toyData <- read.csv("https://raw.githubusercontent.com/alicebalard/Article_IntensityEimeriaHMHZ/master/data/cleanedData.csv")
toyData <-toyData[toyData$Aspiculuris_Syphacia != 0,]
m <- mlHyb(Aspiculuris_Syphacia~., data = toyData, model = "negbin", hybridEffect = TRUE)
m
m0 <- mlHyb(Aspiculuris_Syphacia~., data = toyData, model = "negbin", hybridEffect = FALSE)
m0
lrt.mlHyb(m)
##### to do
# groups
mlHyb(Aspiculuris_Syphacia~Sex, data = toyData, model = "negbin", hybridEffect = TRUE)
mlHyb(Aspiculuris_Syphacia~., data = toyData, model = "negbin", hybridEffect = TRUE)
all.vars(Aspiculuris_Syphacia~.)[2]
mytest <- function(formula, data, model, hybridIndex = "HI", myparamBounds = "default",
hybridEffect = TRUE,
config = list(optimizer = "optimx", method = c("L-BFGS-B", "bobyqa"), control = list(follow.on = TRUE))){
# extract response from formula
response <- all.vars(formula)[1]
# so far, implemented for 1 categorical group (e.g. sex)
group <- all.vars(formula)[2]
group}
mlHyb(Aspiculuris_Syphacia~Sex, data = toyData, model = "negbin", hybridEffect = TRUE)
# extract factor
length(all.vars(Aspiculuris_Syphacia~Sex))
length(all.vars(Aspiculuris_Syphacia~.))
length(all.vars(Aspiculuris_Syphacia~Sex*group))
# fix sides?
# print table 4 hypothesis
# plots
# plot(lm(Aspiculuris_Syphacia~Sex, data = toyData)) Ca ira
# > lm.fit
# function (x, y, offset = NULL, method = "qr", tol = 1e-07, singular.ok = TRUE,
# ...)
# {
# if (is.null(n <- nrow(x)))
# stop("'x' must be a matrix")
# if (n == 0L)
# stop("0 (non-NA) cases")
# p <- ncol(x)
# if (p == 0L) {
# return(list(coefficients = numeric(), residuals = y,
# fitted.values = 0 * y, rank = 0, df.residual = length(y)))
# }
# ny <- NCOL(y)
# if (is.matrix(y) && ny == 1)
# y <- drop(y)
# if (!is.null(offset))
# y <- y - offset
# if (NROW(y) != n)
# stop("incompatible dimensions")
# if (method != "qr")
# warning(gettextf("method = '%s' is not supported. Using 'qr'",
# method), domain = NA)
# chkDots(...)
# z <- .Call(C_Cdqrls, x, y, tol, FALSE)
# if (!singular.ok && z$rank < p)
# stop("singular fit encountered")
# coef <- z$coefficients
# pivot <- z$pivot
# r1 <- seq_len(z$rank)
# dn <- colnames(x)
# if (is.null(dn))
# dn <- paste0("x", 1L:p)
# nmeffects <- c(dn[pivot[r1]], rep.int("", n - z$rank))
# r2 <- if (z$rank < p)
# (z$rank + 1L):p
# else integer()
# if (is.matrix(y)) {
# coef[r2, ] <- NA
# if (z$pivoted)
# coef[pivot, ] <- coef
# dimnames(coef) <- list(dn, colnames(y))
# dimnames(z$effects) <- list(nmeffects, colnames(y))
# }
# else {
# coef[r2] <- NA
# if (z$pivoted)
# coef[pivot] <- coef
# names(coef) <- dn
# names(z$effects) <- nmeffects
# }
# z$coefficients <- coef
# r1 <- y - z$residuals
# if (!is.null(offset))
# r1 <- r1 + offset
# if (z$pivoted)
# colnames(z$qr) <- colnames(x)[z$pivot]
# qr <- z[c("qr", "qraux", "pivot", "tol", "rank")]
# c(z[c("coefficients", "residuals", "effects", "rank")], list(fitted.values = r1,
# assign = attr(x, "assign"), qr = structure(qr, class = "qr"),
# df.residual = n - z$rank))
# }
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