Displaying 20 results from an estimated 1000 matches similar to: "nlminb and optim"
2009 Nov 29
1
optim or nlminb for minimization, which to believe?
I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard
2011 Apr 13
0
ddply and nlminb
Hello
I'm new to R (one week) so please excuse any obvious mistakes in my code
or posting.
I am attempting to fit a non linear function defining the relationship
between dependent variable A and the variables PAR and T grouped by the
condition Di.
The following steps are taken in the Rcode below:
1) load the data (not shown)
2) define the function to be fit
3) define the starting values
2009 Jun 03
1
Using constrOptim() function
I have a function myFunction(beta,x) where beta is a vector of coefficients
and x is a data frame (think of it as a matrix). I want to optimize the
function myFunction() by ONLY changing beta, i.e. x stays constant, with 4
constraints. I have the following code (with a separate source file for the
function):
rm(list=ls())
source('mySourceFile')
2010 Oct 04
1
Help with apply
Suppose I have the following data:
tmp <- data.frame(var1 = sample(c(0:10), 3, replace = TRUE), var2 = sample(c(0:10), 3, replace = TRUE), var3 = sample(c(0:10), 3, replace = TRUE))
I can run the following double loop and yield what I want in the end (rr1) as:
library(statmod)
Q <- 2
b <- runif(3)
qq <- gauss.quad.prob(Q, dist = 'normal', mu = 0, sigma=1)
rr1 <- matrix(0,
2012 Jul 15
1
how to extract p-value in GenMatch function
Dear R-Users,
I have a problem on extracting T-Stat and P-Value. I have written R-code below
library("Matching")
data("lalonde")
attach(lalonde)
names(lalonde)
Y <- lalonde$re78
Tr <- lalonde$treat
glm1 <- glm(Tr~age+educ+black+hisp+married+nodegr+re74+re75,family=binomial,data=lalonde)
pscore.predicted <- predict(glm1)
rr1 <-
2009 Jul 06
1
transform multi skew-t to uniform distribution
Hi R-users,
I have a data from multi skew t and would like to transform each of the data to uniform data. I tried using 'pmst' but only got one output:
> rr1 <- as.vector(r1);rr1
[1] 0.7207582 5.2250906 1.7422237 0.5677233 0.7473555 -0.6020626 -2.1947872 -1.1128313 -0.6587316 -1.1409261
> pmst(rr1, xi=rep(0,10), Omega=diag(10), alpha=rep(1,10), df=5)
[1] 3.676525e-09
2000 Mar 28
1
the function lme in package nlme
Dear people,
A somewhat clueless question follows:
I just discovered that the lme function in contrib package nlme for R,
while similar to the lme function in Splus, does not use the cluster
function option. This difference does not appear to be documented in the
V&R `R Complements' file.
I have data which is divided into 6 groups
The lme model is of the form (simplified from the actual
2005 Nov 08
2
retrieve most abundant species by sample unit
Hi R-users:
[R 2.2 on OSX 10.4.3]
I have a (sparse) vegetation data frame with 500 rows (sampling
units) and 177 columns (plant species) where the data represent %
cover. I need to summarize the cover data by returning the names of
the most dominant and the second most dominant species per plot. I
reduced the data frame to omit cover below 5%; this is what it looks
like stacked. I have
2012 Oct 22
1
Matlab code to R code
Dear r-users,
I would like to convert my Matlab code to R-code, however it dies not work as expected. Hope somebody can help me to match Matlab and r codes.
R code:
rr <- function(r,cxn)
{
tol <- 1E-4;
for(i in 1:n)
{
t1 <- (1+(i-1)*r)*log((1+(i-1)*r))
t2 <- (i-1)*(1-r)*log(1-r)
rri <- ((t1+t2)/i*log(i))-cxn
rr <- rri > tol
}
round(rr,4)
}
rr1 <- rr(0.5,0.0242) ; rr1
2008 Aug 26
2
svymeans question
I have the following code which produces the output below it
clus1 <- svydesign(ids = ~schid, data = lower_dat)
items <- as.formula(paste(" ~ ", paste(lset, collapse= "+")))
rr1 <- svymean(items, clus1, deff='replace', na.rm=TRUE)
> rr1
mean SE DEff
W525209 0.719748 0.015606 2.4932
W525223 0.508228 0.027570 6.2802
W525035 0.827202
2013 Feb 25
3
Empirical Bayes Estimator for Poisson-Gamma Parameters
Dear Sir/Madam,
I apologize for any cross-posting. I got a simple question, which I thought
the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~
Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes
Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t)
are needed.
