"""Example using OneWayMixed


Created on Sat Dec 03 10:15:55 2011

Author: Josef Perktold

This example constructs a linear model with individual specific random
effects, and uses OneWayMixed to estimate it.

This is a variation on ex_mixed_lls_0.py. Here we only have a single
individual specific constant, that is just a random effect without exogenous
regressors.

"""

import numpy as np

from statsmodels.sandbox.panel.mixed import OneWayMixed, Unit

examples = ["ex1"]

if "ex1" in examples:
    # np.random.seed(54321)
    np.random.seed(978326)
    nsubj = 2000
    units = []

    nobs_i = 4  # number of observations per unit, changed below

    nx = 0  # number fixed effects
    nz = 1  # number random effects
    beta = np.ones(nx)
    gamma = 0.5 * np.ones(nz)  # mean of random effect
    gamma[0] = 0
    gamma_re_true = []
    for i in range(nsubj):
        # create data for one unit

        # random effect/coefficient
        gamma_re = gamma + 0.2 * np.random.standard_normal(nz)
        # store true parameter for checking
        gamma_re_true.append(gamma_re)

        # for testing unbalanced case, let's change nobs per unit
        if i > nsubj // 4:
            nobs_i = 6

        # generate exogenous variables
        X = np.random.standard_normal((nobs_i, nx))
        Z = np.random.standard_normal((nobs_i, nz - 1))
        Z = np.column_stack((np.ones(nobs_i), Z))

        noise = 0.1 * np.random.randn(nobs_i)  # sig_e = 0.1

        # generate endogenous variable
        Y = np.dot(X, beta) + np.dot(Z, gamma_re) + noise

        # add random effect design matrix also to fixed effects to
        # capture the mean
        # this seems to be necessary to force mean of RE to zero !?
        # (It's not required for estimation but interpretation of random
        # effects covariance matrix changes - still need to check details.
        X = np.hstack((X, Z))

        # create units and append to list
        new_unit = Unit(Y, X, Z)
        units.append(new_unit)

    m = OneWayMixed(units)

    import time

    t0 = time.time()
    m.initialize()
    res = m.fit(maxiter=100, rtol=1.0e-5, params_rtol=1e-6, params_atol=1e-6)
    t1 = time.time()
    print("time for initialize and fit", t1 - t0)
    print("number of iterations", m.iterations)
    # print dir(m)
    # print vars(m)
    print("\nestimates for fixed effects")
    print(m.a)
    print(m.params)
    bfixed_cov = m.cov_fixed()
    print("beta fixed standard errors")
    print(np.sqrt(np.diag(bfixed_cov)))

    print(m.bse)
    b_re = m.params_random_units
    print("RE mean:", b_re.mean(0))
    print("RE columns std", b_re.std(0))
    print("np.cov(b_re, rowvar=0), sample statistic")
    print(np.cov(b_re, rowvar=0))
    print("std of above")
    # need atleast_1d or diag raises exception
    print(np.sqrt(np.diag(np.atleast_1d(np.cov(b_re, rowvar=0)))))
    print("m.cov_random()")
    print(m.cov_random())
    print("std of above")
    print(res.std_random())
    print(np.sqrt(np.diag(m.cov_random())))

    print("\n(non)convergence of llf")
    print(m.history["llf"][-4:])
    print("convergence of parameters")
    # print np.diff(np.vstack(m.history[-4:])[:,1:],axis=0)
    print(np.diff(np.vstack(m.history["params"][-4:]), axis=0))
    print("convergence of D")
    print(np.diff(np.array(m.history["D"][-4:]), axis=0))

    # zdotb = np.array([np.dot(unit.Z, unit.b) for unit in m.units])
    zb = np.array([(unit.Z * unit.b[None, :]).sum(0) for unit in m.units])
    """if Z is not included in X:
    >>> np.dot(b_re.T, b_re)/100
    array([[ 0.03270611, -0.00916051],
           [-0.00916051,  0.26432783]])
    >>> m.cov_random()
    array([[ 0.0348722 , -0.00909159],
           [-0.00909159,  0.26846254]])
    >>> #note cov_random does not subtract mean!
    """
    print("\nchecking the random effects distribution and prediction")
    gamma_re_true = np.array(gamma_re_true)
    print("mean of random effect true", gamma_re_true.mean(0))
    print("mean from fixed effects   ", m.params[-2:])
    print("mean of estimated RE      ", b_re.mean(0))

    print()
    absmean_true = np.abs(gamma_re_true).mean(0)
    mape = ((m.params[-2:] + b_re) / gamma_re_true - 1).mean(0) * 100
    mean_abs_perc = (
        np.abs((m.params[-2:] + b_re) - gamma_re_true).mean(0) / absmean_true * 100
    )
    median_abs_perc = (
        np.median(np.abs((m.params[-2:] + b_re) - gamma_re_true), 0)
        / absmean_true
        * 100
    )
    rmse_perc = ((m.params[-2:] + b_re) - gamma_re_true).std(0) / absmean_true * 100
    print("mape           ", mape)
    print("mean_abs_perc  ", mean_abs_perc)
    print("median_abs_perc", median_abs_perc)
    print("rmse_perc (std)", rmse_perc)
    # from numpy.testing import assert_almost_equal
    # assert is for n_units=100 in original example
    # I changed random number generation, so this will not work anymore
    # assert_almost_equal(rmse_perc, [ 34.14783884,  11.6031684 ], decimal=8)

    # now returns res
    print("llf", res.llf)  # based on MLE, does not include constant
    print("tvalues", res.tvalues)
    print("pvalues", res.pvalues)
    print(res.t_test([1]))
    print("test mean of both random effects variables is zero")
    print(res.f_test([[1]]))
    plots = res.plot_random_univariate(bins=50)
    # fig = res.plot_scatter_pairs(0, 1) # no pairs
    import matplotlib.pyplot as plt

    plt.show()
