dotwhisker: Remaking the Examples from Kastellec and Leoni (2007)

Figure 6: Presenting a Single Regression Model Using a Dot Plot with Error Bars.

Original, with very light edits, via Kastellec and Leoni’s http://svn.tables2graphs.com/tables2graphs/Rcode/Final%20Code%20for%20Website/figure_6_stevens_table_2.R

#Create vectors for coefficients, standard errors and variable names 
    #we place coefficient as last element in each vector rather than 1st
    #since it is least important predictor, and thus we place it at the bottom of the graph
#note: we exclude the constant, since it is substantively meaningless

coef.vec <- c( 1.31, .93, 1.46, .07, .96, .2, .22, -.21, -.32, -.27,.23, 
    0, -.03, .13, .15, .31, -.10)
se.vec <- c( .33, .32, .32, .37, .37, .13, .12, .12, .12, .07, .07, .01, .21,
    .14, .29, .25, .27)

var.names <- c("Argentina", "Chile", "Colombia", "Mexico", "Venezuela", #for longer names, we split into 2 lines using "\n" function
    "Retrospective Egocentric", "Prospective Egocentric",
    "Retrospective Sociotropic", "Prospective Sociotropic",
    "Distance from President", "Ideology", "Age", "Female", "Education",
    "Academic Sector", "Business Sector", "Government Sector")

    
y.axis <- c(length(coef.vec):1)#create indicator for y.axis, descending so that R orders vars from top to bottom on y-axis

par(mar=c(2, 13, 0, 0))#set margins for plot, leaving lots of room on left-margin (2nd number in margin command) for variable names
plot(coef.vec, y.axis, type = "p", axes = F, xlab = "", ylab = "", pch = 19, cex = 1.2,#plot coefficients as points, turning off axes and labels. 
    xlim = c(-2,2.5), xaxs = "r", main = "") #set limits of x-axis so that they include mins and maxs of 
        #coefficients + .95% confidence intervals and plot is symmetric; use "internal axes", and leave plot title empty
#the 3 lines below create horiztonal lines for 95% confidence intervals, and vertical ticks for 90% intervals
segments(coef.vec-qnorm(.975)*se.vec, y.axis, coef.vec+qnorm(.975)*se.vec, y.axis, lwd =  1.5)#coef +/-1.96*se = 95% interval, lwd adjusts line thickness

axis(1, at = seq(-2,2,by=.5), labels = NA, tick = T,#draw x-axis and labels with tick marks
    cex.axis = 1.2, mgp = c(2,.7,0))#reduce label size, moves labels closer to tick marks
axis(1, at = seq(-2,2,by=1), labels =  c(-2,  -1,  0, 1,2), tick = T,#draw x-axis and labels with tick marks
    cex.axis = 1.2, mgp = c(2,.7,0))#reduce label size, moves labels closer to tick marks    

axis(2, at = y.axis, label = var.names, las = 1, tick = T, mgp = c(2,.6,0),
    cex.axis = 1.2) #draw y-axis with tick marks, make labels perpendicular to axis and closer to axis
segments(0,0,0,17,lty=2) # draw dotted line through 0
#box(bty = "l") #place box around plot

#use following code to place model info into plot region
x.height <- .57
text(x.height, 10, expression(R^{2} == .15), adj = 0, cex = 1) #add text for R-squared
text(x.height, 9, expression(paste("Adjusted ", R^{2} == ".12", "")), adj = 0, cex = 1)#add text for Adjusted-R-squared
text(x.height, 8, "n = 500", adj = 0, cex = 1)#add text for sample size

Redone using dotwhisker:

#install.packages("dotwhisker") # uncomment to install from CRAN
library(dplyr)
library(dotwhisker)
library(dplyr)

# Format data as tidy dataframe
results_df <- data.frame(term=var.names, estimate=coef.vec,
                               std.error=se.vec)

# Draw dot-and-whisker plot
results_df %>% dwplot + theme_bw() + theme(legend.position="none") +
    ggtitle("Determinants of Authoritarian Aggression\nStevens, Bishin, and Barr (2006)\nvia Kastellec and Leoni (2007)") +
    geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +
    annotate("text", x = 1.05, y = 10, size = 4, hjust = 0,
             label = "R^2 == .15", parse = TRUE) +
    annotate("text", x = 1.05, y = 9, size = 4, hjust = 0,
             label = "Adjusted~R^2 == .12", parse = TRUE) +
    annotate("text", x = 1.05, y = 8, size = 4, hjust = 0,
              label = "n = 500") 

Figure 7: Using Parallel Dot Plots with Error Bars to Present Two Regression Models.

