[R] Loop for in R to generate several variables
Hi everybody,
I have to create several variables of this form:
Yind = L0 + L1*X1 + L2*X2 + L3*X3 + K*Cind + n
where ind varires in {1,...,10}
I thought to this loop for but it does not work:
for (ind in 1:10) {
Yind = L0 + L1*X1 + L2*X2 + L3*X3 + K*Cind + n
}
Any suggestions?
Thank you.
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[R] How to predict probabilities after using lmer
Dear R-users,
I'm using lmer to fit two-level logistic models and I'm interested in
predicted probabilities that I get in this way (using "fitted"):
glm1 = lmer(XY$T1~X1 + X2 + X3 + (1|Cind), family=binomial) #estimation of a
two-level logit model
fit1=fitted(glm1) # I get the fitted linear predictor
ilog = function(x) { 1/(1 + exp(-x)) }
ps1=ilog(fit1) # In order to get the estimated probabilities
Is this procedure correct? In this way I'm getting the "conditional
probabilities", right? Is there any function I can use in order to get the
"empirical bayes (EB) probabilities"? Any suggestion?
And more generally, can you suggest me any paper/textbook/notes clarifying
when it's more suitable to use one kind of probability than the other?
Here are the formulas for what I labelled as conditional and EB probability:
The model is: logit(P(Y=1)) = a + bX + u
conditional: P(Y=1/u=u^) = 1/(1 + exp(-(a^ + b^X + u^)))
EB: ∫[1/(1 + exp(-(a^ + b^X + u)))] x Posterior (u/Y, X) du
(u is the random effect; ^ indicates estimated)
Many thanks
--
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http://www.nabble.com/How-to-predict-probabilities-after-using-lmer-tp20678825p20678825.html
Sent from the R help mailing list archive at Nabble.com.
__
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] predicted probabilities after lmer
Dear R-users,
I'm using lmer to fit two-level logistic models and I'm interested in
predicted probabilities that I get in this way (using "fitted"):
glm1 = lmer(XY$T1~X1 + X2 + X3 + (1|Cind), family=binomial) #estimation of a
two-level logit model
fit1=fitted(glm1) # I get the fitted linear predictor
ilog = function(x) { 1/(1 + exp(-x)) }
ps1=ilog(fit1) # In order to get the estimated probabilities
Is this procedure correct? In this way I'm getting the "conditional
probabilities", right? Is there any function I can use in order to get the
"empirical bayes (EB) probabilities"? Any suggestion?
And more generally, can you suggest me any paper/textbook/notes clarifying
when it's more suitable to use one kind of probability than the other?
Here are the formulas for what I labelled as conditional and EB probability:
The model is: logit(P(Y=1)) = a + bX + u
conditional: P(Y=1/u=u^) = 1/(1 + exp(-(a^ + b^X + u^)))
EB: ∫[1/(1 + exp(-(a^ + b^X + u)))] x Posterior (u/Y, X) du
(u is the random effect; ^ indicates estimated)
Many thanks
--
View this message in context:
http://www.nabble.com/predicted-probabilities-after-lmer-tp20796391p20796391.html
Sent from the R help mailing list archive at Nabble.com.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
