LowSim: Data - Low dimensional simulation

In Laksafoss/CausDiscEqVar: Causal Discovery with Equal Variance Assumption

Description

A dataset containing the result of a large scale simulation of the low dimensional setting.

Usage

LowSim

Format

a data frame with 18 variables:

p

number of parameters

graph_setting

Low dimensional sparse, dense or high dimensional A/B

l

lower bound for absolute ausal effect

u

upper bound for sbaolute causal effect

uniquq_ordering

TRUE / FALSE

n

number of observations

sigma

standard deviation of the common error

measure

Model selection parameter, "mse", "mae", "deviance", "AIC", "BIC"

which

Model selection parameter, "min" or "1se"

method

"TD", "BU", "HTD", "HBU"

max.degree

HTD parameter

search

"full", "B&B" or "OMP"

M

Tuning parameter in organic lasso

Kendall

Kendalls tau btween the true and esimated topological ordering

Hamming

Structual Hamming Distance between true and estimated graph

Recall

percent of arrows true positive arrows in estimated graph

Flipped

percent of arrows flipped compared to true graph

FDR

percent of arrows false positivesin the estimateed graph

Laksafoss/CausDiscEqVar documentation built on May 23, 2019, 1:58 a.m.