RMark Output

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RMark Output

Postby MercyMelo » Mon Dec 05, 2022 8:44 am

Hello RMark Users,
I am attempting to run my first survival analysis using RMark, but am faced with odd results that are quite confusing. My dataset includes a capture history of 14 encounters for each individual as well as 4 covariates (season, site, sex, and age) that I wish to use to inform survival (S) and recapture probability (p) of 238 individuals I've tracked over the past 3 years. To do this, I ran the following code:

Encounter <- read.csv("EncounterDataNewSeason.csv",
colClasses=c("character", "factor", "factor","factor",
"factor","factor","factor"),
header = TRUE, sep = ",")

#Process Data and filter groups
Surv <- process.data(Encounter, model = "Burnham", groups = c("Sex", "Site", "Season", "Age"))

#Create Design Data
Surv.ddl <- make.design.data(Surv)

#Create function to run models specified within function
run.surv <- function() {
S.sex <- list(formula=~Sex)
S.site <- list(formula=~Site)
S.season <- list(formula=~Season)
S.age <- list(formula=~Age)
s.dot <- list(formula=~1)
p.sex <- list(formula=~Sex)
p.site <- list(formula=~Site)
p.season <- list(formula=~Season)
p.age <- list(formula=~Age)
p.dot <- list(formula=~1)
F.dot <- list(formula=~1)
r.dot <- list(formula=~1)

# Create model list
model.list = create.model.list("Burnham")
Surv.results = mark.wrapper(model.list, data = Surv,
ddl = Surv.ddl, output = FALSE, invisible = TRUE, threads = 2)

# Return model table and list of models
return(Surv.results)
}

#View results
Surv.results = run.surv()
Surv.results

This code seems to work, and I am able to compare AIC values for different models representing each of the combinations of covariates (ex. S(sex), p(site); S(sex), p(age); etc.) The issue arises once I run just my top model (which I specify as follows):

top <- Surv.results$S.season.p.site.r.dot.F.dot
top$results

This provides output, but the $beta estimates are very large (not 0 to 1 as I would expect for survival estimates), $beta confidence intervals are matching (lcl = ucl on many occasions), and $real estimates appear to include all covariates in the model even though the model should only include 2 of the covariates (ex. model of S(season), p(site) has $real estimates for models with sex, site, season, and age all incorporated).

As this is my first time working with RMark, I am unsure if output like this is normal or if the results are nonsensical like I suspect. Any guidance in correctly interpreting the output and/or pointing out issues in the code would be greatly appreciated! Thank you so much in advance for your time and thoughts on this!
MercyMelo
 
Posts: 3
Joined: Fri Dec 02, 2022 3:02 pm

Re: RMark Output

Postby jlaake » Mon Dec 05, 2022 10:25 am

Have you read the workshop notes for RMark in the documentation archive? Have you read any of the Cooch and White book?

Beta values are for link functions and are not estimates of survival or capture probability. Real estimates are survival or capture probability and the like. They are reported for all groups(covariate combinations) but they can all be the same depending on the model. Also RMark uses all different PIMS by default so you'll get a real estimate for each. Don't use threads=2. That was specifically used for CRAN examples to reduce number of processors used.
jlaake
 
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Joined: Fri May 12, 2006 12:50 pm
Location: Escondido, CA


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