Package 'MIAmaxent'

Calculates variable contributions as RVA

Description

Calculates the Relative Variation Accounted for (Halvorsen et al. 2015), for the selected model or a chosen model from the results of selectEV.

Usage

calculateRVA(selectedEV, formula = NULL)

Arguments

selectedEV	The list returned by selectEV.
formula	If null, RVA is calculated for the selected model in selectedEV. Otherwise, a model formula (in the form ~ x + ...) specifying a model in the trail of forward selection (selectEV$selection) for which to calculate RVA. Response variable is irrelevant.

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.

Examples

## Not run: 
# From vignette:
calculateRVA(grasslandEVselect, formula("~ prbygall + geoberg + lcucor1 +
tertpi09 + geolmja1"))

## End(Not run)

---

Trains a model containing the explanatory variables specified.

Description

chooseModel trains a model based on the formula provided. The formula specifies which explanatory variables (EVs) — and potentially first-order interactions between these — should be included in the model. Each EV can be represented by 1 or more derived variables (see deriveVars). The function may be employed to choose a model from the selection pathway of selectEV other than the model selected under the provided alpha value.

Usage

chooseModel(dvdata, formula, algorithm = "maxent")

Arguments

dvdata	A list containing first the response variable, followed by data frames of selected derived variables for a given explanatory variable (e.g. the first item in the list returned by selectDVforEV).
formula	A model formula (in the form y ~ x + ...) specifying the independent terms (EVs) to be included in the model. The item in dvdata is still taken as the response variable, regardless of formula.
algorithm	Character string matching either "maxent" or "LR", which determines the type of model built. Default is "maxent".

Details

Explanatory variables should be uniquely named. Underscores ('_') and colons (':') are reserved to denote derived variables and interaction terms respectively, and chooseModel will replace these — along with other special characters — with periods ('.').

Examples

## Not run: 
# From vignette:
grasslandmodel <- chooseModel(grasslandDVselect$dvdata,
                              formula("~ pr.bygall + geoberg + lcucor1 +
                                      tertpi09 + geolmja1"))

## End(Not run)

---

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 pre-selected by the criteria described under Details.

Usage

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

Arguments

data	Data frame containing the response variable in the first column and explanatory variables in subsequent columns. The response variable should represent either presence and background (coded as 1/NA) or presence and absence (coded as 1/0). The explanatory variable data should be complete (no NAs). See readData.
transformtype	Specifies the types of transformations types to be performed. Default is the full set of the following transformation types: L (linear), M (monotone), D (deviation), HF (forward hinge), HR (reverse hinge), T (threshold), and B (binary).
allsplines	Logical. Keep all spline transformations created, rather than pre-selecting particular splines based on fraction of total variation explained.
algorithm	Character string matching either "maxent" or "LR", which determines the type of model used for spline pre-selection. See Details.
write	Logical. Write the transformation functions to .Rdata file? Default is FALSE.
dir	Directory for file writing if write = TRUE. Defaults to the working directory.
quiet	Logical. Suppress progress messages from spline pre-selection?

Details

The linear transformation "L" is a simple rescaling to the range [0, 1].

The monotone transformation "M" performed is a zero-skew transformation (Økland 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. Chi-square test of the single-variable model from the given DV compared to the null model gives a p-value < 0.05.

3. The single-variable model from the given DV shows a local maximum in fraction of variation explained (D^2, sensu Guisan & Zimmerman, 2000) compared to DVs from the neighboring 4 knots.

The models used in this pre-selection procedure may be maxent models (algorithm="maxent") or standard logistic regression models (algorithm="LR").

For categorical variables, 1 binary derived variable (type "B") is created for each category.

The maximum entropy algorithm ("maxent") — which is implemented in MIAmaxent as an infinitely-weighted logistic regression with presences added to the background — is conventionally used with presence-only occurrence data. In contrast, standard logistic regression (algorithm = "LR"), is conventionally used with presence-absence occurrence data.

Explanatory variables should be uniquely named. Underscores ('_') and colons (':') are reserved to denote derived variables and interaction terms respectively, and deriveVars will replace these — along with other special characters — with periods ('.').

