gravity {GeNetIt}

Gravity model

Description

Implements Murphy et al., (2010) gravity model via a linear mixed effects model

Usage

gravity(
  y,
  x,
  d,
  group,
  data,
  fit.method = c("REML", "ML"),
  ln = TRUE,
  constrained = TRUE,
  ...
)

Arguments

y

Name of dependent variable

x

Character vector of independent variables

d

Name of column containing distance

group

Name of grouping column (from or to)

data

data.frame object containing model data

fit.method

Method used to fit model c("REML", "ML")

ln

Natural log transform data (TRUE/FALSE)

constrained

Specify constrained model, if FALSE a linear model (lm) is run (TRUE/FALSE)

...

Additional argument passed to nlme or lm

Details

The "group" factor defines the singly constrained direction (from or to) and the grouping structure for the origins. To specify a null (distance only or IBD) model just omit the x argument.

By default constrained models are fit by maximizing the restricted log-likelihood (REML), for maximum likelihood use the type="ML" argument which is passed to the lme function. If ln=TRUE the input data will be log transformed

Value

formula Model formula call

fixed.formula Model formula for fixed effects

random.formula Model formula for random (group) effects (only for constrained models)

gravity Gravity model

fit Model Fitted Values

AIC AIC value for selected model

RMSE Root Mean Squared Error (based on bias corrected back transform)

log.likelihood Restricted log-likelihood at convergence

group.names Column name of grouping variable

groups Values of grouping variable

x data.frame of x variables

y Vector of y variable

constrained TRUE/FALSE indicating if model is constrained

Note

Depends: nlme, lattice

Author(s)

Jeffrey S. Evans <[e-mail]> and Melanie A. Murphy <[e-mail]>

References

Murphy, M. A. & J.S. Evans. (in prep). GenNetIt: graph theoretical gravity modeling for landscape genetics

Murphy M.A., R. Dezzani, D.S. Pilliod & A.S. Storfer (2010) Landscape genetics of high mountain frog metapopulations. Molecular Ecology 19(17):3634-3649

See Also

groupedData for how grouping works in constrained model

lme for constrained model ... options

lm for linear model ... options

Examples

library(nlme)
data(ralu.model)

# Gravity model	
x = c("DEPTH_F", "HLI_F", "CTI_F", "cti", "ffp")
( gm <- gravity(y = "DPS", x = x, d = "DISTANCE", group = "FROM_SITE", 
                data = ralu.model, ln = FALSE) )

#' # Plot gravity results
 par(mfrow=c(2,3))
   for (i in 1:6) { plot(gm, type=i) } 

# log likelihood of competing models 
 x = c("DEPTH_F", "HLI_F", "CTI_F", "cti", "ffp")
 for(i in x[-1]) {
   x1 = c(x[1], x[-which(x %in% i)])  
   ll <- gravity(y = "DPS", x = x1, d = "DISTANCE", group = "FROM_SITE", 
                 data = ralu.model, ln = FALSE)$log.likelihood
  cat("log likelihood for parameter set:", "(",x1,")", "=", ll, "\n") 
 }

# Distance only (IBD) model
gravity(y = "DPS", d = "DISTANCE", group = "FROM_SITE", 
        data = ralu.model, ln = FALSE)