![]() ![]() These synthetic datasets preserve the complexity of the original data sources relevant to estimating the desired estimand and can be used in lieu of formal data pooling of the original datasets to conduct pooled analyses. 15– 17 Here, we propose a simulation-based method leveraging partial information across datasets that draws on researchers’ prior understanding of the causal structure to guide creation of synthetic data, permitting pooled analyses in the context of privacy restrictions that adjust for all desired confounders. Although some synthetic data approaches have applied causal discovery algorithms 13, 14, some controversy about the validity and utility of causal discovery algorithms remains, and epidemiologists typically rely on expert knowledge to generate causal models. ![]() 7– 12 Most prior work has conceptualized data generation as a statistical problem with the goal of posting synthetic datasets for analyses, 7– 12 without grounding the data generation in prior knowledge of the causal structure. Partially or fully synthetic data approaches may permit data sharing and pooled analyses while remaining consistent with data privacy goals. While data from multiple studies can be combined by meta-analysis 1, related Bayesian approaches 2, 3, coordinated analsyes 4, 5, or aggregate-data based approaches 6, analyses may not be perfectly parallel due to differences in covariate sets or parameterizations, and summary measures may remain confounded. Unfortunately, privacy restrictions often preclude full data pooling. Print("Bounds:", ax.get_xbound(), ax.Combining data from multiple studies can enhance research by broadening the diversity of study participants or by improving statistical power. ![]() # Colors are set to `red` and `green` intentionallyĪx.plot(lons, -80*np.ones(len(lons)), transform=ccrs.Geodetic(), lw=0.5, color="red", zorder=30)Īx.plot(lons, -70*np.ones(len(lons)), transform=ccrs.Geodetic(), lw=0.5, color="green", zorder=30) # Draw the missing parts of the parallel lines at 70, 80 deg_S Gl2 = ax.gridlines(draw_labels=True, crs=ccrs.PlateCarree(), \ # The lines are terminated at the meridian of the dateline! # Second set of gridlines: parallels of latitude only. Xlim=) # get these from ax.get_xbound() of previous run Gl1 = ax.gridlines(draw_labels=True, crs=ccrs.PlateCarree(), \ # proper `xlim` is needed to get all the x-labels' visibility set correctly #ax.add_feature(, zorder=1, edgecolor='none', alpha=0.3) import matplotlib.pyplot as pltĪx = plt.axes(, projection=proj) However, if you still need the original plot extent, here is another approach. The plot involves special boundary, the parallels crossing the international dateline - these may be the causes of the problem. Gridlines plotting is problematic in this particular case. Vertices = \Īx.set_boundary(boundary, transform=ccrs.PlateCarree())Īx.set_extent(, ccrs.PlateCarree())Īx.add_feature(, zorder=1, edgecolor='k') Lons = np.linspace(lonmin, lonmax, lonmax - lonmin 1) Lats = np.linspace(latmax, latmin, latmax - latmin 1) Proj = ccrs.Stereographic(central_longitude=228, central_latitude=-70) I have tried many solutions (mostly for rectangular projection), but failed. When adding gridlines, the region (180~60W) lacks gridlines. I am drawing custom shape boundary map which focuses on the Pacific Sector of Southern Ocean (160E~180~60W,-60S~-90S). ![]()
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