Gravity model

Description

Implements Murphy et al., (2010) gravity model

Usage

gravity(y, x, d, group, data, ln = TRUE, constrained = TRUE, ...)

Arguments

Value

formula Model formula

gravity Gravity model

AIC AIC value for selected model

x data.frame of independent variables

y Vector of dependent variable

groups Ordered factor vector of grouping variable

fit Model Fitted Values

Note

The "group" factor defines the singly constrained direction (from or to) and the grouping structure for the origins.

Depends: nlme, lattice

Author(s)

Jeffrey S. Evans <jeffrey_evans@tnc.org> and Melanie Murphy <melanie.murphy@uwyo.edu>

References

Murphy, M. A. & J.S. Evans. (in prep). "GenNetIt: gravity analysis in R 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

Examples

library(nlme)
data(ralu.model)

# Gravity	
x = c("DISTANCE", "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) } 

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GeNetIt)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GeNetIt/gravity.Rd_%03d_medium.png", width=480, height=480)
> ### Name: gravity
> ### Title: Gravity model
> ### Aliases: gravity
> 
> ### ** Examples
> 
> library(nlme)
> data(ralu.model)
> 
> # Gravity	
> x = c("DISTANCE", "DEPTH_F", "HLI_F", "CTI_F", "cti", "ffp")
> ( gm <- gravity(y = "DPS", x = x, d = "DISTANCE", group = "FROM_SITE", 
+                 data = ralu.model, ln = FALSE) )
[1] "Running singly-constrained gravity model"
Gravity model

Linear mixed-effects model fit by REML
 Data: gdata 
        AIC       BIC   logLik
  -280.1398 -251.2545 149.0699

Random effects:
 Formula: ~1 | FROM_SITE
        (Intercept)  Residual
StdDev:  0.01875411 0.1007295

Fixed effects: list(fmla) 
                Value Std.Error  DF   t-value p-value
(Intercept) -5.926103 1.4920658 168 -3.971744  0.0001
DISTANCE    -0.109680 0.0118781 168 -9.233851  0.0000
DEPTH_F      0.025633 0.0094633  15  2.708728  0.0162
HLI_F        0.156871 0.1316072  15  1.191962  0.2518
CTI_F       -0.048095 0.0401198  15 -1.198791  0.2492
cti         -0.197073 0.0869938 168 -2.265362  0.0248
ffp          0.910712 0.1430268 168  6.367426  0.0000
 Correlation: 
         (Intr) DISTAN DEPTH_ HLI_F  CTI_F  cti   
DISTANCE -0.270                                   
DEPTH_F  -0.579  0.066                            
HLI_F    -0.812  0.125  0.833                     
CTI_F     0.329 -0.072 -0.674 -0.583              
cti      -0.050  0.461  0.028  0.035 -0.115       
ffp      -0.797  0.145  0.124  0.321  0.009 -0.159

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-3.19045601 -0.46459488  0.01573003  0.66508837  1.87341262 

Number of Observations: 190
Number of Groups: 19 
> 
> # Plot gravity results
>  par(mfrow=c(2,3))
>    for (i in 1:6) { plot(gm, type=i) } 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>