algorithms.statistics.onesample¶
Module: algorithms.statistics.onesample
¶
Utilities for one sample ttests
Functions¶

nipy.algorithms.statistics.onesample.
estimate_mean
(Y, sd)¶ Estimate the mean of a sample given information about the standard deviations of each entry.
Parameters: Y : ndarray
Data for which mean is to be estimated. Should have shape[0] == number of subjects.
sd : ndarray
Standard deviation (subject specific) of the data for which the mean is to be estimated. Should have shape[0] == number of subjects.
Returns: value : dict
This dictionary has keys [‘effect’, ‘scale’, ‘t’, ‘resid’, ‘sd’]

nipy.algorithms.statistics.onesample.
estimate_varatio
(Y, sd, df=None, niter=10)¶ Estimate variance fixed/random effects variance ratio
In a onesample random effects problem, estimate the ratio between the fixed effects variance and the random effects variance.
Parameters: Y : np.ndarray
Data for which mean is to be estimated. Should have shape[0] == number of subjects.
sd : array
Standard deviation (subject specific) of the data for which the mean is to be estimated. Should have shape[0] == number of subjects.
df : int or None, optional
If supplied, these are used as weights when deriving the fixed effects variance. Should have length == number of subjects.
niter : int, optional
Number of EM iterations to perform (default 10)
Returns: value : dict
This dictionary has keys [‘fixed’, ‘ratio’, ‘random’], where ‘fixed’ is the fixed effects variance implied by the input parameter ‘sd’; ‘random’ is the random effects variance and ‘ratio’ is the estimated ratio of variances: ‘random’/’fixed’.