## ----echo=FALSE---------------------------------------------------------------
suppressPackageStartupMessages(library(knitr))
opts_chunk$set(
  echo = TRUE, warning = TRUE, message = TRUE, cache = FALSE,
  fig.align = "center", collapse = TRUE
)
opts_knit$set(progress = TRUE, verbose = TRUE)

## -----------------------------------------------------------------------------
suppressPackageStartupMessages(library(plantmix))
suppressPackageStartupMessages(library(ggplot2))

## ----time_0, echo=FALSE-------------------------------------------------------
## Execution time (see the appendix):
t0 <- proc.time()

## -----------------------------------------------------------------------------
set.seed(12345)

## -----------------------------------------------------------------------------
levSpecies <- c("S1", "S2")

nbGenos <- c("S1" = 500, "S2" = 500)
levGenos <- list(
  "S1" = sprintf(
    fmt = paste0("gS1_%0", floor(log10(nbGenos["S1"])) + 1, "i"),
    1:nbGenos["S1"]
  ),
  "S2" = sprintf(
    fmt = paste0("gS2_%0", floor(log10(nbGenos["S2"])) + 1, "i"),
    1:nbGenos["S2"]
  )
)

nbSnps <- c("S1" = 1000, "S2" = 1000)
levSnps <- list(
  "S1" = sprintf(
    fmt = paste0("sS1_%0", floor(log10(nbSnps["S1"])) + 1, "i"),
    1:nbSnps["S1"]
  ),
  "S2" = sprintf(
    fmt = paste0("sS2_%0", floor(log10(nbSnps["S2"])) + 1, "i"),
    1:nbSnps["S2"]
  )
)

## -----------------------------------------------------------------------------
nb_pops <- 10
weak_div_pops <- diag(nb_pops)
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)]
snpGenos <- lapply(levSpecies, function(species) {
  tmp <- rep(nbGenos[species] / nb_pops, nb_pops - 1)
  tmp <- c(tmp, nbGenos[species] - sum(tmp))
  simulGenosDoseStruct(
    nb_genos = tmp,
    nb_snps = nbSnps[species],
    div_pops = weak_div_pops,
    geno_IDs = levGenos[[species]],
    snp_IDs = levSnps[[species]]
  )
})
names(snpGenos) <- levSpecies
sapply(snpGenos, dim)
snpGenos$S1[1:3, 1:4]
table(snpGenos$S1)

## -----------------------------------------------------------------------------
snpAFs <- lapply(snpGenos, function(M) {
  colSums(M) / (2 * nrow(M))
})
GRMs_vr <- lapply(levSpecies, function(species) {
  GRM <- estimGRM(snpGenos[[species]], snpAFs[[species]])
  as.matrix(Matrix::nearPD(GRM)$mat)
})
names(GRMs_vr) <- levSpecies

## ----class.source='fold-hide'-------------------------------------------------
species <- "S1"
GRM <- GRMs_vr[[species]]
image(Matrix(GRM), main = paste0("GRM for ", species))
hist(diag(GRM), main = paste0("GRM for ", species))
hist(GRM[upper.tri(GRM)], main = paste0("GRM for ", species))

## -----------------------------------------------------------------------------
nbGenosTrial <- c("S1" = 300, "S2" = 2)
levGenosTrial <- lapply(levSpecies, function(species) {
  sample(levGenos[[species]], nbGenosTrial[species])
})
names(levGenosTrial) <- levSpecies

GRMs_vr_trial <- list()
GRMs_vr_trial$S1 <- GRMs_vr$S1[levGenosTrial$S1, levGenosTrial$S1]
GRMs_vr_trial$S2 <- diag(nbGenosTrial["S2"]) # diag because modeled as fixed
dimnames(GRMs_vr_trial$S2) <- list(levGenosTrial$S2, levGenosTrial$S2)

set.seed(12345)
out <- simulDBVSBVinter(GRMs_vr_trial) # see ?simulDBVSBVinter for more details
names(out)
tmp <- list2env(out, envir = environment())

## ----fig.width=10-------------------------------------------------------------
plantmix:::plotAllocSchemeInterMixDesign(datW)

