Extract regression coefficient values

Question

I have a regression model for some time series data investigating drug utilisation. The purpose is to fit a spline to a time series and work out 95% CI etc. The model goes as follows:

id <- ts(1:length(drug$Date)) a1 <- ts(drug$Rate) a2 <- lag(a1-1) tg <- ts.union(a1,id,a2) mg <-lm (a1~a2+bs(id,df=df1),data=tg)  

The summary output of mg is:

Call: lm(formula = a1 ~ a2 + bs(id, df = df1), data = tg)  Residuals:      Min       1Q   Median       3Q      Max  -0.31617 -0.11711 -0.02897  0.12330  0.40442   Coefficients:                   Estimate Std. Error t value Pr(>|t|)     (Intercept)        0.77443    0.09011   8.594 1.10e-11 *** a2                 0.13270    0.13593   0.976  0.33329     bs(id, df = df1)1 -0.16349    0.23431  -0.698  0.48832     bs(id, df = df1)2  0.63013    0.19362   3.254  0.00196 **  bs(id, df = df1)3  0.33859    0.14399   2.351  0.02238 *   --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  

I am using the Pr(>|t|) value of a2 to test if the data under investigation are autocorrelated.

Is it possible to extract this value of Pr(>|t|) (in this model 0.33329) and store it in a scalar to perform a logical test?

Alternatively, can it be worked out using another method?

Answer 1

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.

Answer 2

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.value 1 (Intercept)   37.285   1.8776    19.858 8.242e-19 2          wt   -5.344   0.5591    -9.559 1.294e-10  is.data.frame(tidy(lmfit)) [1] TRUE