search for: parameterizes

Hello, I have a package which includes some parameterized r-markdown report which I would also like to build as package vignettes. Is there a way to run the parameterized vignette creation with the package build or package check? Thank you -- Witold Eryk Wolski

Hello all (especially MCLUS users). I'm trying to make use of the MCLUST package by C. Fraley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where

Hi all, I encountered this problem with parameterization in R: I have two factors in a regression. how about if I want to set constraint so that for each factor, the sum of their coefficients equals to zero(instead of choosing a reference category)? for example, I have factor(variable) A(with three categories) and factor(variable) B(with 4 categories), and I want to parameterize so that the sum

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to express Inverse Wishart as IW(0.5 * DOF,0.5* Scale). (DOF-> Degree of freedom, Scale -> Scale

Dear R users: I am wondering how to ask for *fixed* number of distributions under parameterized Gaussian mixture model. I know that em() and some related functions can predict the parameterized Gaussian mixture model. However, there seems no parameter to decide number of distributions to be mixed (if we known the value in advance). That is, assume I know the (mixed)data is from 3 different

Dear R community, I am new to R, a reforming SAS user :) I am running R 2.10.1 on a Windows XP machine. I would like to write linear functions of my coefficient parameter estimates from a glm, but am having a difficult time understanding the parameterization R uses. In the toy example below I am running a glm on binomial data, with clones and lines within clones as fixed effects, each with 6

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

I''ve been reading the Docs on this, and its not very clear. I want to have a parameterized class that takes arrays and objects as parameters. I''m using puppet 2.6.16. I define a hostclass like this: hostclass :test, :arguments => { :param1 => "blah" } do end I call from Puppet DSL: class {"test": } I get this as an error: err: Could not

Hi list, how are ya? So, my current project is just begging for the ability to have parameterized associations in my ActiveRecord classes. Basically I need something that is a cross between named scopes and standard associations (has_many specifically). I''m wondering if such a thing exists or, if not, if anyone else has an elegant, equivalent solution? Example: class Sprocket <

I am working with estimates of vegetation height derived from radar data. We have a nonlinear model to correct these estimates for errors associated with viewing geometry. I am trying to estimate a single parameter in this model while accounting for spatial (spherical structure) autocorrelation. I'd also like to statistically test the influence of several vegetation parameters. The gnls()

Dear R users; I'm looking for some hint about how to deal with the following situation: Response = Y Factor A = levels: 0, 1 Factor B = levels: 0, 1 Factor C = levels: 1,2,3,4 Model: Logistic 3-parms. where th1~1+A+C, th2~1+C; th3~1 For 'simplicity' (for me) I'm using the SAS contrast parameterization. The output looks like Beta p-value th1.(Intercept) 550

Hello, I have been successfully using nls to fit a non-linear, self-limiting function to several sets of data collected in 2010 (example found below). To generate confidence intervals for parameter estimates, I've been attempting to bootstrap my sample. Unfortunately, I have meet with singularity gradients and failures to converge. I have altered maxiter and minFactor statements, used

Hello, I want to access variable of a parameterized class but it fails with "class has not been evaluated". Tried to evaluate it first adding stages and requires, but still same. I''ll explain better with an example: stage { pre: before => Stage[main] } class directory($path) { file { $path: ensure => directory, } } class directory::subdirectory { file {

Has anyone run into this issue before? http://projects.puppetlabs.com/issues/5046#note-17 Is there something obvious that I''m missing? -- You received this message because you are subscribed to the Google Groups "Puppet Users" group. To unsubscribe from this group and stop receiving emails from it, send an email to puppet-users+unsubscribe@googlegroups.com. To view this

Bother! This cold has made me accident-prone. I meant to hit Reply-all. Clarification below. -------- Original Message -------- Subject: Re: [R] Parameterization puzzle Date: Fri, 21 Jul 2006 19:10:03 +1200 From: Murray Jorgensen <maj at waikato.ac.nz> To: Prof Brian Ripley <ripley at stats.ox.ac.uk> References: <44C063E5.3020703 at waikato.ac.nz>

The following is an extraction from the Operand class hierarchy of Target.td. I am trying to define a parameterized version of AsmOperandClass with a passed-in bit size. // from Target.td class AsmOperandClass { string Name; } class Operand { AsmOperandClass ParserMatchClass; } // A parameterized AsmOperandClass class myAsmOperandClass<int n> : AsmOperandClass { string Name =

I have a model called Product with an attribute called file of type :binary (DB BLOB). If I do product.save (where product is an instance of my Product model), here is SQL statement that the mysql adapter for Ruby will do: INSERT INTO `PRODUCT` COLUMNS(NAME, PRICE, FILE) VALUES(''PROD1'', 4.56, X''00DEF033423023220'') All is hunky dory for as long as my BLOB is

Hi, I would like to write a function that finds parameters of a log-normal distribution with a 1-alpha CI of (x_lcl, x_ucl): However, I don't know how to optimize for the two unknown parameters. Here is my unsuccessful attempt to find a lognormal distribution with a 90%CI of 1,20: prior <- function(x_lcl, x_ucl, alpha, mean, var) { a <- (plnorm(x_lcl, mean, var) - (alpha/2))^2 b

Although the parameterized samples have allowed people to get a firewall up and running quickly, they have unfortunately set the wrong level of expectation among those who have used them. I am therefore withdrawing support for the samples and I am recommending that they not be used in new Shorewall installations. -Tom -- Tom Eastep \ Shorewall - iptables made easy AIM: tmeastep \

Hello, Thank you for the questions as well as remaining me of the default parameters in the yaml session. Indeed this seems to be the solution. But how would I assign package data as a default parameter? So originally I thought to render the markdown with : <code> data(sample_analysis) data(skylineconfig) x <- rmarkdown::render("report.Rmd", output_format =