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) head(skylark,3) # inspect the dataset #> 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.
Note that the data is passed to
trim as an R data.frame. Information on which columns in the data frame represent the counts, the site ID’s etc is encoded in the first argument, which is of the special type `formula’. Because site identifiers and time points are treated differently by the model, the order matters (see also model specification).
Alternatively, one can just pass the data frame as argument 1, and explictly tell
trim in which columns the counts etc are:
Note that although the name
year_col suggests that counts must be on an annual interval, this is not necesarily the case.
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.
summary(m1) # summarize the model #> 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
totals(m1) # Return time-totals #> 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
gof(m1) # Retrieve goodness-of-fit #> 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
coefficients(m1) # Extract the coefficients #> from upto add se_add mul se_mul #> 1 1 8 0.05482546 0.01043636 1.056356 0.01102452
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.
It is also possible to apply weights by specifyinga
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$changepoints #>  1 2 3 4 5 6 7
In this case, no change points are deleted.
In this example, the data sets consists of 8 time points, so time points 1 to 7 are explicitly specified as change point. This notation, which requires the prior identification of the number of time points present within the data, can be replaced by the more convenient expression
stepwise algorithm can be used. This algorithm removes changepoints when the slope does not change significantly from before to after a changepoint, yielding a simpler (more sparse) model.
m4 <- trim(count ~ site + time + Habitat, data=skylark, model=2 , overdisp = TRUE, serialcor = TRUE, changepoints=1:7, stepwise = TRUE) m4$changepoints #>  1 2
Again, the explicit setting of initial changepoints can be replaced by the more convenient
changepoints="auto", which combines
The original TRIM software can be controlled with text files containing a series of commands that specify both the location and format of the data, an the model (or models) to compute. Such TRIM command files (usually stored with the extension
.tcf) should be considered legacy but for backwards compatability they can be used from R.
To try this, execute the code below to create a
tcf file and a TRIM data file in the current working directory of R.
library(rtrim) tmp <- "FILE skylark.dat TITLE skylark-1d NTIMES 8 NCOVARS 2 LABELS Habitat Cov2 END MISSING 999 WEIGHT Absent COMMENT Example 1; using linear trend model WEIGHTING off OVERDISP on SERIALCOR on MODEL 2 " write(tmp,file="skylark.tcf") data(skylark) skylark[is.na(skylark)] <- 999 write.table(skylark,file="skylark.dat",col.names=FALSE,row.names=FALSE)
Executing a TRIM command file is as easy as reading the file using
read_tcf and passing the result to
trim object can be evaluated as described above. For example
tc, resulting from
read_tcf is an object of class
trimcommand. It stores all commands defined in the TRIM command file. Note that logical parameters such as
WEIGHT are transformed to
logical in R.
tc #> Object of class trimcommand: #> file: skylark.dat #> title: skylark-1d #> ntimes: 8 #> ncovars: 2 #> labels: Habitat, Cov2 #> missing: 999 #> weight: FALSE #> comment: Example 1; using linear trend model #> weighting: FALSE #> serialcor: TRUE #> overdisp: TRUE #> basetime: #> model: 2 #> covariates: #> changepoints: #> stepwise: FALSE #> autodelete: FALSE #> outputfiles: #> overallchangepoints: #> impcovout: FALSE #> covin: FALSE
NOTE. Be aware that R has its own present working directory. If relative paths (that is, file names not starting with the full path to their location) are used in the TRIM command file, R will interpret them as relative to the current working directory.
TRIM data files are basically space-separated, tabular textfiles where the order and type of columns is fixed by a few parameters. Given such a specification, a file can be read with
An overview of count data can be obtained with the function
data(skylark) count_summary(skylark) #> 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. Note the use of
year_col to specify a non-default column name.
check_observations(skylark, model=2, year_col="time", changepoints=c(1,4)) #> $sufficient #>  TRUE #> #> $errors #> $errors$changepoint #> numeric(0)
The result is a
list with boolean element
sufficient==FALSE, the element
errors contains a
data.frame with the sites/times/covariates with insufficient counts.