 0.5),
                   yield_qt_ha=c(50, 40, 25, 22)))
dat$yield_g_m2 <- (dat$yield_qt_ha / 10^4) * 10^6
dat$yield_g_plant <- dat$yield_g_m2 / (sow_density * dat$prop)
dat <- RII(dat, colY = "yield_g_plant")
RIInet(dat)

Relative yield (RY)

Description

Computes the "relative yield" (RY) as defined in Fowler (1982, doi:10.2307/2259865) citing de Wit (1960): RY_ij = Y_ij / Y_i where Y_ij is the yield of i when mixed with j and Y_i is the yield of i in monovarietal culture. Note that equation 1 of table 2 in Williams and McCarthy (2001, doi:10.1046/j.1440-1703.2001.00368.x) is called "RY" but in fact it corresponds to "RYP" (relative yield per plant). Note also that the equation for "RY" in Reiss and Drinkwater (2018, doi:10.1002/eap.1629) in fact corresponds to "RYM" (relative yield of mixture). Missing values are forbidden.

Usage

RY(
  dat,
  colIDstand = "ID",
  colIDfocal = "focal",
  colProp = "prop",
  colY = "yield",
  avgIfReps = FALSE
)

Arguments

dat

data.frame with one yield value per row and with at least four columns: the identifier of each stand, the identifier of the focal genotype or species in each stand, the sowing (plant) proportion of the focal in each stand, and the focal yield in each stand (see the example below); a stand with a single proportion equals to 1 will be interpreted as a monovarietal culture ("pure stand") and a mixture otherwise; note that contrary to RYP, the proportions will not be used for anything else

colIDstand

column name for the stand identifiers

colIDfocal

column name for the focal identifiers

colProp

column name for the proportions

colY

column name for the yield values

avgIfReps

if TRUE, replicates of monovarietals will be averaged; if FALSE, the presence of replicates will return an error

Value

input data.frame with an extra column named "RY"

Author(s)

Timothee Flutre

Examples

(dat <- data.frame(ID=c("geno1", "geno2", "mixg1g2", "mixg1g2"),
                   focal=c("geno1", "geno2", "geno1", "geno2"),
                   prop=c(1, 1, 0.5, 0.5),
                   yield=c(50, 40, 25, 22)))
RY(dat)

Relative yield of mixture (RYM)

Description

Computes the "relative yield of mixture" (RYM) as defined initially by Wilson (1988, doi:10.2307/2403626) for binary mixtures sowed in equal proportions, and extended by Williams and McCarthy (2001, doi:10.1046/j.1440-1703.2001.00368.x) to binary mixtures of unequal proportions. A RYM above 1 means that the mixture yielded better than the sum of the pure-stand yields, each weighted by their proportion in the mixture. It corresponds to equation 35 in Weigelt and Jolliffe (2003, doi:10.1046/j.1365-2745.2003.00805.x), as well as equation 25b (and not to 25a!) of the same paper where the "control" treatment is the expected mixture yield calculated based on the weighted component monovarietal yields. Note that the RYM was unfortunately called "relative yield" (RY) by Reiss and Drinkwater (2018, doi:10.1002/eap.1629). Missing values are forbidden.

Usage

RYM(
  dat,
  colIDstand = NULL,
  colIDcomps = "comps",
  colProps = NULL,
  colY = "yield",
  sep = "-",
  colOut = "RYM"
)

Arguments

dat

data.frame with one yield value per row and with at least four columns: the identifier of each stand, the identifiers of the components of each stand (separated by a specific symbol in the case of mixtures), the sowing (plant) proportion of the components in each stand (separated by the same symbol in the case of mixtures, and the yield of each stand (see the example below); a stand with a single component should have its proportion equal to 1 and will be interpreted as a monovarietal culture ("pure stand"), a mixture otherwise (and its proportions should sum to 1)

colIDstand

column name for the stand identifiers (they should be unique); if NULL, the code will attempt to use "colIDcomps"

colIDcomps

column name for the identifiers of the stand components (separated by sep)

colProps

column name for the stand proportions (separated by sep and summing to 1); if NULL, the code will assume all components of a given mixture are equiprobable

colY

column name for the yield values

sep

separator

colOut

column name for the output RYM

Value

input data.frame with an extra column named from "colOut"

Examples

(dat <- data.frame(ID=c("geno1", "geno2", "mixg1g2"),
                   comps=c("geno1", "geno2", "geno1-geno2"),
                   props=c("1", "1", "0.6-0.4"),
                   yield=c(50, 40, 47)))
RYM(dat)

Relative yield per plant (RYP)

Description

Computes the relative yield per plant (RYP) as defined in Fowler (1982, doi:10.2307/2259865): RYP_ij = Y_ij / (p Y_i) where Y_ij is the yield of i when mixed with j at a proportion of p, and Y_i is the yield of i in monovarietal culture. Note that, unfortunately, the equation of RYP was called "relative yield" (RY) in table 2 of Williams and McCarthy (2001, doi:10.1046/j.1440-1703.2001.00368.x), although these authors do have the right equation, i.e., they only dropped the "per plant". Missing values are forbidden.

Usage

RYP(
  dat,
  colIDstand = "ID",
  colIDfocal = "focal",
  colProp = "prop",
  colY = "yield"
)

Arguments

dat

colIDstand

column name for the stand identifiers

colIDfocal

column name for the focal identifiers

colProp

column name for the proportions

colY

column name for the yield values

Value

input data.frame with an extra column named "RYP"

Author(s)

Timothee Flutre

Examples

(dat <- data.frame(ID=c("geno1", "geno2", "mixg1g2", "mixg1g2"),
                   focal=c("geno1", "geno2", "geno1", "geno2"),
                   prop=c(1, 1, 0.5, 0.5),
                   yield=c(50, 40, 25, 22)))
RYP(dat)

Relative yield total (RYT)

Description

Computes the "relative yield total" (RYT) of a mixture from de Wit and Den Bergh (1965) as the sum of all relative yields (RY) of the mixture. It corresponds to equation 26 (based on 25a and not 25b!) in Weigelt and Jolliffe (2003, doi:10.1046/j.1365-2745.2003.00805.x), and is equivalent to the index called "land equivalent ratio" (LER) corresponding to equation 28 of the same paper. Missing values are forbidden.

