| Title: | Modelling Framework for the Estimation of Salmonid Abundance |
| Version: | 1.2.0 |
| Date: | 2020-10-18 |
| Description: | A set of functions to estimate capture probabilities and densities from multipass pass removal data. |
| Depends: | R (≥ 3.0), TMB |
| Imports: | Matrix, dplyr, methods, mgcv |
| LinkingTo: | TMB, RcppEigen |
| Suggests: | testthat |
| License: | MIT + file LICENSE |
| LazyData: | TRUE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | yes |
| Packaged: | 2020-10-26 12:09:53 UTC; colin |
| Author: | Colin Millar [aut, cre], Rob Fryer [aut], Iain Malcolm [aut], Ross Glover [aut], Marine Scotland Science [cph] |
| Maintainer: | Colin Millar <colin.millar@ices.dk> |
| Repository: | CRAN |
| Date/Publication: | 2020-11-03 12:10:02 UTC |
Modelling Framework for the Estimation of Salmonid Abundance in Scottish Rivers
Description
A set of functions to estimate capture probabilities and densities from multipass pass removal data.
Adjusted BIC function
Description
Complete function for returning overdispersion estimates
Usage
BICadj(model, data, overdispersion.output)
Arguments
model |
a fitted ef model |
data |
the data used to fit the model |
overdispersion.output |
output form the function overdispersion |
Value
the adjusted BIC
Note
When calling the function, you need to specify the data source for the model so that the number of site visits can be determined. You also need to specify the output from the overdispersion model to get the measure
Coversion of an efp fit to a gam fit for plotting
Description
Coversion of an efp fit to a gam fit for plotting
Usage
as.gam(object)
Arguments
object |
an efp fitted model |
Value
gam type object
Counts of salmon fry and parr from electrofishing.
Description
A dataset containing counts from multipass electrofishing samples over three sites and 5 years.
Usage
ef_data
Format
A data frame with 90 rows and 9 variables:
- siteID
Unique identifier for each elecrofishing site
- year
year of data collection
- date
date of data collections
- EFpasscount:
the total number of electrofishing passes
- pass
the electrofishing pass on which the fish were caught
- species
fish species, Salmon
- lifestage
fish lifestage, Fry or Parr
- count
the number of fish caught per pass for each site visit and species, etc.
- area
the area of river fished
Source
https://www2.gov.scot/Topics/marine/Salmon-Trout-Coarse/Freshwater/Monitoring/temperature
Estimate capture probabilites from electrofishing data
Description
This function uses the marginal likelihood of capture probabilities to estimate model parameters
Usage
efp(
formula,
data = NULL,
pass = pass,
id = id,
offset = NULL,
verbose = FALSE,
init = "0",
hessian = TRUE,
fit = TRUE,
sample_re = FALSE
)
Arguments
formula |
a formula object |
data |
a data.frame containing all relavent info |
pass |
a vector of integers giving the pass number of the observation |
id |
a vector of integers identifying an observation (a set of electrofishing passes) |
offset |
an possible offset for the linear predictor of capture probability |
verbose |
if TRUE stan optimiser messages are printed to the screen |
init |
should initialisatiom be random? |
hessian |
if TRUE the hessian is computed and the covariance matrix of the parameters is returned via Vb |
fit |
if TRUE model is fitted if FALSE the data that would be passed to the optimiser is returned |
sample_re |
