def fit_gamma(samples):
samples = [double(n) for n in samples if n > 0]#because rpy does not like longs!
r.library('MASS')
f = r.fitdistr(samples,'gamma')
shap,rat = f['estimate']['shape'],f['estimate']['rate']
qp = r.qgamma(r.ppoints(samples),shape=shap,rate=rat)
return qp,shape,rat
def fit_weibull(samples):
#samples = [double(n) for n in samples if n > 0]#because rpy does not like longs!
r.library('MASS')
f = r.fitdistr(samples,'weibull')
sc,sh = f['estimate']['scale'],f['estimate']['shape']
qp = r.qweibull(r.ppoints(samples),scale=sc,shape=sh)
return qp,sc,sh
def fit_weibull(samples):
#samples = [double(n) for n in samples if n > 0]#because rpy does not like longs!
r.library('MASS')
f = r.fitdistr(samples, 'weibull')
sc, sh = f['estimate']['scale'], f['estimate']['shape']
qp = r.qweibull(r.ppoints(samples), scale=sc, shape=sh)
return qp, sc, sh
def fit_exponential(samples):
samples = [float(n) for n in samples]#because rpy does not like longs!
r.library('MASS')
f = r.fitdistr(samples,'exponential')
rat = f['estimate']['rate']
qp = r.qexp(r.ppoints(samples),rate=rat)
return qp, rat
def fit_exponential(samples):
samples = [float(n) for n in samples] #because rpy does not like longs!
r.library('MASS')
f = r.fitdistr(samples, 'exponential')
rat = f['estimate']['rate']
qp = r.qexp(r.ppoints(samples), rate=rat)
return qp, rat
def fit_gamma(samples):
samples = [double(n) for n in samples
if n > 0] #because rpy does not like longs!
r.library('MASS')
f = r.fitdistr(samples, 'gamma')
shap, rat = f['estimate']['shape'], f['estimate']['rate']
qp = r.qgamma(r.ppoints(samples), shape=shap, rate=rat)
return qp, shape, rat
def _ltm(_locals):
'''Supports method core_R.ltm()'''
class LtmError(Exception):
pass
# Get self
self = _locals['damonObj']
coredata = dmn.tools.get_damon_datadict(self)["coredata"]
#convert all nanvals to nanvals of r environment.
coredata[coredata == self.nanval] = r.NAN
#convert coredata into a dictionary with keys equal to items
data_dict = {
str(x): coredata[:, x - 1]
for x in range(1, len(self["collabels"][0]))
}
#this statement is not necessary , BASIC_CONVERSION is default..
#rpy.set_default_mode(rpy.NO_CONVERSION)
#import ltm library using r object, (to install libraries in R, you have '
#to go to R console. as much i know, there is no way to do it from python. )
r.library("ltm")
#assign a object df to R environment.
r.assign('df', data_dict)
#convert df to r data frame to be passed to ltm function
r("df = data.frame(df)")
#this is the formula to be passed to ltm function, r("") is used to call
#those statements which are not posssible to be called from python environment..
formula = r("df ~ " + _locals['formula_rightside'])
#make the input arguments suitable for r.
if _locals['na_action'] is not None:
_locals['na_action'] = r[_locals['na_action']]
#call r.ltm
try:
ltm_out = r.ltm(formula,
na_action=_locals['na_action'],
IRT_param=_locals['irt_param'],
constraint=_locals['constraint'],
start_val=_locals['start_val'],
control=_locals['control'])
except:
exc = 'Unable to find r.ltm(). Make sure the package resides in the R library.'
raise LtmError(exc)
return ltm_out
def _grm(_locals):
'''Supports method core_R.grm()'''
# Get self
self = _locals['damonObj']
coredata = dmn.tools.get_damon_datadict(self)["coredata"]
#convert all nanvals to nanvals of r environment.
coredata[coredata == self.nanval] = r.NAN
#convert coredata into a dictionary with keys equal to items
data_dict = {
str(x): coredata[:, x - 1]
for x in range(1, len(self["collabels"][0]))
}
#this statement is not necessary , BASIC_CONVERSION is default..
rpy.set_default_mode(rpy.NO_CONVERSION)
#import ltm library using r object, (to install libraries in R, you have to go to R console. as much i know, there is no way to do it from python. )
r.library("ltm")
#make the input arguments suitable for r.
if _locals['na_action'] != None:
_locals['na_action'] = r[_locals['na_action']]
#assign a object df to R environment.
r.assign('df', data_dict)
print _locals['control']
#convert df to r data frame to be passed to ltm function
r("df = data.frame(df)")
#get the robj of data frame created in r.
r_data_frame = r("df")
#call r.ltm
grm_out = r.grm(r_data_frame,
constrained=_locals['constrained'],
na_action=_locals['na_action'],
IRT_param=_locals['irt_param'],
Hessian=_locals['hessian'],
start_val=_locals['start_val'],
control=_locals['control'])
return grm_out
def fit_nbinom(samples):
r.library('MASS')
f = r.fitdistr(samples,'negative binomial')
s,m = f['estimate']['size'],f['estimate']['mu']
qp = r.qnbinom(r.ppoints(samples),size=s,mu=m)
return qp,s,m
def fit_poisson(samples):
r.library('MASS')
f = r.fitdistr(samples,'poisson')
l = f['estimate']['lambda'] #predicted mean
qp = r.qpois(r.ppoints(samples),l)
return qp,l
def fit_poisson(samples):
r.library('MASS')
f = r.fitdistr(samples, 'poisson')
l = f['estimate']['lambda'] #predicted mean
qp = r.qpois(r.ppoints(samples), l)
return qp, l
def fit_nbinom(samples):
r.library('MASS')
f = r.fitdistr(samples, 'negative binomial')
s, m = f['estimate']['size'], f['estimate']['mu']
qp = r.qnbinom(r.ppoints(samples), size=s, mu=m)
return qp, s, m