You would be well advised to check that code carefully. If you look at the number of cases with complete data the stating numbers in risk sets are significantly different: sum(na.omit(colon)$rx=="Obs"... View answer 2 votes The "absolute risk" is the events per unit time divided by the number of individuals susceptible to the event under observation. It is specifically not the value of S(t). During periods of no risk the ... View answer Accepted answer 2 votes This would have been better presented with a test data object but based on your comments (that change the question) you could try using offset() on K and T and I() on your proposed "one" variable: &... View answer 2 votes If the model is: log(y) ~ a + b*X1 + c*X2 with Poisson errors, .... then it would be as simple as: glm( y ~ X1 + X2, data = dfrm, family="poisson") You could get one kind of "additive" model with: ... View answer 2 votes If the items you put up on the web are derived using ks::kde then the 'estimate' component is a 3D matrix with 51 points in each dimension. Try doing this: sum( UD1$estimate ) # should be close to ...

Edit after data and input statement added: data1 <- read.table(text=" base 0 1 2 3 4 5 6 7 8 707 1 1389 430 493.6 0.3096 0.3554 525.47 0.3783 170.29 0.1226 708 4 ...

I think you are proceeding incorrectly. You should be constructing : d=c(x1,x2,x3) And then examining the statistics of interest before applying them to the samples.

This is more an extended comment than an effort to improve on the specific suggestion of @MånsT: Statistical test by and large are not tests for what distribution produced data but rather which ones ...

Th R package 'msm' by Christopher Jackson is entitled "Multi-state Markov and hidden Markov models in continuous time" and provides for the censoring as well as estimating transition probabilities ...

When interpreting the output of cox.zph it is just as much (or even more) the "flatness" of the line, as it is the straightness of the line, that is important. If the line is straight but slanted ...

@chl has pointed to a specific answer to your question. The 'rms' package's cph function will produce a Somers-D which can be transformed trivially into a c-index. However, Harrell (who introduced the ...

It depends on your state of knowledge before the study. If you went into the study knowing that there were variables that were highly likely to be "significant" predictors of the outcome, and you were ...

As I see it there are two survival analysis paradigms that could be used. The Cox regression framework allows time varying covariates and would produce an estimate for the risk of cancellation ...

The Maxwell distribution is the classical limit under conditions of high temperature and non-interacting wave functions of both Fermi-Dirac statistics and Bose-Einstein statistics. I would expect that ...

The function that is specifically designed for this task is ave(). By default it returned the mean within a group and returns a vector of the same length as the two input arguments. It is designed to ...

Engineering failure-time analysis is very similar to demographic analysis of human survival. In both cases there is typically an early failure rate (infant mortality) and an aging process. In ...

Generally an R regression 'control' list defaults to the first item in the parameter character vector (which looking at the Usage section of the help page is "inbag"). So if you did offer a &...

There wouldn't be any uncertainty for the mass but the same errors in position would seem to exist for the position of the starting point as exist for the displaced locations of the spring end-point ...

If you modeled this with Poisson regression, you would still be able to incorporate the time aspect and not violate the assumptions of the model since the outcome can accommodate multiple hits. You ...

The way to get times for particular probabilities rather than probability for particular time you need the inverse of the survival function which is the quantile function. The flexsurv package also ...

In general it is unproductive to attempt interpretation of individual coefficients. It is more direct to create predictions along the range of values for the continuous variables with specific values ...

Klein and Moeschberger's text "Survival Analysis" (p 195-197) has a nice comparison of these tests and a few others. They differ in the weighting of events along the course of the study: The Gehan ...

When I read this question:"Does frequency of occurrence (FO) of pieces eaten differ between species or year?", I do not immediately think of using a 2-factor interaction model, but rather will have ...

I think you might make progress by asking your audience to assume that these values are distributed on the range [0,5] in the set {(0:10)/2} with a beta-binomial distribution. The beta-binomial ...

The Fisher Exact Test is not set up to handle covariates. There are exact tests that do handle covariates, but they require additional packages in R. I would set the data up like this: > res2 ...

The formula interface for regression functions in R would allow you to automatically generate n-way interactions very simply. Unfortunately this results in a combinatoric explosion. You could take ...

The default type of spline with splinefun is "fmm" and the help page says: If method = "fmm", the spline used is that of Forsythe, Malcolm and Moler (an exact cubic is fitted through the four ...

There is at least one discrete variable, parm3 and it's possible that there are other un-labeled groupings. I'd start by redo that graphic while labeling the parm3 values with color coding. Ten you ...