Confidence Intervals of Effect Differences
Confidence Intervals of Effect Differences
confints(X1, X2, confidence = 0.95, df.used = 0) ## S3 method for class 'confints' plot( x, y, xlab = "", ylab = "normalised log2", sorted = TRUE, labels = FALSE, nonZero = FALSE, xlim = NULL, ylim = NULL, text.pt = 12, ... )
X1 |
|
X2 |
|
confidence |
Level of confidence, default = 0.95. |
df.used |
Optional argument indicating how many degrees of freedom have been consumed during deflation. Default = 0. |
x |
Object of class |
y |
Not used. |
xlab |
X label ( |
ylab |
Y label ( |
sorted |
Logical indicating if intervals should be sorted according to their mean values, or a vector of indices/labels to sort by. |
labels |
Logical indicating if sample labels should be used on x axis. |
nonZero |
Logical indicating if intervals are required not to include zero. |
xlim |
Limits of the horizontal scale. |
ylim |
Limits of the vertical scale. |
text.pt |
Size scaling of text in the plot (default = 16). |
... |
Further arguments to |
An object of class confints, which holds
the information needed to perform statistics or plot the
confidence intervals is returned from confints.
The plotting routine returns a ggplot structure for plotting.
data(MS)
# Compare MS and non-MS patients within cluster 1
conf <- with(MS, confints(proteins[MS == "yes" & cluster == 1,],
proteins[MS == "no" & cluster == 1,]))
p1 <- plot(conf)
p2 <- plot(conf, nonZero = TRUE) # Only intervals without 0.
grid.arrange(p1,p2)
# Shorter plot with labels
confShort <- conf[1:10,]
p1 <- plot(confShort, labels = TRUE)
p2 <- plot(confShort, labels = TRUE, nonZero = TRUE)
grid.arrange(p1,p2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.