similar to: Combination of Bias and MSE ?

Dear R Friends , I want to name my variables( more than 2 variables in a single plot) within a plot to distinct them from each other, but I cann't. How it is possible? I don't mean x and y axis using xlab or ylab. At the below , it follows some lines, only as an example that you could try please, if it is possible. I really thanks for your attention. Amir library(graphics) y<-

Dear R-experts, Here below my R code with an error message. Can somebody help me to fix this error?? Really appreciate your help. Best, ############################################################ #?MSE CROSSVALIDATION Lasso regression? library(glmnet) ?

No error message shown Please include the error message so that it is not necessary to rerun your code. This might enable someone to see the problem without running the code (e.g. downloading packages, etc.) -- Bert On Sun, Oct 22, 2023 at 1:36?PM varin sacha via R-help <r-help at r-project.org> wrote: > > Dear R-experts, > > Here below my R code with an error message. Can

Dear David, Dear Friends, After any running svm I receive different results of Error estimation of 'svm' using 10-fold cross validation. What is the reason ? It is caused by the algorithm, libsvm , e1071 or something els? Which value can be optimal one ? How much run can reach to the optimality.And finally, what is difference between Error estimation of svm using 10-fold cross validation

Several ways: 1. Read ?randomForest, especially the `Value' section. 2. Look at str(myforest.rf). 3. Look at print.randomForest. If the forest has 100 trees, then the mse and rsq are vectors with 100 elements each, the i-th element being the mse (or rsq) of the forest consisting of the first i trees. So the last element is the mse (or rsq) of the whole forest. HTH, Andy > From: David

?s 20:12 de 23/10/2023, varin sacha via R-help escreveu: > Dear R-experts, > > I really thank you all a lot for your responses. So, here is the error (and warning) messages at the end of my R code. > > Many thanks for your help. > > > Error in UseMethod("predict") : > ? no applicable method for 'predict' applied to an object of class

Dear R-experts, I really thank you all a lot for your responses. So, here is the error (and warning) messages at the end of my R code. Many thanks for your help. Error in UseMethod("predict") : ? no applicable method for 'predict' applied to an object of class "c('matrix', 'array', 'double', 'numeric')" > mean(unlist(lst)) [1] NA

For what it's worth it looks like spm2 is specifically for *spatial* predictive modeling; presumably its version of CV is doing something spatially aware. I agree that glmnet is old and reliable. One might want to use a tidymodels wrapper to create pipelines where you can more easily switch among predictive algorithms (see the `parsnip` package), but otherwise sticking to glmnet

Hi, R guys: I'm using L-BFGS-B method of optim for minimization problem. My function called besselI function which need non-negative parameter and the besselI will overflow if the parameter is too large. So I set the constraint box which is reasonable for my problem. But the point outside the box was test, and I got error. My program and the error follows. This program depends on CircStats

I have been using random forest on a data set with 226 sites and 36 explanatory variables (continuous and categorical). When I use "tune.randomforest" to determine the best value to use in "mtry" there is a fairly consistent and steady decrease in MSE, with the optimum of "mtry" usually equal to 1. Why would that occur, and what does it signify? What I would

I am trying to understand what is the difference between linear and polynomial kernel: linear: u'*v polynomial: (gamma*u'*v + coef0)^degree It would seem that polynomial kernel with gamma = 1; coef0 = 0 and degree = 1 should be identical to linear kernel, however it gives me significantly different results for very simple data set, with linear kernel

Dear useRs, I am working on a series of field experiments (159 in total) carried out in different years in several locations. The cultivars in each experiment are not always the same, in fact they change over time. I would like to get the fitted values and MSE of the individual fits from the following lmList object, so I can use them to fit a mixed model using the fitted values and weight each

Hello - I am using glmnet to generate a model for multiple cohorts i. For each i, I run 5 separate models, each with a different x variable. I want to compare the fit statistic for each i and x combination. When I use auc, the output is in some cases is < .5 (.49). In addition, if I compare mean MSE (with upper and lower bounds) ... there is no difference across my various x variables, but

Dear friends, I have written the following lines in R console wich already exist in pdf file systemfit: data( "GrunfeldGreene" ) library( "plm" ) GGPanel <- plm.data( GrunfeldGreene, c( "firm", "year" ) ) greeneSur <- systemfit( invest ~ value + capital, method = "SUR", + data = GGPanel ) greenSur I have obtained the following incomplete

I have the following function, a MSE calc based on some Multivariate normals: MV.MSE<-function(n,EP,X,S){ (dmvnorm(X,mean=rep(0,2),I+S+EP)-dmvnorm(X,mean=rep(0,2),I+S))^2 + 1/n*(dmvnorm(X,mean=rep(0,2),1+S+EP/2)*det(4*pi*EP)^-.5- (dmvnorm(X,mean=rep(0,2),I+S+EP ))^2)} I can get the MV.MSE for given values of the function e.g

Dear Random Forests gurus, I have a question about R^2 provided by randomForest (for regression). I don't succeed in finding this information. In the help file for randomForest under "Value" it says: rsq: (regression only) - "pseudo R-squared'': 1 - mse / Var(y). Could someone please explain in somewhat more detail how exactly R^2 is calculated? Is "mse"

Dear R Users, Suppose we are interested for generating a new vector ( x ) from a current vector (y) of length 1000 so that x includes the sum of every 5 values in y respectively from the first to the end of length y. The same length of y for x is desired, so that other 4 positions (indices) in x are filled out with NA. For generating such a new vector, I have no idea. I tried in some

I would compile a table of all the words in the dataset (maybe you have it already), then create a list where each component is an integer vector of indices of words. That is, replace words by their positions in the table. >From that sparse form you could create binary features to use with standard classification methods, or for example compute the X'X matrix for linear regression

Hi, I am using nnet to learn transfer functions. For each transfer function I can estimate the best possible Mean Squared Error (MSE). So, rather than trying to grind the MSE to 0, I would like to use abstol to stop training once the best MSE is reached. Can anyone confirm that the abstol parameter in the nnet function is the MSE, or is it the Sum-of-Squares (SSE)? Best regards, Sam.

Dear R Users, For running wavelet functions using dwt( ), modwt( ), and mra( ), a wavelet filter algorithm is applied. For all these functions, default is "la8" and other possibility is "haar". In related documents, another possibilities like as symlet and coiflet ... are not cited. Besides "la8" and "haar", which wavelet filters can be used? Thank