Description Usage Arguments Value Author(s) References See Also Examples
View source: R/coxph_mpl_dc.control.R
This is used to set various numeric parameters controlling a Cox model fit using coxph_mpl_dc. Typically it would only be used in a call to coxph_mpl_dc.
1 2 3 4 5 6 7 8 9 10 11  coxph_mpl_dc.control(ordSp,
binCount, tie,
tau, copula,
pent, smpart, penc, smparc,
maxit2, maxit,
mid, asy, ac, cv,
ac.theta, ac.gamma, ac.Utheta, ac.Ugamma,
min.theta, min.gamma,
min.ht, min.hc, min.St, min.Sc, min.C, min.dC,
eps, tol.thga, tol.bph, cat.smpar, tol.smpar
)

ordSp 
the order of spline for the basis function for baseline hazard for both T and C,
can be 'piecewise constant' if 
binCount 
the number of subjects in each discretized bin, can be selected either by trial and error or AIC method
Default is 
tie 
tie='No' if tied observations are not existed, otherwise tied observations existed. Default is 
tau 
the kendall’s correlation coefficient between T and C. Default is 
copula 
Archimedean copula type, i.e. 'independent', 'clayton', 'gumbel' and 'frank'. Default is 
pent 
penalty function type for T, i.e. mat1 (first order difference) or mat2 (second order difference) for piecewise constant basis, penalty_mspl for mspline basis
Default is 
smpart 
value of smoothing parameter for T, can be selected by either trial and error or cross validation method.
Note that smpart can be also estimated by restricted maximum likelihood (i.e. 
penc 
penalty function type for C, i.e. mat1 (first order difference) or mat2 (second order difference) for piecewise constant basis, penalty_mspl for mspline basis
Default is 
smparc 
value of smoothing parameter for C, can be selected by either trial and error or cross validation method.
Note that smparc can be also estimated by restricted maximum likelihood (i.e. 
maxit2 
maximum number of iterations for smpart and smparc. Defualt is 
maxit 
maximum number of iteration for updating beta, phi, theta and gamma given fixed smpart and smparc.
Default is 
mid 
the middle matrix selection for the sandwich formula that used to computed the asymptotic covariance matrix,
i.e. 
asy 

ac 

cv 

ac.theta 
the minimum value of theta for active contraints. Default is 
ac.gamma 
the minimum value of gamma for active contraints. Default is 
ac.Utheta 
the minimum value of Utheta (the first derivative of the penalized loglikelihood with respect to theta) for active contraints. Default is 
ac.Ugamma 
the minimum value of Ugamma (the first derivative of the penalized loglikelihood with respect to gamma) for active contraints. Default is 
min.theta 
a value indicating the minimal baseline hazard parameter value theta updated at each iteration.
Baseline hazard parameter theta estimates at each iteration lower than min.theta will be considered as min.theta. Default is 
min.gamma 
a value indicating the minimal baseline hazard parameter value gamma updated at each iteration.
Baseline hazard parameter gamma estimates at each iteration lower than min.gamma will be considered as min.gamma. Default is 
min.ht 
a value indicating the minimal baseline hazard of T updated at each iteration. Baseline hazard estimates of T at each iteration lower than min.ht will be considered as min.ht.
Default is 
min.hc 
a value indicating the minimal baseline hazard of C updated at each iteration. Baseline hazard estimates of C at each iteration lower than min.hc will be considered as min.hc.
Default is 
min.St 
a value indicating the minimal baseline survival of T updated at each iteration. Baseline survival estimates of T at each iteration lower than min.St will be considered as min.St.
Default is 
min.Sc 
a value indicating the minimal baseline survival of C updated at each iteration. Baseline survival estimates of C at each iteration lower than min.Sc will be considered as min.Sc.
Default is 
min.C 
a value indicating the minimal copula K(u,v) at each iteration, lower than min.C will be considered as min.C.
Default is 
min.dC 
a value indicating the minimal first i.e. dK(u,v)/du and dK(u,v)/dv and second i.e. d^2K(u,v)/dudv derivatives of copula K(u,v) at each iteration,
lower than min.dC will be considered as min.dC. Default is 
eps 
a small positive value added to the diagonal of a square matrix. Default value is 
tol.thga 
the convergence tolerence value for both theta and gamma.
Convergence is achieved when the maximum absolute difference between the parameter estimates at iteration k and iteration k1 is smaller than tol.thga.
Default is 
tol.bph 
the convergence tolerence value for both beta and phi.
Convergence is achieved when the maximum absolute difference between the parameter estimates at iteration k and iteration k1 is smaller than tol.bph.
Default is 
cat.smpar 
cat.smpar='Yes' to display the smoothing parameters estimation process, otherwise not to display.
Default is 
tol.smpar 
the convergence tolerence value for both smpart and smparc.
Convergence is achieved when the maximum absolute difference between the parameter estimates at iteration k and iteration k1 is smaller than tol.smpar.
Default is 
A list containing the values of each of the above arguments for most of the inputs of Coxph_mpl_dc.
Jing Xu, Jun Ma, Thomas Fung
Ma, J. and Heritier, S. and Lo, S. (2014). "On the Maximum Penalised Likelihood Approach forProportional Hazard Models with Right Censored Survival Data". Computational Statistics and Data Analysis 74, 142156.
Xu J, Ma J, Connors MH, Brodaty H. (2018). "Proportional hazard model estimation under dependent censoring using copulas and penalized likelihood". Statistics in Medicine 37, 2238–2251.
plot.coxph_mpl_dc
, coxph_mpl_dc
, coef.coxph_mpl_dc
1 2 3 4 5 6  control < coxph_mpl_dc.control(ordSp=4,
binCount=40,
tau=0.8, copula='frank',
pent='penalty_mspl', smpart='REML', penc='penalty_mspl', smparc='REML',
cat.smpar='No'
)

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