mcmc_analyze {demodelr}

Markov Chain parameter estimates

Description

mcmc_analyze Computes summary histograms and model-data comparisons from and Markov Chain Monte Carlo parameter estimate for a given model

Usage

mcmc_analyze(
  model,
  data,
  mcmc_out,
  mode = "emp",
  initial_condition = NULL,
  deltaT = NULL,
  n_steps = NULL,
  verbose = TRUE
)

Arguments

model

the model equations that we use to compute the result.

data

the data used to assess the model

mcmc_out

A dataframe: the first column is the accept flag of the mcmc run (TRUE/FALSE), the log likelihood, and the parameter values

mode

two choices: emp –> empirical (default) or de –> differential equations. The estimator works differently depending on which is used.

initial_condition

The initial condition for the differential equation (DE mode only)

deltaT

The length between timesteps (DE mode only)

n_steps

The number of time steps we run the model (DE mode only)

verbose

TRUE / FALSE indicate if parameter estimates should be printed to console (option, defaults to TRUE)

Value

Two plots: (1) fitted model results compared to data, and (2) pairwise parameter histograms and scatterplots to test model equifinality.

See Also

mcmc_estimate

Examples

## Example with an empirical model:
## Step 1: Define the model and parameters
phos_model <- daphnia ~ c * algae^(1 / theta)

phos_param <- tibble::tibble( name = c("c", "theta"),
lower_bound = c(0, 1),
upper_bound = c(2, 20))

## Step 2: Determine MCMC settings
# Define the number of iterations
phos_iter <- 1000

## Step 3: Compute MCMC estimate
phos_mcmc <- mcmc_estimate(model = phos_model,
data = phosphorous,
parameters = phos_param,
iterations = phos_iter)

## Step 4: Analyze results:
mcmc_analyze(model = phos_model,
data = phosphorous,
mcmc_out = phos_mcmc)

## Example with a differential equation:
## Step 1: Define the model, parameters, and data
## Define the tourism model
tourism_model <- c(dRdt ~ resources * (1 - resources) - a * visitors,
dVdt ~ b * visitors * (resources - visitors))

# Define the parameters that you will use with their bounds
tourism_param <- tibble::tibble( name = c("a", "b"),
lower_bound = c(10, 0),
upper_bound = c(30, 5))

## Step 2: Determine MCMC settings
# Define the initial conditions
tourism_init <- c(resources = 0.995, visitors = 0.00167)
deltaT <- .1 # timestep length
n_steps <- 15 # must be a number greater than 1
# Define the number of iterations
tourism_iter <- 1000

## Step 3: Compute MCMC estimate
tourism_out <- mcmc_estimate(
 model = tourism_model,
 data = parks,
 parameters = tourism_param,
 mode = "de",
 initial_condition = tourism_init, deltaT = deltaT,
 n_steps = n_steps,
 iterations = tourism_iter)

## Step 4: Analyze results
mcmc_analyze(
 model = tourism_model,
 data = parks,
 mcmc_out = tourism_out,
 mode = "de",
 initial_condition = tourism_init, deltaT = deltaT,
 n_steps = n_steps
)