pred.felmdir | R Documentation |
Perform estimation or prediction under the functional envelope linear model, using the direct estimation.
pred.felmdir(X, Y, ux, uy, t1, t2, Xnew, knots = c(0, 0.25, 0.5, 0.75, 1))
X |
Predictor function. An n by T1 matrix, T1 is number of observed time points, which is the length of t1. Here we assume that each function is observed at the same time points. |
Y |
Response function. An n by T2 matrix, T2 is number of observed time points, which is the length of t2. Here we assume that each function is observed at the same time points. |
ux |
Dimension of the predictor envelope. An integer between 0 and number of knots +2. |
uy |
Dimension of the response envelope. An integer between 0 and number of knots +2. |
t1 |
The observed time points for the predictor functions. |
t2 |
The observed time points for the response functions. |
Xnew |
The value of X with which to estimate or predict Y. A T1 dimensional vector. The observed time points should be the same as those of X. |
knots |
The location of knots of the cubic splines used for estimation. Locations should be positive. The default location of the knots are 0, 0.25, 0.5, 0.75, 1. |
This function evaluates the functional envelope linear model at new value Xnew. It can perform estimation: find the fitted value when X = Xnew, or prediction: predict Y when X = Xnew. The covariance matrix and the standard errors are also provided. The estimation method uses the direct estimation in Su et al. (2022) with cubic splines.
The output is a list that contains following components.
value |
The fitted value or the predicted value evaluated at Xnew. The fitted or predicted values are at the same observation points as Y. |
covMatrix.estm |
The covariance matrix of the fitted value at Xnew. |
SE.estm |
The standard error of the fitted value at Xnew. |
covMatrix.pred |
The covariance matrix of the predicted value at Xnew. |
SE.pred |
The standard error of the predicted value at Xnew. |
Su, Z., Li, B. and Cook, R. D. (2022+) Envelope model for function-on-function linear regression.
data(NJdata)
dataX <- matrix(NJdata[,6], nrow = 21)
X <- as.matrix(dataX[, 32:61])
dataY <- matrix(NJdata[,3], nrow = 21)
Y <- as.matrix(dataY[, 32:61])
t1 <- 0:29
t2 <- t1
m <- pred.felmdir(X, Y, 3, 1, t1, t2, X[1,])
m$value
m$SE.estm
m$SE.pred
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