similar to: Force evaluation of variable when calling partialPlot

Greg, Two thoughts: 1. It might be possible that 'vars' is a reserved word of sorts and if you change the name of your vector RF might be happier 2. A way that works for me is to call importance as follows: sel.imp <- importance(sel.rf, class=NULL, scale=TRUE, type=NULL) and then use the 'names' of the imp data frame to be absolutely clear to RF you are talking about the

Dear R Group I had an observation that in some cases, when I use the randomForest model to create partialPlot in R using the package "randomForest" the y-axis displays values that are more than -1! It is a classification problem that i was trying to address. Any insights as to how the y axis can display value more than -1 for some variables? Am i missing something! Thanks Regards

Hola erreros. A ver si alguien podría decirme qué son los dos ejes del plot que resulta de aplicar partialPlot en un Randomforest. Encuentro que: Partial dependence plot gives a graphical depiction of the marginal effect of a variable on the class probability (classification) or response (regression) que nos indica como varía la VR en función de la variable considerada, manteniendo el

I'm having trouble calling randomForest::partialPlot programmatically. It tries to use name of the (R) variable as the data column name. Example: library(randomForest) iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE, proximity=TRUE) partialPlot(iris.rf, iris, Sepal.Width) # works partialPlot(iris.rf, iris, "Sepal.Width") # works (function(var.name)

Muchas gracias Carlos; ¡tu siempre al pié del cañón! (lo puse el día de reyes a la 1.20h y me contestas a las 2.45h) Una cosa más: si el eje y es la probabilidad ¿por qué va de 0 a 10? En un RF para clasificación me da valores parecidos a los de tu ejemplo, y en otro para regresión, valores de y entre 45 y 55. Para regresión, el último parámetro no puede ser una categoría, como

Hello, I need to draw a partial dependence bar graph. My the my predictor vectors are continous and so is the response variable. Iam using the partialPlot function of the randomForest package. I get a line graph. How can I edit it to get a bar graph instead? (partialPlot(randomForest object ,data-matrix, number of predictor vectors, "Temp")) -- Daudi Jjingo [[alternative HTML

All, I'm trying to set up a function which calls the partialPlot function but am getting an error that I can't seem to solve. Here's a simplified version of the function and error... > pplot <- function(rf,pred.var){partialPlot(x=rf,pred.data=acoust,x.var=pred.var)} > > attach(acoust) > acoust.rf <-

I am trying to run a partialPlot with Random Forest (as I have done many times before). First I run my forest... Cell is a 6 level factor that is the dependent variable - all other variables are predictors, most of these are factors as well. predCell<-randomForest(x=tempdata[-match("Cell",names(tempdata))],y=tempdata$Cell,importance=T) Then I try my partial plot to look at the

Mi matriz de datos inicial estaba muy desbalanceada (5% de la clase minoritaria), por lo que he creado con el algoritmo SMOTE un dataset balanceado con el que he creado el modelo, y luego sobre ese modelo he creado la matriz de confusión con los datos originales. Respecto a lo que me comentas, Carlos, creo que además de todo lo que comentas, que está bien, en micaso es necesario también saber no

Hello there, I am working on an extremely unbalanced two class classification problems. I wanna use "classwt" with "down sampling" together. By checking the rfNews() in R, it looks that classwt is not working yet. Then I looked at the software from Salford. I did not find the down sampling option. I am wondering if you have any experience to deal with this problem. Do you

Hello, I am running randomForest models on a number of species. I would like to be able to automate the printing of dependence plots for the most important variables in each model, but I am unable to figure out how to enter the variable names into my code. I had originally thought to extract them from the $importance matrix after sorting by metric (e.g. %IncMSE), but the importance matrix is n

Help, I've been having a difficult time trying to create 3d partial dependence plots using "rgl". It looks like this question has been asked a couple times, but I'm unable to find a clear answer googling. I've tried creating x, y, and z variables by extracting them from the partialPlot output to no avail. I've seen these plots used several times in articles, and I

No no, eso lo he sacaod, es decir, tengo la matriz de confusión para las OK/NOK, lo que no entiendo es como extraer las conclusiones sobre el modelo, de cara a como afectan las variables. He seguido dos estrategias: 1-Crear arboles de clasificacion con las variables más importantes del random Forest, pero el modelo se empobrece bastante. 2- Sacar los partialPlot, para ver la influencia de cada

Hello, I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases. I've seen that although there are only 32 explanatory variables the best classification performance is reached when

Hi everybody, I used randomForest to regress invertebrates abundances in least impaired river reaches from some environmental parameters. Then I used these models to predict invertebrates abundances in impaired reaches. Now I would like to model the deviation (observation - prediction) with a set of chemical parameters to see if the deviations from predictions could be explained with water

Hi, I'm trying to make a partial plot with package randomForest in R. After I perform my random forest object I type partialPlot(data.rforest, pred.data=act2, x.var=centroid, "C") where data.rforest is my randomforest object, act2 is the original dataset, centroid is one of the predictor and C is one of the classes in my response variable. Whatever predictor or response class I

Dear R-help, This is about randomForest's handling of NA and NaNs in test set data. Currently, if the test set data contains an NA or NaN then predict.randomForest will skip that row in the output. I would like to change that behavior to outputting an NA. Can this be done with flags to randomForest? If not can some sort of wrapper be built to put the NAs back in? thanks, Clayton

Hi all, I have a dataset where each point is assigned to a class A, B, C, or D. Each point is also assigned to a study site. Each study site is coded with a number ranging between 1-100. This information is stored in the vector studySites. I want to run randomForests using stratified sampling, so I chose the option strata = factor(studySites) But I am not sure how to control the number of

Has anyone sucessfully used the maxnodes feature in randomForest? I tried setting it, but when it is non-NULL I always get back a forest in which all trees have size 1. I am using a continuous response (regression). Any help would be appreciated. Thanks. [[alternative HTML version deleted]]

Gracias por la pronta respuesta, pero tras leer la contestación de la gente, sigo sin entender muy bien la explicación. Le responden lo siguiente: "Each point on the partial dependence plot is the average vote percentage in favor of the "Yes trees" class across all observations, given a fixed level of TRI. It's not a probability of correct classification. It has absolutely