Extract Linear Predictor Values and Standard Errors For A GAMLSS Model
lpred is the GAMLSS specific method which extracts the linear predictor and its (approximate) standard errors
for a specified parameter from a GAMLSS objects.
The lpred can be also used to extract the fitted values (with its approximate standard errors) or specific terms in the model
(with its approximate standard errors) in the same way that the predict.lm() and predict.glm() functions can be used for
lm or glm objects.
The function lp extract only the linear predictor. If prediction is required for new data values then use the
function predict.gamlss().
lpred(obj, what = c("mu", "sigma", "nu", "tau"), parameter= NULL,
type = c("link", "response", "terms"),
terms = NULL, se.fit = FALSE, ...)
lp(obj, what = c("mu", "sigma", "nu", "tau"), parameter= NULL, ... )obj |
a GAMLSS fitted model |
what |
which distribution parameter is required, default |
parameter |
equivalent to |
type |
|
terms |
if |
se.fit |
if TRUE the approximate standard errors of the appropriate type are extracted |
... |
for extra arguments |
If se.fit=FALSE a vector (or a matrix) of the appropriate type is extracted from the GAMLSS object for the given parameter in what.
If se.fit=TRUE a list containing the appropriate type, fit, and its (approximate) standard errors, se.fit.
Mikis Stasinopoulos
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
(see also https://www.gamlss.com/).
data(aids) mod<-gamlss(y~poly(x,3)+qrt, family=PO, data=aids) # mod.t <- lpred(mod, type = "terms", terms= "qrt") mod.t mod.lp <- lp(mod) mod.lp rm(mod, mod.t,mod.lp)
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