geostatistics

R: How to or should I drop an insignificant orthogonal polynomial basis in a linear model?

由 こ雲淡風輕ζ 提交于 2019-12-11 05:07:43
问题 I have soil moisture data with x-, y- and z-coordinates like this: gue <- structure(list(x = c(311939.1507, 311935.4607, 311924.7316, 311959.553, 311973.5368, 311953.3743, 311957.9409, 311948.3151, 311946.7169, 311997.0803, 312017.5236, 312006.0245, 312001.5179, 311992.7044, 311977.3076, 311960.4159, 311970.6047, 311957.2564, 311866.4246, 311870.8714, 311861.4461, 311928.7096, 311929.6291, 311929.4233, 311891.2915, 311890.3429, 311900.8905, 311864.4995, 311870.8143, 311866.9257, 312002.571,

GAM with “gp” smoother: how to retrieve the variogram parameters?

由 我们两清 提交于 2019-12-11 01:46:13
问题 I am using the following geoadditive model library(gamair) library(mgcv) data(mack) mack$log.net.area <- log(mack$net.area) gm2 <- gam(egg.count ~ s(lon,lat,bs="gp",k=100,m=c(2,10,1)) + s(I(b.depth^.5)) + s(c.dist) + s(temp.20m) + offset(log.net.area), data = mack, family = tw, method = "REML") Here I am using an exponential covariance function with range = 10 and power = 1 ( m=c(2,10,1) ). How can I retrieve from the results the variogram parameters (nugget, sill)? I couldn't find anything

Multi-output spatial statistics with gaussian processes

由 故事扮演 提交于 2019-12-05 02:53:05
问题 I've been investigating Gaussian processes lately. The perspective of probabilistic multi-output is promising in my field. In particular, spatial statistics. But I encountered three problems: multi-ouput overfitting and anisotropy. Let me run a simple case study with the meuse data set (from the R package sp ). UPDATE : The Jupyter notebook used for this question, and updated according to Grr's answer, is here. import pandas as pd import numpy as np import matplotlib.pylab as plt %matplotlib

Multi-output spatial statistics with gaussian processes

由 百般思念 提交于 2019-12-03 20:18:32
I've been investigating Gaussian processes lately. The perspective of probabilistic multi-output is promising in my field. In particular, spatial statistics. But I encountered three problems: multi-ouput overfitting and anisotropy. Let me run a simple case study with the meuse data set (from the R package sp ). UPDATE : The Jupyter notebook used for this question, and updated according to Grr's answer , is here . import pandas as pd import numpy as np import matplotlib.pylab as plt %matplotlib inline meuse = pd.read_csv(filepath_or_buffer='https://gist.githubusercontent.com/essicolo
订阅 geostatistics