## ----class.source='fold-hide'-------------------------------------------------
## Reformat and compute
is_mix <- which(datW$type == "IC")
true_RYTs <- list()
true_RYPs <- list()
for (idx in is_mix) {
  true_y1_IC <- as.vector(truth$mu[["S1"]]["IC"]) + datW$true_gen_yield_S1[idx]
  true_y2_IC <- as.vector(truth$mu[["S2"]]["IC"]) + datW$true_gen_yield_S2[idx]
  g1 <- as.character(datW$geno_S1[idx])
  g2 <- as.character(datW$geno_S2[idx])
  true_y1_SC <- as.vector(truth$mu[["S1"]]["SC"]) +
    datW$true_gen_yield_S1[datW$geno_S1 == g1 &
      is.na(datW$geno_S2)]
  true_y2_SC <- as.vector(truth$mu[["S2"]]["SC"]) +
    datW$true_gen_yield_S2[datW$geno_S2 == g2 &
      is.na(datW$geno_S1)]
  true_y2_SC <- unique(true_y2_SC)
  yields <- data.frame(
    SCcrop = c(true_y1_SC, true_y2_SC),
    intercrop = c(true_y1_IC, true_y2_IC),
    row.names = c(g1, g2)
  )
  tmp <- LER(yields)
  mixId <- paste0(g1, "-", g2)
  true_RYTs[[mixId]] <- c(
    "RY_S1" = as.numeric(tmp$pLER[1]),
    "RY_S2" = as.numeric(tmp$pLER[2]),
    "RYT" = tmp$LER
  )
  true_RYPs[[mixId]] <- c(
    "RYP_S1" = true_y1_IC /
      (props[["S1"]] * true_y1_SC),
    "RYP_S2" = true_y2_IC /
      (props[["S2"]] * true_y2_SC)
  )
}
true_RYTs <- data.frame(
  mix = names(true_RYTs),
  geno_S1 = sapply(strsplit(names(true_RYTs), "-"), `[`, 1),
  geno_S2 = sapply(strsplit(names(true_RYTs), "-"), `[`, 2),
  as.data.frame(do.call(rbind, true_RYTs)),
  stringsAsFactors = TRUE
)
str(true_RYTs)
summary(true_RYTs[, grep("RY_", colnames(true_RYTs))])
summary(true_RYTs[, grep("RYT", colnames(true_RYTs))])
true_RYPs <- data.frame(
  mix = names(true_RYPs),
  geno_S1 = sapply(strsplit(names(true_RYPs), "-"), `[`, 1),
  geno_S2 = sapply(strsplit(names(true_RYPs), "-"), `[`, 2),
  as.data.frame(do.call(rbind, true_RYPs)),
  stringsAsFactors = TRUE
)
str(true_RYPs)
summary(true_RYPs[, grep("RYP", colnames(true_RYPs))])

if (FALSE) {
  ## using the RYT() function
  keys <- unique(paste0(datL$focal, " in ", datL$standID))
  tmp <- do.call(rbind, strsplit(keys, " in "))
  datLavg <- data.frame(
    key = keys,
    focal = tmp[, 1],
    standID = tmp[, 2],
    stringsAsFactors = TRUE
  )
  datLavg$type <- "IC"
  datLavg$type[as.character(datLavg$focal) == as.character(datLavg$standID)] <- "SC"
  datLavg$focal_species <- "S1"
  datLavg$focal_species[grep("^gS2_", datLavg$focal)] <- "S2"
  datLavg$prop <- 1
  datLavg$prop[datLavg$type == "IC" & datLavg$focal_species == "S1"] <- props["S1"]
  datLavg$prop[datLavg$type == "IC" & datLavg$focal_species == "S2"] <- props["S2"]
  for (i in 1:nrow(datLavg)) {
    idx <- which(datL$focal == datLavg$focal[i] &
      datL$standID == datLavg$standID[i])
    datLavg$focal_yield[i] <- mean(datL$focal_yield[idx])
  }
  true_RYTs2 <- RYT(datLavg, "standID", "focal", "prop", "focal_yield")
  true_RYTs2 <- droplevels(true_RYTs2[!is.na(true_RYTs2$RYT), ])
  true_RYTs2 <- droplevels(true_RYTs2[!duplicated(true_RYTs2$standID), ])
}