Usage

RYT(
  dat,
  colIDstand = "ID",
  colIDfocal = "focal",
  colProp = "prop",
  colY = "yield"
)

Arguments

dat

data.frame with one yield value per row and with at least four columns: the identifier of each stand, the identifier of the focal genotype or species in each stand, the sowing (plant) proportion of the focal in each stand, and the focal yield in each stand (see the example below); a stand with a single proportion equals to 1 will be interpreted as a monovarietal culture ("pure stands") and a mixture otherwise; note that contrary to RYP, the proportions will not be used for anything else

colIDstand

column name for the stand identifiers

colIDfocal

column name for the focal identifiers

colProp

column name for the proportions

colY

column name for the yield values

Value

input data.frame with an extra column named "RYT"

Examples

(dat <- data.frame(ID=c("geno1", "geno2", "mixg1g2", "mixg1g2"),
                   focal=c("geno1", "geno2", "geno1", "geno2"),
                   prop=c(1, 1, 0.5, 0.5),
                   yield=c(50, 40, 25, 22)))
RYT(dat)

Estimate a GRM

Description

Estimates a genomic relationship matrix with the first estimator from VanRaden (2008).

Usage

estimGRM(M, AFs = NULL)

Arguments

M

matrix of SNP genotypes, with individuals in rows and SNPs in columns, encoded in allele dose in \[0,2\]

AFs

vector of allele frequencies; if NULL, will be estimated from M

Value

matrix

Author(s)

Timothee Flutre

Examples

strong_div_pops <- diag(3)
strong_div_pops[upper.tri(strong_div_pops)] <- 0.5
strong_div_pops[lower.tri(strong_div_pops)] <- strong_div_pops[upper.tri(strong_div_pops)]
strong_div_pops
M <- simulGenosDoseStruct(div_pops=strong_div_pops)
A <- estimGRM(M)
imageMat(A, "Strong divergence")

RYM with "rep" effect

Description

Computes the relative yield of mixtures (RYMs) per replicate (e.g., blocks), then fits a linear model correcting for this rep effect, and quantifies the uncertainty around it.

Usage

estimRYMRep(
  data,
  colY,
  colID,
  colRep,
  mix2genos,
  conf_level = 0.95,
  plotDiag = FALSE,
  title = ""
)

Arguments

data

data frame

colY

column name specifying the response variable

colID

column name specifying the stand identifiers

colRep

column name specifying the replicate

mix2genos

named list with one component pe rmixture, each component being a vector of genotype names

conf_level

confidence level for the uncertainty intervals

plotDiag

if TRUE, diagnostics will be plotted

title

title for the diagnostics plots

Value

data frame with the inference summaries of RYM per mixture

Author(s)

Timothee Flutre [aut], Arnaud Gauffreteau [ctb]

Examples

## generate fake data
set.seed(1234)
nbGenos <- 25
genos <- sprintf("g%02i", 1:nbGenos)
pairs <- t(combn(x=genos, m=2))
mixIDs <- sample(paste(pairs[,1], pairs[,2], sep="_"), size=75)
nbBlocks <- 3
blocks <- LETTERS[1:nbBlocks]
dat <- do.call(rbind, lapply(blocks, function(block){
  data.frame(ID=c(genos, mixIDs),
             block=block,
             stringsAsFactors=TRUE)
}))
listContr <- list(block="contr.sum")
X <- model.matrix(~ 1 + block, data=dat, contrasts=listContr)
Z_GMA <- mkZGMA(dat, "ID", "_")
Z_SMA <- mkZSMA(dat, "ID", "_", inc_SMA_ii="only_pur")
truth <- list("intercept"=100, "var_GMA"=10, "var_SMA"=4, "var_error"=1)
truth[["blockEffs"]] <- rnorm(n=nbBlocks - 1, mean=0, sd=2)
truth[["GMAs"]] <- rnorm(n=nbGenos, mean=0, sd=sqrt(truth$var_GMA))
truth[["SMAs"]] <- rnorm(n=nlevels(dat$ID), mean=0, sd=sqrt(truth$var_SMA))
truth[["errors"]] <- rnorm(n=nrow(dat), mean=0, sd=sqrt(truth$var_error))
y <- X %*% c(truth$intercept, truth$blockEffs) +
  Z_GMA %*% truth$GMAs +
  Z_SMA %*% truth$SMAs +
  truth$errors
dat$yield <- y[,1]
hist(dat$yield, breaks=20, las=1, main="Simulated data")
boxplot(yield ~ block, data=dat, las=1, main="Simulated data")

## compute the average yield per ID over blocks, and then compute the RYMs:
avg <- tapply(dat$yield, dat$ID, mean)
avg <- data.frame(ID=names(avg), yield=avg, stringsAsFactors=TRUE)
avg <- RYM(avg, colIDcomps="ID", colY="yield", sep="_")

## compute the RYMs per block, and then correct for the "block" effect:
mix2genos <- strsplit(levels(dat$ID), "_")
names(mix2genos) <- levels(dat$ID)
out <- estimRYMRep(dat, "yield", "ID", "block", mix2genos)
head(out)

