similar to: confidence interval in rpart

hi, I am using rpart with a continuous response variable. Is there a way to extract 95% confidence intervals for the predicted mean at each node? cheers. -- View this message in context: http://www.nabble.com/rpart-confidence-intervals--tp18601587p18601587.html Sent from the R help mailing list archive at Nabble.com.

Hello, When considering linear function where dependent variable is a function of lags of dependent variables ( co-variates are kind of auto regressive).Question is .....Can i take entire data set for rpart . Then split data into training and testing and validate the model for testing data.In testing data previously predicted value will be the lag of dependent variable for next prediction.

Hello all, I've come across an online posting http://www.biostat.wustl.edu/archives/html/s-news/2001-10/msg00119.html that described how to get confidence intervals for predicted values from predict.glm. These instructions were meant for S-Plus. Yet, it generally seems to work with R too, but I am encountering some problems. I am explaining my procedure in the following and would be most

I am trying to recreate an analysis that has been done by another group (in SAS I believe). I'm stuck on one part, I think because my stats knowledge is lacking, and while it's OT, I'm hoping someone here can help. Given this dataframe; foo*<-*structure(list(OBS = structure(1:18, .Label = c("1", "2", "3", "4", "5",

Hello, I?ve got a statistical problem that I hope you can help me with. It doesn?t have to do directly with R, so if there?s another forum which would suit better, please tell me! Now here?s the problem: I want to derive confidence intervals for a variable X, which is - given the descriptive statistics - obviously negatively skewed and leptokurtic (i.e. peaked). My aim is to make a statement

I have switched from The Unscrambler to R for pls regression analysis and have been able to calculate scores, coefficients, RMSEP from a large number of PLS1 and PLS2 models. The ultimate goal is to use these models for predicting unknown samples, which again is straight-forward with the built-in predict() function. However, I?m struggling with prediction uncertainty (i.e. confidence intervals) on

Dear all, I am interested to calculate confidence interval for fitted values in general for non-linear regressions. Lets say we have y=f(x1,x2,..xN) where f() is a non-linear regression. I would like to calculate a confidence interval for new prediction f(a1,..,aN). I am aware of techniques for calculating confidence intervals for coeffiecients in specific non-linear regressions and with them

Dear all I have tried to estimate the confidence intervals for predicted values of a nonlinear model fitted with nls. The function predict gives the predicted values and the lower and upper limits of the prediction, when the class of the object is lm or glm. When the object is derived from nls, the function predict (or predict.nls) gives only the predicted values. The se.fit and interval aguments

Hello, I am running a model with four categories and want predicted probabilities in each category. Now for this example I wont give a counterfactual just the training data is fine but is there anyway to get a confidence interval around the predicted probabilities in each group? I have tried but it gives me probabilities and I have used interval="confidence", level=.095 and then interval

Dear R users, I know cross-validation does not work in rpart with user defined split functions. As Terry Therneau suggested, one can use the xpred.rpart function and then summarize the matrix of the predicted values into a single "goodness" value. I need only a confirmation: set for example xval=10, if I correctly understood a single column of the matrix obatined by xpred.rpart gives

Based on some recent r-help discussions, I have been trying out plotting confidence intervals using predict and matplot. Matplot appeared to not be plotting the linear regression when using the default column names generated by read.table (V1, V2, etc). On further inspection, the error seemed to be with predict and vector names (V1, V2) rather than with matplot. I was using some textbook

Hi, I want to create upper and lower 95% confidence intervals for a p-p plot of an empirical distribution with a theoretical gamma distribution. This is my code: x<-rgamma(100,shape=2, rate=1) # empirical data fitdistr(x,"gamma") # fit a gamma distribution dist<-pgamma(x,shape=1.9884256 ,rate=0.8765314 ) # fitted distribution, using the loglikelihood estimated parameters

R-helpers, I am using the package "drc" to fit a 4 parameter logistic model. When I use the predict function to get prediction on a new dataset, I am not getting the requested confidence or prediction intervals. Any idea what is going on? Here is code to reproduce the problem: --- library(drc) # Fit model to existing dataset in package spinach.model <- drm(SLOPE~DOSE, data =

R-helpers, I recently submitted a help request for the predict.drm function found in the drc package. I am still having issues with the function and I am submitting reproducible code hoping that somebody can help me figure out what is going on. -------- library(drc) # Fit a 4 parameter logistic model to ryegrass dataset fit <- drm(rootl ~ conc, data = ryegrass, fct = LL.4()) summary(fit) #

Dear all, I have two highly correlated variables (y and x), and both of them depend on a third variable (A, for Area). Multiple regression (y=a+(b*x)+(c*A)) would have collinearity problems, so I decided to do a partial regression to predict y. I did it this way: - I regressed y to A, and calculated the residuals (e_y) (reg1) - I regressed x to A, and calculated the residuals (e_x) (reg2)

Hello all, I'm trying to establish some confidence intervals on predictions I am making using >predict(nls(...)) and predict.nls (unfortunately) does not utilize the se.fit option. A little more background is that I am trying to match the output with older SAS routines to maintain consistency. Because predict.nls does not provide se's for individual predictions, I have been using a

Greetings and gratitude, I have 19 persons in each group, and each person walks at 3 different speeds. I can do a nice p value and f value with summary command Please help me learn how to report the 95% confidence interval for this anova? This is easier for my fourth, separate condition: preferred walking speed, where there is no speed factor in the model: aov(variable~group) although I compute

I am studying statistics using R and a book "Understandable Statistics", by Brase and Brase. The book has two worked examples for calculating a confidence interval around a predicted value from a linear model. The answers to the two examples in the book differ from those I get from R. The regression line, the standard error, and the predicted value in R and the book all agree for the

Hi Everyone, This example code results in R 'crashing'; that is the R application closes with no warnings or error messages. #----------------------- myD <- read.table(stdin(), header=TRUE, nrows=20) Broth Salt pH Temp N Y Growth 1 310 9.0 2.92 10 90.0 NA 0 2 615 6.0 7.82 30 1.0 2 1 3 217 2.0 7.34 10 7.0 8

Dear All, I would like to be able to estimate confidence intervals for a linear combination of coefficients for a GLS model. I am familiar with John Foxton's helpful paper on Time Series Regression and Generalised Least Squares (GLS) and have learnt a bit about the gls function. I have downloaded the gmodels package so I can use the estimable function. The estimable function is very