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Linear discriminant analysis (LDA, using the lda function in R) was performed on 16 genes that were significantly different based on both the ANOVA and Mann-Whitney U approaches (see Results).
We performed linear discriminant analyses using the "lda" function in R (MASS package).
LDA for each of the four days was carried out on the normalized data using the lda function from the MASS package of the R language [21].
The resulting linear discriminant function was used as a basis for leave-one-out cross validation (using the lda function in R with the "CV" option) to confirm the ability of the gene expression differences to correctly classify individuals as foundress-reared or worker-reared.
LDA was performed using the lda function from the R package "MASS".
At each iteration, these probesets were used to calculate a linear discriminant analysis (LDA -based formuLDA -basedhe lda formulan from the R package MASS (R Development Core Team, 2011).
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The predictions of Q versus q allele at the QTL mutation for the recombinants were estimated by discriminant analysis or a logistic regression model using the "lda" and "glm" functions of R, respectively.
These results demonstrate that a better performance can always be achieved using the LDA space.
We then used the "predict" function to classify individual flies based on resubstitution using the linear discriminate scores for all three discriminant factors, and cross-validated the classification using the "CV" command in the "lda" function.
In this study we performed stepwise variable selection using the stepclass function with the lda function as implemented in R [ 16].
We predicted the group of the case left out from the training set by using the discriminant scores, which we obtained by multiplying the discriminant coefficients from the lda function and the standardised expression values of each probeset.
More suggestions(15)
using the histogram function
using the repair function
using the despeckle function
using the lda classifier
using the search function
using the likelihood function
using the desirability function
using the lda method
using the duleg function
using the share function
using the image function
using the calibrate function
using the ruler function
using the Trace function
using the table function
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