| catseyesplot {catseyes} | R Documentation |
catseyesplot
Description
The catseyesplot() function plots catseye intervals as a basic R plot() window in one step. Can be called with standard plot parameters to further customize the resulting figure. If xlim & ylim are not specified, these will be generated internally per the provided x, ymean, and yse. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot() function requires pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model – see examples below. NOTE: The drawn vertical range of the outline spans 99.8% of the distribution of the mean.
Usage
catseyesplot(
x,
ymean,
yse,
dx = 0.1,
conf = 0.95,
se.only = TRUE,
col = "black",
shade = rgb(0.05, 0.05, 0.05, 0.2),
lwd = 1,
plot.mean.line = FALSE,
fTransform = NULL,
labels = FALSE,
xlim = NULL,
ylim = NULL,
x_scatter = NULL,
y_scatter = NULL,
jitter_scatter = FALSE,
dx_scatter = 0.05,
pch_scatter = 1,
col_scatter = 1,
cex_scatter = 1,
...
)
Arguments
x |
numeric horizontal position(s); if factor, will be converted to integer in factor level order |
ymean |
numeric mean(s) |
yse |
numeric standard error(s); may use standard deviation(s) for population level plots |
dx |
specifies the width (in x direction) of the catseye interval(s) |
conf |
specifies the confidence of the confidence interval (conf=.95 for alpha=.05) |
se.only |
boolean, if TRUE (default) will shade only +/- 1 standard error about the mean, overriding conf, otherwise if FALSE will shade the confidence interval (per conf) about the mean |
col |
specifies the color of the outline of the catseye, as well as the interval point & line, if shown |
shade |
specifies the color of the shaded confidence region |
lwd |
sets the line width of the interval and outline |
plot.mean.line |
boolean, draws a horizontal line at the position of the mean if TRUE |
fTransform |
Optional function to transform catseye plot from normal distribution (as with analyzing log-tranformed data, see example) |
labels |
Optional, may be logical (if TRUE, uses x) or a character vector |
xlim |
x limits of the plot, as with plot.default |
ylim |
y limits of the plot, as with plot.default |
x_scatter |
numeric x values of corresponding raw data for scatterplot; factors will convert to integer sequence of levels |
y_scatter |
numeric y values of corresponding raw data for scatterplot |
jitter_scatter |
boolean, if TRUE x_scatter will be randomly jittered by jitter function, with amount=jitter_scatter |
dx_scatter |
numeric value specifying amount of jittering used if jitter_scatter is TRUE |
pch_scatter |
pch characters of points in scatterplot; if non-null, must be single value or vector corresponding to x, otherwise selected automatically |
col_scatter |
color of points in scatterplot; if non-null, must be single value or vector corresponding to x, otherwise selected automatically |
cex_scatter |
numeric scaling factor of points in scatterplot |
... |
standard arguments to be passed to the plot function |
Value
Returns a list containing xlim and ylim used in the plot
Author(s)
Clark R. Andersen crandersen@mdanderson.org
References
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629
http://www.psychologicalscience.org/index.php/publications/observer/2014/march-14/theres-life-beyond-05.html
Examples
#Show catseye plots for 4 groups with means of c(-3,2,-1,6)
# and standard errors of c(1,2,4,3)
catseyesplot(1:4,ymean=c(-3,2,-1,6),yse=c(1,2,4,3),xlab="",ylab="",main="4 Groups",xaxt="n")
axis(1,at=1:4,labels = c("Group1","Group2","Group3","Group4"))
#Optionally, add points and lines (usually lines only when joining time sequence)
lines(1:4,c(-3,2,-1,6),type="b")
#Using the labels option
catseyesplot(1:4,ymean=c(-3,2,-1,6),yse=c(1,2,4,3),xlab="",ylab="",labels =
c("Group A","Group B","Group C","Group D"))
catseyesplot(1:4,ymean=c(-3,2,-1,6),yse=c(1,2,4,3),xlab="",ylab="",labels = TRUE)
