rtrim package is an complete reimplementation of the original TRIM software developed by Jeroen Pannekoek and Arco van Strien from the 1990’s onwards. This vignette provides a quick getting started manual that demonstrates the R-based workflow for computing TRIM models.
TRIM was developed to estimate animal populations, based on repeated counts at various sites while counts may be missing for certain sites at certain times. Estimation is based on a model-based imputation method.
We assume that the reader is already familiar with the methodology behind TRIM but in short, TRIM estimates a piecewise loglinear growth model to compute imputations. There are three variants of this model which differ by their basic assumptions.
Note that both Model 1 and Model 3 can be seen as special cases of Model 2 (Model 1 is equivalent with Model 2 when where time effects or growth rate is set to zero; Model 3 is equivalent with Model 2 when growth rates are assumed to change every time point).
For each variant it is possible to include categorical covariates in the model, or to weight sites. Certain simplifying assumptions are made to keep computations tractable. A detailed description of the methodology can be found in the original TRIM3 manual.
We are going to use the
skylark dataset, which is included with the package.
library(rtrim) data(skylark) # inspect the dataset head(skylark,3)
## site time count Habitat Deposition ## 1 1 1 11 2 2 ## 2 1 2 8 2 2 ## 3 1 3 5 2 2
skylark is a regular R
The central function for computing TRIM models is called
trim. Calling this function is very similar to calling basic R modeling functions like
lm. Here, we compute TRIM model 2.
m1 <- trim(count ~ site + time, data=skylark,model=2)
time are treated differently by the model, the order matters (see also model specification). Alternatively, one can do this:
m1 <- trim(skylark, count.id="count", site.id="site", time.id="time", model=2)
The result is an object of class
trim. Just like with objects of class
lm, its various components can be extracted using specialized functions. Here are some examples.
# summarize the model summary(m1)
## Call: ## tools::buildVignettes(dir = ".", tangle = TRUE) ## ## Model : 2 ## Method : ML (Convergence reached after 3 iterations) ## ## Coefficients: ## from upto add se_add mul se_mul ## 1 1 8 0.05482546 0.01043636 1.056356 0.01102452 ## ## ## Goodness of fit: ## Chi-square = 210.53, df=146, p=0.0004 ## Likelihood Ratio = 204.63, df=146, p=0.0010 ## AIC (up to a constant) = -87.37
## time imputed se_imp ## 1 1 438 21 ## 2 2 392 20 ## 3 3 432 21 ## 4 4 433 21 ## 5 5 474 22 ## 6 6 521 23 ## 7 7 556 25 ## 8 8 591 28
## Goodness of fit: ## Chi-square = 210.53, df=146, p=0.0004 ## Likelihood Ratio = 204.63, df=146, p=0.0010 ## AIC (up to a constant) = -87.37
Extract the coefficients
## from upto add se_add mul se_mul ## 1 1 8 0.05482546 0.01043636 1.056356 0.01102452
Plot with overall slope
These are just a few of of the functions that can be used to analyse the model. See any of their help files for a complete list of links to all analyses functions.
The names of variables in the dataset are not important and neither is their order. However, since TRIM models are designed to estimate the number of counts at counting sites, the formula specifying the model has to satisfy certain rules.
For example, to use the variable
Habitat as covariate when analysing the
skylark dataset (under model 2) one does the following.
m2 <- trim(count ~ site + time + Habitat, data=skylark, model=2)
It is also possible to specify weights by passing
weights argument. The TRIM options
overdisp (for overdispersion) and
serialcor (for serial correlation), are simple
TRUE/FALSE toggles. The breaks of the piecewise loglinear model can be specified with the
changepoints option. The
trim function will give an error when too little observations are present in a time segment, except when the
autodelete option is set to
TRUE. In that case time segments are combined until enough observations are present for a model to be estimated. See
?trim for a precise description of all options. Below is an example where we specify the maximum number of changepoints and let
trim delete change points where necessary.
m3 <- trim(count ~ site + time + Habitat, data=skylark, model=2 , overdisp = TRUE, serialcor = TRUE, changepoints=1:7, autodelete=TRUE) m3$changepoints
##  1 2 3 4 5 6 7
In this case, no change points are deleted.
In this example, the data sets consists of 8 time points, so time points 1 to 7 are explicitly specified as change point. This notation, which requires the prior identification of the number of time points present within the data, can be replaced by the more convenient expression
stepwise algorithm can be used. This algorithm removes changepoints when the slope does not change significantly from before to after a changepoint, yielding a simpler (more robust) model.
m4 <- trim(count ~ site + time + Habitat, data=skylark, model=2 , overdisp = TRUE, serialcor = TRUE, changepoints=1:7, stepwise = TRUE) m4$changepoints
##  1 2
Again, the explicit setting of initial changepoints can be replaced by the more convenient
changepoints="auto", which combines
An overview of count data can be obtained with the function
## Total number of sites 55 ## Sites without positive counts (0): ## Number of observed zero counts 0 ## Number of observed positive counts 202 ## Total number of observed counts 202 ## Number of missing counts 238 ## Total number of counts 440
The result is an overview similar to the one that used to be printed at the start of TRIM output files.
The TRIM model can only be computed when sufficient data is present. With the function
check_observations one can check if a certain model can be computed.
check_observations(skylark, model=2, changepoints=c(1,4))
## $sufficient ##  TRUE ## ## $errors ## $errors$changepoint ## numeric(0)
The result is a
list with booean element
sufficient==FALSE, the element
errors contains a
data.frame with the sites/times/covariates with insuffiecient counts.