Solved – Confidence intervals with gamlss package

My question regards the use of the gamlss package. I am using gamlss package to fit a dataset to a logistic function. There is only one predictor, let me denote it with x and because the overall dependence is not exactly sigmoid, a better model is achieved by wrapping the predictor x in a smoother function. I chose the cubic spline smoother (cs). The response is binomial. I'll denote it with y:

y = cbind(number of successful events, total number - number of successful events).  

The R code is the following:

cs15 <- gamlss(y~cs(x, df=15),, family=BI,  data=mydata, trace=FALSE)  

I want to estimate predicted response for a set of predictors not contained in the original data set. I know this can be achieved with the function:

cs15fit = predict(cs25, newdata=data.frame(x=xnew), type="response") 

However, my problem is that I also want to estimate the standard errors, which should be done by adding

cs15fit=predict(cs25, newdata=data.frame(x=xnew), type="response", 

But the addition of = T produces the following error: = TRUE is not supported for new data values at the moment

Anyone know how I can still find standard errors for the new values?

See the answer to this question: How are the standard errors computed for the fitted values from a logistic regression?

The linked answer relates to a glm. I am not sure whether using a smoothing term as you have done affects the standard error calculation, but it should give you an idea of how standard errors are computed in predict.

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