## compare both approaches:
op <- par(mfrow=c(1,2))
hist(avg$RYM, las=1, main="Estimated RYM by averaging over blocks", xlab="RYM")
abline(v=1, lwd=2); abline(v=mean(avg$RYM, na.rm=TRUE), col="red", lwd=2)
hist(out$estim, las=1, main="Estimated RYM after correcting the 'block' effect", xlab="RYM")
abline(v=1, lwd=2); abline(v=mean(out$estim), col="red", lwd=2)
par(op)

out$avgRYM <- avg[rownames(out), "RYM"]
plot(out$avgRYM, out$estim, las=1, type="n",
     xlab="RYM averaged over blocks",
     ylab="RYM after correcting the 'block' effect",
     main="Estimated RYMs")
idx <- which(out$pv > 0.05)
points(out$avgRYM[idx], out$estim[idx], pch=19, col="black")
idx <- which(out$pv <= 0.05)
points(out$avgRYM[idx], out$estim[idx], pch=19, col="red")
abline(a=0, b=1, h=1, v=1, lty=2)
segments(x0=as.numeric(out$avgRYM), y0=out$cil,
         x1=as.numeric(out$avgRYM), y1=out$ciu,
         col="grey", lty=2, lwd=2)

RY with "rep" effect

Description

Computes the relative yields (RYs) per replicate (e.g., blocks), then fits a linear model correcting for this rep effect, and quantifies the uncertainty around it.

Usage

estimRYRep(
  data,
  colY,
  colIDstand,
  colIDfocal,
  colRep,
  mix2genos,
  conf_level = 0.95,
  plotDiag = FALSE
)

Arguments

data

data frame

colY

column name specifying the response variable

colIDstand

column name specifying the stand identifiers

colIDfocal

column name for the focal identifiers

colRep

column name specifying the replicate

mix2genos

named list with one component per mixture, each component being a vector of genotype names; see getMixtureList

conf_level

confidence level for the uncertainty intervals

plotDiag

if TRUE, diagnostic plots

Value

data frame with the inference summaries of RY per mixture

Author(s)

Timothee Flutre [aut], Arnaud Gauffreteau [ctb]

Examples

(dat <- data.frame(ID=c("geno1", "geno1",
                        "geno2", "geno2",
                        "mixg1g2", "mixg1g2", "mixg1g2", "mixg1g2"),
                   focal=c("geno1", "geno1",
                           "geno2", "geno2",
                           "geno1", "geno2",
                           "geno1", "geno2"),
                   prop=c(1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5),
                   block=c("A","B","A","B","A","A","B","B"),
                   yield=c(51,45, 39,43, 25,22,26,25)))
estimRYRep(dat, "yield", "ID", "focal", "block", list("mixg1g2"=c("geno1","geno2")))

Fit DBV-SBV models for interspecific mixtures

Description

Fits DBV-SBV models for interspecific mixtures.

Usage

fitDBVSBVinter(
  listY,
  listX,
  listZ,
  listVCov,
  REML = TRUE,
  lOptions = NULL,
  verbose = FALSE
)

Arguments

listY

named list of response variables (a matrix Y_IC and, optionally, a vector y_SC_f and a vector y_SC_t)

listX

named list of design matrices for the fixed-effect explanatory factors (a matrix X_IC and, optionally, a matrix X_SC_f and a matrix X_SC_t)

listZ

named list of design matrices of random-effect explanatory factors

listVCov

named list of square, symmetric matrices used, after re-scaling, as variance-covariance matrices for the random-effect factors; named "K" for the BVs (DBVs and SBVs) and "Kmixpair" for the DBVxSBVs; these matrices must have dimnames (both rows and columns) coherent with the column names of the design matrices in listZ

REML

logical

lOptions

named list of options (for experts)

verbose

verbosity level

Value

list

Author(s)

Jemay Salomon, Timothee Flutre

Examples

## simulate a data set with both sole crops and intercrops:
GRMs <- list("S1" = diag(100),
             "S2" = diag(2))
dimnames(GRMs$S1) <- list(paste0("gS1_", 1:100), paste0("gS1_", 1:100))
dimnames(GRMs$S2) <- list(paste0("gS2_", 1:2), paste0("gS2_", 1:2))
out <- simulDBVSBVinter(GRMs)
names(out)
datW <- out$datW
str(datW)

## fit the model using intercrops only:
idxIC <- which(!is.na(datW$geno_S1) & !is.na(datW$geno_S2))
datW_IC <- droplevels(datW[idxIC, ])
listY <- list(Y_IC = datW_IC[, c("yield_S1", "yield_S2")])
listX <- list(X_IC = model.matrix(~ 1 + block + geno_S2, datW_IC,
              contrasts.arg = list("block" = "contr.sum",
                                   "geno_S2" = "contr.sum")))
listZ <- list(Z_DS_f = model.matrix(~ 0 + geno_S1, datW_IC))
colnames(listZ$Z_DS_f) <- gsub("^geno_S1", "", colnames(listZ$Z_DS_f))
listVCov <- list(K = GRMs$S1[levels(datW_IC$geno_S1), levels(datW_IC$geno_S1)])
fitTmb <- fitDBVSBVinter(listY, listX, listZ, listVCov,
                         lOptions = list(iter.max = 20), REML = TRUE)
names(fitTmb)
fitTmb$sdr

## see the third vignette for more details

Fit GMA-SMA models

Description

Fits GMA-SMA models.