## Plot
ggplot(true_RYTs) +
  aes(x = RY_S1) +
  geom_histogram(color = "white", bins = 30) +
  geom_vline(
    xintercept = sowingDensities$S1["IC"] /
      sowingDensities$S1["SC"],
    col = "red", linewidth = 2
  ) +
  labs(
    title = "True relative yields (partial land-equivalent ratios) of species 1 for all mixtures",
    x = "RY (partial LER) of species 1"
  ) +
  theme_bw()

ggplot(true_RYTs) +
  aes(x = RY_S2) +
  geom_histogram(color = "white", bins = 30) +
  geom_vline(
    xintercept = sowingDensities$S2["IC"] /
      sowingDensities$S2["SC"],
    col = "red", linewidth = 2
  ) +
  labs(
    title = "True partial land-equivalent ratio of species 2 for all mixtures",
    x = "RY (partial LER) of species 2"
  ) +
  theme_bw()

ggplot(true_RYTs) +
  aes(x = geno_S2, y = RYT) +
  geom_violin(aes(fill = geno_S2), trim = FALSE, show.legend = FALSE) +
  geom_boxplot(width = 0.2) +
  labs(
    title = "True land-equivalent ratio for all mixtures"
  ) +
  theme_bw()

p <- ggplot(true_RYTs) +
  aes(x = RY_S1, y = RY_S2, color = geno_S2)
for (i in seq(0.75, 2, by = 0.25)) {
  if (i == 1) {
    p <- p + geom_abline(intercept = i, slope = -1, linetype = "solid", color = "black")
  } else {
    p <- p + geom_abline(intercept = i, slope = -1, linetype = "dotted", color = "black")
  }
}
p + geom_abline(intercept = 0, slope = 1, linetype = "dotted", color = "black") +
  geom_point(size = 2) +
  labs(
    title = "True relative yields (RYs, a.k.a. partial LERs)",
    x = "relative yield (partial LER) of species 1",
    y = "relative yiedl (partial LER) of species 2",
    color = "Tester"
  ) +
  ## guides(color="none") +
  scale_x_continuous(breaks = seq(0, 1.4, by = 0.1)) +
  scale_y_continuous(breaks = seq(0, 1.4, by = 0.1)) +
  coord_cartesian(xlim = c(0, 1.4), ylim = c(0, 1.4)) +
  theme(aspect.ratio = 1) +
  theme_bw()

ggplot(true_RYPs) +
  aes(x = RYP_S1, y = RYP_S2, color = geno_S2) +
  geom_abline(intercept = 0, slope = 1, linetype = "solid", color = "black") +
  geom_hline(yintercept = 1) +
  geom_vline(xintercept = 1) +
  geom_point(size = 2) +
  labs(
    title = "True relative yields per plant (RYPs)",
    x = "RYP of species 1",
    y = "RYP of species 2",
    color = "Tester"
  ) +
  ## guides(color="none") +
  scale_x_continuous(breaks = seq(0, 6.5, by = 1)) +
  scale_y_continuous(breaks = seq(0, 6.5, by = 1)) +
  coord_cartesian(xlim = c(0, 6.5), ylim = c(0, 6.5)) +
  theme(aspect.ratio = 1) +
  theme_bw()

## ----class.source='fold-hide', fig.width=10-----------------------------------
## Reformat and compute
tmp <- datW[, c("geno_S1", "geno_S2", "true_yield_S1", "true_yield_S2")]
tmp$ID <- NA
tmp$props <- NA
tmp$true_yield <- NA
## sole crop of species 1:
idx <- which(!is.na(tmp$geno_S1) & is.na(tmp$geno_S2))
tmp$ID[idx] <- as.character(tmp$geno_S1[idx])
tmp$props[idx] <- "1"
tmp$true_yield[idx] <- tmp$true_yield_S1[idx]
## sole crop of species 2:
idx <- which(is.na(tmp$geno_S1) & !is.na(tmp$geno_S2))
tmp$ID[idx] <- as.character(tmp$geno_S2[idx])
tmp$props[idx] <- "1"
tmp$true_yield[idx] <- tmp$true_yield_S2[idx]
## intercrops of species 1 and 2:
idx <- which(!is.na(tmp$geno_S1) & !is.na(tmp$geno_S2))
sep <- "|"
tmp$ID[idx] <- as.character(paste0(
  tmp$geno_S1[idx], sep,
  tmp$geno_S2[idx]
))
prop1 <- props["S1"]
prop2 <- props["S2"]
stopifnot(prop1 + prop2 == 1)
prop1 <- round(prop1, 2)
prop2 <- 1 - prop1
tmp$props[idx] <- paste0(prop1, sep, prop2)
tmp$true_yield[idx] <- tmp$true_yield_S1[idx] + tmp$true_yield_S2[idx]
stopifnot(all(!is.na(tmp$ID)))
tmp$ID <- factor(tmp$ID)
tmp$props <- factor(tmp$props)
## keep only one yield (the true one) per modality
dupIDs <- table(as.character(tmp$ID))
(dupIDs <- names(dupIDs)[dupIDs > 1])
for (dupID in dupIDs) {
  idx <- which(as.character(tmp$ID) == dupID)
  tmp <- droplevels(tmp[-idx[2:length(idx)], ])
}
rm(dupIDs)

