Is there a reason why we need to do BOTH local SA and global SA when we want to determine the most influential parameters?
I am planning to do sensitivity analysis using normalised forward sensitivity index (local SA) and the LHS/PRCC approach (global SA). I am wondering, is one better than the other? Or just performing either one of it will suffice?
It depends on your study purpose. The local SA is mainly used to conduct parameter prioritization (which parameters are important to model output), but the global SA is used for parameter fixing (which parameters are "not" important to model output).
In a nutshell, if you have a metamodel with dozens of parameters that need to be calibrated with the advanced statistical approach (e.g., Bayesian), you might need to consider using global SA to find the non-influential parameters and fix them to improve the computational efficiency. Otherwise, you can just use local SA to investigate which parameters that dominate the model output.
Related
I want to measure whether the impact of a company's headquarter country on my independent variable (goodwill paid) is stronger during recessions. After some researching, I found out that the differences-in-differences analysis could solve my problem. However, in the internet they always show a diagram (see example under: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.publichealth.columbia.edu%2Fresearch%2Fpopulation-health-methods%2Fdifference-difference-estimation&psig=AOvVaw1yMN6knTtOEahZ9vstJpnV&ust=1676208292554000&source=images&cd=vfe&ved=0CAwQjRxqFwoTCLjbrNDIjf0CFQAAAAAdAAAAABAE ) with the "treatment" and "parallel trends". So two lines that increase or decrease in the same way until the treatment and then one line increase/decreases more than the other.
My question now is what is my treatment and what is my control variable in my example? The treatment cannot be recessions because otherwise I just have the treatment group after the treatment and the control group before the recessions. If you think another statistical test may be better, I would be happy to consider that.
Furthermore, I just want to make sure that I created my model correctly: Goodwil Paid=B0+B1ressions+B2Country+B3ressionsCountry
Would that tell me whether the impact of the country is stronger during recessions?
Thanks a lot for your help.
I have a question not on spatstat but on use and limitation of spatsat.
During the calculation of metrics like pcf and k function equivalents on linear networks, a shortest path distance is used instead of euclidean distance. I have the spatsat book from 2015 and I remember reading somewhere in the text that the shortest path calculation on networks is not sensitive to grade separations like flyover, bridges, underpass and therefore caution should be exercised in selecting the study area or be aware of this limitation while interpreting results.
Is there any publication that discusses this limitation of grade separation in detail and may be suggesting some workarounds? Or limitations of network equivalents in general?
Thank you
The code for linear networks in spatstat can handle networks which contain flyovers, bridges, underpasses and so on.
Indeed the dataset dendrite, supplied with spatstat, includes some of these features.
The shortest-path calculation takes account of these features correctly.
The only challenge is that you can't build the network structure using the data conversion function as.linnet.psp, because it takes a list of line segments and tries to guess which segments are connected at a vertex. In this context it will guess wrongly.
The connectivity information has to be specified somehow! You can use the constructor function linnet to build the network object when you have this information. The connectivity can be edited interactively using clickjoin.
This is explained briefly on page 713 of the book (which also mentions dendrite).
The networks that can be handled by spatstat are slightly more general than the simple model described on page 711. Lines can cross over without intersecting.
I'm sorry the documentation is terse, but much of this information has been kept confidential until recently (while our PhD students were finishing).
I want to test for heteroskedasticity and autocorrelation in a large unbalanced panel dataset.
I do so using the following code:
* Heteroskedasticity test
// iterated GLS with only heteroskedasticity produces
// maximum-likelihood parameter estimates
xtgls adjusted_volume ibn.rounded_time i.id i.TRD_EVENT_DT, igls panels(heteroskedastic)
estimates store hetero
* Autocorrelation
findit xtserial
net sj 3-2 st0039
net install st0039
xtserial adjusted_volume ibn.rounded_time i.id i.TRD_EVENT_DT
Though I use the calculation power of high process center, because of the iteration method, this procedure takes more than 15 hours.
What is the most efficient program to perform these tests using Stata?
This question is borderline off-topic and quite broad, but i suspect still of
considerable interest to new users. As such, here i will try to consolidate our
conversation in the comments as an answer.
I strongly advise in the future to refrain from using highly subjective
words such as 'best', which can mean different things to different people. Or
terms like 'efficient', which can have a different meaning in a different context.
