def diagnostics(data, params, nafc=2, sigmoid="logistic", core="ab", cuts=None, gammaislambda=False):
# here we need to hack stuff, since data can be either 'real' data, or just
# a list of intensities, or just an empty sequence
# in order to remain compatible with psipy we must check for an empty
# sequence here, and return a specially crafted return value in that case.
# sorry..
if op.isSequenceType(data) and len(data) == 0:
pmf, nparams = sfu.make_pmf(
sfr.PsiData([0], [0], [0], 1), nafc, sigmoid, core, None, gammaislambda=gammaislambda
)
thres = np.array([pmf.getThres(params, cut) for cut in sfu.get_cuts(cuts)])
slope = np.array([pmf.getSlope(params, th) for th in thres])
return np.array([]), np.array([]), 0.0, thres, np.nan, np.nan
shape = np.shape(np.array(data))
intensities_only = False
if len(shape) == 1:
# just intensities, make a dataset with k and n all zero
k = n = [0] * shape[0]
data = [[xx, kk, nn] for xx, kk, nn in zip(data, k, n)]
intensities_only = True
else:
# data is 'real', just do nothing
pass
dataset, pmf, nparams = sfu.make_dataset_and_pmf(data, nafc, sigmoid, core, None, gammaislambda=gammaislambda)
cuts = sfu.get_cuts(cuts)
params = sfu.get_params(params, nparams)
predicted = np.array([pmf.evaluate(intensity, params) for intensity in dataset.getIntensities()])
if intensities_only:
return predicted
else:
deviance_residuals = pmf.getDevianceResiduals(params, dataset)
deviance = pmf.deviance(params, dataset)
thres = np.array([pmf.getThres(params, cut) for cut in cuts])
slope = np.array([pmf.getSlope(params, th) for th in thres])
rpd = pmf.getRpd(deviance_residuals, params, dataset)
rkd = pmf.getRkd(deviance_residuals, dataset)
return predicted, deviance_residuals, deviance, thres, slope, rpd, rkd
def diagnostics(data,
params,
nafc=2,
sigmoid='logistic',
core='ab',
cuts=None,
gammaislambda=False):
""" Some diagnostic statistics for a psychometric function fit.
This function is a bit messy since it has three functions depending on the
type of the `data` argument.
Parameters
----------
data : variable
real data : A list of lists or an array of data.
The first column should be stimulus intensity, the second column should
be number of correct responses (in 2AFC) or number of yes- responses (in
Yes/No), the third column should be number of trials. See also: the examples
section below.
intensities : sequence of floats
The x-values of the psychometric function, then we obtain only the
predicted values.
no data : empty sequence
In this case we evaluate the psychometric function at the cuts. All
other return values are then irrelevant.
params : sequence of len nparams
parameter vector at which the diagnostic information should be evaluated
nafc : int
Number of responses alternatives for nAFC tasks. If nafc==1 a Yes/No task is
assumed.
sigmoid : string
Name of the sigmoid to be fitted. Valid sigmoids include:
logistic
gauss
gumbel_l
gumbel_r
See `swignifit.utility.available_sigmoids()` for all available sigmoids.
core : string
\"core\"-type of the psychometric function. Valid choices include:
ab (x-a)/b
mw%g midpoint and width
linear a+bx
log a+b log(x)
See `swignifit.utility.available_cores()` for all available sigmoids.
cuts : sequence of floats
Cuts at which thresholds should be determined. That is if cuts =
(.25,.5,.75), thresholds (F^{-1} ( 0.25 ), F^{-1} ( 0.5 ), F^{-1} ( 0.75
)) are returned. Here F^{-1} denotes the inverse of the function
specified by sigmoid. If cuts==None, this is modified to cuts=[0.5].