Usage

fitGMASMA(
  formFix,
  data,
  listZ,
  pkg = "lme4",
  listVCov,
  contrasts = NULL,
  REML = TRUE,
  ...
)

Arguments

formFix

formula whose right-hand side should only include the fixed effects, the random effects being deduced based on the names of "listZ"

data

data frame; missing data will be removed

listZ

named list of design matrices for the random effects; its names should be "GMA" (compulsory) and, optionally, one of "SMA", "SMA_ij" or "SMA_ii"

pkg

package used to fit the model among lme4, MM4LMM, TMB, INLA and breedR; lme4 and INLA do not use "listVCov"; breedR does not use "contrasts"

listVCov

named list of variance-covariance matrices for the random effects; the names of this list should be the same as the names of "listZ"

contrasts

see stats::model.matrix()

REML

logical

...

additional arguments specific to each package

Value

depends on the chosen package

Author(s)

Timothee Flutre

Examples

## see the first and second vignettes

Optimize design for binary crop mixtures

Description

Optimizes a design for binary crop mixtures

Usage

getDesignBinaryCropMix(
  levGenos1,
  levGenos2,
  nbMixes,
  seed = NULL,
  showPlots = FALSE,
  verbose = FALSE
)

Arguments

levGenos1

character vector with the names of the genotypes of the first species

levGenos2

character vector with the names of the genotypes of the second species

nbMixes

number of mixtures

seed

seed for the pseudo-random number generator

showPlots

logical indicating if plots should be showed

verbose

verbosity level

Value

list

Author(s)

Timothee Flutre

Examples

nbGenos1 <- 30
levGenos1 <- sprintf(fmt=paste0("S1_%0", floor(log10(nbGenos1))+1, "i"),
                     1:nbGenos1)
nbGenos2 <- 8
levGenos2 <- sprintf(fmt=paste0("S2_%0", floor(log10(nbGenos2))+1, "i"),
                     1:nbGenos2)
nbMixes <- 60 # only binary and balanced
design <- getDesignBinaryCropMix(levGenos1, levGenos2, nbMixes, seed=12345)
plotDesignCropMix(design$graph, levGenos1, levGenos2)
ggplot(design$entropies) + aes(x=iteration, y=entropy, group=type) + geom_point() + geom_line() +
  geom_hline(yintercept = design$max_ent) + theme_bw() + facet_wrap(~ type)

Optimize design for binary varietal mixtures

Description

Optimizes a design for binary varietal mixtures

Usage

getDesignBinaryVarMix(
  levGenos,
  nbMixes,
  seed = NULL,
  showPlots = FALSE,
  verbose = FALSE
)

Arguments

levGenos

character vector with the names of the genotypes

nbMixes

number of mixtures

seed

seed for the pseudo-random number generator

showPlots

logical indicating if plots should be showed

verbose

verbosity level

Value

list

Author(s)

Timothee Flutre

Examples

nbGenos <- 25
levGenos <- sprintf(fmt=paste0("geno%0", floor(log10(nbGenos))+1, "i"),
                    1:nbGenos)
nbMixes <- 75 # only binary and balanced
design <- getDesignBinaryVarMix(levGenos, nbMixes, seed=12345)
plotDesignVarMix(design$graph, levGenos)
ggplot(design$entropies) + aes(x=iteration, y=entropy) + geom_point() + geom_line() +
  geom_hline(yintercept = design$max_ent) + theme_bw()

Reformat composition of mixtures into a list

Description

Reformats composition of mixtures into a list.

Usage

getMixtureList(mixtures, sep = NULL)

Arguments

mixtures

vector, matrix, data.frame or list containing of mixtures

sep

required only if mixtures is a vector; will be given to strsplit

Value

list of vectors, with one component per mixture, the vector containing the mixture components

Author(s)

Timothee Flutre

Examples

nbGenos <- 25
levGenos <- sprintf(fmt=paste0("geno%0", floor(log10(nbGenos))+1, "i"),
                    1:nbGenos)
nbMixes <- 75 # only binary and balanced
design <- getDesignBinaryVarMix(levGenos, nbMixes, seed=12345)
mixtures <- getMixtureList(design$combs)
str(mixtures, list.len=10)

Get mixture(s) per genotype

Description

Returns the list of mixtures per genotype.

Usage

getMixturesPerGeno(mix2genos)

Arguments

mix2genos

list with one component per mixture, listing the genotypes it contains (output of getMixtureList)

Value

list with one vector per genotype

Author(s)

Timothee Flutre

Examples

nbGenos <- 25
levGenos <- sprintf(fmt=paste0("geno%0", floor(log10(nbGenos))+1, "i"),
                    1:nbGenos)
nbMixes <- 75 # only binary and balanced
design <- getDesignBinaryVarMix(levGenos, nbMixes, seed=12345)
mixtures <- getMixtureList(design$combs)
str(mixtures, list.len=10)
geno2mixes <- getMixturesPerGeno(mixtures)
geno2mixes[c(1,2)]
table(sapply(geno2mixes, length))

Image of a matrix

Description

Plots an image of a matrix.

Usage

imageMat(mat, title, col, breaks)

Arguments

mat

matrix

title

optional title of the plot; if missing, it will be the matrix name and dimensions

col

optional vector of colors; if missing, it will be a grey color gradient

breaks

optional vector of breaks to go with col

Value

nothing

Author(s)

Timothee Flutre

Examples

M <- diag(c(rep(2, 3), rep(15, 5)))
imageMat(M) # see how the the "2" values are ignored!
imageMat(M, col=c("lightgrey","red","blue"), breaks=c(-1,1,3,20))

Information criterion

Description

Returns an information criterion, among AIC, AICc and BIC. Caution: choosing k and n may not be straightforward for certain models, such as mixed models.

Usage

infoCriterion(k, lnLmax, n = NULL, type = "AIC")

Arguments

k

number of parameters, sometimes also called "effective degrees of freedom"

lnLmax

value of the log-likelihood when maximized

n

sample size

type

specific information criterion to be returned

Value

numeric with attributes k and n (if not NULL)

Author(s)

Timothee Flutre

Inverse vec operator

Description

Applies the inverse vec operator to a vector.

