Predict subject-specific curves based on a fit from "face.sparse".
# S3 method for face.sparse
predict(object, newdata,...)A "face.sparse" fit
Input
Predicted/estimated objects at
the observation time points in newdata
if calculate.scores in object is TRUE (typically FALSE),
then predicted scores rand_eff$scores will be calculated.
...
a fitted object from the R function "face.sparse".
a data frame with three arguments:
(1) argvals: observation times;
(2) subj: subject indices;
(3) y: values of observations.
NA values are allowed in "y" but not in the other two.
further arguments passed to or from other methods.
Luo Xiao <lxiao5@ncsu.edu>
This function makes prediction based on observed data for each subject. So for each subject,
it requires at least one observed data. For the time points prediction is desired but
no observation is available, just make the corresponding data$y as NA.
Luo Xiao, Cai Li, William Checkley and Ciprian Crainiceanu, Fast covariance estimation for sparse functional data, Stat. Comput., tools:::Rd_expr_doi("10.1007/s11222-017-9744-8").
#See the examples for "face.sparse".
Run the code above in your browser using DataLab