predictMMSE: Prediction of MMSE scores in their natural scale.
In NormPsy: Normalisation of Psychometric Tests

Description Usage Arguments Value Author(s) See Also Examples

From the estimates of a linear mixed model applied on normalized MMSE scores, the function computes the predicted values of MMSE in its natural 0-30 scale. The function also provides 95% confidence intervals computed by a Monte Carlo method.

1 2	predictMMSE(model, VarTime, Timelim, nTime, Xprofile, methInteg = "GH", nsim = 20, draws = FALSE, ndraws = 2000)

model

a hlme object representing a latent class linear mixed model

`VarTime`	a character string containing the name of the time variable in the model
`Timelim`	a numeric vector indicating the time limits of the prediction
`nTime`	an integer indicating the number of prediction times
`Xprofile`	a named vector containing the values of the model's covariate
`methInteg`	integration method used to compute the numerical integration. If 0 or 'GH' (the default) a Gauss-Hermite numerical approximation is employed. Value 1 or 'MC' specifies a Monte Carlo method.
`nsim`	number of integration points used for the integration method specified in argument methInteg
`draws`	optional logical indicating if 95% confidence intervals of the predictions should be calculated. If TRUE, the posterior distribution of the predicted values is approximated by a Monte Carlo method. The predicted value is given by the median value, and the confidence interval is given by the 2.5% and 97.5% percentiles of this distribution. If FALSE (the default), a point prediction is provided.
`ndraws`	optional integer indicating the number of draws in the Monte Carlo approximation if draws=TRUE.

a predMMSE object consisting in a matrix containing the prediction time, the predicted values and, if draws=TRUE, the lower and upper limits of the confidence interval.

Cecile Proust-Lima, Viviane Philipps

plot.predMMSE,normMMSE

#import data paquid from lcmm package
library(lcmm)
data(paquid)

# computation of the normalized MMSE
paquid$MMSEnorm <- normMMSE(paquid$MMSE)

# estimation of a model on the normalized MMSE scores
m <- hlme(MMSEnorm~I(age-65)*CEP, random=~I(age-65), subject="ID", data=paquid)

# prediction from the model in the natural scale of MMSE
predictMMSE(m,VarTime="age",Timelim=c(65,85),nTime=30,
Xprofile=c(CEP=1),methInteg='MC',nsim=200,draws=FALSE)