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• Regression - Save Variable(s) - Probabilities of Each Response (category Regression)
  Creates a new variable containing predicted probabilities of each response from a model (e.g., from Regression - Ordered Logitordered logit). Running this
  2 KB (151 words) - 00:52, 8 May 2023
• Machine Learning - Save Variable(s) - Probabilities of Each Response
  of probabilities of a binary outcome variable. Uses the Probabilities method from the R package flipData. Unlike the defaults in most R packages, this implementation
  1 KB (73 words) - 00:57, 21 August 2023
• Regression - Linear Regression (category Regression) (section SAVE VARIABLE(S))
  Go to Insert > Regression > Linear Regression 2. Under Inputs > Outcome, select your dependent variable 3. Under Inputs > Predictor(s), select your independent
  18 KB (3,629 words) - 05:23, 20 October 2023
• Regression - Ordered Logit (category Regression) (section SAVE VARIABLE(S))
  for certain types of outcome variable. Linear Appropriate for a continuous outcome variable. See Regression - Linear Regression. Binary Logit Appropriate
  19 KB (3,646 words) - 05:23, 20 October 2023
• Regression - Binary Logit (category Regression) (section SAVE VARIABLE(S))
  for certain types of outcome variable. Linear Appropriate for a continuous outcome variable. See Regression - Linear Regression. Binary Logit Appropriate
  20 KB (3,631 words) - 05:23, 20 October 2023
• Regression - Poisson Regression (category Regression) (section SAVE VARIABLE(S))
  Poisson regression requires a count variable as the dependent variable. In Displayr, the best data format for this type is Numeric. A count variable must
  19 KB (3,781 words) - 05:23, 20 October 2023
• Regression - Generalized Linear Model (category Regression) (section SAVE VARIABLE(S))
  when the dependent variable is continuous. See Regression - Linear Regression. Binary Logit The Binary Logit is a form of regression analysis that models
  19 KB (3,813 words) - 05:23, 20 October 2023
• Regression - NBD Regression (category Regression) (section SAVE VARIABLE(S))
  1. Go to Insert > Regression > NBD Regression 2. Under Inputs > Outcome, select your dependent variable 3. Under Inputs > Predictor(s), select your independent
  19 KB (3,754 words) - 05:23, 20 October 2023
• Regression - Multinomial Logit (category Regression) (section SAVE VARIABLE(S))
  for certain types of outcome variable. Linear Appropriate for a continuous outcome variable. See Regression - Linear Regression. Binary Logit Appropriate
  20 KB (3,811 words) - 05:23, 20 October 2023
• Regression - Quasi-Poisson Regression (category Regression) (section SAVE VARIABLE(S))
  to Insert > Regression > Quasi-Poisson Regression 2. Under Inputs > Outcome, select your dependent variable 3. Under Inputs > Predictor(s), select your
  19 KB (3,733 words) - 05:23, 20 October 2023
• Release Notes
  number of levels and variables per case to save for the Text Analysis - Save Variable(s) - Categories and Text Analysis - Save Variable(s) - First Category
  84 KB (10,645 words) - 06:37, 2 April 2024
• Machine Learning - Classification And Regression Trees (CART) (section SAVE VARIABLE(S))
  Outcome variable and 'Age', 'Gender' and 'Exercise Frequency' as the Predictor variables. Outcome Variable to be predicted. Predictors Variables which will
  20 KB (2,119 words) - 05:23, 20 October 2023
• Regression - Save Variable(s) - Propensity Weight (category Regression)
  or Regression model that has an outcome variable with only two categories such as a Binary Logit regression). Assume the outcome variable [math]\displaystyle{
  2 KB (530 words) - 00:51, 8 May 2023
• Machine Learning - Linear Discriminant Analysis (section SAVE VARIABLE(S))
  prior probabilities used in computing the probabilities of group membership of the Outcome (Machine Learning - Save Variable(s) - Probabilities of Each
  23 KB (2,444 words) - 05:23, 20 October 2023
• Machine Learning - Random Forest (section SAVE VARIABLE(S))
  a numeric variable, in which case a forest of regression trees is estimated, or classification trees if categorical. Predictors The variable(s) to predict
  23 KB (2,358 words) - 05:23, 20 October 2023
• Machine Learning - Deep Learning (section SAVE VARIABLE(S))
  Outcome The variable to be predicted by the predictor variables. It may be either a numeric or categorical variable. Predictors The variable(s) to predict
  20 KB (2,045 words) - 05:23, 20 October 2023
• Machine Learning - Gradient Boosting (section SAVE VARIABLE(S))
  may be either a numeric or categorical variable. Predictors The variable(s) to predict the outcome. Algorithm The machine learning algorithm. Defaults to
  21 KB (2,266 words) - 05:23, 20 October 2023
• Machine Learning - Ensemble of Models (section SAVE VARIABLE(S))
  provided. Machine Learning - Save Variable(s) - Predicted Values and Machine Learning - Save Variable(s) - Probabilities of Each Response may be used to
  17 KB (1,559 words) - 05:23, 20 October 2023
• Regression - Driver Analysis (category Regression) (section SAVE VARIABLE(S))
  integer values). See Regression - Poisson Regression. Quasi-Poisson Appropriate for count outcomes. See Regression - Quasi-Poisson Regression. NBD Appropriate
  23 KB (4,336 words) - 05:23, 20 October 2023
• Machine Learning - Support Vector Machine (section SAVE VARIABLE(S))
  Outcome The variable to be predicted by the predictor variables. It may be either a numeric or categorical variable. Predictors The variable(s) to predict
  27 KB (3,019 words) - 05:23, 20 October 2023

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