Value

List of 2:

1. dvdata: List containing first the response variable, followed data frames of derived variables produced for each explanatory variable. This item is recommended as input for dvdata in selectDVforEV.

2. transformations: List containing first the response variable, followed by all the transformation functions used to produce the derived variables.

References

Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological modelling, 135(2-3), 147-186.

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.

Økland, R.H., Økland, T. & Rydgren, K. (2001). Vegetation-environment relationships of boreal spruce swamp forests in Østmarka Nature Reserve, SE Norway. Sommerfeltia, 29, 1-190.

Examples

toydata_dvs <- deriveVars(toydata_sp1po, c("L", "M", "D", "HF", "HR", "T", "B"))
str(toydata_dvs$dvdata)
summary(toydata_dvs$transformations)

## Not run: 
# From vignette:
grasslandDVs <- deriveVars(grasslandPO,
                           transformtype = c("L","M","D","HF","HR","T","B"))
summary(grasslandDVs$dvdata)
head(summary(grasslandDVs$transformations))
length(grasslandDVs$transformations)
plot(grasslandPO$terslpdg, grasslandDVs$dvdata$terslpdg$terslpdg_D2, pch=20,
     ylab="terslpdg_D2")
plot(grasslandPO$terslpdg, grasslandDVs$dvdata$terslpdg$terslpdg_M, pch=20,
     ylab="terslpdg_M")

## End(Not run)

---

Plot Frequency of Observed Presence (FOP).

Description

plotFOP produces a Frequency of Observed Presence (FOP) plot for a given explanatory variable. An FOP plot shows the rate of occurrence of the response variable across intervals or levels of the explanatory variable. For continuous variables, a local regression ("loess") of the FOP values is added to the plot as a line. Data density is plotted in the background (grey) to help visualize where FOP values are more or less certain.

Usage

plotFOP(
  data,
  EV,
  span = 0.5,
  intervals = NULL,
  ranging = FALSE,
  densitythreshold = NULL,
  ...
)

Arguments

data	Data frame containing the response variable in the first column and explanatory variables in subsequent columns. The response variable should represent either presence and background (coded as 1/NA) or presence and absence (coded as 1/0). See Details for information regarding implications of occurrence data type. See also readData.
EV	Name or column index of the explanatory variable in data for which to calculate FOP.
span	The proportion of FOP points included in the local regression neighborhood. Should be between 0 and 1. Irrelevant for categorical EVs.
intervals	Number of intervals into which the continuous EV is divided. Defaults to the minimum of N/10 and 100. Irrelevant for categorical EVs.
ranging	Logical. If TRUE, will range the EV scale to [0,1]. This is equivalent to plotting FOP over the linear transformation produced by deriveVars. Irrelevant for categorical EVs.
densitythreshold	Numeric. Intervals containing fewer than this number of observations will be represented with an open symbol in the plot. Irrelevant for categorical EVs.
...	Arguments to be passed to plot or barplot to control the appearance of the plot. For example: lwd for line width cex.main for size of plot title space for space between bars

Details

A list of the optimum EV value and a data frame containing the plotted data is returned invisibly. Store invisibly returned output by assigning it to an object.

In the local regression ("loess"), the plotted FOP values are regressed against their EV values. The points are weighted by the number of observations they represent, such that an FOP value from an interval with many observations is given more weight.

For continuous variables, the returned value of 'EVoptimum' is based on the loess-smoothed FOP values, such that a point maximum in FOP may not always be considered the optimal value of EV.

If the response variable in data represents presence/absence data, the result is an empirical frequency of presence curve, rather than a observed frequency of presence curve (see Støa et al. [2018], Sommerfeltia).

Value

In addition to the graphical output, a list of 2:

1. EVoptimum. The EV value (or level, for categorical EVs) at which FOP is highest

2. FOPdata. A data frame containing the plotted data. Columns in this data frame represent the following: EV interval ("int"), number of observations in the interval ("n"), mean EV value of the observations in the interval ("intEV"), mean RV value of the observations in the interval ("intRV"), and local regression predicted intRV ("loess"). For categorical variables, only the level name ("level"), the number of observations in the level ("n"), and the mean RV value of the level ("levelRV") are used.

