The function will return a list output by default, and if the parameter
.auto_gen_empirical
is set to TRUE
then the empirical data given to the
parameter .x
will be run through the tidy_empirical()
function and combined
with the estimated normal data.
Three different methods of shape parameters are supplied:
MLE/MME
MVUE
Arguments
- .x
The vector of data to be passed to the function.
- .auto_gen_empirical
This is a boolean value of TRUE/FALSE with default set to TRUE. This will automatically create the
tidy_empirical()
output for the.x
parameter and use thetidy_combine_distributions()
. The user can then plot out the data using$combined_data_tbl
from the function output.
Details
This function will attempt to estimate the normal gaussian mean and standard deviation parameters given some vector of values.
See also
Other Parameter Estimation:
util_bernoulli_param_estimate()
,
util_beta_param_estimate()
,
util_binomial_param_estimate()
,
util_cauchy_param_estimate()
,
util_exponential_param_estimate()
,
util_gamma_param_estimate()
,
util_geometric_param_estimate()
,
util_hypergeometric_param_estimate()
,
util_logistic_param_estimate()
,
util_lognormal_param_estimate()
,
util_negative_binomial_param_estimate()
,
util_pareto_param_estimate()
,
util_poisson_param_estimate()
,
util_uniform_param_estimate()
,
util_weibull_param_estimate()
Other Gaussian:
tidy_inverse_normal()
,
tidy_normal()
,
util_normal_stats_tbl()
Examples
library(dplyr)
library(ggplot2)
x <- mtcars$mpg
output <- util_normal_param_estimate(x)
output$parameter_tbl
#> # A tibble: 2 × 8
#> dist_type samp_size min max method mu stan_dev shape_ratio
#> <chr> <int> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 Gaussian 32 10.4 33.9 EnvStats_MME_MLE 20.1 5.93 3.39
#> 2 Gaussian 32 10.4 33.9 EnvStats_MVUE 20.1 6.03 3.33
output$combined_data_tbl %>%
tidy_combined_autoplot()
t <- rnorm(50, 0, 1)
util_normal_param_estimate(t)$parameter_tbl
#> # A tibble: 2 × 8
#> dist_type samp_size min max method mu stan_dev shape_ratio
#> <chr> <int> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 Gaussian 50 -1.81 1.59 EnvStats_MME_MLE -0.166 0.759 -0.219
#> 2 Gaussian 50 -1.81 1.59 EnvStats_MVUE -0.166 0.767 -0.217