Output
------
(predicted, deviance_residuals, deviance, thres, Rpd, Rkd)
predicted : numpy array of length nblocks
predicted values associated with the respective stimulus intensities
deviance_residuals : numpy array of length nblocks
deviance residuals of the data
deviance float
deviance of the data
thres : numpy array length ncuts
the model prediction at the cuts
Rpd : float
correlation between predicted performance and deviance residuals
Rkd : float
correlation between block index and deviance residuals
Example
-------
>>> x = [float(2*k) for k in xrange(6)]
>>> k = [34,32,40,48,50,48]
>>> n = [50]*6
>>> d = [[xx,kk,nn] for xx,kk,nn in zip(x,k,n)]
>>> prm = [2.75, 1.45, 0.015]
>>> pred,di,D,thres,slope,Rpd,Rkd = diagnostics(d,prm)
>>> D
8.0748485860836254
>>> di[0]
1.6893279652591433
>>> Rpd
-0.19344675783032755
"""
# here we need to hack stuff, since data can be either 'real' data, or just
# a list of intensities, or just an empty sequence
# in order to remain compatible with psipy we must check for an empty
# sequence here, and return a specially crafted return value in that case.
# sorry..
# TODO after removal of psipy we can probably change this.
if op.isSequenceType(data) and len(data) == 0:
pmf, nparams = sfu.make_pmf(sfr.PsiData([0], [0], [0], 1),
nafc,
sigmoid,
core,
None,
gammaislambda=gammaislambda)
thres = np.array(
[pmf.getThres(params, cut) for cut in sfu.get_cuts(cuts)])
slope = np.array([pmf.getSlope(params, th) for th in thres])
return np.array([]), np.array([]), 0.0, thres, np.nan, np.nan
shape = np.shape(np.array(data))
intensities_only = False
if len(shape) == 1:
# just intensities, make a dataset with k and n all zero
k = n = [0] * shape[0]
data = [[xx, kk, nn] for xx, kk, nn in zip(data, k, n)]
intensities_only = True
else:
# data is 'real', just do nothing
pass
dataset, pmf, nparams = sfu.make_dataset_and_pmf(
data, nafc, sigmoid, core, None, gammaislambda=gammaislambda)
cuts = sfu.get_cuts(cuts)
params = sfu.get_params(params, nparams)
predicted = np.array([
pmf.evaluate(intensity, params)
for intensity in dataset.getIntensities()
])
if intensities_only:
return predicted
else:
deviance_residuals = pmf.getDevianceResiduals(params, dataset)
deviance = pmf.deviance(params, dataset)
thres = np.array([pmf.getThres(params, cut) for cut in cuts])
slope = np.array([pmf.getSlope(params, th) for th in thres])
rpd = pmf.getRpd(deviance_residuals, params, dataset)
rkd = pmf.getRkd(deviance_residuals, dataset)
return predicted, deviance_residuals, deviance, thres, slope, rpd, rkd
def diagnostics(data, params, nafc=2, sigmoid='logistic', core='ab', cuts=None, gammaislambda=False):
""" Some diagnostic statistics for a psychometric function fit.
This function is a bit messy since it has three functions depending on the
type of the `data` argument.
Parameters
----------
data : variable
real data : A list of lists or an array of data.
The first column should be stimulus intensity, the second column should
be number of correct responses (in 2AFC) or number of yes- responses (in
Yes/No), the third column should be number of trials. See also: the examples
section below.
intensities : sequence of floats
The x-values of the psychometric function, then we obtain only the
predicted values.
no data : empty sequence
In this case we evaluate the psychometric function at the cuts. All
other return values are then irrelevant.
params : sequence of len nparams
parameter vector at which the diagnostic information should be evaluated
nafc : int
Number of responses alternatives for nAFC tasks. If nafc==1 a Yes/No task is
assumed.
sigmoid : string
Name of the sigmoid to be fitted. Valid sigmoids include:
logistic (1+exp(-x))**-1 [Default]
gauss Phi(x)
gumbel_l 1 - exp(-exp(x))
gumbel_r exp(-exp(-x))
exponential x>0: 1 - exp(-x); else: 0
cauchy atan(x)/pi + 0.5
id x; only useful in conjunction with NakaRushton core
See `swignifit.utility.available_sigmoids()` for all available sigmoids.
