Displaying 20 results from an estimated 445 matches for "parametrizes".

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parametrized

2005 Jan 20

2

(no subject)

Hello
I would like to compare the results obtained with a classical non
parametric proportionnal hazard model with a parametric proportionnal
hazard model using a Weibull.
How can we obtain the equivalence of the parameters using coxph(non
parametric model) and survreg(parametric model) ?
Thanks
Virginie

2006 Jul 07

6

parametric proportional hazard regression

Dear all,
I am trying to find a suitable R-function for
parametric proportional hazard regressions. The
package survival contains the coxph() function which
performs a Cox regression which leaves the base hazard
unspecified, i.e. it is a semi-parametric method. The
package Design contains the function pphsm() which is
good for parametric proportional hazard regressions
when the underlying base

2018 Jan 18

2

MCMC Estimation for Four Parametric Logistic (4PL) Item Response Model

Good day Sir/Ma'am! This is Alyssa Fatmah S. Mastura taking up Master of
Science in Statistics at Mindanao State University-Iligan Institute
Technology (MSU-IIT), Philippines. I am currently working on my master's
thesis titled "Comparing the Three Estimation Methods for the Four
Parametric Logistic (4PL) Item Response Model". While I am looking for a
package about Markov

2006 Mar 15

1

Log Cholesky parametrization in lme

Dear R-Users
I used the nlme library to fit a linear mixed model (lme). The random effect standard errors and correlation reported are based on a Log-Cholesky parametrization. Can anyone tell me how to get the Covariance matrix of the random effects, given the above mentioned parameters based on the Log-Cholesky parametrization??
Thanks in advance
Pryseley

2018 Jan 18

0

MCMC Estimation for Four Parametric Logistic (4PL) Item Response Model

I know of no existing functions for estimating the parameters of this model using MCMC or MML. Many years ago, I wrote code to estimate this model using marginal maximum likelihood. I wrote this based on the using nlminb and gauss-hermite quadrature points from statmod.
I could not find that code to share with you, but I do have code for estimating the 3PL in this way and you could modify the

2004 Feb 13

1

parametric bootstrap and computing factor scores

We have a project on which we need to compare various methods for
computing factor scores. Are there any R routines available that do
parametric bootstrap or compute factor scores?

2007 Jun 08

1

pointwise confidence bands or interval values for a non parametric sm.regression

Dear all,
Is there a way to plot / calculate pointwise confidence bands or
interval values for a non parametric regression like sm.regression?
Thank you in advance.
Regards,
Martin

2007 Aug 01

1

RWeka cross-validation and Weka_control Parametrization

Hello,
I have two questions concerning the RWeka package:
1.) First question:
How can one perform a cross validation, -say 10fold- for a given data set and given model ?
2.) Second question
What is the correct syntax for the parametrization of e.g. Kernel classifiers interface
m1 <- SMO(Species ~ ., data = iris, control =

2008 Nov 09

1

estimated variance for parametric fit

Hi,
What formula is appropriate for calculating the estimate of variance
from the parametric fit? I have a linear regression model i.e. lm(a~b)
Thanks,
cruz

2010 Jun 09

0

non-parametric repeated measures anova using Proportional Odds Model - examples?!

Hello dear R-help mailing list,
I wish to perform a non-parametric repeated measures anova.
If what I read online is true, this could be achieved using a mixed Ordinal
Regression model (a.k.a: Proportional Odds Model).
I found two packages that seems relevant, but couldn't find any vignette on
the subject:
http://cran.r-project.org/web/packages/repolr/

2009 May 12

0

Trouble with parametric bootstrap

Hi,
I'm having trouble understanding how to construct a random number generator
for a parametric bootstrap. My aim is to bootstrap a Likelihood Ratio
statistic (under the null) for a linear model. The function at this point
is given by
boot.test.n01 <- function(data, indeces, maxit=20) {
y1 <- fit1+se(e2)*rnorm(314)
mod1 <- glm(y1 ~ X1-1, maxit=maxit)
y2 <-

2011 Oct 26

1

Performing a non parametric Friedman Test

My data looks like this:
(treatments)
T1 T2 T3
DK 8 5 3
JP 5 4 1
AS 9 7 4
MK 8 4 4
DK, JP, AS, and MK are 4 different people (blocks) I am using.
This is my code

2013 Jan 10

1

Semi Parametric Bootstrap

Greetings to you all,
I am performing a semi parametric bootstrap in R on a Gamma Distributed
data and a Binomial distributed data. The main challenge am facing is
the fact that the residual variance depends on the mean (if I am correct).
I strongly feel that the script below may be wrong due to mean-variance
relationship
#####R code#######
fit1s

2009 Feb 18

0

[LLVMdev] Parametric polymorphism

Why do you say that people who compile, e.g., functional languages
would benefit from type variables in LLVM?
I like the level the LLVM is at, and would prefer to deal with
instantiating parametric polymorphism at a higher level.
On Wed, Feb 18, 2009 at 10:43 PM, DeLesley Hutchins
<delesley.spambox at googlemail.com> wrote:
>> I think many people were confused by this at first but an

2009 Feb 18

2

[LLVMdev] Parametric polymorphism

> Why do you say that people who compile, e.g., functional languages
> would benefit from type variables in LLVM?
> I like the level the LLVM is at, and would prefer to deal with
> instantiating parametric polymorphism at a higher level.
I'm surprised you're happy with a non-polymorphic llvm. Does
Cayenne target llvm? Dependent types take polymorphism to new
heights -- but

2003 Oct 22

1

2 D non-parametric density estimation

I have spatial data in 2 dimensions - say (x,y). The correlation
between x and y is fairly substantial. My goal is to use a
non-parametric approach to estimate the multivariate density describing
the spatial locations. Ultimately, I would like to use this estimated
density to determine the area associated with a 95% probability contour
for the data.
Given the strong correlation between x and

2007 Feb 23

1

Repeated measures in Classification and Regresssion Trees

Dear R members,
I have been trying to find out whether one can use multivariate
regression trees (for example mvpart) to analyze repeated measures data.
As a non-parametric technique, CART is insensitive to most of the
assumptions of parametric regression, but repeated measures data raises
the issue of the independence of several data points measured on the
same subject, or from the same plot

2009 Feb 18

2

[LLVMdev] Parametric polymorphism

> I think the problem is deeper than that, in that LLVM has no official
> concept of a subtype, so I don't see how the idea of polymorphism
> could be defined in it.
Parametric polymorphism is different from subtype polymorphism; you
can have one without the other. Parametric polymorphism just means
that you can use type variables (like T) in the IR, which are later
instantiated
to

2008 Aug 29

2

non-parametric Anova and tukeyHSD

I have insect data from twelve sites and like most environmental data
it is non-normal mostly. I would like to preform an anova and a means
seperation like tukey's HSD in a nonparametric sense (on some sort of
central tendency measure - median?). I am searching around at this
time on the internet. Any suggestions, books, etc. would be greatly
appreciated.
--
Stephen Sefick
Research

2007 Oct 16

2

survreg's algorithm

Hi,
I'm using survreg() from the survival package for parametric survival
regression (modelling inter-arrival times of patients to a waiting list
as exponentially distributed, with various regressors such as queue size
and season).
Does anyone know which algorithm survreg() uses for this?
Thanks,
Gad
--
Gad Abraham
Department of Mathematics and Statistics
The University of Melbourne