This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected.

Software swa

How to install the R package swa, swa()

How to run swa()

Details of the algorithm

The swa package includes a main R code swa(). It takes in the design matrix and the response as inputs and outputs a list of selected variables and a final multiple linear regression model using the selected variables.

> install.packages("swa") # install swa package > library(swa) # load swa package > detach(package:swa) # unload swa package

You may also first download the package to your computer and install locally.

> install.packages("swa_0.8.1.tar.gz", repos = NULL, type = "source")

Upload and get help:

> library(swa) > ?swa

Consider a simple example below.

> set.seed(1) > x <- matrix(rnorm(20*100), ncol=20) > b = c(0.5,1,1.5,2,3); > y <- x[,1:5]%*%b + rnorm(100) > y <- as.numeric(y<0) > X <- t(x) > swa(X,y,c(3,5,10,15,20),5,500,MPplot = TRUE) ;

In this example, the design is a 100 by 20 matrix with 20 independent variables. Only the first five independent variables bear true signals that influence the response. Variable 5 has a strong signal while variable 1 has a weak signal. When the MPplot option is set on, then a multi-panel plot is produced.

The MP plots show that the upper arm sets, i.e., the points above the elbow point of each panel plot, started to be stabilized at s = 10. Thus, we fix s = 10 below.

> sum_swa <- swa(X,y,10,5,500) > sum_swa $index [1] 5 3 2 4 1 $summary Call: lm(formula = yy ~ ., data = dataXY) Residuals: Min 1Q Median 3Q Max -2.98810 -1.06555 -0.02721 1.17308 2.29243 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1927 0.1394 1.382 0.170266 X5 -0.9973 0.1238 -8.058 2.42e-12 *** X3 -0.4877 0.1265 -3.854 0.000212 *** X2 -0.4290 0.1265 -3.391 0.001019 ** X4 -0.4216 0.1371 -3.074 0.002763 ** X1 -0.1090 0.1356 -0.804 0.423531 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.375 on 94 degrees of freedom Multiple R-squared: 0.5559, Adjusted R-squared: 0.5323 F-statistic: 23.54 on 5 and 94 DF, p-value: 2.885e-15 $step_sum Call: lm(formula = yy ~ X5 + X3 + X2 + X4, data = dataXY) Residuals: Min 1Q Median 3Q Max -2.92167 -1.13646 0.03455 1.22929 2.29080 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1942 0.1391 1.396 0.165992 X5 -1.0122 0.1221 -8.287 7.47e-13 *** X3 -0.4897 0.1263 -3.878 0.000194 *** X2 -0.4266 0.1262 -3.379 0.001055 ** X4 -0.4152 0.1366 -3.039 0.003070 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.372 on 95 degrees of freedom Multiple R-squared: 0.5529, Adjusted R-squared: 0.5341 F-statistic: 29.37 on 4 and 95 DF, p-value: 6.774e-16

The final model perfectly recovers the five true variables. The stepwise selection removes X1, which has the weakest signal (hence is not expected to be recovered.) We next check the results with a larger m = 1000.

> sum_swa <- swa(X,y,10,5,1000) > sum_swa $index [1] 5 3 2 4 6 $summary Call: lm(formula = yy ~ ., data = dataXY) Residuals: Min 1Q Median 3Q Max -2.85483 -1.06089 -0.00216 1.19698 2.40598 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1904 0.1391 1.369 0.174247 X5 -1.0010 0.1225 -8.172 1.39e-12 *** X3 -0.4856 0.1262 -3.847 0.000218 *** X2 -0.4245 0.1261 -3.365 0.001109 ** X4 -0.4016 0.1371 -2.929 0.004271 ** X6 0.1574 0.1459 1.079 0.283566 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.371 on 94 degrees of freedom Multiple R-squared: 0.5583, Adjusted R-squared: 0.5349 F-statistic: 23.77 on 5 and 94 DF, p-value: 2.247e-15 $step_sum Call: lm(formula = yy ~ X5 + X3 + X2 + X4, data = dataXY) Residuals: Min 1Q Median 3Q Max -2.92167 -1.13646 0.03455 1.22929 2.29080 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1942 0.1391 1.396 0.165992 X5 -1.0122 0.1221 -8.287 7.47e-13 *** X3 -0.4897 0.1263 -3.878 0.000194 *** X2 -0.4266 0.1262 -3.379 0.001055 ** X4 -0.4152 0.1366 -3.039 0.003070 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.372 on 95 degrees of freedom Multiple R-squared: 0.5529, Adjusted R-squared: 0.5341 F-statistic: 29.37 on 4 and 95 DF, p-value: 6.774e-16

It now captured X2,X3,X4,X5, and also a noise variable X6 that is not as significant as the former 4 covariates X2-X4.

Let be the (total) dimension and be the number of true variables. We let be the number of subsamplings, be the sampling size, be both the number of subsamplings to keep, and the number of semifinalist variables.

For

Subsample a subset of variables, , from the variables with absolute marginal -statistic greater than .

Run LDA on the data which yields the area under the ROC curve (AUC-ROC) and -statistic for each variable .

Keep the best subsampled sets with the highest AUC-ROC . Let be the index set of these subsamples.

For each variable , calculate its average absolute value of the -statistic, reweighted by the square root of the AUC-ROC value of the set, .

Let be the index set of the variables with the highest

Run LDA on the data . For each variable , the -value is adjusted using a multiple comparison method such as the Bonferroni method or the Benjamini-Hochberg procedure.