r - How to vectorize extracting significant predictor variables? -

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i run glm , results ok. name of predictors significant @ 95% i.e. p-value less or equal significance level 5e-2. run:

fit <- glm(data=dfa, formula=response~.) sig <- summary(fit)$coefficients[,4]   (intercept)       close0       close1       close2       close3       close4      closema        open0  0.000000e+00 3.147425e-19 7.210909e-04 1.046019e-02 4.117580e-03 2.778701e-01 2.829958e-05 0.000000e+00         open1        open2        open3        open4       openma         low0         low1         low2  8.627202e-30 1.138499e-02 1.112236e-03 7.422145e-03 3.967735e-03 3.036329e-42 3.033847e-05 3.237155e-01          low3         low4        lowma        high0        high1        high2        high3        high4  8.198750e-01 6.647138e-02 4.350488e-05 6.177130e-58 2.625192e-02 4.143373e-01 3.964651e-01 3.694272e-01        highma      volume0      volume1      volume2      volume3      volume4     volumema  1.416310e-05 8.027502e-02 1.975302e-01 1.630341e-09 8.979313e-03 1.274195e-06 8.246661e-01  > str(sig)   named num [1:31] 0.00 3.15e-19 7.21e-04 1.05e-02 4.12e-03 ...   - attr(*, "names")= chr [1:31] "(intercept)" "close0" "close1" "close2" ...

what "named num" type anyway?

i have array of column names because predictor variables have p-value below significance level 5e-2 i.e.

best <- c('close0', 'close1', 'close2', 'close3', 'closema', ... etc)

note close4 not there ... how can extract these column names in vectorized fashion?

update: worked out how in loop

fit <- glm(data=dfa, formula=response~.) summary(fit) sig <- summary(fit)$coefficients[,4] best <- null columnlabels <- names(sig) (columnlabel in columnlabels) {     if (as.numeric(sig[columnlabel]) <= 5e-2) {         if (is.null(best)) {             best <- columnlabel         } else {             best <- c(best, columnlabel)         }     } }

names(sig)[sig <= 0.05] looking for. names(sig) returns names , sig <= 0.05 helps extract desirable subset.


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