PoissonGLM(design_matrix, spk_times, spk_clu, binwidth=0.02, metric='dsq', model='default', alpha=0, train=0.8, blocktrain=False, mintrials=100, subset=False)¶
Utility function for computing D^2 (pseudo R^2) on a given set of weights and intercepts. Is be used in both model subsetting and the mother score() function of the GLM.
weights (pd.Series) – Series in which entries are numpy arrays containing the weights for a given cell. Indices should be cluster ids.
intercepts (pd.Series) – Series in which elements are the intercept fit to each cell. Indicies should match weights.
dm (numpy.ndarray) – Design matrix. Should not contain the bias column. dm.shape should be the same as the length of an element in weights.
binned (numpy.ndarray) – nT x nCells array, in which each column is the binned spike train for a single unit. Should be the same number of rows as dm.
the squared deviance of the model (Compute) –
how much variance beyond the null model (i.e.) –
poisson process with the same mean ((a) –
by the intercept (defined) –
every time step) the (at) –
which was fit explains. (model) –
a detailed explanation see https (For) –
A series in which the index are cluster IDs and each entry is the D^2 for the model fit to that cluster
- Return type