similar to: Why can't I figure this out? :S

Hi, trying to do a robudt regression of a two-way linear model, I keep getting the following error: > lqs(obs ~ y + s -1,method="lms", contrasts=list(s=("contr.sum"))) Error: lqs failed: all the samples were singular Robust regression with M-estimators works (also regular least square fits, of course): rlm.formula(formula = obs ~ y + s - 1, method = "M",

I have some data files in which some fields have multiple values. For example first last sex major John Smith M ANTH Jane Doe F HIST,BIOL What's the best R-like way to handle these data (Jane's major in my example), so that I can do things like summarize the other fields by them (e.g., sex by major)? Right now I'm processing the files (in excel since they're

Hello, I have a file which contains a column with age, which is represented in the two following patterns 1. "007/A" or ''007/a" or ''7 /a" ..... In this case A or a means year and I would like to extract only the numeric values eg 7 in the above case if this pattern exits in a line of file. 2. "004/M" or "004/m" where M or m means month

Hi, I have been using the lqs function with method='lms'. However the results I get are a little different from the results noted by Rousseeuw & Leroy (Robust Regression and Outlier Detection) and I was wondering how to use these results for outlier detection. I'm using the stackloss dataset, for which the original Rousseeuw et al. program points out that observations 1,2,3,4

Similar to --base-allocation, this is a common enough configuration that cannot be performed with -c when using --uri, but which makes life easier in scripting. And like --base-allocation, I'm starting with only a long option spelling, rather than burning a short option letter. --- sh/nbdsh.pod | 21 +++++++++++++++++++-- python/nbdsh.py | 6 ++++++ sh/test-context.sh | 32

Hello, I have found a problem with using the glm function with a gamma family. I have a vector of data, assumed to be generated by a gamma distribution. The parameters of this gamma distribution are estimated in two ways (i) using the glm() function, (ii) "by hand", using the optim() function. I find that the -2*likelihood at the maximum found by (i) is substantially larger than that