the value of p:
Users can input the base mutation probability to simulate the new crown mutation process. It must be ensured that all base mutation probabilities are greater than or equal to 0. And the sum of all base mutation probabilities is 1.
The i+1th sequence mutation will be above the sequence that has been mutated i times. i is greater than or equal to 0. The sequence with 0 mutation is the 2019nCov reference sequence.
In order to be certain that there is no random trend or to determine a trend, we need to perform a stationarity test. Otherwise there will be a "pseudo-regression" problem. Pseudo-regression means that sometimes the data are highly correlated simply because the two are trending up or down over time at the same time, and there is no real connection. The model also has statistical significance only if it passes the stationarity test. When the p-value is less than 0.05, the results pass the stationarity test.
The blue curve in the figure represents the unit root in adf. When an autoregressive process: , if the lag term coefficient b is 1, it is called a unit root. When the unit root exists, the relationship between the independent and dependent variables is deceptive, because any error in the residual series does not decay as the sample size (i.e. the number of epochs) increases, that is, the residuals in the model Poor effects are permanent. This regression is also called pseudo-regression. If the unit root exists, the process is a random walk
The i+1th sequence mutation will be above the sequence that has been mutated i times. i is greater than or equal to 0. The sequence with 0 mutation is the 2019nCov reference sequence.
In order to be certain that there is no random trend or to determine a trend, we need to perform a stationarity test. Otherwise there will be a "pseudo-regression" problem. Pseudo-regression means that sometimes the data are highly correlated simply because the two are trending up or down over time at the same time, and there is no real connection. The model also has statistical significance only if it passes the stationarity test. When the p-value is less than 0.05, the results pass the stationarity test.
The blue curve in the figure represents the unit root in adf. When an autoregressive process: , if the lag term coefficient b is 1, it is called a unit root. When the unit root exists, the relationship between the independent and dependent variables is deceptive, because any error in the residual series does not decay as the sample size (i.e. the number of epochs) increases, that is, the residuals in the model Poor effects are permanent. This regression is also called pseudo-regression. If the unit root exists, the process is a random walk