Absolutely! Even more, consult a local expert in applying mixed effects
models. The op's strategy sounded to me like a prescription to produce
irreproducible results (due to over fitting).
Cheers,
Bert
On Friday, February 19, 2016, Don McKenzie wrote:
> This is a complicated and subtle stati
Hello, Wilbert,
You did give a good procedure for lme model selection! thanks! I learn some.
I am also working on similar problem recently, maybe you can take a
look at "glmmLasso" package, which allows model selection in
generalized linear mixed effects models using the LASSO shrinkage
method.
Your function, buildGui(), what does it use, Tcl/Tk or something else?
If it's Tcl/Tk, I believe you need a normal R console opened. My .bat file
only works for a command line, which is fine if the user interface opens up
in a webpage, but I'm pretty sure it doesn\t work with Tcl/Tk.
What kind of w
Hi Sarah,
Thank you for the response. But it is said in its description that after each
run (sample), each observation in the whole dataset is assigned to the closest
cluster. So how is it possible for one observation to be wrongly allocated,
even with clara?
Behnam
Behnam
On Fri, Feb 19,
This is a complicated and subtle statistical issue, not an R question, the
latter being the purpose of this list. There are people on the list who could
give you literate answers,
to be sure, but a statistically oriented list would be a better match.
e.g.,
http://stats.stackexchange.com/
>
clara() is a version of pam() adapted to use large datasets.
pam() uses the entire dataset, and should give results identical to
your manual procedure, or nearly so. clara() works on subsets of the
data, so it may give a slightly different result each time you run it.
The default parameters for c
Adriana,
My GUI file is a function returning a window. This function is named
buildGui(). How should I create this batch file using the piece of code you've
written?
[[alternative HTML version deleted]]
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R-help@r-project.org mailing list -
Ah, my guess about the confusion was wrong, then. You're
misunderstanding silhouette() instead.
>From ?silhouette:
Observations with a large s(i) (almost 1) are very well clustered,
a small s(i) (around 0) means that the observation lies between
two clusters, and observations with
You need to think more carefully about the details of the clara() method.
The algorithm draws repeated samples of sampsize from the larger
dataset, as specified by the arguments to the function.
It clusters each sample in turn, and saves the best one.
It uses the medoids from the best one to assig
That means that points have been assigned to the wrong groups. This
may readily happen with a clustering method like cluster::clara() that
uses a subset of the data to cluster a dataset too large to analyze as
a unit. Negative silhouette numbers strongly suggest that your
clustering parameters shou
Hello R-users,
I'm trying to build a SIR-like model under R,
using the "deSolve" package. I'm trying to do simulations of its dynamic
over time, with three differential equations. I'm also looking to
calculate the equilibrium state.
So far, my code looks like this
library(deSolve)
#This is
Hello Jim,
I ran str() on the vector and it returned character:str("DF_exp.xlsx") chr
"DF_exp.xlsx"
I tried df2_TZ$DateTimeStamp <-
strptime(as.Date(as.character(df2_TZ$DateTimeStamp, format = "%m/%d/%Y %H:%M",
tz = "GMT"))), which produced an error: Error in charToDate(x) : character
string
Hi,
We know that clustering methods in R assign observations to the closest
medoids. Hence, it is supposed to be the closest cluster each observation can
have. So, I wonder how it is possible to have negative values of silhouette ,
while we are supposedly assign each observation to the closest
Dear all,
Mixed-effects models are wonderful for analyzing data, but it is always a
hassle to find the best model, i.e. the model with the lowest AIC,
especially when the number of predictor variables is large.
Presently when trying to find the right model, I perform the following
steps:
1.
Hi,
I am using CLARA (in 'cluster' package). This method is supposed to assign each
observation to the closest 'medoid'. But when I calculate the distance of
medoids and observations manually and assign them manually, the results are
slightly different (1-2 percent of occurrence probability).
sqldf does not use Tk so you can ignore this.
On Fri, Feb 19, 2016 at 12:32 PM, Divakar Reddy
wrote:
> Dear R users,
>
> I'm getting Waring message while trying to load "sqldf" package in R3.2.3
> and assuming that we can ignore this as it's WARNING Message and not an
> error message.
> Can you g
This is a mailing list. I don't know how you are interacting with it... using a
website rather than an email program can lead to some confusion since there can
be many ways to accomplish the task of interacting with the mailing list. My
email program has a "reply-all" button when I am looking at
Dear R users,
I'm getting Waring message while trying to load "sqldf" package in R3.2.3
and assuming that we can ignore this as it's WARNING Message and not an
error message.
Can you guide me if my assumption is wrong?
> library(sqldf);
Loading required package: gsubfn
Loading required package:
Please keep the mailinglist in cc.
You should be able to solve this after reading the helpfiles of
?dplyr::mutate and ?tidyr::spread
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biomet
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