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Initiate MAR analysis

run.mar.Rd

Allows the user to select variables and restrict interactions, finds best-fit MAR model, and applies a bootstrap to the best-fit model

Usage

run.mar(data, variables=NULL, restrictions=NULL, search=c("random","exhaustive",
  "fwdstep","exhaustive.true"), boot=500, ntop=10, export=FALSE)

Arguments

data

Data frame with continuous time-block variable in first column, ordered by dates in second column, followed by columns of taxa abundance time-series

variables

A vector as long as the number of columns in data indicating how each taxon column should be assigned for the analysis (first two values for the time-block and date columns should be 0):

  • 0 : not included

  • 1 : included as a variate

  • 2 : included as a covariate

Alternatively, an object of class MAR resulting from a previous call to run.mar from which to extract variable assignments

restrictions

A matrix with n variate rows and n variate + n covariate columns of values indicating the potential of a direct effect of each column variable on each row variable:

  • 0.5 : possible (may be included in model)

  • 0 : unlikely/implausible (won't be included in model)

  • 1 : probable (will be included in model)

Alternatively, on object of class MAR resulting from a previous call to run.mar from which to extract interaction restrictions

search

A character string indicating the type of search that should be used to find the best-fit model; either "random" (default), "exhaustive", "fwdstep", or "exhaustive.true"; see "Details" section below for descriptions of search types

boot

Either an integer indicating the number of iterations that should be performed in the model bootstrap or FALSE to skip bootstrapping

ntop

If search="random", "exhaustive", or "exhaustive.true", the number of top best-fit models from the random search to be returned for potential comparison to the selected best-fit model

export

If set to TRUE, a call to export.MAR is executed at the end of the analysis. export.MAR creates a new directory and saves all components of the MAR model object in that directory as csv files

Details

Variables and Constraints:

If the variables or restrictions arguments are not provided, the function creates windows that allow the user to pick which column variables in data should be included in the MAR model as variates or covariates and to set restrictions on potential interactions between model variables. Unless the variables argument is provided in the function call, the restrictions argument must be NULL.

Search Types:

If search="random", which is the default, a random search is performed to find the best-fit model (as determined by AIC) for the included variate time-series. For each variate, 100 random models are constructed according to the restrictions that were set, and the model with the lowest AIC of these models is retained. This process is repeated 100 times, resulting in 100 "best-of-100" models. If any variable occurs in less than 15 of the 100 "best-of-100" models, that variable is discarded (i.e., the probability of that variable occurring in the random search is set to 0) and the search is repeated until the number of variables in successive searches remains constant (resulting in at least 2 search iterations per variate unless all variables are retained in the first iteration). The model with the lowest AIC of the final 100 "best-of-100" models is retained.

If search="exhaustive", a search through possible models for each variate with respect to restrictions is performed using a leap and bound algorithm (Furnival and Wilson,1974) to find the "best-fit" (lowest AIC) model of all potential variable combinations without explicitly examining all possible subsets.

If search="fwdstep", the best-fit model for each variate is built up from the NULL intercept model by sequentially adding whichever variable most improves the model AIC from the pool of potential variables. The model from the series with the lowest AIC is retained.

If search="exhaustive.true", a true exhaustive search through all potential variable combinations with respect to restrictions is performed.

Statistics:

The coefficients of the final "best-fit" MAR model for all variates are attained using least-squares estimation. The coefficients of the B- and C-matrices represent interaction strengths of the column variables on the row variables. If the data were z-scored prior to analysis (see prepare.data), the A intercept values will not be significantly different from 0. Estimates of the stationary distribution mean and covariance for each variate, of the process errors, and of community stability (resilience and reactivity) are calculated following Ives (2003). If bootstrap is not set to FALSE, these statistics are also calculated for the bootstrapped model.

Value

Returns a list of class MAR containing:

variables.selected

corresponds to variables argument

restrictions.set

corresponds to restrictions argument

search.type

corresponds to search argument

search.time.s

time (in seconds) the best-fit model search took


And for each of $bestfit and $bootstrap:

A

each row is the a-value for the variate

B

B-matrix interaction coefficients of columns on rows

C

C-matrix interaction coefficients of columns on rows

log.likelihood

log.likelihood value for model

AIC

AIC value for model

BIC

BIC value for model

R2.values

R^2 and conditional R^2 values for each variate


stationary.distribution

mean means of variates' stationary distributions
covariance covariance matrix of stationary distribution


process.errors


residuals E
covariance sigma
corrmatrix correlation matrix


stability

resilience

eigB eigenvalues of the B matrix
detB determinant of the B matrix
maxeigB max eigenvalue of B matrix
maxeigkrB max eigenvalue of B matrix kronecker products

reactivity

sigma.over.Vinf -tr(sigma)/tr(Vinf)
maxeigBxB max eigenvalue of B'B matrix ("worst-case" reactivity)


If bootstrapping is not performed, $bootstrap will be NULL. Otherwise, in addition to the statistics above, $bootstrap will also contain a $limits list with the upper and lower 95% confidence limits of the best-fit model elements.

If search="random", "exhaustive", or "exhaustive.true", the result will also contain $top.bestfit, an array of the top best-fit models tested during the model search (the first of which is the best-fit model that was selected). The number of top models returned may be less than the value set for the ntop argument if ntop exceeds the potential number of model configurations that can be tested for the selected variables and search method. The dimension of the array in which each top best-fit model is stored is named by its respective AIC value.

References

Furnival GM, Wilson Jr RW (1974) Regressions by leaps and bounds. Technometrics 16:499-511

Ives AR, Dennis B, Cottingham KL, and Carpenter SR (2003) Estimating community stability and ecological interactions from time-series data. Ecological Monographs 73:301-330

Author

LP Scheef

Warning

The "random" model search may select different best-fit models when run multiple times on the same data, particularly for searches including a large number of variables.

The "exhaustive.true" search can become very time-consuming for models with more than 12 variables.

See also

prepare.data, plot.MAR, export.MAR

Packages used for exhaustive search methods:
leaps, bestglm

Examples


if (FALSE) {
## These examples take 1-2 minutes to run

## construct a MAR model using 'run.mar' arguments to set variables and restrictions 
data(L4.mar)

myvar <- c(0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 2, 2, 2) # 8 variates, 3 covariates
myres <- matrix(0.5,
  nrow = length(which(myvar == 1)),
  ncol = length(which(myvar != 0))
) # no restrictions (all 0.5)

run1 <- run.mar(L4.mar, variables = myvar, restrictions = myres, search = "exhaustive")

run1 # only some elements of the object are printed
str(run1) # to see all elements
summary(run1) # some summary statistics for the model
plot(run1)

# set a few restrictions on taxa interactions
myres[1, c(1, 6, 9)] <- c(1, 0, 0) # included, not included, not included

# re-run the analysis with same variates as 'run1' and new restrictions
run1b <- run.mar(L4.mar, run1, myres, "exhaustive")
plot(run1, run1b)

# 'run1' variables and restrictions with a different search method
run1c <- run.mar(L4.mar, run1, run1, "fwdstep")
plot(run1, run1c, legend = TRUE) # plot with legend

## construct a MAR model using windows to select variables and restrictions ##
run2 <- run.mar(L4.mar, search = "exhaustive")
run2
summary(run2)
plot(run2)
}