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 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