search for: fitfunc

...9;) in this code _not_ evaluate in the local environment of function `f' but rather in the global environment (if `f' is called from there)? #---------------------------------------------------------- f <- function () { a <- 1 x <- 1 model <- y ~ a * x fitfunc <- deriv(model[[3]], c("a"), c("a", "x")) call.fitfunc <- c(list(fitfunc), as.name("a"), as.name("x")) call.fitfunc <- as.call(call.fitfunc) curenv <- environment() cat(" eval(call.fitfunc)\n") r1 = eval(c...

...ing `deriv' to provide analytic derivatives. the following fragment gives the idea: #----------------------------------------- f <- function(n = 4) { x <- seq(0, 5, length = n) y <- 2 * exp(-1*x) + 2; y <- rnorm(y,y, 0.01*y) model <- y ~ a * exp (-b*x) + c fitfunc <- deriv(model[[3]], c("a", "b", "c"), c("a", "b", "c", "x")) #"standard" call of nls: res1 <- nls(y ~ fitfunc(a, b, c, x), start = c(a=1, b=1, c=1)) call.fitfunc <- c(list(fitfunc), as.name(&quo...

...t complication when using `deriv' to provide analytic derivatives. the following fragment gives the idea: #----------------------------------------- f <- function(n = 4) { x <- 1:n y <- 2 * exp(-1*x) + 2; y <- rnorm(y,y, 0.01*y) model <- y ~ a * exp (-b*x) + c fitfunc <- deriv(model[[3]], c("a", "b", "c"), c("a", "b", "c", "x")) res1 <- nls(y ~ fitfunc(a, b, c, x), start = c(a=1, b=1, c=1)) call.fitfunc <- c(list(fitfunc), as.name("a"), as.name("b"), as....

binequality looks like just the package I need, specifically for obtaining Gini income coefficients for binned data. I have upgraded to the latest version of R, the latest version of RStudio, and updated all packages. Still I have a persistent problem using fitFunc(). To keep things simple, I am testing things using the example code in the binequality manual, page 6 (or, "?fitFunc"). However, it exhibits the same problems as does my code and another example found elsewhere on the Internet, namely NA results for all fields of interest, as follows:...

...,0,10) # mock dependent variable a = runif(30) # dummy covariate b = runif(30) # another dummy covariate c = runif(30) # an another one x = as.factor(round(runif(30),1))# dummy grouping factor # run exhaustive screening with lmer: bab <- glmulti(y~a*b*c, level = 2, fitfunc = lmer.glmulti, random = "+(1|x)") This works fine. The problem is when I tell it to use the genetic algorithm: babs <- glmulti(y~a*b*c, level = 2, fitfunc = lmer.glmulti, random = "+(1|x)", method = "g") It just keeps running indefinitely and the AIC...

...erating a confidence set of 200 models using clm() and the 'glmulti' R package. This produces these errors: /> model.2.10 <- glmulti(as.factor(dependent) ~ predictor_1*predictor_2*predictor_3*predictor_4*predictor_5*predictor_6*predictor_7*predictor_8*predictor_9, data = database, fitfunc = clm, link = "probit", method = "g", crit = aicc, confsetsize = 200, marginality = TRUE) ... After 670 generations: Best model: as.factor(dependent)~1+predictor_1+predictor_2+predictor_3+predictor_4+predictor_5+predictor_6+predictor_8+predictor_9+predictor_4:predictor_3+predi...

...e the variable "Ozone" into all models with the following code: glm.redefined = function(formula, data, always="", ...)? {glm(as.formula(paste(deparse(formula), always)), data=data, ...)} then run glmulti: output<-glmulti(with(tempData,Temp~Solar.R+Wind),? ? ? ? ? ? ? ? ? fitfunc=glm.redefined,? ? ? ? ? ? ? ? ? level=1,? ? ? ? ? ? ? ? ? crit=aic,? ? ? ? ? ? ? ? ? method="h",? ? ? ? ? ? ? ? ? always= "+Ozone") which will obviously fail once you give it a try... any thoughts on how to identify the best model using glmulti in this fashion? that would fit...

...ulti). To include the variable "Geslacht" in all models: > glm.redefined = function(formula, data, always="", ...) {glm(as.formula(paste(deparse(formula), always)), data=data, ...)} > output<-glmulti(ZBK3~ Onafhankelijk*Zelfconcept*Hyperactief*Populariteit, data=IME, fitfunc=glm.redefined, level=2, crit=aic, method="g", always= "+Geslacht") However, in the output, the variable "Geslacht" is not included... : > print(output) glmulti.analysis Method: g / Fitting: glm.redefined / IC used: aic Level: 2 / Marginality: FALSE >From 100 mod...