References

Støa, B., R. Halvorsen, S. Mazzoni, and V. I. Gusarov. (2018). Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models. Sommerfeltia 38:1–53.

Examples

FOPev11 <- plotFOP(toydata_sp1po, 2)
FOPev12 <- plotFOP(toydata_sp1po, "EV12", intervals = 8)
FOPev12$EVoptimum
FOPev12$FOPdata

## Not run: 
# From vignette:
teraspifFOP <- plotFOP(grasslandPO, "teraspif")
terslpdgFOP <- plotFOP(grasslandPO, "terslpdg")
terslpdgFOP <- plotFOP(grasslandPO, "terslpdg", span = 0.75, intervals = 20)
terslpdgFOP
geobergFOP <- plotFOP(grasslandPO, 10)
geobergFOP

## End(Not run)

---

Plot model response.

Description

Plots the response of a given model over any of the explanatory variables (EVs) included in that model. For categorical variables, a bar plot is returned rather than a line plot. Single-effect response curves show the response of a model containing the explanatory variable of interest only, while marginal effect response curves show the response of the model when all other explanatory variables are held constant at their mean values (cf. plotResp, plotResp2).

Usage

plotResp(model, transformations, EV, logscale = FALSE, ...)

plotResp2(model, transformations, EV, logscale = FALSE, ...)

Arguments

model	The model for which the response is to be plotted. This may be the object returned by chooseModel, or the 'selectedmodel' returned by selectEV.
transformations	Transformation functions used to create the derived variables in the model. I.e. the 'transformations' returned by deriveVars. Equivalently, the full file pathway of the 'transformations.Rdata' file saved as a result of deriveVars.
EV	Character. Name of the explanatory variable for which the response curve is to be plotted. Interaction terms not allowed.
logscale	Logical. Plot the common logarithm of PRO rather than PRO itself.
...	Arguments to be passed to plot or barplot to control the appearance of the plot. For example: lwd for line width cex.main for size of plot title space for space between bars

Functions

• plotResp(): Plot single-effect model response.

• plotResp2(): Plot marginal-effect model response.

Examples

## Not run: 
# From vignette:
plotResp(grasslandmodel, grasslandDVs$transformations, "pr.bygall")
plotResp(grasslandmodel, grasslandDVs$transformations, "geolmja1")

plotResp2(grasslandmodel, grasslandDVs$transformations, "pr.bygall")

## End(Not run)

---

Project model across explanatory data.

Description

projectModel calculates model predictions for any points where values of the explanatory variables in the model are known. It can be used to get model predictions for the training data, or to project the model to a new space or time.

Usage

projectModel(
  model,
  transformations,
  data,
  clamping = FALSE,
  raw = FALSE,
  rescale = FALSE,
  filename = NULL
)

Arguments

model	The model to be projected. This may be the object returned by chooseModel, or the 'selectedmodel' returned by selectEV.
transformations	Transformation functions used to create the derived variables in the model. I.e. the 'transformations' returned by deriveVars. Equivalently, the full file pathway of the 'transformations.Rdata' file saved as a result of deriveVars.
data	Data frame of all the explanatory variables (EVs) included in the model (see readData). Alternatively, an object of class 'SpatRaster' containing rasters for all EVs included in the model. Column or raster names must match EV names.
clamping	Logical. Do clamping sensu Phillips et al. (2006). Default is FALSE.
raw	Logical. Return raw maxent output instead of probability ratio output (PRO)? Default is FALSE. Irrelevant for "LR"-type models.
rescale	Logical. Linearly rescale model output (PRO or raw) with respect to the projection data? This has implications for the interpretation of output values with respect to reference values (e.g. PRO = 1). See details. Irrelevant for "LR"-type models.
filename	Full file pathway to write raster model predictions if data is an object of class 'SpatRaster'. File format is inferred from the filename extension as in terra::writeRaster.

Details

Missing data (NA) for a continuous variable will result in NA output for that point. Missing data for a categorical variable is treated as belonging to none of the categories.

