# 5 Multiple linear regression models (I)

## 5.1 Seminar

Create a new script with the following lines at the top, and save it as seminar5.R

rm(list = ls())
setwd("~/PUBL0055")

Next, we need to load a couple of packages. We need a package called foreign for loading the dataset and texreg for printing formatted regression tables.

library(foreign)
library(texreg)

Today, we load the full standard (cross-sectional) dataset from the Quality of Government Institute (this is a newer version that the one we used in week 3). This is a great data source for comparativist political science research. The codebook is available from their main website. You can also find time-series and cross-section data sets on this page.

The dataset is in Stata format (.dta). Loading it requires the foreign package and the read.dta() function which operates similar to read.csv().

world_data <- read.dta("http://uclspp.github.io/datasets/data/qog_std_cs_jan15.dta")

Check the dimensions of the dataset

dim(world_data)
[1]  193 2037

The dataset contains 2037 variables but we’re only interested in the following:

Variable Description
wbgi_pse A measure of political stability (larger values mean more stability)
lp_lat_abst Distance to the equator or latitude
dr_ing Index for the level of globalization
ti_cpi Transparency International’s Corruptions Perceptions Index (larger values mean better quality institutions, i.e. less corruption)
br_dem Factor variable stating whether the country is a democracy or not (with labels 1. Democracy and 0. Dictatorship)

Our dependent variable (also called response or outcome variable) is wbgi_pse. We will rename the variables we care about to something meaningful.

CAUTION: When renaming variables, do not use spaces or special characters in the name. You can, however, use a period (.) or underscore (_) to make the names more readable.

names(world_data)[names(world_data) == "cname"] <- "country"
names(world_data)[names(world_data) == "wbgi_pse"] <- "pol_stability"
names(world_data)[names(world_data) == "lp_lat_abst"] <- "latitude"
names(world_data)[names(world_data) == "dr_ig"] <- "globalization"
names(world_data)[names(world_data) == "chga_demo"] <- "democracy"
names(world_data)[names(world_data) == "ti_cpi"] <- "inst_quality"

Now Let’s look at some of these variables using the summary() function.

summary(world_data$pol_stability)  Min. 1st Qu. Median Mean 3rd Qu. Max. -3.10637 -0.72686 -0.01900 -0.06079 0.78486 1.57240  If you think about political stability, and how one could measure it, you know there is an order implicit in the measurement – more or less stability. From there, what you need to know is whether the more or less is ordinal or interval scaled. Checking pol_stability you see a range from roughly -3 to 1.60. The variable is numerical and has decimal places. This tells you that the variable is at least interval scaled. You will not see ordinally scaled variables with decimal places. Examine the summaries of the other variables and determine their level of measurement. Now let’s look at the variables that we think can explain political stability. We can use the summary() function on more than one variable by combining their names in a vector. summary(world_data[c('latitude', 'globalization', 'inst_quality', 'democracy')])  latitude globalization inst_quality democracy Min. :0.0000 Min. :24.35 Min. :1.010 0. Dictatorship: 74 1st Qu.:0.1444 1st Qu.:45.22 1st Qu.:2.400 1. Democracy :118 Median :0.2444 Median :54.99 Median :3.300 NA's : 1 Mean :0.2865 Mean :57.15 Mean :3.988 3rd Qu.:0.4444 3rd Qu.:68.34 3rd Qu.:5.100 Max. :0.7222 Max. :92.30 Max. :9.300 NA's :12 NA's :12 NA's :12  The variables latitude, globalization and inst_quality have 12 missing values each marked as NA. democracy has 1 missing value. Missing values could cause trouble because operations including an NA will produce NA as a result (e.g.: 1 + NA = NA). We will drop these missing values from our data set using the is.na() function and square brackets. The exlamation mark in front of is.na() means “not”. So, we keep all rows that are not NA’s on the variable latitude. world_data <- world_data[ !is.na(world_data$latitude), ]

Generally, we want to make sure we drop missing values only from variables that we care about. Now that you have seen how to do this, drop missings from globalization, inst_quality, and democracy yourself.

