MIDS | Medical Imaging Data Structure

The following dataset is available

In my react project, I have a function getDetails that takes in the data retrieved from a database call (filteredVenue), and then console logs that data. This function exists inside a component called MidSection.js. When this component is rendered and getDetails is invoked, the following is logged to the brower:

MidSection.js:9 [ ]
MidSection.js:9 [{…}]

The problem is this - initially, an empty array is being returned which means that is I try to access the data inside filteredReviews, I get an undefined error.

As an example the following code

I am trying to build a graphical audio spectrum analyzer on Linux. I run an FFT function on each buffer of PCM samples/frames fed to the audio hardware so I can see which frequencies are the most prevalent in the audio output. Everything works, except the results from the FFT function only allocate a few array elements (bins) to the lower and mid frequencies. I understand that audio is logarithmic, and the FFT works with linear data. But with so little allocation to low/mid frequencies, I'm not sure how I can separate things cleanly to show the frequency distribution graphically. I have tried with window sizes of 256 up to 1024 bytes, and while the larger windows give more resolution in the low/mid range, it's still not that much. I am also applying a Hann function to each chunk of data to smooth out the window boundaries.

For example, I test using a mono audio file that plays tones at 120, 440, 1000, 5000, 15000 and 20000 Hz. These should be somewhat evenly distributed throughout the spectrum when interpreting them logarithmically. However, since FFTW works linearly, with a 256 element or 1024 element array only about 10% of the return array actually holds values up to about 5 kHz. The remainder of the array from FFTW contains frequencies above 10-15 kHz.

Here's roughly the result I'm after:

But this is what I'm actually getting:

Again, I understand this is probably working as designed, but I still need a way to get more resolution in the bottom and mids so I can separate the frequencies better.

What can I do to make this work?

I have imputed data saved as a mids object and am trying to adapt my usual workflow around imputed data. However, I cannot figure out how to use sjPlot's tab_corr() and tab_df() and psych's describe on a mids object.

My goal is to generate a table of descriptive statistics and a correlation matrix without averaging the imputed datasets together. I was able to generate correlations using miceadds::micombine.cor, but the output isn't formatted like a typical correlation matrix. I also can individually compute means, SDs, etc. of variables from the mids object, but I'm looking for something that will generate a table.

I always get the exception: "Exception: Service timed out: Spreadsheets" Why does this happen and would it help to try the method updateSpec(spec)? It always happens when I got to this specific line:

non-linear least square (nls() function in core R) generates a list object with a bunch of useful info inside, is there also the original data that were the input of the model? I suspect yes, as functions as predict() or fitted() are actually defined on this object and generate predictions in ranges of the original fitted data. So, how can one access this data through the model object?

Imagine this setting

I'd like to add histograms in base R to the margins of a plot, I've tried many ways, and used points and segments to plot the values calculated from a histogram. The challenge is that after plotting the data, the width of the bars have to be set manually in order to show a histogram like margin plot.

I am trying to write a function that will calculate the mean and SD for a variable from a multiply imputed dataframe (mids). The code works fine outside of the function (as shown in two examples below), but will produce unreliable results when placed inside of a function. The function seems to keep giving results for bmi despite calling upon chl.

Any insight into this issue is appreciated. Eventually I would like this function to be able to calculate means and SDs for multiple variables at once (i.e., bmi and chl) but that is likely a separate question.

I have run a multiple imputation (m=45, 10 iterations) using MICE and am attempting to fit a series of confirmatory factor analysis and structural equation models on the imputed datasets using the runMI function from semTools. Nearly all of my variables are Likert scales, coded as ordered/ordinal. Here is my code for the first CFA, where mi.res.train is the mice-generated mids object: