__init__(endog, exog[, smoothers, family]) |
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cont() |
condition to continue iteration loop |
df_resid() |
degrees of freedom of residuals, ddof is sum of all smoothers df |
estimate_scale([Y]) |
Return Pearson’s X^2 estimate of scale. |
fit(Y[, rtol, maxiter]) |
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fit_constrained(constraints[, start_params]) |
fit the model subject to linear equality constraints |
from_formula(formula, data[, subset]) |
Create a Model from a formula and dataframe. |
hessian(params[, scale, observed]) |
Hessian, second derivative of loglikelihood function |
hessian_factor(params[, scale, observed]) |
Weights for calculating Hessian |
information(params[, scale]) |
Fisher information matrix. |
initialize() |
Initialize a generalized linear model. |
loglike(*args) |
Loglikelihood function. |
next() |
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predict(params[, exog, exposure, offset, linear]) |
Return predicted values for a design matrix |
score(params[, scale]) |
score, first derivative of the loglikelihood function |
score_factor(params[, scale]) |
weights for score for each observation |
score_obs(params[, scale]) |
score first derivative of the loglikelihood for each observation. |
score_test(params_constrained[, ...]) |
score test for restrictions or for omitted variables |