![]() ![]() The sheer lunacy of anxiously watching two little numbers occasionally dip on the top left of my screen finally broke me. GPU-Z? I like that kid’s hustle… still, CUT. Actually, I purged any program on my PC that went towards monitoring my graphics cards’ performance. Something had to give.Īnd then it happened. Battlefield 1 would frequently drop to 57 fps, Rise Of The Tomb Raider’s taxing Geothermal Valley hub area refused to run at a locked 60 frames regardless of which settings I lowered, while no amount of graphics card tag teaming could prevent fugly, niche deer-stalking sim The Hunter: Call of the Wild from moments where it dipped into the high 40s. Because SLI scaling is such an afterthought in so many games, that second $500 card barely registered most of the time. This deep-seated fretting over a Fraps counter spread to many games. That’s right: I stopped playing one of the best PC games of the last decade because my crippling fps anxiety couldn’t tolerate the occasional six measly dropped frames. Why? Because every time I crossed the busy city squares of Novigrad, my framerate would ‘plummet’ from a locked 60 fps to sporadic moments of 54 fps. One of the greatest games of all time, right? The razor-sharp, pithy dialogue those vistas the seamless sense of time and place realtime beard growth! Despite these amazing production values, I just couldn’t fully immerse myself in Geralt’s sweeping adventure. Sure, my beefy rig could handle most games at that ludicrously lofty resolution/framerate combo, but it was the few games that fell agonisingly short which fuelled my obsession, ruining my enjoyment in the process. The wrong-headed anxiety that went into chasing the 4K/60fps dream just wasn’t worth it. As you can see, the proportion of highly biased estimates decreased greatly, but there are still some there, and again only in the N = ~ 120 - 140 range.Even with a GTX 1080, I can’t quite constantly hit 4K/60 fps on The Witcher 3.Īnd you know what? I could care less. I decreased that to 1%.Ģ) I included one bin beyond the furthest observation in each dataset (by changing the function defining the breaks to:īreaks <- c(0, round((max(y$distance+1)/cats)*(1:(cats +1))))ģ) I included a wider range of sample sizes, just to see what would happen. This morning I tried a few things to see if I could fix it:ġ) In the above example I was truncating the furthest 10% of data. ![]() (Note that I capped bias at 2 so that the blue gam() line fit on the plot.) When simulating a lot of datasets (in the same way I produced the dataset in my original post) and analyzing the results, the bias looks like this: ![]() Obviously, there might be an error somewhere else in my code (but I haven't been able to figure out where.). With the data that I'm simulating, it only seems to be an issue when the sample size is between ~ 122 and 148 observations. I noticed that the estimates look fairly flat (at a glance), but I'm not convinced that entirely explains the issue. Unmarked.estimates <- ame(Item = c("density", "sigma"),Įstimated = c(unmarkeddensity_bias, unmarkedsigma_bias)) Unmarkedsigma <- backTransform(hn_Null, type='det') ![]() # extract estimate of detection parameter & calculate bias Unmarkeddensity <- backTransform(hn_Null, type="state") # extract density estimate & calculate bias # 2 different options for distance breaks. ![]()
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