similar to: postResample R² and lm() R²

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

Hi, I've built a linear model using multiple linear regression which leads me a R-squared value of 73.58%. After that, I used this model to predicted some values based on the test data. Now I'm wondering how: 1. can I measure de R-squared value between the predicted(by the model) and real (observed) values.? 2. Measure the RMSE error . Example: suppose my data its below: REAL

Dear R-experts, Here below a toy example with some error messages, especially at the end of the code (Tuning the parameters). Your help to correct my R code would be highly appreciated. ####################################### #libraries library(lattice) library(ggplot2) library(caret) library(randomForest) ?? #Data

Dear r-helpers, I just read in an article by Virtanen et al. (2006) where vegetation-environment relationships are studied by fitting smoothed surfaces on an NMDS ordination using GAMs (Wood 2000). The authors describe, that they used R? as goodness-of-fit statistic, which they compare to the R? of fitted vectors. Calculations were carried out using the package vegan (Oksanen). I know that I can

Hola: tengo un pequeño problemita que es más bien estadístico que específico de R, pero de todos modos espero me puedan dar una mano. Quiero encontrar una forma de reportar la Bondad de ajuste de un modelo No Lineal. Estuve buscando y leyendo y parece que tal cosa no es posible o al menos no es fiable, desde el punto de vista estadístico. El tema es que quiero mandar un trabajo a una

Hola compañeros de la lista. Los molesto con la siguiente duda. En un diseño central compuesto (CCD) con dos factores (V1 y V2) y una variable de respuesta (R), utilizando valores codificados (-1.4142, -1, 0, 1, 1.4182), al aplicar la orden: rsm.segundo.orden <- rsm(R ~ Bloque + SO(V1, V2), data = DATOS.Codificados) Obtengo el siguiente modelo: R = 103.92 -2.16

Dear List, how can I obtain the value of r suqared for a non-linear model? For linear models it can be found in the summary() of the model but for non-linear models I just don't know. Please help! Anna

Buenas a todos, acá estoy yo de nuevo con problemas de loops. Tengo el siguiente problema: un vector de datos (y) y una serie de efectos. El loop lo que intenta es evaluar el R² de modelos incrementando por vuelta una variable efecto. Seria algo así: for(i in 1:x) { modelo=lm(y~efectos[,1:i]) ... codigo para guardar R² y otros por cada vuelta... } apply() puede ir columna a columna pero no

Dear Developers, I am a data science student. I wish to implement Neuro fuzzy Classifier, Adaboost, MLkNN Multi label algorithms for web document classification. I could not find packages and steps for above mention algorithms in mlr <https://mlr-org.github.io/mlr-tutorial/devel/html/multilabel/index.html>. Hence i request you to give your valuable suggestions. In need of your guidance,

Hi, Dirk: On 12/20/2016 10:56 AM, Dirk Eddelbuettel wrote: > On 20 December 2016 at 17:40, Martin Maechler wrote: > | >>>>> Steve Bronder <sbronder at stevebronder.com> > | >>>>> on Tue, 20 Dec 2016 01:34:31 -0500 writes: > | > | > Thanks Henrik this is very helpful! I will try this out on our tests and > | > see if gcDLLs()

Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get ?NA? in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached

>>>>> Steve Bronder <sbronder at stevebronder.com> >>>>> on Tue, 20 Dec 2016 01:34:31 -0500 writes: > Thanks Henrik this is very helpful! I will try this out on our tests and > see if gcDLLs() has a positive effect. > mlr currently has tests broken down by learner type such as classification, > regression, forecasting,

Muchas gracias Jorge! Si la opción del loop con for ya la tenía implementada y me demora bastante por eso quería probar con apply o en este caso sapply, muchas gracias! Saludos!! F Macedo El 03/07/15 a las 23:09, Jorge I Velez escribió: > Hola Fernando, > > Podrias considerar las siguientes opciones: > > R2 <- vector("list", x) > for(i in 1:x){ > modelo

This is a request to increase MAX_NUM_DLLS in Rdynload.c in from 100 to 500. On line 131 of Rdynload.c, changing #define MAX_NUM_DLLS 100 to #define MAX_NUM_DLLS 500 In development of the mlr package, there have been several episodes in the past where we have had to break up unit tests because of the "maximum number of DLLs reached" error. This error has been an inconvenience that

Thanks Henrik this is very helpful! I will try this out on our tests and see if gcDLLs() has a positive effect. mlr currently has tests broken down by learner type such as classification, regression, forecasting, clustering, etc.. There are 83 classifiers alone so even when loading and unloading across learner types we can still hit the MAX_NUM_DLLS error, meaning we'll have to break them

On reason for hitting the MAX_NUM_DLLS (= 100) limit is because some packages don't unload their DLLs when they being unloaded themselves. In other words, there may be left-over DLLs just sitting there doing nothing but occupying space. You can remove these, using: R.utils::gcDLLs() Maybe that will help you get through your tests (as long as you're unloading packages). gcDLLs() will

On 20 December 2016 at 17:40, Martin Maechler wrote: | >>>>> Steve Bronder <sbronder at stevebronder.com> | >>>>> on Tue, 20 Dec 2016 01:34:31 -0500 writes: | | > Thanks Henrik this is very helpful! I will try this out on our tests and | > see if gcDLLs() has a positive effect. | | > mlr currently has tests broken down by learner type

Hi, I'm new to R (and statistics) and my boss has thrown me in the deep-end with the following task: We want to evaluate the impact that sampling size has on our ability to create a robust model, or evaluate how robust the model is to sample size for the purpose of cross-validation i.e. in our current project we have collected a series of independent data at 250 locations, from which

Hi all, I have a dataset with a response variable with three categories (1, 2, 3) and a lot of continuous variables. I'd like to make a MLR with these variables. I've been watching the libraries nnet and zelig for this purpose but I don't understand them well. I use a training sample data to make the MLR. train.set <- sample(1:1000,1000*0.7) I have done this: library(nnet) net

Hi, I had a piece of code which looped over a decimal vector like this: for( i in where ){ thisdata <- subset(herde, herde$mlr >= i) # do stuff with thisdata.. } 'where' is a vector like seq(-1, 1, by=0.1) My problem was: 'nrow(thisdata)' in loop repetition 0.4 was different if 'where' was seq(-1, 1, by=0.1) than when 'where' was seq(-0.8, 1,