Dear list,
I have 50 sites where information was recorded over a 45 year time period. The
recorded data could take one of four forms: Fishing effort, Environmental, Both
or Inconclusive.
What i am aiming to do is cluster sites based on their similarity through time,
essentially i view this as
Dear list,
I have 50 sites where information was recorded over a 45 year time period.
The recorded data could take one of four forms: Fishing effort,
Environmental, Both or Inconclusive.
What i am aiming to do is cluster sites based on their similarity through
time, essentially i view this
Dear List,
I am unsure if this is the correct list to post to, if it isn't I apologise.
I am using SSH to access a Linux version of R on a remote computer as it
offers more memory and processing power. The model will take 1-2 days to
run, I am accessing R through Putty and when I close the
I hadn't seen that page Dennis, that makes the case much more succinctly than
my anti stepwise ramblings!
Furthermore, "pigpigmeow" if you are using a random effects model i.e lmer -
where are you getting your p-values from? And what do they mean in this
context? I would strongly advise using i
Why not use AIC / BIC based model selection which in this case makes more sense
than artificial P-Values, and eliminates the problems of stepwise regression.
For background read Burnham and anderson - multimodel selection.
Chris.
On 6 Oct 2011, at 08:14, pigpigmeow wrote:
If I used mixed mode
Dear list,
I am unsure how to structure my model, i have tried something and it makes
sense but i am unsure if i am interpreting it correctly?
i have a continuous response variable - the observed quantity of evolutionary
history - EH
Then i have a number of species which have a hierarchical st
this helps.
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Fri, 20
Is the dput output included not usable either? I have tested it on my machine
and it works fine.
Thanks
On 20 May 2011, at 22:57, David Winsemius wrote:
On May 20, 2011, at 5:50 PM, Chris Mcowen wrote:
> Sorry for not including the data, i did intend to.
You included the data, just not
.Names = c("ECO_NAME", "Order",
"Family", "Genus"), class = "data.frame", row.names = c(NA, -27L
))
On 20 May 2011, at 22:33, jim holtman wrote:
use the 'sqldf' package. Also use 'dput' to include sample data since
it was
Dear List,
I am looking to calculate two things from my data frame and was after some
advice. For the example below i want to know.
1. How many unique Orders/Families and Genera there are per eco-name
2. How many incidences are there for each Order/Family and Genus there are per
eco-region
I
Thanks for this,
With the data i have what is the best method to convert it into the required
matrix, as i am a little unsure how it would be done - i imagine this must be a
common task?
Chris
On 16 May 2011, at 16:05, Duncan Murdoch wrote:
On 16/05/2011 10:57 AM, Chris Mcowen wrote:
> So
000217105, -0.00890313, -0.003368547,
-0.003134873, -0.002463031, -0.006515148)
On 16 May 2011, at 15:50, Duncan Murdoch wrote:
On 16/05/2011 8:40 AM, Chris Mcowen wrote:
> Dear List,
>
> i am trying to produce a 3d plot using wireframe using the code:
>
> wireframe(Residuals_FD ~
Dear List,
i am trying to produce a 3d plot using wireframe using the code:
wireframe(Residuals_FD ~ Elevation * Temperature, data = data2, scales =
list(arrows = FALSE), drape = TRUE, colorkey = TRUE)
As you can see when the code (using the data below) is run the plot area is
set-up correctly
Dear list,
I have the model below which i am using to account for spatial autocorrelation:
exponential <-corExp(form = ~ Longitude + Latitude)
explanation_mod_all <-
gls(Lower_PD~Area+Elevation+Temperature+Preceipitation+Agriculture+Urban+Human.footprint+Population,
correlation = exponential)
Dear list i have a sample question
I have a dataframe of 1500 species and 13 life history traits.
small example code:
traits <- data.frame(letters[1:9],
sample(letters, 9),
sample(letters, 9),
sample(letters, 9),
sample(letters, 9),
sample(letters, 9),
Dear David,
Thats great, thanks very much for the help, much appreciated.
On 6 Jan 2011, at 15:53, David Winsemius wrote:
On Jan 6, 2011, at 6:36 AM, Chris Mcowen wrote:
> Dear List,
>
> I have a data frame called trait with roughly 800 species in, each species
> have 1
variable? From your description, I don't see how you would
link a species to a community. I mean if you select species a in df1 how would
you know what community it is in?
--- On Thu, 1/6/11, Chris Mcowen wrote:
> From: Chris Mcowen
> Subject: [R] Multiple subsets of data
>
Dear List,
I have a data frame called trait with roughly 800 species in, each species have
15 columns of information:
Species 1 2 3 etc..
a t y h
b f j u
c r y u
etc..
Dear List,
I have a data frame called trait with roughly 800 species in, each species have
15 columns of information:
Species 1 2 3 etc..
a t y h
b f j u
c r y u
etc..
Hi,
I am not sure if it is more robust than a discriminant function but it is
certainly capable if differentiating between species based on morphology. I
used 12 measurements in my fish.
