Represent pdqr-function as a set of points
Function enpoint() suggests a reasonable default ways of converting
pdqr-function into a data frame of numerical values (points) with desirable
number of rows. Representation of pdqr-function as a set of numbers helps to
conduct analysis using approaches outside of 'pdqr' package. For example, one
can visually display pdqr-function with some other plotting functionality.
enpoint(f, n_points = 1001)
f |
A pdqr-function. |
n_points |
Desired number of points in the output. Not used in case of
"discrete" type p-, d-, and q-function |
P-functions are represented with "x" (for "x" values) and "p" (for cumulative probability at "x" points) columns:
For "continuous" type, "x" is taken as an equidistant grid (with
n_points elements) on input's support.
For "discrete" type, "x" is taken directly from "x_tbl" metadata without using n_points argument.
D-functions are represented with "x" column and one more (for values of d-function at "x" points):
For "continuous" type, second column is named "y" and is computed as
values of f at elements of "x" column (which is the same grid as in
p-function case).
For "discrete" it is named "prob". Both "x" and "prob" columns are taken from "x_tbl" metadata.
Q-functions are represented almost as p-functions but in inverse
fashion. Output data frame has "p" (probabilities) and "x" (values of
q-function f at "p" elements) columns.
For "continuous" type, "p" is computed as equidistant grid (with
n_points elements) between 0 and 1.
For "discrete" type, "p" is taken from "cumprob" column of "x_tbl" metadata.
R-functions are represented by generating n_points elements from
distribution. Output data frame has columns "n" (consecutive point number,
basically a row number) and "x" (generated elements).
Note that the other way to produce points for p-, d-, and q-functions is
to manually construct them with form_regrid() and meta_x_tbl(). However,
this method may slightly change function values due to possible
renormalization inside form_regrid().
A data frame with n_points (or less, for "discrete" type p-, d-, or
q-function f) rows and two columns with names depending on f's class
and type.
pdqr_approx_error() for diagnostics of pdqr-function approximation
accuracy.
Pdqr methods for plot() for a direct plotting of pdqr-functions.
form_regrid() to change underlying grid of pdqr-function.
d_norm <- as_d(dnorm) head(enpoint(d_norm)) # Control number of points with `n_points` argument enpoint(d_norm, n_points = 5) # Different pdqr classes and types produce different column names in output colnames(enpoint(new_p(1:2, "discrete"))) colnames(enpoint(new_d(1:2, "discrete"))) colnames(enpoint(new_d(1:2, "continuous"))) colnames(enpoint(new_q(1:2, "continuous"))) colnames(enpoint(new_r(1:2, "continuous"))) # Manual way with different output structure df <- meta_x_tbl(form_regrid(d_norm, 5)) ## Difference in values due to `form_regrid()` renormalization plot(enpoint(d_norm, 5), type = "l") lines(df[["x"]], df[["y"]], col = "blue")
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