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# Test loglik functions G and W #
# Test GEV function
test_that("GEV loglik test", {
data(Mass, envir = environment())
data(MassClimate, envir = environment())
funcenv <- environment()
xvar <- MassClimate$Temp
bdate <- Mass$Date
cdate <- MassClimate$Date
furthest <- 2
closest <- 1
duration <- (furthest-closest) + 1
baseline <- lm(Mass ~ 1, data = Mass)
cmatrix <- matrix(ncol = (duration), nrow = length(bdate))
nullmodel <- AICc(baseline)
funcenv$modno <- 1
funcenv$DAICc <- list()
funcenv$par_shape <- list()
funcenv$par_scale <- list()
funcenv$par_location <- list()
cont <- convertdate(bdate = bdate, cdate = cdate, xvar = xvar,
cinterval = "day", type = "variable", spatial = NULL) # create new climate dataframe with continuous daynumbers, leap days are not a problem
for (i in 1:length(bdate)){
for (j in closest:furthest){
k <- j - closest + 1
cmatrix[i, k] <- xvar[match(cont$bintno[i] - j,cont$cintno)] #Create a matrix which contains the climate data from furthest to furthest from each biological record#
}
}
modeldat <- model.frame(baseline)
modeldat$climate <- matrix(ncol = 1, nrow = nrow(cmatrix), seq(from = 1, to = nrow(cmatrix), by = 1))
test <- modloglik_G(par = c(3, 0.2, 0), baseline = baseline, k = 0,
modeloutput = lm(Mass ~ climate, data = modeldat),
duration = duration, cmatrix = cmatrix,
nullmodel = nullmodel, funcenv = funcenv)
# Test that modloglik_G produces a deltaAIC output
expect_false(is.na(test))
# Test that the output is less than 2 (i.e. AIC value is reasonable)
expect_true(test <= 2)
#Test that values are the same as previous R version
expect_true(round(test, 1) == -2.7)
})
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