search for: modeler

On Mon, Sep 14, 2020 at 8:42 AM Yedidyah Bar David <didi@redhat.com> wrote: > > On Mon, Sep 14, 2020 at 12:28 AM wodel youchi <wodel.youchi@gmail.com> wrote: > > > > Hi, > > > > Thanks for the help, I think I found the solution using this link : https://www.berrange.com/posts/2018/06/29/cpu-model-configuration-for-qemu-kvm-on-x86-hosts/ > > > >

My question is about where certain kinds of logic sit in the Model and Controller objects or more specifically the relationship between Model and Controllers. Does an Model class contain a Controller class, the otherway round, or are they two seperate classes that talk? 1. Should the Models be treated as the primary objects within a program and the matching controller is simply a way of

I want to create a list based on the information from a data.frame, Model. So I tried the following: MyList <- list(colnames(Model)[2] = levels(Model$(colnames(Model)[2]))) but it failed with an error: Error: unexpected '=' in "list(colnames(Model)[2] =" I have the following problems with this command line: (1) I wanted to use colnames(Model)[2] as a tag for the list:

My question is, how to compute hazard function(H(t)) after building the coxph model. I even aware of the terminology that differs from hazard function(H(t)) and the hazard rate(h(t)). Here onward I wish to calculate both. Here what I have done in two different methods; ##########################################################################################

Sorry if double posted... might have sent this to old mailing list address... ---------- Forwarded message ---------- From: Stephen Thomas <stephen.warner.thomas at gmail.com> Date: Wed, Aug 5, 2015 at 10:39 AM Subject: How to use CostModel? To: LLVM Dev <llvmdev at cs.uiuc.edu> Hi, I'm trying to use the built-in CostModel class to estimate the number of machine instructions

Hi, For some reason do.call on anova fails if the models are named lmer objects. Consider the following example: library(lme4) models <- list( lmer(Reaction ~ Days + (1| Subject), sleepstudy), lmer(Reaction ~ Days + (Days | Subject), sleepstudy)) # # models is an unnamed list, do.call works (although with warning): do.call(anova, models) # # after labeling the models, do.call gives an

For the vcdExtra package, I'm exploring methods to generate and fit collections of glm models, and handling lists of such model objects, of class "glmlist". The simplest example is fitting all k-way models, from k=0 (null model) to the model with the highest-order interaction. I'm having trouble writing a function, Kway (below) to do what is done in the example below >

Dear r-developers: I am struggling with some fundamental aspects of model.frame(). Conceptually, I think of a flow from data -> model.frame() -> model.matrix; the data contain _input variables_, while model.matrix contains _predictor variables_: data have been transformed, splines and polynomials have been expanded into their corresponding multi-dimensional bases, and factors have been

Hi all, First of all, I'm a novice R user (less that a week), so perhaps my code isn't very efficient. Using the MBoost package I created a model using the following command and saved it to a file for later use: model <- gamboost(fpfm,data=SampleClusterData,baselearner="bbs") # Creating a model save(model,file="model.RData") # Saving a model After this, during a

I?ve used logistic regression to create models to assess the effect of 3 variables on the presence or absence of a species, including the interaction terms between variables and model averaging using MuMI: model.avg The top models (delta<4) include several models with interaction terms and some models without; model weights are quite low for all models (<0.25). My problem is that the models

Dear All, is there a simple way that covers all regression models to extract the number of samples from a data frame/matrix actually used in a regression model? For instance I might have a data of 100 rows and 4 colums (1 response + 3 explanatory variables). If 3 samples have one or more NAs in the explanatory variable columns these samples will be dropped in any model: my.model = lm(y ~ x + w

Sorry, I'm stuck. :) I am writing a function, which would fit either one linear model or another one, depending on its argument model.type. And I don't want to use several if's, because R allows doing it with much more beauty. That is I am looking for prettier alternative to the following AB2C<-function(a,b,model.type="S") { # or HK, and return NULL if this argument has

Hello, All: What's the simplest way to convert a data.frame into a model.matrix? One way is given by the following example, modified from the examples in help(model.matrix): dd <- data.frame(a = gl(3,4), b = gl(4,1,12)) ab <- model.matrix(~ a + b, dd) ab0 <- model.matrix(~., dd) all.equal(ab, ab0) What do you think about replacing "model.matrix(~ a +

Hello, I am writing a package with a collection of several models. In order to allow users to play interactively with the models (in contrast to hacking lengthy scripts), I want to put all what is needed to run a particular model into a single list object for each model. Then there will be a collection of functions to run the model or to modify parameters, time steps, integration method ...,

Dear R list members, I'm encountering a problem with expand.model.frame(): Suppose that I define the following simple function (meant just to illustrate the problem): > fun <- function(model){ + expand.model.frame(model, all.vars(formula(model))) + } > and I have the following model, created with an explicit data argument: > mod Call:

Dear List-members, Hopefully someone will help through my confusion: In order to get the same coefficients as we get from the following ## require (MASS) summary ( lm(Gas ~ Insul/Temp - 1, data = whiteside) ) ...................... we need to do the following (if we use model.matrix to specify the model) ## summary ( lm(Gas ~ model.matrix(~ Insul/Temp - 1) - 1, data = whiteside) )

Hi R Forum I am a newbie to R and I have been amazed by what I can get my team to accomplish just by implementing Scripting routines of R in all my team's areas of interest.. Recently i have been trying to adopt R scripting routine for some analysis with longitudanal data.. I am presenting my R script below that I have tried to make to automate data analysis for longitudanal data by employing

Hi everybody, If I am correct, you can compare a model with random effect with the same model without the random effect by using the nlme function, like this: no.random.model <- gls(Richness ~ NAP * fExp, method = "REML", data = RIKZ) random.model <- lme(Richness ~NAP * fExp, data = RIKZ, random = ~1 | fBeach, method = "REML")

The subject says it all really. Question 1. Here is some code created to illustrate my problem, can anyone spot where I'm going wrong? Question 2. The reason I'm following a manual specification of models relates to the fact that in reality I am using mgcv::gam, and I'm not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request,

Hi, I am running a Cox Mixed Effects Hazard model using the library coxme. I am trying to model time to onset (age_sym1) of thought problems (e.g. hearing voices) (sym1). As I have siblings in my dataset, I have decided to account for this by including a random effect for family (famid). My covariate of interest is Mother's diagnosis where a 0 is bipolar, 1 is control, and 2 is major