### For testing competition, we perform a simulation excercise ###
set.seed(12345)
grid.size <- c(x = 20, y = 25) # x: columns, y: rows
# dist.rc <- c(x = 3, y = 5)
coord <- expand.grid(sapply(grid.size, seq))
Nobs <- prod(grid.size)
Nparents <- c(mum = 20, dad = 20)
rho <- -.7 # genetic correlation between additive and competitive values
# covariance = -.7 * sqrt(2) = -0.98
sigma2_a <- 2 # additive genetic variance
sigma2_c <- 1 # competitive genetic variance
sigma2_p <- .5 # permanent environment effect of competition
sigma2 <- .5 # residual variance
ped.obs <- data.frame(id = 1:Nobs + sum(Nparents),
mum = sample(Nparents['mum'],
size = Nobs,
replace = TRUE),
dad = sample(Nparents['dad'],
size = Nobs,
replace = TRUE) + Nparents['mum'])
fullped <- build_pedigree(1:3, data = ped.obs)
# checks
# stopifnot(all(xtabs( ~ mum + dad, data = ped.obs) > 0))
stopifnot(all(check_pedigree(fullped)))
# Additive matrix
# Precision matrices are more sparse
# # Workaround with package pedigree
# makeAinv(as.data.frame(fullped))
# Ai <- read.table("Ainv.txt")
# nInd <- nrow(as.data.frame(fullped))
# Ainv <- matrix(0,nrow = nInd,ncol = nInd)
# Ainv[as.matrix(Ai[,1:2])] <- Ai[,3]
# dd <- diag(Ainv)
# Ainv <- Ainv + t(Ainv)
# diag(Ainv) <- dd
Ainv <- pedigreemm::getAInv(fullped)
# Covariance matrix for the genetic effects (a, c)
S_ac <- matrix(c(sigma2_a, rho*sqrt(sigma2_a*sigma2_c),
rho*sqrt(sigma2_a*sigma2_c), sigma2_c), 2, 2)
Q <- kronecker(solve(S_ac), Ainv)
# Simulate genetic effects
ped <- as.data.frame(fullped)
gen_a_c <- matrix(spam::rmvnorm.prec(n = 1,
Q = Q),
ncol = 2,
dimnames = list(NULL, c('a', 'c')))
# checks
# plot(gen_a_c, pch = 19)
# cor(gen_a_c)
# Randomly distribute the trees over the grid
dat <- data.frame(coord[sample(Nobs),],
ped.obs,
tail(gen_a_c, Nobs),
pec = rnorm(Nobs, sd = sqrt(sigma2_p)),
e = rnorm(Nobs, sd = sqrt(sigma2)))
# check
# BVs must be more similar for members of the same family
# with(dat, qplot(paste(mum, dad, sep='_'), c))
# Simulate phenotype
# Each tree is affected by the competition value of its neighbours
# Compute IFCs and corresponding neighbours. Assume equal row/col spacing and
# competition intensity decreasing with inverse distance
# neighbours and weighted coefficients of IC
X <- local({
rect.dist = 1
diag.dist = sqrt(2)
# It is convenient to work with matrices representing the spatial arrangement
ord <- order(dat$x, dat$y)
matlst <- lapply(dat[ord, c('id', 'a', 'c', 'e', 'pec')],
function(x) matrix(x, nrow = grid.size['y']))
rect <- breedR:::neighbours.at.list(matlst, c('N', 'S', 'E', 'W'))
diag <- breedR:::neighbours.at.list(matlst, c('NE', 'SE', 'SW', 'NW'))
dat <- c(rect$id,
diag$id,
ifelse(is.na(rect$id), NA, 1/rect.dist),
ifelse(is.na(diag$id), NA, 1/diag.dist),
rect$c,
diag$c,
rect$pec,
diag$pec)
# Four-dimensional array
# two first dimensions are spatial
# third dimension is direction of neighbourhood: N, S, ..., NW (8)
# fourth dimension is
# 1 = neighbour idx
# 2 = neighbour IFC
# 3 = neighbour c
# 4 = neighbour pec
x <- array(dat, dim = c(rev(grid.size), 8, 4))
# normalize to make all squared-coefficients add up to one throughout dim. 3
normalizing.constant = apply(x[,,,2]**2, 1:2, sum, na.rm = TRUE)
x[,,,2] <- x[,,,2] / as.vector(sqrt(normalizing.constant))
# check
stopifnot(all(sapply(apply(x[,,,2]**2, 1:2, sum, na.rm = TRUE), all.equal, 1)))
# result in tabular form
# first eight cols are neighbour idx, last eight are coefs that add up to 1
res <- data.frame(as.vector(matlst$id),
array(x, dim = c(prod(grid.size), 32)))
colnames(res) <- c('idx',
paste('n', 1:8, sep = ''),
paste('ifc', 1:8, sep = ''),
paste('c', 1:8, sep = ''),
paste('pec', 1:8, sep = ''))
rownames(res) <- NULL
# check
stopifnot(all(sapply(apply(res[,10:17]**2, 1, sum, na.rm = TRUE), all.equal, 1)))
# return results in the original order of the data frame
res[order(ord),]
})
# Contribution to phenotype of neighbouring genetic-competition effects
dat$wnc <- rowSums(X[,10:17] * X[,17+1:8], na.rm = TRUE)
dat$pec <- rowSums(X[,10:17] * X[,25+1:8], na.rm = TRUE)
# Simulated phenotype (with or without pec)
dat <- transform(dat, z = a + wnc + pec + e)
# dat <- transform(dat, z = a + wnc + e)
#### Fitting the competition model with remlf90 ################################
context('Fitting competition models')
expect_error(
res <- suppressMessages(
remlf90(
fixed = z ~ 1,
genetic = list(model = c('comp'),
pedigree = dat[, c('id', 'mum', 'dad')],
id = 'id',
coord = dat[, c('x', 'y')],
competition_decay = 1,
pec = list(present = TRUE)),
data = dat,
method = 'em',
debug = F)
),
NA)
# ggplot2::qplot(dat$z - dat$e, fitted(res)) +
# ggplot2::geom_abline(intercept = 0, slope = 1, col = 'darkgray')
#### Context: Extraction of results from competition model #####################
context("Extraction of results from competition model")
n.fixed <- 1
nlevels.fixed <- 1
n.bvs <- nrow(as.data.frame(fullped)) # one set for direct, another for comp.
