I am new to generalised linear modelling. I ran the negative binomial model, and then try to estimate the residuals from the model.
Here is what I did:
Run a negative binomial regression model with nbreg command in stata 17.
Run the predict command to estimate the predicted values.
Then, generate the residual by subtracting predicted values from observed values.
Did I do it correctly?
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I'm using scatter_matrix for correlation visualization and calculating correlation values using corr(). Is it possible to have the scatter_matrix visualization draw the regression line in the scatter plots?
I think this is a misleading question/thought process.
If you think of data in strictly 2 dimension then a regression line on a scatter plot makes sense. But let's say you have 5 dimensions of data you are plotting in your scatter matrix. In this case the regression for each pair of dimensions is not an accurate representation of the global regression.
I would be wary presenting that to anyone as I can easily see where it could create confusion.
That being said if you don't care about a regression across all of your dimensions then you could write your own function to do this. A quick walk through of steps may be:
1. Identify number of dimensions N
2. Create figure
3. Double for loop on N, first will walk down rows, second will walk across rows
4. At each point add subplot, calculate regression (if not kde/hist position), plot scatter cloud and regression line or kde/hist
how to estimate linear regression using OLS with stata command 'regress', how to transform the slope of a in order to meet the following regression form
enter image description here
a=E/pib
regress bc a
I think your question seems to be about (1-b2).
After regress bc a, your coefficient is exactly 1-b2, so if you want to get b2, then just subtract it from 1 .
I'm using PROC LOGISTIC procedure in SAS and option SELECTION=SCORE which gives me few logistic regression models and their Chi-Square values. My question would be which model is better - with smaller Chi-Square or bigger?
In general, the larger chi-squared statistic will correspond with a lower p-value (more significance). However, it is important to know the shape of the chi-squared distribution and also the number of degrees of freedom. As you can see in the graph, the relationship between p and chi-squared changes based on the degrees of freedom.
Score for Chi-Square is larger, the model is better.
I'm using a LMM in SAS and, I would like to get an estimation (and a p-value) of a linear combination of some of the regression coefficients.
Say that the model is:
b0+b1Time+b2X1+b3X2+b4(Time*X1)
and say that, I want to get an estimate and a p-value for the b1+b4.
What should I do?
I'm estimating the parameters of a GMM using EM,
When I use my Matlab script And run the EM code i get a single value of "log-likelihood"..
However in opencv the output of EM.train gives a matrix which contained the loglikelihood value of every sample.
How do I get a single log likelihood value? Do I need to take the minimum of all the loglikelihood values of all samples or the sum of all loglikelihood values?
You need sum of log probabilities of datapoints which you use to estimate probability density function. You'll get loglikelihood of your estimation.
You can find good explanation in "Pattern Recognition and Machine Learning" book