SPSsmoot(USPS)
SPSsmoot()所属R语言包：USPS
Spline Smoothing of Outcome by Treatment in Supervised Propensiy Scoring
样条平滑的治疗在监督Propensiy评估结果
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Express Expected Outcome by Treatment as Spline Functions of Fitted Propensity Scores.
明确的预期结果，样条函数拟合的倾向得分的处理。
用法----------Usage----------
SPSsmoot(dframe, trtm, pscr, yvar, faclev=3, df=5, spar=NULL, cv=FALSE, penalty=1)
参数----------Arguments----------
参数：dframe
data.frame of the form returned by SPSlogit().
数据框由SPSlogit（）返回的形式。
参数：trtm
the two-level factor on the left-hand-side in the formula argument to SPSlogit().
式参数对SPSlogit（）中的左手侧上的两个级别的因素。
参数：pscr
fitted propensity scores of the form returned by SPSlogit().
合身的倾向分数的形式返回由SPSlogit（）。
参数：yvar
continuous outcome measure or result unknown at the time patient was assigned (possibly non-randomly) to treatment; "NA"s are allowed in yvar.
连续的测量结果或结果未知的时候病人被分配了（可能非随机的）治疗，“NA”被允许在yvar的。
参数：faclev
optional; maximum number of distinct numerical values a variable can assume and yet still be converted into a factor variable; faclev=1 causes a binary indicator to be treated as a continuous variable determining a proportion.
可选的，一个变量可以假设，但仍然被转换成一个因素变量的不同数值的最大数目; faclev = 1导致一个二进制的指示器被处理作为一个连续变量确定的比例。
参数：cv
optional; ordinary cross-validation (T) or generalized cross-validation, GCV (F).
可选的;的普通交叉验证（T）或广义交叉验证，GCV（F）。
参数：df
optional; degrees-of-freedom of B-spline fit.
可选的;度的B-样条拟合的自由。
参数：spar
spar argument for smooth.spline() function.
晶石smooth.spline（）函数的参数。
参数：penalty
coefficient of penalty for df in the GCV criterion.
在GCV标准的DF系数的罚款。
Details
详细信息----------Details----------
Once one has fitted a somewhat smooth curve through scatters of observed outcomes, Y, versus the fitted propensity scores, X, for the patients in each of the two treatment groups, one can consider the question: "Over the range where both smooth curves are defined (i.e. their common support), what is the (weighted) average signed difference between these two curves?"
一旦一个人配一个比较平滑的曲线，通过散射观测到的结果，Y，与拟合的倾向得分，X，的两个治疗组的患者中，其中一个可以考虑的问题：“在范围内都流畅的曲线定义（即它们的共同支持），什么是（加权）平均签署了这两条曲线之间的差异？“
If the distribution of patients (either treated or untreated) were UNIFORM over this range, the (unweighted) average signed difference (treated minus untreated) would be an appropriate estimate of the overall difference in outcome due to choice of treatment.
如果分布的患者（无论是处理或未经处理）是在这个范围内的一致，（未加权）平均带符号差（处理过的减去未处理）的总体差异的结果，由于选择的治疗将是一个适当的估计。
Histogram patient counts within 100 cells of width 0.01 provide a naive "non-parametric density estimate" for the distribution of total patients (treated or untreated) along the propensity score axis. The weighted average difference (and standard error) displayed by SPSsmoot() are based on an R density() smooth of these counts.
的直方图病人数在100个单元的宽度0.01提供了一个天真的“非参数密度估计”为沿倾向得分轴的总例（处理或未经处理）的分布。加权平均差异（和标准错误）显示由SPSsmoot（）的基于对R密度（）这些计数光滑的。
In situations where the propensity scoring distribution for all patients in a therapeutic class is known to differ from that of the patients within the current study, that population weighted average would also be of interest. Thus the SPSsmoot() output object contains two data frames, ssgrid and ssfit, useful in further computations.
在倾向得分在目前的研究中的患者，所有患者在治疗类是已知的不同分布的情况下，加权平均，人口也有兴趣。因此，SPSsmoot（）输出的对象包含两个数据的的框架，ssgrid和ssfit，可用于进一步的计算。
值----------Value----------
An output list object of class SPSsmoot:
输出列表对象类SPSsmoot：
参数：ssgrid
spline grid data.frame containing 11 variables and 100 observations. The PS variable contains propensity score "cell means" of 0.005 to 0.995 in steps of 0.010. Variables F0, S0 and C0 for treatment 0 and variables F1, S1 and C1 for treatment 1 contain fitted smooth spline values, standard error estimates and patient counts, respectively. The DIF variable is simply (F1\-F0), the SED variable is sqrt(S1\^2+S0\^2), the HST variable is proportional to (C0+C1), and the DEN variable is the estimated probability density of patients along the PS axis.
样条网格数据框包含11个变量和100个观测。 PS变量包含“倾向得分”电池装置0.005，步长为0.010~0.995。变量F0，S0和C0为处理0和变量F1，S1和C1包含安装的光滑样条值，标准误差估计和病人数，分别为处理1。的DIF变量是简单的（F1 \-F0），SED变量是sqrt（S1 \ ^ 2 + S0 \ ^ 2），的HST的变量是正比于（C0 + C1），以及对DEN变量是估计的概率密度患者的PS轴。
参数：spsub0, spsub1
spline fit data.frames containing 4 variables for each distinct PS value in ssfit. These 4 variables are named PS, YAVG, TRT==0 and 1, respectively, and FIT = spline prediction.
样条拟合data.frames的含有4个变量为每个不同的PS值在ssfit。这4个变量的名为PS，YAVG，TRT == 0和1的，分别和FIT =样条预测。
参数：df
smooth.spline() degrees-of-freedom
smooth.spline度的自由（）
参数：sptdif
outcome treatment difference mean.
结果治疗效果差的意思。
参数：sptsde
outcome treatment difference standard deviation.
结果治疗效果差的标准差。
（作者）----------Author(s)----------
Bob Obenchain <wizbob@att.net>
参考文献----------References----------
A Roughness Penalty Approach. Chapman and Hall.
density estimation. J Roy Statist Soc B 53: 683-690.
参见----------See Also----------
SPSloess, SPSbalan and SPSoutco.
SPSloess，SPSbalan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")
SPScbss7 <- SPSsmoot(lindSPS, abcix, PSfit, cardbill, df=7)
SPScbss7
plot(SPScbss7)
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 14:18:51
