spellchecked

R/chooseModel.R

@@ -10,7 +10,7 @@
 #'
 #' Explanatory variables should be uniquely named. Underscores ('_') and colons
 #' (':') are reserved to denote derived variables and interaction terms
-#' repectively, and \code{chooseModel} will replace these --- along with other
+#' respectively, and \code{chooseModel} will replace these --- along with other
 #' special characters --- with periods ('.').
 #'
 #' @param dvdata A list containing first the response variable, followed by data

R/deriveVars.R

@@ -32,14 +32,14 @@
 #' for each category.
 #'
 #' The maximum entropy algorithm ("maxent") --- which is implemented in
-#' MIAmaxent as an infinitly-weighted logisitic regression with presences added
+#' MIAmaxent as an infinitely-weighted logistic regression with presences added
 #' to the background --- is conventionally used with presence-only occurrence
-#' data. In contrast, standard logisitic regression (algorithm = "LR"), is
+#' 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
-#' repectively, and \code{deriveVars} will replace these --- along with other
+#' respectively, and \code{deriveVars} will replace these --- along with other
 #' special characters --- with periods ('.').
 #'
 #' @param data Data frame containing the response variable in the first column
@@ -48,7 +48,7 @@
 #'   and absence (coded as 1/0). The explanatory variable data should be
 #'   complete (no NAs). See \code{\link{readData}}.
 #' @param transformtype Specifies the types of transformations types to be
-#'   performed. Default is the full set of the following transfomation types: L
+#'   performed. Default is the full set of the following transformation types: L
 #'   (linear), M (monotonous), D (deviation), HF (forward hinge), HR (reverse
 #'   hinge), T (threshold), and B (binary).
 #' @param allsplines Logical. Keep all spline transformations created, rather

R/readData.R

@@ -27,7 +27,7 @@
 #' variables, so these files should be uniquely named. \code{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, repectively.
+#' derived variables and interaction terms, respectively.
 #'
 #' @param occurrence Full pathway of the '.csv' file of occurrence data. The
 #'   first column of the CSV should code occurrence (see Details), while the

R/selectDVforEV.R

@@ -17,16 +17,16 @@
 #' 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 infinitly-weighted logisitic regression with presences added to the
-#' background --- is conventionally used with presence-only occurrence data. In
-#' contrast, standard logisitic regression (algorithm = "LR"), is conventionally
-#' used with presence-absence occurrence data.
+#' 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
-#' repectively, and \code{selectDVforEV} will replace these --- along with other
-#' special characters --- with periods ('.').
+#' respectively, and \code{selectDVforEV} will replace these --- along with
+#' other special characters --- with periods ('.').
 #'
 #' @param dvdata List containing first the response variable, followed by data
 #'   frames of derived variables produced for each explanatory variable (e.g.

R/selectEV.R

@@ -22,14 +22,14 @@
 #' products of all combinations of their component DVs (Halvorsen, 2013).
 #'
 #' The maximum entropy algorithm ("maxent") --- which is implemented in
-#' MIAmaxent as an infinitly-weighted logisitic regression with presences added
+#' MIAmaxent as an infinitely-weighted logistic regression with presences added
 #' to the background --- is conventionally used with presence-only occurrence
-#' data. In contrast, standard logisitic regression (algorithm = "LR"), is
+#' 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
-#' repectively, and \code{selectEV} will replace these --- along with other
+#' respectively, and \code{selectEV} will replace these --- along with other
 #' special characters --- with periods ('.').
 #'
 #' @param dvdata List containing first the response variable, followed by data

R/testAUC.R

@@ -3,7 +3,7 @@
 #' For a given model, \code{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 occurence data that is independent from the data used to train the
+#' used with occurrence data that is independent from the data used to train the
 #' model, to obtain an unbiased measure of model performance.
 #'
 #' If plotted, the point along the ROC curve where the discrimination threshold

vignettes/a-modeling-example.Rmd

@@ -150,7 +150,7 @@ The output of `deriveVars()` is a list consisting of 2 parts:
 * data frames of DVs for each EV (named "dvdata")  
 * the transformation functions used to produce each DV (named "transformations").
 
-Both list elements also contain the RV vaector.
+Both list elements also contain the RV vector.
 
 In our grasslands analysis, the contents of the list items look like this:
 ```{r}
@@ -357,7 +357,7 @@ grasslandPreds <- projectModel(model = grasslandmodel,
                                data = EVstack)
 ```
 
-It is often easier to vizualize probability-ratio values on a log scale, so we plot the raster object again as log2(PRO + 1):
+It is often easier to visualize probability-ratio values on a log scale, so we plot the raster object again as log2(PRO + 1):
 ```{r fig.show='hold', fig.width=5.5, fig.height=5.5}
 plot(log2(grasslandPreds$output + 1))
 ```
@@ -384,7 +384,7 @@ The evaluation metric which is used most commonly for distribution models and is
 
 The `testAUC()` function takes a data frame of presence and absence locations, along with the corresponding values of EVs at those locations, and calculates testAUC. The evaluation data can easily be read into R using the `readData()` function with `PA = TRUE` if desired, as shown below.
 
-In our example, 122 test locations in Øsfold County, Norway, were visited to record presence or absence of semi-natural grasslands:
+In our example, 122 test locations in Østfold County, Norway, were visited to record presence or absence of semi-natural grasslands:
 ```{r}
 grasslandPA <- readData(
   occurrence = system.file("extdata", "occurrence_PA.csv", package="MIAmaxent"),