Original, with very light edits, via Kastellec and Leoni’s http://svn.tables2graphs.com/tables2graphs/Rcode/Final%20Code%20for%20Website/figure_7_pekkanen_table_1.R

#Create Vectors for coefs and standard errors for each model, and variable names
    #note that we  exclude "margin squared" since it doesn't appear in either model

coef.matrix <- matrix(c(-.039, NA, .048, -.133, .071, -.795, 1.47, 
                        -.036, NA, .036, -.142, .07, -.834, 1.70, 
                        -.051, NA, .017, .05, .011, -.532, .775, 
                        -.037, -.02, .047, -.131,.072, -.783, 1.45,
                        -.034, -.018, -.035, -.139, .071, -.822, 1.68,
                        -.05, -.023, .016,-.049, .013, -.521, .819),nr=7)
## R2 of the models
R2<-  c(0.910,  0.910,  0.940,  0.910,  0.910,  0.940)

##standard error matrix, n.variables x n.models
se.matrix <- matrix(c(.003, NA, .011, .013, .028, .056, .152, .003, NA, .012, .014, .029, .059, .171, .003, NA,
                      .01, .013, .024, .044, .124, .003, .005, .011, .013, .028, .055, .152, .003, .005, .021, .014,
                      .029, .059, .17, .003,.006, .01, .013, .024, .044, .127),nr=7)

##variable names
coef.vec.1<- c(0.18, -0.19,-0.39,-0.09, NA, 0.04,-0.86, 0.39,-3.76, -1.61,
    -0.34, -1.17, -1.15,-1.52, -1.66, -1.34,-2.89,-1.88,-1.08, 0.20)
se.vec.1 <-  c(0.22, 0.22, 0.18,.29, NA, 0.08,0.26,0.29,0.36,.19,0.19, 0.22,
    0.22,0.25,0.28,0.32,0.48, 0.43,0.41, 0.20)
coef.vec.2 <-  c(0.27,-0.19, NA, NA, 0.005, 0.04,-0.98,-.36,-3.66, -1.59,
     -0.45, -1.24, -1.04, -1.83, -1.82, -1.21, -2.77, -1.34, -0.94, 0.13)
se.vec.2 <- c(0.22,0.24, NA, NA, 0.004, 0.09 , .31 , .30 , .37 , .21 , .21 , .24 , .24,
     .29 , .32 , .33 , .49 , .46 , .49 , .26)
var.names <- c("Zombie" , "SMD Only", "PR Only", "Costa Rican in PR", 
    "Vote Share Margin", "Urban-Rural Index","No Factional\nMembership",
    "Legal Professional", "1st Term", "2nd Term", "4th Term",
    "5th Term","6th Term","7th Term","8th Term","9th Term","10th Term",
    "11th Term","12th Term", "Constant")

y.axis <- length(var.names):1#create indicator for y.axis, descending so that R orders vars from top to bottom on y-axis
adjust <- .15 #create object that we will use to adjust points and lines up and down to distinguish between models
    
layout(matrix(c(2,1),1,2), #in order to add variable categories and braces to left side of plot, 
    widths = c(1.5, 5))#we use layout command, create a small second panel on left side.
                #using c(2,1) in matrix command tells R to create right panel 1st
#layout.show(2) #can use this command to check results of layout command (but it must be commented out when creating PDF).

