similar to: sign of the y axis in partialPlot for randomForest regression

Dear list, I performed a multivariate analysis on freshwater invertebrates data. So I obtained coordinates of my samples on the axes defining the first factorial plane (F1 and F2). I would like to see if the positions on my factorial plan could be linked to levels of impairment ('low' vs 'significant') for several water quality pressure categories and which pressure categories

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)

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 <-

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

Dear R Users, I'm using the randomForest package and would like to generate partial dependence plots, one after another, for a variety of variables: m <- randomForest( s, ... ) varnames <- c( "var1", "var2", "var3", "var4" ) # var1..4 are all in data frame s for( v in varnames ) { partialPlot( x=m, pred.data=s, x.var=v ) } ...but this

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

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

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

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

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

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

Hi all I have been trying to use the randomForest package to model insect species abundance in different habitats and identify the key variables (landscape/climate etc) in determining abundance, which has all worked fine and I get nice variable importance plots etc. Many thanks to everyone on this help forum who has given tips/advice along the way. But the percentage variance explained /pseudo r

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

Dear All, I am using a Procrustes analysis to compare two NMDS ordinations for the same set of sites. One ordination is based on fish data, the other is based on invertebrate data. Ordinations were derived using metaMDS() from the {vegan} library as follows: fish.mds<-metaMDS(fish.data, distance="bray", k=3, trymax=100, wascores=TRUE, trace=TRUE, zero="add")

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 all~ I am interested in clarifying something more conceptual, so I won't be providing any data or code here. >From what I understand, partial dependence plots can help you understand the relative dependence on a variable, and the subsequent values of that variable, after "averaging out the effects" of the other input variables. This is great, but what I am interested in

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