search for: folate

Displaying 4 results from an estimated 4 matches for "folate".

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 t...
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 e...
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 look...
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