search for: curvatures

Dear colleagues, Von Bertalanffy model is commonly adjust to data on fish length (TL) and age (AGE) TL= Linf*(1-exp(-K*(AGE-t0)). Linf, K and t0 are parameters of the model. One main goal of the growth study is the comparison of growth parameter estimates between sexes of the same species, or estimates from different populations. The realibility statistical tests normally applied are highly

...ates et al (1983) article, Seber & Wild (1989) and Ratkowsky (1983) books. Those sources show steps and algorithm to get this measures but I don't know translate C code to R language and I've no success until now. I know and I use rms.curv() in MASS package but I would like the maximum curvatures measures. Does someone have this implemented in R? Or know some paper illustrating this calculation with real data (but not so trivial) that can able me to create my functions? Thanks a lot. Walmes Zeviani. Bates; Hamilton; Watts. Calculation onf intrinsic and parameter-effects curvatures for no...

I have a function in R^2, say f <- function(x,y) { ...skipped } I want to plot this function using contour, persp. wireframe, etc. I know that the function has a global minimum at (x0, y0) The naive approach is to evaluate the function on the outer product of two arrays, like this: sx <- c(seq(-3, x0, len = 100), seq(x0, 3, len = 100)[-1]) sy <- c(seq(-3, y0, len = 100), seq(y0, 3,

Dear R-users, I'd appreciate your statistical opinion on the following problem. I'm fitting the four parameter logistic model [f(x) = a + (b - a)/(1 + exp((c - x)*d))] to assay data. We have a lot of samples to fit and my aim is to classify these samples into following groups: 1. no interrelation all results about =~ 0 too low concentration 2. only full

Dear all, I'm interested in testing 2 nonlinear restrictions on coefficients of a nls object. Is there a package for doing this? Something in the lines of `test(nls object, res=c("res 1","res 2"),...)' I only found the function delta.method in the alr3 library that calculates the se of a singleton nonlinear restriction of a nls object using the delta method. Thanks in

Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

I used the wrong address. This was meant as a reply to another post. On 10/17/07 10:55, thomas.schwander at mvv.de wrote: > Hi Jonathan, > > I read your post in the R-Help. Did you get rid off the problem? I'm standing inf > ront of the same problem... If you've got an answer to me to drae a curly bracket, > could you please be so kind to tell me who you did? I missed

I'm not sure I'm barking up the right tree here, but would I need to make use of groups to plot two separate datasets within ONE panel in xyplot? The desired end result is a single xy plot of two separate (but similar in values and ranges). Full code follows, xyplot code at bottom #########Determine Frequencies ##########coastal_slope #needs the maptools package to read ESRI grid

Dear All, I want to know if there is some easy and reliable way to estimate the number of dominant eigenvalues when applying PCA on sample covariance matrix. Assume x-axis is the number of eigenvalues (1, 2, ....,n), and y-axis is the corresponding eigenvalues (a1,a2,..., an) arranged in desceding order. So this x-y plot will be a decreasing curve. Someone mentioned using the elbow (knee)

Dear R experts, Did someone implemented French Curve yet? Or can anyone point me some papers that I can follow to implement it? thanks in advance for your help. Paul

Hello, I've been searching for a method of converting Lat/Lon decimal coordinates into actual distances between points, and taking into account the curvature of the earth. Is there such a package in R? I've looked at the GeoR package, but this does not seem to contain what I am looking for. Ideally the output would be a triangular matrix of distances. Thanks in advance, Paul Brewin

Hi, this seems to work (assuming that your problem was the setting of colours...): --- snip --- network %>% ggraph(., layout = "auto") + # This produces an error... # geom_edge_arc(curvature=0.3, aes(width=(E(network)$weight/10), color=c("darkblue", "red")[as.factor(edge_list$relationship)], alpha=0.5)) + # ... this works :-)

Hi R People: Sorry about the off topic question. Does anyone know the reference for "-2 Log Lambda is approx dist. Chi square", please? It may be Bartlett, but I'm not sure.... thanks in advance! Sincerely, Laura Holt mailto: holtlaura at gmail.com

Hello, This code builds a simple example of 2 wireframes : require(lattice) x <- c(1:10) y <- c(1:10) g <- expand.grid(x = 1:10, y = 1:10, gr = 1:2) g$z <- c(as.vector(outer(x,y,"*")), rep(50,100)) wireframe(z ~ x * y, data = g, groups = gr, scales = list(arrows = FALSE)) However, the intersection between the wireframes is not properly drawn. Is there a way to fix this

I have a data assimilation problem that might be amenable to the use of GAMS, but I am not sure how feasible it is to implement. I was told the R mailing list was a great resource. My observations are spatiotemporal salinity in the San Francisco Bay at a number of instruments over a few days. The thing that I want to fit is the initial condition for a salt transport model at the beginning of this

Dear All, I've been?searching for?appropriate codes to compute the rate of change and the curvature?of ?nonparametric regression model whish was denoted by a smooth function?but?unfortunately?don't manage to?do?it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators. The following are the example of fitting a

Actually, I re-read the post and think it needs clarification. We may both be right. If the question is "I am building a model and want to know if I should retain this random effect?" (or something like that) then the LRT should be used to compare the fitted model against another model. This would be accomplished via anova(). In other multilevel programs, the variance components are

Dear community Find enclosed the full working example. Many thanks Sibylle Test_cat.csv Names Subcategory_type sources.cyto source Factor A.A material "A" A 1 B.B material "B" B 1 C.C regulation "C" C 1 D.D regulation "D" D 1 E.E habitat "E" E 1 F.F cultural "F" F 1 Test_adjac.csv

Dear community I am using ggraph to plot a network analysis. See part 2 in the working example. Besides different colors for different groups of nodes: --> geom_node_point(aes(size = V(network)$hub_score*200, color= as.factor(V(network)$community))) I additionally want to consider different colors for different edge groups The grouping is defined in the edge_list$relationship: negative

Dear all Please, can you advice me how to compute an error, standard deviation or another measure of variability of computed value. I would like to do something like: var(y) = some.function(var(x1),var(x2),var(x3)) for level F1 (2,3,...) Let say I have some variables - x1, x2, x3 (two computed for levels of factor F and one which is same for all levels) and I want to compute y =