swd(SpherWave)
swd()所属R语言包：SpherWave
Decomposition
分解
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function performs decomposition with multi-sale SBF's.
这个函数执行分解与多出售SBF。
用法----------Usage----------
swd(sbf)
参数----------Arguments----------
参数：sbf
an object of class "sbf"
对象类SBF
Details
详细信息----------Details----------
This function performs decomposition with multi-sale SBF's.
这个函数执行分解与多出售SBF。
值----------Value----------
An object of class spherical wavelet decomposition("swd"). This object is a list with the following components. <table summary="R valueblock"> <tr valign="top"><td>obs</td> <td> observations</td></tr> <tr valign="top"><td>latlon</td> <td> grid points of observation sites in degree</td></tr> <tr valign="top"><td>netlab</td> <td> vector of labels representing sub-networks</td></tr> <tr valign="top"><td>eta</td> <td> bandwidth parameters for Poisson kernel</td></tr> <tr valign="top"><td>method</td> <td> extrapolation methods, ""ls"" or ""pls""</td></tr> <tr valign="top"><td>approx</td> <td> if TRUE, approximation is used.</td></tr> <tr valign="top"><td>grid.size</td> <td> grid size (latitude, longitude) of extrapolation site</td></tr> <tr valign="top"><td>lambda</td> <td> smoothing parameter for penalized least squares method</td></tr> <tr valign="top"><td>p0</td> <td> starting level for extrapolation. Resolution levels p0+1, \ldots, L is used for extrapolation.</td></tr> <tr valign="top"><td>gridlon</td> <td> longitudes of extrapolation sites in degree</td></tr> <tr valign="top"><td>gridlat</td> <td> latitudes of extrapolation sites in degree</td></tr> <tr valign="top"><td>nlevels</td> <td> the number of multi-resolution levels</td></tr> <tr valign="top"><td>coeff</td> <td> interpolation coefficients</td></tr> <tr valign="top"><td>field</td> <td> extrapolation on grid.size</td></tr> <tr valign="top"><td>density1</td> <td> density of SBF</td></tr> <tr valign="top"><td>latlim</td> <td> range of latitudes in degree</td></tr> <tr valign="top"><td>lonlim</td> <td> range of longitudes in degree</td></tr> <tr valign="top"><td>global</td> <td> List of successively smoothed data</td></tr> <tr valign="top"><td>density</td> <td> density of SW coefficients</td></tr> <tr valign="top"><td>detail</td> <td> List of details at different resolution levels</td></tr> <tr valign="top"><td>swcoeff</td> <td> SW coefficients</td></tr> <tr valign="top"><td>thresh.info</td> <td> ""None""</td></tr> </table>
对象的类球面小波分解（社署）。此对象是一个具有下列组件列表。 <table summary="R valueblock"> <tr valign="top"> <TD> obs</ TD> <TD>的意见</ TD> </ TR> <tr valign="top"> < latlon TD> </ TD> <TD>网格点的观测点度</ TD> </ TR> <tr valign="top"> <TD> netlab</ TD> < TD>矢量表示子网络的标签</ TD> </ TR> <tr valign="top"> <TD>eta </ TD> <TD>带宽参数Poisson核</ TD> < / TR> <tr valign="top"> <TD> method</ TD> <TD>外推法“，”LS“或”PLS“</ TD> </ TR> <tr valign="top"> <TD> approx </ TD> <TD>如果为TRUE，使用近似。</ TD> </ TR> <tr valign="top"> <TD >grid.size</ TD> <TD>网格尺寸（纬度，经度）外推网站</ TD> </ TR> <tr valign="top"> <TD>lambda</ TD > <TD>平滑参数补偿最小二乘法</ TD> </ TR> <tr valign="top"> <TD> p0 </ TD> <TD>开始外推。分辨率级别p0+1, \ldots, L使用外推。</ TD> </ TR> <tr valign="top"> <TD>gridlon </ TD> <TD>东经度的外推网站< / TD> </ TR> <tr valign="top"> <TD> gridlat </ TD> <TD>纬度程度的外推网站</ TD> </ TR> <TR VALIGN =“顶部“> <TD> nlevels </ TD> <TD>数多分辨率水平</ TD> </ TR> <tr valign="top"> <TD>coeff</ TD> <TD>插值系数</ TD> </ TR> <tr valign="top"> <TD> field </ TD> <TD>外推法grid.size </ TD> </ TR > <tr valign="top"> <TD> density1 </ TD> <TD>密度SBF </ TD> </ TR> <tr valign="top"> <TD>latlim </ TD> <TD>范围的纬度度</ TD> </ TR> <tr valign="top"> <TD>lonlim </ TD> <TD>范围内的经度度< / TD> </ TR> <tr valign="top"> <TD> global </ TD> <TD>连续平滑的数据列表</ TD> </ TR> <TR VALIGN =“顶” > <TD> density </ TD> <TD>密度的SW系数</ TD> </ TR> <tr valign="top"> <TD> detail</ TD> <TD >列表在不同的分辨率级别的详细信息</ TD> </ TR> <tr valign="top"> <TD>swcoeff </ TD> <TD> SW系数</ TD> </ TR> < TR VALIGN =“顶”> <TD>thresh.info </ TD> <TD>“”无“”</ TD> </ TR> </ TABLE>
参考文献----------References----------
Oh, H-S. and Li, T-H. (2004) Estimation of global temperature fields from scattered observations by a spherical-wavelet-based spatially adaptive method. Journal of the Royal Statistical Society Ser. B, 66, 221–238.
参见----------See Also----------
sbf, swthresh, swr.
sbf，swthresh，swr。
实例----------Examples----------
### Observations of year 1967[＃＃1967年的观察]
#data(temperature)[数据（温度）]
#names(temperature)[名称（温度）]
# Temperatures on 939 weather stations of year 1967 [1967年的939个气象站的温度]
#temp67 <- temperature$obs[temperature$year == 1967] [temp67 < - 温度美元OBS [温度年== 1967]]
# Locations of 939 weather stations [939个气象站的位置]
#latlon <- temperature$latlon[temperature$year == 1967, ][latlon < - 的温度美元latlon [温度年== 1967年]]
### Network design by BUD[＃＃网络设计BUD]
#data(netlab)[数据（NETLAB）]
### Bandwidth for Poisson kernel[＃＃泊松核的带宽]
#eta <- c(0.961, 0.923, 0.852, 0.723, 0.506)[ETA - C（0.961，0.923，0.852，0.723，0.506）]
### SBF representation of the observations by pls[＃＃的SBF表示，观察PLS]
#out.pls <- sbf(obs=temp67, latlon=latlon, netlab=netlab, eta=eta, [SBF（OBS = temp67，latlon latlon，NETLAB = NETLAB，η= ETA，out.pls < - ]
# method="pls", grid.size=c(50, 100), lambda=0.89)[方法=“PLS”，grid.size = C（50，100），λ= 0.89）]
### Decomposition[＃＃分解]
#out.dpls <- swd(out.pls)[out.dpls < - 社署（out.pls）]
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注：
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发表于 2012-9-30 15:13:43