It is also difficult to provide specific advice regarding the use of commands
when we know nothing about what you are trying to do.
In my view, the 'best' choice, is the choice that gets the job done as accurately
as possible given the available data. Speed is an important consideration nowadays, but accuracy is still the most fundamental one. As you continue to use Stata, you will see that it has a considerable number of commands, often with overlapping functionality. Depending on the use case, sometimes opting for one implementation over another can be 'better', in the sense that it may be more practical or faster in achieving the desired end result.
Case in point, your comment in your previous post where the noconstant option is unavailable in rreg. In that particular context you can get a reasonably good alternative using regress with the vce(robust) option. In fact, this alternative may often be adequate for several use cases.
In this particular example, xtgls will be considerably faster if the igls
option is not used. This will be especially true with larger and more 'difficult' datasets. In cases where MLE is necessary, the iterate option will allow you to specify a fixed number of iterations, which could speed things up but can be a recipe for disaster if you don't know what you are doing and is thus not recommended. This option is usually used for other purposes. However, is xtgls the only command you could use? Read here why this may in fact not necessarily be the case.
Regarding speed, Stata in general is slow, at least when the ado language is used. This is because it is an interpreted language. The only realistic option for speed gains here is through parallelisation if you have Stata MP. Even in this case, whether any gains are achieved it will depend on a number of factors,
including which command you use.
Finally, xtserial is a community-contributed command, something which you
fail to make clear in your question. It is customary and useful to provide this
information right from the start, so others know that you do not refer to an
official, built-in command.
I want to use spark ALS for multi-behavior implicit feedback recommendation. There are several kinds of implicit user behavior data, such as browses, carts, deals etc.
I have checked numerous online sources for ALS implicit feedback recommendation, but almost all of them utilized only single source of data, in shopping case, the deal data.
I am wonder if whether only the deal data is needed or utilize all kinds of data for better results?
There is no general purpose, principled way to use ALS with multiple behaviors. Sometimes different behaviors are used to vary implicit ratings -- for example, viewing an item might be worth 0.1, viewing in multiple sessions might be worth 0.3, putting it in a cart 0.5, and a purchase 1.0. But this feels a bit hacky, and doesn't readily provide a way to exploit all the data you might have.
For a more principled approach that scales to handling lots of different features, I would take a look at the Universal Recommender. Disclaimer: I've never used it, I just think it sounds promising.
yes, you'd better using all deal data and user data. You use ALS to acquire user vector and deal vector, then compute the similarity of deal and user, if the user or deal have no vector, we can't get the similarity for next recommendation.
I had a test for ALS, and used the similarity of user and deal for training my model, it gave me big surprisingļ¼the auc follow as :
2018-06-05 21:25:28,138 INFO [21:25:28] [58] test-auc:0.968764 train-auc:0.972966
2018-06-05 21:25:28,442 INFO [21:25:28] [59] test-auc:0.968865 train-auc:0.973075
because I use all the deal and user information to train the model. but rmse is 3.6, maybe I should tune my parameter.
I have a set of examples, which are each annotated with feature data. The examples and features describe the settings of an experiment in an arbitrary domain (e.g. number-of-switches, number-of-days-performed, number-of-participants, etc.). Certain features are fixed (i.e. static), while others I can manually set (i.e. variable) in future experiments. Each example also has a "reward" feature, which is a continuous number bounded between 0 and 1, indicating the success of the experiment as determined by an expert.
Based on this example set, and given a set of static features for a future experiment, how would I determine the optimal value to use for a specific variable so as to maximise the reward?
Also, does this process have a formal name? I've done some research, and this sounds similar to regression analysis, but I'm still not sure if it's the same thing.
The process is called "design of experiments." There are various techniques that can be used depending on the number of parameters, and whether you are able to do computations between trials or if you have to pick all your treatments in advance.
full factorial - try each combination, the brute force method
fractional factorial - eliminate some of the combinations in a pattern and use regression to fill in the missing data
Plackett-Burman, response surface - more sophisticated methods, trading off statistical effort for experimental effort
...and many others. This is an active area of statistical research.
Once you've built a regression model from the data in your experiments, you can find an optimum by applying the usual numerical optimization techniques.