par(mar=c(2,8,.5,1), lheight = .8)#set margins for regression plot
plot(coef.vec.1, y.axis+adjust, type = "p", axes = F, xlab = "", ylab = "", pch = 19, cex = 1.2, #plot model 1 coefs using black points (pch = 19, default = black), adding the "adjust amount" to the y.axis indicator to move points up
    xlim = c(min((coef.vec.1-qnorm(.975)*se.vec.1 -.1), (coef.vec.2-qnorm(.975)*se.vec.2 -.1), na.rm = T), #set xlims at mins and maximums (from both models) of confidence intervals, plus .1 to leave room at ends of plots
    max((coef.vec.1+qnorm(.975)*se.vec.1 -.1), (coef.vec.2+qnorm(.975)*se.vec.2 -.1), na.rm = T)),  #use na.rm=T since vectors have missing values
    ylim = c(min(y.axis), max(y.axis)), main = "")
axis(1,at = seq(-4,1, by = 1), label = seq(-4,1, by = 1), mgp = c(2,.8,1), cex.axis = 1.3)#add x-axis and labels; "pretty" creates a sequence of  equally spaced nice values that cover the range of the values in 'x'-- in this case, integers
axis(2, at = y.axis, label = var.names, las = 1, tick = T, cex.axis =1.3)#add y-axis and labels; las = 1 makes labels perpendicular to y-axis
#axis(3,pretty(coef.vec.1, 3))#same as x-axis, but on top axis 
abline(h = y.axis, lty = 2, lwd = .5, col = "light grey")#draw light dotted line at each variable for dotplot effect
#box(bty="l")#draw box around plot
segments(coef.vec.1-qnorm(.975)*se.vec.1, y.axis+adjust, coef.vec.1+qnorm(.975)*se.vec.1, y.axis+adjust, lwd =  1.3)#draw lines connecting 95% confidence intervals

abline(v=0, lty = 2) # draw dotted line through 0 for reference line for null significance hypothesis testing

#add 2nd model
    #because we are using white points and do want the lines to go "through" points rather than over them
        #we draw lines first and the overlay points
segments(coef.vec.2-qnorm(.975)*se.vec.2, y.axis-adjust, coef.vec.2+qnorm(.975)*se.vec.2, y.axis-adjust, lwd =  1.3)#draw lines connecting 95% confidence intervals
points(coef.vec.2, y.axis-adjust, pch = 21, cex = 1.2, bg = "white" ) #add point estimates for 2nd model; pch = 21 uses for overlay points, and "white" for white color

#add legend (manually) to identify which dots denote model 1 and which denote model 2
#legend(-4.5, 20, c("Model 1", "Model 2"), pch = c(19,21),bty = "n")
points(-4, 19.5, pch = 19, cex  = 1.2)
text(-3.7, 19.5, "Model 1", adj = 0,cex  = 1.2)#left-justify text using adj  = 0
points(-4, 18.5, pch = 21,cex  = 1.2)
text(-3.7, 18.5, "Model 2", adj = 0,cex  = 1.2)#left-justify text using adj  = 0

#Create Variable Categories and Braces to go in 2nd plot

par(mar=c(2,0,.5,0)) #set margins--- bottom (1st number) and top (3rd number) must be the same as in 1st plot
plot(seq(0,1,length=length(var.names)), y.axis, type = "n", axes = F,  xlab = "", ylab = "")#call empty plot using type="n"
    #use a sequence of length 20 so that x and y have same length

left.side <- .55#use this to manipulate how far segments are from y-axis
    #note:  getting braces and text in proper place requires much trial and error
segments(left.side,20.2,left.side,16.5) #add brackets around MP Type vars
segments(left.side,20.2,left.side+.15,20.2) #1 segment at a time
segments(left.side,16.5,left.side+.15,16.5)
text(.4, 18.5, "MP Type", srt = 90, font = 3, cex  = 1.5)#Add text; "srt" rotates to 90 degrees, font = 3 == italics

#don't add "Electoral Strength" since it's only 1 variable

segments(left.side,15.5,left.side,12.3) #add brackets around "Misc Controls"
segments(left.side,15.5,left.side+.15,15.5) #one segment at a time
segments(left.side,12.3,left.side+.15,12.3)
text(.3, 14, "Misc\nControls", srt = 90, font = 3, cex  = 1.5)#Add text; "srt" rotates to 90 degrees, font = 3 == italics

segments(left.side,12.15,left.side,1.8) #add brackets around "Seniority"
segments(left.side,12.15,left.side+.15,12.15)   #one segment at a time
segments(left.side,1.8,left.side+.15,1.8)
text(.4, 7, "Seniority", srt = 90, font = 3, cex  = 1.5)#Add text; "srt" rotates to 90 degrees, font = 3 == italics