n.pec <- nrow(dat)
test_that("coef() gets a named vector of coefficients", {
expect_is(coef(res), 'numeric')
expect_equal(length(coef(res)), nlevels.fixed + 2*n.bvs + n.pec)
expect_named(coef(res))
})
test_that("ExtractAIC() gets one number", {
expect_is(extractAIC(res), 'numeric')
expect_equal(length(extractAIC(res)), 1)
})
test_that("fitted() gets a vector of length N", {
expect_is(fitted(res), 'numeric')
expect_equal(length(fitted(res)), Nobs)
})
test_that("fixef() gets a named list of numeric vectors with estimated values and s.e.", {
x <- fixef(res)
expect_is(x, 'breedR_estimates')
expect_named(x)
expect_equal(length(x), n.fixed)
for (f in x) {
expect_is(f, 'numeric')
expect_false(is.null(fse <- attr(f, 'se')))
expect_is(fse, 'numeric')
expect_equal(length(fse), length(f))
}
})
test_that("get_pedigree() returns the given pedigree", {
expect_identical(get_pedigree(res), fullped)
})
test_that("logLik() gets an object of class logLik", {
expect_is(logLik(res), 'logLik')
})
test_that("model.frame() gets an Nx2 data.frame with a 'terms' attribute", {
x <- model.frame(res)
expect_is(x, 'data.frame')
expect_is(terms(x), 'terms')
expect_equal(dim(x), c(Nobs, n.fixed + 1))
})
test_that("model.matrix() gets a named list of incidence matrices", {
x <- model.matrix(res)
expect_is(x, 'list')
expect_named(x, get_efnames(res$effects))
expect_equal(dim(x$Intercept), c(Nobs, nlevels.fixed))
for (m in x$random) {
expect_is(m, 'sparseMatrix')
}
expect_equal(dim(x$genetic_direct), c(Nobs, n.bvs))
expect_equal(dim(x$genetic_competition), c(Nobs, n.bvs))
expect_equal(dim(x$pec), c(Nobs, Nobs))
})
test_that("nobs() gets the number of observations", {
expect_equal(nobs(res), Nobs)
})
test_that("plot(, type = *) returns ggplot objects after providing coords", {
## Even when there is no spatial effect,
## the coordinates must be recovered from the competition
expect_is(plot(res, type = 'phenotype'), 'ggplot')
expect_is(plot(res, type = 'fitted'), 'ggplot')
expect_is(plot(res, type = 'residuals'), 'ggplot')
## But still get errors for the absent spatial components
expect_error(plot(res, type = 'spatial'), 'no spatial effect')
expect_error(plot(res, type = 'fullspatial'), 'no spatial effect')
})
test_that("print() shows some basic information", {
## Not very informative currently...
expect_output(print(res), 'Data')
})
test_that("ranef() gets a ranef.breedR object with random effect BLUPs and their s.e.", {
x <- ranef(res)
expect_is(x, 'ranef.breedR')
expect_equal(length(x), 3)
expect_named(x, c('genetic_direct', 'genetic_competition', 'pec'))
for (y in x) {
expect_is(y, 'numeric')
#expect_equal(length(y), n.bvs) # pec is of length 500
expect_false(is.null(xse <- attr(y, 'se')))
expect_is(xse, 'numeric')
#expect_equal(length(xse), n.bvs)
}
})
test_that("residuals() gets a vector of length N", {
rsd <- residuals(res)
expect_is(rsd, 'numeric')
expect_equal(length(rsd), Nobs)
})
test_that("summary() shows summary information", {
expect_output(print(summary(res)), 'Variance components')
})
test_that("vcov() gets the covariance matrix of the genetic components of the observations", {
for (ef in paste('genetic', c('direct', 'competition'), sep = '_')) {
x <- try(vcov(res, effect = 'genetic_direct'))
expect_false(inherits(x, 'try-error'))
expect_is(x, 'sparseMatrix')
expect_equal(dim(x), rep(Nobs, 2))
}
})
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