The function performs a linear discriminant analysis (by using the MASS::lda function).
Compared to the MASS::lda function, the ldaPlus
function enable to consider the prior probabilities to predict the values of a categorical variable, it
provides with predicted values and with (Jack-knife) classification table and also with statistical test of canonical correlations
between the variable that represents groups and numeric variables.
ldaPlus(x, grouping, pred = TRUE, CV = TRUE, usePriorBetweenGroups = TRUE, ...)The following objects are also a part of what is returned by the MASS::lda function.
prior - Prior probabilities of class membership taken to estimate the model (it can be estimated based on the sample data or it can be provided by a reseacher).
counts - Number of units in each category of categorical variable taken to estimate the model.
means - Group means.
scaling - Matrix that transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical.
lev - Levels (groups) of the categorical variable.
svd - Singular values, that give the ratio of the between-group and within-group standard deviations on linear discriminant variables. Their squares are the canonical F-statistics.
N - Number of observations used.
call - the (matched) function call.
The additional following objects are generated by the multiUS::ldaPlus function.
standCoefWithin - Standardized coefficients (within groups) of discriminant function.
standCoefTotal - Standardized coefficients of discriminant function.
betweenGroupsWeights - Proportions/priors used when estimating the model.
sigTest - Test of canonical correlations between the variable that represent groups (binary variable) and numeric variables (see function testCC for more details) (Ho: The current and all the later canonical correlations equal to zero.).
eigModel - Table with eigenvalues and canonical correlations (see function testCC for more details).
centroids - Means of discriminant variables by levels of categorical variable (not predicted, but actual).
corr - Pooled correlations within groups (correlations between values of numerical variables and values of linear discriminat function(s)).
pred
class - Predicted values of categorical variable
posterior - Posterior probabilities (the values of the Fisher's calcification linear discrimination function)
x - Estimated values of discriminat function(s) for each unit
class - Classification table:
orgTab - Frequency table.
perTab - Percentages.
corPer - Percentage of correctly predicted values (alternatively, percentage of correctly classified units).
classCV - Similar to class but based on cross validation (Jack-knife).
A data frame with values of numeric variables.
Categorical variable that defines groups.
Whether to return the predicted values based on the model. Default is TRUE.
Whether to do cross-validation in addition to "ordinary" analysis, default is TRUE.
Whether to use prior probabilities also in estimating the model (compared to only in prediction); default is TRUE.
Arguments passed to function MASS::lda.
Aleš Žiberna
The specified prior is not taken into account when computing eigenvalues and all statistics based on them (everything in components eigModel and sigTest of the returned value).
R Data Analysis Examples: Canonical Correlation Analysis, UCLA: Statistical Consulting Group. From http://www.ats.ucla.edu/stat/r/dae/canonical.htm (accessed Decembar 27, 2013).
ldaPlus(x = mtcars[,c(1, 3, 4, 5, 6)], grouping = mtcars[,10])
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