search for: isotropic

Hi there, I was wondering if by the way the isotropic bivariate function works in the mgcv package, one can use highly correlated coordinates (given the shape of the study area) without worrying about the potential problems of correlation between explanatory variables, i.e., does s(LON, LAT) deal with that by considering their combined effect? Altho...

...riogram of spatial data ? 2) does anyone know if there exist the possibility in R of performing kriging with an arbitrary covariance function ? If I have well understood the Krig procedure offers only three possibilities (expcov, gauscov and sphercov) that are the common models for stationary and isotropic processes. Thanks, Paola. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !)...

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

...object. However, for Jcross I must specify the two marks I want to analyse. That is, usually I would get the Jcross doing: Jc <- Jcross(PPPData,"Aun","Qsu") For marks "Aun" and "Qsu". For L function, I can make: K <- Kest (PPPData, correction="isotropic") plot (K, r-sqrt(iso/pi)~r) But I could not understand how to put all that together. Resuming, I do not understand how can I use the envelope function: - to plot Jcross (where and how do I specify types/marks ??) - to plot L (where do I write that I do not want K, but r-sqrt(iso/pi)~r, in...

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

...s and a binary response variable [0 or 1]. There is no current understanding of the predictors' relationship to response so I am relying on GAM for selection of appropriate predictors. Thanks Savrina *mgcv package 1.3-12: # I start with specifying the full model with 116 predictors including isotropic smooth of 3D location variables (when I specify only the first 14 predictors I get no error message) > m0<-gam(label~s(x,y,z,k=50),s+(feature4)+s(feature5)+s(feature6)+...+s(feature116),data=k.data, family=binomial) Error in smooth.construct.tp.smooth.spec(object, data, knots): A term h...

...The main new features are: * A generalized additive mixed modelling function `gamm' (which uses lme from the nlme library of glmmPQL from the MASS library for fitting). * Tensor product smooths as an alternative way of representing smooths of more than one variable. These are useful when isotropic smoothing is inappropriate. Tensor products of any available smooth can be used. See ?te * An object oriented approach to smooth terms that allows new classes of smooths to be added easily. See ?smooth.construct for details and example R code implementing `p-splines'. * A built in cyc...

...The main new features are: * A generalized additive mixed modelling function `gamm' (which uses lme from the nlme library of glmmPQL from the MASS library for fitting). * Tensor product smooths as an alternative way of representing smooths of more than one variable. These are useful when isotropic smoothing is inappropriate. Tensor products of any available smooth can be used. See ?te * An object oriented approach to smooth terms that allows new classes of smooths to be added easily. See ?smooth.construct for details and example R code implementing `p-splines'. * A built in cyc...

...a normal data frame? If I do this in package gstat: library(gstat) g.dummy <- gstat(formula = z~1, locations = ~x+y, dummy = TRUE, beta = 0, model = vgm(1,"Exp",15), nmax = 20) set.seed(1) iso <- predict(g.dummy, newdata = xy, nsim = 10) gridded(iso) = ~x+y # spplot(iso,main="isotropic") # not essential for this question # I could make a dataframe like: diso<-data.frame(iso) diso[1:4,] # which results in: # x y sim1 sim2 sim3 sim4 sim5 sim6 sim7 sim8 sim9 sim10 # 1 1 1 0.4093776 1.0752794 -1.5654566 0.6183240 -2.196812...

...lso be that my questions reflect a lack of knowledge pertaining to random fields in general and not the RandomFields package specifically, in which case someone can set me straight. I like the versatility of the extended definition form that can be used in RandomFields. For example, specifying an isotropic exponential model with a nugget of 1 as: modelA=list("+", list("$", var=2, scale=2, list("exp")), list("$", var=1, list("nugget")) ) Ultimately I would like to work with ani...

...width and height of the > symbols are taken to be those of the x axis and y axis, respectively..." > at least for 'boxplots' symbols this is empirically the actual > behaviour. if other symbols (e.g. circles) actually _are_ using x axis > units, the difference between 'isotropic' and 'anisotropic' symobls > should be made clear. > > the current text gave me the false impression that I needed to set asp=1 > in order to get correct heights for the boxplots symbols. > > joerg > > ______________________________________________ > R-devel a...

Hi--I'm trying to run a Ripley's K analysis on a point pattern within a window of 1 square km. The maximum distance I want to use is the diagonal of the window, around 1400m, run in 50m increments. The function defaults to a 250m maximum--I thought this was simply the way I had plotted it, but when I exported the data there was nothing over 250m. I've looked into seq and ginterval,

hi! I did Riply's K function and envelope in the package "SPATSTAT". When the species number is less, it can work well and it is quickly. But when the species number is more(example:2000), it told me"memory limit". So I change "memory limit". But the speed is very slow. I took 13 hours to run the programe. Perhaps there are other methods that can do it. Or just

...ige.method, x=xkrig,y=ykrig,grid=TRUE, model=list( list(m="exp", v=Variance,s=Range,a=anisoMatrix) ), given=points2,data=data) ###### #END# ###### As an error message, R writes : Error: (in PrepareModel) do not mix isotropic definitions with anisotropic ones I don't know why. - The problem comes from adding the anisotropy parameter (a=anisoMatrix, in the function Kriging). When putting the anisotropy coefficient to one (ie isotropy), it doesn't work as well. - I have tested the script for several theoritical mo...

Dear all; First of all, this is probably a more conceptual question than a R-related one, but still want to give it a try. When working with the interpolation function "interp" from the package akima and the triangulation function "tri.mesh" from package tripack I've got NA's for the interpolation and "error" for the triangulation. The data is arranged in a

Dear all, 1. How can I compute the intersecting area between 2 polygons ? 2. I have polygons with coordinates in decimal degrees (i.e. 13 deg 30 min = 13.5 decimal degrees). I want to compute their area and get the results in square meters or square kiometers. Can anyone give me a conversion coefficient or a pointer where I can find this information (sorry for this off topic question) ? Thanks

I have a general question about using thin plate splines in the surf and ordisurf routines. My rudimentary knowledge of a gam is that with each predictive variable there is a different smooth for each one and then they are added together with no real interaction term (because they don't handle this well?). Now, If I have two variables that have a high D^2 score and a low GCV score (I am

I am a little bit confusing about the following help message on how to fit a GAM model with interaction between factor and smooth terms from http://rss.acs.unt.edu/Rdoc/library/mgcv/html/gam.models.html: ?Sometimes models of the form: E(y)=b0+f(x)z need to be estimated (where f is a smooth function, as usual.) The appropriate formula is: y~z+s(x,by=z) - the by argument ensures that the smooth

...all Load. ole32: Implement saving of the data cache. ole32: Call SetWindowOrgEx and SetWindowExtEx in OleMetafilePictFromIconAndLabel so that the created metafile scales correctly. ole32: The METAFILEPICT created by OleMetafilePictFromIconAndLabel should have its mapping mode be anisotropic, not isotropic. ole32: Use the default icon title font when writing the caption text out in OleMetafilePictFromIconAndLabel. ole32: Fill in the xExt and yExt fields in OleMetafilePictFromIconAndLabel. ole32: The width of the metafile drawn should be 3 times the icon width in OleMe...