similar to: Accuracy of dnorm (PR#8586)

Howdee, One of my student spotted something I can't explain: a probability >1 vs a normal probability density function. > dnorm(x=1, mean=1, sd=0.4) [1] 0.9973557 > dnorm(x=1, mean=1, sd=0.39) [1] 1.022929 > dnorm(x=1, mean=1, sd=0.3) [1] 1.329808 > dnorm(x=1, mean=1, sd=0.1) [1] 3.989423 > dnorm(x=1, mean=1, sd=0.01) [1] 39.89423 > dnorm(x=1, mean=1, sd=0.001) [1]

Hello all, I have a question about basic statistics. Given a PDF value of 0.328161, how can I find out the value of -0.625 in R? It is like reversing the dnorm function but I do not know how to do it in R. > pdf.xb <- dnorm(-0.625) > pdf.xb [1] 0.328161 > qnorm(pdf.xb) [1] -0.444997 > pnorm(pdf.xb) [1] 0.628605 Many thanks, Edwin -- View this message in context:

I'm trying to provide different parameters to the integrate function for various probability functions. I'm using dnorm as the simplest example here. For instance integrate(dnorm, -1.96, 1.96) produces the correct answer for a normal distribution with mean 0 and standard deviation 1. I've tried two ways to use mean=2.0 and standard deviation 1, but with no luck. The examples follow.

Dear R-users Idea: Plot a dnorm line using specific mean/sd to complete a histogram (skewed). xs:range of y-values, ys: dnorm function Problem: I expected to multiply the ys function with the sample size (n=250-300). I was wondering about a factor between 12'000 and 30'000 to match the size of the dnorm line with the specific histogram. Thanks Sibylle hist(Biotree[Ld,]$Height2008,

Dear R community, I was trying to estimate density at point zero of a multivariate distribution (9 dimensions) and for this I was using a multinormal approximation and the function dmvnorm , gtools package. To have a sense of the error I tried to look the mismatch between a unidimensional version of my distribution and estimate density at point zero with function density, dmvnorm and dnorm. At

Hi All, Why would calls to dnorm and dmvnorm return values that are above 1? For example, > dnorm(0.00003,mean=0, sd=0.1) [1] 3.989423 This is happening on two different installations of R that I have. Thank you. Hailu [[alternative HTML version deleted]]

I am attempting to wrap the histogram function in my own custom function, so that I can quickly generate some standard plots. A part of what I want to do is to draw a normal curve over the histogram: > x <- rnorm(1000) > hist(x, freq=F) > curve(dnorm(x), lty=3, add=T) (for normal use, x would be a vector of empirical values, but the rnorm() function works for testing) That

Hi, Apropos of a recent Inf question, I've previously wondered if dnorm "does the right thing" with dnorm(0, 0, -Inf) which gives zero. Should that be zero or NaN (or NA)? The help says "'sd < 0' is an error and returns 'NaN'" and since -Inf < 0 is TRUE, then... is this a bug? Thank you, Stephen Rochester, MN USA

Dear all, How is it possible in R to calculate the following integral: Integral(-Inf, Inf)[log(dnorm(mean = 3, sd = 1))] how can I define that the density dnorm is taken on (-Inf, Inf) Thanks a lot! [[alternative HTML version deleted]]

Can someone help me understand the following: > D(expression(dnorm(x, mean)), "mean") [1] 0 > sessionInfo() R version 2.2.1, 2005-12-20, i386-pc-mingw32 attached base packages: [1] "methods" "stats" "graphics" "grDevices" "utils" "datasets" [7] "base" By my computations, this should be

Hello, 1. I have a number of snow depth measurements (centimeters depth) that were acquired in a row (linear transect). A: I am trying to plot the probability density function (PDF) of these points. When I use the "dnorm" command and line type = points, the PDF is displayed correctly. When I change the line type = line, it displays the PDF curve with numerous lines connecting multiple

Hi, I have one basic doubt. Suppose X ~ N(50,10). I need to calculate Probability X = 50. dnorm(50, 50, 10) gives me [1] 0.03989423 My understanding is (which is bit statistical or may be mathematical) on a continuous scale, Probability of the type P(X = .....) are nothing but 1/Infinity i.e. = 0. So as per my understanding P(X = 50) should be 0, but even excel also gives 0.03989422. Obviously

Yes, that looks like a bug and an easily fixable one too. However, I spy another issue: Why do we check the !R_FINITE(x) && mu == x before checking for sd < 0 ? The difference is whether we return ML_NAN; or ML_ERR_return_NAN; but surely negative sd should always be an error? I'd be inclined to do if (sigma < 0) ML_ERR_return_NAN; if(!R_FINITE(sigma)) return R_D__0;

#Quick one hopefully. Shouldn't dnorm be returning the pdf? Last time I checked, #a probability shouldn't be greater than 1 as produced by: curve(dnorm(x,0,.1),from=-1,to=t) #Shouldn't I be getting an axis more like that produced by: f=function(x,m,s){ y=rep(NA,length(x)) for(i in 1:length(x)){ y[i]=integrate( dnorm , upper=x[i]+sqrt(.Machine$double.eps) ,

Hi All, I just started to learn compiling C codes for R usage, and got a problem when I was playing with my 'hello world' code. #include <R.h> #include <Rdefines.h> #include <Rmath.h> SEXP test( ) { double x; x=dnorm(1.0,0.0,1.0,1); printf(" x value is: %d \n",x); return(R_NilValue); } I got the result : x value is: -466460838 Could

Hi, In R, qnorm is the inversion function of pnorm. (c.d.f) But there is no inversion function for dnorm. (p.d.f). Is there any easy (and quick) way to compute the inversion function of p.d.f in R ? Thanks ...

Hello: The example "integrate(dnorm,0,20000)" says it "fails on many systems". I just got 0 from it, when I should have gotten either an error or something close to 0.5. I got this with R 2.12.0 under both Windows Vista_x64 and Linux (Fedora 13); see the results from Windows below. I thought you might want to know. Thanks for all your work in creating

Dear R people The function below should be decreasing, convex, and tend to zero when x tends to infinity. curve((1-pnorm(x))/dnorm(x),from=0, to=9) >From the plot we see that for x between 8.0 and 8.3 the function is fluctuating. As far as I understand, this is due to the function pnorm() not being sufficiently accurate in the tails. I am using pnorm() in a way that has probably not been

Dear all I have a problem in fitting lines() of the normal distributions identified with Mclust on a histogram or a mclust1Dplot. Here is some sample code to explain : set.seed(22) foo <- c(rnorm(400, 10, 2), rnorm(500, 17, 4)) mcl <- Mclust(foo, G=2) mcl.sd <- sqrt(mcl$parameters$variance$sigmasq) mcl.size <- c(length(mcl$classification[mcl$classification==2]),

Hi, Is there anyone know if BUGS language allows the combination of variables as response such as Y[i] <- a*X1[i]+b*X2[i] Y[i] ~ dnorm(c,d) It seems doesn't work in my model. The problem is between two ######. The error message is > modelCheck("BayesBioMarker.BUGS") model is syntactically correct > modelData(paste("BUGS_data.txt",sep="")) data