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Mostly updated the documentation and validated the examples.

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Per.Andreas.Brodtkorb 14 years ago
parent 9d10be02be
commit e8ab556234
2 changed files with 214 additions and 159 deletions
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pywafo/src/wafo/stats/distributions.py
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@ -1249,7 +1249,7 @@ class rv_continuous(rv_generic):
Accurate confidence interval with profile loglikelihood Accurate confidence interval with profile loglikelihood
>>> lp = phat3.profile() >>> lp = phat3.profile()
>>> lp.plot() >>> lp.plot()
>>> lp.get_CI() >>> lp.get_bounds()
""" """
@ -6898,19 +6898,19 @@ def main():
phat = weibull_min.fit(R, 1, 1, par_fix=[nan, 0, nan]) phat = weibull_min.fit(R, 1, 1, par_fix=[nan, 0, nan])
Lp = phat.profile(i=0) Lp = phat.profile(i=0)
Lp.plot() Lp.plot()
Lp.get_CI(alpha=0.1) Lp.get_bounds(alpha=0.1)
R = 1. / 990 R = 1. / 990
x = phat.isf(R) x = phat.isf(R)
# CI for x # CI for x
Lx = phat.profile(i=0, x=x) Lx = phat.profile(i=0, x=x)
Lx.plot() Lx.plot()
Lx.get_CI(alpha=0.2) Lx.get_bounds(alpha=0.2)
# CI for logSF=log(SF) # CI for logSF=log(SF)
Lpr = phat.profile(i=1, logSF=log(R), link=phat.dist.link) Lpr = phat.profile(i=1, logSF=log(R), link=phat.dist.link)
Lpr.plot() Lpr.plot()
Lpr.get_CI(alpha=0.075) Lpr.get_bounds(alpha=0.075)
dlaplace.stats(0.8, loc=0) dlaplace.stats(0.8, loc=0)
# pass # pass

365
pywafo/src/wafo/stats/estimation.py
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@ -7,10 +7,10 @@ Author: Per A. Brodtkorb 2008
''' '''
from __future__ import division from __future__ import division
import warnings
from wafo.plotbackend import plotbackend from wafo.plotbackend import plotbackend
from wafo.misc import ecross, findcross from wafo.misc import ecross, findcross
#from scipy.misc.ppimport import ppimport
import numdifftools import numdifftools
from scipy import special from scipy import special
@ -19,7 +19,7 @@ from scipy import optimize
import numpy import numpy
import numpy as np import numpy as np
from numpy import alltrue, arange, ravel, ones, sum, zeros, log, sqrt, exp from numpy import alltrue, arange, ravel, sum, zeros, log, sqrt, exp
from numpy import (atleast_1d, any, asarray, nan, pi, reshape, repeat, from numpy import (atleast_1d, any, asarray, nan, pi, reshape, repeat,
product, ndarray, isfinite) product, ndarray, isfinite)
from numpy import flatnonzero as nonzero from numpy import flatnonzero as nonzero
@ -134,84 +134,93 @@ class Profile(object):
''' Profile Log- likelihood or Product Spacing-function. ''' Profile Log- likelihood or Product Spacing-function.
which can be used for constructing confidence interval for which can be used for constructing confidence interval for
either phat(i), probability or quantile. either phat(i), probability or quantile.
Call
-----
Lp = Profile(fit_dist,**kwds)
Parameters Parameters
---------- ----------
fit_dist : FitDistribution object with ML or MPS estimated distribution parameters. fit_dist : FitDistribution object
with ML or MPS estimated distribution parameters.
**kwds : named arguments with keys **kwds : named arguments with keys
i - Integer defining which distribution parameter to i : scalar integer
profile, i.e. which parameter to keep fixed defining which distribution parameter to profile, i.e. which
(default index to first non-fixed parameter) parameter to keep fixed (default index to first non-fixed parameter)
pmin, pmax - Interval for either the parameter, phat(i), prb, or x, pmin, pmax : real scalars
used in the optimization of the profile function (default Interval for either the parameter, phat(i), prb, or x, used in the
is based on the 100*(1-alpha)% confidence interval optimization of the profile function (default is based on the
computed using the delta method.) 100*(1-alpha)% confidence interval computed using the delta method.)
