Nothing
require(LocalControlStrategy)
# Use the radon data of Krstic & Obenchain (2016).
data(radon)
# outcome: lcanmort ...continuous
# treatment: lnradon ...continuous Exposure (natural log, Winsorized to exceed -3.0)
# Define Cluster Hierarchy for UNSUPERVISED, nonparametric analyses...
xvars <- c("obesity","over65","cursmoke")
hclobj <- LCcluster(radon, xvars) # defalut clustering method = "ward.D"
hclobj
plot(hclobj)
# Save Local Control basic parameter settings to an environment that will be Updated...
e <- LCsetup(hclobj, radon, lnradon, lcanmort)
ls.str(e)
# Compute and Save LRC distributions for a range of K = Number of Clusters...
mort010 <- lrcagg( 10, e)
mort050 <- lrcagg( 50, e) # Average Cluster Size: ~58 US Counties
plot(mort050, show="ecdf", e)
mort100 <- lrcagg(100, e)
mort200 <- lrcagg(200, e) # Average Cluster Size: Only ~14 US Counties
plot(mort200, show="ecdf", e)
# "Sensitivity Analysis" Summary...
LCcompare(e)
# LTD Distribution for 50 Clusters appears to Optimize Variance-Bias Trade-Offs...
# Save and plot IV distributions for 2 values of K = Number of Clusters...
iv050 <- ivadj(mort050)
plot(iv050) # graphical display
iv200 <- ivadj(mort200)
plot(iv200) # graphical display
# Confirm: Does the Observed LRC distribution for 50 clusters truly differ from
# the Random Permutation NULL distribution assuming x_Covariates are Ignorable?
system.time( conf050 <- confirm(mort050) ) # Simulation takes ~6 seconds.
conf050
plot(conf050)
# Simulate maximum p-value for observed Kolmogorov-Smirnov D-statistic...
system.time( ksd050 <- KSperm(conf050) ) # Simulation takes ~12 seconds.
ksd050
plot(ksd050)
# Example: "Most-Like-Me" Visualizations for Residents of
# Teller County, Colorado:
# The "fips" code is a unique identifier of US Counties...
subset(radon, fips == 8119)
# xvars: "obesity" "over65" "cursmoke"
xvecTC <- c(17.2, 7.5, 24.4)
mlmeTC <- mlme(e, hclobj, mort050, xvecTC )
# Implicit print...
mlmeTC
plot(mlmeTC, NN = 250)
# Summary Statistics...
mlme.stats(mlmeTC, NN = 250)
### End of demo(radon) ##############################################
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