Dear list -
A simple question, i hope!
With this truncated output from a GLS model the intercept is 0.004634487 but
where is the slope?
Coefficients:
Value Std.Error
t-valuep-value
(Intercept) 0.004634487 0.0006773
17 Dec 2010, at 15:19, John Haart wrote:
Hi,
I have two matrices with a common field = species what i want to do is make a
matrix that combines the data held in the other two based on the species name.
I.e ( simple example)
Matrix 1 - monocot
SPECIES V1
A 2
B
Hi,
I have two matrices with a common field = species what i want to do is make a
matrix that combines the data held in the other two based on the species name.
I.e ( simple example)
Matrix 1 - monocot
SPECIES V1
A 2
B 3
C 4
Dear All,
I am having trouble getting my data into R as i need it! I am used to using
read.delim("") to open .txt files to do work on. The function i am using
requires a matrix like the one below.
My data is from excel and then saved as a txt file. I have tried the usual
read.delim("") approa
Dear List,
I have a table i have read into R:
NameYes/No
John0
Frank 1
Ann 0
James 1
Alex1
etc - 800 different times.
What i want to do is shuffle yes/no and randomly re-assign them to the name.
I have used sample() and permute(), however there is no way to do thi
p = 0.0748). The variance on that expectation
will be p * (1-p) * Fn.
If you do your simulation that's the result you'll get. Perhaps to
initial identify families with disproportionate observed extinction
rates all you need is the dbinom function ?
Michael
On 15 October 2010 22:29, John
ected to be at risk in EACH family under the
random binomial distribution ( assuming every species has a 7.48% chance of
being at risk.
Thanks
John
On 15 Oct 2010, at 11:19, Dennis Murphy wrote:
Hi:
I don't believe you've provided quite enough information just yet...
On Fri, Oc
Dear List,
I am doing some simulation in R and need basic help!
I have a list of animal families for which i know the number of species in each
family.
I am working under the assumption that a species has a 7.48% chance of being at
risk.
I want to simulate the number of species expected to
Dear List and Frank,
I have calculated the log-odds for my models but maybe i am not getting
something but i am not understanding how for a categorical factor this helps?
On all the examples i have see it relates to continuous factors where moving
from one number to another shows either a incre
Thanks Frank and Greg,
This makes alot more sense to me now. I appreciate you are both very busy, but
i was wondering if i could trouble you for one last piece of advice. As my data
is a little complicated for a first effort at R let alone modelling!
The response is on a range from 1-6, which
Frank and list,
The reason I am trying to assign them is because I have a data set where i have
arrived at the most likely model that describes the data and now I have
another dataset where I know the factors but not the response.
Therefore, surely I need to assign the predicted values to a r
Frank,
Thats great thanks for the advice, i appreciate that brier score, AUC etc are a
better method of validation and discrimination but when it comes to
predictions of new data
> d <- data.frame(x1=c(.1,.5),x2=c(.5,.15))
> predict(f, d, type="fitted.ind")
>
> y=good y=better
Dear list,
I am relatively new to ordinal models and have been working through the example
given by Frank Harrell in the predict.lrm {Design} help
All of this makes sense to me, except for the responses, i,e how do i interpret
them? i would be extremely grateful if someone could explain the re
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