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For me, grep is more R-ish. Strongly related is the recent package by Hadley Wickhem: stringr, A package for "modern, consistent string processing" including grep like functions. … He recently published a paper on it in the R Journal. See also my answer on stackoverflow on an identical issue. …

Additionally, there exists a very popular connection to R, ESS, developed by, inter alia, R core members Kurt Hornik and Martin Maechler. … One org-mode buffer with the document, one ESS mode R script to keep code and try out different things, and one R console being accessible from both scripts. This works really great. …

Next to optim there is another function in base R that allows for what you want: nlminb. Check ?nlminb and ?optim for examples of the usage. There are a bunch of packages that can do optimizations. …

You could analyze this easily in R using package afex (for which you will need an id variable giving a unique code for each object of observation): ez.glm("id", "age", your.data, betwenn = c("gender", …

I think that the main issue is that you need to increase the number of possible iterations the optimizer is allowed to have (in my humble opinion the default of 10,000 is a little low). This is what i …

The R-world doesn't like Type 3 SS to much. One of the usually cited references is the "Exegeses on Linear Models" by Bill Venables (2000). …

You should note that T is none of your model's a random effects terms, but a fixed effect. Random effects are only those effects that appear after the | in a lmer formula! A more thorough discussion …

You could simply use my afex package (on CRAN) with function ez.glm. Alternatively you could use ezANOVA from the ez package (also on CRAN). Both functions are basically wrappers for car::Anova(). H …

Although I almost exclusively use R, I really admire the profiler in Matlab. When your program is kind of slow you normally want to know where the bottleneck is. … Although I recommend R (because it is just great: free, extremely powerful, ...) if you know you have to profile your code a lot, Matlab is way better. …

This can be solved by using the ingenious combination of afex with lsmeans (and also multcomp if one desires so, but this is usually not necessary). Furthermore, thanks to afex functionality to aggreg …

The R-sig-mixed modeling FAQ says the following regarding this: Tests of single parameters From worst to best: Wald Z-tests For balanced, nested LMMs where df can be computed: Wald t-tests …

One relatively straight-forward way could be to use likelihood-ratio tests via anova as described in the lme4 FAQ. We start with a full model in which the variances are unconstrained (i.e., two diffe …

Models m2 and m3 can only be estimated if you have repetitions on the level of the participant-item combination. For m3 for the full factorial design. Otherwise this random effect is confounded with t …

I have the feeling that pretty recent "Introduction to Scientific Programming and Scientific Simulation Using R" by Owen Jones, Robert Maillardet, and Andrew Robinson (2009) could be what you are looking …

A sketch of one solution (for another see below): Data needs to be in the long format (i.e., on value per row) instead of in the wide format as in SPSS (i.e., one subject per row), see the reshape p …