def plot_3d_comp_multinom(model, data, vmin=None, vmax=None,
resid_range=None, fig_num=None,
pop_ids=None, residual='Anscombe', adjust=True):
"""
Multinomial comparison between 3d model and data.
model: 3-dimensional model SFS
data: 3-dimensional data SFS
vmin, vmax: Minimum and maximum values plotted for sfs are vmin and
vmax respectively.
resid_range: Residual plot saturates at +- resid_range.
fig_num: Clear and use figure fig_num for display. If None, an new figure
window is created.
pop_ids: If not None, override pop_ids stored in Spectrum.
residual: 'Anscombe' for Anscombe residuals, which are more normally
distributed for Poisson sampling. 'linear' for the linear
residuals, which can be less biased.
adjust: Should method use automatic 'subplots_adjust'? For advanced
manipulation of plots, it may be useful to make this False.
This comparison is multinomial in that it rescales the model to optimally
fit the data.
"""
model = Inference.optimally_scaled_sfs(model, data)
plot_3d_comp_Poisson(model, data, vmin=vmin, vmax=vmax,
resid_range=resid_range, fig_num=fig_num,
pop_ids=pop_ids, residual=residual,
adjust=adjust)
def plot_1d_comp_multinom(model,
data,
fig_num=None,
residual='Anscombe',
plot_masked=False):
"""
Mulitnomial comparison between 1d model and data.
model: 1-dimensional model SFS
data: 1-dimensional data SFS
fig_num: Clear and use figure fig_num for display. If None, an new figure
window is created.
residual: 'Anscombe' for Anscombe residuals, which are more normally
distributed for Poisson sampling. 'linear' for the linear
residuals, which can be less biased.
plot_masked: Additionally plots (in open circles) results for points in the
model or data that were masked.
This comparison is multinomial in that it rescales the model to optimally
fit the data.
"""
model = Inference.optimally_scaled_sfs(model, data)
plot_1d_comp_Poisson(model, data, fig_num, residual, plot_masked)
def plot_1d_comp_multinom(model, data, fig_num=None, residual='Anscombe',
plot_masked=False):
"""
Mulitnomial comparison between 1d model and data.
model: 1-dimensional model SFS
data: 1-dimensional data SFS
fig_num: Clear and use figure fig_num for display. If None, an new figure
window is created.
residual: 'Anscombe' for Anscombe residuals, which are more normally
distributed for Poisson sampling. 'linear' for the linear
residuals, which can be less biased.
plot_masked: Additionally plots (in open circles) results for points in the
model or data that were masked.
This comparison is multinomial in that it rescales the model to optimally
fit the data.
"""
model = Inference.optimally_scaled_sfs(model, data)
plot_1d_comp_Poisson(model, data, fig_num, residual,
plot_masked)
maxiter=50)
print('Optimized parameters', str(repr(popt)))
model = func_ex(popt[0], ns, pts_l)
ll_opt = dadi.Inference.ll_multinom(model, pop)
print('Optimized log-likelihood:', str(ll_opt))
print('Theta0_2:', str(theta0))
##Print out the scaled SFS and the Anscombe Poisson residuals
print("==============================================")
print(Inference.optimally_scaled_sfs(model, pop))
rescaled = Inference.optimally_scaled_sfs(model, pop)
print(Inference.Anscombe_Poisson_residual(rescaled,pop))
dadi.Plotting.plot_1d_comp_multinom(model,pop)
print("==============================================")
####Neutral Fit#########
###########################
print("NEUTRAL MODEL")
params = [1]
