Displaying 4 results from an estimated 4 matches for "folat".
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2010 Sep 10
2
pairwise.t.test vs t.test
Dear all, I am perplexed when trying to get the same results using pairwise.t.test and t.test.
I'm using examples in the ISwR library, 
>attach(red.cell.folate)
I can get the same result for pairwise.t.test and t.test when I set the variances to be non-equal, but not when they are assumed to be equal. Can anyone explain the differences, or what I'm doing wrong?
Here's an example where I compare the first two ventilations with pairwise.t.test and...
2008 Dec 19
1
Misuse of $<matn expressions>$ in Rd files
'Writing R Extensions' tells you that $ needs to be escaped in Rd files 
outside \code and similar.  So I was surprised to find that ca 80 CRAN 
packages have constructions like (from ISwR)
   \item{\code{folate}}{
     a numeric vector, folate concentration ($\mu$g/l).
   }
This does not render sensibly in non-latex conversions, and it is what we 
have \eqn{} for.
That $ needs to be escaped is an implementation restriction that we hope 
to remove so that things like (package fields)
 	greater than or...
2008 Nov 17
2
The use of F for False and T for True
...ng with and without replacement
I seem unable to use "replace = F" when I want to sample without 
replacement. I would think
that it comes down to "F is not a legitimate abbreviation for FALSE." 
except that
Dalgaard (p. 118) uses F for FALSE and it works
 "pairwise.t.test(folate, ventilation, pool.sd = F)"
I am having trouble when I try to sample a vector without replacement.
The following code illustrates my problem.
 >b <- c(1:8)
 >sample(b)
[1] 7 8 3 5 1 6 2 4    # That works correctly--no replacement
  (This would be my preferred form, but when I loo...
2006 May 20
5
Can lmer() fit a multilevel model embedded in a regression?
I would like to fit a hierarchical regression model from Witte et al. 
(1994; see reference below).  It's a logistic regression of a health 
outcome on quntities of food intake; the linear predictor has the form,
X*beta + W*gamma,
where X is a matrix of consumption of 82 foods (i.e., the rows of X 
represent people in the study, the columns represent different foods, 
and X_ij is the amount of