Usage

invvec(x, n_col, n_row = NULL, sep = NULL)

Arguments

x

vector

n_col

number of columns of the output matrix

n_row

number of rows of the output matrix

sep

separator used to retrieve row and column names, only if x has names

Value

matrix

Author(s)

Timothee Flutre

Examples

mat <- matrix(c(1, 2, 3,
                4, 5, 6),
              nrow = 2, ncol = 3, byrow = TRUE,
              dimnames = list(as.character(1:2), letters[1:3]))
mat
(x <- vec(mat))
invvec(x, n_col = 3, sep = "-")

Inverse vec for mixtures

Description

Tranforms a vector of component observations into a matrix with one row per mixture and components in colums. Requires all mixtures to have the same number of components, and all the obervationss from the same mixture to be one after the others, in the same order for each mixture.

Usage

invvecMixes(x, nb_comps)

Arguments

x

vector; make sure that it is correctly sorted beforehand

nb_comps

number of components in each mixture

Value

matrix with nb_comps columns

Author(s)

Timothee Flutre

Examples

datL <- data.frame(ID = c("g1+g2", "g1+g2", "g1+g2", "g1+g2"),
  focal = c("g1", "g2", "g1", "g2"),
  neighbor = c("g2", "g1", "g2", "g1"),
  block = c("A", "A", "B", "B"),
  yield = c(1, 2, 3, 4)
)
invvecMixes(datL$yield, 2)

Sowing

Description

Computes the seed weight per genotype for each stand, assuming equal sowing proportions.

Usage

mixSowingWeight(pureSowingWeights, stands)

Arguments

pureSowingWeights

vector which names are genotypes and values are sowing weights in pure stands

stands

vector which names are stand identifiers and values are genotype(s), a single one if it is a pure stand, several ones if it is a mixed stand (separated by a dash "-", e.g., "var1-var8")

Value

list which components are stands and values are named vectors with sowing weight per genotype for each stand

Author(s)

Timothee Flutre

Examples

sowingArea <- 9.52
sowingDensity <- 160
(tmp <- nbSeedsToSownInPure(sowingArea, sowingDensity))
TKWs <- c("var1"=38.605, "var2"=40.051, "var6"=36.251, "var8"=33.368)
pureSowingWeights <- TKWs * tmp
stands <- c("var1"="var1", "mix8"="var1-var2", "mix34"="var1-var8",
            "mix50"="var1-var6-var8")
mixSowingWeight(pureSowingWeights, stands)

Design matrices of SMAs for GMA-SMA models

Description

Makes the design matrices with the contribution of specific mixing abilities (SMAs). More details in the help of mkZSMA and in the vignette.

Usage

mkAllZSMA(
  df,
  col,
  sep,
  models = c("2p", "2", "2pp", "3", "3p"),
  verbose = FALSE
)

Arguments

df

data frame

col

name of the column containing the names of pure and mixed stands

sep

separator used to separate the genotype names in "col"

models

vector of model identifiers; see the vignette

verbose

verbosity level

Value

list of SMA design matrices

Author(s)

Timothee Flutre

Examples

dat <- data.frame(mix=paste0("mix", 1:3),
                  varieties=c("var3","var1-var3","var1-var2"),
                  pheno=c(10, 7, 9))
(listZSMA <- mkAllZSMA(df=dat, col="varieties", sep="-"))

Design matrix of GMAs for GMA-SMA models

Description

Makes a design matrix with the contribution of general mixing abilities (GMAs). The design is based on the names of pure and mixed stands, in which the genotype names are separated by a specific symbol.

Usage

mkZGMA(df, col, sep = ",")

Arguments

df

data frame

col

name of the column containing the names of pure and mixed stands

sep

separator used to separate the genotype names in "col"

Value

matrix

Author(s)

Timothee Flutre

Examples

dat <- data.frame(mix=paste0("mix", 1:3),
                  varieties=c("var3","var1-var3","var1-var2"),
                  pheno=c(10, 7, 9))
(Z_GMA <- mkZGMA(df=dat, col="varieties", sep="-"))

Design matrix of SMAs for GMA-SMA models

Description

Makes a design matrix with the contribution of specific mixing abilities (SMAs). Before Forst et al (2019), there was only one kind of SMA, the inter-genotypic SMA (SMA_ij). Forst et al (2019) introduced a second kind of SMA, the intra-genotypic SMA (SMA_ii). As a result, various design matrices can be made depending on which kinds of SMAs are modeled. The design is based on the names of pure and mixed stands, in which the genotype names are separated by a specific symbol.

Usage

mkZSMA(df, col, sep = ",", inc_SMA_ii = "no", skipUnusedCols = TRUE)

Arguments

df

data frame

col

name of the column containing the names of pure and mixed stands

sep

separator used to separate the genotype names in "col"

inc_SMA_ii

specify how the intra-genotypic SMA should be included, with "no" meaning no SMA_ii, "only_pur" meaning that the SMA_ii is only included in the pure stands (this is model 2 of Forst et al, 2019), "pur_mix" meaning that the SMA_ii is included in both the pure and the mixed stands (this is model 3 of Forst et al, 2019)

skipUnusedCols

skip unused columns, i.e., full of zeroes and not present in the data frame

Value

matrix

Author(s)

Timothee Flutre

Examples

dat <- data.frame(mix=paste0("mix", 1:3),
                  varieties=c("var3","var1-var3","var1-var2"),
                  pheno=c(10, 7, 9))
(Z_SMA <- mkZSMA(df=dat, col="varieties", sep="-", inc_SMA_ii="only_pur")) # model 2
(Z_SMA <- mkZSMA(df=dat, col="varieties", sep="-", inc_SMA_ii="pur_mix")) # model 3

Design matrices for DBV-SBV models in an inter-specific trial

Description

Makes the design matrices for direct and social breeding values per species when the trial includes binary intercrops and, possibly, sole crops.

