similar to: lda predict

Hi all, Easy question for you (which I failed to find the answer to in the FAQ etc). I've recently been forced to switch from linux to windows (currently windows NT), and my usual habit of putting lots of "cat" statements in slow functions to get an idea of the progress rate is no longer useful. Why -- because R waits until the function is completely finished before printing the cat

Hello all, A colleague at work set me the challenge to convert some MATLAB code into R, to see which is faster. We'd seen that benchmark comparing MATLAB 6.5 to R1.90 (and others), and so I thought that I should be able to get roughly comparable speeds. The code has lots of multiplications of matrixes, transposes, and MATLAB's "repmat". I did the code conversion, and R was about

Hello all, A really noddy question for you all: I''m trying without success to do some subhypothesis testing. Using simple anova model, with a toy dataset from a book. I have four factors A,B,C,D, and wish to test mu_C = mu_D. This is what I have tried: > contrasts(infants$group,how.many=1) <- c(0,0,1,-1) > contrasts(infants$group) [,1] A 0 B 0 C 1

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

IHi all, have a dataset with rows as plots and environmental data as columns. I have predicted the values using the following ed.pred<-predict(lda.ed,ed) #lda.ed the model, ed the env. variables used for the prediction plots I am wanting to know the formula used by predict.lda for calculating the posterior probabilities. Can anyone point me in the right direction? Thanks

Hello, Probably a stupidly easy question, but I have done the following in order to make predictions from a fitted glm with new data: my.glm <- glm(lt96~so296[,1:17],family=binomial(link=logit)) p96 <- predict.glm(my.glm,newdata=so293[,1:17],type="response") but I always get the fitted linear predictors from the original model, ie there doesn't seem to be acknowledgement of

Hello All I am having a bit of trouble formatting my HTML with the desired number of digits after the decimal place. Am I doing something wrong/misunderstanding or is it a bug? Looking at the example supplied with ?HTML.data.frame: HTML(iris[1:2,1:2],nsmall=c(3,1),file="") Gives html output that includes the lines: </tr> <tr><td

Hi R guru coders I wrote a bit of code to add a new column onto a "topTable" dataframe. That is a list of genes processed using the limma package. I used a for loop but I kept feeling there was a better way using a more vector oriented approach. I looked at several commands such as "apply", "by" etc but could not find a good way to do it. I have this feeling there

Full_Name: Rob Foxall Version: 2.2.0 OS: Windows XP Submission from: (NULL) (149.155.96.5) I was using multinom from nnet package, when I did something stupid -- I entered in an incorrect factor variable as response. This factor had only one level. Instead of R telling me not to be so dumb, it crashed, clicking on debug coming up with the message "An exception 'Unhandled Win32

using an example from r online help > state <- c("tas", "sa", "qld", "nsw", "nsw", "nt", "wa", "wa", "qld", "vic", "nsw", "vic", "qld", "qld", "sa", "tas", "sa", "nt", "wa", "vic",

Hi, I am wondering if it is possible to get an estimate of standard error of the predicted posterior probability from LDA using lda() from MASS? Logistic regression using glm() would generate a standard error for predicted probability with se.fit=T argument in predict(), so would it make sense to get standard error for posterior probability from lda() and how? Another question about standard

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:

I apologize for the off-topic post, but my Google search did not turn up much and I thought people on this list my have knowledge of this. I am looking for examples of data brushing (i.e. dynmaic linked plots) either on a web site, or in a web-based application, such as an AJAX app. Even better if there is a way to do this in R. Thanks for any help. -Roy M. **********************

Is there a package (other than xgobi which requires an X server) that will do scatterplot brushing? I see a mention in the mail archive of R-orca by Anthony Rossini but it is not in the current list of packages. My OS is Windows XP version 5.1, service pack 2 R version 2.4.1 (2006-12-18) Thanks [[alternative HTML version deleted]]

Hi, I'm currently using the R package e1071 to train naive bayes classifiers and came across a bug: When the posterior probabilities of all classes are small, the result from the predict.naiveBayes function become NaNs. This is an issue with the treatment of the log-transformed probabilities inside the predict.naiveBayes function. Here is an example to demonstrate the problem (you might need

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

Dear All, It is not a typical R question (though I use R for this) but I thought someone will help me. For the list of P values, I have calculated FDR using p.adjust() in R (bioconductor). But my FDR values are same for all the P values. When do we get same FDR values? Does the smallest P values should less than 1/N? (where N is the number of P values) Thanks in advance. Kind regards, Ezhil

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

Hi all, I would very much appreciate any suggestions on any online resources, or even a decent book to purchase with the focus of brushing up on Linux command line tools. The focus is on troubleshooting type commands, adding users from command line and so forth. While I am reasonably comfortable using the command line, I will be the first to admit I am slow on some things and have gaps in

Hello everybody, In order to predict income for different time points, I fitted a linear model with polynomial effects using BMA (bicreg(...)). It works fine, the results are consistent with what we are looking for. Now, we would like to predict income for a future time point t_next and of course draw the prediction interval around the estimated value for this point t_next. I've found the