search for: model1

Hi everyone, I have recently experienced a strange behavior (strange from my knowledge) in rails. In my controllers ''new'' action, I am creating a few instance variables in the following manner : @controllerModel = ControllerModel.new @model1 = Model1.all @model2 = Model2.all in my ''new'' view, I am using the @controllerModel to create the form for new and I am using the @model1 & @model2 within the form to populate 2 combo boxes in the following manner : f.select :model1_id, @model1.collect { |m1| [m1.name, m1.id...

...m using a custom function to fit ancova models to a dataset. The data are divided into 12 groups, with one dependent variable and one covariate. When plotting the data, i'd like to add separate regression lines for each group (so, 12 lines, each with their respective individual slopes). My 'model1' uses the group*covariate interaction term, and so the coefficients to plot these lines should be contained within the 'model1' object (there are 25 coefficients and it looks like I need the last 12). The problem is I can't figure out how to extract the relevant coefficients from ...

...I am struggling to identify to the formula the name of the column in a way which is meaningful to the formula. It errors becasue it is not evaluated proporly. (below is a pilot with only 7 to 33 columns, my actual has 200,000 columns) My attempts: for (i in 7:33) { label <- colnames(n)[i] model1 <- glm(AS~label*interaction,family=binomial("logit"),data=n) X <- summary(model1)$coefficients[2,1] Y <- c(label,X) vector <- rbind(vector,Y) } #variable lengths differ Error in model.frame.default(formula = AS ~ label, data = n, drop.unused.levels = TRUE) : variable le...

I was working on a project involving a linear model, and wanted to extract the standard error of a predictor. I am able to do so, but not in the way I would expect. I would have expected that if a created a model such as Model1 <- lm(y~x,z,d), the object Model1 would contain that information even though it does not print it out when I simply type Model1. I would also have (wrongly) suspected that if I type summary(Model1) R would simply look at the object Model1 and find whatever it needs. But it doesn't work...

...amespace". The only solution I have found is to detach the TSA package, (detach("package:TSA")) , which results in the loss of all the TSA specific functions. Does anyone have another solution? The following code illustrates the problem: Y1<-arima.sim(n=100,list(ar=c(.95,-0.2))) model1<-arima(Y1,order=c(2,0,0)) tsdiag(model1) library(TSA) tsdiag(model1) stats::tsdiag(model1) detach("package:TSA") tsdiag(model1) R Saba

...ng to figure this out for a few days now (I'm quite new to R), have read manuals, list posts, etc., and not finding precisely what I need. I am accustomed to working in Stata, but I am currently working through the multilevel package right now. In Stata, I would include the statement "if model1 == 1" at the end of my regression statement, but I can't seem to find the correct syntax for doing the same thing in R. I have successfully converted Stata datasets, merged, aggregated, run lm, etc., but this simple task is evading me entirely. Here's what I have tried (and vario...

...family = poisson, data = epil3.) fit0 <- lmer(y ~ 1+(1 | subject), family = poisson, data = epil3.) chisq.sim[i] <- anova(fit0, fit1)[2, "Chisq"] } ************simulation (MM)************ iter <- 10 chisq.sim <- rep(NA, iter) Zt <- slot(model1,"Zt") # see ?lmer-class n.grps <- dim(ranef(model1)[[1]])[1] sd.ran.effs <- sqrt(VarCorr(model1)[[1]][1]) X <- slot(model1,"X") # see ?lmer-class fix.effs <- matrix(rep(fixef(model1),dim(X)[1]), nrow=dim(X)[1], byrow=T) model.parms <- X*fix.eff...

I''d like to create scaffolds for each of the tables in my database, but would like to access them below the path: localhost:3000/admin For example localhost:3000/admin/table1 localhost:3000/admin/table2 Where table1 and table2 would be the controller names respectively, calling their own list.rhtml, show.rhtml, edit.rhtml, etc.. Any recommendations on how/where to

...regressions, and 2) compile the slopes of all regression into one vector. Sample input data are: y1<-rnorm(100, mean=0.01, sd=0.001) y2<-rnorm(100, mean=0.1, sd=0.01) x<-(c(10,400)) #I have gotten this far with the loop for (i in 1:100) { #create the linear model for each data set model1<-lm(c(y1[i],y2[i])~x) slope1<-model1$coefficients[2] } How can I compile the slopes from all 100 regressions into one vector? Thanks, Jake [[alternative HTML version deleted]]

Dear R users, I would like to extract df and Mean Sq values. I tried several things (e.g., str(model1), names(model1)) but I can't find a way to extract these values. I also tried to search using RSiteSearch. Any help will be appreciated. Thanks Taka, model1<-aov(dv~iv.1*iv.2*iv.3) summary(model1) Df Sum Sq Mean Sq iv.1 1 3.200 3.200 iv.2...

