I have what is probably a very simple problem but I would be very
grateful to the list for any help or pointers to implement a solution in
R.
We have two types of measurements on the eye that we have collected in
62 patients at 5 fixed time points during a clinical visit over an
office day. We
I think this is an easy question, but I would be grateful for any advice
on how to implement this in R.
I simply have a response variable (y) that I am trying to predict with
one explanatory variable (x) but the shape of the scatter plot is
distinctly bilinear. It would be best described by two
I am interested in calculating Age-Specific normal reverence intervals,
using non-parametric methods - or ideally something called the LMS
method (which as I understand it uses cubic splines fitted to the data).
Any packages in R that you think might help me? Any other advice
gratefully received.
I hope you can help with what might be an easy query.
I am doing some simple simulations where I am generating series of data
from a normal distribution using rnorm. I am then treating these as a
time series. I want to know how I can incorporate correlation in the
series (autocorrelation) i.e.
We have a clinical measurement on patients over time. Each patient has
about 5 of these measurements over a period of two years, but the
measurement are not necessarily taken at equal space in time. We want to
use this data to establish test-retest variability. My first thought was
to look at the
I would be very grateful for any help from members of this list for what
might be a simple problem...
We are trying to simulate the behaviour of a clinical measurement in a
series of computer experiments. This is simple enough to do in R if we
assume the measurements to be Gaussian, but their
I would be grateful if members of the list could point me in the
direction of any code (preferably in R) that will allow me to estimate
95th percentiles from a set of repeated measurements. For example, we
are interested in a clinical measurement where we have 3 measures for 14
subjects and 2