search for: submodel

Here's a more general question following up on the specific question I asked earlier: Can anybody recommend an R command other than mle.aic() (from the wle package) that will give back a ranked list of submodels? It seems like a pretty basic piece of functionality, but the closest I've been able to find is stepAIC(), which as far as I can tell only gives back the best submodel, not a ranking of all submodels. Thanks in advance, Alexandra

...d to experiment with the small-sample correction AICc, I dug around in the code for the functions glm.fit stepAIC dropterm.glm addterm.glm extractAIC.glm and came across something I just don't understand. stepAIC() passes dropterm.glm() a model object. dropterm.glm() then fits a number of submodels, computing for each some measure DeltaFit of the relative change in goodness of fit. It then returns to stepAIC() with some indication of which submodel is best. addterm.glm behaves similarly. The problem is, both functions use the submodel deviances to compute the DeltaFits, not the submodel AICs...

...'ve only just started using AIC for model comparison and after a bunch of different keyword searches I've failed to find a page laying out what the differences are between the AIC scores assigned by AIC() and mle.aic() using default settings. I started by using mle.aic() to find the best submodels, but then I wanted to also be able to make comparisons with a couple of submodels that were nowhere near the top, so I started calculating AIC values using AIC(). What I found was that not only the scores, but also the ranking of the models was different. I'm not sure if this has to do with...

...> customRF$fit <- function(x, y, wts, param, lev, last, weights, > classProbs, ...) { > > > > randomForest(x, y, maxnodes = param$maxnodes, ntree=param$ntree, ...) > > > > } > > > > customRF$predict <- function(modelFit, newdata, preProc = NULL, > submodels = NULL) > > > > predict(modelFit, newdata) > > > > customRF$prob <- function(modelFit, newdata, preProc = NULL, submodels = > NULL) > > > > predict(modelFit, newdata, type = "prob") > > > > customRF$sort <- function(x) x[order(x[...

I would like to try a likelihood ratio test in place of waldtest. Ideally I'd like to provide two glm models, the second a submodel of the first, in the style of lrt (http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt takes farimsa objects] Does anyone know of such a likelihood ratio test? Chris Elsaesser, PhD Principal Scientist, Machine Learning SPADAC Inc. 7921 Jones Branch Dr. Suite 600 McLean,...

...RF$grid <- function(x, y, len = NULL, search = "grid") {} customRF$fit <- function(x, y, wts, param, lev, last, weights, classProbs, ...) { ?randomForest(x, y, maxnodes = param$maxnodes, ntree=param$ntree, ...) } customRF$predict <- function(modelFit, newdata, preProc = NULL, submodels = NULL) predict(modelFit, newdata) customRF$prob <- function(modelFit, newdata, preProc = NULL, submodels = NULL) ? predict(modelFit, newdata, type = "prob") customRF$sort <- function(x) x[order(x[,1]),] customRF$levels <- function(x) x$classes ? # Set grid search paramete...

...e case of two categorical factors, say a and b, once I have fixed the constrasts, the model matrix is set according to these contrasts with "lm", and the t-tests for the significance of the parameters provided by "summary" indeed concern the comparison of the model with each submodel obtained by removing the corresponding column of the model matrix. So : options(contrasts=c("contr.sum", "contr.poly")) mod = lm(Y ~ a*b, x=TRUE) print(mod$x) print(summary(mod)) provide the expected results. When using "anova" for the corresponding two-way anova,...

...function called jointModelBayes(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a Cox model object fit returned by function coxph() of package survival. * jointModelBayes() allows for joint models with relative risk survival submodels with Weibull or B-spline approximated baseline hazard functions (controlled by argument 'survMod'). * In addition, argument 'param' of jointModelBayes() specifies the association structure between the longitudinal and survival processes; available options are: - "td-va...

...function called jointModelBayes(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a Cox model object fit returned by function coxph() of package survival. * jointModelBayes() allows for joint models with relative risk survival submodels with Weibull or B-spline approximated baseline hazard functions (controlled by argument 'survMod'). * In addition, argument 'param' of jointModelBayes() specifies the association structure between the longitudinal and survival processes; available options are: - "td-va...

