我跑glm了,结果好了.现在我想得到那些在95%显着的预测因子的名称,即p值小于或等于显着性水平5e-2.我跑:
fit <- glm(data=dfa, formula=response~.)
sig <- summary(fit)$coefficients[,4]
(Intercept) close0 close1 close2 close3 close4 closema open0
0.000000e+00 3.147425e-19 7.210909e-04 1.046019e-02 4.117580e-03 2.778701e-01 2.829958e-05 0.000000e+00
open1 open2 open3 open4 openma low0 low1 low2
8.627202e-30 1.138499e-02 1.112236e-03 7.422145e-03 3.967735e-03 3.036329e-42 3.033847e-05 3.237155e-01
low3 low4 lowma high0 high1 high2 high3 high4
8.198750e-01 6.647138e-02 4.350488e-05 6.177130e-58 2.625192e-02 4.143373e-01 3.964651e-01 3.694272e-01
highma volume0 volume1 volume2 volume3 volume4 volumema
1.416310e-05 8.027502e-02 1.975302e-01 1.630341e-09 8.979313e-03 1.274195e-06 8.246661e-01
> str(sig)
Named num [1:31] 0.00 3.15e-19 7.21e-04 1.05e-02 4.12e-03 ...
- attr(*, "names")= chr [1:31] "(Intercept)" "close0" "close1" "close2" ...
什么是"命名数"类型？
我希望有一个像这样的列名数组,因为那些预测变量的p值低于显着性水平5e-2即
best <- c('close0', 'close1', 'close2', 'close3', 'closema', ... etc)
注意close4不存在......如何以矢量化方式提取这些列名？
更新:我研究了如何在循环中完成它
fit <- glm(data=dfa, formula=response~.)
summary(fit)
sig <- summary(fit)$coefficients[,4]
best <- NULL
columnLabels <- names(sig)
for (columnLabel in columnLabels) {
if (as.numeric(sig[columnLabel]) <= 5e-2) {
if (is.null(best)) {
best <- columnLabel
} else {
best <- c(best, columnLabel)
}
}
}