oneStation_DOlogprob: Log posterior probability for the DO curve given empirical...

In mtalluto/NSmetabolism: N-Station Stream Metabolism Modelling

Description

Log posterior probability for the DO curve given empirical data

Usage

oneStation_DOlogprob(params, data, prior = list(), ...)

Arguments

params	A named vector of model parameters; see details
data	A list of model data, see details
prior	A list of prior hyperparameters; see details
...	Additional parameters to be passed to DO_predict()

Details

#' params must be a vector including the following named items:

• logP1 $(W min g^-1 O_2)$ log(inverse of the slope of a photosynthesis–irradiance curve) at low light intensity

• logP2 $(m^2 min g^-1 O_2)$ log(inverse maximum photosynthesis rate)

• logk600 coefficient of gas exchange for a gas with a Schmidt number of 600; (see kT())

• logMinusER24_20 log(-1 * daily ecosystem respiration rate), standardized at 20 degrees C

• logsd log(Error standard deviation)

data is a named list of (constant) data items, including the following:

• DO 2-column data frame; first column is dissolved oxygen, second is time of observation

• PAR 2-column data frame; first column is light, second is time of observation

• temp 2-column data frame; first column is temperature, second is time of observation

• P pressure, in atmospheres

• z Depth, in meters

prior is a list of hyperparameters for the priors on parameters listed under params. All priors are normal. Thus, each list item should be named (following the names from params), and each item should be a vector of length two giving the mean and standard deviation of the prior for that parameter. Note that each parameter is optional (as is the entire list); missing items will get a minimally informative normal(0,10) distribution.

Value

The unnormalized log probability of the model given the data

mtalluto/NSmetabolism documentation built on May 3, 2021, 7:51 p.m.