6
Notice the y-axis range of the data (~20000), compared to the plot of the model (~0.005) . If you add an amplitude parameter to the model, it works much better.
peakresponse[a_, β_, e1_, e2_] :=
a (Abs[β*e2*π])^(-1/2)*Exp[(-(e1 - e2)^2/(2*β*e2))]
nlmbeta =
NonlinearModelFit[databeta, peakresponse[a, β, e1, e2],
{{a, 3000000}, {β, 80}, {e2, 100}}, ...
4
You'll likely want NonlinearModelFit rather than LogitModelFit as the latter assumes that the response variable has a binomial distribution given the prediction model.
Consider the following "logit" model: $y=a/(1+\exp(-k(t-b)) + e$ where $e\sim N(0,\sigma^2)$ and $a$, $b$, $k$, and $\sigma^2$ are parameters to be estimated. That's 4 parameters with only ...
3
If "good data" is data surrounded by similar values, then using the variance, standard deviation or range of 3 or more neighboring points might be an approach. Below I've used the standard deviation of 3 consecutive points to decide on whether to keep the middle point.
sd = Table[{i, data[[i]], StandardDeviation[data[[i - 1 ;; i + 1]]]}, {i, 2, Length[data]...
1
As already suggested above, using Export followed by CopyFile to a CloudObject certainly works.
You can also Export directly to a CloudObject, or (even more concisely) use CloudExport. In terms of the format, you might consider "CVS" instead of "TXT". Then this would become:
CloudExport[data, "CSV", "data.csv"]
To bring it back into WL, you would use ...
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