kld_measure {rnmamod} | R Documentation |
Function for the Kullback-Leibler Divergence of two normally distributed treatment effects for the same pairwise comparison
Description
The user specify the (posterior) mean and standard error (or posterior standard deviation) of two estimated treatment effects, X and Y, that refer to the same pairwise comparison and are assumed to follow a normal distribution. The function returns the Kullback-Leibler Divergence (KLD) measure of 1) approximating X with Y, 2) approximating Y with X, and 3) their average.
Usage
kld_measure(mean_y, sd_y, mean_x, sd_x)
Arguments
mean_y |
A real number that refers to the mean of the estimated treatment effect Y on the scale of the selected effect measure (in logarithmic scale for relative effect measures). |
sd_y |
A positive integer that refers to the posterior standard deviation or the standard error of the estimated treatment effect Y on the scale of the selected effect measure (in logarithmic scale for relative effect measures). |
mean_x |
A real number that refers to the mean of the estimated treatment effect X on the scale of the selected effect measure (in logarithmic scale for relative effect measures). |
sd_x |
A positive integer that refers to the posterior standard deviation or the standard error of the estimated treatment effect X on the scale of the selected effect measure (in logarithmic scale for relative effect measures). |
Value
The function return the following numeric results:
kld_sym | The symmetric KLD value as the average of two KLD values . |
kld_x_true | The KLD value when approximating X by Y (X is the 'truth'). |
kld_y_true | The KLD value when approximating Y by X (Y is the 'truth'). |
References
Kullback S, Leibler RA. On information and sufficiency. Ann Math Stat 1951;22(1):79–86. doi: 10.1214/aoms/1177729694
See Also
kld_inconsistency
,
kld_inconsistency_user
, robustness_index
,
robustness_index_user