similar to: optimize weights for a weighted average?

Hi, I'm trying to solve the following system of nonlinear equations P1 - F2 = x[1] + (1/2) * x[3] * x[1]^2 P2 - F2 = x[2] + (1/2) * x [3] * x[2]^2 F1 - F2 = -(1/2) * x[1] - (1/2) * x[2] + (1/8) * x [3] * (x[1] + x[2])^2 B1 - F2 = (1/4) * x[1] - (1/4) * x[2] + (1/16) * x[3] * (x[1] - x[2])^2 B2 - F2 = (1/4) * x[1] + (1/4) * x[2] + (1/16) * x[3] * (x[1]

Hello I have a table describing butterfly range traits. It is composed of three columns as below Species name range size (X) latitude of range midpoint (Y) There are 11 kinds of butterflies. Each has its range size, and the latitude of each range midpoint ranges from 9 to 19. I would like to have the average range size of every degree of latitude. For example, the average range

Thank you for reading. I am trying to get sphericity values, and I understood I need to use mlm, but how do I implement a nested within subject design in mlm? I already read the R newsletter, fox chapter appendix, EZanova, and whatever I could find online. My original ANOVA anova(aov(resp ~ sucrose*citral, random =~1 | subject, data = p12bl, subset = exps==1)) Or anova(aov(resp ~

Hi, I have 2 huge datasets - May and Jun - a miniscule sample of one is given below. I am trying to do 2 things with these datasets. I need to verify if the weighted average of variable A for a Reason in Jun is same/different from the same for May. To do this I am first computing the weighted average for each SubReason using a function I wrote. Where I need help is applying the function on both

I am attempting to optimize a regression model's parameters to meet a specific target for the sum of positive errors over sum of the dependent variable (minErr below). I see two courses of action , 1) estimate a linear model then iteratively reduce the regressors to achieve the desired positive error threshold (naturally the regressors and predicted values are biased - but this is

hi thierry thanks for your reply yes, you are right, your solution is more straightforward best Da: "Thierry Onkelinx" <thierry.onkelinx at inbo.be> A: "Massimo Bressan" <massimo.bressan at arpa.veneto.it> Cc: "r-help" <r-help at r-project.org> Inviato: Gioved?, 9 novembre 2017 15:17:31 Oggetto: Re: [R] weighted average grouped by

Dear R Users, I have looked for a solution to the following problem and I have not been able to find it on the archive, through Google or in the R documentation. I have a data frame, say df, which has 4 variables, one of which I would like to use as a grouping variable (g), another one that I would like to use for my weights (w) The other two variables are variables (x1 and x2) for which I would

Hello, Using base R only, the following seems to do what you want. with(mydf, ave(speed, date_time, type, FUN = weighted.mean, w = n_vehicles)) Hope this helps, Rui Barradas Em 09-11-2017 13:16, Massimo Bressan escreveu: > Hello > > an update about my question: I worked out the following solution (with the package "dplyr") > > library(dplyr) > > mydf%>% >

Dear Massimo, It seems straightforward to use weighted.mean() in a dplyr context library(dplyr) mydf %>% group_by(date_time, type) %>% summarise(vel = weighted.mean(speed, n_vehicles)) Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team

Hi, I'm having a problem getting the panel.average function to work as I expect it to in a lattice plot. I wish to draw lines between the averages of groups of y-values at specific x-values. I have created a dataset below which is similar to my real data. I also show an example of using panel.loess in place of panel.average; it performs in a manner similar to what I want panel.average to do

Hi all I want to construct a generalised nonlinear model (binomial family) using gnm, of the form: Response = a + b variable1 + c variable2 + d variable3 - d b variable4 - d c variable5, with the parameters b, c, and d appearing more than once. Hence, I think I need to use a custom nonlin function with gnm. One of my predictor variables is categorical, so I have created a dummy variable for

Dear R useRs, i try to compute the posterior mean for the parameters omega and beta for the following posterior density. I have simulated data where i know that the true values of omega=12 and beta=0.01. With the function postMeanOmega and postMeanBeta i wanted to compute the mean values of omega and beta by numerical integration, but instead of omega=12 and beta=0.01 i get omega=11.49574 and

hello, how i cant to charge in form dynamic a checkbutton, try to do it with a vector be charged automaticamente but not works, for example library(tcltk) tt<-tktoplevel() f<-tkframe(tt) tkpack(f) i<-2 if (i==1) {b1<-tkcheckbutton (f,text="b1",variable="b1",relief="raised");tkpack(b1);print(tclvalue ("b1"))}else if (i==2) {b1<-tkcheckbutton

I sent this in with an old version, but it's in latest version as well. The fix is simple. In the summary.table function, the parameter is calculated incorrectly for a test of independence among all cells when the table is more than 2-way table. Example: Consider X: > X a b c 1 A1 B2 C1 2 A3 BA3 C2 3 A2 B1 C4 4 A1 B2 C3 5 A3 BA3 C2 6 A1 BA3 C1 7 A2 BA3 C2 8 A1

Dear List-Members, since 3 weeks I have been heavily working on reproducing the results of an economic paper. The method there uses the numerical solution of an integral within nonlinear least squares. Within the integrand there is also some parameter to estimate. Is that in the end possible to implement in R [Originally it was done in GAUSS]? I'm nearly into giving up. I constucted an

Hi I searched in the list and only found questions without answers e.g. http://finzi.psych.upenn.edu/R/Rhelp02a/archive/19955.html : Is there a way to get the same results with lme as with aov with Error()? Can anybody reproduce the following results with lme: id<-c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5)

7 Jan 09 Hello, I am using R version 2.7.0 in a Windows XP context. I am also using the glmm.admb package (created by Dave Fournier, Hans Skaug, and Anders Nielson) to run mixed-effects negative binomial models. To the best of my knowledge and ability, I have searched and studied the R-help, R-sig-mixed models, and ADMB NBMM for R (through Otter Research Ltd) list servs; R help

I have a complex function that I want to maximize (I have multiplied this function by -1 so that it becomes a minimization problem in the code below). This function has two equality constraints. I get the programs to run but the answer isn't correct because, when it does converge, at least one of the constraints is violated. Any suggestions? Code below Violated constraint (an easy check):

Commit-ID: bae97a14a3dab910cd57c1d36003b18a869f788f Gitweb: http://git.kernel.org/?p=libs/klibc/klibc.git;a=commit;h=bae97a14a3dab910cd57c1d36003b18a869f788f Author: Martijn Dekker <martijn at inlv.org> AuthorDate: Wed, 7 Mar 2018 17:32:29 +0000 Committer: Ben Hutchings <ben at decadent.org.uk> CommitDate: Fri, 25 Jan 2019 02:57:21 +0000 [klibc] builtin: Greater resolution

For an application, I have formulas defined in terms of a left Kronecker product of matrices, A,B, meaning A \otimes_L B = {A * B[i,j]} -- matrix on the left multiplies each element on the right. The standard kronecker() function is the right Kronecker product, A \otimes_R B = {A[i,j] * B} -- matrix on the right multiplies each element on the left. The example below shows the result of