search for: curvature

...n of growth parameter estimates between sexes of the same species, or estimates from different populations. The realibility statistical tests normally applied are highly dependent on the nonlinearity of the model used. Are there any developed routines in R ? to: 1) calculate the maximum intrinsic curvature 2) maximum parameter affecting curvature 3) plot the two dimensional cross sections of confidence regions (for different alpha values) of the model parameters Regards, Ivone Figueiredo [[alternate HTML version deleted]]

Hi all, I'm looking for the algorithm to calculate maximum intrinsic and parameter effects curvature of Bates & Watts (1980). I have Bates & Watts (1980) original article, Bates 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 unti...

...ter 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, len = 100)[-1]) fout <- outer( sx, sy, f) persp(fout) This works pretty well, but I would like to achieve better results by using information o the curvature of the function. I know that the curvature of the function is very high in a neighborhood of (x0, y0), but it is rather flat for (x,y) not belonging to this neighborhood. So in principle I have to choices: increase the number of points were the function is evaluated; evaluate the function more den...

...lation and saturation Is there a way to model these classes, and compare their significance by means of an analysis of the residuals (ANOVA)? Something like model 1 = linear & constant =~ 0 & slope = 0 model 2 = linear & constant =~ 1 & slope = 0 model 3 = ???? some curvature model 4 = ???? some curvature model 5 = linear & slope > 0 model 6 = full four parameter logistic model with the procedure: Starting with the linear model and testing for any curvature. -> curvature not significant ==> result = model 1, 2 or 3, depending on signific...

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

...e two-way (and one three-way) interactions that I know are of interest (I know this from previous logistic regression modelling of the dataset). For those who like to see the actual R code, here are two fits I've tried: gam(outcome ~ te(acl, dispersal) + te(amplitude, dispersal) + te(slope, curvature, amplitude), family=binomial, data=rla, method="REML") gam(outcome ~ te(slope) + te(curvature) + te(amplitude) + te(acl) + te(dispersal) + te(slope, curvature) + te(slope, amplitude) + te(curvature, amplitude) + te(acl, dispersal) + te(amplitude, dispersal) + te(slope, curvature, amplitu...

...function(x1,y1,x2,y2,h) { x2 <- x2-x1; y2 <- y2-y1 v1 <- viewport(x=x1,y=y1,width=sqrt(x2^2+y2^2), height=h,angle=180*atan2(y2,x2)/pi, just=c("left","bottom"),gp=gpar(col="red")) pushViewport(v1) grid.curve(x2=0,y2=0,x1=.125,y1=.5,curvature=.5) grid.move.to(.125,.5) grid.line.to(.375,.5) grid.curve(x1=.375,y1=.5,x2=.5,y2=1,curvature=.5) grid.curve(x2=1,y2=0,x1=.875,y1=.5,curvature=-.5) grid.move.to(.875,.5) grid.line.to(.625,.5) grid.curve(x2=.625,y2=.5,x1=.5,y1=1,curvature=.5) popViewport()} grid.newpage() grid.move.to(.25,.3) grid....

...to the table, result is high prob of exceed is in row with low slope #and low probabibility is in row with high slope freqs$rank<-z write.table(freqs, "C:/R_PLots/coastslopefreqs.txt", sep=",", col.names=TRUE, row.names=TRUE, quote=TRUE, na="NA") ##########coastal_curvature #needs the maptools package to read ESRI grid require(maptools) #import the curvature grid basin.map <- readAsciiGrid("C:/R_PLots/coastal_crv.asc", colname="curv") basin_curv <- (basin.map$curv) #read the curvs into a dataframe freqs<-as.data.frame(table(basin_curv)) #r...

...le 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) method to find the point that the maximal curvature of this curve occurs. The number at this point would be the number of dominant eigenvalues. But I could not find any reference papers on this idea. Does anyone has tried this method or knows more details on this? Thanks for your point. Fred [[alternative HTML version deleted]]

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 Paul E Brewin (PhD) Center for Research in Biological Syst...

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 :-) geom_edge_arc(curvature=0.3, aes(width=E(network)$weight/10, color=edge_list$relationship), alpha=.5) + scale_edge_color_manual(v...

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

...t 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 time period. The spline basis functions, parameters and curvature penalties would all be purely spatial, though the cross-validation would be in the space of the data. The modification I need to make is as follows: 1. evaluate each spatial basis function at every computational point in my mesh (dense, but not part of the GAM) 2. use the bases members as individu...

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 nonparametric re...

...he best literature available at the moment: > > 1. Doug Bates in his PhD dissertation and later in his book > with Don Watts (1988) Nonlinear Regression Analysis and Its > Applications (Wiley) split approximation errors in nonlinear least > squares into "intrinsic curvature" and "parameter effects curvature". > He quantified these two problems in the context of roughly three dozen > published examples, if my memory is correct, and found that in not > quite all cases, the parameter effects were at least an order of > magnitude greater tha...

...dge.color = ifelse(edge_list$relationship == "pos", "blue", "red"), layout=layout_with_mds(.)) tiff("figures/Test_network_bysubcatecory.tiff", width=1000, height=900, res=120) network %>% ggraph(., layout = "auto")+ geom_edge_arc(curvature=0.3, aes(width=(E(network)$weight/10), color=c("darkblue", "red")[as.factor(edge_list$relationship)], alpha=0.5)) + geom_node_point(aes(size = V(network)$hub_score*200, color= as.factor(V(network)$community))) + geom_node_text(aes(label = V(network)$name), size=3, color=...

...defined in the edge_list$relationship: negative relationship = red and positive relationship = darkblue. The code is working in the way that the groups are separated by two colors. However, the code uses not the assigned colors. Does anyone have any idea how to adapt the code? --> geom_edge_arc(curvature=0.3, aes(width=(E(network)$weight/10), color=c("darkblue", "red")[as.factor(edge_list$relationship)], alpha=0.5)) + Kind regards Sibylle Working example library(circlize) library(ggplot2) library(igraph) library(tidyverse) library(RColorBrewer) library(stringi) library(sci...

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 =