nsf, Tm, CparamCalculation, power.matrix, base, SmoothedEstimate {NPHazardRate} | R Documentation |
Auxiliary functions for discrete semiparametric and kernel smooth hazard rate estimation
nsf(xin, cens, xout)
Tm(tk, xout, distribution, par1, par2)
CparamCalculation(gamparam, VehHazard)
power.matrix(M, n)
base(m, b)
SmoothedEstimate(NonParEst, VehHazard, gammapar, SCproduct, Cpar)
xin |
A vector of data points. Missing values not allowed. |
cens |
A vector of 1s and zeros, 1's indicate uncensored observations, 0's correspond to censored obs. |
xout |
The points where the estimate should be calculated. |
tk |
desing points for the NPMLE estimate. |
distribution |
which distribution to use? |
par1 |
distribution parameter 1 |
par2 |
distribution parameter 2 |
gamparam |
gamma parameter |
M |
a matrix to be raised to a power |
n |
the power the matrix will be raised at |
m |
express m as a power of b |
b |
express m as a power of b |
NonParEst |
The crude nonparametric hazard rate estimate. |
VehHazard |
Vehicle hazard rate |
gammapar |
gamma parameter |
SCproduct |
SC product, the result of DetermineSCprod |
Cpar |
C parameter, the result of CparamCalculation. |
Auxiliary functions for discrete hazard rate estimators. The function nsf
is used for the kernel smooth estimate TutzPritscher
.
Tm used to calculate \max(t_k; 1-\sum_{l=0}^k \eta_l > \epsilon), \epsilon>0
in the implementation of the semiparametric estimate
CparamCalculationreturns the C smoothing parameter calculated as C= \gamma/\max_{k \geq 0} ( \lambda(t_{k-1}) + \lambda(t_k) + \lambda(t_{k+1}) )
DetermineSCprodthis finds SC = \gamma((n+1) \hat B_1)^{-1} \hat V_1
n = number of obs, gammapar = sum of vehicle haz at xout (computed elsewhere)
A vector with the values of the hazard rate estimates.