N - Max number of points used in Lp (default 100) N : scalar integer
x - Quantile (return value) Max number of points used in Lp (default 100)
logSF - log survival probability,i.e., SF = Prob(X>x;phat) x : real scalar
link - function connecting the quantile (x) and the Quantile (return value) (default None)
survival probability (SF) with the fixed distribution logSF : real scalar
parameter, i.e.: self.par[i] = link(x,logSF,self.par,i), log survival probability,i.e., SF = Prob(X>x;phat) (default None)
where logSF = log(Prob(X>x;phat)). link : function connecting the quantile (x) and the survival probability
This means that if: (SF) with the fixed distribution parameter, i.e.:
1) x is not None then x is profiled self.par[i] = link(x,logSF,self.par,i), where logSF = log(Prob(X>x;phat)).
2) logSF is not None then logSF is profiled This means that if:
3) x and logSF both are None then self.par[i] is profiled (default) 1) x is not None then x is profiled
alpha - confidence coefficent (default 0.05) 2) logSF is not None then logSF is profiled
3) x and logSF both are None then self.par[i] is profiled (default)
alpha : real scalar
confidence coefficent (default 0.05)
Returns Returns
------- -------
Lp : Profile log-likelihood function with parameters phat given Lp : Profile log-likelihood function with parameters phat given
the data, phat(i), probability (prb) and quantile (x) (if given), i.e., the data, phat(i), probability (prb) and quantile (x) (if given), i.e.,
Lp = max(log(f(phat|data,phat(i)))), Lp = max(log(f(phat|data,phat(i)))),
or or
Lp = max(log(f(phat|data,phat(i),x,prb))) Lp = max(log(f(phat|data,phat(i),x,prb)))
Member methods Member methods
plot() -------------
get_CI() plot() : Plot profile function with 100(1-alpha)% confidence interval
get_bounds() : Return 100(1-alpha)% confidence interval
Member variables Member variables
fit_dist - fitted data object. ----------------
data - profile function values fit_dist : FitDistribution data object.
args - profile function arguments data : profile function values
alpha - confidence coefficient args : profile function arguments
Lmax - Maximum value of profile function alpha : confidence coefficient
alpha_cross_level - Lmax : Maximum value of profile function
alpha_cross_level :
PROFILE is a utility function for making inferences either on a particular PROFILE is a utility function for making inferences either on a particular
component of the vector phat or the quantile, x, or the probability, SF. component of the vector phat or the quantile, x, or the probability, SF.
This is usually more accurate than using the delta method assuming This is usually more accurate than using the delta method assuming
asymptotic normality of the ML estimator or the MPS estimator. asymptotic normality of the ML estimator or the MPS estimator.