tmp <- RYM(tmp,
  colIDstand = "ID", colIDcomps = "ID", colProps = "props",
  colY = "true_yield", sep = "|"
)
summary(tmp$RYM)

## Plot
ggplot(tmp) +
  aes(x = RYM) +
  geom_histogram(na.rm = TRUE, bins = 30, color = "white") +
  geom_vline(xintercept = 1, linewidth = 2) +
  geom_vline(xintercept = mean(tmp$RYM, na.rm = TRUE), linewidth = 2, color = "red") +
  labs(title = "True relative yields of mixtures (RYMs)") +
  theme_bw()

## -----------------------------------------------------------------------------
str(datW)
tapply(datW$type, datW$block, table)

## ----class.source='fold-hide'-------------------------------------------------
ggplot(datL) +
  aes(x = block, y = focal_yield) +
  geom_violin(aes(fill = block)) +
  geom_boxplot(fill = "white", width = 0.2) +
  theme_bw() +
  facet_grid(focal_species ~ type)

is_mix <- datW$type == "IC"
subDatW <- droplevels(datW[is_mix, ])
ggplot(subDatW) +
  aes(x = yield_S1, y = yield_S2, color = geno_S1, shape = geno_S2) +
  geom_abline(intercept = seq(0, 200, by = 10), slope = -1, linetype = "dotted", color = "black") +
  geom_point(size = 2) +
  labs(
    title = "Partial yields in intercrop",
    x = "species 1 (in qt.ha-1)", y = "species 2 (in qt.ha-1)",
    shape = "Tester (species S2)"
  ) +
  guides(color = "none") +
  theme_bw()

## ----class.source='fold-hide', fig.width=10-----------------------------------
## Add the empty micro-plots:
coords <- data.frame(
  x = rep(sort(unique(datW$x)), each = length(unique(datW$y))),
  y = sort(unique(datW$y)),
  block = "A",
  plot = NA
)
coords$block[coords$x >= min(datW$x[datW$block != "A"])] <- "B"
coords$plot <- paste0(coords$x, coords$block, coords$y)
length(idx <- which(!coords$plot %in% as.character(datW$plot)))
tmp <- as.data.frame(matrix(nrow = length(idx), ncol = ncol(datW)))
colnames(tmp) <- colnames(datW)
tmp[, c("x", "y", "block", "plot")] <- coords[idx, ]
datWSupp <- rbind(
  datW,
  as.data.frame(tmp)
)

## Plot
xranges <- do.call(rbind, tapply(datW$x, datW$block, range))
p <- ggplot(datWSupp) +
  aes(x = x, y = y) +
  theme_bw() +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank()
  ) +
  scale_x_continuous(breaks = sort(unique(datW$x))) +
  scale_y_continuous(
    breaks = sort(unique(datW$y)),
    sec.axis = dup_axis()
  ) +
  guides(x = guide_axis(angle = 90)) +
  geom_tile(na.rm = TRUE) +
  geom_rect(aes(xmin = x - 0.5, xmax = x + 0.5, ymin = y - 0.5, ymax = y + 0.5),
    color = "white"
  ) +
  geom_text(
    x = sum(xranges[1, ]) / 2, y = 10.7, label = "Block A",
    hjust = 0, color = "black"
  ) +
  geom_text(
    x = sum(xranges[2, ]) / 2, y = 10.7, label = "Block B",
    hjust = 0, color = "black"
  ) +
  geom_vline(
    xintercept = max(datW$x[datW$block == "A"]),
    color = "black", linetype = "dashed", linewidth = 1
  )

p + aes(fill = type) +
  labs(title = "Types of microplots") +
  scale_fill_discrete()

scaleCols <- c("#CB2027", "#ffec1b", "#b3e93e", "#60BD68", "#059748")
scaleLim <- range(datW$tot_yield)
p + aes(fill = tot_yield) +
  labs(title = "Total yield for each microplot") +
  scale_fill_continuous(type = "viridis")

