def test_zero_penalty():
x, y, poly = multivariate_sample_data()
alphas = [0, 0]
gam_gs = GLMGam(y, smoother=poly, alpha=alphas)
gam_gs_res = gam_gs.fit()
y_est_gam = gam_gs_res.predict()
glm = GLM(y, poly.basis).fit()
y_est = glm.predict()
assert_allclose(y_est, y_est_gam)
def fit_logistic(X_hold,Y_hold,Firth=False,resBase=None,LRtest=True):
"""
Fits a logistic regression model using standard (when Firth = False) or Firth's method (when Firth = True).
resBase is the result of a previous call to a regression that is used to store data for Firth's method.
LRtest indicates if the likelihood ratio test should be reported.
"""
if not Firth:
res = GLM(Y_hold, X_hold, family=families.Binomial()).fit()#XXX Confirm this with logistic using older XXXX
# AICc adjustment
res.aicc = statsmodels.tools.eval_measures.aicc(res.llf, nobs=res.nobs, df_modelwc=res.df_model+1)
# Correct BIC
res.bic = statsmodels.tools.eval_measures.bic(res.llf, nobs=res.nobs, df_modelwc=res.df_model+1)
else:
if resBase is None:
sys.stderr.write('resBase must be provided to do Firth regression\n')
sys.exit(1)
elif type(resBase) is not statsmodels.genmod.generalized_linear_model.GLMResultsWrapper:
sys.stderr.write('resBase must be type statsmodels.genmod.generalized_linear_model.GLMResultsWrapper\n')
sys.exit(2)
else:
res = resBase
#Do Firth's logistic regression
(rint, rbeta, rbse, rfitll, pi) = fit_firth(Y_hold, X_hold, start_vec = None)
if LRtest:
# LRT
null_X = np.delete(arr=X_hold,obj=range(int(np.size(X_hold)/len(X_hold)))[1:int(np.size(X_hold)/len(X_hold))],axis=1)
(null_intercept, null_beta, null_bse, null_fitll, null_pi) = fit_firth(Y_hold, null_X, start_vec = None)
lrstat = -2.*(null_fitll - rfitll)
lrt_pvalue = 1.
if lrstat > 0.: # non-convergence
lrt_pvalue = stats.chi2.sf(lrstat, 1)
res.llnull = null_fitll
res.lrstat = lrstat
res.lrt_pval = lrt_pvalue
# AICc adjustment for Firth model
aicc = statsmodels.tools.eval_measures.aicc(rfitll, nobs=len(Y_hold), df_modelwc=np.shape(X_hold)[1])
# AIC
aic = statsmodels.tools.eval_measures.aic(rfitll, nobs=len(Y_hold), df_modelwc=np.shape(X_hold)[1])
# BIC
bic = statsmodels.tools.eval_measures.bic(rfitll, nobs=len(Y_hold), df_modelwc=np.shape(X_hold)[1])
#Store parameters, standard errors, likelihoods, and statistics
rint = np.array([rint])
rbeta = np.array(rbeta)
res.params = np.concatenate([rint,rbeta])
res.bse = rbse
res.llf = rfitll
res.aicc = aicc
res.aic = aic
res.bic = bic
#Get Wald p vals for parameters
res.pvalues = 1. - chi2.cdf(x=(res.params/res.bse)**2, df=1)
#Add predicted y
res.predict = pi
return res
def test_zero_penalty():
x, y, poly = multivariate_sample_data()
alphas = [0, 0]
gam_gs = GLMGam(y, smoother=poly, alpha=alphas)
gam_gs_res = gam_gs.fit()
y_est_gam = gam_gs_res.predict()
glm = GLM(y, poly.basis).fit()
y_est = glm.predict()
assert_allclose(y_est, y_est_gam)
def test_predict(self):
np.random.seed(382304)
endog = np.random.randint(0, 10, 100)
exog = np.random.normal(size=(100, 3))
exposure = np.random.uniform(1, 2, 100)
mod1 = GLM(endog,
exog,
family=sm.families.Poisson(),
exposure=exposure).fit()
exog1 = np.random.normal(size=(10, 3))
exposure1 = np.random.uniform(1, 2, 10)
# Doubling exposure time should double expected response
pred1 = mod1.predict(exog=exog1, exposure=exposure1)
pred2 = mod1.predict(exog=exog1, exposure=2 * exposure1)
assert_almost_equal(pred2, 2 * pred1)
# Check exposure defaults
pred3 = mod1.predict()
pred4 = mod1.predict(exposure=exposure)
pred5 = mod1.predict(exog=exog, exposure=exposure)
assert_almost_equal(pred3, pred4)
assert_almost_equal(pred4, pred5)
# Check offset defaults
offset = np.random.uniform(1, 2, 100)
mod2 = GLM(endog, exog, offset=offset,
family=sm.families.Poisson()).fit()
pred1 = mod2.predict()
pred2 = mod2.predict(offset=offset)
pred3 = mod2.predict(exog=exog, offset=offset)
assert_almost_equal(pred1, pred2)
