Displaying 16 results from an estimated 16 matches for "downweight".
2011 Nov 07
2
ordination in vegan: what does downweight() do?
Can anyone point me in the right direction of figuring out what downweight()
is doing?
I am using vegan to perform CCA on diatom assemblage data. I have a lot of
rare species, so I want to reduce the influence of rare species in my CCA. I
have read that some authors reduce rare species by only including species
with an abundance of at least 1% in at least one sample (ot...
2008 Apr 22
0
Downweighting of cases in GLM
...porated as a predictor
because of some sort of arbitrariness. Some trees were logged while others
were not but to both cases land use as a category would apply. However, at
last I cannot decide if absences occur truly because of unsuitability of
conditions at sampling location. Therefore, I want to downweight these
"wrong" absences based on a certain algorithm producing decimal numbers so
that they become less influential (i.d. I have a column "weights"
(1>=weights>0) of the same lenght as predictors and response variable). I
tried to figure this out checking the forum, but are...
2011 Mar 28
1
ordination in vegan
...axes.
Also after doing envfit() and plotting, is there a way to label sites based
on certain environmental factors? For instance, I want to highlight plots
that have a certain treatment (or have different colored points for the
different treatments.
I don't know if it matters but I used downweight() before applying
decorana().
down <-downweight(spp2009bttl, fraction = 5)
down.dca <- decorana(down)
scores(down.dca)
summary(down.dca)
Thanks so much!
-Akane
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2006 Apr 06
5
pros and cons of "robust regression"? (i.e. rlm vs lm)
Can anyone comment or point me to a discussion of the
pros and cons of robust regressions, vs. a more
"manual" approach to trimming outliers and/or
"normalizing" data used in regression analysis?
2012 Dec 17
2
How to get transparent colors to sum to complete opacity?
Dear List,
I want to use transparency in R to represent downweighting of
observations based on clusters (repeated observations in a dataset).
Some clusters will have identical covariate values in a parameter space
-- in the 2D x,y case, these represent a bunch of semi-tranparent dots
in the same place. I'd like these overlapping dots to be completely
op...
2002 Oct 09
1
s.window in stl()
Hi,
This is actually a theory question.
I'm a bit confused by the s.window parameter in the stl() function (which
is in the ts package). For example, in the stl documentation it uses the
nottem data, and then:
plot(stl(nottem, s.win = 4, t.win = 50, t.jump = 1))
What does it mean by s.win = 4? Is it because a year has 4 seasons
(namely Spring, Summer, Autumn and Winter)? If so will it
2004 Mar 18
2
logistic regression with temporal correlation
Hello
We would like to perform a logistic regression analysis weighting the
independent variable in a temporal fashion, i.e. events occuring most
recent get highest weight. Does anyone know how to do this in R??
Regards
S. Merser and S. Lophaven
2005 Jul 27
2
GAM weights
...rare plants so we always have less than 50
1x1 km presences, and the total area is about 550.000 square km. So we have a
real problem, when we perform a GAM, if we consider only the same amount of
absences than presences.
We have thought to use a greater number of absences but in this case we shoud
downweight them.
Does anybody know how to use the ?wheight? term?
thank you in advance
daniel
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2009 Feb 08
0
library vegan - cca - versus CANOCO
...uot;good for my eyes",
however, when I did summary(CCA1), the first two axis accounted 0.04 CCA1
and similar in CCA2....then the variation explained by each axis was small.
On the other hand, when I performed CCA in CANOCO, without selecting the
option Log-transforming data matrix and without downweighting rare species,
the results were the opposite from the CCA performed in R. The
axes accounted high percentage of variation, the model was also significant,
but the plot had little sense.
Thank you very much!
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2009 Feb 19
1
Difference between GEE and Robust Cluster Standard Errors
Hello,
I know that two possible approaches to dealing with clustered data would be
GEE or a robust cluster covariance matrix from a standard regression. What
are the differences between these two methods, or are they doing the same
thing? Thanks.
--
View this message in context: http://www.nabble.com/Difference-between-GEE-and-Robust-Cluster-Standard-Errors-tp22092897p22092897.html
Sent from the
1999 Aug 14
1
leaps and bounds
Dear friends. On the Bayesian averaging homepage http://www.research.att.com/~volinsky/bma.html I found
some S code some of which perhaps may run in R. There was a call to an algorithm possibly within S but not supported by R 64.1: "leaps and bounds". I guess it is a minimization step. Can anyone clarify the algorithm and perhaps even give a pointer to some code ?
I guess this may be
2005 Apr 13
1
logistic regression weights problem
Hi All,
I have a problem with weighted logistic regression. I have a number of
SNPs and a case/control scenario, but not all genotypes are as
"guaranteed" as others, so I am using weights to downsample the
importance of individuals whose genotype has been heavily "inferred".
My data is quite big, but with a dummy example:
> status <- c(1,1,1,0,0)
> SNPs <-
2012 May 30
0
Survival with different probabilities of censoring
...We've tried various approaches for zero-heavy data but I think this
increasingly looks like a survival analysis to me. The question is, how can
I adapt a survival analysis so that it takes into account the different
probability of censoring (the different random probability of being a zero)
and downweights the uninformative zeroes? (With the added fun of double
censoring, non-integer values and a small number of negative values).
Remember that what we have is the number of species fractions changing
status in a single time period, not the more usual "time to event".
Please also remember:...
2008 Dec 08
1
residual standard error in rlm (MASS package)
Hi,
I would appreciate of someone could explain how the residual standard
error is computed for rlm models (MASS package). Usually, one would
expect to get the residual standard error by
> sqrt(sum((y-fitted(fm))^2)/(n-2))
where y is the response, fm a linear model with an intercept and slope
for x and n the number of observations. This does not seem to work for
rlm models and I am wondering
2007 Sep 12
2
k-means clustering
Dear list, first apologies for this is not strictly an R question but
a theoretical one.
I have read that use of k-means clustering assumes sphericity of data
distribution. Can anyone explain me what this means? My statistical
background is too poor. Is it another kind of distribution, like
gaussian or binomial? What does it happen if the distribution is not
spherical? Could you give me an
2006 May 20
1
(PR#8877) predict.lm does not have a weights argument for newdata
Dear R developers,
I am a little disappointed that my bug report only made it to the
wishlist, with the argument:
Well, it does not say it has.
Only relevant to prediction intervals.
predict.lm does calculate prediction intervals for linear models from
weighted regression, so they should be correct, right?
As far as I can see they are bound to be wrong in almost all cases, if
no weights