Usage

mkZinterspe(df, genosPerSp, colIDfocal = "focal", colIDneighbors = "neighbors")

Arguments

df

data frame with one row per sole-crop plot or two rows per binary intercrop plot, with a factor column corresponding to the focal identifiers ("DBV" a.k.a. "DGE" or "Pr") and a column corresponding to the "neighbor(s)" identifiers ("SBV" a.k.a. "IGE" or "As")

genosPerSp

list of two components, one per species, each being a vector with the genotype identifiers of the given species (they will be sorted)

colIDfocal

name of the column containing the identifiers of the focal genotypes

colIDneighbors

name of the column containing the identifiers of the neighbour genotypes

Value

list of two components, one per species, each being a list of two matrices, the first for the DBVs and the second for the SBVs; the column names of these matrices correspond to the genotype identifiers after sorting

Author(s)

Timothee Flutre

Examples

(dat <- data.frame(focal=c("wheat01", "pea02",
                           "wheat01", "pea01",
                           "wheat01"),
                   neighbors=c("pea02", "wheat01",
                               "pea01", "wheat01",
                               "wheat01"),
                   name=c("wheat01-pea02", "wheat01-pea02",
                          "wheat01-pea01", "wheat01-pea01",
                          "wheat01-wheat01"),
                   stand = c("inter", "inter", "inter", "inter", "sole"),
                   species = c("wheat", "pea", "wheat", "pea", "wheat")))
(out <- mkZinterspe(dat,
                    list("wheat"="wheat01",
                         "pea"=c("pea01","pea02"))))

Sowing

Description

Computes the number of seeds of a given genotype to be sowned as pure stand based on a given sowing area and density.

Usage

nbSeedsToSownInPure(sowingArea = 8.4, sowingDensity = 160)

Arguments

sowingArea

area to be sowned in square meters

sowingDensity

number of seeds per square meter

Value

number of seeds (in thousands)

Author(s)

Timothee Flutre

Examples

sowingArea <- 9.52
sowingDensity <- 160
nbSeedsToSownInPure(sowingArea, sowingDensity)
sowingArea <- 8.66
sowingDensity <- 200
nbSeedsToSownInPure(sowingArea, sowingDensity)

Normalized bias error

Description

Returns the normalized bias error (NBE).

Usage

normBiasError(estim, truth, perc = TRUE)

Arguments

estim

numeric vector of estimates

truth

numeric vector of true values

perc

logical; if TRUE, the return value will be in percentage

Value

numeric vector

Author(s)

Timothee Flutre

Parametric bootstrap with TMB

Description

Performs parametric bootstrap with TMB and nlminb on a fitted model to allow uncertainty quantification.

Usage

paramBoot4TMB(fit, nb_boot = 5, mc.cores = 1)

Arguments

fit

list corresponding to a model fitted with TMB

nb_boot

number of parametric bootstraps to perform

mc.cores

the number of cores to use, i.e. at most how many child processes will be run simultaneously (see the "parallel" package)

Value

list with as many components as nb_boot

Author(s)

Jemay Salomon, Timothee Flutre

Examples

## see the example of `fitDBVSBVinter`
## then run (slow if nb_boot is high!): paramBoot4TMB(fitTmb)

Pivot mixture data into the long format

Description

Pivots mixture data into the long format, from one row per stand into one row per component of each stand (i.e., several rows if the stand is a mixture).

Usage

pivotMixData2Long(
  df,
  genos,
  colC,
  sep = "-",
  prefixY = NULL,
  sepY = "_",
  colIDfocal = "focal",
  colIDneighbors = "neighbor"
)

Arguments

df

data frame

genos

named list of vectors, one per species, containing all the identifiers of the genotypes of the given species

colC

name of the column containing the component(s) of each stand

sep

separator used to separate the genotype names in "col"; see the "split" argument of strsplit

prefixY

prefix of the columns containing the yield data (one column per mixture component); it is assumed that the components in colC are in the same order as the yield data

sepY

separator used in the name of the new "yield" columns made by concatenating colY and colIDfocal or colIDneighbors

colIDfocal

name of the column in the output that will contain the identifiers of the focal genotypes

colIDneighbors

name of the column in the output that will contain the identifiers of the neighbor genotypes; for monovarietal stands, the entries in this neighbor column will be identical to the entries in the focal column

Value

data.frame with more rows

Examples

## example of species mixtures:
(dat <- data.frame(name=c("wheat01-pea02","wheat01-pea01", "wheat01"),
                   stand=c("inter","inter","sole"),
                   x=c(1, 1, 1),
                   y=c(1, 2, 3),
                   "yield_cereal" = c(10, 11, 20),
                   "yield_legume" = c(9, 8, NA),
                   check.names = FALSE,
                   stringsAsFactors=TRUE))
pivotMixData2Long(df=dat, colC="name", sep="-", prefixY="yield",
                  genos=list("cereal"=c("wheat01"),
                             "legume"=c("pea01","pea02")))

## example of varietal mixtures:
(dat <- data.frame(name=c("g1+g2","g1+g2","g1","g2"),
                   "yield_focal" = c(10, 12, 20, 18),
                   "yield_neighbor" = c(11, 9, NA, NA),
                   check.names = FALSE,
                   stringsAsFactors=TRUE))
pivotMixData2Long(df=dat, colC="name", sep="+", prefixY="yield",
                  genos=list(c("g1","g2")))

Pivot mixture data into the wide format

Description

Pivots mixture data into the wide format, from one row per component of each stand (i.e., several rows if the stand is a mixture) into one row per stand.

