plot.SPSloess(USPS)
plot.SPSloess()所属R语言包：USPS
Display LOESS Smooth of Outcome by Treatment in Supervised Propensiy Scoring
显示治疗在监督Propensiy评估的黄土平整度的结果
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Express Expected Outcome by Treatment as LOESS Smooths of Fitted Propensity Scores.
快速治疗黄土预期的结果平滑合身的倾向得分。
用法----------Usage----------
## S3 method for class 'SPSloess'
plot(x, tcol="blue", ucol="red", dcol="green3", ...)
参数----------Arguments----------
参数：x
output list object of class SPSloess.
输出列表类SPSloess对象。
参数：tcol
optional; quoted name of color for treated patient smooth.
治疗的患者平滑的颜色可选;引用名。
参数：ucol
optional; quoted name of color for untreated patient smooth.
可选的，引用的颜色名称为未经治疗的病人光滑。
参数：dcol
optional: quoted name of color for combined patient density.
可选：引用名患者联合密度的颜色。
参数：...
optional; argument(s) passed on to plot().
可选参数（S）通过上图（）。
Details
详细信息----------Details----------
Plots of SPSloess objects display each patient's propensity score versus his/her observed (continuous) outcome. Patients receiving the "standard" treatment (trtm=0) are represented by cyan circles, while patients receiving the "new" treatment (trtm=1) are represented by magenta triangles. The smooth fits of outcome to propensity score within treatment cohorts are show as cyan (trtm=0) and magenta (trtm=1) curves, respectively, superimposed upon the scatter.
SPSloess对象的绘图显示每个病人的倾向得分与他/她的观察（连续）的结果。接收到“标准”治疗的患者（trtm = 0）表示的青色圆圈，而接收到“新的”治疗的患者（trtm = 1）表示的品红三角形。光滑的配合结果倾向得分治疗世代内显示为青色（trtm = 0）和品红色（trtm = 1）的曲线，分别叠加在分散。
Because smooth fits can be difficult to see when the scatters contain many points, a second plot rescaled to show only the two smooth (lowess or spline) fits, again using cyan (trtm=0) and magenta (trtm=1) curves. For details, see the returned lofit data frame.
因为流畅的配合是很难看时，散射包含了很多点，第二个图重新缩小到只有两个光滑（LOWESS或样条曲线）适合再次使用青色（trtm = 0）和品红色曲线（trtm = 1）。有关详细信息，请参阅返回lofit数据框。
Finally, a third plot shows total patient frequencies (black circles) within a 100-cell histogram along the propensity score axis as well as the corresponding density() smooth in red. For details, see the returned logrid data frame.
最后，第三个图显示沿倾向得分轴内的小区100的直方图以及光滑红色对应的密度（）的的忍耐总频率（黑色圆圈）。有关详细信息，请参阅返回logrid数据框。
Winsorizing Cost data: PSframe$TRIMBILL <- pmin( PSframe$cardbill, 50000)
极值调整的成本数据：PSframe $ TRIMBILL < - PMIN（PSframe cardbill，50000美元）
The fam="symmetric" default option of SPSloess tends to be fairly robust to outlying outcomes, at least when the loess span is wide enough. Thus reducing (Winsorizing) outlying cardbill values to \$50K (as illustrated above) should have little effect on a fitted loess smooth with an appropriate span. Looking for the effects of Winsorizing on SPSloess() or SPSsmoot() constitutes "sensitivity analysis."
FAM =“对称”的默认选项SPSloess往往是相当强大的到边远结果，至少在黄土范围足以。因此的减少（Winsorizing），\ $ 50K（如上图所示）的边远cardbill值应该有一个的装黄土顺利用适当的跨度上的影响不大。上SPSloess（）或SPSsmoot极值调整的影响（）构成“灵敏度分析”。
The original lowess() function of Cleveland and Devlin (1988) could be used here because only one X variable (namely, fitted propensity score) is involved, but I choose loess() instead to give users flexibility to choose between fam="gaussian" and fam="symmetric" option, which provides some resistance to outlying outcome values.
可以使用原来的LOWESS（）函数的克利夫兰和Devlin（1988），在这里，因为只有一个X变量（即，合身的倾向得分）参与，但我选择黄土（），而不是让用户能够灵活选择FAM =“高斯“和FAM =”对称“的选项，它提供了一些阻力到边远结果值。
SPSloess() fits can tend to look rather "rough" compared to SPSsmoot() fits. Cubic spline smoothing appears to give answers that are interpretable as smoothed mean values for highly skewed distributions. Loess smoothing, at least when fam="symmetric," tends to give answers more easily interpretable as modes or medians of highly skewed distributions. This median versus mean analogy may help explain why the weighted average signed treatment differences from SPSloess() tend to seem more precise than those from SPSsmoot() for highly skewed distributions.
SPSloess（）配合往往可以看，而“粗糙”相比SPSsmoot（）符合。三次样条平滑解释为平滑的高度偏斜分布的平均值给出答案。黄土平滑，至少在FAM =“对称”，往往会给出答案更容易解释模式或高度偏斜分布的中位数。中位数与平均数的比喻可能有助于解释为什么签署的加权平均待遇上的差别往往显得比高度偏斜分布从SPSsmoot（）的精确从SPSloess（）。
值----------Value----------
NULL
NULL
（作者）----------Author(s)----------
Bob Obenchain <wizbob@att.net>
参考文献----------References----------
approach to regression analysis by local fitting. J Amer Stat Assoc 83: 596-610.
Statistical Models in S eds Chambers JM and Hastie TJ. Wadsworth & Brooks/Cole.
参见----------See Also----------
SPSlogit, SPSsmoot and SPSoutco.
SPSlogit，SPSsmoot和SPSoutco。
实例----------Examples----------
data(lindner)
PStreat <- abcix~stent+height+female+diabetic+acutemi+ejecfrac+ves1proc
logtSPS <- SPSlogit(lindner, PStreat, PSfit, PSrnk, PSbin, appn="lindSPS")
SPScbls5 <- SPSloess(lindSPS, abcix, PSfit, cardbill, span=.5)
SPScbls5
plot(SPScbls5)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-10-1 14:18:06
