similar to: Invalid parameter when assigning parameterized class to a node

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

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

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 {

I tried to use named parameters in my SQL query , using find_by_sql Order.find_by_sql ([select * from orders where amount > ? and quantity > ?", [ @amount, @quantity ] works;.. but Order.find_by_sql ([select * from orders where amount > :amount and quantity > :quantity ", [ :amount => @amt, :quantity => @qty ] is not working a I wrong or should I use a plain select

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 <

Hi all, I have three parameterized classes, named mq, mq_gsk and apache. The mq class always calls the mq_gsk class, but the apache class should only call the mq_gsk class when the mq class is not used in a hosts'' node definition Here is my configuration class mq ( $gsk_version = undef ) { require mq::params < ..... > class { ''mq_gsk'':

I have this: class foo::foobar ($version) { } which is called like this: class facility::sanjose inherits facility::common { class { foo::foobar: version => "1.3.13" } } puppet complains with this on the client: Could not retrieve catalog from remote server: Error 400 on SERVER: Could not find parent resource type ''facility::sanjose'' of type hostclass in

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 \

Can you reverse the color scheme order in rcolorbrewer. If so, how? [[alternative HTML version deleted]]

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

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

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()

On Thu, Jul 18, 2019 at 12:55:50PM +0000, ? jiang wrote: > This change makes ring buffer reclaim threshold num_free configurable > for better performance, while it's hard coded as 1/2 * queue now. > According to our test with qemu + dpdk, packet dropping happens when > the guest is not able to provide free buffer in avail ring timely. > Smaller value of num_free does decrease

On Thu, Jul 18, 2019 at 10:01:05PM +0800, Jason Wang wrote: > > On 2019/7/18 ??9:04, Michael S. Tsirkin wrote: > > On Thu, Jul 18, 2019 at 12:55:50PM +0000, ? jiang wrote: > > > This change makes ring buffer reclaim threshold num_free configurable > > > for better performance, while it's hard coded as 1/2 * queue now. > > > According to our test with

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

Dear all, Apologies if this question is bit theoretical and for the longish email. I am meta-analyzing the coefficients and standard errors from multiple studies where the raw data is not available. Each study analyst runs a model that includes an interaction term for, say, between sex and smoking and age. Here is an illustrative example example for one study: set.seed(1066) status

On 2019/7/18 ??10:43, Michael S. Tsirkin wrote: > On Thu, Jul 18, 2019 at 10:42:47AM -0400, Michael S. Tsirkin wrote: >> On Thu, Jul 18, 2019 at 10:01:05PM +0800, Jason Wang wrote: >>> On 2019/7/18 ??9:04, Michael S. Tsirkin wrote: >>>> On Thu, Jul 18, 2019 at 12:55:50PM +0000, ? jiang wrote: >>>>> This change makes ring buffer reclaim threshold

Dear all, I would like to fit a non-linear model of the form: y=g*x/(a+b*x) with nls(). However this model is somehow overparameterized and I get the error message about singular gradient matrix at initial parameter estimates. What I am interested in is to make inference about parameters b and g, so this has to be taken into account in the model formulation. What options do I have? Also, how is

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>