Created
September 11, 2019 13:16
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Multivariate Adaptive Regression Splines smoother for GAMLSS models
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ma <- function (formula, method = c("earth"), control = list(NULL), | |
...) { | |
method <- match.arg(method) | |
scall <- deparse(sys.call(), width.cutoff = 200L) | |
if (!is(formula, "formula")) | |
stop("formula argument in ma() needs a formula starting with ~") | |
rexpr <- grepl("gamlss", sys.calls()) | |
for (i in length(rexpr):1) { | |
position <- i | |
if (rexpr[i] == TRUE) | |
break | |
} | |
gamlss.env <- sys.frame(position) | |
if (sys.call(position)[1] == "predict.gamlss()") { | |
Data <- get("data", envir = gamlss.env) | |
} | |
else if (sys.call(position)[1] == "gamlss()") { | |
if (is.null(get("gamlsscall", envir = gamlss.env)$data)) { | |
Data <- model.frame(formula) | |
} | |
else { | |
Data <- get("gamlsscall", envir = gamlss.env)$data | |
} | |
} | |
else { | |
Data <- get("data", envir = gamlss.env) | |
} | |
Data <- data.frame(eval(substitute(Data))) | |
len <- dim(Data)[1] | |
xvar <- rep(0, len) | |
attr(xvar, "formula") <- formula | |
attr(xvar, "method") <- method | |
attr(xvar, "control") <- control | |
attr(xvar, "gamlss.env") <- gamlss.env | |
attr(xvar, "data") <- as.data.frame(Data) | |
attr(xvar, "call") <- substitute(gamlss.ma(data[[scall]], | |
z, w, ...)) | |
attr(xvar, "class") <- "smooth" | |
xvar | |
} | |
gamlss.ma <- function (x, y, w, xeval = NULL, ...) | |
{ | |
formula <- attr(x, "formula") | |
formula <- as.formula(paste("y", deparse(formula, width.cutoff = 500L), | |
sep = "")) | |
method <- attr(x, "method") | |
control <- as.list(attr(x, "control")) | |
gamlss.env <- as.environment(attr(x, "gamlss.env")) | |
OData <- attr(x, "data") | |
Data <- if (is.null(xeval)) | |
OData | |
else OData[seq(1, length(y)), ] | |
Data <- data.frame(eval(substitute(Data)), y, w) | |
rexpr <- regexpr("gamlss", sys.calls()) | |
fit <- if (method == "earth") { | |
control$x <- model.matrix(formula, Data)[,-1] | |
control$y <- model.response(model.frame(formula, Data)) | |
control$weights <- w | |
do.call(earth, control) | |
} | |
df <- ncol(model.matrix(fit)) | |
residuals <- resid(fit) | |
fv <- predict(fit) | |
if (is.null(xeval)) { | |
list(fitted.values = fv, residuals = residuals, nl.df = df, | |
lambda = NA, coefSmo = fit, var = NA) | |
} | |
else { | |
ndata <- subset(OData, source == "newdata") | |
pred <- predict(fit, newdata = ndata) | |
} | |
} |
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