catseyes (version 0.2.5)

catseyesplot: 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

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

# NOT RUN {
#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"))
# }