core : string
\"core\"-type of the psychometric function. Valid choices include:
ab (x-a)/b [Default]
mw%g midpoint and width, with "%g" a number larger than 0 and less than 0.5.
mw%g corresponds to a parameterization in terms of midpoint and width of
the rising part of the sigmoid. This width is defined as the length of the
interval on which the sigmoidal part reaches from "%g" to 1-"%g".
linear a+b*x
log a+b*log(x)
weibull 2*s*m*(log(x)-log(m))/log(2) + log(log(2))
This will give you a weibull if combined with the gumbel_l sigmoid and a
reverse weibull if combined with the gumbel_r sigmoid.
poly (x/a)**b Will give you a weibull if combined with an exp sigmoid
NakaRushton The Naka-Rushton nonlinearity; should only be used with an id core
See `swignifit.utility.available_cores()` for all available cores.
cuts : sequence of floats
Cuts at which thresholds should be determined. That is if cuts =
(.25,.5,.75), thresholds (F^{-1} ( 0.25 ), F^{-1} ( 0.5 ), F^{-1} ( 0.75
)) are returned. Here F^{-1} denotes the inverse of the function
specified by sigmoid. If cuts==None, this is modified to cuts=[0.5].
Output
------
(predicted, deviance_residuals, deviance, thres, Rpd, Rkd)
predicted : numpy array of length nblocks
predicted values associated with the respective stimulus intensities
deviance_residuals : numpy array of length nblocks
deviance residuals of the data
deviance float
deviance of the data
thres : numpy array length ncuts
the model prediction at the cuts
Rpd : float
correlation between predicted performance and deviance residuals
Rkd : float
correlation between block index and deviance residuals
Example
-------
>>> x = [float(2*k) for k in xrange(6)]
>>> k = [34,32,40,48,50,48]
>>> n = [50]*6
>>> d = [[xx,kk,nn] for xx,kk,nn in zip(x,k,n)]
>>> prm = [2.75, 1.45, 0.015]
>>> pred,di,D,thres,slope,Rpd,Rkd = diagnostics(d,prm)
>>> D
8.0748485860836254
>>> di[0]
1.6893279652591433
>>> Rpd
-0.19344675783032755
"""
# here we need to hack stuff, since data can be either 'real' data, or just
# a list of intensities, or just an empty sequence
# in order to remain compatible with psipy we must check for an empty
# sequence here, and return a specially crafted return value in that case.
# sorry..
# TODO after removal of psipy we can probably change this.
if op.isSequenceType(data) and len(data) == 0:
pmf, nparams = sfu.make_pmf(sfr.PsiData([0],[0],[0],1), nafc, sigmoid, core, None, gammaislambda=gammaislambda )
thres = np.array([pmf.getThres(params, cut) for cut in sfu.get_cuts(cuts)])
slope = np.array([pmf.getSlope(params, th ) for th in thres])
return np.array([]), np.array([]), 0.0, thres, np.nan, np.nan
shape = np.shape(np.array(data))
intensities_only = False
if len(shape) == 1:
# just intensities, make a dataset with k and n all zero
k = n = [0] * shape[0]
data = [[xx,kk,nn] for xx,kk,nn in zip(data,k,n)]
intensities_only = True
else:
# data is 'real', just do nothing
pass
dataset, pmf, nparams = sfu.make_dataset_and_pmf(data, nafc, sigmoid, core, None, gammaislambda=gammaislambda)
cuts = sfu.get_cuts(cuts)
params = sfu.get_params(params, nparams)
predicted = np.array([pmf.evaluate(intensity, params) for intensity in
dataset.getIntensities()])
if intensities_only:
return predicted
else:
deviance_residuals = pmf.getDevianceResiduals(params, dataset)
deviance = pmf.deviance(params, dataset)
thres = np.array([pmf.getThres(params, cut) for cut in cuts])
slope = np.array([pmf.getSlope(params, th ) for th in thres])
rpd = pmf.getRpd(deviance_residuals, params, dataset)
rkd = pmf.getRkd(deviance_residuals, dataset)
return predicted, deviance_residuals, deviance, thres, slope, rpd, rkd