Fit and validate Generalized Linear Models
Usage
fit_glm(
data,
response,
predictors,
predictors_f = NULL,
select_pred = FALSE,
partition,
thr = NULL,
fit_formula = NULL,
poly = 2,
inter_order = 0
)Arguments
- data
data.frame. Database with response (0,1) and predictors values.
- response
character. Column name with species absence-presence data (0,1).
- predictors
character. Vector with the column names of quantitative predictor variables (i.e. continuous variables). Usage predictors = c("aet", "cwd", "tmin")
- predictors_f
character. Vector with the column names of qualitative predictor variables (i.e. ordinal or nominal variables type). Usage predictors_f = c("landform")
- select_pred
logical. Perform predictor selection. If TRUE predictors will be selected based on backward step wise approach. Default FALSE.
- partition
character. Column name with training and validation partition groups.
- thr
character. Threshold used to get binary suitability values (i.e. 0,1), needed for threshold-dependent performance metrics. More than one threshold type can be used. It is necessary to provide a vector for this argument. The following threshold criteria are available:
lpt: The highest threshold at which there is no omission.
equal_sens_spec: Threshold at which the sensitivity and specificity are equal.
max_sens_spec: Threshold at which the sum of the sensitivity and specificity is the highest (aka threshold that maximizes the TSS).
max_jaccard: The threshold at which the Jaccard index is the highest.
max_sorensen: The threshold at which the Sorensen index is highest.
max_fpb: The threshold at which FPB (F-measure on presence-background data) is highest.
sensitivity: Threshold based on a specified sensitivity value. Usage thr = c('sensitivity', sens='0.6') or thr = c('sensitivity'). 'sens' refers to sensitivity value. If it is not specified a sensitivity values, function will use by default 0.9
If more than one threshold type is used they must be concatenated, e.g., thr=c('lpt', 'max_sens_spec', 'max_jaccard'), or thr=c('lpt', 'max_sens_spec', 'sensitivity', sens='0.8'), or thr=c('lpt', 'max_sens_spec', 'sensitivity'). Function will use all thresholds if no threshold is specified.
- fit_formula
formula. A formula object with response and predictor variables (e.g. formula(pr_ab ~ aet + ppt_jja + pH + awc + depth + landform)). Note that the variables used here must be consistent with those used in response, predictors, and predictors_f arguments
- poly
integer >= 2. If used with values >= 2 model will use polynomials for those continuous variables (i.e. used in predictors argument). Default is 0.
- inter_order
integer >= 0. The interaction order between explanatory variables. Default is 0.
Value
A list object with:
model: A "glm" class object from stats package. This object can be used for predicting.
predictors: A tibble with quantitative (c column names) and qualitative (f column names) variables use for modeling.
performance: Performance metrics (see
sdm_eval). Threshold dependent metric are calculated based on the threshold specified in thr argument .data_ens: Predicted suitability for each test partition. This database is used in
fit_ensemble
Examples
if (FALSE) { # \dontrun{
data("abies")
abies
# Using k-fold partition method
abies2 <- part_random(
data = abies,
pr_ab = "pr_ab",
method = c(method = "kfold", folds = 5)
)
abies2
glm_t1 <- fit_glm(
data = abies2,
response = "pr_ab",
predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
predictors_f = c("landform"),
select_pred = FALSE,
partition = ".part",
thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
poly = 0,
inter_order = 0
)
glm_t1$model
glm_t1$predictors
glm_t1$performance
glm_t1$data_ens
# Using second order polynomial terms and first-order interaction terms
glm_t2 <- fit_glm(
data = abies2,
response = "pr_ab",
predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
predictors_f = c("landform"),
select_pred = FALSE,
partition = ".part",
thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
poly = 2,
inter_order = 1
)
# Using repeated k-fold partition method
abies2 <- part_random(
data = abies,
pr_ab = "pr_ab",
method = c(method = "rep_kfold", folds = 3, replicates = 5)
)
abies2
# Using third order polynomial terms and second-order interaction terms
glm_t3 <- fit_glm(
data = abies2,
response = "pr_ab",
predictors = c("ppt_jja", "pH", "awc"),
predictors_f = c("landform"),
select_pred = FALSE,
partition = ".part",
thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
poly = 3,
inter_order = 2
)
} # }