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    Interface DlmFitResultMatlab

    MATLAB DLM-compatible result layout and names. Produced by toMatlab. State arrays use the MATLAB convention: x[stateIdx][timeIdx], C[i][j][timeIdx], xstd[timeIdx][stateIdx].

    interface DlmFitResultMatlab {
        C: FloatArray[][];
        C0: number[][];
        Cf: FloatArray[][];
        class: "dlmfit";
        Cp: FloatArray;
        F: number[] | number[][];
        G: number[][];
        lik: number;
        m: number;
        mape: number;
        mse: number;
        n: number;
        nobs: number;
        p: number;
        resid: FloatArray;
        resid0: FloatArray;
        resid2: FloatArray;
        s2: number;
        ssy: number;
        v: FloatArray;
        V: FloatArray;
        W: number[][];
        x: FloatArray[];
        x0: number[];
        xf: FloatArray[];
        xstd: FloatArray[];
        XX: number[][];
        y: FloatArray;
        yhat: FloatArray;
        ystd: FloatArray;
    }

    Properties

    C: FloatArray[][]

    Smoothed covariances: C[i][j][time]

    C0: number[][]

    Initial state covariance

    Cf: FloatArray[][]

    Filtered covariances: Cf[i][j][time]

    class: "dlmfit"

    Class identifier

    Innovation covariances

    F: number[] | number[][]

    Observation matrix. p=1: [m] row vector (backward compat). p>1: [p, m].

    G: number[][]

    State transition matrix

    lik: number

    -2 · log-likelihood

    m: number

    State dimension

    mape: number

    Mean absolute percentage error

    mse: number

    Mean squared error

    n: number

    Number of observations

    nobs: number

    Number of non-NaN observations

    p: number

    Observation dimension

    resid: FloatArray

    Scaled residuals

    resid0: FloatArray

    Raw residuals

    resid2: FloatArray

    Standardized residuals

    s2: number

    Residual variance

    ssy: number

    Sum of squared residuals

    Innovations

    Observation noise std devs

    W: number[][]

    State noise covariance

    Smoothed states: x[state][time]

    x0: number[]

    Initial state mean

    Filtered states: xf[state][time]

    xstd: FloatArray[]

    Smoothed state std devs: xstd[time][state]

    XX: number[][]

    Covariates matrix

    Observations

    Fitted values

    Prediction standard deviations