Convert the parameter(s) of a distribution to summary statistics

Convert the parameters for a range of distributions to a number of summary statistics. All summary statistics are calculated analytically given the parameters.

Usage

convert_params_to_summary_stats(x, ...)

# S3 method for class 'character'
convert_params_to_summary_stats(
  x = c("lnorm", "gamma", "weibull", "nbinom", "geom", "norm"),
  ...
)

# S3 method for class 'epiparameter'
convert_params_to_summary_stats(x, ...)

Arguments

x

An R object.

...

<dynamic-dots> Numeric named parameter(s) used to convert to summary statistics. An example is the meanlog and sdlog parameters of the lognormal (lnorm) distribution.

Value

A list of eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient of variation (cv), skewness, and excess kurtosis (ex_kurtosis).

Details

The distribution names and parameter names follow the style of distributions in R, for example the lognormal distribution is lnorm, and its parameters are meanlog and sdlog.

Distribution names are matched without regard to capitalisation, and the full name of a distribution can be given as well as its R name, so "nbinom", "negbinom" and "Negative binomial" are all accepted.

See also

convert_summary_stats_to_params()

Examples

# example using characters
convert_params_to_summary_stats("lnorm", meanlog = 1, sdlog = 2)
#> $mean
#> [1] 20.08554
#> 
#> $median
#> [1] 2.718282
#> 
#> $mode
#> [1] 0.04978707
#> 
#> $var
#> [1] 21623.04
#> 
#> $sd
#> [1] 147.0477
#> 
#> $cv
#> [1] 7.321076
#> 
#> $skewness
#> [1] 414.3593
#> 
#> $ex_kurtosis
#> [1] 9220557
#> 
convert_params_to_summary_stats("gamma", shape = 1, scale = 1)
#> $mean
#> [1] 1
#> 
#> $median
#> [1] 0.6931472
#> 
#> $mode
#> [1] 0
#> 
#> $var
#> [1] 1
#> 
#> $sd
#> [1] 1
#> 
#> $cv
#> [1] 1
#> 
#> $skewness
#> [1] 2
#> 
#> $ex_kurtosis
#> [1] 6
#> 
convert_params_to_summary_stats("nbinom", prob = 0.5, dispersion = 2)
#> $mean
#> [1] 2
#> 
#> $median
#> [1] 1
#> 
#> $mode
#> [1] 1
#> 
#> $var
#> [1] 4
#> 
#> $sd
#> [1] 2
#> 
#> $cv
#> [1] 1
#> 
#> $skewness
#> [1] 1.5
#> 
#> $ex_kurtosis
#> [1] 3.25
#> 

# example using <epiparameter>
epiparameter <- epiparameter_db(single_epiparameter = TRUE)
#> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan
#> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other
#> Epidemiological Characteristics of 2019 Novel Coronavirus Infections
#> with Right Truncation: A Statistical Analysis of Publicly Available
#> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538
#> <https://doi.org/10.3390/jcm9020538>.. 
#> To retrieve the citation use the 'get_citation' function
convert_params_to_summary_stats(epiparameter)
#> $mean
#> [1] 9.7
#> 
#> $median
#> [1] 2.576957
#> 
#> $mode
#> [1] 0.1818772
#> 
#> $var
#> [1] 1239.04
#> 
#> $sd
#> [1] 35.2
#> 
#> $cv
#> [1] 3.628866
#> 
#> $skewness
#> [1] 58.67393
#> 
#> $ex_kurtosis
#> [1] 46586.04
#> 

# example using <epiparameter> and specifying parameters
epiparameter <- epiparameter_db(
  disease = "Influenza",
  author = "Virlogeux",
  subset = prob_dist == "weibull"
)
#> Returning 4 results that match the criteria (3 are parameterised). 
#> Use subset to filter by entry variables or single_epiparameter to return a single entry. 
#> To retrieve the citation for each use the 'get_citation' function
convert_params_to_summary_stats(epiparameter[[2]], shape = 1, scale = 1)
#> $mean
#> [1] 1
#> 
#> $median
#> [1] 0.6931472
#> 
#> $mode
#> [1] 0
#> 
#> $var
#> [1] 1
#> 
#> $sd
#> [1] 1
#> 
#> $cv
#> [1] 1
#> 
#> $skewness
#> [1] 2
#> 
#> $ex_kurtosis
#> [1] 6
#>