[R] power law plot

Hello all, I have a distribution of numbers with an abundance of zeros (zero-inflated). I thought about plotting the data in a log-log plot but then I cannot plot the zeros in such a plot. Also log-log plots are considered biased and not so robust. I have seen CCDF plots being used instead of that

[R] summary(object) not showing all values of a factor

Dear all, I am encountering some odd results from the summary(object) command for coxph and hurdle models. In both cases the result of summary(object) function leaves out one of the categories of a categorical variable used in the model. It is typically the first category if sorted alphabetically.

Re: [R] coxph diagnostics

Thank you for your response, Terry. To put the discussion into perspective, my data set is quite large with over 160,000 samples and 38 variables. The event is true for all samples in this dataset. The distribution is zero-inflated (i.e. most events occur at time = 0). The result of the cox.zph l

Re: [R] coxph diagnostics

ssume proportional assumption of coxph does not hold? Thanks! On Sun, Aug 11, 2013 at 6:30 AM, Göran Broström wrote: > > > On 08/11/2013 06:14 AM, Soumitro Dey wrote: > >> Hello all, >> >> This may be a naive question but since I'm new to R/survival models, I >> ca

[R] coxph diagnostics

Hello all, This may be a naive question but since I'm new to R/survival models, I cannot figure it out the problem myself. I have a coxph model for my data and I am trying to test if the proportional hazards assumption holds. Using cox.zph on the model I get a global score: GLOBAL NA 4.20e+02 0

Re: [R] X matrix deemed to be singular and cbind

ith a local statistician to decide > how best to handle this, as you seem to be out of your depth with > regard to model fitting. > > ... unless I have misunderstood, of course. > > Cheers, > Bert > > On Fri, Jul 26, 2013 at 7:55 AM, Soumitro Dey > wrote: > > H

[R] X matrix deemed to be singular and cbind

Hi list, While the "X matrix deemed to be singular" question has been answered in the list for quite a few times, I have a twist to it. I am using the coxph model for survival analysis on a dataset containing over 160,000 instances and 46 independent variables and I have 2 scenarios: 1. If I use

[R] percent correctly predicted (PCP) zeros for hurdle model

Hello all, I am using the hurdle model for fitting my count data using the pscl package which is working fine. However, I am stuck with the problem of calculating the percent correctly predicted (PCP) zeros for hurdle model. The method I am trying to use to achieve this is 'hitmiss' in the pscl pa