Description Usage Arguments Details Value Author(s) References See Also Examples
Make prediction based on a fitted FuncompCGL
object.
1 2 3 4 5 |
object |
fitted |
Znew |
data frame or matrix |
Zcnew |
matrix |
s |
value(s) of the penalty parameter |
T.name |
a character string specifying names of the time variable and the Subject
ID variable in |
ID.name |
a character string specifying names of the time variable and the Subject
ID variable in |
Trange, interval, insert, basis_fun, degree, method |
the same as those in |
sseq |
full set of potential time points of observations;
used for interpolation when |
... |
not used. |
s
is the vector at which predictions are requested. If s
is not in the lam
sequence used for fitting the model, the predict
function uses linear interpolation.
If the data frame X
is provided in FuncompCGL
mode, the integral
for new data newx
is taken the same as that in the fitted
FuncompCGL
model. This means that the parameters degree
,
basis_fun
, insert
, method
, inteval
,
Trange
, and K
are exactly the same as these in the provided
object
. If insert="X"
or "basis"
, sseq
is the
sorted sequence of all the observed time points in fitting FuncompCGL
model and
all the observed time points in newx
. Then interpolation is
conducted on sseq
. If matrix X
after integral is provided in
the FuncompCGL
object, these parameters are required.
predicted values at the requested value(s) for s
.
Zhe Sun and Kun Chen
Sun, Z., Xu, W., Cong, X., Li G. and Chen K. (2020) Log-contrast regression with functional compositional predictors: linking preterm infant's gut microbiome trajectories to neurobehavioral outcome, https://arxiv.org/abs/1808.02403 Annals of Applied Statistics
FuncompCGL
, and coef
,
plot
and print
methods for "FuncompCGL"
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | p = 30
n_train = 50
n_test = 30
df_beta = 5
beta_C_true = matrix(0, nrow = p, ncol = df_beta)
beta_C_true[1, ] <- c(-0.5, -0.5, -0.5 , -1, -1)
beta_C_true[2, ] <- c(0.8, 0.8, 0.7, 0.6, 0.6)
beta_C_true[3, ] <- c(-0.8, -0.8 , 0.4 , 1 , 1)
beta_C_true[4, ] <- c(0.5, 0.5, -0.6 ,-0.6, -0.6)
Data <- Fcomp_Model(n = n_train, p = p, m = 0, intercept = TRUE,
SNR = 2, sigma = 2,
rho_X = 0, rho_T = 0.5, df_beta = df_beta,
n_T = 20, obs_spar = 1, theta.add = c(3,4,5),
beta_C = as.vector(t(beta_C_true)))
m1 <- FuncompCGL(y = Data$data$y, X = Data$data$Comp , Zc = Data$data$Zc,
intercept = Data$data$intercept, k = df_beta)
arg_list <- as.list(Data$call)[-1]
arg_list$n <- n_test
TEST <- do.call(Fcomp_Model, arg_list)
predmat <- predict(m1, Znew = TEST$data$Comp, Zcnew = TEST$data$Zc)
predmat <- predict(m1, Znew = TEST$data$Comp, Zcnew = TEST$data$Zc, s = c(0.5, 0.1, 0.05))
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