Extract regression coefficient values

A summary.lm object stores these values in a matrix called 'coefficients'. So the value you are after can be accessed with:

a2Pval <- summary(mg)$coefficients[2, 4]

Or, more generally/readably, coef(summary(mg))["a2","Pr(>|t|)"]. See here for why this method is preferred.

The package broom comes in handy here (it uses the "tidy" format).

tidy(mg) will give a nicely formated data.frame with coefficients, t statistics etc. Works also for other models (e.g. plm, ...).

Example from broom's github repo:

lmfit <- lm(mpg ~ wt, mtcars)require(broom)    tidy(lmfit)      term estimate std.error statistic   p.value1 (Intercept)   37.285   1.8776    19.858 8.242e-192          wt   -5.344   0.5591    -9.559 1.294e-10is.data.frame(tidy(lmfit))[1] TRUE

Just pass your regression model into the following function:

    plot_coeffs <- function(mlr_model) {      coeffs <- coefficients(mlr_model)      mp <- barplot(coeffs, col="#3F97D0", xaxt='n', main="Regression Coefficients")      lablist <- names(coeffs)      text(mp, par("usr")[3], labels = lablist, srt = 45, adj = c(1.1,1.1), xpd = TRUE, cex=0.6)    }

Use as follows:

model <- lm(Petal.Width ~ ., data = iris)plot_coeffs(model)