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类别排队分析及小鼠弗氏细胞数据(CBQ Analysis and Mouse Vertebrae Data)_算法理论_科研数据集

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类别排队分析及小鼠弗氏细胞数据(CBQ Analysis and Mouse Vertebrae Data)
数据摘要:
The files comprise the following: fb5ml.data -- Whin Sill data; qcet2.data -- mouse vertebrae data; cbq9.r -- R routines to carry out cbq analysis; example.r -- sample file of commands to reproduce output of relevant paper.

中文关键词:
算法,统计,弗氏细胞,鼠,基于类别排队,

英文关键词:
algorithm,statistic,Vertebrae,Mouse,CBQ,

数据格式:
TEXT

数据用途:

design of the algorithm

数据详细介绍:
CBQ Analysis and Mouse Vertebrae Data

The files comprise the following: fb5ml.data -- Whin Sill data; qcet2.data -- mouse vertebrae data; cbq9.r -- R routines to carry out cbq analysis; example.r -- sample file of commands to reproduce output of the paper. The R routines have not been packaged as a library, but are in the file cbq9.r The routines are not very polished and may need refining for awkward datasets, but here is a brief description of how to use them. 1. Read in the R routines using source("cbq9.r") 2. The data should be presented in the form of a complex n x p matrix, zh, say where each row is a unit vector. 3. Carry out some preliminary calculations by setting pre=preliminaries(zh) 4. Set nparam=1,2, or 3 depending on whether you wish to estimate all the parameters, just the concentration parameters, or just the mean parameters (with any parameters not being estimated fixed at the tangent estimator values). 5. Do some preparations for the saddlepoint aprrox by prep=cbq.sadint5.prep(pre$Omega0.half%*%pre$Omega0.half,niter=1) 6. Set con.ind=1,2,3 or 4 depending on which approximation to the normalizing constant is to be used (simple asymptotic, refined asymptotic, saddlepoint or exact (the last only available if p=2). Then run

param=pre$param0; p=pre$p; p2=2*p-2 if(nparam==2) param=param[1:(p2*(p2+1)/2)] if(nparam==3) param=param[(p2*(p2+1)/2)+(1:p2)] out=nlm.vm(cbq.mlden8.std,param,pre=pre,prep=prep,constr=constr.cbq.std, con.ind=con.ind, nparam=nparam,niter=1,verbose=0) 7. Then "out" contains various pieces of information about the fitted parameters. To get the parameter estimates in the mean-standardized coordinate", run process(zh,pre=pre,out) 8. The parameters can be mapped back to the original coordinates using pre$g0 which corresponds to the complex conjugate of G(mu) in the paper. 9. Some further information about comparing parameter estimates can be found in the file example.r

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