When rescale = FALSE the scale of the maxent model output (PRO or raw) returned by this function is dependent on the data used to train the model. For example, a location with PRO = 2 can be interpreted as having a probability of presence twice as high as an average site in the training data (Halvorsen, 2013, Halvorsen et al., 2015). When rescale = TRUE, the output is linearly rescaled with respect to the data onto which the model is projected. In this case, a location with PRO = 2 can be interpreted as having a probability of presence twice as high as an average site in the projection data. Similarly, raw values are on a scale which is dependent on the size of either the training data extent (rescale = FALSE) or projection data extent (rescale = TRUE).

Value

List of 2:

1. output: A data frame with the model output in column 1 and the corresponding explanatory data in subsequent columns, or a raster containing predictions if data is a SpatRaster.

2. ranges: A list showing the range of data compared to the training data, on a 0-1 scale.

If data is a SpatRaster, the output is also plotted.

References

Halvorsen, R. (2013) A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia, 36, 1-132.

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, 172-183.

Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231-259.

Examples

## Not run: 
# From vignette:
EVfiles <- c(list.files(system.file("extdata", "EV_continuous", package="MIAmaxent"),
                        full.names=TRUE),
             list.files(system.file("extdata", "EV_categorical", package="MIAmaxent"),
                        full.names=TRUE))
EVstack <- rast(EVfiles)
names(EVstack) <- gsub(".asc", "", basename(EVfiles))
grasslandPreds <- projectModel(model = grasslandmodel,
                               transformations = grasslandDVs$transformations,
                               data = EVstack)
grasslandPreds

## End(Not run)

---

Read in data object from files.

Description

readData reads in occurrence data in CSV file format and environmental data in ASCII or GeoTIFF raster file format and produces a data object which can be used as the starting point for the functions in this package. This function is intended to make reading in data easy for users familiar with the maxent.jar program. It is emphasized that important considerations for data preparation (e.g. cleaning, sampling bias removal, etc.) are not treated in this package and must be dealt with separately!

Usage

readData(
  occurrence,
  contEV = NULL,
  catEV = NULL,
  maxbkg = 10000,
  PA = FALSE,
  XY = FALSE,
  duplicates = FALSE
)

Arguments

occurrence	Full pathway of the '.csv' file of occurrence data. The first column of the CSV should code occurrence (see Details), while the second and third columns should contain X and Y coordinates corresponding to the raster coordinate system. The first row of the csv is read as a header row.
contEV	Pathway to a directory containing continuous environmental variables in either '.asc' (ASCII) or '.tif' (GeoTIFF) file format.
catEV	Pathway to a directory containing categorical environmental variables in either '.asc' (ASCII) or '.tif' (GeoTIFF) file format.
maxbkg	Integer. Maximum number of grid cells randomly selected as uninformed background locations for the response variable. Default is 10,000. Irrelevant for presence/absence data (PA = TRUE) and ignored for presence-only data (PA = FALSE) if occurrence contains 'NA' values. See Details.
PA	Logical. Does occurrence represent presence/absence data? This argument affects how the values in occurrence are interpreted, and controls what type of data object is produced. See Details.
XY	Logical. Include XY coordinates in the output. May be useful for spatial plotting. Note that coordinates included in the training data used to build the model will be treated as explanatory variables.
duplicates	Logical. Include each coordinate in occurrence as a separate row in the output, even if multiple coordinates fall in the same raster cell. If TRUE, a presence data point in a given cell does not preclude absence data points in the same cell.

Details

When occurrence represents presence-only data (PA = FALSE), all rows with values other than 'NA' in column 1 of the CSV file are treated as presence locations. If column 1 contains any values of 'NA', these rows are treated as the uninformed background locations. Thus, 'NA' can be used to specify a specific set of uninformed background locations if desired. Otherwise uninformed background locations are randomly selected from the full extent of the raster cells which are not already included as presence locations. Only cells which contain data for all environmental variables are retained as presence locations or selected as uninformed background locations.

When occurrence represents presence/absence data (PA = TRUE), rows with value '0' in column 1 of the CSV are treated as absence locations, rows with value 'NA' are excluded, and all other rows are treated as presences. If duplicates = FALSE, raster cells containing both presence and absence locations result in a single presence row.

The names of the input raster files are used as the names of the explanatory variables, so these files should be uniquely named. readData replaces underscores '_', spaces ' ' and other special characters not allowed in names with periods '.'. In MIAmaxent, underscores and colons are reserved to denote derived variables and interaction terms, respectively.

Value

Data frame with the Response Variable (RV) in the first column, and Explanatory Variables (EVs) in subsequent columns. When PA = FALSE, RV values are 1/NA, and when PA = TRUE, RV values are 1/0.

Examples

toydata_sp1po <- readData(system.file("extdata/sommerfeltia", "Sp1.csv", package = "MIAmaxent"),
   contEV = system.file("extdata/sommerfeltia", "EV_continuous", package = "MIAmaxent"))
toydata_sp1po

## Not run: 
# From vignette:
grasslandPO <- readData(
 occurrence=system.file("extdata", "occurrence_PO.csv", package="MIAmaxent"),
  contEV=system.file("extdata", "EV_continuous", package="MIAmaxent"),
  catEV=system.file("extdata", "EV_categorical", package="MIAmaxent"),
  maxbkg=20000)
str(grasslandPO)

# From vignette:
grasslandPA <- readData(
  occurrence = system.file("extdata", "occurrence_PA.csv", package="MIAmaxent"),
  contEV = system.file("extdata", "EV_continuous", package="MIAmaxent"),
  catEV = system.file("extdata", "EV_categorical", package="MIAmaxent"),
  PA = TRUE, XY = TRUE)
head(grasslandPA)
tail(grasslandPA)

## End(Not run)

---

Select parsimonious sets of derived variables.

Description

For each explanatory variable (EV), selectDVforEV selects the parsimonious set of derived variables (DV) which best explains variation in a given response variable. The function uses a process of forward selection based on comparison of nested models using inference tests. A DV is selected for inclusion when, during nested model comparison, it accounts for a significant amount of remaining variation, under the alpha value specified by the user. See Halvorsen et al. (2015) for a more detailed explanation of the forward selection procedure.

Usage

selectDVforEV(
  dvdata,
  alpha = 0.01,
  retest = FALSE,
  test = "Chisq",
  algorithm = "maxent",
  write = FALSE,
  dir = NULL,
  quiet = FALSE
)

Arguments

dvdata	List containing first the response variable, followed by data frames of derived variables produced for each explanatory variable (e.g. the first item in the list returned by deriveVars).
alpha	Alpha-level used for inference testing in nested model comparison. Default is 0.01.
retest	Logical. Test variables that do not meet the alpha criterion in a given round in subsequent rounds? Default is FALSE.
test	Character string matching either "Chisq" or "F" to determine which inference test is used in nested model comparison. The Chi-squared test is implemented by stats::anova, while the F-test is implemented as described in Halvorsen (2013, 2015). Default is "Chisq".
algorithm	Character string matching either "maxent" or "LR", which determines the type of model used during forward selection. Default is "maxent".
write	Logical. Write the trail of forward selection for each EV to .csv file? Default is FALSE.
dir	Directory for file writing if write = TRUE. Defaults to the working directory.
quiet	Suppress progress bar?

Details

The F-test available in selectDVforEV is calculated using equation 59 in Halvorsen (2013).

If using binary-type derived variables from deriveVars, be aware that a model including all of these DVs will be considered equal to the the closest nested model, due to perfect multicollinearity (i.e. the dummy variable trap).

The maximum entropy algorithm ("maxent") — which is implemented in MIAmaxent as an infinitely-weighted logistic regression with presences added to the background — is conventionally used with presence-only occurrence data. In contrast, standard logistic regression (algorithm = "LR"), is conventionally used with presence-absence occurrence data.

Explanatory variables should be uniquely named. Underscores ('_') and colons (':') are reserved to denote derived variables and interaction terms respectively, and selectDVforEV will replace these — along with other special characters — with periods ('.').

Value

List of 2:

1. dvdata: A list containing first the response variable, followed by data frames of selected DVs for each EV. EVs with zero selected DVs are dropped. This item is recommended as input for dvdata in selectEV.

2. selection: A list of data frames, where each data frame shows the trail of forward selection of DVs for a given EV.

References

Halvorsen, R. (2013). A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia, 36, 1-132.

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.

