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.
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_pareto1()
,
tidy_pareto()
,
tidy_t()
,
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 1.43 -3.98 0.000213 0.923 1.43
#> 2 1 2 0.662 -3.83 0.000592 0.746 0.662
#> 3 1 3 0.280 -3.68 0.00144 0.610 0.280
#> 4 1 4 -0.893 -3.52 0.00308 0.186 -0.893
#> 5 1 5 -1.69 -3.37 0.00579 0.0457 -1.69
#> 6 1 6 -2.72 -3.22 0.00961 0.00329 -2.72
#> 7 1 7 -1.66 -3.06 0.0142 0.0487 -1.66
#> 8 1 8 -0.897 -2.91 0.0188 0.185 -0.897
#> 9 1 9 0.0596 -2.76 0.0230 0.524 0.0596
#> 10 1 10 1.06 -2.61 0.0271 0.856 1.06
#> # … with 40 more rows