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), sigma.fo=~1, 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 `se.fit=T`

:

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

But the addition of `se.fit = T`

produces the following error:

se.fit = TRUE is not supported for new data values at the moment

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

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#### Best Answer

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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