assert_almost_equal(pred2, pred3)
# Check that offset shifts the linear predictor
mod3 = GLM(endog, exog, family=sm.families.Poisson()).fit()
offset = np.random.uniform(1, 2, 10)
pred1 = mod3.predict(exog=exog1, offset=offset, linear=True)
pred2 = mod3.predict(exog=exog1, offset=2 * offset, linear=True)
assert_almost_equal(pred2, pred1 + offset)
def test_predict(self):
np.random.seed(382304)
endog = np.random.randint(0, 10, 100)
exog = np.random.normal(size=(100,3))
exposure = np.random.uniform(1, 2, 100)
mod1 = GLM(endog, exog, family=sm.families.Poisson(),
exposure=exposure).fit()
exog1 = np.random.normal(size=(10,3))
exposure1 = np.random.uniform(1, 2, 10)
# Doubling exposure time should double expected response
pred1 = mod1.predict(exog=exog1, exposure=exposure1)
pred2 = mod1.predict(exog=exog1, exposure=2*exposure1)
assert_almost_equal(pred2, 2*pred1)
# Check exposure defaults
pred3 = mod1.predict()
pred4 = mod1.predict(exposure=exposure)
pred5 = mod1.predict(exog=exog, exposure=exposure)
assert_almost_equal(pred3, pred4)
assert_almost_equal(pred4, pred5)
# Check offset defaults
offset = np.random.uniform(1, 2, 100)
mod2 = GLM(endog, exog, offset=offset, family=sm.families.Poisson()).fit()
pred1 = mod2.predict()
pred2 = mod2.predict(offset=offset)
pred3 = mod2.predict(exog=exog, offset=offset)
assert_almost_equal(pred1, pred2)
assert_almost_equal(pred2, pred3)
# Check that offset shifts the linear predictor
mod3 = GLM(endog, exog, family=sm.families.Poisson()).fit()
offset = np.random.uniform(1, 2, 10)
pred1 = mod3.predict(exog=exog1, offset=offset, linear=True)
pred2 = mod3.predict(exog=exog1, offset=2*offset, linear=True)
assert_almost_equal(pred2, pred1+offset)
def local_fdr(zscores,
null_proportion=1.0,
null_pdf=None,
deg=7,
nbins=30,
alpha=0):
"""
Calculate local FDR values for a list of Z-scores.
Parameters
----------
zscores : array_like
A vector of Z-scores
null_proportion : float
The assumed proportion of true null hypotheses
null_pdf : function mapping reals to positive reals
The density of null Z-scores; if None, use standard normal
deg : int
The maximum exponent in the polynomial expansion of the
density of non-null Z-scores
nbins : int
The number of bins for estimating the marginal density
of Z-scores.
alpha : float
Use Poisson ridge regression with parameter alpha to estimate
the density of non-null Z-scores.
Returns
-------
fdr : array_like
A vector of FDR values
References
----------
B Efron (2008). Microarrays, Empirical Bayes, and the Two-Groups
Model. Statistical Science 23:1, 1-22.
Examples
--------
Basic use (the null Z-scores are taken to be standard normal):
>>> from statsmodels.stats.multitest import local_fdr
>>> import numpy as np
>>> zscores = np.random.randn(30)
>>> fdr = local_fdr(zscores)
Use a Gaussian null distribution estimated from the data:
>>> null = EmpiricalNull(zscores)
>>> fdr = local_fdr(zscores, null_pdf=null.pdf)
"""
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.genmod.generalized_linear_model import families
from statsmodels.regression.linear_model import OLS
# Bins for Poisson modeling of the marginal Z-score density
minz = min(zscores)
maxz = max(zscores)
bins = np.linspace(minz, maxz, nbins)
# Bin counts
zhist = np.histogram(zscores, bins)[0]
# Bin centers
zbins = (bins[:-1] + bins[1:]) / 2
# The design matrix at bin centers
dmat = np.vander(zbins, deg + 1)
# Rescale the design matrix
sd = dmat.std(0)
ii = sd > 1e-8
dmat[:, ii] /= sd[ii]
start = OLS(np.log(1 + zhist), dmat).fit().params
# Poisson regression
if alpha > 0:
md = GLM(zhist, dmat,
family=families.Poisson()).fit_regularized(L1_wt=0,
alpha=alpha,
start_params=start)
else:
md = GLM(zhist, dmat,
family=families.Poisson()).fit(start_params=start)
# The design matrix for all Z-scores
dmat_full = np.vander(zscores, deg + 1)
dmat_full[:, ii] /= sd[ii]
