This function will generate n
random points from a Gaussian
distribution with a user provided, .mean
, .sd
- standard deviation and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the dnorm
, pnorm
and qnorm
data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number
The current simulation number.x
The current value ofn
for the current simulation.y
The randomly generated data point.dx
Thex
value from thestats::density()
function.dy
They
value from thestats::density()
function.p
The values from the resulting p_ function of the distribution family.q
The values from the resulting q_ function of the distribution family.
Arguments
- .n
The number of randomly generated points you want.
- .mean
The mean of the randomly generated data.
- .sd
The standard deviation of the randomly generated data.
- .num_sims
The number of randomly generated simulations you want.
- .return_tibble
A logical value indicating whether to return the result as a tibble. Default is TRUE.
Details
This function uses the underlying stats::rnorm()
, stats::pnorm()
,
and stats::qnorm()
functions to generate data from the given parameters. For
more information please see stats::rnorm()
See also
Other Continuous Distribution:
tidy_beta()
,
tidy_burr()
,
tidy_cauchy()
,
tidy_chisquare()
,
tidy_exponential()
,
tidy_f()
,
tidy_gamma()
,
tidy_generalized_beta()
,
tidy_generalized_pareto()
,
tidy_geometric()
,
tidy_inverse_burr()
,
tidy_inverse_exponential()
,
tidy_inverse_gamma()
,
tidy_inverse_normal()
,
tidy_inverse_pareto()
,
tidy_inverse_weibull()
,
tidy_logistic()
,
tidy_lognormal()
,
tidy_paralogistic()
,
tidy_pareto()
,
tidy_pareto1()
,
tidy_t()
,
tidy_triangular()
,
tidy_uniform()
,
tidy_weibull()
,
tidy_zero_truncated_geometric()
Other Gaussian:
tidy_inverse_normal()
,
util_normal_param_estimate()
,
util_normal_stats_tbl()
Examples
tidy_normal()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 -1.86 -4.13 0.000243 0.0313 -1.86
#> 2 1 2 -1.29 -3.97 0.000638 0.0977 -1.29
#> 3 1 3 -0.915 -3.81 0.00152 0.180 -0.915
#> 4 1 4 -2.67 -3.66 0.00329 0.00382 -2.67
#> 5 1 5 0.465 -3.50 0.00651 0.679 0.465
#> 6 1 6 2.16 -3.34 0.0118 0.985 2.16
#> 7 1 7 1.03 -3.18 0.0197 0.849 1.03
#> 8 1 8 -0.129 -3.02 0.0306 0.449 -0.129
#> 9 1 9 -0.420 -2.86 0.0444 0.337 -0.420
#> 10 1 10 -1.53 -2.71 0.0609 0.0625 -1.53
#> # ℹ 40 more rows