Inheritance diagram for nipy.algorithms.statistics.models.family.family:
Bases: nipy.algorithms.statistics.models.family.family.Family
Binomial exponential family.
Methods
deviance | |
devresid | |
fitted | |
predict | |
variance | |
weights |
Deviance of (Y,mu) pair. Deviance is usually defined as the difference
DEV = (SUM_i -2 log Likelihood(Y_i,mu_i) + 2 log Likelihood(mu_i,mu_i)) / scale
Binomial deviance residual
Fitted values based on linear predictors eta.
Linear predictors based on given mu values.
Weights for IRLS step.
w = 1 / (link’(mu)**2 * variance(mu))
Bases: object
A class to model one-parameter exponential families.
link – a Link instance variance – a variance function (models means as a function
of mean)
Methods
deviance | |
devresid | |
fitted | |
predict | |
weights |
Deviance of (Y,mu) pair. Deviance is usually defined as the difference
DEV = (SUM_i -2 log Likelihood(Y_i,mu_i) + 2 log Likelihood(mu_i,mu_i)) / scale
The deviance residuals, defined as the residuals in the deviance.
Without knowing the link, they default to Pearson residuals
resid_P = (Y - mu) * sqrt(weight(mu))
Fitted values based on linear predictors eta.
Linear predictors based on given mu values.
Weights for IRLS step.
w = 1 / (link’(mu)**2 * variance(mu))
Bases: nipy.algorithms.statistics.models.family.family.Family
Gamma exponential family.
Methods
deviance | |
devresid | |
fitted | |
predict | |
variance | |
weights |
Deviance of (Y,mu) pair. Deviance is usually defined as the difference
DEV = (SUM_i -2 log Likelihood(Y_i,mu_i) + 2 log Likelihood(mu_i,mu_i)) / scale
The deviance residuals, defined as the residuals in the deviance.
Without knowing the link, they default to Pearson residuals
resid_P = (Y - mu) * sqrt(weight(mu))
Fitted values based on linear predictors eta.
Linear predictors based on given mu values.
Weights for IRLS step.
w = 1 / (link’(mu)**2 * variance(mu))
Bases: nipy.algorithms.statistics.models.family.family.Family
Gaussian exponential family.
Methods
deviance | |
devresid | |
fitted | |
predict | |
variance | |
weights |
Deviance of (Y,mu) pair. Deviance is usually defined as the difference
DEV = (SUM_i -2 log Likelihood(Y_i,mu_i) + 2 log Likelihood(mu_i,mu_i)) / scale
Gaussian deviance residual
Fitted values based on linear predictors eta.
Linear predictors based on given mu values.
Weights for IRLS step.
w = 1 / (link’(mu)**2 * variance(mu))
Bases: nipy.algorithms.statistics.models.family.family.Family
InverseGaussian exponential family.
Methods
deviance | |
devresid | |
fitted | |
predict | |
variance | |
weights |
Deviance of (Y,mu) pair. Deviance is usually defined as the difference
DEV = (SUM_i -2 log Likelihood(Y_i,mu_i) + 2 log Likelihood(mu_i,mu_i)) / scale
The deviance residuals, defined as the residuals in the deviance.
Without knowing the link, they default to Pearson residuals
resid_P = (Y - mu) * sqrt(weight(mu))
Fitted values based on linear predictors eta.
Linear predictors based on given mu values.
Weights for IRLS step.
w = 1 / (link’(mu)**2 * variance(mu))
Bases: nipy.algorithms.statistics.models.family.family.Family
Poisson exponential family.
Methods
deviance | |
devresid | |
fitted | |
predict | |
variance | |
weights |
Deviance of (Y,mu) pair. Deviance is usually defined as the difference
DEV = (SUM_i -2 log Likelihood(Y_i,mu_i) + 2 log Likelihood(mu_i,mu_i)) / scale
Poisson deviance residual
Fitted values based on linear predictors eta.
Linear predictors based on given mu values.
Weights for IRLS step.
w = 1 / (link’(mu)**2 * variance(mu))