Examples Examples
-------- --------
#MLE and better CI for phat.par[0] # MLE
>>> import numpy as np >>> import numpy as np
>>> R = weibull_min.rvs(1,size=100); >>> import wafo.stats as ws
>>> phat = FitDistribution(ws.weibull_min, R,1,scale=1, floc=0.0) >>> R = ws.weibull_min.rvs(1,size=100);
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
# Better CI for phat.par[i=0]
>>> Lp = Profile(phat, i=0) >>> Lp = Profile(phat, i=0)
>>> Lp.plot() >>> Lp.plot()
>>> Lp.get_CI(alpha=0.1) >>> phat_ci = Lp.get_bounds(alpha=0.1)
>>> SF = 1./990 >>> SF = 1./990
>>> x = phat.isf(SF) >>> x = phat.isf(SF)
# CI for x # CI for x
>>> Lx = phat.profile(i=1,x=x,link=phat.dist.link) >>> Lx = phat.profile(i=0, x=x, link=phat.dist.link)
>>> Lx.plot() >>> Lx.plot()
>>> Lx.get_CI(alpha=0.2) >>> x_ci = Lx.get_bounds(alpha=0.2)
# CI for logSF=log(SF) # CI for logSF=log(SF)
>>> Lpr = phat.profile(i=1,logSF=log(SF),link = phat.dist.link) >>> Lsf = phat.profile(i=0, logSF=log(SF), link=phat.dist.link)
>>> Lsf.plot()
>>> sf_ci = Lsf.get_bounds(alpha=0.2)
''' '''
def __init__(self, fit_dist, **kwds): def __init__(self, fit_dist, **kwds):
try:
i0 = (1 - numpy.isfinite(fit_dist.par_fix)).argmax()
except:
i0 = 0
self.fit_dist = fit_dist self.fit_dist = fit_dist
self.data = None self.data = None
self.args = None self.args = None
@ -220,7 +229,7 @@ class Profile(object):
self.ylabel = '' self.ylabel = ''
self.i_fixed, self.N, self.alpha, self.pmin, self.pmax, self.x, self.logSF, self.link = map(kwds.get, self.i_fixed, self.N, self.alpha, self.pmin, self.pmax, self.x, self.logSF, self.link = map(kwds.get,
['i', 'N', 'alpha', 'pmin', 'pmax', 'x', 'logSF', 'link'], ['i', 'N', 'alpha', 'pmin', 'pmax', 'x', 'logSF', 'link'],
[0, 100, 0.05, None, None, None, None, None]) [i0, 100, 0.05, None, None, None, None, None])
self.ylabel = '%g%s CI' % (100 * (1.0 - self.alpha), '%') self.ylabel = '%g%s CI' % (100 * (1.0 - self.alpha), '%')
if fit_dist.method.startswith('ml'): if fit_dist.method.startswith('ml'):
@ -252,14 +261,10 @@ class Profile(object):
self.alpha_cross_level = Lmax - self.alpha_Lrange self.alpha_cross_level = Lmax - self.alpha_Lrange
#lowLevel = self.alpha_cross_level - self.alpha_Lrange / 7.0 #lowLevel = self.alpha_cross_level - self.alpha_Lrange / 7.0
## Check that par are actually at the optimum
phatv = fit_dist.par.copy() phatv = fit_dist.par.copy()
self._par = phatv.copy() self._par = phatv.copy()
phatfree = phatv[self.i_free].copy()
## Set up variable to profile and _local_link function ## Set up variable to profile and _local_link function
self.profile_x = not self.x == None self.profile_x = not self.x == None
self.profile_logSF = not (self.logSF == None or self.profile_x) self.profile_logSF = not (self.logSF == None or self.profile_x)
self.profile_par = not (self.profile_x or self.profile_logSF) self.profile_par = not (self.profile_x or self.profile_logSF)
@ -279,16 +284,29 @@ class Profile(object):
p_opt = self.logSF p_opt = self.logSF
self.x = fit_dist.isf(exp(p_opt)) self.x = fit_dist.isf(exp(p_opt))
self._local_link = lambda fix_par, par : self.link(self.x, fix_par, par, self.i_fixed) self._local_link = lambda fix_par, par : self.link(self.x, fix_par, par, self.i_fixed)
self.xlabel = 'log(R)' self.xlabel = 'log(SF)'
else: else:
raise ValueError("You must supply a non-empty quantile (x) or probability (logSF) in order to profile it!") raise ValueError("You must supply a non-empty quantile (x) or probability (logSF) in order to profile it!")
