multiLocalFDR is a package for multi-dimensional local-FDR estimation using a semiparametric mixture method. The two pillars of the proposed approach are Efron's empirical null principle and log-concave density estimation for the alternative distribution. A unique feature of our method is that it can be extended to compute the local false discovery rates by combining multiple lists of p-values.
localFDR() provides estimates of local-fdr for given lists of p-values.SPestimate() provides estimates of null and alternative distribution of our method.normmix() provides estimates of null and alternative distribution of normal mixture model. arrangeNE() arranges given data as an increasing order for multi-dimensional data.You can learn more about them in
vignette("multiLocalFDR").
You can install multiLocalFDR from GitHub.
# install.packages("devtools")
devtools::install_github("JungiinChoi/multiLocalFDR")multiLocalFDR imports the modified version of fmlogcondens from my GitHub.
If you already have the original fmlogcondens, multiLocalFDR will overwrite this package and give a warning.
# install.packages("devtools")
devtools::install_github("JungiinChoi/multiLocalFDR")
#> Warning message:
#> package 'multiLocalFDR' overwrites 'fmlogcondens' to modified version.library(multiLocalFDR)
starwars %>%
filter(species == "Droid")
#> # A tibble: 6 x 14
#> name height mass hair_color skin_color eye_color birth_year sex gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 C-3PO 167 75 <NA> gold yellow 112 none masculi…
#> 2 R2-D2 96 32 <NA> white, blue red 33 none masculi…
#> 3 R5-D4 97 32 <NA> white, red red NA none masculi…
#> 4 IG-88 200 140 none metal red 15 none masculi…
#> 5 R4-P17 96 NA none silver, red red, blue NA none feminine
#> # … with 1 more row, and 5 more variables: homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
starwars %>%
select(name, ends_with("color"))
#> # A tibble: 87 x 4
#> name hair_color skin_color eye_color
#> <chr> <chr> <chr> <chr>
#> 1 Luke Skywalker blond fair blue
#> 2 C-3PO <NA> gold yellow
#> 3 R2-D2 <NA> white, blue red
#> 4 Darth Vader none white yellow
#> 5 Leia Organa brown light brown
#> # … with 82 more rows
starwars %>%
mutate(name, bmi = mass / ((height / 100) ^ 2)) %>%
select(name:mass, bmi)
#> # A tibble: 87 x 4
#> name height mass bmi
#> <chr> <int> <dbl> <dbl>
#> 1 Luke Skywalker 172 77 26.0
#> 2 C-3PO 167 75 26.9
#> 3 R2-D2 96 32 34.7
#> 4 Darth Vader 202 136 33.3
#> 5 Leia Organa 150 49 21.8
#> # … with 82 more rows
starwars %>%
arrange(desc(mass))
#> # A tibble: 87 x 14
#> name height mass hair_color skin_color eye_color birth_year sex gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Jabba … 175 1358 <NA> green-tan,… orange 600 herm… mascu…
#> 2 Grievo… 216 159 none brown, whi… green, ye… NA male mascu…
#> 3 IG-88 200 140 none metal red 15 none mascu…
#> 4 Darth … 202 136 none white yellow 41.9 male mascu…
#> 5 Tarfful 234 136 brown brown blue NA male mascu…
#> # … with 82 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
starwars %>%
group_by(species) %>%
summarise(
n = n(),
mass = mean(mass, na.rm = TRUE)
) %>%
filter(
n > 1,
mass > 50
)
#> # A tibble: 8 x 3
#> species n mass
#> <chr> <int> <dbl>
#> 1 Droid 6 69.8
#> 2 Gungan 3 74
#> 3 Human 35 82.8
#> 4 Kaminoan 2 88
#> 5 Mirialan 2 53.1
#> # … with 3 more rowsIf you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub. For questions and other discussion, feel free to contact me: Jungin Choi (serimtech07 at snu.ac.kr).