data visualizationdescriptive statisticsr
From page 166 of the Open Intro to Stats, what is this type of graph called? It represents 25 point estimates of a mean, together with their confidence intervals, centered around the actual population mean. The red line is a point estimate whose 95% confidence interval fails to capture the true population mean.
Further, is there a default R command to plot these graphs? Most graphs in the book seem to have been generated with R…
Is this much different from doing two local polynomials of degree 2, one for below the threshold and one for above with smooth at $K_i$ points? Here's an example with Stata:
use votex // the election-spending data that comes with rd
tw
(scatter lne d, mcolor(gs10) msize(tiny))
(lpolyci lne d if d<0, bw(0.05) deg(2) n(100) fcolor(none))
(lpolyci lne d if d>=0, bw(0.05) deg(2) n(100) fcolor(none)), xline(0) legend(off)
Alternatively, you can just save the lpoly smoothed values and standard errors as variables instead of using twoway. Below $x$ is the bin, $s$ is the smoothed mean, $se$ is the standard error, and $ul$ and $ll$ are the upper and lower limits of the 95% Confidence Interval for the smoothed outcome.
lpoly lne d if d<0, bw(0.05) deg(2) n(100) gen(x0 s0) ci se(se0)
lpoly lne d if d>=0, bw(0.05) deg(2) n(100) gen(x1 s1) ci se(se1)
/* Get the 95% CIs */
forvalues v=0/1 {
gen ul`v' = s`v' + 1.95*se`v'
gen ll`v' = s`v' - 1.95*se`v'
};
tw
(line ul0 ll0 s0 x0, lcolor(blue blue blue) lpattern(dash dash solid))
(line ul1 ll1 s1 x1, lcolor(red red red) lpattern(dash dash solid)), legend(off)
As you can see, the lines in the first plot are the same as in the second.
I am going to use R. I used dput after reading in the data to make all this reproducible. Define the data and the levels:
example <- structure(list(
V1 = structure(c(4L, 7L, 8L, 3L, 6L, 10L, 11L, 1L, 5L, 12L, 2L, 9L),
.Label = c("12.7.", "14.11.", "14.4.", "15.1.", "15.10.", "15.5.", "17.2.",
"18.3.", "22.12.", "22.6.", "24.6.", "27.10."), class = "factor"),
V2 = c(NA, NA, NA, 7L, 42L, 57L, 41L, 17L, NA, NA, NA, NA),
V3 = c(NA, NA, 22L, 71L, 135L, 175L, 139L, 103L, 29L, NA, NA, NA),
V4 = c(NA, 43L, 109L, 175L, 244L, 256L, 299L, 240L, 152L, 77L, 22L, NA),
V5 = c(95L, 165L, 245L, 300L, 374L, 375L, 400L, 375L, 299L, 200L, 95L, 45L),
V6 = c(180L, 252L, 334L, 421L, 470L, 400L, 529L, 555L, 440L, 330L, 175L, 125L),
V7 = c(237L, 325L, 495L, 500L, 540L, 535L, 626L, 616L, 557L, 440L, 225L, 189L),
V8 = c(257L, 356L, 450L, 575L, 600L, 602L, 650L, 663L, 616L, 475L, 303L, 199L),
V9 = c(245L, 355L, 455L, 550L, 597L, 602L, 657L, 678L, 643L, 499L, 357L, 232L),
V10 = c(259L, 401L, 500L, 521L, 576L, 575L, 655L, 645L, 375L, 400L, 295L, 218L),
V11 = c(222L, 295L, 375L, 495L, 527L, 579L, 599L, 585L, 518L, 400L, 245L, 175L),
V12 = c(157L, 230L, 313L, 398L, 415L, 425L, 517L, 481L, 400L, 310L, 166L, 120L),
V13 = c(67L, 121L, 195L, 255L, 299L, 305L, 382L, 332L, 275L, 99L, 65L, 21L),
V14 = c(NA, NA, 89L, 109L, 208L, 265L, 225L, 201L, 118L, 43L, NA, NA),
V15 = c(NA, NA, NA, 48L, 108L, 121L, 118L, 70L, 12L, NA, NA, NA),
V16 = c(NA, NA, NA, NA, 22L, 39L, 21L, NA, NA, NA, NA, NA)),
.Names = c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8",
"V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16"),
class = "data.frame",
row.names = c(NA, -12L))
example.levels <- c(115,170,250,330,385,600)
Then we plot twelve subplots. In each subplot, we add your levels as horizontal lines. Note that I am constraining the $y$ axis to be identical across plots so we can visually compare them:
opar <- par(mfrow=c(3,4),mai=c(.2,.3,.3,.1)+.02)
for ( ii in 1:12 ) {
plot(1:15,as.numeric(example[ii,-1]),xlab="",ylab="",
xaxt="n",main=example[ii,1],ylim=c(0,700),type="o")
abline(h=example.levels,col="grey")
}
par(opar)
I am not putting the times on the x axis since they will be hard to read anyway, but perhaps one could truncate the minutes and just note the hours. Result:
Best Answer
I think this is a Forest plot. See Forest plots using R and ggplot2