Re: [R] Fit CMARS with R possible (Packages) ?

Hi, Like Bert, I was not able to find anything built in R. It is possible that CMARS has not yet been implemented in R, or may be in development but not yet ready for release. I found several references to the use of MOSEK (https://www.mosek.com) along with MATLAB, but both are commercial

Re: [R] Fit CMARS with R possible (Packages) ?

rseek.org might be a better place to search if you haven't tried t herealready. However, my minimal effort there did not turn up any R software. Maybe you can do better. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." --

[R] Fit CMARS with R possible (Packages) ?

Dear R-experts, We can fit MARS regression using the packages "earth" and/or "mda" or others packages. However, I am wondering if it is possible to fit a CMARS (Conic multivariate adaptive regression splines) using R ? I have googled "conic MARS with R software", I did not get anything, so

Re: [R] Accessing Data Frame

Here is another approach that uses only the default packages: > onecar <- mtcars[10,] > w <- which(duplicated(rbind(mtcars,onecar), fromLast = TRUE)) > w [1] 10 > mtcars.subset <- mtcars[-w,] > > > threecars <- mtcars[c(1,10,15),] > w <- which(duplicated(rbind(mtcars,threecars), fromLast=TRUE)) >

Re: [R-es] Función mosaicplot() con alpha segun valor.

Muchas gracias, Marcelino y Carlos. Con respecto a lo que comentás, Marcelino, me parece que voy a tener que seguir experimentando. De momento, solamente conseguí que mosaicplot haga tantos colores como niveles tiene el eje Y. Quiero decir, con un 1:9, si solo hay tres niveles en Y solo usa 1:3.

Re: [R] How to perform Mixed Design ANOVA on MICE imputed dataset in R?

You might wish to post on the r-sig-mixed-models list, which is specifically devoted to mixed effects models, instead of here. You are more likely to find both interest and expertise there. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and

Re: [R] Error in names(x) <- value: 'names' attribute must be the same length as the vector in gam function

mgcv::s() does not appear to work with objects of class "Date". E.g., > d <- data.frame(date=seq(as.Date("2018-12-20"),len=10,by="week"), response=log2(1:10)%%1) > model <- gam(data=d, response ~ s(date)) Error in names(dat) <- object$term : 'names' attribute [1] must be the same length as the

Re: [R-es] Función mosaicplot() con alpha segun valor.

Hola, Prueba también las opciones que ofrecen los paquetes vcd/vcdExtra (Visualizing Categorial Data). Incluye varias funciones para representar tablas de frecuencias. Mira esta viñeta que detalla el cómo: https://cloud.r-project.org/web/packages/vcdExtra/vignettes/vcd-tutorial.pdf Saludos,

Re: [R] How to perform Mixed Design ANOVA on MICE imputed dataset in R?

Hi Lisa, The package web page at http://stefvanbuuren.github.io/mice/ has all the info you need to get started. Best, Ista On Fri, Jan 4, 2019 at 3:29 AM Lisa Snel wrote: > > Hi all, > > I have a question about performing a Mixed Design ANOVA in R after multiple > imputation using MICE. My

Re: [R-es] Función mosaicplot() con alpha segun valor.

Hola, Juan: Creo que hay varios problemas en tu código. Por un lado, no hay una función "alpha()" en R básico ni un argumento "alpha" en mosaicplot(). Por otro lado "1:3" son tres colores, y tabla1 tiene 9 celdas para colorear. Podrías hacer algo así: mosaicplot(tabla1,

[R-es] Función mosaicplot() con alpha segun valor.

Feliz año a todos! Y al que sepa y tenga tiempo, una duda: Quiero hacer un análisis de distribución de léxico (supongo que da igual eso) mediante mosaicplot(), en particular me interesa que me muestre el peso de la distribución según una proporción que ya conseguí sacar; la de el uso de x termino

[R] Error in gam function in names(x) <- value: 'names' attribute must be the same length as the vector

Outlook Dear R users, I am using the *mgcv package* to model the ozone pollution concentration according to some environmental covariates. The model takes the form : model1