...e, mainly in a splitplot design. ML make significative variables that REML dont make. I read an article that is made a stepwise procedure using GENSTAT. from article: "Terms were dropped from a model in a stepwise procedure by assessing the change in deviance between the full model and the submodel." All are made using REML. It is possible?! I dont know GENSTAT. Thanks Ronaldo -- aquadextrous, adj.: Possessing the ability to turn the bathtub faucet on and off with your toes. -- Rich Hall, "Sniglets" -- | // | \\ [***********************************] |> ( ? ? )...

Hello all, I am trying to figure out an optimal linear model by using stepwise regression which requires partial f-test, I did some Googling on the Internet and realised that someone seemed to ask the question before: Jim Milks <jrclmilks at joimail.com> writes: > Dear all: > > I have a regression model that has collinearity problems (between > three regressor variables). I

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

...eate all possible combinations dd1<-gsub("formula = ","",dd1,fixed=TRUE) #delete characters dd1<-gsub(" + 1","",dd1,fixed=TRUE) #delete characters dd1#inspect model formulae } lm1<-lm(data$BiRich.o~log(data$HaArea,10)+log(data$HaPeri,10)) #saturated submodel dredgeit(lm1) [[alternative HTML version deleted]]

...family=gaussian) target.model <- dredge(mig.stds, subset=datam.std$temp_ran) error in eval(expr, envir, enclos), 'temp_ran' not found Q3 showing sub-models for two assigned explanatory variables The manual only explains how to exclude two variables. Please advise how to contain submodels regarding certain two or more variables. Elaine [[alternative HTML version deleted]]

...) + sqrt(h_t)*epsilon_t h_t = alpha0 + alpha1*epsilon_(t-1)^2 + beta1 * h_(t-1). Alpha refers to a risk-free return, lambda to the risk-premium. I've implemented it like this: #specification of the model spec = ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1,1), submodel = NULL, external.regressors = NULL, variance.targeting = FALSE), mean.model = list( armaOrder = c(0,0), include.mean = TRUE, archm = TRUE, archpow = 1, arfima = FALSE, external.regressors = NULL, archex = FALSE), distribution.model = "jsu", start.pars = list(), fixed.pars = list()) #fit...

...ch accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either function coxph() or function survreg() of package survival. In addition, the method argument of jointModel() specifies the type of the survival submodel to be fitted and the type of the numerical integration technique; available options are: * "ph-GH": the time-dependent version of a proportional hazards model with unspecified baseline hazard function. This option corresponds to the joint model proposed by Wulfsohn and Tsiatis, B...

...l types of residuals are supported for the longitudinal and time-to-event outcomes. Moreover, for the longitudinal outcome there is also the option to compute multiple-imputation-based residuals, as described in Rizopoulos, Verbeke and Molenberghs (Biometrics 2009, to appear). * the Weibull submodel for the time-to-event outcome is now available under both the relative risk and accelerated failure time formulations. * this new version of the package features new and more robust algorithms for numerical integration and optimization -- these updates could lead to different results, epseci...

Does anyone know how to do an Hausman test? I?ve estimate a modell (some alternatives) with clogit an wanted to test the IIA test (Independence of Irrelevant Alternatives) after estimating a multinomial logit model? Thank you

...ch accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either function coxph() or function survreg() of package survival. In addition, the method argument of jointModel() specifies the type of the survival submodel to be fitted and the type of the numerical integration technique; available options are: * "ph-GH": the time-dependent version of a proportional hazards model with unspecified baseline hazard function. This option corresponds to the joint model proposed by Wulfsohn and Tsiatis, B...

...l types of residuals are supported for the longitudinal and time-to-event outcomes. Moreover, for the longitudinal outcome there is also the option to compute multiple-imputation-based residuals, as described in Rizopoulos, Verbeke and Molenberghs (Biometrics 2009, to appear). * the Weibull submodel for the time-to-event outcome is now available under both the relative risk and accelerated failure time formulations. * this new version of the package features new and more robust algorithms for numerical integration and optimization -- these updates could lead to different results, epseci...