similar to: R's AIC values differ from published values

I am using the AICmodavg package and using R version 2.15.1. I need help with code that is instead being read as text. Below is a subset of code... I actually have 12 models, but I am trying to get this to work for 2 below right now. Everything 'appears' to work through the line starting with Modnames. After that the code starting with aictab and beyond is recognized as text and not

Hi R-helpers, I have a character object called dd that has 32 elements each of which is a model formula contained within quotation marks. Here's what it looks like: > dd [1] "lm(y ~ 1,data=Cement)" "lm(y ~ X,data=Cement)" "lm(y ~ X1,data=Cement)" [4] "lm(y ~ X2,data=Cement)" "lm(y ~

Dear moderator, If possible I would like to send in the following question for R-help: I am analyzing a small data set using PGLS with phylogenetic uncertainty taken into account and thereby including 100 potential phylogenetic tree scenarios. I've managed to run models on all of the different trees and performed model averaging to get parameter estimates for the intercept and most of the

Hi All: the package "MuMIn" can be used to select the model based on AIC or AICc. The code is as follows: data(Cement) lm1 <- lm(y ~ ., data = Cement) dd <- dredge(lm1,rank="AIC") print(dd) If I want to select the model by BIC, what code do I need to use? And when to select the best model based on AIC, what the differences between the function "dredge" in

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,

Hello, I'd like to sum the weights of each independent variable across linear models that have been evaluated using AIC. For example: > library(MuMIn) > data(Cement) > lm1 <- lm(y ~ ., data = Cement) > dd <- dredge(lm1, beta = TRUE, eval = TRUE, rank = "AICc") > get.models(dd, subset = delta <4) There are 5 models with a Delta AIC Score of

Hello, I am using package MuMIn to calculate AIC for a full model with 10 explanatory variables. Thanks in advance in sharing your experience. Q1 In the AIC list of all models, each model is differentiated by model number. Please kindly advise if it is possible to find the corresponding explanatory variable(s) for the model number. Q2 error message I tried to display sub-model with only

I'm doing these manipulations on the data frame and wondering why does R have to remember historical data on my operation and not just keep the needed info. Probably a basic fundamentals of the way R handles data .. Pls point me to the manual if possible .. I have this Index data: > head(NIFTY_INDX) Constituents.list.of.S.P.CNX.Nifty X X.1 X.2 X.3

All, In the function below I have 24 individuals and 6 calculations per individual. The 6 calculations are collected each time in a 1:24 loop when calculating "delta". I'd like to collect all 144 = 24*6 calculations in one vector ("delta.patient.comb"). The function works as is via indexing, but is there an easier way to collect the measurements via appendinng the 6

I am still searching for a solution to what i think is a simple problem i am having with building a vector in a for loop. I have built a more understandable example so hopefully that will help..help you, help me, if you know what i mean. dev=400 #test location model TAZs to reference cands=c(101,105,109) #Create Object of length of cands candslength=length(cands) #TEST TAZ Vector

Hi All: Recently I am researching my dissertation about the quantile model selection by bayesian approach. I have the dependent variable(return) and 16 independent variables and I need to select the best variable for each quantile of return. And the method I used is the bayesian approach, which is based on calculating the posterior distibution of model identifier. In other words, I need to obtain

Hello, This is a question motivated by curiosity, not a pressing problem. Any responses are much appreciated! In the following code, function reg1 calls glm with a formula object while reg2 uses a string. In both cases, glm works; however, in the second case, the add1 function fails with the following message: "Error in eval(predvars, data, env) : invalid 'envir' argument."

I just r-sync'ed the r-devel version of R-1.7.0 (2003-03-11), compiled it under RH-8.0 and ran make check. The reg-tests-2 fails at the very end with the message stating that "object cement was not found". It looks like this piece of the regression test is new to version 1.7. The the piece of code generating the error is: if(require(MASS)) { teststep <- function(formula,

I just r-sync'ed the r-devel version of R-1.7.0 (2003-03-11), compiled it under RH-8.0 and ran make check. The reg-tests-2 fails at the very end with the message stating that "object cement was not found". It looks like this piece of the regression test is new to version 1.7. The the piece of code generating the error is: if(require(MASS)) { teststep <- function(formula,

With new kernel, appeared some problems with build proccess here. After many minutes waiting for rpmbuild were over, a gpg problem appears!!: ... scripts/modsign/mod-extract.c:311: warning: format '%lx' expects type 'long unsigned int', but argument 3 has type 'Elf64_Xword' + KEYFLAGS='--no-default-keyring --homedir ..' + KEYFLAGS='--no-default-keyring

Hi, I have been experimenting for a while with tryCandidate() method of the pre-RA mischeduler. I have by chance found some parameters that give quite good results on benchmarks on SystemZ (on average 1% improvement, some improvements of several percent and very little regressions). Basically, I add a "latency heuristic boost" just above processor resources checking:

Dear List, I want to ask a AIC question based on package library(MuMIn) The relative importance of 16 explanatory variables are assessed using delta AIC in a generalized linear model. Please kindly advise if it is possible to show models with any two only certain variables. Thank you. Elaine I asked a similar question and got a great help for models with only one variable as below.

Hello all! I've readed somes docs and I found nothing about the following trouble : I can't connect to my sbmd server using the following : '\\MYRH72SMBDSERVERNAME\'. I always got a error message like : 'Share name could not be found, make sure you typed it correctly....' , but if I utilize \\10.x.x.x\ all is working fine (I don't want to access using the \\IP\

I am using the "predict" function on a support vector machine (svm) object, and I don't understand why I can't predict on a dataset with more observations than the training dataset. I think this problem is a generic "predict" problem, but I'm not sure. The original svm was fit on 50 observations.

Hi all, I have two sets: dig<-c("DAVID ADAMS","PIERS AKERMAN","SHERYLE BAGWELL","JULIAN BAJKOWSKI","CANDIDA BAKER") import<-c("by DAVID ADAMS","piersAKERMAN","SHERYLE BagWEL","JULIAN BAJKOWSKI with ","Cand BAKER","smith green")     I want to get the following result from