Usage

pivotMixData2Wide(
  df,
  colIDstand = "ID",
  colIDfocal = "focal",
  colIDneighbors = "neighbor",
  colPlot = c("x", "y"),
  colY = "yield",
  sepY = "_",
  sepFocalNeighbors = NULL
)

Arguments

df

data frame

colIDstand

name of the column containing the identifier of each stand

colIDfocal

name of the column containing the identifiers of the focal genotypes

colIDneighbors

optional name of the column containing the identifiers of the neighbor genotypes

colPlot

name of the column(s) allowing to uniquely identify a plot

colY

name of the column containing the yield data

sepY

separator used in the name of the new "yield" columns made by concatenating colY and colIDfocal or colIDneighbors

sepFocalNeighbors

in case colIDneighbors is unspecified or does not exist in df, the distinction between focal and neighbor(s) will be retrieved by splitting colIDstand using sepFocalNeighbors

Value

data.frame

Author(s)

Timothee Flutre

Examples

## only binary mixtures:
dat0 <- data.frame(
  focal = c("wheat01", "pea02", "wheat01", "pea01"),
  neighbor = c("pea02", "wheat01", "pea01", "wheat01"),
  name = c(
    rep("wheat01-pea02", 2),
    rep("wheat01-pea01", 2)
  ),
  stand = rep("inter", 4),
  x = 1,
  y = c(1, 1, 2, 2),
  yield = c(10, 11, 12, 13),
  stringsAsFactors = TRUE
)
dat0
(dat1 <- pivotMixData2Wide(dat0, colIDstand="name"))

## binary mixtures and a monovarietal:
dat0 <- data.frame(
  focal = c("wheat01", "pea02", "wheat01", "pea01", "wheat01"),
  neighbor = c("pea02", "wheat01", "pea01", "wheat01", "wheat01"),
  name = c(
    rep("wheat01-pea02", 2),
    rep("wheat01-pea01", 2),
    "wheat01"
  ),
  stand = c(rep("inter", 4), "sole"),
  x = 1,
  y = c(1, 1, 2, 2, 3),
  yield = c(10, 11, 12, 13, 20.5),
  stringsAsFactors = TRUE
)
dat0
(dat1 <- pivotMixData2Wide(dat0, colIDstand="name"))

## reverse conversion
dat1v2 <- dat1
colnames(dat1v2)[colnames(dat1v2) == "yield_focal"] <- "yield-cereal"
colnames(dat1v2)[colnames(dat1v2) == "yield_neighbor"] <- "yield-legume"
dat1v2$yield <- apply(dat1v2[,c(7,8)], 1, sum, na.rm=TRUE)
(dat1v2 <- dat1v2[,-c(1,2)])
(dat2 <- pivotMixData2Long(dat1v2, colC="name", sepY="-",
                           genos=list("cereal"=c("wheat01","wheat02"),
                                      "legume"=c("pea01","pea02"))))
all.equal(dat2, dat0)

Plot design for crop mixtures

Description

Plots design for crop mixtures, as a graph and as a diallel.

Usage

plotDesignCropMix(graph, levGenos1, levGenos2, main = NULL)

Arguments

graph

graph

levGenos1

character vector with the names of the genotypes of the first species

levGenos2

character vector with the names of the genotypes of the second species

main

main title for the graph plot

Value

nothing

Author(s)

Timothee Flutre

Examples

nbGenos1 <- 30
levGenos1 <- sprintf(fmt=paste0("S1_%0", floor(log10(nbGenos1))+1, "i"),
                     1:nbGenos1)
nbGenos2 <- 8
levGenos2 <- sprintf(fmt=paste0("S2_%0", floor(log10(nbGenos2))+1, "i"),
                     1:nbGenos2)
nbMixes <- 60 # only binary and balanced
design <- getDesignBinaryCropMix(levGenos1, levGenos2, nbMixes, seed=12345)
plotDesignCropMix(design$graph, levGenos1, levGenos2)

Plot design for varietal mixtures

Description

Plots design for varietal mixtures, as a graph and as a diallel.

Usage

plotDesignVarMix(
  graph,
  levGenos,
  main = NULL,
  subplots = c("graph", "diallel")
)

Arguments

graph

graph

levGenos

character vector with the names of the genotypes

main

main title for the graph plot

subplots

character vector indicating which object(s) to plot

Value

nothing

Author(s)

Timothee Flutre

Examples

nbGenos <- 25
levGenos <- sprintf(fmt=paste0("geno%0", floor(log10(nbGenos))+1, "i"),
                    1:nbGenos)
nbMixes <- 75 # only binary and balanced
design <- getDesignBinaryVarMix(levGenos, nbMixes, seed=12345)
plotDesignVarMix(design$graph, levGenos)

Plot diallel

Description

Plots a diallel matrix.

Usage

plotDiallel(diallel, main = NULL)

Arguments

diallel

matrix

main

title

Value

nothing

Author(s)

Timothee Flutre

Examples

nbGenos <- 25
levGenos <- sprintf(fmt=paste0("geno%0", floor(log10(nbGenos))+1, "i"),
                    1:nbGenos)
nbMixes <- 75 # only binary and balanced
design <- getDesignBinaryVarMix(levGenos, nbMixes, seed=12345)
plotDiallel(design$diallel)

Matrix-variate Normal distribution

Description

Random generation for the matrix-variate Normal distribution. See https://en.wikipedia.org/wiki/Matrix_normal_distribution.

Usage

rmatnorm(n = 1, M, U, V, pivot = c(U = "auto", V = "auto"))

Arguments

n

number of observations

M

mean matrix; if missing, will be replaced by a matrix of zeroes; if dimnames(M) is not NULL, the column and row names will be compared to those of U and V (if any) and, in the absence of conflict, propagated to the output

U

between-row covariance matrix

V

between-column covariance matrix

pivot

2-element vector with values TRUE/FALSE/"auto", where TRUE (FALSE) means using pivoting (or not) for Choleski decomposition of U and/or V (see chol); useful when U and/or V are singular; with "auto", this will be automatically determined

Value

array

Author(s)

Timothee Flutre

Examples

set.seed(1859)
Sigma <- matrix(c(3,2,2,4), nrow=2, ncol=2)
rho <- Sigma[2,1] / prod(sqrt(diag(Sigma)))
samples <- rmatnorm(n=100, M=matrix(0, nrow=10^3, ncol=2),
                    U=diag(10^3), V=Sigma)
tmp <- t(apply(samples, 3, function(mat){
  c(var(mat[,1]), var(mat[,2]), cor(mat[,1], mat[,2]))
}))
summary(tmp) # corresponds well to Sigma

Simulate w.r.t. a DBV-SBV model for interspecific mixtures

Description

Simulates an incomplete (sparse) yet balanced, tester-based design with respect to a DBV-SBV model for interspecific mixtures.

