pcvr (version 1.2.0)

mvSS: Ease of use multi-value trait model helper function.

Description

This function provides a simplified interface to modeling multi-value traits using growthSS. Output from this should be passed to fitGrowth to fit the specified model.

Usage

mvSS(
  model = "linear",
  form,
  sigma = NULL,
  df,
  start = NULL,
  pars = NULL,
  type = "brms",
  tau = 0.5,
  hierarchy = NULL,
  spectral_index = c("none", "ari", "ci_rededge", "cri550", "cri700", "egi", "evi",
    "gdvi", "mari", "mcari", "mtci", "ndre", "ndvi", "pri", "psnd_chlorophyll_a",
    "psnd_chlorophyll_b", "psnd_caroteniods", "psri", "pssr_chlorophyll_a",
    "pssr_chlorophyll_b", "pssr_caroteniods", "rgri", "rvsi", "savi", "sipi", "sr",
    "vari", "vi_green", "wi", "fvfm", "fqfm")
)

Value

A named list of plots showing prior distributions that growthSS would use, optionally with a plot of simulated growth curves using draws from those priors.

Arguments

model

A model specification as in growthSS.

form

A formula similar to label | value ~ time + id/group where label is a column of histogram bins, value is the counts within those bins, time is an optional time variable, id identifies an individual, and group contains the treatment groups. If the time variable is not included then the individual variable should also not be included.

sigma

Distributional models passed to growthSS.

df

Data passed to growthSS.

start

Starting values or priors, passed to growthSS.

pars

Parameters to vary, passed to growthSS.

type

Backend to use, passed to growthSS.

tau

Quantile to model, passed to growthSS.

hierarchy

Formulae describing any hierarchical models, see growthSS.

spectral_index

Optionally, a spectral index from those calculated by PlantCV. If this is given then the appropriate truncation and model family (if applicable) will be included for the index you are using without you having to write it in the formula.

See Also

fitGrowth for fitting the model specified by this list.

Examples

set.seed(123)
mv_df <- mvSim(dists = list(rnorm = list(mean = 100, sd = 30)), wide = FALSE)
mv_df$group <- rep(c("a", "b"), times = 900)
mv_df <- mv_df[mv_df$value > 0, ]
mv_df$label <- as.numeric(gsub("sim_", "", mv_df$variable))

ss1 <- mvSS(
  model = "linear", form = label | value ~ group, df = mv_df,
  start = list("A" = 5), type = "brms", spectral_index = "none"
)
# \donttest{
mod1 <- fitGrowth(ss1, backend = "cmdstanr", iter = 1000, chains = 1, cores = 1)
growthPlot(mod1, ss1$pcvrForm, df = ss1$df)
# }

# when the model is longitudinal the same model is possible with growthSS

m1 <- mvSim(
  dists = list(
    rnorm = list(mean = 100, sd = 30),
    rnorm = list(mean = 110, sd = 25),
    rnorm = list(mean = 120, sd = 20),
    rnorm = list(mean = 135, sd = 15)
  ),
  wide = FALSE, n = 6
)
m1$time <- rep(1:4, times = 6 * 180)
m2 <- mvSim(
  dists = list(
    rnorm = list(mean = 85, sd = 25),
    rnorm = list(mean = 95, sd = 20),
    rnorm = list(mean = 105, sd = 15),
    rnorm = list(mean = 110, sd = 15)
  ),
  wide = FALSE, n = 6
)
m2$time <- rep(1:4, times = 6 * 180)
mv_df2 <- rbind(m1, m2)
mv_df2$group <- rep(c("a", "b"), each = 4320)
mv_df2 <- mv_df2[mv_df2$value > 0, ]
mv_df2$label <- as.numeric(gsub("sim_", "", mv_df2$variable))
ss_mv0 <- mvSS(
  model = "linear", form = label | value ~ group, df = mv_df2,
  start = list("A" = 50), type = "brms", spectral_index = "ci_rededge"
)
ss_mv0 # non longitudinal model setup

ss_mv1 <- mvSS(
  model = "linear", form = label | value ~ time | group, df = mv_df2,
  start = list("A" = 50), type = "brms", spectral_index = "ci_rededge"
)
ss_mv1
ss_mv2 <- growthSS(
  model = "skew_normal: linear",
  form = label | resp_weights(value) + trunc(lb = -1, ub = Inf) ~ time | group,
  df = mv_df2, start = list("A" = 50)
)
ss_mv2
# ignoring environments and other such details these are identical except for the
# function call.
unlist(lapply(names(ss_mv1), function(nm) {
  if (!identical(ss_mv1[[nm]], ss_mv2[[nm]],
    ignore.environment = TRUE,
    ignore.srcref = TRUE
  )) {
    if (!identical(as.character(ss_mv1[[nm]]), as.character(ss_mv2[[nm]]))) {
      nm
    }
  }
}))

# \donttest{
if (rlang::is_installed("mnormt")) {
  m2 <- fitGrowth(ss_mv1, backend = "cmdstanr", iter = 1000, chains = 1, cores = 1)
  growthPlot(m2, ss_mv1$pcvrForm, df = ss_mv1$df)
}
# }