#Demontration of inclusion of scatterplots
datTest=data.frame(x=c(rep(1,10),rep(2,10),rep(3,10)),y=rnorm(10,30))
datTest$y[datTest$x==2]=datTest$y[datTest$x==2]+7
datTest$y[datTest$x==3]=datTest$y[datTest$x==3]+5
means=c(mean(datTest$y[datTest$x==1]),mean(datTest$y[datTest$x==2]),
mean(datTest$y[datTest$x==3]))
ses=c(sd(datTest$y[datTest$x==1]),sd(datTest$y[datTest$x==2]),
sd(datTest$y[datTest$x==3]))/sqrt(10)
catseyesplot(1:3,ymean=means,yse=ses,xlab="Group",ylab="",x_scatter = datTest$x,
y_scatter = datTest$y)
catseyesplot(1:3,ymean=means,yse=ses,xlab="Group",ylab="",x_scatter = datTest$x,
y_scatter = datTest$y,jitter_scatter = TRUE,xaxt="n")
axis(1,at=1:3,labels = c("Group1","Group2","Group3"))
#Demonstration of plotting of factor estimates by direct prediction from lm model
datTest$x=factor(datTest$x)
lm1=lm(y~x,data=datTest)
newdata=data.frame(x=c("1","2","3"))
pred_lm=predict(lm1,se.fit = TRUE,newdata=newdata,type="response")
catseyesplot(1:3,ymean=pred_lm$fit,yse=pred_lm$se.fit,xlab="Group",ylab="",
plot.mean.line = TRUE,labels=TRUE,
x_scatter = datTest$x,y_scatter = datTest$y,jitter_scatter = TRUE,xaxt="n")
#Demonstration of plotting of factor estimates from emmeans package
require(emmeans)
emmeans1=emmeans(lm1,~x)
#Assess differences between levels of x
pairs(emmeans1,adjust="tukey")
preds=confint(emmeans1)
catseyesplot(1:3,ymean=preds$emmean,yse=preds$SE,xlab="Group",ylab="",
plot.mean.line = TRUE,labels=TRUE,
x_scatter = datTest$x,y_scatter = datTest$y,jitter_scatter = TRUE,xaxt="n")
#Plot with variable x positions
catseyesplot(c(1,3.5,5),ymean=pred_lm$fit,yse=pred_lm$se.fit,xlab="Group",
plot.mean.line = TRUE,labels=TRUE,
ylab="",x_scatter = datTest$x,y_scatter = datTest$y,jitter_scatter = TRUE,xaxt="n")
#Demonstrate use of transformation function fTransform
#Create skewed y
set.seed(3142)
datTest=data.frame(x=c(rep(1,10),rep(2,10),rep(3,10)),y=rnorm(30,mean=0))
datTest$y[datTest$x==2]=datTest$y[datTest$x==2]+1
datTest$y[datTest$x==3]=datTest$y[datTest$x==3]+.5
datTest$y=exp(datTest$y)#Create skewed y
datTest$log_y=log(datTest$y+1)#Transform skewed y to normal distribution for analysis
qqnorm(datTest$y)
qqnorm(datTest$log_y)
plot(datTest$x,datTest$y)
plot(datTest$x,datTest$log_y)
means=c(mean(datTest$log_y[datTest$x==1]),mean(datTest$log_y[datTest$x==2]),
mean(datTest$log_y[datTest$x==3]))
ses=c(sd(datTest$log_y[datTest$x==1]),sd(datTest$log_y[datTest$x==2]),
sd(datTest$log_y[datTest$x==3]))/sqrt(10)
#Plot on log scale
catseyesplot(1:3,ymean=means,yse=ses,xlab="Group",ylab="",x_scatter = datTest$x,
y_scatter = datTest$log_y,jitter_scatter = TRUE,xaxt="n",yaxt="n")
axis(1,at=1:3,labels = c("Group1","Group2","Group3"))
axis(2,at=log(c(0,1,2,4,8,16)+1),labels = c(0,1,2,4,8,16))
#Show catseye plot on original (skewed) scale
#Define function to invert data from log_y scale to y scale
fInvertLog<-function(y_vals) {exp(y_vals)-1}
catseyesplot(1:3,ymean=means,yse=ses,xlab="Group",ylab="",x_scatter = datTest$x,
y_scatter = datTest$y,jitter_scatter = TRUE,xaxt="n",fTransform=fInvertLog)
axis(1,at=1:3,labels = c("Group1","Group2","Group3"))
#Logistic regression example (2 groups)
set.seed(3333)
datBin=data.frame(Group=factor(c(rep("A",15),rep("B",15))),
Y=c(rbinom(15,1,.8),rbinom(15,1,.5)))
sum(datBin$Y[datBin$Group=="A"])/sum(datBin$Group=="A")
sum(datBin$Y[datBin$Group=="B"])/sum(datBin$Group=="B")
glm1=glm(Y~Group-1,family = binomial,data=datBin)
summary(glm1)
(smr=coefficients(summary(glm1)))
#Plot Results on logit=log(odds) Scale
catseyesplot(1:2,smr[,1],smr[,2],xaxt="n",ylab="log(odds)",xlab="Group")
axis(1,at=c(1,2),labels = c("A","B"))
#Plot Results on Probability Scale
fInvLogit<-function(yy) {exp(yy)/(1+exp(yy))}
catseyesplot(1:2,smr[,1],smr[,2],xaxt="n",ylab="Probability",xlab="Group",
fTransform = fInvLogit,ylim=c(0,1))
axis(1,at=c(1,2),labels = c("A","B"))