similar to: randomForest partial dependence plot variable names

I have a question about predictor importances in randomForest. Once I've run randomForest and got my object, I get their importances: rfresult$importance I also get the "standard errors" of the permutation-based importance measure: rfresult$importanceSD I have 2 questions: 1. Because I am dealing with regressions, I am getting an importance object (rfresult$importance) with two

Hi everyone, I have another "Random Forest" package question: - my (presumably incorrect) understanding of the varImpPlot is that it should plot the "% increase in MSE" and "IncNodePurity" exactly as can be found from the "importance" section of the model results. - However, the plot does not, in fact, match the "importance"

I've run the function randomForest with importance=T. All my variables (predictors and the dependent variable) are numeric. rf<-randomForest(formula, data=mydata, importance=T, etc.) my results object "rf" contains predictor importances: rf$importance I am seeing two columns: %IncMSE IncNodePurity V1 -0.01683558 58.10910 V2 0.04000299 71.27579 V3 0.01974636

Dear R Helpers, I need help with a slightly unusual situation in which I am trying to select some columns from a data frame. I know how to use the subset statement with column names as in: x=as.data.frame(matrix(c(1,2,3, 1,2,3, 1,2,2, 1,2,2, 1,1,1),ncol=3,byrow=T)) all.cols<-colnames(x) to.keep<-all.cols[1:2] Kept<-subset(x,select=to.keep) Kept

I am trying to use the package RandomForest performing regression. The variable importance estimates are given as: "%IncMSE" and "IncNodePurity" Can anyone explain me what these refer to and how they are calculated? I found a lot of information on variable importance measures for classification problems, but nothing on regression. Thanks a lot. Mareike

Hi I am having a problem using varImpPlot in randomForest. I get the error message "Error in plot.window(xlim = xlim, ylim = ylim, log = "") : need finite 'xlim' values" When print $importance, several variables have NaN under %IncMSE. There are no NaNs in the original data. Can someone help me figure out what is happening here? Thanks! [[alternative HTML

Hi, I'm experimenting with random forests and want to perform a binary classification task. I've tried some of the sample codes in the help files and things run, but I get a message to the effect 'you don't have very many unique values in the target - are you sure you want to do regression?' (sorry, don't know exact message but r is busy now so can't check). In

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

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

The behaviour of within() with list input changes if you delete 2 or more variables, compared to deleting one: l <- list(x=1, y=2, z=3) within(l, { rm(z) }) #$x #[1] 1 # #$y #[1] 2 within(l, { rm(y) rm(z) }) #$x #[1] 1 # #$y #NULL # #$z #NULL When 2 or more variables are deleted, the list entries are instead set to NULL. Is this intended?

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

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

>>>>> peter dalgaard <pdalgd at gmail.com> >>>>> on Mon, 26 Jun 2017 13:43:28 +0200 writes: > This seems to be due to changes made by Martin Maechler in > 2008. Presumably this fixed something, but it escapes my > memory. Yes: The change set (svn -c46441) also contains the following NEWS entry BUG FIXES o

Hello Given the following data for a data set called airquality. To identify the nature of the objects from the data set airquality example "Ozone" would it be best to use the command is. like is.character(airquality$Ozone) ....... I tried attributes(airquality$Ozone) but it came up null. Would there be a better way to identify these objects. Thanking you in advance for your

Dear all, Is there a less cumbersome way to rename a column by name (as opposed to index) than -- names( X)[ names[ X] == "bob"]<-"sue" ? A semi-related question: how does one get the index of a column by name, something along the lines of col.index( X, "sue") ? Chip Barnaby --------------------------------------------------------- Chip Barnaby

My apologies in advance for my thickness but I can't seem to solve the following, seemingly simple, data manipulation problem: I have a data frame that contains multiple factors and multiple continuous response variables, but duplicates of some factor combinations. The duplicates contain bad data, so I would like to eliminate the duplicates. I would like to retain the entire rows

Hi all. 1) If I have a dataframe with variable names as follow: PC1 PC2 ... PCn and I want to pass only some of them to a function, e.g. glm(resp~from PC1 to PC10, PC15, etc.,...) is there a faster way than simply writing each variable name in the formula? 2) Again, I have a dataframe, say ali.df, with tha following variables: ali1, ali2, ...ali78 I want to sum, for example, ali1+al2+ali7+f rom

I would like to add a column to the airquality dataset that contains the date 1950-01-01 in each row. This method does not appear to work: > attach(airquality) > data1 <- transform(airquality,Date=as.Date("1950-01-01")) Error in data.frame(list(Ozone = c(41L, 36L, 12L, 18L, NA, 28L, 23L, 19L, : arguments imply differing number of rows: 153, 1 I can't decipher what

I think I've gotten my Emacs/Sweave/R system set up correctly, thanks to Vincent and Jim, but I haven't been successful getting my first document produced. I'm trying to use one of Friedrich Leisch's examples, http://www.ci.tuwien.ac.at/~leisch/Sweave/example-1.Snw. I cut and pasted the text into a document sweaveexample.Rnw in Emacs. It seemed to be processed successfully with R:

Hi, all I have a question on mgcv and ns. Now I want to compare the results from glm, gam and ns. Take a simple model y~x for example. glm1 = glm(y~x, data=data1) gam1 = gam(y~s(x), data=data1) ns1 = glm(y~ns(x),data=data1) In order to confirm the result from glm1 is consistent to those from gam1 and ns1, I want to define degree=1 in mgcv and ns. I am wondering if there is somebody can give me