self.xlabel = self.xlabel + ' (' + fit_dist.dist.name + ')' self.xlabel = self.xlabel + ' (' + fit_dist.dist.name + ')'
## Check that par are actually at the optimum
phatfree = phatv[self.i_free].copy()
foundNewphat = False
mylogfun = self._nlogfun
if True:
phatfree = optimize.fmin(mylogfun, phatfree, args=(phatv[self.i_fixed],) , disp=0)
LLt = -mylogfun(phatfree,phatv[self.i_fixed])
if LLt>Lmax:
foundNewphat = True
warnings.warn('Something wrong with fit')
dL = Lmax-LLt
self.alpha_cross_level -= dL
self.Lmax = LLt
pvec = self._get_pvec(p_opt) pvec = self._get_pvec(p_opt)
mylogfun = self._nlogfun
self.data = numpy.empty_like(pvec) self.data = numpy.empty_like(pvec)
self.data[:] = nan self.data[:] = nan
k1 = (pvec >= p_opt).argmax() k1 = (pvec >= p_opt).argmax()
@ -362,6 +380,7 @@ class Profile(object):
else: else:
pvec = linspace(self.pmin, self.pmax, self.N) pvec = linspace(self.pmin, self.pmax, self.N)
return pvec return pvec
def _myinvfun(self, phatnotfixed): def _myinvfun(self, phatnotfixed):
mphat = self._par.copy() mphat = self._par.copy()
mphat[self.i_notfixed] = phatnotfixed; mphat[self.i_notfixed] = phatnotfixed;
@ -382,39 +401,39 @@ class Profile(object):
probability (return period) or distribution parameter probability (return period) or distribution parameter
''' '''
par = self._par par = self._par.copy()
par[self.i_free] = free_par par[self.i_free] = free_par
# _local_link: connects fixed quantile or probability with fixed distribution parameter # _local_link: connects fixed quantile or probability with fixed distribution parameter
par[self.i_fixed] = self._local_link(fix_par, par) par[self.i_fixed] = self._local_link(fix_par, par)
return self.fit_dist.fitfun(par) return self.fit_dist.fitfun(par)
def get_CI(self, alpha=0.05): def get_bounds(self, alpha=0.05):
'''Return confidence interval '''Return confidence interval
''' '''
if alpha < self.alpha: if alpha < self.alpha:
raise ValueError('Unable to return CI with alpha less than %g' % self.alpha) raise ValueError('Unable to return CI with alpha less than %g' % self.alpha)
cross_level = self.Lmax - 0.5 * chi2isf(alpha, 1) #_WAFODIST.chi2.isf(alpha, 1) cross_level = self.Lmax - 0.5 * chi2isf(alpha, 1)
ind = findcross(self.data, cross_level) ind = findcross(self.data, cross_level)
N = len(ind) N = len(ind)
if N == 0: if N == 0:
#Warning('upper bound for XXX is larger' warnings.warn('''Number of crossings is zero, i.e.,
#Warning('lower bound for XXX is smaller' upper and lower bound is not found!''')
CI = (self.pmin, self.pmax) CI = (self.pmin, self.pmax)
elif N == 1: elif N == 1:
x0 = ecross(self.args, self.data, ind, cross_level) x0 = ecross(self.args, self.data, ind, cross_level)
isUpcrossing = self.data[ind] > self.data[ind + 1] isUpcrossing = self.data[ind] > self.data[ind + 1]
if isUpcrossing: if isUpcrossing:
CI = (x0, self.pmax) CI = (x0, self.pmax)
#Warning('upper bound for XXX is larger' warnings.warn('Upper bound is larger')
else: else:
CI = (self.pmin, x0) CI = (self.pmin, x0)
#Warning('lower bound for XXX is smaller' warnings.warn('Lower bound is smaller')
elif N == 2: elif N == 2:
CI = ecross(self.args, self.data, ind, cross_level) CI = ecross(self.args, self.data, ind, cross_level)
else: else:
# Warning('Number of crossings too large!') warnings.warn('Number of crossings too large! Something is wrong!')
CI = ecross(self.args, self.data, ind[[0, -1]], cross_level) CI = ecross(self.args, self.data, ind[[0, -1]], cross_level)
return CI return CI
@ -449,7 +468,7 @@ class FitDistribution(rv_frozen):
Data to use in calculating the ML or MPS estimators Data to use in calculating the ML or MPS estimators
args : optional args : optional
Starting values for any shape arguments (those not specified Starting values for any shape arguments (those not specified
will be determined by _fitstart(data)) will be determined by dist._fitstart(data))
kwds : loc, scale kwds : loc, scale
Starting values for the location and scale parameters Starting values for the location and scale parameters
Special keyword arguments are recognized as holding certain Special keyword arguments are recognized as holding certain
@ -501,31 +520,33 @@ class FitDistribution(rv_frozen):
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0) >>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
#Plot various diagnostic plots to asses quality of fit. #Plot various diagnostic plots to asses quality of fit.
>>> phat.plotfitsumry() >>> phat.plotfitsummary()
#phat.par holds the estimated parameters #phat.par holds the estimated parameters
#phat.par_upper upper CI for parameters #phat.par_upper upper CI for parameters
#phat.par_lower lower CI for parameters #phat.par_lower lower CI for parameters
#Better CI for phat.par[0] #Better CI for phat.par[0]
>>> Lp = Profile(phat,i=0) >>> Lp = phat.profile(i=0)
>>> Lp.plot() >>> Lp.plot()
>>> Lp.get_CI(alpha=0.1) >>> p_ci = Lp.get_bounds(alpha=0.1)
>>> SF = 1./990 >>> SF = 1./990
>>> x = phat.isf(SF) >>> x = phat.isf(SF)
# CI for x # CI for x
>>> Lx = phat.profile(i=0,x=x,link=phat.dist.link) >>> Lx = Profile(phat, i=0,x=x,link=phat.dist.link)
>>> Lx.plot() >>> Lx.plot()
>>> Lx.get_CI(alpha=0.2) >>> x_ci = Lx.get_bounds(alpha=0.2)
# CI for logSF=log(SF) # CI for logSF=log(SF)
>>> Lpr = phat.profile(i=0,logSF=log(SF),link = phat.dist.link) >>> Lsf = phat.profile(i=0, logSF=log(SF), link=phat.dist.link)
>>> Lsf.plot()
>>> sf_ci = Lsf.get_bounds(alpha=0.2)
''' '''
def __init__(self, dist, data, *args, **kwds): def __init__(self, dist, data, *args, **kwds):
extradoc = ''' extradoc = '''
plotfitsumry() plotfitsummary()
Plot various diagnostic plots to asses quality of fit. Plot various diagnostic plots to asses quality of fit.
plotecdf() plotecdf()
Plot Empirical and fitted Cumulative Distribution Function Plot Empirical and fitted Cumulative Distribution Function
@ -685,7 +706,16 @@ class FitDistribution(rv_frozen):
optimizer = getattr(optimize, optimizer) optimizer = getattr(optimize, optimizer)
except AttributeError: except AttributeError:
raise ValueError, "%s is not a valid optimizer" % optimizer raise ValueError, "%s is not a valid optimizer" % optimizer
vals = optimizer(func,x0,args=(ravel(data),),disp=0) loglike = numpy.inf
for num_times in range(5):
vals = optimizer(func,x0,args=(ravel(data),),disp=0)
ll = func(vals, ravel(data))
if ll<loglike:
loglike = ll
x0 = vals
else:
vals = x0
vals = tuple(vals) vals = tuple(vals)
else: else:
vals = tuple(x0) vals = tuple(x0)
@ -716,87 +746,99 @@ class FitDistribution(rv_frozen):
def fitfun(self, phat): def fitfun(self, phat):
return self._fitfun(phat, self.data) return self._fitfun(phat, self.data)
def _fxfitfun(self, phat10):
self.par[self.i_notfixed] = phat10
return self._fitfun(self.par, self.data)
def profile(self, **kwds): def profile(self, **kwds):
''' Profile Log- likelihood or Log Product Spacing- function, ''' Profile Log- likelihood or Log Product Spacing- function,
which can be used for constructing confidence interval for which can be used for constructing confidence interval for
either phat(i), probability or quantile. either phat(i), probability or quantile.