## -----------------------------------------------------------------------------
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_vr_trial$S1[
  levels(datW_IC$geno_S1),
  levels(datW_IC$geno_S1)
])

## ----echo=FALSE, eval=FALSE---------------------------------------------------
# if (FALSE) {
#   listZ$Z_DxS_f <- model.matrix(~ 0 + standID, datW_IC)
#   colnames(listZ$Z_DxS_f) <- gsub("^standID", "", colnames(listZ$Z_DxS_f))
#   listVCov$Kmixpair <- diag(nlevels(datW_IC$standID))
#   dimnames(listVCov$Kmixpair) <- list(
#     colnames(listZ$Z_DxS_f), colnames(listZ$Z_DxS_f)
#   )
#   ## TODO: listVCov$invKmixpair <- getInvKmixpairDxS(dat, "focal", "neighbor", sep="+")
# }

## -----------------------------------------------------------------------------
fitsTmb <- list()
i <- 1
for (REML in c(TRUE, FALSE)) {
  print(paste0("fit model with ", ifelse(REML, "REML", "ML"), "..."))
  st <- system.time(
    fitTmb <- fitDBVSBVinter(listY, listX, listZ, listVCov,
      lOptions = list(iter.max = 20, quantifUncertain = TRUE),
      REML = REML, verbose = 0
    )
  )
  print(st)
  fitsTmb[[i]] <- fitTmb
  i <- i + 1
  break # skip ML to speed-up
}

## ----class.source='fold-hide'-------------------------------------------------
for (i in seq_along(fitsTmb)) {
  fitTmb <- fitsTmb[[i]]
  p <- ggplot(fitTmb$trace) +
    aes(x = iter, y = objfn) +
    geom_point() +
    geom_line() +
    labs(
      title = "Optimization convergence",
      subtitle = paste0("REML=", fitTmb$REML)
    ) +
    theme_bw()
  print(p)
}

## ----fig.width=9--------------------------------------------------------------
for (i in seq_along(fitsTmb)) {
  fitTmb <- fitsTmb[[i]]
  print(paste0("REML=", fitTmb$REML))

  print("Check fixed effects:")
  checks <- data.frame(
    param = rownames(fitTmb$B_IC),
    truth = c(truth$B_IC),
    fitTmb$B_IC,
    row.names = NULL,
    check.names = FALSE
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(checks)

  print("Check (co)variances of random genetic effects:")
  checks <- data.frame(
    param = rownames(fitTmb$vcov_BV_f),
    truth = c(
      truth$var_DBV["S1"],
      truth$var_SBV["S1"],
      truth$cor_DBV_SBV["S1"]
    ),
    fitTmb$vcov_BV_f,
    row.names = NULL,
    check.names = FALSE
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(checks)

  print("Check (co)variances of residual errors:")
  checks <- data.frame(
    param = rownames(fitTmb$vcov_E_IC),
    truth = c(truth$var_err_IC, truth$cor_err_IC),
    fitTmb$vcov_E_IC,
    row.names = NULL,
    check.names = FALSE
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(checks)

  print("Check random genetic effects of the focal species:")
  checks <- data.frame(
    param = c(
      rep(c("DBV", "SBV"), each = nrow(truth$BV$S1)),
      rep("BV_IC", length(truth$BV_IC$S1))
    ),
    geno = levels(datW_IC$geno_S1),
    truth = c(
      truth$BV$S1[levels(datW_IC$geno_S1), ],
      truth$BV_IC$S1[levels(datW_IC$geno_S1)]
    ),
    rbind(
      fitTmb$DBV_f[levels(datW_IC$geno_S1), ],
      fitTmb$SBV_IC_f[levels(datW_IC$geno_S1), ],
      fitTmb$BV_IC_f[levels(datW_IC$geno_S1), ]
    ),
    row.names = NULL,
    check.names = FALSE
  )
  checks$param <- factor(checks$param,
    levels = c("BV_IC", "DBV", "SBV")
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(tapply(1:nrow(checks), checks$param, function(idx) {
    cor(checks$truth[idx], checks$Estimate[idx])
  }))
  p <- ggplot(checks) +
    aes(x = Estimate, y = truth) +
    geom_hline(yintercept = 0, linetype = "dotted") +
    geom_vline(xintercept = 0, linetype = "dotted") +
    geom_abline(slope = 1, intercept = 0, linetype = "dotted") +
    geom_point() +
    labs(
      title = "Results with intercrop-only data",
      subtitle = paste0("REML=", fitTmb$REML)
    ) +
    theme_bw() +
    facet_wrap(~param)
  print(p)
}

