Factor covariates fail to produce results

Working from the example code associated with the function make.detectability, I wanted to simulate a study with multiple strata and multiple species within each stratum.

The make.detectability example works, as far as it goes, but I added to the example, a survey design and a simulation to actually simulate a survey.

Minimum reproducable example

library(dsims)

# Multi-strata example (make sf shape)
s1 = matrix(c(0,0,0,2,1,2,1,0,0,0),ncol=2, byrow=TRUE)
s2 = matrix(c(1,0,1,2,2,2,2,0,1,0),ncol=2, byrow=TRUE)
pol1 = sf::st_polygon(list(s1))
pol2 = sf::st_polygon(list(s2))
sfc <- sf::st_sfc(pol1,pol2)
strata.names <- c("low", "high")

mytrunc <- 0.2
sf.pol <- sf::st_sf(strata = strata.names, geom = sfc)

region <- make.region(region.name = "Multi-strata Eg",
                      strata.name = strata.names,
                      shape = sf.pol)

mydesign <- make.design(region=region, design="systematic", 
                        truncation=mytrunc, samplers = 30)
somelines <- generate.transects(mydesign)
plot(region, somelines, covered.area=TRUE)

density <- make.density(region = region,
                        x.space = 0.22,
                        constant = c(20,50))

covs <- list()
covs$size <- list(list(distribution = "poisson", lambda = 25),
                  list(distribution = "poisson", lambda = 15))
covs$sex <- data.frame(level = rep(c("male", "female"),2),
                       prob = c(0.5, 0.5, 0.6, 0.4),
                       strata = c(rep("low",2),rep("high",2)))

# Define the population description (this time using the density to determine
# the population size)
popdesc <- make.population.description(region = region,
                                       density = density,
                                       covariates = covs,
                                       fixed.N = FALSE)

cov.param <- list()
cov.param$size <- c(log(1.02),log(1.005))
cov.param$sex <- data.frame(level = c("male", "female", "male", "female"),
                            param = c(log(1.5), 0, log(1.7), log(1.2)),
                            strata = c("low","low","high","high"))

# define the detecability
detect <- make.detectability(key.function = "hn",
                             scale.param = 0.08,
                             cov.param = cov.param,
                             truncation = mytrunc)

plot(detect, popdesc)

myds <- make.ds.analysis(truncation=mytrunc)

simmake <- make.simulation(reps=10, design=mydesign,
                           population.description=popdesc,
                           detectability=detect,
                           ds.analysis=myds)
seeit <- run.survey(simmake)
plot(seeit, region)
out <- run.simulation(simmake)

No replicate from the simulation produced results, because no animals were detected.

Digging into the code, I discovered the following via debug():

My first stop in debugging was simulate.detections that generated the error messages.

`simulate.detections`

determines what animals are detected
uses poss.distances
- how is that constructed
because scale.param within poss.distances is NA

However, the problem did not arise in simulate.detections, but rather in calculate.scale.param

`calculate.scale.param`

does what it is named
creates scale parameter value for each animal
problems when there are factor covariates
- logic doesn't work when creating factor.cols (lines 61-82)
  - result is that sum.of.factors (line 80) is filled with NA When I remove the factor covariate (sex) and work only with the continuous covariate (size), no problems arise

Therefore the solution to the problem of using factor covariates in the detection function lays within the portion of code following this line

DistanceDevelopment / dsims