similar to: how to calcaulate matrices for two subsets

I am an R beginner and trying to run a SUR model in R framework. subset(esasp500, Obs <=449 & Obs>=197, select = -Date) ->ev13sub c(Obs>=397) & c(Obs<=399) ->d13 c(Obs>=400) & c(Obs<=449) ->f13 SP500*f13 ->SP500f13 BBC~SP500+d13+SP500f13 ->sur132 BOW~SP500+d13+SP500f13 ->sur133 CSK~SP500+d13+SP500f13 ->sur134

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

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 ~

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

Dear list-members, a model is fit with lmer, but I want to force the variance parameter values to be as defined by me I thought, use 'start' to specify initial values and only allow for one iteration ? my question is how to do that ? to specify the 2x2 matrix of variance parameter values: start=list(groups=array(2,-0.5,1),dim=c(2,2)) now I need to make sure the mean structure is

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

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

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

?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,

Dear Group, I am having a function that I am running in a loop that generated two results for each loop The result1 is a zoo object The result2 is a data frame Now I want to put both of them in a list or some structure ... that I can access or output to a file after the loop is done. For e.g. for (i in 1:20){ niceFunction(x[i],i) } niceFunction (x,i) { result1 = someOperations() #zoo