small changes

Author: ASuGuit

R/predict.R

@@ -77,7 +77,7 @@
 #'   "name" = c("James", "Leah", "Thomas", "Sophie", "Mathew", "Andrew"),
 #'   "surname" = c("Wilson", "Thompson", "Davies", "Clarks", "Robins", "Scots")
 #' )
-#' predict(model, df_new_1, df_new_2, set_construction = "size")
+#' predict(model, df_new_1, df_new_2)
 #' @export
 predict.rec_lin_model <- function(object,
                                   newdata_A,

R/supervised_learning.R

@@ -111,7 +111,7 @@ train_rec_lin <- function(
   }
 
   if (is.null(prob_ratio)) {
-    prob_ratio <- "1"
+    prob_ratio <- "2"
   }
 
   if (any(methods %in% c("binary", "continuous_parametric"))) {

README.Rmd

@@ -46,7 +46,6 @@ df_1 <- data.frame(
   city = c("New York", "Los Angeles", "Chicago", "Houston", "Phoenix",
            "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose")
 )
-
 df_2 <- data.frame(
   name = c(
     "Emma", "Liam", "Olivia", "Noah",
@@ -68,7 +67,7 @@ df_1
 df_2
 ```
 
-Specify key variables used for record linkage. Select a comparison function (i.e. a function to compare pairs of records) for each variable. For example, use the `jarowinkler_complement` function from the `automatedRecLin` package (1 - Jaro-Winkler distance). Choose a method for estimating the probability or density ratio for each variable. The available methods are: `"binary"`, `"continuous_parametric"` and `"continuous_nonparametric"`.
+Specify the key variables used for record linkage. Select a comparison function (i.e. a function to compare pairs of records) for each variable. For example, use the `jarowinkler_complement` function from the `automatedRecLin` package (1 - Jaro-Winkler distance). Choose a method for estimating the probability or density ratio for each variable. The available methods are: `"binary"`, `"continuous_parametric"` and `"continuous_nonparametric"`.
 
 ```{r}
 variables <- c("name", "surname", "city")
@@ -92,7 +91,7 @@ Perform record linkage using the `mec` function. The output contains the followi
 + the method for constructing the predicted set of matches (default: `"size"`),
 + estimated false link rate (FLR),
 + estimated missing match rate (MMR),
-+ estimated parameters for variables using the `"binary"` or `"continuous_parametric"` methods.
++ estimated parameters for the variables using the `"binary"` or `"continuous_parametric"` methods.
 
 ```{r}
 set.seed(1)
@@ -160,7 +159,8 @@ df_2_new
 Predict matches using the `predict` function. The output has a similar structure to that of the `mec` function.
 
 ```{r}
-predict(model, df_1_new, df_2_new)
+result_sup <- predict(model, df_1_new, df_2_new)
+result_sup
 ```
 
 ## Funding

README.md

@@ -42,7 +42,6 @@ df_1 <- data.frame(
   city = c("New York", "Los Angeles", "Chicago", "Houston", "Phoenix",
            "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose")
 )
-
 df_2 <- data.frame(
   name = c(
     "Emma", "Liam", "Olivia", "Noah",
@@ -88,7 +87,7 @@ df_2
 #> 12     Lucas     Clark      Denver
 ```
 
-Specify key variables used for record linkage. Select a comparison
+Specify the key variables used for record linkage. Select a comparison
 function (i.e. a function to compare pairs of records) for each
 variable. For example, use the `jarowinkler_complement` function from
 the `automatedRecLin` package (1 - Jaro-Winkler distance). Choose a
@@ -121,7 +120,7 @@ following information:
   `"size"`),
 - estimated false link rate (FLR),
 - estimated missing match rate (MMR),
-- estimated parameters for variables using the `"binary"` or
+- estimated parameters for the variables using the `"binary"` or
   `"continuous_parametric"` methods.
 
 ``` r
@@ -246,7 +245,8 @@ Predict matches using the `predict` function. The output has a similar
 structure to that of the `mec` function.
 
 ``` r
-predict(model, df_1_new, df_2_new)
+result_sup <- predict(model, df_1_new, df_2_new)
+result_sup
 #> The algorithm predicted 5 matches.
 #> The first 5 predicted matches are:
 #>        a     b ratio / 1000