dls | R Documentation |
Calculate the log partial likelihood and derivatives with respect to the subject log hazard ratio (compared to the baseline) for Cox proportional hazard additive model described in gamcox
. Results are used to update estimates in gamcox
function.
dls(Y,X,which,eta,span=0.5,adjust=TRUE)
Y |
a list including two elements: |
X |
a data frame containing the variables in the model. The data must be structured so that the X and Y coordinates for two-dimensional predictor (e.g., geolocation) are in the 1st and 2nd columns, respectively. |
which |
matrix index for smooth term. |
eta |
current estimated subject log hazard ratio compared to the baseline. |
span |
smoothing parameter that been used to smoothing the second derivative of the log partial likelihood. |
adjust |
|
For data that having tied failure times, Efron's approximation method is used to calculate the log partial likelihood and correspongding derivatives. Let \eta
denote the log hazard ratio, and l denote the partial likelihood. When fitting a Cox proportional hazard additive model, \eta
is updated by
\eta^{new} = \eta^{old} - \frac{dl/d{\eta}}{smooth(d^2l/d\eta^2)}
deltaeta |
difference between the input |
w |
inverse of smoothed second derivatives. |
l |
partial likelihood baed on input |
Lu Bai and Scott Bartell
Send bug reports to sbartell@uci.edu.
Hastie TJ, Tibshirani RJ. Generalized Additive Models. (Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Boca Raton, Florida, 1990).
Bristow RE, Chang J, Ziogas A, Gillen DL, Bai L, Vieira VM. Spatial Analysis of Advanced-stage Ovarian Cancer Mortality in California. American Journal of Obstetrics and Gynecology 2015, 213(1), e1-43).
gamcox
,
predict.gamcox
.
data(CAdata)
Y = CAdata[,c("time","event")]
X = CAdata[,c(3:5)]
eta = coxph(Surv(time,event)~AGE,data=CAdata)$linear.predictors
result = dls(Y,X,1:2,eta,span=0.2)
plot(eta,result$deltaeta)
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