uCL | μCL — CLion for Microcontrollers | Plugin library

You are binding incorrect column name to the grid columns.

You are using DataTable.Columns.Add(string? columnName, Type type, string expression) overload:

Try the below code to get your desired csv. I would recommend not to use dict as name for your dictionary. I have changed it to d:

Many things mixed in here!

Clarifications:

• A matlab container is only equivalent to a python dictionary, not a list/tuple/general iterable.

• Both of your containers are created the same. You seem to be naming them comma and space, but this distinction does not even reach the definition of the container. Both {'Open University','UCL',' University of Edinburgh','Birkbeck'} and {'Open University' 'UCL' ' University of Edinburgh' 'Birkbeck'} create the exact same cell array, so the input to container.map is the same. you are comparing a=[5]; and b=5, as a MATLAB-valid analogy. They are the same.

• For objects, most programing languages (including python!) will give you false when you compare two objects that contain the same values, yet are different objects. Only basic variables tend to compare by value, and not by some sort of objecID. In your case, simply doing isequal(co_comma,co_space) will return true, so their values are the same (as we already know, from the previous point)

• Containers are not generally used in MATLAB, unless you specifically want a dictionary.

After each of the places that you send a response, you need to return so your function doesn't continue executing and cause it to attempt to send another response. I see multiple places in the /api/rules route where you need to add the return after sending a response because if you don't, it executes the res.end(response) even though you've already send a response. That is what causes the error you see.

your n=n-1 instruction isn't indented correctly. Your solution must be:

The duration and the currentTime are measured in seconds.

displaying them in the format you want is just a matter of doing some simple calculations:

Try to specify User-Agent in request headers=:

Hard to know for sure, but the most obvious reason would be that there's a strong positive correlation between the estimates. In general the variance of (A-B) is Var(A)+Var(B)-2*Cov(A,B), so (for example) if A, B, C all had variances (and therefore std errors) of 1 and correlations (== covariances in this case) of 0.8 between all pairs; then the variance of any pairwise difference is is 1+1-2*0.8=0.4, so the std dev would be 0.63.However, reducing the standard errors by two orders of magnitude would require a very strong correlation.

A related possibility is that this is a case of complete separation (which would lead to large coefficients and even larger standard errors, although I would typically expect these to be even larger (e.g. |beta|>8, SD>5*beta)

What is cov2cor(vcov(Model)) ?

The function emmeans::qdrg() can sometimes be used to create the needed object for a model not directly supported by emmeans. See its documentation. In very simple models (e.g., inheriting from lm, it may be enough to supply the object and data arguments.

That usually does not work for more sophisticated models, in which case you will need to specify data, the fixed-effects formula for the conditional or zero part of the model, and the associated regression coefficients (coef) and variance-covariance matrix (vcov) for the part of the model in question. Often with models like this with multiple components, you likely will have to pick a subset of the coefficients and covariance matrix. These all must conform: the length of coef must equal the number of rows and columns of vcov and the number of columns in the model matrix generated by formula [which may be checked via model.matrix(formula, data = data)].

qdrg() will not work for a multivariate model -- or at least it's tricky -- because the implied model involves other factor(s) that delineate the levels of the multivariate response. If there are special provisions for, say, spline smoothing, that is another instance where qdrg() probably can't be made to work.

Once qdrg() actually runs and produces results, it is a good idea to use it to estimate some contrasts that are estimated by the model parameterization. For example, suppose that the model was fitted with the default contr.treatment contrasts. Then the regression coefficients are interpretable as a comparison with the first level as a reference level. Accordingly, if we computedrg <- qdrg(...), and one of the factors is "treat", look at contrast(rg, "trt.vs.ctrl1", simple = "treat"), and check to see if the first set of estimated contrasts matches the main-effect estimates for treat.

I will illustrate all of this with a simple lm model, ignoring the fact that it is already supported by emmeans.