What did your PCA results show?
Unfortunately I haven't got round to publishing my data yet but I can send
Hi,
I did this exact thing for my masters, with intertidal fish, I just used a PCA?
have you tried that?
Sent from my iPhone
On 16 Nov 2010, at 17:01, Mike Gibson wrote:
>
> My objective is to look at differences in two species of fish from
> morphometric measurements. My morphometric me
Use ?points for more info on changing the appearance of points. The cex
function is what you are looking for.
Chris
On 16 Oct 2010, at 20:12, Hongwei Dong wrote:
Hi, R users,
Can anyone tell me how I can change the size of points in my plot?
For example:
x <- c(1,3,6,9,12)
y <- c(1.5,2,7,8,1
Hi Wolfgang,
Thanks for this, it makes sense.
I should of been more detailed when i described my model, it is in fact
binomial - sell or not.
> remove the Mag factor from the model, you get a model with just an intercept,
> reflecting the overall mean
This is true, but what i was trying to
Dear List,
I am looking to run a host of models (60) with three methods - lmer,glm and lrm.
Is there a way to output the key stats into a table that i can copy to excel?
I.e for lmer i would want AIC,BIC etc
for lrm i would want Brier score, r2, c-value etc
At present i am running the mode
Dear List,
I am looking to run a host of models (60) with three methods - lmer,glm and lrm.
Is there a way to output the key stats into a table that i can copy to excel?
I.e for lmer i would want AIC,BIC etc
for lrm i would want Brier score, r2, c-value etc
At present i am running the mode
Dear List,
I have developed a model and am looking to predict a response for 1-6 ( it is
ordered i.e the difference between level 1 and 2 is the same as between level 2
and 3 etc.
I have used the predict function for a polr model (below) and a lrm model, and
both give similar results, however
Dear List,
I have developed a model and am looking to predict a response for 1-6 ( it is
ordered i.e the difference between level 1 and 2 is the same as between level 2
and 3 etc.
I have used the predict function for a polr model (below) and a lrm model, and
both give similar results, however
Right, that makes sense, thanks
The reason i used type= response was i wanted to convert the predicted
probabilities to the response scale, as surely this is the scale at which a
95CI value is most useful for?
I.e
>> pp <- predict(model1,se.fit=TRUE, type = "response")
1 0.68
Probability
Dear List,
I am looking to perform a cross validation on my glm using the cv.binary
function in DAAG.
However i am getting the following error -
> > cv.binary(modelhe)
> Error in sample(nfolds, m, replace = TRUE) : invalid 'size' argument
Any help would be greatly appreciated.
Chris
Hi Christofer,
I have just repeated this and changed the code a little and it gives the
correct result
> any(as.integer(c(1, 3)) == 3)
[1] TRUE
> any(as.integer(c(1, 3)) == 2)
[1] FALSE
> any(as.integer(c(1, 3)) == 1)
[1] TRUE
HTH
Chris
On 24 Sep 2010, at 10:07, Christofer Bogaso wrote:
> a
Thats great thanks,
I suppose it is hard to move away from a more "traditional" measure of
performance such a percentage correct, at least for the relatively amateur
statisticians among us who have been graded on such a system.
The difficulty comes in reporting the effectiveness of the model to
Thats great thanks
I guess it is hard to not use % as a performance measure when that is what is
commonly used in everyday life.
So when i come to predicting the response of new data ( using the estimated
mean Y ) which i am more comfortable with i can say -
Species A - 2.12 - Therefore this i
rank
Frank E Harrell Jr Professor and ChairmanSchool of Medicine
Department of Biostatistics Vanderbilt University
On Mon, 20 Sep 2010, Chris Mcowen wrote:
> Dear Professor Harell
> I am familier with binary models, however i am now trying to get predictions
rank
Frank E Harrell Jr Professor and ChairmanSchool of Medicine
Department of Biostatistics Vanderbilt University
On Mon, 20 Sep 2010, Chris Mcowen wrote:
> Dear Professor Harell
> I am familier with binary models, however i am now trying to get predictions
quot; after predict
- Not sure why your variable names are all upper case; harder to read this way
Good luck
Frank
Frank E Harrell Jr Professor and ChairmanSchool of Medicine
Department of Biostatistics Vanderbilt University
On Mon, 20 Sep 2010, Chris Mcowen wrote:
&g
quot; after predict
- Not sure why your variable names are all upper case; harder to read this way
Good luck
Frank
Frank E Harrell Jr Professor and ChairmanSchool of Medicine
Department of Biostatistics Vanderbilt University
On Mon, 20 Sep 2010, Chris Mcowen wrote:
Dear List,
I am familier with binary models, however i am now trying to get predictions
from a ordinal model and have a question.
I have a data set made up of 12 categorical predictors, the response variable
is classed as 1,2,3,4,5,6, this relates to threat level of the species ( on the
IUCN
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