Howdy everyone
I’m trying to get Odds ratio and OR confidence intervals using a probit model,
but I'm not getting.
Do you think you can help me?
I’m new with R L
naive =
summary(glm(pcr.data[,7]~boldBeta_individual+pcr.data$age,family=binomial(link=probit)))
naive_answer = c(naive$coefficients[,1:3])
#naive estimates for
#alpha (first 4 collumns: intercept; beta_intercept, beta_slope and age) and
#and SE(last 4 collumns: intercept; beta_intercept, beta_slope and age)
OR.naive = exp(1.6*coef(naive))
(till here works, the problem is with the confidence interval)
I tried to get the Standard error from the variance, but I’m not sure if this
can be done as I’ve done.
Var_coef <- 1.6^2*var(coef(naive))
SE_coef <- Var_coef/sqrt(nsample) ########## I thi k this is
correct
OR.naive.inf <- exp(OR.naive - (1.96 * SE_coef))
OR.naive.sup <- exp(OR.naive + (1.96 * SE_coef))
if I used logit link I would get the CI with confint(naïve) command, but with
probit I don't think so. Is there a way?
What should I do?
Atenciosamente,
Rosa Oliveira
--
____________________________________________________________________________
Rosa Celeste dos Santos Oliveira,
E-mail: [email protected] <mailto:[email protected]>
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Linkedin: https://pt.linkedin.com/in/rosacsoliveira
<https://pt.linkedin.com/in/rosacsoliveira>
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