CALL: Lp = RV.profile(**kwds) Parameters
----------
**kwds : named arguments with keys
RV = object with ML or MPS estimated distribution parameters. i : scalar integer
Parameters defining which distribution parameter to profile, i.e. which
---------- parameter to keep fixed (default index to first non-fixed parameter)
**kwds : named arguments with keys: pmin, pmax : real scalars
i - Integer defining which distribution parameter to Interval for either the parameter, phat(i), prb, or x, used in the
profile, i.e. which parameter to keep fixed optimization of the profile function (default is based on the
(default index to first non-fixed parameter) 100*(1-alpha)% confidence interval computed using the delta method.)
pmin, pmax - Interval for either the parameter, phat(i), prb, or x, N : scalar integer
used in the optimization of the profile function (default Max number of points used in Lp (default 100)
is based on the 100*(1-alpha)% confidence interval x : real scalar
computed using the delta method.) Quantile (return value) (default None)
N - Max number of points used in Lp (default 100) logSF : real scalar
x - Quantile (return value) log survival probability,i.e., SF = Prob(X>x;phat) (default None)
logSF - log survival probability,i.e., R = Prob(X>x;phat) link : function connecting the quantile (x) and the survival probability
link - function connecting the quantile (x) and the (SF) with the fixed distribution parameter, i.e.:
survival probability (R) with the fixed distribution self.par[i] = link(x,logSF,self.par,i), where logSF = log(Prob(X>x;phat)).
parameter, i.e.: self.par[i] = link(x,logSF,self.par,i), This means that if:
where logSF = log(Prob(X>x;phat)). 1) x is not None then x is profiled
This means that if: 2) logSF is not None then logSF is profiled
1) x is not None then x is profiled 3) x and logSF both are None then self.par[i] is profiled (default)
2) logSF is not None then logSF is profiled alpha : real scalar
3) x and logSF both are None then self.par[i] is profiled (default) confidence coefficent (default 0.05)
alpha - confidence coefficent (default 0.05) Returns
Returns -------
-------- Lp : Profile log-likelihood function with parameters phat given
Lp = Profile log-likelihood function with parameters phat given the data, phat(i), probability (prb) and quantile (x) (if given), i.e.,
the data, phat(i), probability (prb) and quantile (x) (if given), i.e., Lp = max(log(f(phat|data,phat(i)))),
Lp = max(log(f(phat|data,phat(i)))), or
or Lp = max(log(f(phat|data,phat(i),x,prb)))
Lp = max(log(f(phat|data,phat(i),x,prb)))
Member methods
PROFILE is a utility function for making inferences either on a particular -------------
component of the vector phat or the quantile, x, or the probability, R. plot() : Plot profile function with 100(1-alpha)% confidence interval
This is usually more accurate than using the delta method assuming get_bounds() : Return 100(1-alpha)% confidence interval
asymptotic normality of the ML estimator or the MPS estimator.
Member variables
----------------
Examples fit_dist : FitDistribution data object.
-------- data : profile function values
# MLE and better CI for phat.par[0] args : profile function arguments
>>> R = weibull_min.rvs(1,size=100); alpha : confidence coefficient
>>> phat = weibull_min.fit(R) Lmax : Maximum value of profile function
>>> Lp = phat.profile(i=0) alpha_cross_level :
>>> Lp.plot()
>>> Lp.get_CI(alpha=0.1) PROFILE is a utility function for making inferences either on a particular
>>> R = 1./990 component of the vector phat or the quantile, x, or the probability, SF.
>>> x = phat.isf(R) This is usually more accurate than using the delta method assuming
asymptotic normality of the ML estimator or the MPS estimator.
