resample_dat_scale | R Documentation |
Creates a data frame where each sample loaded into the microplate wells has a separate attribute.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute. NA values are retained for more control.
A function that takes tuples or rows consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
A function that takes tuples or rows consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample. NA values are retained.
A function that takes tuples or rows consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
A function that takes tuples or rows consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute. NA values are retained.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute.
resample_dat_scale(df, tnp, cycles)
resample_dat_scale_naretainer(df, tnp, cycles)
resample_dat_scale_alt(df, tnp, cycles, na_omit = NULL)
resample_dat_scale_alt_na(df, tnp, cycles)
resample_dat_scale_alt_bf_na(df, tnp, cycles)
resample_dat_scale_alt_bfv(df, tnp, cycles)
resample_dat_scale_optimus(df, tnp, cycles)
resample_dat_scale_optimus_na(df, tnp, cycles)
resample_dat_scale_optimus_backend(df, tnp, cycles, na_omit = NULL)
resample_vect_scale(df, tnp, cycles, method = c("normal", "brute", "vector"))
df |
A clean data frame with attributes or tuples containing a mixture of samples. |
tnp |
A numeric value indicating the number of rows used. TNP is used as an acronym for Test, Negative, Positive. |
cycles |
A numeric value indicating the number of cycles selected by the user when running the FLUOstar instrument. |
na_omit |
Takes a string "yes" OR "no". |
method |
A string 'normal', 'brute' or 'vector' to specify the method of resampling. |
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
This function builds on or scales-up @seealso resample_dat()
, hence the suffix scale.
This function is less optimized than @seealso resample_dat_scale_optimus()
.
This function builds on or scales-up @seealso resample_dat()
, hence the suffix scale.
This function is less optimized than @seealso resample_dat_scale_optimus()
.
This function builds on or scales-up @seealso resample_dat()
, hence the suffix scale.
This function is more optimized than @seealso resample_dat_scale()
, hence the suffix scale_optimus.
This function builds on or scales-up @seealso resample_dat()
, hence the suffix scale.
This function is more optimized than @seealso resample_dat_scale()
, hence the suffix scale_optimus.
This function builds on or scales-up @seealso resample_dat()
, hence the suffix scale.
This function is more optimized than @seealso resample_dat_scale()
, hence the suffix scale_optimus.
This is the pseudo-vectorized approach and should be a more efficient function. This function will produce a vertical layout as defined in this package. This function inspired by the lapply approach pretty much applies the
Tingwei Adeck
resample_dat()
resample_dat()
resample_dat_alt()
resample_dat_alt()
resample_dat_alt()
, resample_dat_scale_alt()
resample_dat_alt()
, resample_dat_scale_alt()
resample_dat()
resample_dat()
resample_dat()
resample_dat_vect()
resample_dat_vect()
. As a matter of fact, I took this approach to
create compatibility with lapply and rapply but that failed.
## Not run:
fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale_naretainer(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale_alt(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale_alt_na(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale_alt_bf_na(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale_alt_bfv(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale_optimus(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale_optimus_na(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
resampled_scaled <- resample_dat_scale_optimus_backend(nocomma_dat, tnp=3, cycles=40)
## End(Not run)
## Not run:
fpath <- system.file("extdata", "dat_3.dat", package = "normfluodbf", mustWork = TRUE)
dat_df <- read.table(file=fpath)
nocomma_dat <- clean_odddat_optimus(dat_df)
alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'brute')
alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'normal')
alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'vector')
alt_test_scale_norm <- lapply(alt_test_scale, min_max_norm)
## End(Not run)
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