Usage

simulDBVSBVinter(
  GRMs,
  parsGenetics = list(mu = list(S1 = c(SC = 65, IC = 32), S2 = c(SC = 30, IC = 27)),
    sdBlocks = 4, CV_g = c(S1 = 0.08, S2 = 0.08), prop_var_SBV = c(S1 = 0.2, S2 = 0.2),
    prop_var_SIGV = c(S1 = 0.5, S2 = 0), cor_DBV_SBV = c(S1 = -0.9, S2 = -0.9),
    prop_var_DBVxSBV = c(S1 = 0, S2 = 0), use_GRM_for_DBVxSBV = TRUE, cor_DxS = 0, H2_SC
    = c(S1 = 0.7, S2 = 0.7), T2 = c(S1 = 0.7, S2 = 0.7), cor_err_IC = -0.2),
  parsDesign = list(nbBlocks = 2, nbPlotsPerBl = 500, nbYs = 10, sowingDensities =
    list(S1 = c(SC = 300, IC = 150), S2 = c(SC = 40, IC = 40)), testersS2 = TRUE,
    incomplete_balanced = TRUE),
  sep = "+",
  seed = NULL
)

Arguments

GRMs

named list with a genomic relationship matrix per species, named S1 and S2; should be a diagonal for S2 if it is a tester (see parsDesign)

parsGenetics

list of genetic parameters

parsDesign

list of design parameters

sep

character separating the names of a mixture components

seed

seed for the generation of pseudo-random numbers

Value

list

Author(s)

Timothee Flutre

Examples

## simulate a data set with both sole crops and intercrops:
GRMs <- list("S1" = diag(100),
             "S2" = diag(2))
dimnames(GRMs$S1) <- list(paste0("gS1_", 1:100), paste0("gS1_", 1:100))
dimnames(GRMs$S2) <- list(paste0("gS2_", 1:2), paste0("gS2_", 1:2))
out <- simulDBVSBVinter(GRMs)
names(out)
str(out$datW)
str(out$datL)

## see the third vignette for more details

Simulate SNP genotypes

Description

Simulates SNP genotypes as allele dose additively encoded, i.e. 0,1,2, using correlated allele frequencies to mimick genetic structure.

Usage

simulGenosDoseStruct(
  nb_genos = c(100, 120, 80),
  nb_snps = 1000,
  div_pops = diag(3) * 0.5 + 0.5,
  geno_IDs = NULL,
  snp_IDs = NULL
)

Arguments

nb_genos

number of genotypes (i.e. individuals) per population

nb_snps

number of SNPs

div_pops

matrix of divergence among populations, with a diagonal of 1's; the closer off-diagonal values are from 1; the weaker the divergence; the further, the stronger

geno_IDs

vector of genotype identifiers (if NULL, will be "geno001", etc)

snp_IDs

vector of SNP identifiers (if NULL, will be "snp001", etc)

Value

matrix with genotypes in rows and SNPs in columns

Author(s)

Timothee Flutre thanks to code from Andres Legarra

Examples

## weak divergences among populations:
weak_div_pops <- diag(3)
weak_div_pops[upper.tri(weak_div_pops)] <- 0.9
weak_div_pops[lower.tri(weak_div_pops)] <- weak_div_pops[upper.tri(weak_div_pops)]
weak_div_pops

## strong divergences among populations:
strong_div_pops <- diag(3)
strong_div_pops[upper.tri(strong_div_pops)] <- 0.5
strong_div_pops[lower.tri(strong_div_pops)] <- strong_div_pops[upper.tri(strong_div_pops)]
strong_div_pops

M <- simulGenosDoseStruct(div_pops=weak_div_pops)
A <- estimGRM(M)
imageMat(A, "Weak divergence")

M <- simulGenosDoseStruct(div_pops=strong_div_pops)
A <- estimGRM(M)
imageMat(A, "Strong divergence")

Model summary

Description

Summarizes the GMA-SMA models.

Usage

summarizeGMASMAs(fits)

Arguments

fits

named list of "merMod" objects returned by lmer, as is done by fitGMASMA with pkg="lme4"

Value

matrix

Author(s)

Timothee Flutre

Examples

## see the first vignette

Vec operator

Description

Applies the vec operator to a matrix, i.e., concatenate its columns, and save the names, too, if any.

Usage

vec(mat, sep = "-")

Arguments

mat

matrix

sep

separator of column and row names; used only if mat has dimnames

Value

vector, possibly with names as <rowname><sep><colname>

Author(s)

Timothee Flutre

Examples

mat <- matrix(c(1, 2, 3,
                4, 5, 6),
              nrow = 2, ncol = 3, byrow = TRUE,
              dimnames = list(as.character(1:2), letters[1:3]))
mat
vec(mat)

Package {plantmix}

The plantmix package

Description

Introduction

Optimizing experimental designs

Fitting GMA-SMA models

Fitting DBV-SBV models

Simulating mixture data

Interaction indices

Miscellaneous

Author(s)

Change in contribution

Description

Usage

Arguments

Value

Author(s)

Examples

Land equivalent ratio (LER)

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Relative interaction index (RII)

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Net Relative interaction index (RIInet)

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Relative yield (RY)

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Relative yield of mixture (RYM)

Description

Usage

Arguments

Value

See Also

Examples

Relative yield per plant (RYP)

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Relative yield total (RYT)

Description

Usage

Arguments

Value

See Also

Examples

Estimate a GRM

Description

Usage

Arguments

Value

Author(s)

See Also

Examples