[R] add median value and standard deviation bar to lattice plot
Bert Gunter
bgunter.4567 at gmail.com
Thu Mar 16 15:41:58 CET 2017
Just add whatever further code to decorate the groups as you like
within the panel.groups function. I believe I have given you
sufficient information in my code for you to do that if you study the
code carefully. Depending on what you decide to do -- which is
statistical and OT here (and not something I would offer specific
advice on remotely anyway) -- you may also have to pass down
additional arguments based on computations that you do with *all* the
data from *all* groups together.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Thu, Mar 16, 2017 at 1:38 AM, Luigi Marongiu
<marongiu.luigi at gmail.com> wrote:
> dear Bert,
> thank you for the solution, it worked perfectly. However I still would
> like to know how reliable are the dots that are plotted, that is why i
> would like to have individual bars on each dot (if possible). the
> standard deviation maybe is not the right tool and the confidence
> interval is perhaps better, but the procedure should be the same: draw
> an arrow from the lower to the upper limit. is that possible?
> regards,
> luigi
>
> PS sorry for the formatting, usually plain text is my default; it
> should have switched to html when i replied to a previous email but
> the difference does not show up when i type...
>
> On Wed, Mar 15, 2017 at 4:28 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote:
>> There may be a specific function that handles this for you, but to
>> roll your own, you need a custom panel.groups function, not the
>> default. You need to modify the panel function (which is
>> panel.superpose by default) to pass down the "col" argument to the
>> panel.segments call in the panel.groups function.
>>
>> This should get you started:
>>
>> useOuterStrips(
>> strip = strip.custom(par.strip.text = list(cex = 0.75)),
>> strip.left = strip.custom(par.strip.text = list(cex = 0.75)),
>> stripplot(
>> average ~ type|target+cluster,
>> panel = function(x,y,col,...)
>> panel.superpose(x,y,col=col,...),
>> panel.groups = function(x,y,col,...){
>> panel.stripplot(x,y,col=col,...)
>> m <- median(y)
>> panel.segments(x0 = x[1] -.5, y0 = m,
>> x1 = x[1] +.5, y1 = m,
>> col=col, lwd=2
>> )
>> },
>> my.data,
>> groups = type,
>> pch=1,
>> jitter.data = TRUE,
>> main = "Group-wise",
>> xlab = expression(bold("Target")), ylab = expression(bold("Reading")),
>> col = c("grey", "green", "red"),
>> par.settings = list(strip.background = list(col=c("paleturquoise",
>> "grey"))),
>> scales = list(alternating = FALSE, x=list(draw=FALSE)),
>> key = list(
>> space = "top",
>> columns = 3,
>> text = list(c("Blank", "Negative", "Positive"), col="black"),
>> rectangles = list(col=c("grey", "green", "red"))
>> )
>> )
>> )
>>
>> FWIW, I think adding 1 sd bars is a bad idea statistically.
>>
>> And though it made no difference here, please post in pain text, not HTML.
>>
>> Bert Gunter
>>
>> "The trouble with having an open mind is that people keep coming along
>> and sticking things into it."
>> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>>
>>
>> On Wed, Mar 15, 2017 at 2:22 AM, Luigi Marongiu
>> <marongiu.luigi at gmail.com> wrote:
>>> Dear all,
>>> I am analyzing some multivariate data that is organized like this:
>>> 1st variable = cluster (A or B)
>>> 2nd variable = target (a, b, c, d, e)
>>> 3rd variable = type (blank, negative, positive)
>>> 4th variable = sample (the actual name of the sample)
>>> 5th variable = average (the actual reading -- please not that this is the
>>> mean of different measures with an assumed normal distribution, but the
>>> assumption might not always be true)
>>> 6th variable = stdev (the standard deviation associated with each reading)
>>> 7th variable = ll (lower limit that is average stdev)
>>> 8th variable = ul (upper limit that is average + stdev)
>>>
>>> I am plotting the data using lattice's stripplot and I would need to add:
>>> 1. an error bar for each measurement. the bar should be possibly coloured
>>> in light grey and semitransparent to reduce the noise of the plot.
