I am tring to add error bars to data in an xyplot. Below is the
content of a CSV file that I am trying to use and the code which plots
the data points and 45 degree lines in each of the panes of the
trellice. I want to add error bars that go up and down to the limits
set by the lcl and ucl values in the data set. How the heck can I
specify the error bars in a lattice plot?
If anyone knows how to do this can you please tell me how to do it and
more importantly point me toward a book/paper that explains how to do
this and has examples. :~) As always, help is profoundly appreciated.
fig2 = read.xls("figure2.csv")
library(lattice)
library(grid)
with(fig2,
xyplot(y ~ x | group * Method,
xlab = "",
xlim = c(-5, 1),
ylab = "",
ylim = c(-5, 1),
col = "black",
groups = group,
aspect = 1,
lx = lcl,
ux = ucl,
pch = 16 ,
panel = function(x, y, ...) {
panel.superpose(x, y, ...)
panel.abline(0, 1)
},
)
)
###### some sample data for a CSV file ############
Method,group,x,y,lcl,ucl
A,Black,-3.018626791,-1.461326791,-2.068936791,-0.867106791
A,Black,-3.304632403,-2.608322403,-3.463632403,-1.768972403
A,Black,-2.673500283,-2.100820283,-2.708560283,-1.507230283
A,Black,-1.700734807,-0.887104807,-1.275424807,-0.524394807
A,Black,-0.586121342,-0.848638342,-1.169118342,-0.551044342
A,Yellow,-3.136580929,-1.120380929,-1.720710929,-0.550799929
A,Yellow,-2.757574147,-1.021674147,-1.474644147,-0.600298147
A,Yellow,-2.17890461,-0.89563461,-1.34797461,-0.47309861
A,Yellow,-1.812551095,-0.920201095,-1.215711095,-0.639398095
A,Yellow,-1.054284002,-1.195239002,-1.453922002,-0.956836002
A,Green,-1.639312507,-4.174222507,-4.174222507,-4.174222507
A,Green,-1.23917727,-0.30492427,-0.68852327,-0.21611447
A,Green,-0.66966805,-1.13230605,-1.99186605,-0.49901105
A,Green,0.278895635,0.096514635,-0.301298365,0.300602935
A,Green,-0.504911532,-0.894806332,-1.378339332,-0.603663332
A,Blue,-2.969196277,-3.693096277,-3.820696277,-1.384526277
A,Blue,-2.419573959,-2.280913959,-2.894093959,-1.672893959
A,Blue,-1.773659064,-1.850719064,-2.450169064,-1.260569064
A,Blue,-1.510092543,-0.851495543,-1.439472543,-0.411152543
A,Blue,-0.595168746,-0.998596746,-1.208087746,-0.805696746
B,Black,-2.459224263,-1.757114263,-2.364714263,-1.162324263
B,Black,-1.817291831,-1.704651831,-2.557361831,-0.868041831
B,Black,-2.495591512,-2.614881512,-3.456411512,-1.789931512
B,Black,-1.216945891,-0.928095891,-1.315455891,-0.557498891
B,Black,-0.500977651,-0.518668651,-0.817970651,-0.249855651
B,Yellow,-2.473182951,-1.310892951,-1.909672951,-0.741403951
B,Yellow,-2.085555297,-0.856005297,-1.252555297,-0.489987297
B,Yellow,-2.249602851,-1.539112851,-2.010652851,-1.093110851
B,Yellow,-1.406241001,-0.872741001,-1.183891001,-0.577481001
B,Yellow,-0.953045757,-0.981668757,-1.241857757,-0.740871757
B,Green,-2.487720209,-4.310240209,-4.310240209,-4.310240209
B,Green,-2.472235352,-0.579902352,-0.963501352,-0.491092552
B,Green,-1.35388464,-0.39377464,-0.99212464,0.00267836
B,Green,-1.20222519,-0.58276419,-1.05847819,-0.30239519
B,Green,-0.155542679,-0.302979679,-0.795866679,-0.000590679
B,Blue,-2.467137219,-2.879317219,-3.541517219,-2.219037219
B,Blue,-2.093208678,-2.467378678,-3.037568678,-1.901558678
B,Blue,-1.968546702,-1.171728702,-1.881726702,-0.642598702
B,Blue,-1.067931387,-1.109401387,-1.445171387,-0.785811387
B,Blue,-0.936799562,-1.166112562,-1.361216562,-0.987562562
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