I found two packages that implement sample entropy,pracma and nonlinearTimeSeries. I looked intononlinearTimeSeries first but the data structure seemed a bit too complex on first glance (for me!).So I decided to go for pracma. When you are ok with the default parameters, then you can simple callsample_entropy().

In R the PerformanceAnalytics library I tested HurstIndex on SPY data for the last 20 years and got a very different result compared to using hurstexponent from the pracma library: one suggested randomness, the other suggested very marginal persistence in the data long term, to possible mean reversion. I am unsure as to which I should rely on for the Hurst r/s statistic between 0 and 1, to imply persistence, mean reversion or randomness.`

Download Pracma Library R

Download File 🔥 https://tinurll.com/2yGbtX 🔥

Without data or output from your function calls it's difficult comments specifically, but the help pages for the two functions indicate that one is the raw value and the other is the corrected value. The help(pac=pracma, hurstexp) help page indicates that many variants of the Hurst exponent are provided. (The Hurst Index is not limited to a range of 0-1.)

The HurstIndex calculation from the Performance Analytics library is wrong (or was?) that is why they differ. I left a bug report for the maintainers on the Github repo, last time I checked it had not been looked at or addressed.

The only reliable commonly used libraries for Hurst calculations are the pracma and fractal libraries. Notebly, they quoted that they adapted the HurstIndex statistic directly from the matlab code, somehow a bug has crept in.

I'm trying to detect the max peaks in an ECG/PPG sample with findpeaks function from pracma package, but the results aren't as expected, not sure if I missing filters or the function isn't for that. The function returns some other peaks in the middle and in the bottom of the wave.

The first issue is with how the CSV file gets imported. Note that I'm assuming here that you are importing the exact same CSV file you posted online. If that's not the case, this part might not apply to you.

actually, to be easier, I have exported the variable to a csv file. I have filtered this variable with other libraries and functions previously. So probably when I exported it, something may have gone wrong. I didn't post all code to not be so extended.

I suspected you might be accessing your data in a different fashion than in the example. For the future, I strongly recommend learning how to use the reprex package to format reproducible examples for posting in forums like this one (I actually wrote almost that entire post in an R script file and reprex::reprex() turned it into what you see above, including the image upload ). By including the output along with the code and enforcing a decent amount of self-contained-ness, reprex makes it much easier to focus your helpers on the most important problem.

R language provides a very efficient method to calculate the cross product of two vectors. By using cross() method which is available in the pracma library. This function computes the cross or vector product of vectors in 3 dimensions. In the case of matrices, it takes the first dimension of length 3 and computes the cross product between corresponding columns or rows.

With the introduction of ChatGPT I was interested to learn how ChatGPT in pharmacometrics might be applied and how this will impact my code writing tasks, especially for doing pharmacokinetic analysis and creating a starting point for basic R applications. My objective of this post was to see whether ChatGPT could write the R code for a shiny application that is capable of a basic non-compartmental analysis, calculating the Cmax, Tmax, and AUC (linear interpolation) of a PK profile.

ChatGPT is a large language model developed by OpenAI that can generate human-like text. It has been trained on a diverse range of internet text and can answer questions, write stories, and even code.

I apologize for the confusion, trapz is not a built-in R function, it comes from the pracma library, which is not loaded by default. You will need to install and load the pracma library before using the trapz function. You can do this by running the following code:

The ease of use and the clean R code that has been generated for this simple project is a game-changer. There is no need to go to Google or StackOverflow for every question or to get a piece of template code. Furthermore, ChatGPT has already shown to also generate the code for NONMEM models and I can only imagine what the impact will be for the field of pharmacometrics. As always, a modeller/coder should critically check the code as there will be errors present that need to be fixed manually, but ChatGPT is capable of assisting you in fixing those errors as well.

