search for: probabilties

AFAIK, R only has "pnorm" which computes the probability of getting a value smaller or equal to "x" from a normal distribution N[mean,stdev]. For example: R> pnorm(0, 4, 10) [1] 0.3446 means there is 34.46% chance of getting a value equal to or smaller than 0 from a N(4, 10) distribution. What I intend to get is: given the observed value "x", mean, and stdev

Hi Guy's I was wondering if someone could point me in the right direction. dbinom(10,1,0.25) I am using dbinom(10,1,0.25) to calculate the probabilty of 10 judges choosing a certain brand x times. I was wondering how I would go about simulating 1000 trials of each x value ? regards Brendan -- View this message in context:

Hi All, In package e1071 for SVM based classification, one can get a probability measure for each prediction. I like to know what is method that is used for calculating this probability. Is it calculated using logistic link function? Thanks for your help. Regards, Raj

Hi all, I am a freshman of R,but I am interested in it! Those days,I am learning pages on NIST,with url http://www.itl.nist.gov/div898/handbook/eda/section3/probplot.htm, I am meeting a problem about probability plot and I don't know how to plot a data set with R. Could somebody tell me the answer,and a example is the best! I will look forward to your answer. Thank you very much.

Estimate region of highest probabilty density Dear R-community I have data consisting of x and y. To each pair (x,y) a z value (weight) is assigned. With kde2d I can estimate the densities on a regular grid and based on this make a contour plot (not considering the z-values). According to an earlier post in the list I adjusted the kde2d to kde2d.weighted (see code below) to estimate the

Dear R users, i try to calculate the probabilty to survive a given time by using the estimated survival curve by kaplan meier. What is the right way to do that? as far as is see i cannot use the predict-methods from the survival package? library(survival) set.seed(1) time <- cumsum(rexp(1000)/10) status <- rbinom(1000, 1, 0.5) ## kaplan meier estimates fit <- survfit(Surv(time,

Dear list, I would like to plot the empirical cumulative distribution of the time needed by a treatment to attain a certain goal. A number of experiments is run with a strict time limit. In some experiments the goal is attained before the time limit, in other experiments time expires before the goal is attained. The situation is very similar to survivial analysis with censored data. I tryed

Hi, I have a question about logistic regression in R. Suppose I have a small list of proteins P1, P2, P3 that predict a two-class target T, say cancer/noncancer. Lets further say I know that I can build a simple logistic regression model in R model <- glm(T ~ ., data=d.f(Y), family=binomial) (Y is the dataset of the Proteins). This works fine. T is a factored vector with levels cancer,

...observed table, compute the probability of this table conditional on the marginal totals. 2) Rank the possible tables in order of a measure of discrepancy between the table and the null hypothesis of "no association". 3) Locate the observed table, and compute the sum of the probabilties, computed in (1), for this table and more "extreme" tables in the sense of the ranking in (2). The question is: what "measure of discrepancy" is used in 'fisher.test' corresponding to stage (2)? (There are in principle several possibilities, e.g. value of a Pears...

please help me on this ----- Message Text ----- Dear all R users first, sorry for that this question might not be appropriate to ask here. I wanna know theories or techinques aimed at following questions: I have a sample, say,K(at the

Dear All, I am very new to R projects.May be i am wrong in some steps.I have given the code which i tried for drawing 3d surface using persp.I need to label the axes with scales z <- array(topnew2$V2, dim=c(600,2)) x <- 10 * (1:nrow(z)) y <- (1:ncol(z)) persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue", xlab ="fluidlevel", ylab

DeaR list I would like to know if there is a direct method to compute the expected remaining survival time for a subject having survived up to time t. survexp gives me the probabilty for subject S to survive up to day D Thanks -- Anne

Dear R-helpers, I need to compute probabilties of multinomial observations, eg by doing the following: y=sample(1:3,15,1) prob=matrix(runif(45),15) prob=prob/rowSums(prob) diag(prob[,y]) However, my question is whether this is the most efficient way to do this. In the call prob[,y] a whole matrix is computed which seems a bit of a waste. Is...

Hi r-users,   I try to draw histogram (in terms of probabilty) and superimpose with the gamma pdf. Using this code below I can get the plots BUT the y scale for the density is out of scale.  How do I change the y-axis scale to max 1?  Another thing, how I do draw smooth line rather that points?   Nota that my observed data is hume_pos and the fitted data is rgam1.   hist(hume_pos,prob=TRUE)

Dear All, I try to simulate correlated binary data for a clinical research project. Unfortunately, I do not come to grips with bindata(). Consider corr<-data.frame(ID=as.factor(rep(c(1:10), each=5)), task=as.factor(rep(c(1:5),10))) [this format might be more appropriate:] corr2<-data.frame(ID=as.factor(rep(c(1:10),5)), tablet=as.factor(rep(c(1:5),each=10))) Now, I want to

Dear R-helpers, In a data frame I have 100 securities,monthly closing value,from 1995 to present,which I have to 1. Sampling with replacement,make 50 samples of 10 securities each,each sample hence will be a data frame with 10 columns. 2. With uniform probabilty,mark a month from 2000 onwards as a "special" month,t=0. 3. I have to subtract the market index from each column of each

In https://en.wikipedia.org/wiki/Pareto_distribution, it is clear what the parameters are for the pareto distribution: *xmin *the scale parameter and *a* the shape parameter. I am using rmutil to generate random deviates from a pareto distribution. It says in the documentation that the probabilty density of the pareto distribution The Pareto distribution has density f(y) = s (1 + y/(m

HI Guys, I know that this forum is not for homework but I am trying to interpret R output code. I was just wondering if someone might be able to help. I have been given the following. For (X1,X2) distributed bivariate normal with parameters mu1 = 5.8 mu2 = 5.3 sd1 = sd2 = 0.2 and p = 0.6 The r-code and inpit/output are as follows input m <- 5.3 + 0.6*(6.3 - 5.8) s <-

...e data from a complexe stratified survey. The population is divide in 12 regions and a region consist to and urban area and rural one. there to region just with urbain area. stratification variable is a combinaison of region and area type (urban/rural) In rural area, subdivision are sample with probabilties proporionnal to size in population then enuration area are sample in selected division and finally households are selected in those EA. In urban area, EA are directly selected and finally household are selected. to schematise we have: (12 regions) each region is divised in two regions / Ur...

Dear R users, I have been trying to understand what the Weights arguments is doing in the estimation of the parameters when using the Surreg function. I looked through the function's code but I am not sure if I got it or not. For example, if I inclue the Surv function in it: survreg(Surv(vector, status)~1,weights=vector2,dist="Weibull") will it try to maximize the likelihood with