# The height of the estimated marginal density of Z-scores,
# evaluated at every observed Z-score.
fz = md.predict(dmat_full) / (len(zscores) * (bins[1] - bins[0]))
# The null density.
if null_pdf is None:
f0 = np.exp(-0.5 * zscores**2) / np.sqrt(2 * np.pi)
else:
f0 = null_pdf(zscores)
# The local FDR values
fdr = null_proportion * f0 / fz
fdr = np.clip(fdr, 0, 1)
return fdr
def interactplot(x1, x2, y, data=None, filled=False, cmap="RdBu_r",
colorbar=True, levels=30, logistic=False,
contour_kws=None, scatter_kws=None, ax=None, **kwargs):
"""Visualize a continuous two-way interaction with a contour plot.
Parameters
----------
x1, x2, y, strings or array-like
Either the two independent variables and the dependent variable,
or keys to extract them from `data`
data : DataFrame
Pandas DataFrame with the data in the columns.
filled : bool
Whether to plot with filled or unfilled contours
cmap : matplotlib colormap
Colormap to represent yhat in the countour plot.
colorbar : bool
Whether to draw the colorbar for interpreting the color values.
levels : int or sequence
Number or position of contour plot levels.
logistic : bool
Fit a logistic regression model instead of linear regression.
contour_kws : dictionary
Keyword arguments for contour[f]().
scatter_kws : dictionary
Keyword arguments for plot().
ax : matplotlib axis
Axis to draw plot in.
Returns
-------
ax : Matplotlib axis
Axis with the contour plot.
"""
msg = (
"The `interactplot` function has been deprecated and will be removed "
"in a future version."
)
warnings.warn(msg, UserWarning)
if not _has_statsmodels:
raise ImportError("The `interactplot` function requires statsmodels")
from statsmodels.regression.linear_model import OLS
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.genmod.families import Binomial
# Handle the form of the data
if data is not None:
x1 = data[x1]
x2 = data[x2]
y = data[y]
if hasattr(x1, "name"):
xlabel = x1.name
else:
xlabel = None
if hasattr(x2, "name"):
ylabel = x2.name
else:
ylabel = None
if hasattr(y, "name"):
clabel = y.name
else:
clabel = None
x1 = np.asarray(x1)
x2 = np.asarray(x2)
y = np.asarray(y)
# Initialize the scatter keyword dictionary
if scatter_kws is None:
scatter_kws = {}
if not ("color" in scatter_kws or "c" in scatter_kws):
scatter_kws["color"] = "#222222"
if "alpha" not in scatter_kws:
scatter_kws["alpha"] = 0.75
# Intialize the contour keyword dictionary
if contour_kws is None:
contour_kws = {}
# Initialize the axis
if ax is None:
ax = plt.gca()
# Plot once to let matplotlib sort out the axis limits
ax.plot(x1, x2, "o", **scatter_kws)
# Find the plot limits
x1min, x1max = ax.get_xlim()
x2min, x2max = ax.get_ylim()
# Make the grid for the contour plot
x1_points = np.linspace(x1min, x1max, 100)
x2_points = np.linspace(x2min, x2max, 100)
xx1, xx2 = np.meshgrid(x1_points, x2_points)
# Fit the model with an interaction
X = np.c_[np.ones(x1.size), x1, x2, x1 * x2]
if logistic:
lm = GLM(y, X, family=Binomial()).fit()
else:
lm = OLS(y, X).fit()
# Evaluate the model on the grid
eval = np.vectorize(lambda x1_, x2_: lm.predict([1, x1_, x2_, x1_ * x2_]))
yhat = eval(xx1, xx2)
# Default color limits put the midpoint at mean(y)
y_bar = y.mean()
c_min = min(np.percentile(y, 2), yhat.min())
c_max = max(np.percentile(y, 98), yhat.max())
delta = max(c_max - y_bar, y_bar - c_min)
c_min, cmax = y_bar - delta, y_bar + delta
contour_kws.setdefault("vmin", c_min)
contour_kws.setdefault("vmax", c_max)
# Draw the contour plot
func_name = "contourf" if filled else "contour"
contour = getattr(ax, func_name)
c = contour(xx1, xx2, yhat, levels, cmap=cmap, **contour_kws)
# Draw the scatter again so it's visible
ax.plot(x1, x2, "o", **scatter_kws)
# Draw a colorbar, maybe
if colorbar:
bar = plt.colorbar(c)
# Label the axes
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if clabel is not None and colorbar:
clabel = "P(%s)" % clabel if logistic else clabel
bar.set_label(clabel, labelpad=15, rotation=270)
return ax
def interactplot(x1,
x2,
y,
data=None,
filled=False,
cmap="RdBu_r",
colorbar=True,
levels=30,
logistic=False,
contour_kws=None,
scatter_kws=None,
ax=None,
**kwargs):
"""Visualize a continuous two-way interaction with a contour plot.
