logliklihood function Saved to this PC References Mailings Review View Help Tell

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logliklihood function Saved to this PC References Mailings Review View Help Tell me what you want to do 1Normal 1 No Spac.. Heading 1 Heading 2Title Paragraph Styles Let tm, Immm denote an adaptive progressive Type-Il censored sample, with Rm) being the progressive censoring scheme. The maximum likelihood function based on this adaptive progressively type-II censored sample by taking In for the likelihood function is then In L(0: t)-Constant +??-1 In--Travnin 1-2 ??-1 in ( tnt ?(int,n-p)-In ( 1 + e

Explanation / Answer

# redefine log likelihood

l2 = function(para){

beta = matrix(NA, row = len(para) - 1, col = 1)

beta[,1] = para[-length(para)]

sigma    = para[[length(para)]]

minus = -sum(log(dnorm(Y - X %*% beta, 0, sigma)))

return(minus)

}

# regress Y on X1

X <- model.matrix(lm(y ~ x1, data = df))

mle2(ll2, start = c(beta0 = 0.1, beta1 = 0.2, sigma = 1),

vepar = TRUE, parnas = c('beta0', 'beta1', 'sigma'))

#regress Y on X1+X2

X <- model.matrix(lm(y ~ x1 + x2, data = df))

mle2(ll2, start = c(beta0 = 0.1, beta1 = 0.2, beta2 = 0.1, sigma = 1),

      vepar = TRUE, parnas = c('beta0', 'beta1', 'beta2', 'sigma'))

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