Getting_Started

library(MAR1)
#> Loading required package: leaps
#> Loading required package: bestglm
#> Loading required package: tcltk

These examples take 1-2 minutes to run

Set up the data

This uses the [run.mar()] arguments to set variables and restrictions.

data(L4.mar)
colnames(L4.mar)
#>  [1] "contin"       "date"         "cnidarian"    "amphipod"     "chaetognath" 
#>  [6] "krill"        "pteropod"     "tunicate"     "cladoceran"   "calanoid.lg" 
#> [11] "calanoid.sm"  "cyclopoid"    "poecilostom"  "harpact"      "diatom"      
#> [16] "dino"         "other.algae"  "cirripedia"   "mero.grazers" "decapod"     
#> [21] "surface.temp"

Select response (1) and covariates (2) for the model. The variables and covariates are specified with a vector that is the same length as the number of columns. 0s are not uses. 1s are response columns and 2s are covariate columns.

myvar <- c(0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 2, 2, 2)

Create the restriction matrix

The next step is to set any B and D to 0 that will be fixed at 0. The shape of the restriction matrix has rows equal to the number of response variables” and columns equal to number of response variables plus the number of covariates. In this example, we thus have a 8 × 11 matrix.

myres <- matrix(0.5,
  nrow = length(which(myvar == 1)),
  ncol = length(which(myvar != 0))
)

Fit the model

Next we fit the MAR model:

run1 <- run.mar(L4.mar, variables = myvar, restrictions = myres, search = "exhaustive")
#>  
#>  
#> searching for best-fit model...
#>        ...BEST-FIT MODEL SELECTED
#>             ( search time:  0 minutes 1 seconds )
#>  
#>  
#>  
#> identifying 10 lowest AIC models...
#>        ...TOP MODELS RETAINED 
#>  
#>  
#>  
#> bootstrapping best-fit model...
#>        ...BOOTSTRAPPING COMPLETE
#>  
#>  
#> ════════════════════════════════════════════════════════════════════════════════ 
#>  
#>

Show output

Summary

run1
#> 
#> Variables Selected:
#>       contin         date    cnidarian     amphipod  chaetognath        krill 
#>            0            0            0            1            1            0 
#>     pteropod     tunicate   cladoceran  calanoid.lg  calanoid.sm    cyclopoid 
#>            0            0            1            1            1            1 
#>  poecilostom      harpact       diatom         dino  other.algae   cirripedia 
#>            0            0            1            1            0            0 
#> mero.grazers      decapod surface.temp 
#>            2            2            2 
#>  [0 = not included, 1 = variate, 2 = covariate]
#> 
#> Restrictions Set:
#>               amphip chaeto cladoc calano calano cyclop diatom dino  mero.g
#>      amphipod   ·      ·      ·      ·      ·      ·      ·      ·     ·   
#>   chaetognath   ·      ·      ·      ·      ·      ·      ·      ·     ·   
#>    cladoceran   ·      ·      ·      ·      ·      ·      ·      ·     ·   
#>   calanoid.lg   ·      ·      ·      ·      ·      ·      ·      ·     ·   
#>   calanoid.sm   ·      ·      ·      ·      ·      ·      ·      ·     ·   
#>     cyclopoid   ·      ·      ·      ·      ·      ·      ·      ·     ·   
#>        diatom   ·      ·      ·      ·      ·      ·      ·      ·     ·   
#>          dino   ·      ·      ·      ·      ·      ·      ·      ·     ·   
#>               decapo surfac
#>      amphipod   ·      ·   
#>   chaetognath   ·      ·   
#>    cladoceran   ·      ·   
#>   calanoid.lg   ·      ·   
#>   calanoid.sm   ·      ·   
#>     cyclopoid   ·      ·   
#>        diatom   ·      ·   
#>          dino   ·      ·   
#> 
#> Search Type:   "exhaustive" 
#> ________________________________________ 
#> ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
#> Best-fit Model
#> ¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
#>                 B:                                                       
#>               │ amphip chaeto cladoc calano calano cyclop diatom  dino  │
#>      amphipod │  0.08     ·      ·    0.24     ·   -0.14   0.04     ·   │
#>   chaetognath │    ·    0.48     ·      ·   -0.10     ·      ·      ·   │
#>    cladoceran │    ·      ·    0.42     ·      ·      ·      ·      ·   │
#>   calanoid.lg │ -0.30   0.02  -0.14   0.43     ·   -0.19   0.02     ·   │
#>   calanoid.sm │ -0.27     ·      ·    0.11   0.34     ·   -0.08  -0.07  │
#>     cyclopoid │    ·      ·      ·      ·      ·    0.46     ·      ·   │
#>        diatom │    ·      ·      ·   -0.24     ·      ·    0.18     ·   │
#>          dino │  0.11     ·   -0.20  -0.03   0.22  -0.28     ·    0.18  │
#>               C:                  
#>               mero.g decapo surfac
#>      amphipod -0.14     ·      ·  
#>   chaetognath  0.14     ·      ·  
#>    cladoceran    ·      ·      ·  
#>   calanoid.lg  0.04   0.20     ·  
#>   calanoid.sm    ·   -0.03     ·  
#>     cyclopoid    ·      ·      ·  
#>        diatom    ·      ·      ·  
#>          dino  0.07     ·   -0.05 
#> 
#> AIC:  3548.162
#> 
#> R^2 Values:
#>                  R2  R2_D
#>      amphipod  0.10  0.46
#>   chaetognath  0.28  0.28
#>    cladoceran  0.18  0.30
#>   calanoid.lg  0.27  0.44
#>   calanoid.sm  0.22  0.38
#>     cyclopoid  0.21  0.27
#>        diatom  0.11  0.44
#>          dino  0.17  0.43
#> ________________________________________ 
#> ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
#> Bootstrapped Model
#> ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
#>                 B:                                                       
#>               │ amphip chaeto cladoc calano calano cyclop diatom  dino  │
#>      amphipod │    ·      ·      ·    0.19     ·      ·      ·      ·   │
#>   chaetognath │    ·    0.50     ·      ·      ·      ·      ·      ·   │
#>    cladoceran │    ·      ·    0.42     ·      ·      ·      ·      ·   │
#>   calanoid.lg │ -0.31     ·      ·    0.49     ·   -0.19     ·      ·   │
#>   calanoid.sm │ -0.24     ·      ·      ·    0.39     ·      ·      ·   │
#>     cyclopoid │    ·      ·      ·      ·      ·    0.46     ·      ·   │
#>        diatom │    ·      ·      ·   -0.24     ·      ·    0.18     ·   │
#>          dino │    ·      ·   -0.18     ·    0.25  -0.29     ·    0.18  │
#>               C:                  
#>               mero.g decapo surfac
#>      amphipod    ·      ·      ·  
#>   chaetognath    ·      ·      ·  
#>    cladoceran    ·      ·      ·  
#>   calanoid.lg    ·      ·      ·  
#>   calanoid.sm    ·      ·      ·  
#>     cyclopoid    ·      ·      ·  
#>        diatom    ·      ·      ·  
#>          dino    ·      ·      ·  
#> 
#> AIC:  3547.274
#> 
#> R^2 Values:
#>                  R2  R2_D
#>      amphipod  0.04  0.42
#>   chaetognath  0.25  0.25
#>    cladoceran  0.18  0.30
#>   calanoid.lg  0.22  0.41
#>   calanoid.sm  0.20  0.36
#>     cyclopoid  0.21  0.27
#>        diatom  0.11  0.44
#>          dino  0.15  0.42
#> 
#>