Parameters
----------
x1, x2, y, strings or array-like
Either the two independent variables and the dependent variable,
or keys to extract them from `data`
data : DataFrame
Pandas DataFrame with the data in the columns.
filled : bool
Whether to plot with filled or unfilled contours
cmap : matplotlib colormap
Colormap to represent yhat in the countour plot.
colorbar : bool
Whether to draw the colorbar for interpreting the color values.
levels : int or sequence
Number or position of contour plot levels.
logistic : bool
Fit a logistic regression model instead of linear regression.
contour_kws : dictionary
Keyword arguments for contour[f]().
scatter_kws : dictionary
Keyword arguments for plot().
ax : matplotlib axis
Axis to draw plot in.
Returns
-------
ax : Matplotlib axis
Axis with the contour plot.
"""
msg = (
"The `interactplot` function has been deprecated and will be removed "
"in a future version.")
warnings.warn(msg, UserWarning)
if not _has_statsmodels:
raise ImportError("The `interactplot` function requires statsmodels")
from statsmodels.regression.linear_model import OLS
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.genmod.families import Binomial
# Handle the form of the data
if data is not None:
x1 = data[x1]
x2 = data[x2]
y = data[y]
if hasattr(x1, "name"):
xlabel = x1.name
else:
xlabel = None
if hasattr(x2, "name"):
ylabel = x2.name
else:
ylabel = None
if hasattr(y, "name"):
clabel = y.name
else:
clabel = None
x1 = np.asarray(x1)
x2 = np.asarray(x2)
y = np.asarray(y)
# Initialize the scatter keyword dictionary
if scatter_kws is None:
scatter_kws = {}
if not ("color" in scatter_kws or "c" in scatter_kws):
scatter_kws["color"] = "#222222"
if "alpha" not in scatter_kws:
scatter_kws["alpha"] = 0.75
# Intialize the contour keyword dictionary
if contour_kws is None:
contour_kws = {}
# Initialize the axis
if ax is None:
ax = plt.gca()
# Plot once to let matplotlib sort out the axis limits
ax.plot(x1, x2, "o", **scatter_kws)
# Find the plot limits
x1min, x1max = ax.get_xlim()
x2min, x2max = ax.get_ylim()
# Make the grid for the contour plot
x1_points = np.linspace(x1min, x1max, 100)
x2_points = np.linspace(x2min, x2max, 100)
xx1, xx2 = np.meshgrid(x1_points, x2_points)
# Fit the model with an interaction
X = np.c_[np.ones(x1.size), x1, x2, x1 * x2]
if logistic:
lm = GLM(y, X, family=Binomial()).fit()
else:
lm = OLS(y, X).fit()
# Evaluate the model on the grid
eval = np.vectorize(lambda x1_, x2_: lm.predict([1, x1_, x2_, x1_ * x2_]))
yhat = eval(xx1, xx2)
# Default color limits put the midpoint at mean(y)
y_bar = y.mean()
c_min = min(np.percentile(y, 2), yhat.min())
c_max = max(np.percentile(y, 98), yhat.max())
delta = max(c_max - y_bar, y_bar - c_min)
c_min, cmax = y_bar - delta, y_bar + delta
contour_kws.setdefault("vmin", c_min)
contour_kws.setdefault("vmax", c_max)
# Draw the contour plot
func_name = "contourf" if filled else "contour"
contour = getattr(ax, func_name)
c = contour(xx1, xx2, yhat, levels, cmap=cmap, **contour_kws)
# Draw the scatter again so it's visible
ax.plot(x1, x2, "o", **scatter_kws)
# Draw a colorbar, maybe
if colorbar:
bar = plt.colorbar(c)
# Label the axes
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if clabel is not None and colorbar:
clabel = "P(%s)" % clabel if logistic else clabel
bar.set_label(clabel, labelpad=15, rotation=270)
return ax
