create_data: Create data file for fitting time varying run timing...

In ericward-noaa/phenomix: Fit Density Curves to Peak Timing Data that Varies over Time

Create data file for fitting time varying run timing distributions with TMB

Description

Does minimal processing of data to use as argument to fitting function

Usage

create_data(
  data,
  min_number = 0,
  variable = "number",
  time = "year",
  date = "doy",
  asymmetric_model = TRUE,
  mu = ~1,
  sigma = ~1,
  covar_data = NULL,
  est_sigma_re = TRUE,
  est_mu_re = TRUE,
  tail_model = "student_t",
  family = "lognormal",
  max_theta = 10,
  share_shape = TRUE,
  nu_prior = c(2, 10),
  beta_prior = c(2, 1)
)

Arguments

data	A data frame
min_number	A minimum threshold to use, defaults to 0
variable	A character string of the name of the variable in 'data' that contains the response (e.g. counts)
time	A character string of the name of the variable in 'data' that contains the time variable (e.g. year)
date	A character string of the name of the variable in 'data' that contains the response (e.g. day of year). The actual #' column should contain a numeric response – for example, the result from using lubridate::yday(x)
asymmetric_model	Boolean, whether or not to let model be asymmetric (e.g. run timing before peak has a different shape than run timing after peak)
mu	An optional formula allowing the mean to be a function of covariates. Random effects are not included in the formula but specified with the est_mu_re argument
sigma	An optional formula allowing the standard deviation to be a function of covariates. For asymmetric models, each side of the distribution is allowed a different set of covariates. Random effects are not included in the formula but specified with the est_sigma_re argument
covar_data	a data frame containing covariates specific to each time step. These are used in the formulas mu and sigma
est_sigma_re	Whether to estimate random effects by year in sigma parameter controlling tail of distribution. Defaults to TRUE
est_mu_re	Whether to estimate random effects by year in mu parameter controlling location of distribution. Defaults to TRUE
tail_model	Whether to fit Gaussian ("gaussian"), Student-t ("student_t") or generalized normal ("gnorm"). Defaults to Student-t
family	Response for observation model, options are "gaussian", "poisson", "negbin", "binomial", "lognormal". The default ("lognormal") is not a true lognormal distribution, but a normal-log in that it assumes log(y) ~ Normal()
max_theta	Maximum value of log(pred) when limits=TRUE. Defaults to 10
share_shape	Boolean argument for whether asymmetric student-t and generalized normal distributions should share the shape parameter (nu for the student-t; beta for the generalized normal). Defaults to TRUE
nu_prior	Two element vector (optional) for penalized prior on student t df, defaults to a Gamma(shape=2, scale=10) distribution
beta_prior	Two element vector (optional) for penalized prior on generalized normal beta, defaults to a Normal(2, 1) distribution

Examples

data(fishdist)
datalist <- create_data(fishdist,
  min_number = 0, variable = "number", time = "year",
  date = "doy", asymmetric_model = TRUE, family = "gaussian"
)

ericward-noaa/phenomix documentation built on May 6, 2024, 10:20 a.m.