Redone using dotwhisker:

# Format data as tidy dataframe
results_df <- data.frame(term = rep(var.names, times = 2),
                         estimate = c(coef.vec.1, coef.vec.2),
                         std.error = c(se.vec.1, se.vec.2),
                         model = c(rep("Model 1", 20), rep("Model 2", 20)),
                         stringsAsFactors = FALSE)

# Draw dot-and-whisker plot
p <- results_df %>% dwplot + theme_bw() +
    theme(legend.justification=c(0, 1), legend.position=c(0, 1),
           legend.title = element_blank(), legend.background =
              element_rect(color="gray90")) + 
    xlab("Logit Coefficients") +
    geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +
    ggtitle("Electoral Incentives and LDP Post Allocation\nPekkanen, Nyblade, and Krause (2006)\nvia Kastellec and Leoni (2007)")

# Add brackets
grid.draw(p %>% add_brackets(list(c("MP Type", "Zombie", "Costa Rican in PR"), 
                 c("Misc Controls", "Urban-Rural Index", "Legal Professional"),
                 c("Seniority", "1st Term", "12th Term"))))

Figure 8: Using “Small Multiple” Plots to Present Regression Results from Several Models.

Original, with light edits, via Kastellec and Leoni’s http://svn.tables2graphs.com/tables2graphs/Rcode/Final%20Code%20for%20Website/figure_8_ansolabehere_table_4.R

library(grid)

##point estimates, in a n.variables, n.variables x n.models
coef.matrix <- matrix(c(-.039, NA, .048, -.133, .071, -.795, 1.47, 
                        -.036, NA, .036, -.142, .07, -.834, 1.70, 
                        -.051, NA, .017, .05, .011, -.532, .775, 
                        -.037, -.02, .047, -.131,.072, -.783, 1.45,
                        -.034, -.018, -.035, -.139, .071, -.822, 1.68,
                        -.05, -.023, .016,-.049, .013, -.521, .819),nr=7)
## R2 of the models
R2<-  c(0.910,  0.910,  0.940,  0.910,  0.910,  0.940)

##standard error matrix, n.variables x n.models
se.matrix <- matrix(c(.003, NA, .011, .013, .028, .056, .152, .003, NA, .012, .014, .029, .059, .171, .003, NA,
                      .01, .013, .024, .044, .124, .003, .005, .011, .013, .028, .055, .152, .003, .005, .021, .014,
                      .029, .059, .17, .003,.006, .01, .013, .024, .044, .127),nr=7)

##variable names
varnames<- c("% of county\nregistration", "Law change", "Log population", "Log median\nfamily income",
             "% population with\nh.s. education" ,"% population\nAfrican American" ,"Constant")


##exclude intercept
coef.matrix<-coef.matrix[-(7),]
se.matrix<-se.matrix[-7,]

## each panel has at most six models, plotted in pairs.
## in each pair, solid circles will be the models with "law change" in the specification
## empty circles, those without "law change"

##we are making a list, define it first as empty
Y1 <- vector(length=0,mode="list")
#estimates with law change (in the 4th to 6th columns)
Y1$estimate <- coef.matrix[,4:6]
##95% confidence intervals
Y1$lo <- coef.matrix[,4:6]-qnorm(0.975)*se.matrix[,4:6]
Y1$hi <- coef.matrix[,4:6]+qnorm(0.975)*se.matrix[,4:6]
##90% confidence intervals
Y1$lo1 <- coef.matrix[,4:6]-qnorm(0.95)*se.matrix[,4:6]
Y1$hi1 <- coef.matrix[,4:6]+qnorm(0.95)*se.matrix[,4:6]
##name the rows of Y1 estimate
rownames(Y1$estimate) <- varnames[-7] ##no intercept


#estimates without law change
Y2 <- vector(length=0,mode="list")
Y2$estimate <- coef.matrix[,1:3]
Y2$lo <- coef.matrix[,1:3]-qnorm(.975)*se.matrix[,1:3]
Y2$hi <- coef.matrix[,1:3]+qnorm(.975)*se.matrix[,1:3]
Y2$lo1 <- coef.matrix[,1:3]-qnorm(.95)*se.matrix[,1:3]
Y2$hi1 <- coef.matrix[,1:3]+qnorm(.95)*se.matrix[,1:3]
rownames(Y2$estimate) <- varnames[-7]


