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Schraiber committed Oct 30, 2017
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17 changes: 10 additions & 7 deletions paper/ancient.aux
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Impact of admixture}{9}}
\citation{mathieson2015genome}
\citation{10002015global}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Application to ancient humans}{10}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Impact of contamination}{10}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Application to ancient humans}{10}}
\citation{skoglund2014genomic}
\citation{sawyer2012temporal}
\citation{green2010draft}
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\newlabel{admixture}{{4}{32}}
\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Impact of ghost admixture on rejecting continuity. The $x$ axis shows the admixture proportion from the ghost population, and the $y$ axis shows the fraction of simulations in which continuity was rejected. Each line corresponds to a different sampling strategy, as indicated in the legend.}}{33}}
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\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Parameters of the model inferred from ancient West Eurasian samples. Panel A shows $t_1$ on the x-axis and $t_2$ on the y-axis, with each point corresponding to a population as indicated in the legend. Numbers in the legend correspond to the mean date of all samples in the population. Panels B and C show scatterplots of the mean age of the samples in the population (x-axis) against $t_1$ and $t_2$, respectively. Points are described by the same legend as Panel A.}}{34}}
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\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Impact of pooling individuals into populations when estimating model parameters from real data. In both panels, the x-axis indicates the parameter estimate when individuals are analyzed separately, while the y-axis indicates the parameter estimate when individuals are grouped into populations. Size of points is proportional to the coverage of each individual. Panel A reports the impact on estimation of $t_1$, while Panel B reports the impact on $t_2$. Note that Panel B has a broken x-axis. Solid lines in each figure indicate $y = x$.}}{35}}
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\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Details of populations included in analysis. ``pop'' is population name, ``cov'' is mean coverage of individuals in the population, ``date'' is mean date of individuals in the population, ``$t_1$'' is the maximum likelihood estimate of $t_1$ in the full model, ``$t_2$'' is the maximum likelihood estimate of $t_2$ in the full model, ``LnL'' is the maximum likelihood value in the full model, ``$t_1$ (cont)'' is the maximum likelihood estimate of $t_1$ in the model where $t_2 = 0$, ``LnL'' is the maximum likelihood value in the model where $t_2 = 0$.}}{36}}
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\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Impact of contamination on parameter inference. The $x$ axis shows the contamination fraction, and the $y$ axis shows the average parameter estimate from simulations. Each line corresponds to a different sampling strategy, as indicated in the legend. Panel A shows $t_1$, and Panel B shows $t_2$. Dashed lines indicate the true values of $t_1 = 0.02$ and $t_2 = 0.05$}}{34}}
\newlabel{contamination}{{6}{34}}
\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Parameters of the model inferred from ancient West Eurasian samples. Panel A shows $t_1$ on the x-axis and $t_2$ on the y-axis, with each point corresponding to a population as indicated in the legend. Numbers in the legend correspond to the mean date of all samples in the population. Panels B and C show scatterplots of the mean age of the samples in the population (x-axis) against $t_1$ and $t_2$, respectively. Points are described by the same legend as Panel A.}}{35}}
\newlabel{pops_together}{{7}{35}}
\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Impact of pooling individuals into populations when estimating model parameters from real data. In both panels, the x-axis indicates the parameter estimate when individuals are analyzed separately, while the y-axis indicates the parameter estimate when individuals are grouped into populations. Size of points is proportional to the coverage of each individual. Panel A reports the impact on estimation of $t_1$, while Panel B reports the impact on $t_2$. Note that Panel B has a broken x-axis. Solid lines in each figure indicate $y = x$.}}{36}}
\newlabel{sep_vs_pops}{{8}{36}}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Details of populations included in analysis. ``pop'' is population name, ``cov'' is mean coverage of individuals in the population, ``date'' is mean date of individuals in the population, ``$t_1$'' is the maximum likelihood estimate of $t_1$ in the full model, ``$t_2$'' is the maximum likelihood estimate of $t_2$ in the full model, ``LnL'' is the maximum likelihood value in the full model, ``$t_1$ (cont)'' is the maximum likelihood estimate of $t_1$ in the model where $t_2 = 0$, ``LnL'' is the maximum likelihood value in the model where $t_2 = 0$.}}{37}}
\newlabel{params_table}{{1}{37}}
8 changes: 8 additions & 0 deletions paper/ancient.fff
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\end{figure}
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\begin{figure}[h] % figure placement: here, top, bottom, or page
\centering
\includegraphics[width=\textwidth]{contamination_t1_t2.pdf}
\caption{Impact of contamination on parameter inference. The $x$ axis shows the contamination fraction, and the $y$ axis shows the average parameter estimate from simulations. Each line corresponds to a different sampling strategy, as indicated in the legend. Panel A shows $t_1$, and Panel B shows $t_2$. Dashed lines indicate the true values of $t_1 = 0.02$ and $t_2 = 0.05$}
\label{contamination}
\end{figure}
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\begin{figure}[h] % figure placement: here, top, bottom, or page
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