similar to: Curve fitting,FDA for biological data

Hi, I'm new to this mailing list so apologies if this is too basic. I have confocal images 512x512 from which I have extracted x,y positions of the coordiates of labelled cells exported from ImageJ as a.csv file. I also have images that define an underlying pattern in the tissue defined as areas of different pixel values 0 or 255 (also 512x512) I've exported these images as .txt

Dear all, I need to make dendogram from read count in a csv file across 34 samples including biological replicate. Please share R code or package to do this. Do I also need to normalized read count before using read data? Thanks [[alternative HTML version deleted]]

Assistant/Associate Professor of Biometry Department of Crop Sciences College of Agricultural, Consumer and Environmental Sciences University of Illinois at Urbana ? Champaign The Department of Crop Sciences is seeking candidates for an Assistant/Associate Professor of Statistics. The successful candidate will develop a vigorous, externally funded, and nationally recognized research program with

Dear R-gurus, We are planning an experiment to test if plants produced by selfing are less fit than those produced by outcrossing. We have plants from three different alpine valleys, picked randomly among all the possible valleys. In each valley, we have a number of individuals, also picked at random. seeds from this individuals were brought back to the green house and sawned. when they

R-help, I'm trying to estimate some parameters using the Maximum Likehood method. The model describes fish growth using a sigmoidal-type of curve: fn_w <- function(params) { Winf <- params[1] k <- params[2] t0 <- params[3] b <- params[4] sigma <- params[5] what <- Winf * (1-exp(- k *(tt - t0)))^b

Greetings all, I am using Fourier analysis to search for periodicities in IP network traffic by generating periodograms and then visually examining them for large, distinct peaks. However, in many cases it is not readily apparent where there are periodicities. I have no experience with discrete maths so I've come up against a block here: How do I define what the "noise floor"

Sorry if it is a silly question, I haven't found documentation on this and I don't know if it is possible. library(MASS) ## for fitdistr library(msm) ## for dtnorm #prepare truncated normal distribution dtnorm0 <- function(x, mean, sd , log = FALSE) { dtnorm(x, mean, sd, 105, 135, log) } set.seed(1) #Generate normal distribution with the TRUE population mean (day 106 of the

I apologize for accidentally omitting some of the answers I got; here they are: ************** I use R regularly in my research in the pharmaceutical industry. I have used it for projects ranging from sample size and power curve estimation, analysis of laboratory data with mixed effects models, to analysis of mass spectrometry data. I have helped biologists here install and use it in limited

Dear All, With a colleague we are writing a paper where we show how R is a very nice tool to deal with some issues in the analyses of data in evolutionary biology. For the intro, I wonder if 1) Anybody has any rough idea of how many people might be using R or how many people have downloaded R, or similar (I am aware answering this question might require divinatory powers...). 2) Have/are any

Greetings everyone, I was wondering if anyone had experience in using fda (flexible discriminant analysis) in the mda package. Specifically, I can run the model but I cant locate/produce much of anything beyond the predicted values and confusion matrix. I've been searching for more examples or documentation but cant seem to locate any specific to the program itself. I need to be able to find

I would like to create a functional data object where both domain and range are matrices. My erroneous attempts are below. Can you suggest corrections? library(fda) domain<-matrix(c(1,1.1,1.2,2,2.1,2.1,3,3.1,3.2,4,4.1,4.2,5,5.1,5.2), nrow = 5, ncol=3, byrow=TRUE) range<-sin(domain)+matrix(rnorm(15,sd=0.1),nrow=5, ncol=3) myfd<-Data2fd(argvals=domain, y=range) #Error in

Hi all, Has anyone tried to do k-fold cross validation for flexible discriminant analysis ( mda library), for example, using crossval() in bootstrap? The problem is that the function crossval() requires a separate matrix for predictors and another for responses, whereas the function fda(), using the formula argument only. Is there another way of doing k-fold cross validation for functions which

Basically all I want to do is predict a scalar response using some curves. I've got as far as doing a regression (using fRegress from the fda package) but have no idea how to apply the results to a NEW set of curves (for prediction). I have N=536 curves, and 536 scalar responses. Here's what I've done so far: * I've created a basis for the curves. * I've created a

Hello, I would like to use fda library to compare sampled curves that are density profiles. But I encounter some problems to register my data : I know that I must use a registration function to align functions in order to have important features found in each curve occur at roughly the same argument value, prior to do subsequent analysis ( PCA and cluster analysis). But I do not

The Biostatistics Analytics Team at the FDA Center for Food Safety and Applied Nutrition seeks an MS/PhD in computer science (or related field) with experience in bioinformatics. The Analytics Team is responsible for computational support for projects involving large chemical and microbial hazard data sets, including bioinformatic and statistical analysis of bacterial genomic data for foodborne

UseR's- As this is slightly off-topic I will limit the discussion to only the major points; for further information feel free to reply off-line. DIA and FDA are hosting a forum to discuss (as per the Goal of the announcement--see link below) "a vendor-neutral software product to support validated analytic procedures ("tools") and make tool development a collaborative effort

The fda package supports "Functional Data Analysis" and "Applied Functional Data Analysis" by Bernard Silverman and James Ramsay. Functional data analysis, which lots of us like to call "FDA", is about the analysis of information on curves or functions. FDA is a collection statistical techniques for answering questions like, "What are the main ways in which the

The fda package supports "Functional Data Analysis" and "Applied Functional Data Analysis" by Bernard Silverman and James Ramsay. Functional data analysis, which lots of us like to call "FDA", is about the analysis of information on curves or functions. FDA is a collection statistical techniques for answering questions like, "What are the main ways in which the

fda 1.2.3 ======================== Version 1.2.3 of the fda package has just been released. This version adds to previous versions a script to create most of the figures of chapter 6 of "Applied Functional Data Analysis" by Ramsay and Silverman. Other changes offer simpler calls to time warping / registration and functional principal component functions. The fda package supports the

Hello R help list, I am using the mda package and in particular the fda routine to classify a set of 4409 trips. I preformed a flexible discriminant analysis (FDA) using a set of 148 trips. A total of 36 predictors were considered. the resulting FDA rule was applied to the 4409 trips and for the same predictors in order to predict to which group the trip will belong. A consistent error is