search for: probabl

Hello. I have a quick question. I try to use logit regression, to work out probabilities in the sport event. I have work out probabilities for group of 2 players: p1 - probability, what player1 will beat player2 p2 - probability, what player2 will beat player1 pt - tie probability, p1 <- 1 - p1 - p2; Now i want to work out probabilities for group of 3 players, like: pg1 - probability, what

Dear R:ers, I'm using the svm from the e1071 package to train a model with the option "probabilities = TRUE". I then use "predict" with "probabilities = TRUE" and get the probabilities for the data point belonging to either class. So far all is well. My question is why I get different results each time I train the model, although I use exactly the same data.

Probability <- function(N, f, m, b, x, t) { #N is the number of lymph nodes #f is the fraction of Dendritic cells (in the correct node) that have the antigen #m is the number of time steps #b is the starting position (somewhere in the node or somewhere in the gap between nodes. It is a number between 1 and (x+t)) #x is the number of time steps it takes to traverse the gap #t is the number

Dear R Community- I am a new user of support vector machines for species distribution modeling and am using package e1071 to run svm() and predict.svm(). Briefly, I want to create an svm model for classification of a factor response (species presence or absence) based on climate predictor variables. I have used a training dataset to train the model, and tested it against a validation data set

I am a statistician and I come up to an interesting problem in cryptography. I would like to use R since there are some statistical procedures that I need to use. However, I run into a problem when using the modulus operator %%. I am using R 2.2.1 and when I calculate modulus for large numbers (that I need with my problem) R gives me warnings. For instance if one does: a=1:40; 8^a %% 41 one

Dear R Community, Below is a problem with a simple ggplot2 graph. The code returns the error message below. Error: stat_count() must not be used with a y aesthetic. My code is below and the data is attached as a ?text? file. # Graph of the probabilities library(digest) library(DT) datatable(probability) str(probability) probability$Fertilizer <- as.factor(probability$Fertilizer)

..., ((2), 0.121), ((11), 0.185)] and (3 * 0.024) + (5 * 0.049) + (2 * 0.072) + (1 x 0.121) + (1 x 0.185) ~= 1. My question is: What is the most 'correct' way to cluster these probabilities? In my case the problem is not totally unconstrained. I would like to specify the number of buckets (probably will always wish to use either 5 or 6), so I do not need an algorithm which determines the most appropriate number of buckets given some cost function. I just need to know for a given number of buckets, which entrants go in which buckets and what is the representative probability for each bucket....

> x <- train[,c( 2:18, 20:21, 24, 27:31)] > y <- train$out > > svm.pr <- svm(x, y, probability = TRUE, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) > > pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)], decision.values = TRUE, probability = TRUE) > attr(pred, "decision.values")[1:4,]

I've completed an experiment and want to summarize the results. There are two things I like to create. 1) A simple count of things from the data.frame with predictions 1a) Number of predictions with probability greater than x 1b) Number of predictions with probability greater than x that are really true In SQL, this would be, "Select count(predictions) from

svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) # model fitting svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) # find the probability, but can not find. attr(svm.pred, "probabilities") > attr(svm.pred, "probabilities") 1 0 1 0 0 2 0

  Dear R helpers   Suppose I have two sets of ranges (interest rates) as   Range 1 : (7 – 7.50, 7.50 – 8.50, 8.50 – 10.00) with respective probabilities 0.42, 0.22 and 0.36.     Range II : (11-12, 12-14, 14-21) with respective probabilities 0.14, 0.56 and 0.30 respectively.     My problem is to form the combinations of these ranges in a decreasing order of joint probabilities. It is assumed that

I am using SVM to classify categorical data and I would like the probabilities instead of the classification. ?predict.svm says that its only enabled when you train the model with it enabled, so I did that, but it didn't work. I can't even get it to work with iris. The help file shows that probability = TRUE when training the model, but doesn't show an example. Then I try to

I found you online....... Can you help with empirical probability? Hi Partha. I really liked your email that you sent me, it really inspired me. I have been breezing through the chapters, and doing quite well, You should be a teacher. After all the time my college instructor spent with the class on the slopes etc.... There were very few of us who really understood it. However, after reading

Hello! I have data containing a large number of probabilities (about 60) of nonzero coefficients to predict 10 different independent variables (in 10 different BMA models). i've arranged these probabilities in a matrix like so: (IV1) (IV2) (IV3) ... p(b0) p(b0) p(b0) p(b1) p(b1) p(b1) p(b2) p(b2) p(b2) ... where p(b1) for independent variable 1 is p(b1 !=

Hi All, When carrying out hist(samples,breaks=50,probability=TRUE), the column values are considerably greater than 1, which seams very unreasonable. The plot is attached. I think the column value of the hist plot should correspond to x$counts/sum(x$counts) (x=hist(samples,breaks=50,probability=TRUE)). The size of data is a little bit larger, causing failure of uploading. If you need the

Dear helpers, As the title says, my question is not directly related to R. I find, however, that there are many people who are both knowledgeable and kind in this email list, and so decided to give it a try. I do stochastic simulations. Parameter values used in simulation often come from the observations of the real word phenomena. Parameter values are often given as "rates" (of

https://bugzilla.netfilter.org/show_bug.cgi?id=1294 Bug ID: 1294 Summary: Strange --probability behavior Product: iptables Version: 1.4.x Hardware: x86_64 OS: All Status: NEW Severity: enhancement Priority: P5 Component: iptables Assignee: netfilter-buglog at

I need to refresh my memory on Probability Theory, especially on conditional probability. In particular, I want to solve the following two problems. Can somebody point me some good books on Probability Theory? Thank you! 1. Z=X+Y, where X and Y are independent random variables and their distributions are known. Now, I want to compute E(X | Z = z). 2.Suppose that I have $I \times J$ random number

Dear R users, This is a KDE beginner's question. I have this distribution: > length(cap) [1] 200 > summary(cap) Min. 1st Qu. Median Mean 3rd Qu. Max. 459.9 802.3 991.6 1066.0 1242.0 2382.0 I need to compute the sum of the values times their probability of occurence. The graph is fine, den <- density(cap, from=min(cap), to=max(cap), give.Rkern=F)

Hi All, I'm attempting to use eps-regression or nu-regression SVM to compute probabilities but the predict function applied to an svm model object returns values outside [0, 1]: Variable Data looks like: Present X02 X03 X05 X06 X07 X13 X14 X15 X18 1 0 1634 48 2245.469 -1122.0750 3367.544 11105.013 2017.306 40 23227 2 0 1402 40 2611.519 -811.2500 3422.769 10499.425 1800.475 40 13822 3 0 1379