y=c(12,5,17,14)
n=4
What about a Hierarchal B ayes
2010 Feb 05
3
metafor package: effect sizes are not fully independent
In a classical meta analysis model y_i = X_i * beta_i + e_i, data
{y_i} are assumed to be independent effect sizes. However, I'm
encountering the following two scenarios:
(1) Each source has multiple effect sizes, thus {y_i} are not fully
independent with each other.
(2) Each source has multiple effect sizes, each of the effect size
from a source can be categorized as one of a factor levels
2003 Oct 23
1
Variance-covariance matrix for beta hat and b hat from lme
Dear all,
Given a LME model (following the notation of Pinheiro and Bates 2000) y_i
= X_i*beta + Z_i*b_i + e_i, is it possible to extract the
variance-covariance matrix for the estimated beta_i hat and b_i hat from the
lme fitted object?
The reason for needing this is because I want to have interval prediction on
the predicted values (at level = 0:1). The "predict.lme" seems to
2011 Sep 02
1
Using capture.output within a function
Dear R-users
I'm running a maximum likelihood procedure using the spg package. I'd like
to save some output produced in each iteration to a file, but if I put the
capture.output() within the function I get the following message; Error in
spg(par = startval, fn = loglik, gr = NULL, method = 3, lower = lo, :
Failure in initial function evaluation!Error in -fn(par, ...) : invalid
argument
2010 Mar 09
1
penalized maximum likelihood estimation and logistf
Hi, I got two questions and would really appreciate any help from here.
First, is the penalized maximum likelihood estimation(Firth Type Estimation)
only fit for binary response (0,1 or TRUE, FALSE)? Can it be applied to
multinomial logistic regression?
If yes, what's the formula for LL and U(beta_i)? Can someone point me to
the right reference?
Second, when I used *logistf *on a dataset with
2010 Sep 29
1
generalized additive mixed models for ordinal data
? stato filtrato un testo allegato il cui set di caratteri non era
indicato...
Nome: non disponibile
URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20100929/bedab79b/attachment.pl>
2008 Feb 24
0
problem with ML estimation
dear list,
as a part my problem. I have to estimate some parameters using ML
estimation. The form of the likelihood function
is not straight forward and I had to use a for loop to define the function.
I used "optim" to maximise the result but
was not sure of the programme.
To validate my results, I tried to write a function to obtain the MLE of a
bivariate normal in the same manner.
On
2002 Feb 20
2
How to get the penalized log likelihood from smooth.spline()?
I use smooth.spline(x, y) in package modreg and I would like to get
value of penalized log likelihood and preferable also its two parts. To
make clear what I am asking for (and make sure that I am asking for the
right thing) I clarify my problem trying to use the same notation as in
help(smooth.spline):
I want to find the natural cubic spline f(x) such that
L(f) = \sum_{k=1}{n} w[k](y[k] -
2008 May 07
1
dlm with constant terms
Hi,
I am trying to figure how to use dlm with constant terms
(possibly time-dependent) added to both equations
y_t = c_t + F_t\theta_t + v_t
\theta_t = d_t + G_t\theta_{t-1} + w_t,
in the way that S-PLUS Finmetrics does?
Is there any straightforward way to transform the above to
the default setup?
Thanks,
Tsvetan
--------------------------------------------------------
NOTICE: If received in
2008 Sep 19
0
panel data analysis possible with mle2 (bbmle)?
Dear R community,
I want to estimate coefficients in a (non-linear) system of equations using
'mle2' from the "bbmle" package. Right now the whole data is read in as just
one long time series, when it's actually 9 cross sections with 30 observations
each. I would like to be able to test and correct for autocorrelation but
haven't found a way to do this in this package.