“gwr.m”函数命令的调用方式如下所示: #%% 地理加权回归模型MATLAB程序代码如下>> help gwrPURPOSE: compute geographically weighted regression---USAGE: results = gwr(y,x,east,north,info)where: y = dependent variable vectorx = explanatory variable matrixeast = x-coordinatesinspacenorth = y-coordinate...
利用python进行空间自相关的检验并构建地理加权回归(GWR)模型 说到地理加权回归,相信大家肯定不会陌生。作为一种先进的空间数据分析技术,地理加权回归能够充分捕捉空间关系的非平稳性。举个简单的不恰当的例子,我们要对中国各个城市的奢侈品消费量与人均收入进行建模。正常的的理解是人均收入越高,奢侈品消费量就越大,在...
In global regression models, such as Generalized Linear Regression, results are unreliable when two or more variables exhibit multicollinearity (when two or more variables are redundant or together tell the same story). The GWR tool builds a local regression equation for each point in the DataFrame...
% USAGE: results = gwr(y,x,east,north,info) % where: y = dependent variable vector % x = explanatory variable matrix % east = x-coordinates in space % north = y-coordinates in space % info = a structure variable with fields:
The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for ...
从模型的回归效果看,土壤重金属的OLS预测效果总体上不太理想,其中Cu元素的预测效果相对较好,解释度(调节R2)为32.3%,其后为Zn(解释度为28.1%)、Cr(解释度为26.6%)、Ni(解释度为21.2%),而Cd和Pb预测的解释程度则小于20%。在GWR预测模型中,则以Cr元素的预测效果最好,解释度为71.6%,其次为Cu,解释度64.9%,...
from mgtwr.gtwr import GTWR, MGTWR,GTWRResults 1. 2. 3. 4. 5. 1.所需要的数据格式 AI检测代码解析 np.random.seed(10) u = np.array([(i-1)%12 for i in range(1,1729)]).reshape(-1,1) v = np.array([((i-1)%144)//12 for i in range(1,1729)]).reshape(-1,1) ...
R2:R 平方是拟合度的一种度量。其值在 0.0 到 1.0 范围内变化,值越大越好。此值可解释为回归模型所涵盖的因变量方差的比例。R2 计算的分母为因变量值平方和。向模型中再添加一个解释变量不会更改分母但会更改分子;这将出现改善模型拟合的情况(但可能为假象)。请参阅下文中的“校正的 R2”。
R2 local: estos valores están entre 0,0 y 1,0 e indican lo bien que se ajusta el modelo de regresión local a los valores y observados. Los valores muy bajos indican que el modelo local está funcionando mal. Asignar los valores R2 locales para ver dónde GWR hace una buena predicc...
Results showed that the GWR models outperformed the OLS models in term of statistical results such as R2 and the Akaike Information Criterion (AICc). We... M Mostafa,M Elia,V Giannico,... - Fire (2571-6255) 被引量: 0发表: 2024年 Assessment of vegetation dynamics under changed climat...