MARSS-package. Users would not normally work directly with these functions.
is.marssMODEL() ensures model consistency.
MARSS_formname() translates a model list as passed in call to MARSS() into a marssMODEL model object.is.marssMODEL(modelObj, method="kem")marssMODEL object is an R representation of a MARSS model along with the data.
Data in a marssMODEL object consists of multivariate time series data in which time is across columns and the n observed time series are in the n different rows.
The base MARSS model (form=marss) is
[object Object],[object Object],[object Object]
The marssMODEL(form=marss) object describes this MARSS model but written in vec form:
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
In the marssMODEL(form=marss) object, f(t) + D(t)m, is the vec of a matrix M(t), so f_b(t)+D_b(t)b would be vec(B(t)). The estimated parameters are in the column vectors: b, u, q, z, a, r, p, and l. Each matrix M(t) is f(t)+D(t)m so is the sum of a fixed part f(t) and the linear combination, D(t), of the free (or estimated) part m.
The vec form of the MARSS model is specified by 3D matrices for each f and D for each parameter: B, U, Q, Z, A, R, x0, V0. The number of columns in the D matrix for a parameter determines the number of estimated values for that parameter.
The first dimension for f (fixed) and D (free) must be:
[object Object],[object Object],[object Object],[object Object],[object Object]
The third dimension of f (fixed) and D (free) is either 1 (if not time-varying) or TT (if time-varying). The second dimension of f (fixed) is always 1, while the second dimension of D (free) depends on how many values are being estimated for a matrix. It can be 0 (if the matrix is fixed) or up to the size of the matrix (if all elements are being estimated).MARSS, MARSS.marxss, marssMODEL