我试图用recordLinkage包两个数据集连接在一起，其中一个数据集往往给多个最后/中间名，另一只是给了一个姓。目前该活动正用于字符串比较函数但是哈罗 - 温克勒函数返回的得分取决于如何串被偶然匹配起来，而不是如果短字符串的内容被包含在较长的字符串的任何地方。正在创建许多质量差的链路上是领先的。是错误的权重重复的例子如下：
library(RecordLinkage)
data1 <- as.data.frame(list("lname" = c("lolli gaggen nazeem", "lolli gaggen nazeem", "lolli gaggen nazeem"),
"bday" = c("1908-08-08", "1979-12-12", "1560-06-06") ) )
data2 <- as.data.frame(list("lname" = c("lolli", "gaggen", "nazeem"),
"bday" = c("1908-08-08", "1979-12-12", "1560-06-06") ) )
blocking_variable <- c("bday")
pass <- compare.linkage(data1, data2, blockfld = blocking_variable, strcmp = T)
pass_weights <- epiWeights(pass)
getPairs(pass_weights, single.rows = TRUE)
id1 lname.1 bday.1 id2 lname.2 bday.2 Weight
1 1 lolli gaggen nazheem 1908-08-08 1 lolli 1908-08-08 0.9162463
2 2 lolli gaggen nazheem 1979-12-12 2 gaggen 1979-12-12 0.8697165
3 3 lolli gaggen nazheem 1560-06-06 3 nazheem 1560-06-06 0.6995502
我想ID的2＆3接收大致相同的权重为ID＃1但是目前他们要低得多，因为他们的名字最后都没有在两个数据集中完全相同的位置（虽然内容是同意）。有没有一种方法，我可以修改这里所使用的字符串比较函数/数据结构，这样我可以考虑不同的排序吗？
补充笔记：
你有没有想过下面的办法？
记录链接和名称是因为我知道你会知道，困难。理想情况下，你想在其他可用的信息（性别，唯一的标识符，出生日期，位置信息等）的块，然后做名字符串比较。
你提到有千万条记录的大型数据集。不要再观望比data.table包由伟大的马特Dowle（https://stackoverflow.com/users/403310/matt-dowle）。
所述RecordLinkage包相比是缓慢的。你可以很容易地提高了下面的代码考虑使用同音串散列技术，双音位，nysiis等。
# install.packages("data.table")
library(RecordLinkage)
library(data.table)
data1 <- as.data.frame(list("lname" = c("lolli gaggen nazeeem", "lolli gaggen nazeem", "lollly gaggen nazeem", "matt dowle", "john-smith"),
"bday" = c("1908-08-08", "1979-12-12", "1560-06-06", "1979-12-12", "1560-06-06") ) )
data2 <- as.data.frame(list("lname" = c("lolli", "gaggen", "nazeem", "m dowl", "johnny smith"),
"bday" = c("1908-08-08", "1979-12-12", "1560-06-06", "1979-12-12", "1560-06-06") ) )
# Coerce to data.tables
setDT(data1)
setDT(data2)
# Define a regex split (we will split all words based on space or hyphen)
split <- " |-"
# Apply a blocking strategy based on bday. Ideally your dataset would allow for additional blocking strategies(?).
block_pairs <- merge(data1, data2, by = "bday", all = T,
sort = TRUE, suffixes = c(".x", ".y"))
# Store the split up components of each comparison variable.
split1 <- strsplit(block_pairs[["lname.x"]], split)
split2 <- strsplit(block_pairs[["lname.y"]], split)
# Perform jarowinkler comparisons on each combination of components of each string
fc <- jarowinkler(block_pairs[["lname.x"]], block_pairs[["lname.y"]])
pc <- mapply(function(x, y) max(outer(x, y, jarowinkler)), split1, split2)
# Store the max of the full and partial comparisons
block_pairs[, ("winkler.lname") := mapply(function(x,y) max(x,y), fc, pc)]
# Sort by the jarowinkler score
block_pairs <- block_pairs[order(winkler.lname)]
# Inspect
block_pairs
# 0.96 is an appropriate threshold in this instance
block_pairs <- block_pairs[winkler.lname >= 0.96]
此外，我要Khayenes的回答让作为评论概述：
library(gtools)
...
# Store the split up components of each comparison variable.
split1 <- strsplit(block_pairs[["lname.x"]], split)
split2 <- strsplit(block_pairs[["lname.y"]], split)
# Recombine tokens into all possible orderings:
make_combinations <- function(x) {
# Use permutations from the gtools package
split_names <- permutations(length(x),length(x),x)
apply(X=split_names, MARGIN=1, FUN=paste0, collapse=' ')
}
split1 <- lapply(X=split1, FUN=`make_combinations`)
split2 <- lapply(X=split2, FUN=`make_combinations`)
# Perform jarowinkler comparisons on each string combination and append it to the table
block_pairs[ ,("winkler.lname") := mapply(function(x, y) max(outer(x, y, jarowinkler)), split1, split2)]
# Sort by the jarowinkler score
block_pairs <- block_pairs[order(winkler.lname)]
# 0.85 is an appropriate threshold in this instance
block_pairs <- block_pairs[winkler.lname >= 0.85]
bday lname.x lname.y winkler.lname
1: 1908-08-08 lolli gaggen nazeem lolli 0.8526316
2: 1560-06-06 lolli gaggen nazeem nazeem 0.8631579
3: 1979-12-12 lolli gaggen nazeem gaggen 0.8631579
4: 1979-12-12 matt dowle m dowl 0.9200000
5: 1560-06-06 john-smith johnny smith 0.9666667