I think the biggest disadvantage is that even though the responses of ChatGPT always sound convincing, they may be completely wrong. Some examples of code that I have requested just did not make sense and threw errors, even after repeatedly asking for improvements and specifying what was wrong. Especially for junior coders this might give a false sense of confidence that the backbone of their code is correct.

This is amazing! Thank you for writing this post and opening my eyes to the possibilities of using ai in writing code. I came across this post while searching whether ai could help me generate code to create hysteresis plots for pk-pd. The funny coincidence is actually that you were also the one who showed me the online guides that helped me write the R code for those plots over a year ago!

Thanks again for your tutoring and great posts. Keep them coming! ?

In this post, I will present how to plot complex variable functions. To do so, I will plot the gamma function, as it allows illustrating how to plot asymptotic functions. I will plot the function in its real domain first, and then produce 2D and 3D plots of the complex domain. I will use ggplot2 for two dimensional plots, and plotly for the 3D surface plot. To calculate the gamma function for complex numbers with imaginary component I will use the pracma package.

The resulting plot is not satisfactory, as the behavior for non-negative numbers is quite different from the rest of the domain. There seems to be vertical asymptotes for nonpositive integer numbers, and the function grows factorially for positive values. Therefore, I have opted to present two plots for the gamma function:

The dots in the plot represent \(\left(n-1\right)!\) for integer values. Its values come from table df1, and are plotted with geom_point. The gamma function is plotted with stat_function, adding a color argument. I have also added a title with ggtitle.

For the negative values, I do not use stat_function. Instead I defining values for the two axis in the df2 table. When the absolute value of the function is greater than 20 I turn it no NA, so the geom_line is interrupted. Finally, I am adding asymptotes for non-positive integers using geom_vline. Note that the value of xintercept is a vector, so all asymptotes are plotted in a single instruction.

The gamma function for the whole complex domain can be obtained with the gammaz function of the pracma package. To represent the function graphically, I have chosen to assign the real and imaginary component to axis x and y, and the module of the gamma function to axis z. I nave used the expand_grid function to define a grid of points, so we have 10,000 points in a 100x100 grid.

Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

This section gets us started with displaying basic binary classification using 2D data. We first show how to display training versus testing data using various marker styles, then demonstrate how to evaluate our classifier's performance on the test split using a continuous color gradient to indicate the model's predicted score.

We will use Tidymodels for training our model and for loading and splitting data. Tidymodels is a popular Machine Learning (ML) library that offers various tools for creating and training ML algorithms, feature engineering, data cleaning, and evaluating and testing models.

We will train a k-Nearest Neighbors (kNN) classifier. First, the model records the label of each training sample. Then, whenever we give it a new sample, it will look at the k closest samples from the training set to find the most common label, and assign it to our new sample.

Now, we train the kNN model on the same training data displayed in the previous graph. Then, we predict the confidence score of the model for each of the data points in the test set. We will use shapes to denote the true labels, and the color will indicate the confidence of the model for assign that score.

Instead of predicting the confidence for the test set, we can predict the confidence map for the entire area that wraps around the dimensions of our dataset. To do this, we use meshgrid to create a grid, where the distance between each point is denoted by the mesh_size variable.

In hydraulic design it is common to encounter implicit equations thatcannot be directly solved. Traditionally these were solved in aniterative manner, where an initial guess is refined until convergence ofthe solution is achieved. With programmable calculators becoming commonin the late 20th century, and computers and smartphones providing evenmore convenient computation capacity in the 21st century, the need forgraphical aids and lengthy iterative procedures has become largelyobsolete. However, these iterative methods underlie many advanced tools,and having a level of comfort with them can allow the development ofcustom tools to solve other unique problems.

Illustrated below are examples of solving hydraulic equations using avariety of methods. Most of these examples use R, though an example using aspreadsheet is also included. Similar approaches to those used here areavailable in other languages, like Matlab and python. 152ee80cbc

travelers web series download in hindi

episodios de chaves para download

the collector 2 full movie download in hindi 480p