Examples

toydata_seldvs <- selectDVforEV(toydata_dvs$dvdata, alpha = 0.4)

## Not run: 
# From vignette:
grasslandDVselect <- selectDVforEV(grasslandDVs$dvdata, alpha = 0.001)
summary(grasslandDVs$dvdata)
sum(sapply(grasslandDVs$dvdata[-1], length))
summary(grasslandDVselect$dvdata)
sum(sapply(grasslandDVselect$dvdata[-1], length))
grasslandDVselect$selection$terdem

## End(Not run)

---

Select parsimonious set of explanatory variables.

Description

selectEV selects the parsimonious set of explanatory variables (EVs) which best explains variation in a given response variable (RV). Each EV can be represented by 1 or more derived variables (see deriveVars and selectDVforEV). The function uses a process of forward selection based on comparison of nested models using inference tests. An EV is selected for inclusion when, during nested model comparison, it accounts for a significant amount of remaining variation, under the alpha value specified by the user. See Halvorsen et al. (2015) for a more detailed explanation of the forward selection procedure.

Usage

selectEV(
  dvdata,
  alpha = 0.01,
  retest = FALSE,
  interaction = FALSE,
  formula = NULL,
  test = "Chisq",
  algorithm = "maxent",
  write = FALSE,
  dir = NULL,
  quiet = FALSE
)

Arguments

dvdata	List containing first the response variable, followed by data frames of selected derived variables for a given explanatory variable (e.g. the first item in the list returned by selectDVforEV).
alpha	Alpha-level used in F-test comparison of models. Default is 0.01.
retest	Logical. Test variables (or interaction terms) that do not meet the alpha criterion in a given round in subsequent rounds? Default is FALSE.
interaction	Logical. Allow interaction terms between pairs of EVs? Default is FALSE.
formula	A model formula (in the form y ~ x + ...) specifying a starting point for forward model selection. The independent terms in the formula will be included in the model regardless of explanatory power, and must be represented in dvdata, while the remaining explanatory variables in dvdata are candidates for selection. The first list item in dvdata is still taken as the response variable, regardless of formula. Default is NULL, meaning that forward selection starts with zero selected variables.
test	Character string matching either "Chisq" or "F" to determine which inference test is used in nested model comparison. The Chi-squared test is implemented by stats::anova, while the F-test is implemented as described in Halvorsen (2013, 2015). Default is "Chisq".
algorithm	Character string matching either "maxent" or "LR", which determines the type of model used during forward selection. Default is "maxent".
write	Logical. Write the trail of forward selection to .csv file? Default is FALSE.
dir	Directory for file writing if write = TRUE. Defaults to the working directory.
quiet	Logical. Suppress progress messages from EV-selection?

Details

The F-test available in selectEV is calculated using equation 59 in Halvorsen (2013).

When interaction = TRUE, the forward selection procedure selects a parsimonious group of individual EVs first, and then tests interactions between EVs included in the model afterwards. Therefore, interactions are only explored between terms which are individually explain a significant amount of variation. When interaction = FALSE, interactions are not considered. Practically, interactions between EVs are represented by the products of all combinations of their component DVs (Halvorsen, 2013).

The maximum entropy algorithm ("maxent") — which is implemented in MIAmaxent as an infinitely-weighted logistic regression with presences added to the background — is conventionally used with presence-only occurrence data. In contrast, standard logistic regression (algorithm = "LR"), is conventionally used with presence-absence occurrence data.

Explanatory variables should be uniquely named. Underscores ('_') and colons (':') are reserved to denote derived variables and interaction terms respectively, and selectEV will replace these — along with other special characters — with periods ('.').

Value

List of 3:

1. dvdata: A list containing first the response variable, followed by data frames of DVs for each selected EV.

2. selection: A data frame showing the trail of forward selection of individual EVs (and interaction terms if necessary).

3. selectedmodel: the selected model under the given alpha value.

References

Halvorsen, R. (2013). A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia, 36, 1-132.

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.

Examples

## Not run: 
# From vignette:
grasslandEVselect <- selectEV(grasslandDVselect$dvdata, alpha = 0.001,
                              interaction = TRUE)
summary(grasslandDVselect$dvdata)
length(grasslandDVselect$dvdata[-1])
summary(grasslandEVselect$dvdata)
length(grasslandEVselect$dvdata[-1])
grasslandEVselect$selectedmodel$formula

## End(Not run)

---

Calculate model AUC with test data.

