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?

.@John - Why did you use `Pr(>|t|)` value of `a2` and not any one of the first three columns? — Chetan Arvind Patil, Aug 04 '17 at 21:33

score 91 · Accepted Answer · edited May 23 '17 at 12:34

91

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.

edited May 23 '17 at 12:34

Community

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answered Jul 05 '11 at 00:57

wkmor1

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How can we "discover" these properties of the `summary`? How did you find/know this? – WestCoastProjects May 20 '18 at 01:48
You mean e.g. `coef(summary(mg))[, c("t value","Pr(>|t|)")]`? But thanks for the Link - I always forget that! – Christoph Aug 23 '18 at 09:43
@javadba, you can ``str(mg)`` or ``names(mg)``, for instance. – PatrickT Dec 19 '18 at 10:57
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@PatrickT thx. These little things are what make a big difference. I found out about `data.table` and that _transformed_ my outlook on _R_ .. but still need these kinds of tips. – WestCoastProjects Dec 19 '18 at 14:34

Helix123 · Answer 2 · 2015-04-08T22:03:37.253

36

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

edited Apr 08 '15 at 22:03

answered Apr 07 '15 at 09:37

Helix123

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To answer the OP from this: ``td[1, "estimate"]`` or ``td[td$term == "(Intercept)","estimate"]`` or even ``tdt <- as.data.table(td); setkey(tdt); tdt["(Intercept)","estimate"]`` – PatrickT Sep 26 '17 at 20:46

score 9 · Answer 3 · answered Feb 19 '17 at 05:39

9

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)

answered Feb 19 '17 at 05:39

Cybernetic

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I've plundered your code and find it to be useful. You may want to alter it so that it removes na values and it allows for the main title to be entered from the function. – DarrenRhodes Nov 12 '19 at 10:48

score 3 · Answer 4 · edited Nov 03 '18 at 01:23

To answer your question, you can explore the contents of the model's output by saving the model as a variable and clicking on it in the environment window. You can then click around to see what it contains and what is stored where.

Another way is to type yourmodelname$ and select the components of the model one by one to see what each contains. When you get to yourmodelname$coefficients, you will see all of beta-, p, and t- values you desire.

Extract regression coefficient values

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