## S3 method for class 'nmm'
logLik(
  object,
  new_coef = NULL,
  separatenmm = FALSE,
  transform = FALSE,
  methodopt = "NA",
  ...
)

#example  continuous nonlinear
library(systemfit)
data( ppine , package="systemfit")
hg.formula <- hg ~ exp( h0 + h1*log(tht) + h2*tht^2 + h3*elev)
dg.formula <- dg ~ exp( d0 + d1*log(dbh) + d2*hg + d3*cr)
labels <- list( "height.growth", "diameter.growth" )
model <- list( hg.formula, dg.formula )
start.values <- c(h0=-0.5, h1=0.5, h2=-0.001, h3=0.0001,
                  d0=-0.5, d1=0.009, d2=0.25, d3=0.005)
model.sur <- nlsystemfit( "SUR", model, start.values, data=ppine, eqnlabels=labels )
eq_c <- as.character(c(hg.formula, dg.formula))
parl <- c(paste0("h", 0:3),paste0("d", 0:3))
res <- nmm(ppine, eq_c=eq_c, start_v=start.values, par_c=parl, eq_type = "cont",
 best_method = FALSE)
logLik(res)
logLik(res, new_coef=res$estimate)
logLik(res, new_coef=model.sur$b)
#example discrete
library(mlogit)
data("Fishing", package = "mlogit")
Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide", choice = "mode")
## a pure "conditional" model
mres <- summary(mlogit(mode ~ price + catch, data = Fish))
data <- prepare_data(Fish %>% data.frame %>% dplyr::select(-idx),
choice="alt", dummy="mode", PeID="chid", mode_spec_var = c("price", "catch"),
type="long")
eq_d <- c("a1 + p1 * price_1 + p2 * catch_2", "a2 + p1 * price_2 + p2 * catch_2",
             "a3 + p1 * price_3 + p2 * catch_3", "a4 + p1 * price_4 + p2 * catch_4")
par_d <- c(paste0("a", 1:4), paste0("p", 1:2))
res <- nmm(data, eq_d=eq_d,  eq_type="disc", fixed_term=FALSE, par_d=par_d,
best_method=FALSE)
logLik(res)
logLik(res, new_coef=res$estimate)
logLik(res, new_coef=mres$coefficients)