## -----------------------------------------------------------------------------
if (FALSE) { # slow
  system.time(
    pmTmb <- paramBoot4TMB(fitTmb, nb_boot = 5)
  )
  summary(do.call(rbind, pmTmb))
  fitTmb$sry_sdr[names(pmTmb[[1]]), ]
}

## -----------------------------------------------------------------------------
idxIC <- which(!is.na(datW$geno_S1) & !is.na(datW$geno_S2))
datW_IC <- droplevels(datW[idxIC, ])
idxSCf <- which(datL$type == "SC" & datL$focal_species == "S1")
datL_SC_f <- droplevels(datL[idxSCf, ])
idxSCt <- which(datL$type == "SC" & datL$focal_species == "S2")
datL_SC_t <- droplevels(datL[idxSCt, ])

listY <- list()
listY$Y_IC <- datW_IC[, c("yield_S1", "yield_S2")]
listY$y_SC_f <- datL_SC_f$focal_yield
listY$y_SC_t <- datL_SC_t$focal_yield
sapply(listY[-1], length)

listX <- list()
listX$X_IC <- model.matrix(~ 1 + block + geno_S2,
  data = datW_IC,
  contrasts.arg = list(
    "block" = "contr.sum",
    "geno_S2" = "contr.sum"
  )
)
listX$X_SC_f <- model.matrix(~ 1 + block, datL_SC_f,
  contrasts.arg = list(block = "contr.sum")
)
listX$X_SC_t <- model.matrix(~ 1 + block + focal, datL_SC_t,
  contrasts.arg = list(
    block = "contr.sum",
    focal = "contr.sum"
  )
)

listZ <- list()
listZ$Z_DS_f <- model.matrix(~ 0 + geno_S1, datW_IC)
colnames(listZ$Z_DS_f) <- gsub("^geno_S1", "", colnames(listZ$Z_DS_f))
listZ$Z_D_f <- model.matrix(~ 0 + focal, datL_SC_f)
colnames(listZ$Z_D_f) <- gsub("^focal", "", colnames(listZ$Z_D_f))

listVCov <- list(K = GRMs_vr_trial$S1[
  levels(datW_IC$geno_S1),
  levels(datW_IC$geno_S1)
])

## ----echo=FALSE, eval=FALSE---------------------------------------------------
# if (FALSE) {
#   listZ$Z_DxS_f <- model.matrix(~ 0 + standID, datW_IC)
#   colnames(listZ$Z_DxS_f) <- gsub("^standID", "", colnames(listZ$Z_DxS_f))
#   listVCov$Kmixpair <- diag(nlevels(datW_IC$standID))
#   dimnames(listVCov$Kmixpair) <- list(
#     colnames(listZ$Z_DxS_f), colnames(listZ$Z_DxS_f)
#   )
#   ## TODO: listVCov$invKmixpair <- getInvKmixpairDxS(dat, "focal", "neighbor", sep="+")
# }

## -----------------------------------------------------------------------------
fitsTmb <- list()
i <- 1
for (REML in c(TRUE, FALSE)) {
  print(paste0("fit model with ", ifelse(REML, "REML", "ML"), "..."))
  st <- system.time(
    fitTmb <- fitDBVSBVinter(listY, listX, listZ, listVCov,
      lOptions = list(iter.max = 20),
      REML = REML, verbose = 0
    )
  )
  print(st)
  fitsTmb[[i]] <- fitTmb
  i <- i + 1
  break # skip ML to speed-up
}

## ----class.source='fold-hide'-------------------------------------------------
for (i in seq_along(fitsTmb)) {
  fitTmb <- fitsTmb[[i]]
  p <- ggplot(fitTmb$trace) +
    aes(x = iter, y = objfn) +
    geom_point() +
    geom_line() +
    labs(
      title = "Optimization convergence",
      subtitle = paste0("REML=", fitTmb$REML)
    ) +
    theme_bw()
  print(p)
}

