openest.models.mean_size_model module

Mean-Size Model

In Mean-Size models, each point is characterized only by a value and the population size that went into estimating that value. As such, it does not have enough information to generate a full distribution. It can be safely combined with other mean-size models, or approximated with a Gaussian (with a variance which is equal to the absolute value of the mean for size = 1, and a variance that decreases with the square root of the size, according to the Central Limit Theorem).

The format is:

msx1,mean,size
<x0>,<mean0>,<size0>
<x1>,<mean1>,<size1>
...

class openest.models.mean_size_model.MeanSizeModel(xx_is_categorical=False, xx=None, means=None, sizes=None)

Bases: openest.models.univariate_model.UnivariateModel

attribute_list()

static combine(one, two)

copy()

filter_x(xx)

get_attribute(title)

get_mean(x=None)

E[Y | X]

get_sdev(x=None)

sqrt Var[Y | X]

init_from_mean_size_file(file, delimiter, status_callback=None)

interpolate_x(newxx, kind='quadratic')

kind()

static merge(models, treatment='default')

scale_p(a)

Raise the distribution to the power ‘a’ and rescales.

Returns:modifies this model and returns it Return type:self

scale_y(a)

Rescaling of the Parameter Dimension

Produces a new conditional PDF with the $y$ dimension scaled by a constant: $p(z | x) = p( rac{y}{a} | x)$.

write(file, delimiter)

write_file(filename, delimiter)