date:20101031

Re: [R] Constrained Regression

2010-10-31 Thread Jim Silverton

Hello everyone,
I have 3 variables Y, X1 and X2. Each variables lies between 0 and 1. I want
to do a constrained regression such that a0 and (1-a) 0

for the model:

Y = a*X1 + (1-a)*X2

I tried the help on the constrained regression in R but I concede that it
was not helpful.

Any help is greatly appreciated
-- 
Thanks,
Jim.

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[R] make many barplot into one plot

2010-10-31 Thread Sibylle Stöckli

Dear R users


I would like to group my barplot graph (see example on the R help  
link). The proposed R code, adding individual bars to the plot, looks  
really overwhelming. My specific dataset just consists of five groups  
and three different levels within each groups (the individual bars).  
The .txt file is read as matrix (horizontal: group, vertical: levels).

The R trellis barchart (function group=) is an easy function, but  
unfortunately the upper plot part look much different from other  
graphs. I would therefore prefer barplot to stansdardize my plots  
within the manuscript.

It would be very  helpful for me to know if anyone else has worked on  
the barplot group function.

Thanks
Sibylle


http://onertipaday.blogspot.com/2007/05/make-many-barplot-into-one-plot.html

R code from the link
## I have 4 tables like this:satu - array(c(5,15,20,68,29,54,84,119),  
dim=c(2,4), dimnames=list(c(Negative, Positive), c(Black,  
Brown, Red, Blond)))dua - array(c(50,105,30,8,29,25,84,9),  
dim=c(2,4), dimnames=list(c(Negative, Positive), c(Black,  
Brown, Red, Blond)))tiga - array(c(9,16,26,68,12,4,84,12),  
dim=c(2,4), dimnames=list(c(Negative, Positive), c(Black,  
Brown, Red, Blond)))empat - array(c(25,13,50,78,19,34,84,101),  
dim=c(2,4), dimnames=list(c(Negative, Positive), c(Black,  
Brown, Red, Blond)))# rbind() the tables togetherTAB -  
rbind(satu, dua, tiga, empat)# Do the barplot and save the bar  
midpointsmp - barplot(TAB, beside = TRUE, axisnames = FALSE)# Add the  
individual bar labelsmtext(1, at = mp, text = c(N, P),line = 0,  
cex = 0.5)# Get the midpoints of each sequential pair of bars# within  
each of the four groupsat - t(sapply(seq(1, nrow(TAB), by =  
2),function(x) colMeans(mp[c(x, x+1), ])))# Add the group labels !
for each pairmtext(1, at = at, text = rep(c(satu, dua, tiga,  
empat), 4),line = 1, cex = 0.75)# Add the color labels for each  
groupmtext(1, at = colMeans(mp), text = c(Black, Brown, Red,  
Blond), line = 2)
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[R] Need help with lmer model specification syntax for nested mixed model

2010-10-31 Thread Carabiniero

I haven't been able to fully make sense of the conflicting online information
about whether and how to specify nesting structure for a nested, mixed
model. I'll describe my experiment and hopefully somebody who knows lme4
well can help.

We're measuring the fluorescence intensity of brain slices from frogs that
have undergone various treatments. We want to use multcomp to look for
differences between treatments, while accounting for the variance introduced
by the random effects of brain and slice. There are a few measurements per
slice, several slices per brain, and several brains per treatment. In the
data file, the numbering for slices starts over from 1 for each brain, and
the numbering for brains starts over from 1 for each treatment.

In other words: Treatment is a fixed effect, brain is a random effect
nested in treatment, and slice is a random effect nested in brain.

As I understood the documentation, this is the correct specification:

log(Intensity) ~ Treatment + (1|Brain) + (1|Slice)

However, I don't see how lmer understands the correct nesting structure from
that. How does it know brain isn't crossed with treatment?

Here are two other things I tried, and each gave different results:

log(Intensity) ~ Treatment + (1|Slice/Brain/Treatment)
log(Intensity) ~ Treatment + (1|Brain/Treatment) + (1|Slice/Brain)

I'm not sure why these things give different results, or which one (if any)
is right. Can anyone help?

Thanks!
--
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