world_data <- world_data[ !is.na(world_data$globalization), ] world_data <- world_data[ !is.na(world_data$inst_quality), ]
world_data <- world_data[ !is.na(world_data$democracy), ] Let’s check the range of the variable latitude from our summary above. It is between 0 and 1. The codebook clarifies that the latitude of a country’s capital has been divided by 90 to get a variable that ranges from 0 to 1. This would make interpretation difficult. When interpreting the effect of such a variable a unit change (a change of 1) covers the entire range or put differently, it is a change from a country at the equator to a country at one of the poles. We therefore multiply by 90 again. This will turn the units of the latitude variable into degrees again which makes interpretation easier. world_data$latitude <- world_data$latitude * 90 ### 5.1.2 Estimating a Bivariate Regression Is there a correlation between the distance of a country to the equator and the level of political stability? Both political stability (dependent variable) and distance to the equator (independent variable) are continuous. Therefore, we will get an idea about the relationship using a scatter plot. plot(pol_stability ~ latitude, data = world_data, frame.plot = FALSE, pch = 20, xlab = "Latitude", ylab = "Political Stability") Looking at the cloud of points suggests that there might be a positive relationship: increases in our independent variable latitude appear to be associated with increases in the dependent variable pol_stability (the further from the equator, the more stable). We can fit a line of best fit through the points. To do this we must estimate the bivariate regression model with the lm() function. latitude_model <- lm(pol_stability ~ latitude, data = world_data) Now we can create a scatterplot with a regression line using the abline() function. plot(pol_stability ~ latitude, data = world_data, frame.plot = FALSE, pch = 20, xlab = "Latitude", ylab = "Political Stability") abline(latitude_model, col = "red") We can also view a simple summary of the regression by using the screenreg function: screenreg(latitude_model)  ======================= Model 1 ----------------------- (Intercept) -0.58 *** (0.12) latitude 0.02 *** (0.00) ----------------------- R^2 0.11 Adj. R^2 0.10 Num. obs. 170 RMSE 0.89 ======================= *** p < 0.001, ** p < 0.01, * p < 0.05 Thinking back to last week, how can we interpret this regression ouput? • The coefficient for the variable latitude ($$\beta_1$$) indicates that a one-unit increase in a country’s latitude is associated with a 0.02 increase in the measure of political stability, on average. Question: Is this association statistically significant at the 95% confidence level? • The coefficient for the (intercept) term ($$\beta_0$$) indicates that the average level of political stability for a country with a latitude of 0 is -0.58 (where latitude = 0 is a country positioned at the equator) • The $$R^2$$ of the model is 0.11. This implies that 11% of the variation in the dependent variable (political stability) is explained by the independent variable (latitude) in the model. ### 5.1.3 Multivariate Regression The regression above suggests that there is a significant association between these variables However, as good social scientistis, we probably do not think that the distance of a country from the equator is a theoretically relevant variable for explaining political stability. This is because there is no plausible causal link between the two. We should therefore consider other variables to include in our model. We will include the index of globalization (higher values mean more integration with the rest of the world), the quality of institutions, and the indicator for whether the country is a democracy. For all of these variables we can come up with a theoretical story for their effect on political stability. To specify a multiple linear regression model, the only thing we need to change is the formula argument of the lm() function. In particular, if we wish to add additional explanatory variables, the formula argument will take the following form: dependent.var ~ independent.var.1 + independent.var.2 + independent.var.3 ... In our example here, the model would therefore look like the following: inst_model <- lm( pol_stability ~ latitude + globalization + inst_quality + democracy, data = world_data ) Remember, pol_stability is our dependent variable, as before, and now we have four independent variables: latitude, globalization, democracy and inst_quality. Again, just as with the bivariate model, we can view the summarised output of the regression by using screenreg(). As we now have two models (a simple regression model, and a multiple regression model), we can join them together using the list() function, and then put all of that inside screenreg(). screenreg(list(latitude_model, inst_model))  ============================================= Model 1 Model 2 --------------------------------------------- (Intercept) -0.58 *** -1.25 *** (0.12) (0.20) latitude 0.02 *** 0.00 (0.00) (0.00) globalization -0.00 (0.01) inst_quality 0.34 *** (0.04) democracy1. Democracy 0.04 (0.11) --------------------------------------------- R^2 0.11 0.50 Adj. R^2 0.10 0.49 Num. obs. 170 170 RMSE 0.89 0.67 ============================================= *** p < 0.001, ** p < 0.01, * p < 0.05 Including the two new predictors leads to substantial changes. • First, we now explain 50% of the variance of our dependent variable instead of just 11%. • Second, the effect of the distance to the equator is no longer significant. • Third, better quality institutions are associated with more political stability. In particular, a one-unit increase in the measure of instituion quality (which ranges from 1 to 10) is associated with a 0.34 increase in the measure for political stability. • Fourth, there is no significant relationship between globalization and political stability in this data. • Fifth, there is no significant relationship between democracy and political stability in this data. ### 5.1.4 Predicting outcome conditional on institutional quality Just as we did with the simple regression model last week, we can use the fitted model object to calculate the fitted values of our dependent variable for different values of our explanatory variables. To do so, we again use the predict() function. We proceed in three steps. 1. We set the values of the covariates for which we would like to produce fitted values. • You will need to set covariate values for every explanatory variable that you included in your model. • As only one of our variables has a significant relationship with the outcome in the multiple regression model that we estimated above, we are really only interested in that variable (inst_quality). • Therefore, we will calculate fitted values over the range of inst_quality, while setting the values of latitude and globalization to their mean values. • As democracy is a factor variable, we cannot use the mean value. Instead, we will set democracy to be equal to "1. Democracy" which is the label for democratic countries 2. We calculate the fitted values. 3. We report the results (here we will produce a plot). For step one, the following code produces a data.frame of new covariate values for which we would like to calculate a fitted value from our model: democracies <- data.frame( inst_quality = seq(from = 1.4, to = 9.3, by = 1), globalization = mean(world_data$globalization),
latitude = mean(world_data$latitude), democracy = "1. Democracy" ) We’ve just created a data.frame of hypothetical democracies with varying level of institutional quality. Let’s see what this data.frame looks like. democracies  inst_quality globalization latitude democracy 1 1.4 57.93053 25.78218 1. Democracy 2 2.4 57.93053 25.78218 1. Democracy 3 3.4 57.93053 25.78218 1. Democracy 4 4.4 57.93053 25.78218 1. Democracy 5 5.4 57.93053 25.78218 1. Democracy 6 6.4 57.93053 25.78218 1. Democracy 7 7.4 57.93053 25.78218 1. Democracy 8 8.4 57.93053 25.78218 1. Democracy In this data.frame, we have set the inst_quality variable to vary between 1.40 and 9.30, with increments of 1 unit which represents the range of inst_quality in our dataset. min(world_data$inst_quality)
[1] 1.4
max(world_data$inst_quality) [1] 9.3 We have set globalization to be equal to the mean value of globalization in the world_data object, and latitude to be equal to the mean value of latitude in the world_data object. Finally, we have set democracy to be equal to "1. Democracy" (the value for democratic countries). We can now calculate the fitted values for each of these combinations of our explanatory variables using the predict() function. democracies$predicted_pol_stability <- predict(inst_model, newdata = democracies)

