[R] Normalized 2-D cross-correlation
Hi all, I need to do (normalized) 2-D cross-correlation in R. There is a convenient function available in Matlab (see: http://www.mathworks.de/de/help/images/ref/normxcorr2.html). Is there anything comparable in R available? Thanks, Felix [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Agreement and Consistency of 2D data
Hi,
I have two different imaging modalities (for the identification of areas of
infarcted myocardium) that I need to compare regarding agreement and
consistency.
However, I don't think that methods like Cohen's Kappa, PCC, Bland-Altmann
or ICC are sufficient here as there is not just a pairwise but also a
spatial relationship between measured data points. For example if the
results of the two imaging modalities are slightly misaligned their
agreement might be still much better than the mentioned tests might
indicate (at least from a practical point of view). On the other hand the
number of non-infarcted myocardial segments often heavily outweighs the
number of infarcted segments which seems to introduce a bias towards better
agreement in most examinations.
Here is an example data set, where I've calculated the ICC.
What would be the correct way to assess agreement and consistency here?
# modality 1
lgemtx - as.matrix(read.table('
http://cluster010.ovh.net/~myvideoc/R20130305/lge_mtx.csv'))
# modality 2
petmtx - as.matrix(read.table('
http://cluster010.ovh.net/~myvideoc/R20130305/pet_mtx.csv'))
# ICC
# note: modalities are inverse
# thus in modality1 0 denotes a normal segment and 100 a complete
infarction of the segment
# and vice versa in modality2
library(irr)
print(icc(cbind(100 - c(pet_mtx), c(lge_mtx)), model=twoway, type=c))
print(icc(cbind(100 - c(pet_mtx), c(lge_mtx)), model=twoway, type=a))
If the matrices are mapped to polar plots (also called bullseye plots) one
can visually assess their agreement.
Each matrix row defines one circle starting at 0° with columns equally
mapped to segments of 3,6° width.
The first row defines the outmost circle, the last row defines the inner
circle.
Here are the corresponding plots for the matrices given above (red is
infarction).
http://cluster010.ovh.net/~myvideoc/R20130305/LGE.png
http://cluster010.ovh.net/~myvideoc/R20130305/PET.png
I know, this is not a pure R question but more a general statistical one.
Hopefully it is still ok to post it here :-)
Best, Felix
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Re: [R] Non linear Regression: singular gradient matrix at initial parameter estimates
Hi Peter, thank you for your reply. Now I see, that P3 is indeed redundand. But with the simplified model... fit = nls(yeps ~ p1 / (1 + exp(p2 - x)) * exp(p4 * x)) ...nls still produces the same error. Any ideas? Felix 2011/4/12 Peter Ehlers ehl...@ucalgary.ca On 2011-04-11 13:29, Felix Nensa wrote: Hi, I am using nls to fit a non linear function to some data but R keeps giving me singular gradient matrix at initial parameter estimates errors. For testing purposes I am doing this: ### R code ### x- 0:140 y- 200 / (1 + exp(17 - x)/2) * exp(-0.02*x) # creating 'perfect' samples with fitting model yeps- y + rnorm(length(y), sd = 2) # adding noise # results in above error fit = nls(yeps ~ p1 / (1 + exp(p2 - x) / p3) * exp(p4 * x)) ### From what I've found in this list I think that my model is over-parameterized. How can I work around that? Take out p3; it's redundant. Peter Ehlers Thanks, Felix [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Non linear Regression: singular gradient matrix at initial parameter estimates
Hi Mario, yes works great. Thanks! 2011/4/12 Mario Valle mva...@cscs.ch Use a more realistic starting point instead of the default one: fit - nls(yeps ~ p1 / (1 + exp(p2 - x)) * exp(p4 * x), start=list(p1=410,p2=18,p4=-.03)) This works for me: fit Nonlinear regression model model: yeps ~ p1/(1 + exp(p2 - x)) * exp(p4 * x) data: parent.frame() p1p2p4 199.48276 16.28664 -0.01987 residual sum-of-squares: 560.6 Number of iterations to convergence: 5 Achieved convergence tolerance: 5.637e-07 Ciao! mario On 12-Apr-11 18:01, Felix Nensa wrote: fit = nls(yeps ~ p1 / (1 + exp(p2 - x)) * exp(p4 * x)) -- Ing. Mario Valle Data Analysis and Visualization Group| http://www.cscs.ch/~mvalle Swiss National Supercomputing Centre (CSCS) | Tel: +41 (91) 610.82.60 v. Cantonale Galleria 2, 6928 Manno, Switzerland | Fax: +41 (91) 610.82.82 -- Felix Nensa Luisenstr. 15-17 44787 Bochum Germany mail: felix.ne...@googlemail.com mobile: +49 171 958 51 40 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Non linear Regression: singular gradient matrix at initial parameter estimates
Hi, I am using nls to fit a non linear function to some data but R keeps giving me singular gradient matrix at initial parameter estimates errors. For testing purposes I am doing this: ### R code ### x - 0:140 y - 200 / (1 + exp(17 - x)/2) * exp(-0.02*x) # creating 'perfect' samples with fitting model yeps - y + rnorm(length(y), sd = 2) # adding noise # results in above error fit = nls(yeps ~ p1 / (1 + exp(p2 - x) / p3) * exp(p4 * x)) ### From what I've found in this list I think that my model is over-parameterized. How can I work around that? Thanks, Felix [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
