similar to: fix variance parameter values for lmer estimation

could someone explain why the name of FPVAL gets " .value" concatenated onto it when the code below is run and temp is returned. I've been trying to figure this out for too long. It doesn't matter when I put the FPVAL in the return statement. It happens regardless of whether it's first or last. Thanks. f.lmmultenhanced <- function(response, pred1, pred2) {

hi, i'm working with mgcv packages and specially gam. My exemple is: >test<-gam(B~s(pred1)+s(pred2)) >plot(test,pages=1) when ploting test, you can view pred1 vs s(pred1, edf[1] ) & pred2 vs s(pred2, edf[2] ) I would like to know if there is a way to access to those terms (s(pred1) & s(pred2)). Does someone know how? the purpose is to access to equation of smooths terms

THANKS so very much for your help (previous and future!). I have a two follow-up questions. 1) You say that dispersion = 1 by definition ....dispersion changes from 1 to 13.5 when I go from binomial to quasibinomial....does this suggest that I should use the binomial? i.e., is the dispersion factor more important that the 2) Is there a cutoff for too much overdispersion - mine seems to be

Guten Tag, I need to install a software called Topix (www.topix.de) wih wine. It is our main order management software with enterprise recource planing, accounting, stock keeping and customer relation management. I use Kubuntu newest version and Wine 1,10. Here you can see the message list from wine: http://ubuntuusers.de/paste/380352/ Do you have any idea to avoid the software crash? Best

I am an R beginner and trying to run a market model using event study in R framework. First, I run a market model, that is lm(stock security~SP500 index, subset=Obs[197, 396]) ->result1 Then I get predict results for a new dataset using predict (result1, newdata=Obs[397,399]) ->pred1 Pred1 should have three numbers. Now I need to calculate abnormal return by the formula stock

Hi, I am quite new to R and I have two questions regarding ROCR. 1. I have tried to understand how to extract area-under-curve value by looking at the ROCR document and googling. Still I am not sure if I am doing the right thing. Here is my code, is "auc1" the auc value? " pred1 <- prediction(resp1,label1) perf1 <- performance(pred1,"tpr","fpr") plot(

Say I fit a logistic model and want to calculate an odds ratio between 2 sets of predictors. It is easy to obtain the difference in the predicted logodds using the predict() function, and thus get a point-estimate OR. But I can't see how to obtain the confidence interval for such an OR. For example: model <- glm(chd ~age.cat + male + lowed, family=binomial(logit)) pred1 <-

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

Hi, My questions concerns the ROCR package and I hope somebody here on the list can help - or point me to some better place. When evaluating a model's performane, like this: pred1 <- predict(model, ..., type="response") pred2 <- prediction(pred1, binary_classifier_vector) perf <- performance(pred, "sens", "spec") (Where "prediction" and

I'm new to R and am trying to extract the factors of a dataframe using numeric indices (e.g. df[1]) that are input to a function definition instead of the other types of references (e.g. df$out). df[1] is a list(?) whose class is "dataframe". These indexed lists can be printed successfuly but are not agreeable to the plot() and lm() functions shown below as are their df$out

I have a function that, among other things, runs a linear model and returns r2. But, the number of predictor variables passed to the function changes from 1 to 3. How can I change the formula inside the function depending on the number of variables passed in? An example: get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL, pred3.dat = NULL) { res <- lm(response.dat ~

Full_Name: Renaud Lancelot Version: Version 2.3.0 (2006-04-24) OS: MS Windows XP Pro SP2 Submission from: (NULL) (82.239.219.108) I think there is a bug in predict.lme, when a polynomial generated by poly() is used as an explanatory variable, and a new data.frame is used for predictions. I guess this is related to * not * using, for predictions, the coefs used in constructing the orthogonal

Not sure why the data.frame function does not capture the name of the column field when its being built with only one column. Can anyone help? > data out pred1 predd2 1 1 2.0 3.0 2 2 3.5 5.5 3 3 5.5 11.0 > data1=data.frame(data[,1]) > data1 data...1. 1 1 2 2 3 3 > data1=data.frame(data[,1:2]) > data1 out pred1 1 1 2.0 2 2

Hi, May be you can try this: obj_name<- load("arun.RData") Pred1<- get(obj_name[1]) Actual1<- get(obj_name[2]) library(reshape2) dat<-cbind(melt(Pred1,id.vars="S1"),value2=melt(Actual1,id.vars="S1")[,3])? # to reshape to long form colnames(dat)[3:4]<- c("Predict","Actual") dat$variable<- as.character(dat$variable) #not that

The following code crashes R (I know I shouldn't try to estimate such a model; this was a bug in some code of mine). I also tried with R-devel; same result. tab <- structure(list(dob_day = c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE), dob_mon = c(FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE), dob_year = c(FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE), n =

Nella citazione martedì 2 agosto 2011 22:01:13, Carlo Alberto Ferraris ha scritto: > My question is: > what is the best way to > express such relationships in LLVM IR ("best" in the sense of allowing > other optimizations to run effectively)? Bear in mind that in this > example N=2, but it may be way bigger than that. Just to clarify: I already figured out two ways to

Hello r-help, I'm trying to fit birds counts over years using glm. In fact I'm trying to reproduce an analysis already perform with genstat (attach document). I have done (with Estate and year as factors): Model1 <- glm(Females~Estate+Year+offset = log(area)), family = quasipoisson(link = log), na.action = "na.exclude") After I have calculated the prediction using: Pred1

Hi Jean-Marc, thank you for that suggestion! It seems that the file "fixed_c6x.h" is not part of the Opus sources, so the compiler cannot find it after enabling the TI_C6X_ASM config option. Maybe it was only part of an early version of the Opus sources? I looked for the file in versions V1.1, V1.1.1, V1.2alpha and V1.3 but did not found it. Do you have an idea, where I can get the

Dear R Helpers, I want to test if a procedure within a loop has produced an error or not. If the procedure has produced an error, then I want to ignore its result. If it has not produced an error, then I want to use the result. The problem In order to run the loop without crashing if the procedure produces an error, I place the routine inside a try() statement. So, suppose I am trying to find

?s 12:13 de 13/07/2024, Christofer Bogaso escreveu: > Hi, > > I ran below code > > Dat = read.csv('https://raw.githubusercontent.com/sam16tyagi/Machine-Learning-techniques-in-python/master/logistic%20regression%20dataset-Social_Network_Ads.csv') > head(Dat) > Model = glm(Purchased ~ Gender, data = Dat, family = binomial()) > head(predict(Model,