VARIANCE([distinct|all]x)

本文详细介绍了SQL中的VARIANCE函数用法,包括如何计算选定行中方差,并通过具体实例展示了不同参数(如distinct与all)对方差计算结果的影响。
VARIANCE([distinct|all]x)
【功能】统计数据表选中行x列的方差。

【参数】all表示对所有的值求方差,distinct只对不同的值求方差,默认为all
如果有参数distinct或all,需有空格与x(列)隔开。

【参数】x,只能为数值型字段

【返回】数字值



【示例】
环境:
create table table3(xm varchar(8),sal number(7,2));
insert into table3 values('gao',1111.11);
insert into table3 values('gao',1111.11);
insert into table3 values('zhu',5555.55);
commit;

执行统计:
select VARIANCE(distinct sal),VARIANCE(all sal),VARIANCE(sal) from table3;
结果: 9876523.4568     6584348.9712     6584348.9712

 

crr {cmprsk} R Documentation Competing Risks Regression Description regression modeling of subdistribution functions in competing risks Usage crr(ftime, fstatus, cov1, cov2, tf, cengroup, failcode=1, cencode=0, subset, na.action=na.omit, gtol=1e-06, maxiter=10, init, variance=TRUE) Arguments ftime vector of failure/censoring times fstatus vector with a unique code for each failure type and a separate code for censored observations cov1 matrix (nobs x ncovs) of fixed covariates (either cov1, cov2, or both are required) cov2 matrix of covariates that will be multiplied by functions of time; if used, often these covariates would also appear in cov1 to give a prop hazards effect plus a time interaction tf functions of time. A function that takes a vector of times as an argument and returns a matrix whose jth column is the value of the time function corresponding to the jth column of cov2 evaluated at the input time vector. At time tk, the model includes the term cov2[,j]*tf(tk)[,j] as a covariate. cengroup vector with different values for each group with a distinct censoring distribution (the censoring distribution is estimated separately within these groups). All data in one group, if missing. failcode code of fstatus that denotes the failure type of interest cencode code of fstatus that denotes censored observations subset a logical vector specifying a subset of cases to include in the analysis na.action a function specifying the action to take for any cases missing any of ftime, fstatus, cov1, cov2, cengroup, or subset. gtol iteration stops when a function of the gradient is < gtol maxiter maximum number of iterations in Newton algorithm (0 computes scores and var at init, but performs no iterations) init initial values of regression parameters (default=all 0) variance If FALSE, then suppresses computation of the variance estimate and residuals Details Fits the 'proportional subdistribution hazards' regression model described in Fine and Gray (1999). This model directly assesses the effect of covariates on the subdistribution of a particular type of failure in a competing risks setting. The method implemented here is described in the paper as the weighted estimating equation. While the use of model formulas is not supported, the model.matrix function can be used to generate suitable matrices of covariates from factors, eg model.matrix(~factor1+factor2)[,-1] will generate the variables for the factor coding of the factors factor1 and factor2. The final [,-1] removes the constant term from the output of model.matrix. The basic model assumes the subdistribution with covariates z is a constant shift on the complementary log log scale from a baseline subdistribution function. This can be generalized by including interactions of z with functions of time to allow the magnitude of the shift to change with follow-up time, through the cov2 and tfs arguments. For example, if z is a vector of covariate values, and uft is a vector containing the unique failure times for failures of the type of interest (sorted in ascending order), then the coefficients a, b and c in the quadratic (in time) model � � + � � � + � � 2 az+bzt+zt 2 can be fit by specifying cov1=z, cov2=cbind(z,z), tf=function(uft) cbind(uft,uft*uft). This function uses an estimate of the survivor function of the censoring distribution to reweight contributions to the risk sets for failures from competing causes. In a generalization of the methodology in the paper, the censoring distribution can be estimated separately within strata defined by the cengroup argument. If the censoring distribution is different within groups defined by covariates in the model, then validity of the method requires using separate estimates of the censoring distribution within those groups. The residuals returned are analogous to the Schoenfeld residuals in ordinary survival models. Plotting the jth column of res against the vector of unique failure times checks for lack of fit over time in the corresponding covariate (column of cov1). If variance=FALSE, then some of the functionality in summary.crr and print.crr will be lost. This option can be useful in situations where crr is called repeatedly for point estimates, but standard errors are not required, such as in some approaches to stepwise model selection. Value Returns a list of class crr, with components $coef the estimated regression coefficients $loglik log pseudo-liklihood evaluated at coef $score derivitives of the log pseudo-likelihood evaluated at coef $inf -second derivatives of the log pseudo-likelihood $var estimated variance covariance matrix of coef $res matrix of residuals giving the contribution to each score (columns) at each unique failure time (rows) $uftime vector of unique failure times $bfitj jumps in the Breslow-type estimate of the underlying sub-distribution cumulative hazard (used by predict.crr()) $tfs the tfs matrix (output of tf(), if used) $converged TRUE if the iterative algorithm converged $call The call to crr $n The number of observations used in fitting the model $n.missing The number of observations removed from the input data due to missing values $loglik.null The value of the log pseudo-likelihood when all the coefficients are 0 $invinf - inverse of second derivative matrix of the log pseudo-likelihood
最新发布
07-21
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