search for: john_szumiloski

Dear useRs, Some time ago I queried the list as to an efficient way of building a function which acts as ls() but with a different default for all.names: http://tolstoy.newcastle.edu.au/R/e6/help/09/03/7588.html I have struck upon a solution which so far has performed admirably. In particular, it uses ls() and not its explicit source code, so only has a dependency on its name and the name of

Dear useRs, I have a longitudinal experiment with several treatment groups, ~20 subjects per group, ~6 timepoints and a continuous dependent variable. I have been successfully been using lattice::xyplot with this data. However, I have been stumped with a particular application of it. I would like to use xyplot on my data, broken into treatment groups with the groups argument, using

Dear useRs, I would like to be able to smoothly use the "log" base graphics argument as an argument to pairs(). Currently in my hands, pairs() accepts the argument, makes transformed plots accordingly, but then stumbles over some annotation commands. Example: ############ begin session > sessionInfo() R version 2.15.0 Patched (2012-04-29 r59255) ### CRAN binary, not

Dear useRs, I have a utility function which is meant to be a clone of ls(), except with the option all.names=TRUE. Currently however, the function merely consists of a copy of the source code of ls(), except the default value of all.names is different. That approach has the drawback of future inconsistency if the code for ls() ever changes. No comment on whether that is likely or not; I would

Dear useRs, I have a complicated function to be optimized with optim(), and whose parameters are passed to another function within its evaluation. This function allows for the parameters to enter as arguments to various probability distribution functions. However, I am violating some scoping convention, as somewhere within the hierarchy of calls a variable is not visible. I will give a

Dear useRs, I am using the gamm function in the mgcv package to model a smooth relationship between a covariate and my dependent variable, while allowing for quantification of the subjectwise variability in the smooths. What I would like to do is to make subjectwise predictions for plotting purposes which account for the random smooth components of the fit. An example. (sessionInfo() is at