We can now look again at the democracies object:

democracies
  inst_quality globalization latitude    democracy predicted_pol_stability
1          1.4      57.93053 25.78218 1. Democracy             -1.00319887
2          2.4      57.93053 25.78218 1. Democracy             -0.66431685
3          3.4      57.93053 25.78218 1. Democracy             -0.32543483
4          4.4      57.93053 25.78218 1. Democracy              0.01344719
5          5.4      57.93053 25.78218 1. Democracy              0.35232921
6          6.4      57.93053 25.78218 1. Democracy              0.69121123
7          7.4      57.93053 25.78218 1. Democracy              1.03009325
8          8.4      57.93053 25.78218 1. Democracy              1.36897527

Hey presto! Now, for each of our explanatory variable combinations, we have the corresponding fitted values as calculated from our estimated regression.

Finally, we can plot these values:

plot(
predicted_pol_stability ~ inst_quality,
data = democracies,
frame.plot = FALSE,
col = "blue",
type = "l",
xlab = "Institution Quality",
ylab = "Fitted value for political stability"
)

We could also use the output from our model to plot two separate lines of fitted values: one for democracies, and one for dictatorships. We have already done this for democracies, so the following code constructs a data.frame of fitted values for dictatorships:

dictatorships <- data.frame(
inst_quality = seq(from = 1.4, to = 9.3, by = 1),
globalization = mean(world_data$globalization), latitude = mean(world_data$latitude),
democracy = "0. Dictatorship"
)

dictatorships\$predicted_pol_stability <- predict(inst_model, newdata = dictatorships)

Now that we have calculated these fitted values, we can add the line for dictatorships to the plot we created above using the lines() function:

plot(
predicted_pol_stability ~ inst_quality,
data = democracies,
frame.plot = FALSE,
col = "blue",
type = "l",
xlab = "Institution Quality",
ylab = "Fitted value for political stability"
)

lines(predicted_pol_stability ~ inst_quality,
data = dictatorships,
col = "red")

We can see from the plot that the fitted values for democracies (blue line) are almost exactly the same as those for dictatorships (red line). This is reassuring, as the estimated coefficient on the democracy variable was very small (0.04) and was not statistically significantly different from 0. Often, however, it can be very illuminating to construct plots like this where we construct a line to indicate how our predicted values for Y vary across one of our explanatory variables (here, institution quality), and we create different lines for different values of another explanatory variable (here, democracy/dictatorship).

### 5.1.5 Seminar Feedback

We want to make sure the seminars are helping you make the most of this module. This super-short survey aims to gather student feedback to inform us how to better support you in the seminar sessions during the second half of the module. The responses you provide are absolutely anonymous.

### 5.1.6 Exercises

1. Create a new file called assignment5.R in your PUBL0055 folder and write all the solutions in it.
2. Go to the QoG website and download the QoG Cross-Section Data and the QoG Standard Codebook.
3. Pretend that you are writing a research paper. Select one dependent variable that you want to explain with a statistical model.
4. Run a simple regression (with one explanatory variable) on that dependent variable and justify your choice of explanatory variable. This will require you to think about the outcome that you are trying to explain, and what you think will be a good predictor for that outcome.
5. Add some additional explanatory variables (no more than 5) to your model and justify the choice again.
6. Produce a regression table of the newer model.
7. Interpret the output of the two models. State your expectations and whether they were met.
8. Calculate fitted values whilst varying at least one of your continuous explanatory variables.
9. Plot the result.
10. Interpret the plot.
11. Save your script, which should now include the answers to all the exercises.
12. Source your script, i.e. run the entire script all at once. Fix the script if you get any error messages.