similar to: Random Forest Error for Factor to Character column

Hello, I have two Random Forest (RF) related questions. 1. How do I view the classifications for the detail data of my training data (aka trainset) that I used to build the model? I know there is an object called predicted which I believe is a vector. To view the detail for my testset I use the below-bind the columns together. I was trying to do something similar for my trainset but

Hi everyone, I'm involved in a discussion with a colleague. He suggested a sample design for a finite-sized process that (to all intents and purposes) involves tossing a coin and examining the unit if the coin shows Heads. I should emphasize that we're both approaching the problem from a design-based sampling theory point of view. So I have no argument about the appropriateness of the

I am wondering how to obtain the model/equation at each level automatically in a regression model with a few factors without looking at summary of the lm model. For example, consider lm.factors <- lm(y ~ x1 + factor(x2)*factor(x3)+x4*factor(x5)) The coefficients of lm.factors in summary(lm.factors) might be complicated. I would like to have the equation at each level from lm.factor. Could you

This is part of my program. I am getting an error, that I cannot figure out, any help would very much appreciated, thanks. # subset variables arc <- arc[,c("SNAP", "code", "ncode", "var", "n_total")] Error in `[.data.frame`(arc, , c("SNAP", "code", "ncode", : undefined columns selected arc$N_eff <-

Dear all, I am facing the following problem in optimization: w = (d, o1, ..., op, m1, ..., mq) is a 1 + p + q vector I want to determine: w = argmin (a - d(w))' A (a - d(w)) where a is a 1xK marix, A is the covariance matrix of vector a, d(w) is a 1xK vector which parameters are functions of parameters d, o1 .. op, m1 .. mq. Is there some function to solve this problem easily? I know

The R "cut" command is entirely different from the UNIX "cut" command. The latter retains selected fields in a line of text. I can do that kind of manipulation using sub() or gsub(), but it is tedious. I assume there is an R function that will do this, but I don't know its name. Can you tell me? I'm also guessing that there is a web page somewhere that will tell

Hi, How i can install the package "adapt" in some version of R for mac? i try in 2.13, 2.9,2.7 and other previous versions... and nothing happens. and another question: There are some packages that do the same but that it is implemented for mac? (calculate integrals in 2 or more dimmensions). help me please, it's for an important work. greetings. -- Matías Hernán Ramírez

Hello. I'm trying to use the functions pandit and treebase. They are in the package apTreeshape. Once I've loaded the package, R responses: - no function pandit/treebase. Somebody knows why or what is the reason? Thanks, Arnau. ------------------------------------------------------------ Arnau Mir Torres Edifici A. Turmeda Campus UIB Ctra. Valldemossa, km. 7,5 07122 Palma de Mca.

Hi, I have three matrices (X,Y,P) with the same dimension. The X,Y grid is regular and I want to perform linear interpolation to pick out certain points. In matlab appropriate call is something like Pout=interp2(X,Y,P,Xout,Yout, method="linear") where Xout and Yout are the locations where I want the Pout data (typically a different grid). (Scipy has this routine in

Dear all, I new to r and I would like your help. I want to explore the patterns (unimodal, monotonically increased/decreased) of species richness~altitude using GAM in R. Although I run the gam function in mgcv package I do not know how to manually define knots and degrees of freedom. Any help would be greatly appreciated. Spyros -- View this message in context:

I'm fitting a regression model for censored data with three categorical predictors, say A, B, C. My final model based on the survreg function is Surv(..) ~ A*(B+C). I know the three-way group mean estimates can be computed using the predict function. But is there any way to obtain two-way group mean estimates, say estimated group mean for (A1, B1)-group? The sample group means don't

Dear community, I uploaded an excel with read.xls. My xls file actually have a column which is an id, ("plot" is the id) : plot height area 34 7.6 5.4 85 3.2 4.1 89 5.4 8.4 121 6.7 6.2 ... 1325 2.1 1.5 However R uses another id, this way: r id plot height area 1 34 7.6 5.4 2 85 3.2 4.1 3 89 5.4 8.4 4 121

I have a vector with lots of different numbers. I need to make a graph showing the Uniform Distribution of the figures. I have created a graph showing all the different values, but now want individual Gaussian Kernel round each point. This is what I have but each time it comes up with an error as I have just based it on the Normal Distribution, but I'm not sure what I need to change to make it

thanks to your guys help I am closer to solving my problem but I have some small problem. So let's say I start with >data number day hour 1 17 10 2 17 11 3 17 6 4 18 4 5 18 10 6 19 8 7 19 8 I want to split to odd days, which I am able to do, I call this object frames, which looks like: > frames $`1` c1 day1 hour1 1 1 17 10 2 2 17 11 3 3 17 6 $`2` c1 day1

I'm trying to find the variance of various outputs in a matrix: for(l in 2:vl){ for(o in 1:(l-1)){ # Make sure the inputs are for the matrix "m" input3=rownames(v)[o] input4=colnames(v)[l] r=t[(t$Rec1==input3 & t$Rec2==input4),output] if(length(r)==0){ r=t[(t$Rec1==input4 & t$Rec2==input3),output] } v[l,o]=var(q,na.rm=TRUE)

Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).

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Hi, I am attempting to fit a ARMA/GARCH regression model without success. ### ARIMA-GARCH model with regressor ### ### Time series data: A multivariate data set. cov.ts.dq = cov.ts[1:4,"dq1"][!is.na(cov.ts[,"dq1"])] cov.ts.day = ts.intersect(dq = diff(q.ts), day = lag(q.ts, -1)) ### The following R scripts work: (summary(no.day.fitr <- garchFit(dq ~ arma(0,3) +

Hi everybody I couldn't find the solution to what must be quite a simple problem. Maybe you can help? treatment session period stage wage_accepted market 1 1 1 1 1 25 public 2 1 1 1 1 19 privat 3 1 1 1 1 15 public 4 1

Dear R users, I'd like to create a block diagonal matrix with each rows in a matrix. Here is a simple example. (In fact, the matrix is big) x <- matrix(1:20, 4,5) > x [,1] [,2] [,3] [,4] [,5] [1,] 1 5 9 13 17 [2,] 2 6 10 14 18 [3,] 3 7 11 15 19 [4,] 4 8 12 16 20 With each rows in matrix x, I'd like to make the matrix below.