similar to: Column of probabilities

> > Hi all, > I have a vector xm say: xm = c(1,2,3,4,5,5,5,6,6) > > I want to return a vector with the corresponding probabilities based on the > amount of times the numbers occurred. For example, I should get the > following vector for xm: > prob.xm = c(1/9, 1/9, 1/9, 1/9, 3/9, 3/9, 3/9, 2/9, 2/9) > Using prop.table gives: Usage (with table) > prob.xm <-

Hi all, I have 2 columns in a mtrix, one of which is a column of probabilities and the other is simply a vector of integers. I want to sum all the probabilities with the same integer value and put it in a new column. For example, If my matrix is: 0.98 2 0.2 1 0.01 2 0.5 1 0.6 6 Then I should get: 0.98 2 0.99 0.2 1 0.70 0.01 2 0.99 0.5 1 0.70 0.6 6

Hello, I would like to be able to update an existing column in a dataframe, like this... data$score[data$type=="1" & data$year=="2001"]<-data$score * 0.111 data$score[data$type=="1" & data$year=="2002"]<-data$score * 0.222 data$score[data$type=="1" & data$year=="2003"]<-data$score * 0.333 ...but, if possible,

Greetings, I'm having trouble finding a simple way to calculate a weighted histogram where there may be zero raw counts in a given interval. Given equal-length vectors of data 'data' and weights 'w', and breaks (intervals) for the histogram, I calculate a weighted histogram as follows (see MASS's 'truehist' for an unweighted histogram): bin <- cut(data,

I am simulating an ANOVA model and get a strange behavior from the summary function. To be more specific: please run the following code and see for yourself: the summary()[["r.squared"]] values of two identical models are quite different!! ## 3 x 3 ANOVA of two factors x and z on outcome y s.size <- 300 # the sample size p.z <- c(0.25, 0.5, 0.25) # the probabilities of factor z ##

// R 2.3.1 Can someone please explain why this error returns? > y=numeric(100) > x=matrix(runif(16),4,4) > for(i in 2:100) + { + y[i]=which(rmultinom(1, size = 1, prob = x[y[i-1], ])==1) + } Error in rmultinom(n, size, prob) : too few positive probabilities thx much ej

  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

Hello, Frank was nice enough to point me to the val.prob function of the Design library. It creates a beautiful graph that really helps me visualize how well my model is predicting probabilities. By default, there are two lines on the graph 1) fitted logistic calibration curve 2) nonparametric fit using lowess Right now, the nonparametric line doesn't look very good. The

Dear all, I am a newbie in R and could not find help on this problem. I am trying to plot an histogram with probabilities in the y axis. This is the code I am using: #TLC uniform n=30 mi=1; mx=6 nrep=1000 xbar=rep(0,nrep) for (i in 1:nrep) {xbar[i]=mean(runif(n,min=mi,max=mx))} hist(xbar,prob=TRUE,breaks="Sturges",xlim=c(1,6),main=paste("n =",n), xlab="Média",

As usual, a web search ("find statistical mode in R") brought up something that is possibly useful -- Did you try this before posting? If not, please do so in future and let us know what your results were if you subsequently post here. Here's what SO suggested: Mode <- function(x) { ux <- unique(x) ux[which.max(tabulate(match(x, ux)))] } # ergo:

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

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 have a column of 1000 datapoints from the normal distribution with mean 2 and variance 4. How can I get a histogram of these observations with 20 bins with each bin having 50 observations? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear All, I need to write an R function which computes values of Probabilities for the (standard) normal distribution, ¦µ(z) for z > 0 by summing this power series. (We should accumulate terms until the sum doesn't change). I also need to make the function work for vector of values z. The initial fomular is ¦µ(z) = ( 1/ sqrt(2¦Ð) )* ¡Ò e^(-t^2/2)dt (¡Ò is from -¡Þ, z)

Thank you. The problem was not finding the mode but applying it the R way (I have the tendency to loop into each line of the dataframes, which I believe is NOT the R way). I'll try them. Best regards Luigi On Sat, Oct 31, 2020 at 5:40 PM Bert Gunter <bgunter.4567 at gmail.com> wrote: > > As usual, a web search ("find statistical mode in R") brought up something that is

Hello everyone, Can someone tell me exactly how to generate data from a null distribution for the fisher exact test? I know I have to use the hypergrometric but exactly what commands do I use? Jim [[alternative HTML version deleted]]

hello, I want to simulate 200 times the mean of a joint probability (y1) and 200 times the mean of another joint distribution (y2), that is I'm expecting to get 200 means of y1 and 200 means of y2. y1 and y2 are probabilities that I calculate from the marginal prob. (z1 and z2 respectively) multiple by the conditional prob. (x1 and x2 respectively), which I generaterd from the binomial

Hi all, I should have given a better explanation of my problem. Here it is. I extracted from my code the bit that gives the error. Place this in a file called test.c #include <math.h> #include <R.h> #include <Rmath.h> #include <float.h> #include <R_ext/Print.h> int main(){ double prob[3] = {0.0, 0.0, 0.0}; double prob_tot = 0.; prob[0] = 0.3*dnorm(2, 0,

Dear all, I have a questions regarding using the c function rmultinom.c. I got the following error message "rbinom: probability sum should be 1, but is 0.999264" Which is thrown by: if(fabs((double)(p_tot - 1.)) > 1e-7) MATHLIB_ERROR(_("rbinom: probability sum should be 1, but is %g"), (double) p_tot); I understand my probabilities do not sum to one close enough. I

Dear r users, I have 4 variables x1,x2,x3,x4 and each one has two levels, for example Y and N. For x1: prob(Y)=0.6, prob(N)=0.4; For x2: prob(Y)=0.5, prob(N)=0.5; For x3: prob(Y)=0.8, prob(N)=0.2; For x4: prob(Y)=0.9, prob(N)=0.1; Therefore, the sample space for (x1, x2, x3, x4)={YYYY, YYYN, YYNY,......} (16 possible combination) and the corresponding probabilities are {(0.6)(0.5)(0.8)(0.9),