Variable names Displays Variable Names in the output instead of labels. Predictors color Color of variable correlations in Scatterplot output. Outcome color Color of group centroids in Scatterplot output. Moonplot A two-dimensional moonplot, using the same assumptions as the scatterplot. The group centroids are scaled to appear on the same scale as the correlations. Also plotted are the correlations between the predictor variables and the first two discriminant function variables. This shows which groups are separated by the first two discriminant function variables. Scatterplot A two-dimensional scatterplot of the group centroids in the space of the first two discriminant function variables. Prediction-Accuracy Table Produces a table relating the observed and predicted outcome. Detail More detailed diagnostics, from the lda function in the R MASS package. Output Means Produces a table showing the means by category, and assorted statistics to evaluate the LDA. Defaults to Linear Discriminant Analysis but may be changed to other machine learning methods. Predictors The numeric variable(s) to predict the outcome.Īlgorithm The machine learning algorithm. Outcome The variable to be predicted by the predictor variables. The inputs used to generate the Linear Discriminant Analysis are shown below. In this example, Coca-Cola is by far the biggest group, so the prior causes the predicted accuracy to be poor. The Prior is at Equal, which assumes that the group sizes in the population are equal.The relationship between the predictors and the outcome is weak.There are two reasons why this model is particularly poor: See Analysis of Variance - One-Way MANOVA for more detail on the interpretation of the table. The R-Squared column shows the proportion of variance within each row that is explained by the groups in all cases it is very poor. We can also see that there are some significant differences relating to Pepsi. It shows, for example, that the 1,799 Coca-Cola drinkers in the sample has significantly lower ratings of health-conscious, older, and traditional (these are the only significant differences, when compared to the mean, which is why they are in bold. The colored shading shows the differences between the means by group. The sub-title shows the predictive accuracy of the model, which in this case is extremely poor, at approximately 7%. The table below shows the results of a linear discriminant analysis predicting brand preference based on the attributes of the brand. Under Inputs > Linear Discriminant Analysis > Predictor(s) select your predictor variables.Ĥ. Under Inputs > Linear Discriminant Analysis > Outcome select your outcome variable.ģ. In Q, select Create > Classifier > Linear Discriminant Analysis.Ģ. In Displayr, select Insert > More > Machine Learning > Linear Discriminant Analysis. The parameters of the discriminant functions can be extracted with Machine Learning - Diagnostic - Table of Discriminant Function Coefficients.ġ. Un-ordered categorical predictors are converted to binary dummy variables. Ordered categorical predictors are coerced to numeric values. Fits linear discriminant analysis (LDA) to predict a categorical variable by two or more numeric variablesįits linear discriminant analysis (LDA) to predict a categorical variable by two or more numeric variables.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |