R2.3, WinXP
Dear all,
I am using the following functions:
f1 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(x))/exp(log(Phi4)))
f2 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(r)-log(x))/exp(log(Phi4)))
subject to the residual weighting
Var(e[i]) = sigma^2 * abs( E(y) )^(2*Delta)
Here is my
Thank you all for your answers... To summarize,
1. with(X, ifelse(V1 V2 | V1 V3, 1, 0))
2. ifelse((foo$x foo$y) | (foo$x foo$z), 1, 0)
3. with(foo, (x y) * (x z))
were the responses to my question (see below).
Kindly,
Greg
--- Original post ---
I have a
I have a question that must have a simple answer (but eludes me).
I need a row-by-row logical comparison across three numeric variables
in
a data frame: foo$x, foo$y, foo$z. The logic is
if( x y || x z ) 1 else 0
for a particular row.
It is simple and very inefficient to use for(i in
Hello,
I am using WinXP, R version 2.3.1, and SAS for PC version 8.1.
I have mostly used SAS over the last 4 years and would like to
compare the output of PROC DISCRIM to that of lda( ) with respect
to a very specific aspect. My data have k=3 populations and there
are 3 variates in the feature
WinXP, Splus7 and R2.3.1.
All,
I have been using the SSfpl and SSlogis self-starting functions
in the nlme library to fit 4PL and restricted 4PL models. I need
to adapt these routines to fit the alternative model
f(x) = A + (B-A)/(1 + abs(x/EC50)^C)
My Question: How do I obtain good
R 2.3.1, WinXP:
I have a puzzling problem that I suspect may be solved using
grep or a regular expression but am at a loss how to actually do it...
My data.frame looks like
Location TimeXY
--- ---
1 0 1.6 9.3
1 3 4.2 10.4
1
R2.3.0, WinXP.
I am trying to fit the model
Y(t) = C1*exp(-exp(a)*t) + C2*exp(-exp(b)*t) - (C1+C2)*exp(-exp(c)*t)
to some pharmacokinetic data. The data derives from a single oral dose
of a drug. Does anyone know how to use peeling and/or other methods to
obtain good starting estimates of
All,
I have been able to successfully use the optim( ) function with
L-BFGS-B to find reasonable parameters for a one-compartment
open pharmacokinetic model. My loss function in this case was
squared error, and I made no assumptions about the distribution
of the plasma values. The model
R2.2, WinXP:
I am using xyplot( ) to generate time plots of plasma concentration data.
The following is an edited version of my code:
xyplot(log.conc ~ time| group, data = foo,
groups = subject,
panel = function(x, y, panel.number, ...)
{
panel.superpose(x, y,
NOTE: WinXP, R2.2.0
All,
a while back I posted a question about using relative filereferencing.
The responses have allowed me to successfully set up the following
directory structure:
...\data\raw
...\data\derived
...\prog
...\lst
...\log
In the \prog directory I have put
:
On Tue, 28 Feb 2006, Uwe Ligges wrote:
On Mon, 27 Feb 2006, Greg Tarpinian wrote:
BACKGROUND:
I use SAS on a daily basis and one of its most powerful features in a
production environment is the use of LIBNAME and FILEREF statements, e.g.:
PROC PRINTTO LOG = LOGDIR.\data
BACKGROUND:
I use SAS on a daily basis and one of its most powerful features in a
production environment is the use of LIBNAME and FILEREF statements, e.g.:
PROC PRINTTO LOG = LOGDIR.\data processing.log NEW;
RUN;
PROC IMPORT
DATAFILE= DATADIR.\blah.xls
OUT = TEMP
DBMS =
R2.2 for WinXP, Splus 6.2.1 for HP 9000 Series, HP-UX 11.0.
I am trying to get a handle on why the same lme( ) code gives
such different answers. My output makes me wonder if the
fact that the UNIX box is 64 bits is the reason. The estimated
random effects are identical, but the fixed effects
R 2.2, WinXP. I am having problems getting the right kind of
xyplot( ) to be generated. The first of these works fine, but
doesn't overlay a reference grid (which I need):
xyplot(Y ~ X | Factor1, type = 'b', groups = GROUP,
col = c(1,13), pch = c(16,6), lty = 1, lwd = 2,
cex = 1.2, data =
, by GROUP,
between = list(x = .5, y = .5))
Using paste() to hardcode an extra space provided me with
the necessary graphical offsets.
Regards,
Greg
--- Deepayan Sarkar [EMAIL PROTECTED] wrote:
On 1/19/06, Henric Nilsson [EMAIL PROTECTED] wrote:
Greg Tarpinian said the following
R for Windows, version 2.2. I am trying the following
code:
BLAH.lme4 - lme(fixed = , data = , random =
)
plot(BLAH.lme4, resid(.,type = p) ~ fitted(.) |
GROUP,
id = 0.05, adj = -0.3,
idLabels = BLAH$VALUE,
main = Pearson Residuals vs. Fitted Values, by
Group)
When
I apologize for the second posting, my other email address
died today.
I am using R for Windows, version 2.2. Here is my code:
plot(FOO.lme4, resid(.,type = p) ~ fitted(.) | LOT,
id = 0.05, adj = -0.3, idLabels = RO54F$VALUE,
main = Pearson Residuals vs.
I apologize for the second posting, my other email address
died today.
I am using R for Windows, version 2.2. Here is my code:
plot(FOO.lme4, resid(.,type = p) ~ fitted(.) | GROUP,
id = 0.05, adj = -0.3,
idLabels = FOO$value,
main = Pearson Residuals vs. Fitted Values,
All,
I have been trying to get the following code to work:
A.quantiles - quantile(foo.frame$A,
probs = seq(from = 0.05, to = 0.95, by = 0.05))
base.quantiles - quantile(Efficacy205$BASELINE_RANK,
probs = seq(from = 0.05, to = 0.95, by = 0.05))
gender - levels(Efficacy205$GENDER)
contrast.1
-
Sorry about the previous email, hadn't finished
editing yet
Here is some code that I have been trying to get
to work:
A.quant -
quantile(foo.frame$A,
probs=seq(from=0.05, to=0.95, by=0.05))
B.quant -
quantile(foo.frame$B,
probs=seq(from=0.05, to=0.95, by=0.05))
gender -
All,
I have been reading Dr. Harrell's excellent
Regression Modeling Strategies book and trying out
the exercises. I understand that contrast( ) is used
to obtain contrasts between two variables for given
levels of other nuisance variables; is there a way
to use contrast( ) to obtain, for
I have successfully fitted the model
loess.fit1 - loess(response ~ X*Y)
and plotted it in 3D using
X.grid - seq(0,10,length=100)
Y.grid - seq(0,1000,length=100)
pred.loess1 - predict(loess.fit1,
expand.grid(x = X.grid, y = Y.grid))
persp(X.grid, Y.grid, pred.loess1, theta = 0, phi
All,
In looking at `optim', it doesn't appear that it is
possible to impose nonlinear constraints on Nelder-
Mead. I am sufficiently motivated to try to code
something in C from scratch and try to call it from
R
Does anyone have some good references to barrier
and/or penalization methods
I have some count data (0 - 1 at each time point for
each test subject) that I would like to model. Since
the 1's are rather sparse, the Poisson distribution
comes to mind but I would also consider the binomial.
The data are censored as the data come from a clinical
trial and subjects were able
All,
I have been learning about mixed models and have been
able to successfully use lme( ) and nlme( ) to fit
some simple linear and 4PL logistic models. As a
relative newbie I am at a loss as to how I can do
the following:
(1) Import a SAS dataset with DATE9. formatted time
values and get
I have found Numerical Recipes in C to be extremely
helpful in my statistical studies. OLS is described
in detail in this book.
Best,
Greg
--- Manoj - Hachibushu Capital [EMAIL PROTECTED]
wrote:
Dear All,
Is it possible to perform OLS using C code? I am
trying to
optimize a
I have been learning about some outlier tests -- Dixon
and Grubb, specifically -- for small data sets. When
I try help.start() and search for outlier tests, the
only response I manage to find is the Bonferroni test
avaiable from the CAR package... are there any other
packages the offer outlier
Actually, it was very recent. I pulled the electronic
version of the article from the Forbes website:
Has SCO's Stance Slowed Linux's Momentum?
Arik Hesseldahl, 04.20.04, 3:00 PM ET
NEW YORK - The open-source Linux operating system may
be mostly free, but there is company that thinks that
if
The following were the replies to my question
about using R's LAPACK and other .h files in
some of my C programs. From what was
said, it appears that buying a ready-made
library (MKL = $200, for example)
or using CLapack according to the Shumway lecture
notes are the best approaches:
I am a relative newcomer to both the R and C/C++
software worlds -- I'm taking a C Programming class
currently. I noticed the other day that the
C:\Program Files\R1_8_1\src\include\R_ext
directory on my WinXP box has the header files
BLAS.h
Lapack.h
Linpack.h
RLapack.h
I am interested in
header files are on one
another? Are they redundant in any way? Which ones
would be recommended?
Any help / advice would be greatly appreciated.
=
Greg Tarpinian
San Diego, CA
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