Recall that y is an indicator for diabetes, so observations where y=0 will be displayed with a navy marker and observations where y=1 will be displayed with a dark-red marker. And the fourth argument uses the ListedColormap() method in the colors module to specify the display colors for categories of y. The third argument, c=y, specifies that different colors are to be used for different values of y. The second argument specifies that HbA1c will be plotted on the vertical axis. The first argument specifies that age will be plotted on the horizontal axis. We can plot the raw data using the scatter() method in the pyplot module. The goal is to use the information in X to distinguish between categories of y. In machine-learning jargon, we would refer to the variables in X as the “feature variables” and y as the “target variable”. In statistical jargon, we would refer to the variables in X as the “independent variables” and the variable in y as the “dependent variable”.
INSTALL PUTEXCEL STATA 12 SERIES
The next two Python statements place the variables age and HbA1c into a pandas data frame named X and the variable diabetes into a pandas series named y. The option convert_missing=False tells pandas to read missing values in the Stata dataset to missing values in the pandas data frame. For example, Stata integers will be converted to integers in the pandas data frame. The option preserve_dtypes=True instructs pandas to read the Stata variables without converting their storage type. The option convert_categoricals=False tells pandas to read labeled numeric data, such as diabetes, as numbers rather than converting the numbers to their categorical labels. Then, we can use the pandas method read_stata() to read the Stata dataset diabetes into a pandas data frame named data.
INSTALL PUTEXCEL STATA 12 CODE
We begin the code block by importing the pandas module using the alias pd, the pyplot module from the matplotlib package using the alias plt, and the colors module from the matplotlib package using the alias mcolors. I have included comments in the code block below to briefly explain each section. Next, let’s read the Stata dataset diabetes.dta into a pandas data frame named data and plot the raw data. We can also tabulate diabetes to verify that it has two categories, where 1 indicates the presence of diabetes and 0 indicates the absence of diabetes. Let’s list the first five observations in the dataset to check our work. The last two lines of the code block erase the temporary datasets age.dta and glucose.dta. Saves the files to Stata datasets, merges the Stata datasets, renames and recodes the variables, keeps the variables diabetes, age, and HbA1c, drops observations with missing values, and saves the variables to a dataset named diabetes.dta. The code block below imports three SAS Transport files from the NHANES website, HbA1c from the glycohemoglobin data, and diabetes from the diabetes data. Specifically, we will be using the variables age from the demographic data, We will be using data from the United States National Health and Nutrition Examination Survey (NHANES). If you are not familiar with Python, it may be helpful to read the first four posts in my Stata/Python integration series before you read further. Younger people with lower HbA1c levels in the blue-shaded area are less likely to have diabetes. An SVM model predicts that older people with higher levels of HbA1c in the red-shaded area of the graph are more likely to have diabetes. The graph below displays diabetics with red dots and nondiabetics with blue dots. Age is measured in years, and HbA1c is a blood test that measures glucose control. We will use age and HbA1c level to differentiate between people with and without diabetes. Our goal is to use an SVM to differentiate between people who are likely to have diabetes and those who are not.
INSTALL PUTEXCEL STATA 12 HOW TO
I am going to give you a brief introduction and show you how to implement an SVM with Python. I don’t have space to explain an SVM in detail, but I will provide some references for further reading at the end. In this post, I will show you how to use one of these algorithms called a “support vector machines” (SVM). These algorithms have exotic-sounding names like “random forests”, “neural networks”, and “spectral clustering”. Machine learning, deep learning, and artificial intelligence are a collection of algorithms used to identify patterns in data.
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