Description

For a given model, testAUC calculates the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) as a threshold-independent measure of binary classification performance. This function is intended to be used with occurrence data that is independent from the data used to train the model, to obtain an unbiased measure of model performance.

Usage

testAUC(model, transformations, data, plot = TRUE, ...)

Arguments

model	The model to be projected. This may be the object returned by chooseModel, or the 'selectedmodel' returned by selectEV.
transformations	Transformation functions used to create the derived variables in the model. I.e. the 'transformations' returned by deriveVars. Equivalently, the full file pathway of the 'transformations.Rdata' file saved as a result of deriveVars.
data	Data frame containing test occurrence data in the first column and corresponding explanatory variables in the model in subsequent columns. The test data should be coded as: 1/0/NA, representing presence, absence, and uninformed. See readData.
plot	Logical. Plot the ROC curve?
...	Arguments to be passed to plot to control the appearance of the ROC plot. For example: lwd for line width main for plot title cex for plot text and symbol size Note that some graphical parameters may return errors or warnings if they cannot be changed or correspond to multiple elements in the plot.

Details

If plotted, the point along the ROC curve where the discrimination threshold is PRO = 1, is shown for reference.

Examples

## Not run: 
# From vignette:
grasslandPA <- readData(
  occurrence = system.file("extdata", "occurrence_PA.csv", package="MIAmaxent"),
  contEV = system.file("extdata", "EV_continuous", package="MIAmaxent"),
  catEV = system.file("extdata", "EV_categorical", package="MIAmaxent"),
  PA = TRUE, XY = TRUE)
head(grasslandPA)
tail(grasslandPA)
testAUC(model = grasslandmodel, transformations = grasslandDVs$transformations,
        data = grasslandPA)

## End(Not run)

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Derived variables and transformation functions, from toy data.

Description

Derived variables and transformation functions for distribution modeling of a small, synthetic data set used in Halvorsen (2013).

Usage

toydata_dvs

Format

List with 2 elements:

1. A list of 5, with the response variable followed by data frames each containing the derived variables produced for a given explanatory variable.

2. A list of the response variable and all the transformation functions used to produce the derived variables.

Source

Produced from toydata_sp1po using deriveVars.

References

Halvorsen, R. (2013) A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia, 36, 1-132.

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Selected derived variables accompanied by selection trails, from toy data.

Description

Selected derived variables and tables showing forward model selection of derived variables for distribution modeling of a small, synthetic data set used in Halvorsen (2013).

Usage

toydata_seldvs

Format

List with 2 elements:

1. A list of 3, with the response variable followed by data frames each containing the derived variables selected for a given explanatory variable.

2. A list of the response variable and forward model selection trails used to select derived variables.

Source

Produced from toydata_dvs using selectDVforEV.

References

Halvorsen, R. (2013) A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia, 36, 1-132.

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Selected explanatory variables accompanied by selection trails, from toy data.

Description

Selected explanatory variables and tables showing forward model selection of explanatory variables for distribution modeling of a small, synthetic data set used in Halvorsen (2013). Each individual explanatory variable is represented by a group of derived variables.

Usage

toydata_selevs

Format

List with 3 elements:

1. A list of 3, with the response variable followed by data frames, represent selected explanatory variables.

2. A trail of forward model selection used to select explanatory variables and interaction terms.

3. The selected model

Source

Produced from toydata_seldvs using selectEV.

References

Halvorsen, R. (2013) A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia, 36, 1-132.

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Occurrence and environmental toy data.

Description

A small, synthetic data set for distribution modeling, consisting of occurrence and environmental data, from Halvorsen (2013). The study area consists of 40 grid cells, with 8 row and 5 columns, in which 10 presences occur.

Usage

toydata_sp1po

Format

A data frame with 40 rows and 5 variables:

RV

response variable, occurrence either presence or uninformed background

EV11

explanatory variable: northing

EV12

explanatory variable: easting

EV13

explanatory variable: modified random uniform

EV14

explanatory variable: random uniform

Source

Halvorsen, R. (2013) A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia, 36, 1-132.

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