##Plot estimates in a single columns 
source2 <- function(file, start, end) {
    file.lines <- scan(file, what=character(), 
                       skip=start - 1, 
                       nlines=end - start + 1, sep='\n')
    file.lines.collapsed <- paste(file.lines, collapse='\n')
    source(textConnection(file.lines.collapsed))
}

source2("http://svn.tables2graphs.com/tables2graphs/Rcode/plotReg.R", 15, 372)

## create the graph (do not print it yet)
tmp <- plot.reg(Y1,Y2,#the lists
                #the model labels
                label.x=c("Full Sample","Excluding counties\nw. partial registration",
                  "Full sample w. \nstate year dummies"),
                ## reference lines
                refline=c(0,0,0,0,0,0),
                ## space left in the bottom (for the x-axis labels)
                hlast=.15,
                ## print the graph?
                print=FALSE,
                ## line width / character size multiplier
                lwd.fact=1.3,
                ## length of the cross- hairs
                length.arrow=unit(0,"mm"),
                ## legend
                ##legend=c("without law change","with law change"),
                ## widths: variable names, plot size, y-axis
                widths=c(.6,.4,.3),
                ## rotation of the variable name labes
                rot.label.y=0,
                ## justification of the variable name labels
                just.label.y="right",
                ## position (x-axis) of the variable name labels)
                pos.label.y=0.95,
                ## size of the symbol
                pch.size=0.6,expand.factor=.2,expand.factor2=0.1,
                ##legend
                legend=c("With law\nchange dummy","Without law\nchange dummy"),leg.mult=.7,
                ##legend font size
                leg.fontsize=11,
                v.grid=TRUE,
                yaxis.at=list(
                NULL,
                NULL,
                seq(-.1,.1,.05),
                seq(-.2,.2,.1),
                seq(-.2,.2,.1),
                NULL##seq(-1,1,.5)
                )
                )
## we rotate the labels of the x-axis 45 degrees. The grid utilities allow
## this modification "on the fly", and it is easy if you are careful at naming the paths
tmp <- editGrob(tmp,gPath("xaxis","labels"),rot=45,just="right",gp=gpar(lineheight=.75))
##tmp is the object we have just created,"xaxis" is the name of element in the object with the x-axis
##elements, and "labels" is the actual object in xaxis that we want to rotate
##just is the justification of the text
grid.draw(tmp) ## print the graph

Redone using dotwhisker:

# Format data as tidy dataframe
model_names <- c("Full Sample\n",
                       "Excluding counties\nw/ partial registration\n",
                       "Full sample w/\nstate year dummies\n")
submodel_names <- c("With","Without")

model_order <- c(4, 1, 5, 2, 6, 3)

results_df <- data.frame(term = rep(varnames[1:6], times = 6),
                         estimate = as.vector(coef.matrix[, model_order]),
                         std.error = as.vector(se.matrix[, model_order]),
                         model = rep(model_names, each = 12),
                         submodel = rep(rep(submodel_names, each = 6), times = 3),
                         stringsAsFactors = FALSE)

small_multiple(results_df) + 
    theme_bw() + ylab("Coefficient Estimate") +
    geom_hline(yintercept = 0, colour = "grey60", linetype = 2) +
    theme(axis.text.x  = element_text(angle = 45, hjust = 1),
          legend.position=c(1, 1), legend.justification=c(1, 1), 
          legend.title = element_text(size=9),
          legend.background = element_rect(color="gray90"),
          legend.margin = unit(-3, "pt"),
          legend.key.size = unit(10, "pt")) +
    scale_colour_hue(name = "Law Change\nDummy") +
    ggtitle("Registration Effects on Turnout\nAnsolabehere and Konisky (2006)\nvia Kastellec and Leoni (2007)")

Affiliation

Frederick Solt

Department of Political Science,

University of Iowa,

324 Schaeffer Hall,

20 E Washington St, Iowa City, IA, 52242

Email: frederick-solt@uiowa.edu

Website: http://myweb.uiowa.edu/fsolt