MIAmaxent (version 0.4.0)

deriveVars: Derive variables by transformation.

Description

deriveVars produces derived variables from explanatory variables by transformation, and returns a list of dataframes. The available transformation types are as follows, described in Halvorsen et al. (2015): L, M, D, HF, HR, T (for continuous EVs), and B (for categorical EVs). For spline transformation types (HF, HR, T), a subset of possible DVs is selected by the criteria described under Details.

Usage

deriveVars(data, transformtype = c("L", "M", "D", "HF", "HR", "T", "B"),
  allsplines = FALSE, dir = NULL)

Arguments

data

Data frame containing the response variable in the first column and explanatory variables in subsequent columns. The response variable should represent presence/background data, coded as: 1/NA. See readData.

transformtype

Specifies the types of transformations types to be performed. Default is the full set of the following transfomation types: L (linear), M (monotonous), D (deviation), HF (forward hinge), HR (reverse hinge), T (threshold), and B (binary).

allsplines

Logical. Keep all spline transformations created, rather than selecting particular splines based on fraction of total variation explained.

dir

Directory to which files will be written during selection of spline-type derived variables. Defaults to the working directory.

Value

List of 2:

1. A list of data frames, with each containing the derived variables produced for a given explanatory variable. This item is recommended as input for dvdata in selectDVforEV.
2. A list of all the transformation functions used to produce the derived variables. This item is recommended as input for transformation in plotResp2, testAUC, and projectModel.

Details

The linear transformation "L" is a simple rescaling to the range [0, 1]. The monotonous transformation "M" performed is a zero-skew transformation (Oekland et al. 2001). The deviation transformation "D" is performed around an optimum EV value that is found by looking at frequency of presence (see plotFOP). Three deviation transformations are created with different steepness and curvature around the optimum. For spline transformations ("HF", "HR", and "T"), DVs are created around 20 different break points (knots) which span the range of the EV. Only DVs which satisfy all of the following criteria are retained:

1. 3 <= knot <= 18 (DVs with knots at the extremes of the EV are never retained).
2. F-test of the single-variable Maxent model from the given DV gives a p-value < 0.05.
3. The single-variable Maxent model from the given DV shows a local maximum in fraction of variation explained (FVA) compared to DVs from the neighboring 4 knots.

For categorical variables, 1 binary derived variable (type "B") is created for each category. Explanatory variables should be uniquely named, and the names must not contain spaces, underscores, or colons. Underscores and colons are reserved to denote derived variables and interaction terms repectively.

References

Halvorsen, R., Mazzoni, S., Bryn, A., & Bakkestuen, V. (2015). Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt. Ecography, 38(2), 172-183. Oekland, R.H., Oekland, T. & Rydgren, K. (2001). Vegetation-environment relationships of boreal spruce swamp forests in Oestmarka Nature Reserve, SE Norway. Sommerfeltia, 29, 1-190.

Examples

## Not run: ------------------------------------
# deriveddat <- deriveVars(dat, transformtype = c("HF", "HR", "T"), allsplines = TRUE,
#    dir = "D:/path/to/modeling/directory")
## ---------------------------------------------

toydata_dvs <- deriveVars(toydata_sp1po, transformtype = c("L", "M", "D", "B"))
str(toydata_dvs$EVDV)
summary(toydata_dvs$transformations)

## Not run: ------------------------------------
# # From vignette:
# grasslandDVs <- deriveVars(grasslandPO, transformtype = c("L", "M", "D", "HF", "HR", "T", "B"))
# summary(grasslandDVs$EVDV) # alternatively: summary(grasslandDVs[[1]])
# head(summary(grasslandDVs$transformations)) # alternatively: head(summary(grasslandDVs[[2]]))
# length(grasslandDVs$transformations)
# plot(grasslandPO$terslpdg, grasslandDVs$EVDV$terslpdg$terslpdg_D2, pch = 20)
# plot(grasslandPO$terslpdg, grasslandDVs$EVDV$terslpdg$terslpdg_HR4, pch = 20)
## ---------------------------------------------