Get states

get.states uses the Viterbi algorithm to globally decode the model and estimate the most probable sequence of states.

Usage

get.states(model)

Arguments

model

fitted hydroState model object.

Value

data frame of results to evaluate the rainfall-runoff states over time

Details

get.states

These dataframe of results include:

• time-step: year and possibly either season or month for subannual analysis

• Viterbi State Number: state number (i.e. 1, 2, or 3) to differentiate states

• Obs. flow: streamflow observations

• Viterbi Flow: flow values of the Viterbi state including the 5\

• Normal State Flow: flow values of the normal state including the 5\

• Conditional Prob: conditional probabilities for each state show the probability of remaining in the given state. When the conditional probability is closer to 1, there is a higher probability that hydroState model remains in that state for the next time-step.

• Emission Density: emission density for each state is the result of multiplying the conditional probabilities by the transition probabilities at each timestep.

Examples

# Load fitted model
data(model.annual.fitted.221201)

## Set initial year to set state names
model.annual.fitted.221201 =
                setInitialYear(model = model.annual.fitted.221201,
                               initial.year = 1990)

## Get states
model.annual.fitted.221201.states =
                get.states(model = model.annual.fitted.221201)