should sample random effects be included |
Value
glm type object
Examples
# create two electrofishing site visits with 3 and 4 passes and 2 lifestages
ef_data <- data.frame(n = c(100, 53, 24, 50, 26, 12,
100, 53, 24, 50, 26, 12),
pass = c( 1, 2, 3, 1, 2, 3,
1, 2, 3, 1, 2, 3),
stage = c( 1, 1, 1, 2, 2, 2,
1, 1, 1, 2, 2, 2),
sample = c( 1, 1, 1, 2, 2, 2,
3, 3, 3, 4, 4, 4))
ef_data2 <- data.frame(n = c(100, 53, 24, 50, 26, 12,
100, 53, 24, 12, 50, 26, 12, 6),
pass = c( 1, 2, 3, 1, 2, 3,
1, 2, 3, 4, 1, 2, 3, 4),
stage = c( 1, 1, 1, 2, 2, 2,
1, 1, 1, 1, 2, 2, 2, 2),
sample = c( 1, 1, 1, 2, 2, 2,
3, 3, 3, 3, 4, 4, 4, 4))
ef_data3 <- data.frame(n = c(100, 53, 24, 50, 26, 12, 40,
100, 53, 24, 12, 50, 26, 12, 6, 40),
pass = c( 1, 2, 3, 1, 2, 3, 1,
1, 2, 3, 4, 1, 2, 3, 4, 1),
stage = c( 1, 1, 1, 2, 2, 2, 1,
1, 1, 1, 1, 2, 2, 2, 2, 2),
sample = c( 1, 1, 1, 2, 2, 2, 5,
3, 3, 3, 3, 4, 4, 4, 4, 6))
# Fit a simple model
m2 <- efp(n ~ 1 + factor(stage), data = ef_data, pass = pass, id = sample)
cbind(ef_data, fit = fitted(m2))
m3 <- efp(n ~ 1 + factor(stage), data = ef_data2, pass = pass, id = sample)
cbind(ef_data2, fit = fitted(m3))
m4 <- efp(n ~ 1 + factor(stage), data = ef_data3, pass = pass, id = sample)
cbind(ef_data3, fit = fitted(m4))
# create two electrofishing site visits with 3 and 4 passes and 2 lifestages
ef_data <- data.frame(n = c(200, 53, 24, 100, 26, 12,
200, 53, 24, 100, 26, 12),
pass = c( 1, 2, 3, 1, 2, 3,
1, 2, 3, 1, 2, 3),
stage = c( 1, 1, 1, 2, 2, 2,
1, 1, 1, 2, 2, 2),
sample = c( 1, 1, 1, 2, 2, 2,
3, 3, 3, 4, 4, 4))
# Fit a simple model
m2 <- efp(n ~ 1 + factor(stage) + factor(replace(pass, pass> 2, 2)),
data = ef_data, pass = pass, id = sample)
out <- cbind(ef_data, p = fitted(m2, type = "p"))
out
Inverse logistic transformation
Description
This function transforms values on the logistic scale to values on the probability scale.
Usage
invlogit(x)
Arguments
x |
a numeric vector |
Value
vector of values between 0 and 1
Logistic transformation
Description
This function transforms values on the probability scale to values on the logistic scale.
Usage
logit(p)
Arguments
p |
a numeric vector with values between 0 and 1 |
Value
vector of values between -Inf and Inf
Estimating overdispersion
Description
Complete function for returning overdispersion estimates
Usage
overdispersion(
data,
siteID,
visitID,
count = "count",
pass = "pass",
lifestage = "lifestage",
pass12 = "pass12",
id,
largemodel
)
Arguments
data |
dataframe containing EF data |
siteID |
site name or unique ID |
visitID |
a number identifying each unique visit |
count |
count of fish (defaults to "count") |
pass |
the EF pass number (defaults to "pass") |
lifestage |
the lifestage (defaults to "lifestage") |
pass12 |
categorical variable with 2 levels where the 1st pass and subsequent passes are treated separately (defaults to "pass12") |
id |
sample ID |
largemodel |
the large model that captures most of the systematic variation in the data - this is specified before running the overdispersion function |
Value
a data.frame summarising overdispersion
Note
ensure column names in function call are in inverted commas
Utility function to convert parameters to probabilities
Description
The matrix G should be of dimension n x p, and the parameter vector should be length p
Usage
transpar(par, G)
Arguments
par |
fitted model parameters |
G |
The design matrix for a model |
Value
a data frame