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Smooth construct, predict, and plot for random effects with numeric inputs
#' Create a random effect basis with integers rather than factors
#' @import mgcv
#' @export
smooth.construct.re2.smooth.spec <- function (object, data, knots) {
if (!is.null(object$id))
stop("random effects don't work with ids.")
if(any(sapply(data, is.numeric))) data <- lapply(data, as.factor) ## <-- All I did was this (and below)
form <- as.formula(paste("~", paste(object$term, collapse = ":"),
"-1"))
object$X <- model.matrix(form, data)
object$bs.dim <- ncol(object$X)
if (inherits(object, "tensor.smooth.spec")) {
object$margin <- list()
maxd <- maxi <- 0
for (i in 1:object$dim) {
form1 <- as.formula(paste("~", object$term[i], "-1"))
object$margin[[i]] <- list(X = model.matrix(form1,
data), term = object$term[i], form = form1,
by = "NA")
class(object$margin[[i]]) <- "random.effect2"
d <- ncol(object$margin[[i]]$X)
if (d > maxd) {
maxi <- i
maxd <- d
}
}
if (maxi < object$dim) {
ns <- object$dim
ind <- 1:ns
ind[maxi] <- ns
ind[ns] <- maxi
object$margin <- object$margin[ind]
object$term <- rep("", 0)
for (i in 1:ns) object$term <- c(object$term, object$margin[[i]]$term)
object$label <- paste0(substr(object$label, 1, 2),
paste0(object$term, collapse = ","), ")", collapse = "")
object$rind <- ind
if (!is.null(object$xt$S))
stop("Please put term with most levels last in 're' to avoid spoiling supplied penalties")
}
}
if (is.null(object$xt$S)) {
object$S <- list(diag(object$bs.dim))
object$rank <- object$bs.dim
}
else {
object$S <- if (is.list(object$xt$S))
object$xt$S
else list(object$xt$S)
for (i in 1:length(object$S)) {
if (ncol(object$S[[i]]) != object$bs.dim || nrow(object$S[[i]]) !=
object$bs.dim)
stop("supplied S matrices are wrong diminsion")
}
object$rank <- object$xt$rank
}
object$null.space.dim <- 0
object$C <- matrix(0, 0, ncol(object$X))
object$form <- form
object$side.constrain <- FALSE
object$plot.me <- TRUE
object$te.ok <- if (inherits(object, "tensor.smooth.spec"))
0
else 2
object$random <- TRUE
object$noterp <- TRUE
class(object) <- if (inherits(object, "tensor.smooth.spec"))
c("random.effect2", "tensor.smooth")
else "random.effect2"
object
}
#' Predict from a random effect basis with integers rather than factors
#' @import mgcv
#' @export
Predict.matrix.random.effect2 <- function (object, data) {
if(any(sapply(data, is.numeric))) data <- lapply(data, as.factor) # <-- And did it again here
X <- model.matrix(object$form, model.frame(object$form, data,
na.action = na.pass))
X[!is.finite(X)] <- 0
X
}
#' Plot a numeric random effect
#' @import mgcv
#' @export
plot.random.effect2 <- function (x, P = NULL, data = NULL, label = "", se1.mult = 1,
se2.mult = 2, partial.resids = FALSE, rug = TRUE, se = TRUE,
scale = -1, n = 100, n2 = 40, n3 = 3, pers = FALSE, theta = 30,
phi = 30, jit = FALSE, xlab = NULL, ylab = NULL, main = NULL,
ylim = NULL, xlim = NULL, too.far = 0.1, shade = FALSE,
shade.col = "gray80", shift = 0, trans = I, by.resids = FALSE,
scheme = 0, ...) {
# Did nothing here at all, just renamed the function to `....random.effect2`
if (is.null(P)) {
if (!x$plot.me)
return(NULL)
else {
raw <- data[x$term][[1]]
p <- x$last.para - x$first.para + 1
X <- diag(p)
if (is.null(xlab))
xlabel <- "Gaussian quantiles"
else xlabel <- xlab
if (is.null(ylab))
ylabel <- "effects"
else ylabel <- ylab
if (!is.null(main))
label <- main
return(list(X = X, scale = FALSE, se = FALSE, raw = raw,
xlab = xlabel, ylab = ylabel, main = label))
}
}
else {
b <- as.numeric(trans(P$fit + shift))
qqnorm(b, main = P$main, xlab = P$xlab, ylab = P$ylab,
...)
qqline(b)
}
}
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