R Regression

Let's assume we want to run a regression with lwage (the logarithm of the woman's wage) as dependent variable and a constant, exper (the years of experience) and the logarithm of the husbands wage (huswage as explanatory variables. First we should note that the logarithm of the woman's wage already exists as variable lwage, but the logarithm of the husband's wage doesn't exist as its own variable. Hence we are yet to calculate it.

The lm() function

The R function that does the heavy lifting for regression analysis is the lm() function (presumably an abbreviation for "linear model") and we will have a close up look at how it works. But let's get our first regression under the belt.

A first example

The following few lines of code (which you should save in a script) import the data, convert missing data to NAs (see R_Data#Data_Types) and eventually runs a regression:

    # This is my first R regression!
    setwd("T:/ECLR/R/FirstSteps")              # This sets the working directory
    mydata <- read.csv("mroz.csv")  # Opens mroz.csv from working directory
     
    # Now convert variables with "." to num with NA
    mydata$wage <- as.numeric(as.character(mydata$wage))
    mydata$lwage <- as.numeric(as.character(mydata$lwage))
     
    # Run a regression
    reg_ex1 <- lm(lwage~exper+log(huswage),data=mydata)

So let's look at the last line in which we ask R to run a regression. Whatever comes in the parenthesis after lm are parameters to the lm() function. Different parameters are separated by commas. So here we have two inputs. Let's start with the second data=mydata. This basically indicates to R that we are drawing the data for the regression from our dataframe called mydata. That means that for the first input, in which we actually specify the model we estimate, we can refer to the variable names of the variables that are contained in mydata. In that first input you should imagine writing down a regression model. The model we want to estimate is the following:

[math]\label{OLSModel} lwage = \beta_0 + \beta_1 * exper + \beta_2 * log(huswage) + \epsilon[/math]

The way how you tell lm() to estimate this model is to leave the coefficients and error term away, and replace the equal sign with a ~. This is where the bold part of

    reg_ex1 <- lm(lwage~exper+log(huswage),data=mydata)

comes from. One additional note here. The log(huswage) part of this model took the huswage variable from our dataframe and applied the log() function to it.

You should be familiar with the left hand side of the command, which assigns the results of the regression to a new object called reg_ex1. You should think of this as some sort of folder in which R has now saved all the regression results. If you look at the object reg_ex1 in your environment, you will see that it is a list object with 13 elements. So far so good, but if you click on the little triangle next to its name and see the detail you will most likely scratch your head and think "What a mess!" and I think you are quite right.

Not using a dataframe

While you will often use a dataframe from which you extract your variables, it may well be possible that you have your data not saved in a dataframe but in individual data vectors. In that case the regression command becomes even simpler. Let's assume that tat your dependent variable was saved in y and your two explanatiry variables in x1 and x2. In that case you would merely have to call

    reg_ex2 <- lm(y~x1+x2)

That's it, nothing else!

Regression Output

You would be familiar with a standard regression output containing estimated coefficients, standard errors and a set of regression statistics. Fortunately it is very easy to obtain this. The command is

    > summary(reg_ex1)
     
    Call:
    lm(formula = lwage ~ exper + log(huswage), data = mydata)
     
    Residuals:
         Min       1Q   Median       3Q      Max 
    -3.10089 -0.31219  0.02919  0.37466  2.11402 
     
    Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
    (Intercept)  0.534866   0.139082   3.846 0.000139 ***
    exper        0.016684   0.004243   3.933 9.81e-05 ***
    log(huswage) 0.236466   0.063684   3.713 0.000232 ***
    ---
    Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
     
    Residual standard error: 0.7031 on 425 degrees of freedom
      (325 observations deleted due to missingness)
    Multiple R-squared:  0.05919,	Adjusted R-squared:  0.05477 
    F-statistic: 13.37 on 2 and 425 DF,  p-value: 2.338e-06

Most things should sound familiar here. One thing you should note is that, on this occasion, R automatically ignored all the missing observations, all 325 of them, which left R to estimate the model with 428 observations

Accessing Regression Output

The next thing you should want to know as a buddying applied econometrician is how you actually access these regression statistics in order to use them in some further analysis. We previously mentioned that reg_ex1 was a list that contained a number of elements some of which we print by using the summary function.

It turns out that it is actually useful to save the summary as its own object:

         reg_ex1_sm <- summary(reg_ex1)

Inspecting reg_ex1_sm reveals that this is a list as well. A list is a collection of objects. Sometimes these objects in lists have names and this is one such occasion. Try names(reg_ex1_sm) in your console and you find the following list of names:

     [1] "call"          "terms"         "residuals"    
     [4] "coefficients"  "aliased"       "sigma"        
     [7] "df"            "r.squared"     "adj.r.squared"
    [10] "fstatistic"    "cov.unscaled"  "na.action"

In fact if you do the same for the reg_ex1 list you will find the following list:

    > names(reg_ex1)
     [1] "coefficients"  "residuals"     "effects"      
     [4] "rank"          "fitted.values" "assign"       
     [7] "qr"            "df.residual"   "na.action"    
    [10] "xlevels"       "call"          "terms"        
    [13] "model"  

If you compare the lists you can see that there is some overlap between the information contained in the two lists. In fact I find that only list member that you would usually want to access from reg_ex1 directly is the "fitted.values". Most other commonly used statistics are available from the summary list.

The question now remains how you access particular members of these lists if you wanted to use them further down in your code. There are a number of ways of accessing members of a list. This isn't really the place to discuss these in detail. This is a good place to go if you want to find out more. I shall just demonstrate one way to do this.

    reg_ex1_resid <- reg_ex1_sm$residuals  # assigns the residual vector to a new object

This will work in a similar manner for any of the other list elements. The $ sign separates the list name from the name of the list member sought. It is worth looking at one other output in particular, the coefficients. As it turns out, while both, the regression output and the regression summary both have a list member called coefficients, they are defined slightly differently:

The version from the regression output reports the coefficients only, while the summary version also contains standard errors, t-statistics and p-values:

    > reg_ex1_sm$coefficients
                   Estimate  Std. Error  t value     Pr(>|t|)
    (Intercept)  0.53486640 0.139081935 3.845693 1.386203e-04
    exper        0.01668435 0.004242588 3.932587 9.811191e-05
    log(huswage) 0.23646570 0.063684482 3.713082 2.320238e-04

The latter is a 3 by 4 matrix. If you were only interested in the t-statistic of the coefficient to exper you would add that you wanted the element from the 2nd row and 3rd column as in the following line:

    > reg_ex1_sm$coefficients[3,4]
    [1] 0.0002320238

Extra parameters to the lm() function

So far we employed the lm() function in its most basic form. It does have some bells and whistles and here I will introduce a few that may become useful on occasions.