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fix use of ggplot's after_stat() command #111

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Dec 2, 2022
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fix use of ggplot's after_stat() command #111

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22 changes: 11 additions & 11 deletions doc/guide/basics.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -224,7 +224,7 @@ other half of observations indexed with :math:`i \in I`
}

g_nonorth = ggplot(data.frame(theta_rescaled=(theta_nonorth - alpha)/se_nonorth)) +
geom_histogram(aes(y=after_stats(density), x=theta_rescaled, colour = "Non-orthogonal ML", fill="Non-orthogonal ML"),
geom_histogram(aes(y=after_stat(density), x=theta_rescaled, colour = "Non-orthogonal ML", fill="Non-orthogonal ML"),
bins = 30, alpha = 0.3) +
geom_vline(aes(xintercept = 0), col = "black") +
suppressWarnings(geom_function(fun = dnorm, aes(colour = "N(0, 1)", fill="N(0, 1)"))) +
Expand Down Expand Up @@ -348,7 +348,7 @@ orthogonalized regressor :math:`V = D - m(X)`. We then use the final estimate
}

g_nosplit = ggplot(data.frame(theta_rescaled=(theta_orth_nosplit - alpha)/se_orth_nosplit), aes(x = theta_rescaled)) +
geom_histogram(aes(y=after_stats(density), x=theta_rescaled, colour = "Double ML (no sample splitting)", fill="Double ML (no sample splitting)"),
geom_histogram(aes(y=after_stat(density), x=theta_rescaled, colour = "Double ML (no sample splitting)", fill="Double ML (no sample splitting)"),
bins = 30, alpha = 0.3) +
geom_vline(aes(xintercept = 0), col = "black") +
suppressWarnings(geom_function(fun = dnorm, aes(colour = "N(0, 1)", fill="N(0, 1)"))) +
Expand Down Expand Up @@ -452,7 +452,7 @@ Cross-fitting performs well empirically because the entire sample can be used fo
}

g_dml = ggplot(data.frame(theta_rescaled=(theta_dml - alpha)/se_dml), aes(x = theta_rescaled)) +
geom_histogram(aes(y=after_stats(density), x=theta_rescaled, colour = "Double ML with cross-fitting", fill="Double ML with cross-fitting"),
geom_histogram(aes(y=after_stat(density), x=theta_rescaled, colour = "Double ML with cross-fitting", fill="Double ML with cross-fitting"),
bins = 30, alpha = 0.3) +
geom_vline(aes(xintercept = 0), col = "black") +
suppressWarnings(geom_function(fun = dnorm, aes(colour = "N(0, 1)", fill="N(0, 1)"))) +
Expand Down Expand Up @@ -525,11 +525,11 @@ The third term :math:`c^*` vanishes in probability if sample splitting is applie
g_all = ggplot(data.frame(t_nonorth=(theta_nonorth - alpha)/se_nonorth,
t_orth_nosplit=(theta_orth_nosplit - alpha)/se_orth_nosplit,
t_dml=(theta_dml - alpha)/se_dml)) +
geom_histogram(aes(x = t_nonorth, y=after_stats(density), colour = "Non-orthogonal ML", fill="Non-orthogonal ML"),
geom_histogram(aes(x = t_nonorth, y=after_stat(density), colour = "Non-orthogonal ML", fill="Non-orthogonal ML"),
bins = 30, alpha = 0.3) +
geom_histogram(aes(x = t_orth_nosplit, y=after_stats(density), colour = "Double ML (no sample splitting)", fill="Double ML (no sample splitting)"),
geom_histogram(aes(x = t_orth_nosplit, y=after_stat(density), colour = "Double ML (no sample splitting)", fill="Double ML (no sample splitting)"),
bins = 30, alpha = 0.3) +
geom_histogram(aes(x = t_dml, y=after_stats(density), colour = "Double ML with cross-fitting", fill="Double ML with cross-fitting"),
geom_histogram(aes(x = t_dml, y=after_stat(density), colour = "Double ML with cross-fitting", fill="Double ML with cross-fitting"),
bins = 30, alpha = 0.3) +
geom_vline(aes(xintercept = 0), col = "black") +
suppressWarnings(geom_function(fun = dnorm, aes(colour = "N(0, 1)", fill="N(0, 1)"))) +
Expand Down Expand Up @@ -643,7 +643,7 @@ estimate :math:`\theta_0` without sample splitting. Again we observe a bias from
}

g_nosplit = ggplot(data.frame(theta_rescaled=(theta_orth_po_nosplit - alpha)/se_orth_po_nosplit), aes(x = theta_rescaled)) +
geom_histogram(aes(y=after_stats(density), x=theta_rescaled, colour = "Double ML (no sample splitting)", fill="Double ML (no sample splitting)"),
geom_histogram(aes(y=after_stat(density), x=theta_rescaled, colour = "Double ML (no sample splitting)", fill="Double ML (no sample splitting)"),
bins = 30, alpha = 0.3) +
geom_vline(aes(xintercept = 0), col = "black") +
suppressWarnings(geom_function(fun = dnorm, aes(colour = "N(0, 1)", fill="N(0, 1)"))) +
Expand Down Expand Up @@ -735,7 +735,7 @@ Using sample splitting, overcomes the bias induced by overfitting.
}

g_dml = ggplot(data.frame(theta_rescaled=(theta_dml_po - alpha)/se_dml_po), aes(x = theta_rescaled)) +
geom_histogram(aes(y=after_stats(density), x=theta_rescaled, colour = "Double ML with cross-fitting", fill="Double ML with cross-fitting"),
geom_histogram(aes(y=after_stat(density), x=theta_rescaled, colour = "Double ML with cross-fitting", fill="Double ML with cross-fitting"),
bins = 30, alpha = 0.3) +
geom_vline(aes(xintercept = 0), col = "black") +
suppressWarnings(geom_function(fun = dnorm, aes(colour = "N(0, 1)", fill="N(0, 1)"))) +
Expand Down Expand Up @@ -780,11 +780,11 @@ Using sample splitting, overcomes the bias induced by overfitting.
g_all = ggplot(data.frame(t_nonorth=(theta_nonorth - alpha)/se_nonorth,
t_orth_nosplit=(theta_orth_po_nosplit - alpha)/se_orth_po_nosplit,
t_dml=(theta_dml_po - alpha)/se_dml_po)) +
geom_histogram(aes(x = t_nonorth, y=after_stats(density), colour = "Non-orthogonal ML", fill="Non-orthogonal ML"),
geom_histogram(aes(x = t_nonorth, y=after_stat(density), colour = "Non-orthogonal ML", fill="Non-orthogonal ML"),
bins = 30, alpha = 0.3) +
geom_histogram(aes(x = t_orth_nosplit, y=after_stats(density), colour = "Double ML (no sample splitting)", fill="Double ML (no sample splitting)"),
geom_histogram(aes(x = t_orth_nosplit, y=after_stat(density), colour = "Double ML (no sample splitting)", fill="Double ML (no sample splitting)"),
bins = 30, alpha = 0.3) +
geom_histogram(aes(x = t_dml, y=after_stats(density), colour = "Double ML with cross-fitting", fill="Double ML with cross-fitting"),
geom_histogram(aes(x = t_dml, y=after_stat(density), colour = "Double ML with cross-fitting", fill="Double ML with cross-fitting"),
bins = 30, alpha = 0.3) +
geom_vline(aes(xintercept = 0), col = "black") +
suppressWarnings(geom_function(fun = dnorm, aes(colour = "N(0, 1)", fill="N(0, 1)"))) +
Expand Down