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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