search for: nareas

...s regarding how to implement the penalty function so it works over all areas? i.e. that the sum of each fleet's effort is 9 across all areas? The code with areas is below. In the obj function, the ??? denote the start of the penalty function and the place where I need help.... *** nfleets<-2 nareas<-2 M<-1 M<-array(M,dim=c(nfleets,nareas)) N<-1000 cost<-c(30,30) cost<-array(cost,dim=c(nfleets,nareas)) Price<-2 Price<-array(Price,dim=c(nfleets,nareas)) q<-array(0.1,dim=c(nfleets,nareas)) f<-1 f<-array(f,dim=c(nfleets,nareas)) init.eff<-array(3,dim=c(nfleets,...

I wantto run sequential ANOVAs (i.e. type I sums of squares), and trying to getresults including ANOVA tables and associated coefficients for predictive variables(I am using the R 3.4.2 version). I think ANOVA tables look right, but believecoefficients are wrong. Specifically, it looks like that the coefficients arefrom ANOVA with ?marginal? (type III sums of squares). I have tried both lme

(This time with the r-help in the recipients...) Be careful when mixing lme4 and lmerTest together -- lmerTest extends and changes the behavior of various lme4 functions. >From the help page for lme4-anova (?lme4::anova.merMod) > ?anova?: returns the sequential decomposition of the contributions > of fixed-effects terms or, for multiple arguments, model >

dear R-friends, i`ve got a large dataset of vegetation-samples with about 500 variables(=species) in the following format: 1 spec1 1 spec23 1 spec54 1 spec63 2 spec1 2 spec2 2 spec253 2 spec300 2 spec423 3 spec20 3 spec88 3 spec121 3 spec200 3 spec450 . . this means: sample 1 (grassland) with the species (=spec) 1, 23, 54, 63 is it possible to get a following data-structure for further

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply

Is there a way to create a 'bubble plot' in R? For example, if we define the following data frame containing the level of y observed for 5 patients at three time points: time<-c(rep('time 1',5),rep('time 2',5),rep('time 3',5))

Dear all, We are trying to fit a non linear model to dispersal data. It seems that sometimes when the fit of the model of the data is not very good we start getting singular gradient errors. However if we modify slightly the data this won't occurr. We have also tried changing the initial parameter values and the algorithm for fitting in nls but didn't help. So we ended up programming a