Description Usage Arguments Value Examples
View source: R/FunsForOptimalV2.R
caculate the gradient value for Maximum Likelihood Estimates in LBC model
1 | ScoreFun(bb, ixx, iyy, iw, iZZ)
|
bb |
initial values for the intercept and slope coefficients |
ixx |
continuous predictor |
iyy |
binary outcome |
iw |
the weighted parameter |
iZZ |
covariates to be incorporated in the model |
a numeric gradient value of the corresponding individual observation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | iZZ <- matrix(0,100,10)
ixx <- matrix(0,100,1)
iyy <- matrix(0,100,1)
iw <-matrix(0,100,1)
# ixx: continuous predictor
# iyy: binary outcome
# iZZ: covariates to be incorporated in the model
# iw: The weighted parameter
# myLBC <-
# maxLik(
# logLik = LogLikeFun,
# grad = ScoreFun,
# start = inits,
# ixx = ixx,
# iyy = iyy,
# iw = iw,
# iZZ = as.matrix(iZZ)
# )
# as.matrix returns all values of iZZ as a matrix
|
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