ssEst  R Documentation 
Allows for the estimation of process steady state of a single stream for a process using flow rate data.
ssEst(y, BTE = c(100, 1000, 1), stationary = FALSE)
y 
Vector of mass flow rate observations. Must be specified sequentially with 
BTE 
Numeric vector giving 
stationary 
Logical indicating if stationarity will be imposed when generating posterior draws. See Details. 
The model of the following form is fit to the data:
y_t = μ + α y_{t1} + ε
Where ε \sim \mathcal{N}(0,σ^2) and t indexes the time step.
A time series is stationary, and predictable, when α< 1. Stationarity can be enforced, using the argument setting stationary = TRUE
. This setting utilizes the priors p(α) \sim \mathcal{N}(0, 1000) truncated at (1,1), and p(μ) \sim \mathcal{N}(0, var(y)*100
) for inference, producing a posterior distribution for α constrained to be within (1,1).
When fitting a model where stationarity is not enforced, the Jeffreys prior of p(μ,α)\propto 1 is used.
The Jeffreys prior of p(σ^2)\propto 1/σ^2 is used for all inference of σ^2
A stationary time series will have an expected value of:
\frac{μ}{1α}
Samples of this expectation are included in the output if stationary = TRUE
or if none of the samples of α lie outside of (1,1).
The output list is a BMB
object, passing the output to plot.BayesMassBal
allows for observation of the results.
Returns a list of outputs

List of vectors containing posterior draws of model parameters 

Logical indicating the setting of the 

Vector of observations initially passed to the 

Character string giving details of the model fit. Primarily included for use with 
## Generating Data y < rep(NA, times = 21) y[1] < 0 mu < 3 alpha < 0.3 sig < 2 for(i in 2:21){ y[i] < mu + alpha*y[i1] + rnorm(1)*sig } ## Generating draws of model parameters fit < ssEst(y, BTE = c(100,500,1))
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