Should the need to model the relationship between bivariate data and a response variable arise, two-dimensional (2D) Gaussian models are often the most appropriate choice. For example, Priebe et al. (2003) characterized motion-sensitive neurons in the brains of macaques by fitting 2D-Gaussian ...
By imposing lower and upper bounds 0<=D<=0, this can also be used to perform pure Gaussian fitting. The submission also provides a helpful tool, namely gaussFcn, for post-plotting and assessing the fit. See help documentation for gaussfitn() and gaussFcn() as well as the accompanying ...
Open Babel is a chemical toolbox designed to speak the many languages of chemical data. - openbabel/src/formats/gaussformat.cpp at master · openbabel/openbabel
fit_gaussian_2D(samp_dat)## Generate a grid of X- and Y- values on which to predictgrid<-expand.grid(X_values=seq(from=-5,to=0,by=0.1),Y_values=seq(from=-1,to=4,by=0.1))## Predict the values using predict_gaussian_2Dgauss_data_ue<-predict_gaussian_2D(fit_object=gauss_fit_...
Graph(a)—intensity of the Gaussian beam vs. propagation distance behind the focus of the lens (black curve) in units of Rayleigh diffraction length (\(z_R\)=3.8 cm). For comparison, the variation of the central peak intensity of the Gauss-Bessel beams vs. distance (in units of\(z_R...
Lpl is the generalized Laguerre polynomial; p (q) is the radial (longitudinal) number (∈); R(z) is the wavefront radius of curvature; w(z) ≫ λ is the Gaussian beam waist, zR = πw2/λ is the Rayleigh Scientific Reports | 6:38156 | DOI: 10.1038/srep3...
Details on fitting Gaussian random fields, including Box-Cox transformationMartin Schlather