ICA.BinBin.CounterAssum | R Documentation |
Shows the results of ICA (binary-binary setting) in the subgroup of results where the counterfactual correlations are assumed to fall within some prespecified ranges.
ICA.BinBin.CounterAssum(x, r2_h_S0S1_min, r2_h_S0S1_max, r2_h_S0T1_min,
r2_h_S0T1_max, r2_h_T0T1_min, r2_h_T0T1_max, r2_h_T0S1_min, r2_h_T0S1_max,
Monotonicity="General", Type="Freq", MainPlot=" ", Cex.Legend=1,
Cex.Position="topright", ...)
x |
An object of class |
r2_h_S0S1_min |
The minimum value to be considered for the counterfactual correlation |
r2_h_S0S1_max |
The maximum value to be considered for the counterfactual correlation |
r2_h_S0T1_min |
The minimum value to be considered for the counterfactual correlation |
r2_h_S0T1_max |
The maximum value to be considered for the counterfactual correlation |
r2_h_T0T1_min |
The minimum value to be considered for the counterfactual correlation |
r2_h_T0T1_max |
The maximum value to be considered for the counterfactual correlation |
r2_h_T0S1_min |
The minimum value to be considered for the counterfactual correlation |
r2_h_T0S1_max |
The maximum value to be considered for the counterfactual correlation |
Monotonicity |
Specifies whether the all results in the fitted object |
Type |
The type of plot that is produced. When |
MainPlot |
The title of the plot. Default |
Cex.Legend |
The size of the legend when |
Cex.Position |
The position of the legend, |
... |
Other arguments to be passed to the |
Wim Van der Elst, Ariel Alonso, & Geert Molenberghs
Alonso, A., Van der Elst, W., Molenberghs, G., Buyse, M., & Burzykowski, T. (submitted). On the relationship between the causal inference and meta-analytic paradigms for the validation of surrogate markers.
Van der Elst, W., Alonso, A., & Molenberghs, G. (submitted). An exploration of the relationship between causal inference and meta-analytic measures of surrogacy.
ICA.BinBin
## Not run: #Time consuming (>5 sec) code part
# Compute R2_H given the marginals specified as the pi's, making no
# assumptions regarding monotonicity (general case)
ICA <- ICA.BinBin.Grid.Sample(pi1_1_=0.261, pi1_0_=0.285,
pi_1_1=0.637, pi_1_0=0.078, pi0_1_=0.134, pi_0_1=0.127,
Monotonicity=c("General"), M=5000, Seed=1)
# Obtain a density plot of R2_H, assuming that
# r2_h_S0S1>=.2, r2_h_S0T1>=0, r2_h_T0T1>=.2, and r2_h_T0S1>=0
ICA.BinBin.CounterAssum(ICA, r2_h_S0S1_min=.2, r2_h_S0S1_max=1,
r2_h_S0T1_min=0, r2_h_S0T1_max=1, r2_h_T0T1_min=0.2, r2_h_T0T1_max=1,
r2_h_T0S1_min=0, r2_h_T0S1_max=1, Monotonicity="General",
Type="Density")
# Now show the densities of R2_H under the different
# monotonicity assumptions
ICA.BinBin.CounterAssum(ICA, r2_h_S0S1_min=.2, r2_h_S0S1_max=1,
r2_h_S0T1_min=0, r2_h_S0T1_max=1, r2_h_T0T1_min=0.2, r2_h_T0T1_max=1,
r2_h_T0S1_min=0, r2_h_T0S1_max=1, Monotonicity="General",
Type="All.Densities", MainPlot=" ", Cex.Legend=1,
Cex.Position="topright", ylim=c(0, 20))
## End(Not run)
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