| Type: | Package |
| Title: | Faster Generation of Quantile Quantile Plots with Large Samples |
| Version: | 0.1.3 |
| Maintainer: | Gudmundur Einarsson <gudmundur.einarsson.phd@gmail.com> |
| Description: | New and faster implementations for quantile quantile plots. The package also includes a function to prune data for quantile quantile plots. This can drastically reduce the running time for large samples, for 100 million samples, you can expect a factor 80X speedup. |
| URL: | https://github.com/gumeo/fastqq |
| BugReports: | https://github.com/gumeo/fastqq/issues |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| Imports: | Rcpp |
| LinkingTo: | Rcpp |
| NeedsCompilation: | yes |
| Packaged: | 2022-10-24 21:42:32 UTC; gudmundureinarsson |
| Author: | Gudmundur Einarsson [aut, cre], Hafsteinn Einarsson [aut] |
| Repository: | CRAN |
| Date/Publication: | 2022-10-25 06:05:10 UTC |
Internal function to prune quantiles of non-important values for visualization.
Description
This function is not exposed, since we want to hard-code the parameters for simplicity of usage.
Usage
drop_dense(x, y, N_hard = 10000)
Arguments
x |
A numeric vector of sample/theoretical points. |
y |
A numeric vector of theoretical/sample points. |
N_hard |
Desired upper bound on the number of points to plot. |
Value
data.frame with o and e pruned as columns.
Creates a Q-Q plot
Description
Creates a quantile-quantile plot from p-values from an association study,
e.g. a genome wide association study (GWAS). We compare
the data quantile with a theoretical quantile from a uniform distribution.
This code is mostly adapted from the qqman package, but improved
for speed. A graph with a hundred million points should only take a few
seconds to generate.
Usage
qq(pvector, ...)
Arguments
pvector |
A numeric vector of p-values. |
... |
Other arguments passed to |
Value
No return value, called for plotting side effects.
Examples
qq(stats::runif(1e6))
Creates a Q-Q plot for comparing with normal quantiles
Description
Faster alternative to stats::qqnorm(). For more than 1e5 points
we remove excess points, that would not be visible in the plot, since the
points are so close. Otherwise this should work exactly the same, and the
code is mostly adapted from stats::qqnorm(). This code produces
more lightweight plots for excessive amounts of data.
Usage
qqnorm(
y,
ylim,
main = "Normal Q-Q Plot",
xlab = "Theoretical Quantiles",
ylab = "Sample Quantiles",
plot.it = TRUE,
datax = FALSE,
...
)
Arguments
y |
sample, to compare to normal quantiles. |
ylim |
graphical limits. |
main |
Plot title. |
xlab |
X label. |
ylab |
Y label. |
plot.it |
Should the plot be created. |
datax |
logical. Should data values be on x-axis? |
... |
Other arguments passed to |
Value
data.frame with sorted sample and normal quantiles, NA
values are excluded.
Examples
qqnorm(stats::rnorm(1e6))
Creates a Q-Q plot
Description
Faster alternative to stats::qqplot(). For more than 1e5 points
we remove excess points, that would not be visible in the plot, since the
points are so close.
Usage
qqplot(
x,
y,
plot.it = TRUE,
xlab = deparse1(substitute(x)),
ylab = deparse1(substitute(y)),
...
)
Arguments
x |
First sample for |
y |
Second sample for |
plot.it |
Should the plot be created. |
xlab |
x label for plot. |
ylab |
y label for plot. |
... |
Other arguments passed to |
Value
list with sorted samples, interpolated to be same size.
Examples
qqplot(stats::runif(1e6),stats::runif(1e6))