## ----fig.width=12, fig.height=9-----------------------------------------------
for (i in seq_along(fitsTmb)) {
  fitTmb <- fitsTmb[[i]]
  print(paste0("REML=", fitTmb$REML))

  print("Check fixed effects:")
  checks <- data.frame(
    param = rownames(fitTmb$B_IC),
    truth = c(truth$B_IC),
    fitTmb$B_IC,
    row.names = NULL,
    check.names = FALSE
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(checks)
  checks <- data.frame(
    param = rownames(fitTmb$beta_SC_f),
    truth = obsMC$blObsContrs$S1[, "SC"],
    fitTmb$beta_SC_f,
    row.names = NULL,
    check.names = FALSE
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(checks)
  checks <- data.frame(
    param = rownames(fitTmb$beta_SC_t),
    truth = c(
      obsMC$blObsContrs$S2[, "SC"],
      obsMC$BVObsContrs$S2[-1, "SC", "DBV"]
    ),
    fitTmb$beta_SC_t,
    row.names = NULL,
    check.names = FALSE
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(checks)

  print("Check (co)variances of random genetic effects:")
  checks <- data.frame(
    param = c(
      rownames(fitTmb$vcov_BV_f),
      rownames(fitTmb$var_SIGV_f)
    ),
    truth = c(
      truth$var_DBV["S1"],
      truth$var_SBV["S1"],
      truth$cor_DBV_SBV["S1"],
      truth$var_SIGV["S1"]
    ),
    rbind(
      fitTmb$vcov_BV_f,
      fitTmb$var_SIGV_f
    ),
    row.names = NULL,
    check.names = FALSE
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(checks)

  print("Check (co)variances of residual errors:")
  checks <- data.frame(
    param = c(
      rownames(fitTmb$vcov_E_IC),
      rownames(fitTmb$var_err_SC_f),
      rownames(fitTmb$var_err_SC_t)
    ),
    truth = c(
      truth$var_err_IC, truth$cor_err_IC,
      truth$var_err_SC
    ),
    rbind(
      fitTmb$vcov_E_IC,
      fitTmb$var_err_SC_f,
      fitTmb$var_err_SC_t
    ),
    row.names = NULL,
    check.names = FALSE
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(checks)

  print("Check random genetic effects of the focal species:")
  checks <- data.frame(
    param = rep(c("DBV", "SBV", "BV_IC", "SIGV", "BV_SC"),
      each = nrow(truth$BV$S1)
    ),
    geno = levels(datW_IC$geno_S1),
    truth = c(
      truth$BV$S1[levels(datW_IC$geno_S1), ],
      truth$BV_IC$S1[levels(datW_IC$geno_S1)],
      truth$SIGVs$S1[levels(datW_IC$geno_S1)],
      truth$BV_SC$S1[levels(datW_IC$geno_S1)]
    ),
    rbind(
      fitTmb$DBV_f[levels(datW_IC$geno_S1), ],
      fitTmb$SBV_IC_f[levels(datW_IC$geno_S1), ],
      fitTmb$BV_IC_f[levels(datW_IC$geno_S1), ],
      fitTmb$SIGV_f[levels(datW_IC$geno_S1), ],
      fitTmb$BV_SC_f[levels(datW_IC$geno_S1), ]
    )
  )
  checks$param <- factor(checks$param,
    levels = c("BV_SC", "BV_IC", "DBV", "SBV", "SIGV")
  )
  checks$nBE <- normBiasError(checks$Estimate, checks$truth)
  print(tapply(1:nrow(checks), checks$param, function(idx) {
    cor(checks$truth[idx], checks$Estimate[idx])
  }))
  p <- ggplot(checks) +
    aes(x = Estimate, y = truth) +
    geom_hline(yintercept = 0, linetype = "dotted") +
    geom_vline(xintercept = 0, linetype = "dotted") +
    geom_abline(slope = 1, intercept = 0, linetype = "dotted") +
    geom_point() +
    labs(
      title = "Results with both sole-crop and intercrop data",
      subtitle = paste0("REML=", fitTmb$REML)
    ) +
    theme_bw() +
    facet_wrap(~param)
  print(p)
}

## ----info---------------------------------------------------------------------
t1 <- proc.time()
t1 - t0
print(sessionInfo(), locale = FALSE)

