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 logistic data.
Three different methods of shape parameters are supplied:
MLE
MME
MMUE
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.xparameter and use thetidy_combine_distributions(). The user can then plot out the data using$combined_data_tblfrom the function output.
Details
This function will attempt to estimate the logistic location and scale parameters given some vector of values.
See also
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_cauchy_param_estimate(),
util_exponential_param_estimate(),
util_gamma_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate()
Other Logistic:
tidy_logistic(),
tidy_paralogistic(),
util_logistic_stats_tbl()
Examples
library(dplyr)
library(ggplot2)
x <- mtcars$mpg
output <- util_logistic_param_estimate(x)
output$parameter_tbl
#> # A tibble: 3 × 10
#> dist_type samp_size min max mean basic_scale method location scale
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MME 20.1 3.27
#> 2 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MMUE 20.1 3.32
#> 3 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MLE 20.1 12.6
#> # ℹ 1 more variable: shape_ratio <dbl>
output$combined_data_tbl %>%
tidy_combined_autoplot()
t <- rlogis(50, 2.5, 1.4)
util_logistic_param_estimate(t)$parameter_tbl
#> # A tibble: 3 × 10
#> dist_type samp_size min max mean basic_scale method location scale
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 Logistic 50 -5.77 7.56 2.51 1.57 EnvStats_MME 2.51 1.57
#> 2 Logistic 50 -5.77 7.56 2.51 1.57 EnvStats_MMUE 2.51 1.58
#> 3 Logistic 50 -5.77 7.56 2.51 1.57 EnvStats_MLE 2.51 1.87
#> # ℹ 1 more variable: shape_ratio <dbl>
