On 23 Apr 2008, at 20:53, Schack Tang, Katie wrote:
1) Does the fact that voiced (vce) fails to reach significance on its own indicate that its effect on F0 is not significantly different from the effect of an implosive on F0?
Yes. Your example suggests you're using the default contrasts.
2) What can I conclude about the sonorant (son) and voiceless (vceless)? Can I just conclude that they both raise F0 compared to implosives?
Yes.
Or can I also conclude that voiceless raises F0 more than sonorant does?
No.
If the former, how can I test the latter--is it possible to specify which level of a factor is withheld in lmer?
Normally, the first factor is withheld; manipulating the contrasts matrix will give you the right results.
Usually, calculating effect sizes and confidence intervals is very informative. First, you will need to manipulate the contrasts: F0 ~ 1/PrecSegment instead of F0 ~ 1 + PrecSegment (the "1" intercept is implicit) should give you a model without explicit intercept, but separate estimates for all PrecSegment levels.
To estimate confidence intervals, I've had comparatively good results with the pvals.fnc function (languageR package) and its Markov Chain Monte-Carlo sampling strategy. I had to slightly patch this function to accept "glmer" rather than just "lmer" type models, and I also understand (p.c.) that recent versions do not yet work well with more than one random effect. There are occasional problems with minimal (i.e. zero-sized) confidence intervals; manipulating the number of simulations (a parameter to the pvals.fnc function) can help.
As far as I know, the confidence intervals that are estimated this way are non-simultaneous. Thus, fishing for effects without defining explicit hypotheses will have the usual consequences. (If you have corpus data, the use of separate samples for each hypothesis should give you reliable p's and confidence intervals in that respect.)
-- David Reitter ICCS/HCRC, Informatics, University of Edinburgh http://www.david-reitter.com
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