gwr.sel(spgwr)
gwr.sel()所属R语言包：spgwr
Crossvalidation of bandwidth for geographically weighted regression
交叉验证的带宽GEO加权回归
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function finds a bandwidth for a given geographically weighted regression by optimzing a selected function. For cross-validation, this scores the root mean square prediction error for the geographically weighted regressions, choosing the bandwidth minimizing this quantity.
函数找到一个带宽对于一个给定的GEO加权回归通过optimzing选定的功能。交叉验证，这个分数的根均方预测误差为GEO加权回归，数量尽量减少这种选择的带宽。
用法----------Usage----------
gwr.sel(formula, data=list(), coords, adapt=FALSE, gweight=gwr.Gauss,
method = "cv", verbose = TRUE, longlat=NULL, RMSE=FALSE, weights,
tol=.Machine$double.eps^0.25)
参数----------Arguments----------
参数：formula
regression model formula as in lm
回归模型公式lm
参数：data
model data frame as in lm, or may be a SpatialPointsDataFrame or SpatialPolygonsDataFrame object as defined in package sp
模型数据框在lm，或可能是一个SpatialPointsDataFrame或SpatialPolygonsDataFrame在套件sp中定义的对象作为
参数：coords
matrix of coordinates of points representing the spatial positions of the observations
代表的观测值的空间位置的坐标的点矩阵
参数：adapt
either TRUE: find the proportion between 0 and 1 of observations to include in weighting scheme (k-nearest neighbours), or FALSE — find global bandwidth
TRUE：找到的比例在0和1之间的观测，包括权重方案（k-最近邻居），或FALSE - 寻找全局带宽
参数：gweight
geographical weighting function, at present gwr.Gauss() default, or gwr.gauss(), the previous default or gwr.bisquare()
GEO加权函数，目前gwr.Gauss()默认情况下，或gwr.gauss()，以前的默认或gwr.bisquare()
参数：method
default "cv" for drop-1 cross-validation, or "aic" for AIC optimisation (depends on assumptions about AIC degrees of freedom)
默认情况下下拉式交叉验证，或“AIC”的AIC优化（AIC自由度依赖于假设的“CV”）
参数：verbose
if TRUE (default), reports the progress of search for bandwidth
如果是TRUE（默认），报告的进展，带宽的搜索
参数：longlat
TRUE if point coordinates are longitude-latitude decimal degrees, in which case distances are measured in kilometers; if x is a SpatialPoints object, the value is taken from the object itself
TRUE，如果点坐标的经纬度小数度，在这种情况下，测量距离（公里），如果x是一个SpatialPoints，对象，对象本身的价值是从
参数：RMSE
default FALSE to correspond with CV scores in newer references (sum of squared CV errors), if TRUE the previous behaviour of scoring by LOO CV RMSE
默认情况下，FALSE对应在新的参考文献（CV得分总和平方CV错误），如果为true，以前的行为得分由LOO CV RMSE
参数：weights
case weights used as in weighted least squares, beware of scaling issues — only used with the cross-validation method, probably unsafe
作为加权最小二乘的情况下，权重，要小心的比例问题 - 仅用于交叉验证的方法，可能是不安全的
参数：tol
the desired accuracy to be passed to optimize
所需的精度将被传递给optimize
值----------Value----------
returns the cross-validation bandwidth.
返回的交叉验证的带宽。
注意----------Note----------
Use of method="aic" results in the creation of an n by n matrix, and should not be chosen when n is large.
使用方法=“AIC”在创建一个n×n矩阵，当n足够大时，不应该选择。
（作者）----------Author(s)----------
Roger Bivand <a href="mailto:Roger.Bivand@nhh.no">Roger.Bivand@nhh.no</a>
参考文献----------References----------
Geographically Weighted Regression, Chichester: Wiley;
参见----------See Also----------
gwr.bisquare, gwr.gauss
gwr.bisquare，gwr.gauss
实例----------Examples----------
data(columbus)
gwr.sel(crime ~ income + housing, data=columbus,
coords=cbind(columbus$x, columbus$y))
## Not run: [＃不运行：]
gwr.sel(crime ~ income + housing, data=columbus,
coords=cbind(columbus$x, columbus$y), gweight=gwr.bisquare)
## End(Not run)[＃（不执行）]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 15:08:28
