Description Usage Arguments Value Examples
Useful to visualize results from regression type analyses, as it shows the estimate, confidence interval, and optionally use the value of the p.value to highlight significant associations. A vertical line is included
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mapping 
Set of aesthetic mappings created by 
data 
The data to be displayed in this layer. There are three options: If A A 
stat 
The statistical transformation to use on the data for this layer, as a string. 
position 
Position adjustment, either as a string, or the result of a call to a position adjustment function. 
... 
other arguments passed on to 
height 
Add ends to the confidence intervals. 
fatten 
A multiplicative factor used to increase the size of the
middle bar in 
na.rm 
If 
show.legend 
logical. Should this layer be included in the legends?

center.linetype 
The linetype for the center line. 
center.linecolour 
Line colour for the center line. 
center.linesize 
Line size for the center line. 
ci.linesize 
Line size for the confidence interval lines. 
inherit.aes 
If 
Adds a ggplot2 geom layer.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44  library(ggplot2)
library(broom)
fit < lm(Fertility ~ 0 + Catholic + Agriculture + Examination +
Education + Infant.Mortality, data = swiss)
fit < tidy(fit, conf.int = TRUE)
fit < transform(fit, model = "nonlog", p.value = discrete_pvalue(fit$p.value))
p < ggplot(fit, aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high))
p + geom_estci()
p + geom_estci(aes(xintercept = 1.1), center.linecolour = "red")
p + geom_estci(aes(size = p.value), linetype = "dotted")
p + geom_estci(aes(colour = p.value, size = p.value), linetype = "dotted")
p + geom_estci(aes(colour = p.value, size = p.value), linetype = "dotted") +
scale_colour_grey(start = 0.75, end = 0)
p + geom_estci(aes(size = p.value, alpha = p.value), linetype = "dotted")
p + geom_estci(aes(size = p.value, alpha = p.value, colour = p.value))
p + geom_estci(aes(alpha = p.value), linetype = "dashed",
center.linetype = "solid")
p + geom_estci(aes(alpha = p.value, xintercept = 1),
colour = "blue", linetype = "dashed", center.linetype = "solid")
p + geom_estci(aes(alpha = p.value, xintercept = 1), center.linesize = 1.5)
p + geom_estci(center.linesize = 0.25, height = 1, fatten = 2)
p + geom_estci(center.linesize = 2, height = 0.5, fatten = 8)
p + geom_estci(ci.linesize = 3)
p + geom_estci(aes(size = p.value, colour = p.value), fatten = 2)
fit_log < lm(log(Fertility) ~ 0 + Catholic + Agriculture + Examination +
Education + Infant.Mortality, data = swiss)
fit_log < tidy(fit_log, conf.int = TRUE)
fit_log < transform(fit_log, model = "log",
p.value = discrete_pvalue(fit_log$p.value))
two_fits < rbind(fit, fit_log)
p < ggplot(two_fits, aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high))
# It might be possible to show groups with 'dodging', but it is currently in development.
# p + geom_estci(aes(group = model, colour = model), position = position_dodge(width = 0.3))
p + geom_estci()
p < ggplot(two_fits, aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high))
p + geom_estci() + facet_grid(~ model)

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