weibull

I'm doing survival calculations in Scipy and can't get the correct values. My code: x, a, c = 1000, 1.5, 5000 vals = exponweib.cdf(x,a,c,loc=0,scale=1) Val should equal 0.085559356392783004, but I'm getting 0 instead. If I define my own function I get the right answer: def weibCumDist(x,a,c): return 1-np.exp(-(x/c)**a) I could just use my own function, but I'm curious as to what I'm doing wrong. Any suggestions? Thanks. You haven't correctly mapped your parameters to those of scipy. To implement the equivalent of your weibCumDist : In [22]: x = 1000 In [23]: a = 1.5 In [24]: c = 5000 In [25]:

I am trying to generate an inverse Weibull distribution using parameters estimated from survreg in R. By this I mean I would like to, for a given probability (which will be a random number in a small simulation model implemented in MS Excel), return the expected time to failure using my parameters. I understand the general form for the inverse Weibull distribution to be: X=b[-ln(1-rand())]^(1/a) where a and b are shape and scale parameters respectively and X is the time to failure I want. My problem is in the interpretation of the intercept and covariate parameters from survreg. I have these

I have the following survreg model: Call: survreg(formula = Surv(time = (ev.time), event = ev) ~ age, data = my.data, dist = "weib") Value Std. Error z p (Intercept) 4.0961 0.5566 7.36 1.86e-13 age 0.0388 0.0133 2.91 3.60e-03 Log(scale) 0.1421 0.1208 1.18 2.39e-01 Scale= 1.15 Weibull distribution I would like to plot the hazard function and the survival function based on the above estimates. I don't want to use predict() or pweibull() (as presented here Parametric Survival or here SO question . I would like to use the curve() function. Any ideas how I can accomplish this? It seems the Weibull

I have a data which is the interval days of customers purchasing products. I try to estimate the shape and scale params by scipy.stat.weibull_min But, the parameters returned from the fit function is not unique and when I try to constrain the scale param to be 1, it does not work. Here is the three results with different ways for input: shape, loc, scale = scipy.stats.weibull_min.fit(data,floc=1,scale=1) #constrain scale to be 1 yellow curve loc:1 shape:0.7318249351 scale:75.22852953 shape, loc, scale = scipy.stats.weibull_min.fit(data,floc=1, f0=1) #constrain shape to be 1 blue curve loc:1