# CI for x
>>> Lx = phat.profile(i=1,x=x,link=phat.dist.link) Examples
>>> Lx.plot() --------
>>> Lx.get_CI(alpha=0.2) # MLE
>>> import numpy as np
# CI for logSF=log(SF) >>> import wafo.stats as ws
>>> Lpr = phat.profile(i=1,logSF=log(R),link = phat.dist.link) >>> R = ws.weibull_min.rvs(1,size=100);
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
See also
-------- # Better CI for phat.par[i=0]
Profile >>> Lp = Profile(phat, i=0)
>>> Lp.plot()
>>> phat_ci = Lp.get_bounds(alpha=0.1)
>>> SF = 1./990
>>> x = phat.isf(SF)
# CI for x
>>> Lx = phat.profile(i=0, x=x, link=phat.dist.link)
>>> Lx.plot()
>>> x_ci = Lx.get_bounds(alpha=0.2)
# CI for logSF=log(SF)
>>> Lsf = phat.profile(i=0, logSF=log(SF), link=phat.dist.link)
>>> Lsf.plot()
>>> sf_ci = Lsf.get_bounds(alpha=0.2)
See also
--------
Profile
''' '''
if not self.par_fix == None:
i1 = kwds.setdefault('i', (1 - numpy.isfinite(self.par_fix)).argmax())
return Profile(self, **kwds) return Profile(self, **kwds)
def plotfitsumry(self): def plotfitsummary(self):
''' Plot various diagnostic plots to asses the quality of the fit. ''' Plot various diagnostic plots to asses the quality of the fit.
PLOTFITSUMRY displays probability plot, density plot, residual quantile PLOTFITSUMMARY displays probability plot, density plot, residual quantile
plot and residual probability plot. plot and residual probability plot.
The purpose of these plots is to graphically assess whether the data The purpose of these plots is to graphically assess whether the data
could come from the fitted distribution. If so the empirical- CDF and PDF could come from the fitted distribution. If so the empirical- CDF and PDF
@ -972,7 +1014,20 @@ class FitDistribution(rv_frozen):
def main(): def main():
import wafo.stats as ws
R = ws.weibull_min.rvs(1,size=100);
phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
# Better CI for phat.par[i=0]
Lp = Profile(phat, i=0)
pass pass
#import doctest
#doctest.testmod()
# _WAFODIST = ppimport('wafo.stats.distributions') # _WAFODIST = ppimport('wafo.stats.distributions')
# #nbinom(10, 0.75).rvs(3) # #nbinom(10, 0.75).rvs(3)
# import matplotlib # import matplotlib
@ -986,19 +1041,19 @@ def main():
# phat = _WAFODIST.weibull_min.fit(R, 1, 1, par_fix=[nan, 0, nan]) # phat = _WAFODIST.weibull_min.fit(R, 1, 1, par_fix=[nan, 0, nan])
# Lp = phat.profile(i=0) # Lp = phat.profile(i=0)
# Lp.plot() # Lp.plot()
# Lp.get_CI(alpha=0.1) # Lp.get_bounds(alpha=0.1)
# R = 1. / 990 # R = 1. / 990
# x = phat.isf(R) # x = phat.isf(R)
# #
# # CI for x # # CI for x
# Lx = phat.profile(i=0, x=x) # Lx = phat.profile(i=0, x=x)
# Lx.plot() # Lx.plot()
# Lx.get_CI(alpha=0.2) # Lx.get_bounds(alpha=0.2)
# #
# # CI for logSF=log(SF) # # CI for logSF=log(SF)
# Lpr = phat.profile(i=0, logSF=log(R), link=phat.dist.link) # Lpr = phat.profile(i=0, logSF=log(R), link=phat.dist.link)
# Lpr.plot() # Lpr.plot()
# Lpr.get_CI(alpha=0.075) # Lpr.get_bounds(alpha=0.075)
# #
# _WAFODIST.dlaplace.stats(0.8, loc=0) # _WAFODIST.dlaplace.stats(0.8, loc=0)
## pass ## pass
@ -1020,4 +1075,4 @@ def main():
# lp = pht.profile() # lp = pht.profile()
if __name__ == '__main__': if __name__ == '__main__':
main() main()

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