...lm (binomial errors) and anova is used two compare two competitive models one can use either an "F" or a "Chi" test. R always performs an F test (Although when test="Chi" the test is labeled as Chi, there isn't any difference between F and Chi test) Let us assume model1 and model2, where model2 a simplified version of model1 model1_glm(y~factor1*factor2,binomial) model2_update(model1,~.-factor2) anova(model1,model2,test="F")# will give the same result as anova(model1,model2,test="Chi") -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-...

...e, (this example is just to show the problems in the plot and it doesn't make any sense the way I analyzed). set.seed(3) datos <-data.frame(fac.A=rep(c("bla","Ur2","pel","arb"),each=3), y= c(rnorm(3,sd=0.5),rnorm(9,sd=2))) model1 <- lm(y~fac.A,data=datos) plot(model1,which=5) # plot1 # this plot1 show that level "arb" has less dispersion than the other levels, # But if I do the plot by myself, look: hii <- lm.influence(model1, do.coef = FALSE)$hat s1 <- sqrt(deviance(model1)/df.residual(mo...

...of encomptest breaking when models are created via the ##nlme::lmList() function. ##Required libraries: library(nlme) library(lmtest) library(plyr) ##Create a noisy slope (spoofing real-world data): fakeData<-c(rnorm(10))+seq(0,5,length=10) ##Create a data frame of data to test. The columns ##model1 and model2 contain my basis functions modelFrame<-as.data.frame( list( srcData=fakeData, model1=seq(0,1,length=10), model2=c(rep(0,5),rep(1,5)) ) ) ##Create all data to be fitted allData<-ldply(1:2,function(x){modelFrame$modelFactor<-x;return(modelFrame)}) ##Apply models...

...ian at NSW Department of Commerce, Sydney. I am using R 2.9.1 on Windows XP. This has reference to the package “MCMCpack”. My objective is to select a better model using various alternatives. I have provided here an example code from MCMCpack.pdf. The matrix of Bayes Factors is: model1 model2 model3 model1 1.000 14.08 7.289 model2 0.071 1.00 0.518 model3 0.137 1.93 1.000 > PostProbMod(BF) model1 model2 model3 0.82766865 0.05878317 0.11354819 I need assistance to interpret the results. If I am wrong, please correct me. 1. By looking...

I would like to know how R computes the probability of an event in a logistic model (P(y=1)) from the score s, linear combination of x and beta. I noticed that there are differences (small, less than e-16) between the fitting values automatically computed in the glm procedure by R, and the values "manually" computed by me applying the reverse formula p=e^s/(1+e^s); moreover I noticed

Hello, I am trying to use the multi_search method, but I keep getting type error on nil objects, I send it [Model1,Model2] and it seems as though the class names keep getting clobbered and turn to nil, somewhere along the multi_index area. I tried to trace what was going on, but I got nothing, also, this only happens when there are actually hits(thank god, most of the time). Perhaps some insight? Thank you! --...

If I have a model line = lm(y~x1) and I want to use a for loop to change the number of explanatory variables, how would I do this? So for example I want to store the model objects in a list. model1 = lm(y~x1) model2 = lm(y~x1+x2) model3 = lm(y~x1+x2+x3) model4 = lm(y~x1+x2+x3+x4) model5 = lm(y~x1+x2+x3+x4+x5)... model10. model_function = function(x){ for(i in 1:x) { } If x =1, then the list will only add model1. If x =2, then the list will add both model1 and model2. If x=3, then the list w...

...time with a total of 7,280 observations, 1,720 students nested witin 60 schools. The data set is not balanced. The model was fit using eg.model.1<-lme(math~year, random=~year|schoolid/childid, data=single). When I call the random effects at all levels using EB.1<-data.frame(ranef(eg.model1, level=1)) and EB.2<-data.frame(ranef(eg.model1, level=2)), I get the shrinkage estimators that I expect. That is, I get 2 random effects for each child (1 intercept and 1 slope) and 2 for each school (1 intercept and 1 slope). However, when I call the fitted values using: fitted<-data.fr...

...with R yet, so forgive me if this question is irrelevant. If I try to find the significance of the fixed factor "spikes" in a generalized linear mixed model, with "site" nested within "zone" as a random factor, I compare following two models with the anova function: model1<-lmer(aphids~spikes+(1|zone:site), method="ML", family=quasipoisson) model2<-lmer(aphids~(1|zone:site), method="ML", family=quasipoisson) anova(model1,model2) This gives me a p< 2.2e-16 ***, concluding that "spikes" has a highly significant effect on "ap...

Hi, I have fitted a generalized linear mixed effects model using lmer (library lme4), and the family = quasibinomial. I have tried to obtain a MCMC sample, but on calling mcmcsamp(model1, 1000) I get the following error which I don't understand at all: Error in .local(object, n, verbose, ...) : Update not yet written traceback() delivers: 4: .Call(mer_MCMCsamp, ans, object) 3: .local(object, n, verbose, ...) 2: mcmcsamp(model1, n = 1000, verbose = FALSE) 1: mcmcsamp(model1,...