Sensitivity analysis refers to methods to see if violations of assumptions of a model make large differences to results. Cases that violate the assumptions are deleted and the analyses re-run. Then results are compared.
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How does Soboljansen deal with categorical input against continuous output?
I have two data.tables which correspond to each other:
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Meta-analysis and homogeneity — what did these guys do?
I appreciate any insight into this meta-analysis.
This is a meta-analysis on alogliptin efficacy and safety. In the 2nd paragraph of the discussion that make this statement:
Although ...
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Local sensitivity around the maximum
After having inferred a statistical model for my physical system $Y=f(X_1, \dots X_n)$, I want to estimate the local sensitivity of each variable $X_i$ (one by one) around the point of maximum ...
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Confusion with sensitivity and specificity within the survivalROC R package
I have a question about sensitivty/specificity in the survivalROC package.
I've been able to successfully use the survivalROC package to draw some ROC curves. As you know ROC curves have ...
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Sensitivity analysis for modeled time series
I have very basic knowledge of stats so my question may sound very simplistic. I have a large time series of measured data and have calibrated a model with five parameters to make predictions. I would ...
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Linearity in local sensitivity analysis
As it is know local sensitivity analysis attempt to quantify the local impact of input factors on the model, through partial derivatives: a derivative of the outputs accordingly to the inputs, when ...
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293 views
Interpreting results from Sobol sensitivity analysis in R
I'm trying to use the sobol2007 model in the R sensitivity package. I'm doing runs on a model with 26 parameters, and using 2 sets of 500 monte-carlo samples to seed the analysis, and nboot=500. This ...
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Using fast99 sensitivity method in R with normal distributions
If I use the fast99 method (from the sensitivity package) with qnorm, it generates parameter sets with -Inf and Inf in, which are not much use for then running models:
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Literature on robustness of regression assumptions
In my OLS regression not all assumptions are perfectly met, but I read that due to a large sample size there is a certain robustness to assumptions (my sample is 2500 people).
E.g. the DV isn't ...
