| Title: | Generating Bootstrap Estimation Distributions of HR Data |
|---|---|
| Description: | Creates plots showing scored HR experiments and plots of distribution of means of ranks of HR score from bootstrapping. Authors (2019) <doi:10.5281/zenodo.3374507>. |
| Authors: | Dan MacLean [aut, cre] |
| Maintainer: | Dan MacLean <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.3.2 |
| Built: | 2024-11-04 04:18:58 UTC |
| Source: | https://github.com/cran/besthr |
estimate carries out estimation of bootstrap confidence intervals on
ranked score data. Returns a hrest object of the result
Proceeeds by calculating score ranks, then bootstrapping ranks in non-control
groups retaining the mean for each bootstrap iteration. Calculates
low and high quantiles of bootstrap mean distributions for each group.
If technical replicates are provided in a second grouping column these will
be averaged before proceeding.
estimate(df, ..., control = "A", nits = 100, low = 0.025, high = 0.975)estimate(df, ..., control = "A", nits = 100, low = 0.025, high = 0.975)
df |
data frame of score and group data. Contains minimally a score and group column |
... |
bare names of columns to use, minimally the score column and the group column in that order. Optionally a third technical replicate column can be provided |
control |
the value of the grouping column taken to be the control group |
nits |
the number of bootstap iterations to be done |
low |
the low probability value of the quantile |
high |
the high probability value of the quantile |
a list object of class "hrest"
d1 <- make_data() estimate(d1, score, group) d2 <- make_data2() estimate(d2, score_column_name, sample_column_name, rep_column_name ) d3 <- make_data3() estimate(d3, score, sample, rep, nits = 1000)d1 <- make_data() estimate(d1, score, group) d2 <- make_data2() estimate(d2, score_column_name, sample_column_name, rep_column_name ) d3 <- make_data3() estimate(d3, score, sample, rep, nits = 1000)
return a sample data set of random values for two groups
make_data()make_data()
tibble of random values for two groups
d1 <- make_data()d1 <- make_data()
return a sample data set of random values for two groups with three technical reps per group
make_data2()make_data2()
tibble of random values for two groups with three technical reps per group
d2 <- make_data2()d2 <- make_data2()
@examples
make_data3()make_data3()
d3 <- make_data3()
tibble of random values for three groups with three technical reps per group
hrest objectreturns a ggplot object representing the hrest object from
estimate. The content of left panel varies according to the
value of the which parameter. If which = "rank_simulation" is
used a plot of rank score values will be plotted in the left panel. In this
case technical replicates will be averaged if provided. If
which = "just_data" a plot of scores only is created and technical
replicates are displayed as is. In each case, the right hand panel shows the
rank bootstrap distribution and confidence interval boundaries for all non-
control groups.
## S3 method for class 'hrest' plot(x, ..., which = "rank_simulation")## S3 method for class 'hrest' plot(x, ..., which = "rank_simulation")
x |
the |
... |
Other parameters |
which |
the type of left hand panel to create. Either "rank_simulation" or "just_data" |
ggplot object
d1 <- make_data() hr_est <- estimate(d1, score, group) plot(hr_est)d1 <- make_data() hr_est <- estimate(d1, score, group) plot(hr_est)
print a summary of the hrest object
## S3 method for class 'hrest' print(x, ...)## S3 method for class 'hrest' print(x, ...)
x |
hrest object |
... |
other parameters |
null
d1 <- make_data() hr_est <- estimate(d1, score, group) print(hr_est)d1 <- make_data() hr_est <- estimate(d1, score, group) print(hr_est)