similar to: standard error for lda()

With predict.lda the posterior probabilities only relate to the existing Class definitions. This is fine for Class definitions like gender but it is a problem when new data does not necessarily belong to an existing Class. Is there a classification method that gives posterior probabilities for Class membership and does not assume the new data must belong to one of the existing Classes? A new

Hi, I am using Partek for LDA analysis. For a binary response variable, it generates 2 discriminant functions, one for each of the 2 levels of the response variable. And I can simply calculate 2 discriminant scores (say d1 and d2) for each sampples using the 2 discriminant functions, then I can use the following formula to compute the posterior probability for the sample:

Hi. I asked a question about lda() and got some answers. However, one question remains (which is not independent of the earlier ones): What output does lda() produce which I can use to compute the posteriors? I know predict(lda())$posterior will give me precisely the posteriors, but suppose I'd like to compute them myself, outside of R. So far, I have not been able to use

Hi, it seems that the lda function in MASS library doesn''t give out the constant for the linear discriminant function under the situation that we don''t use standardized variable, anyone knows how to obtain the constant in order to construct the linear discriminant function? I understand that if the priors are set to be 1/2, the threshold of the discriminant score used to

Hello, I asked this as part of a previous message, but never really figured out a usable solution. So this is a second attempt. I have an process containing an SVM. The end result is the probability that the class is true. That result is added back to the original data. So I wind up with a data.frame that looks like this label,v1,v2,v3,prob_true What I want to do is measure how accurate

Hi all, I appreciate the help this list has given me before. I have a question which has been perplexing me. I have been working on doing a Bayesian calculating inserting studies sequentially after using a non-informative prior to get a meta-analysis type result. I created a function using three iterations of this, my code is below. I insert prior mean and precision (I add precision manually

While you are editing that, you might change its name from 'stderr' to standardError (or standard_error, etc.) so as not to conflict with base::stderr(). Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Sep 13, 2016 at 8:55 AM, Martin Maechler <maechler at stat.math.ethz.ch > wrote: > >>>>> Suharto Anggono Suharto Anggono via R-devel <r-devel at

Hello developers folks! First, congratulations for the wonderful work with R. For science, barplots with error bars are very important. We were wondering that is so easy to use the boxplot function: boxplot(Spores~treatment, col=treatment_colors) But there is no such function for barplots with standard deviation or standard error. It becomes a "journey" to plot a simple graph (e.g.

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

Hello, I am looking for a way to obtain standard errors for emprirical Bayes estimates of a model fitted with lmer (like the ones plotted on page 14 of the document available at http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/2b/b3/94.pdf). Harold Doran mentioned (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) that the posterior modes' variances

Dear R-helpers, I have a model created by lda, and I would like to use this model to make predictions for new or old data. The catch is, I want to do this without using the "predict" function, i.e. only using information directly from the foo.lda object to create my posterior probabilities. In anticipation of likely responses, I will be brushing up my lda knowledge using the

Greetings all, I'm a newcomer to Bayesian stats, and I'm trying to calculate the Deviance Information Criterion "by hand" from some MCMC output. However, having consulted several sources, I am left confused as to the exact terms to use. The most common formula can be written as DIC = 2*Mean(Deviance over the whole sampled posterior distribution) - Deviance(Mean

Hello, for a frequency modelling problem I want to combine expert knowledge with incoming real-life data (which is not available up to now). The frequency has to be modelled with a poisson distribution. The parameter lambda has to be normal distributed (for certain reasons we did not NOT choose gamma althoug it would make everything easier). I've started with the subsequent two functions to

I am attempting to use optim to solve a neural network problem. I would like to optimize coefficients that are currently stored in a matrix Y=270 x 1 X= 27- x 14 b1= 10x14 b2= 11x1 V= 10 x 14 set of prior variances. I have the following function: posterior.mode1=function(y,X,b_0,b2,V) { log.like=function(b1) { a_g=compute(b1) z_g=tanh(a_g); z_g=cbind(1,z_g)

I have a variable that looks like this: > print(pred$posterior) o x 1 2.356964e-03 9.976430e-01 2 8.988153e-01 1.011847e-01 3 9.466137e-01 5.338627e-02 4 2.731429e-11 1.000000e+00 Now what I want to do is to access "o" and "x" How come this approach fail? > print(pred$posterior$o) or >

This question is slightly off topic, but I'll use R to try and make it as relevant as possible. I'm working on a problem where I want to compare estimates from a posterior distribution with a uniform prior with those obtained from a frequentist approach. Under these conditions the estimates should agree. Specifically, I am asking the question, "What is the probability that the true

Dear R-users I am looking for a help in using the "deal" package. I followed the manual and paper from the author's web site to learn it, as shown below, but I could not figure out how to access the local and posterior probability of the nodes in the constructed network. library(deal) data(ksl) ksl.nw <- network(ksl) ksl.prior <- jointprior(ksl.nw) banlist <-

Dear R users, I'm trying to carry out monte carlo integration of a posterior density function which is the product of a normal and a gamma distribution. The problem I have is that the density function always returns 0. How can I solve this problem? Here is my code #generate data x1 <- runif(100, min = -10, max = 10) y <- 2 * x1^2 + rnorm(100) # # # # # # # # Model 0 # # # # # # #

All, I have several years of longitudinal test scores for students (many who switch schools at various points in time). I am using a mixed-effects model with crossed random effects to model student trajectories. The model includes time at level 1 and students crossed with schools at level 2. When I run the model I get the posterior variances on the intercepts and slopes for students and schools,

Dear R list, I have written some code to produce several wireframe plots in a panel. They look good, but when I try to create a pdf, many (but not all) of the details I have specified are not reproduced. For example, the line width I have specified is not reproduced, and neither are the font sizes for the axis labels. I'm an R novice, so I could really use some guidance. Here is the code I am