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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