• R language mixed effects logistic model was used to analyze lung cancer data


    Original link:http://tecdat.cn/?p=22302  Mixed effect logistic regression is used to model binary outcome variables, in which when the data are grouped or there are fixed and random effects at the same time, the logarithmic probability of the result is modeled as a linear combination of predictive variables. Examples of mixed effect logistic regression Example 1:A researcher […]

  • Fluent encapsulates Alibaba cloud player and has released pub.dev: HSP_ aliplayer


    Version 0.2.1: Implementation functions: 1. Support MP4, m3u8 and live broadcast 2. Full screen, double speed, sliding screen setting brightness and volume 3. Support auto play, poster and poster settings Permissions used:                 <uses-permission android:name=”android.permission.INTERNET” />                  <uses-permission android:name=”android.permission.ACCESS_NETWORK_STATE” />                   <uses-permission android:name=”android.permission.ACCESS_WIFI_STATE” />                 <uses-permission android:name=”android.permission.CHANGE_WIFI_STATE” />                 <uses-permission android:name=”android.permission.SYSTEM_ALERT_WINDOW” />                 <uses-permission android:name=”android.permission.CHANGE_WIFI_MULTICAST_STATE” />                 <uses-permission android:name=”android.permission.WRITE_EXTERNAL_STORAGE” />                 <uses-permission android:name=”android.permission.READ_EXTERNAL_STORAGE” […]

  • Partial least squares regression PLS-DA in R language


    Original link:http://tecdat.cn/?p=8890 The principal component regression (PCR) method essentially uses the ordinary least squares (OLS) fitting of the first methodPrincipal components (PC) from predictive variables. This brings many advantages: The number of predictors is virtually unlimited.   The relevant predictive variables do not destroy the regression fitting. However, in many cases, it is much wiser to […]

  • Machine learning (12): Wu Enda’s notes


    This article mainly looks at several problems that should be considered in machine learning. We take spam classification as an example. Given some training sets with tags, spam y = 1, non spam y = 0, we construct a classifier by supervised learning. ###First, consider how to construct the vector X In spam classification, we […]

  • Macro: an often overlooked weapon in hive


    We all know that there is UDF (user defined function) in hive, that is, user-defined function. However, because UDF is written in Java, the memory recovery of heap variables in the code is not under the control of the developer, and UDF programs are nested in hive SQL. For large-scale tables, there are often out […]