Some summary statistics for the model

summary(run1)
#> 
#> Matrix Coefficients:
#> 
#>                   Best-fit           Bootstrap    
#>                ───────────────    ─────────────── 
#>                 B    C   Total     B    C   Total 
#>   total.coef   64   24    88      64   24    88   
#>        zeros   37   17    54      49   24    73   
#>     nonzeros   27    7    34      15    0    15   
#>     positive   15    4    19       9    0     9   
#>     negative   12    3    15       6    0     6   
#> 
#> 
#> Information Criteria:
#> 
#>         Best-fit    Bootstrap
#>         ─────────   ─────────
#>   AIC   3548.162    3547.274 
#>   BIC   3795.015    3733.996 
#> 
#> 
#> R^2 Values:
#> 
#>             Best-fit       Bootstrap 
#>            ───────────    ───────────
#>             R2    R2_D     R2    R2_D
#>   min      0.1    0.27    0.04   0.25
#>   1st qu   0.15   0.29    0.14   0.29
#>   median   0.19   0.4     0.19   0.38
#>   mean     0.19   0.37    0.17   0.36
#>   3rd qu   0.24   0.44    0.21   0.42
#>   max      0.28   0.46    0.25   0.44
#> 
#> 
#> Stability:
#> 
#>                                  Best-fit    Bootstrap
#>                                  ─────────   ─────────
#>   ATTRIBUTE    METRIC              VALUE       VALUE  
#>   resilience   detB                 0.11        0.09  
#>   resilience   maxeigB              0.48        0.50  
#>   resilience   maxeigkrB            0.23        0.25  
#>   reactivity   sigma.over.Vinf     -0.82       -0.83  
#>   reactivity   maxeigBxB           -0.46       -0.47

Plots

plot(run1)

Model with restrictions

Set some elements of the restriction matrix to 1 to force an element to be included (will never be dropped in searches) and set some to 0 to force that element to always be zero (not in the model).

myres[1, c(1, 6, 9)] <- c(1, 0, 0)

re-run the analysis

We will use the same variates as run1 and new restrictions.

run1b <- run.mar(L4.mar, variables = run1, restrictions = myres, "exhaustive")
#>  
#>  
#> searching for best-fit model...
#>        ...BEST-FIT MODEL SELECTED
#>             ( search time:  0 minutes 1 seconds )
#>  
#>  
#>  
#> identifying 10 lowest AIC models...
#>        ...TOP MODELS RETAINED 
#>  
#>  
#>  
#> bootstrapping best-fit model...
#>        ...BOOTSTRAPPING COMPLETE
#>  
#>  
#> ════════════════════════════════════════════════════════════════════════════════ 
#>  
#>

Plot results.

plot(run1, run1b)

run with a different search method

run1c <- run.mar(L4.mar, variables = run1, restrictions = run1, search = "fwdstep")
#>  
#>  
#> searching for best-fit model...
#>        ...BEST-FIT MODEL SELECTED
#>             ( search time:  0 minutes 0.03 seconds )
#>  
#>  
#>  
#> bootstrapping best-fit model...
#>        ...BOOTSTRAPPING COMPLETE
#>  
#>  
#> ════════════════════════════════════════════════════════════════════════════════ 
#>  
#>

Plot

plot(run1, run1c, legend = TRUE)

#> png 
#>   2

construct a MAR model using windows

Alternatively one can not pass in variables and restrictions, and one can interactively select the variables and restrictions.

run2 <- run.mar(L4.mar, search = "exhaustive")