>>> 2. a type-based median bar to show differences in measurements between
>>> blanks, negative and positive samples within each panel.
>>>
>>> How would I do that?
>>> Many thanks,
>>> Luigi
>>>
>>>>>>
>>> cluster <- c(rep("A", 90), rep("B", 100))
>>> sample <- c(
>>> rep(c("cow-01", "cow-02", "cow-03", "cow-04", "cow-05", "cow-06",
>>> "cow-07", "cow-08", "cow-09", "cow-10", "cow-11",
>>> "cow-12", "cow-13", "cow-14", "cow-15", "cow-16", "cow-17",
>>> "blank"), 5),
>>> rep(c("cow-26", "cow-35", "cow-36", "cow-37", "cow-38", "cow-39",
>>> "cow-40", "cow-41", "cow-42", "cow-43", "cow-44", "cow-45",
>>> "cow-46", "cow-47", "cow-48", "cow-49", "cow-50", "cow-51",
>>> "cow-59", "blank"), 5)
>>> )
>>> type <- c(
>>> rep(c("negative", "negative", "negative", "negative", "negative",
>>> "negative", "negative", "negative", "positive", "positive",
>>> "positive", "positive", "positive", "positive", "positive",
>>> "positive", "positive", "blank"), 5),
>>> rep(c("negative", "positive", "negative", "negative", "negative",
>>> "negative", "negative", "negative", "positive", "positive",
>>> "positive", "positive", "positive", "positive", "positive",
>>> "positive", "positive", "positive", "positive", "blank"), 5)
>>> )
>>> target <- c(
>>> c(rep("a", 18), rep("b", 18), rep("c", 18), rep("d", 18), rep("e", 18)),
>>> c(rep("a", 20), rep("b", 20), rep("c", 20), rep("d", 20), rep("e", 20))
>>> )
>>> average <- c(88.5, 49, 41, 33, 35, 45, 95, 30, 41, 64, 22, 29, 59, 71, 128, 39,
>>> 42, 47, 86, 100,
>>> 69, 44, 53, 66, 66, 71, 161, 69, 22.5, 30, 67, 99, 129, 94, 49,
>>> 33, 28, 31, 26, 23,
>>> 30, 41, 35, 23, 38, 43, 15, 21, 45, 51.5, 34, 26, 43, 32.5, 59,
>>> 58.5, 61, 62.5, 58,
>>> 59.5, 60.5, 60, 64, 110, 55, 66, 197, 83.5, 155, 76, 125, 90, 73,
>>> 84, 95.5, 62, 82, 138,
>>> 103.5, 57, 138, 149.5, 57, 54, 245.5, 191, 131, 96, 176, 45, 76,
>>> 33, 37, 51, 44, 50, 54,
>>> 66, 49, 90, 66.5, 42.5, 67, 56, 54, 50, 45, 99, 50, 51.5, 212, 40,
>>> 68, 121, 80, 57,
>>> 81.5, 128, 77, 119.5, 126, 184, 101, 103, 88, 100, 140, 186, 297,
>>> 32, 184, 36, 45, 45, 44,
>>> 86, 65, 61, 76, 62, 136, 84, 80, 56, 109, 116, 54, 59,
>>> 79, 34, 74.5,
>>> 54, 49, 55, 56,
>>> 59, 56, 56, 57, 67, 65, 63, 52, 58, 59, 56, 54, 66, 92, 87, 59,
>>> 33, 58, 51, 54,
>>> 52, 47, 45, 42, 52, 57, 79, 42, 45.5, 47, 47, 36, 50, 53, 49 )
>>> stdev <- c(17.85, 6.31, 3.42, 1.04, 0.51, 6.04, 38.43, 2.78, 5.55, 26.72, 1.83,
>>> 9.92, 4.59, 19, 7.96,
>>> 7.5, 1.06, 9.66, 75.94, 36.79, 50.45, 9.79, 1.55, 11.42, 64.12,
>>> 0.79, 15.14, 16.15, 8.12, 4.04, 92.57, 35.35,
>>> 42.28, 52.96, 7.06, 4.97, 1.15, 4.77, 6.59, 7.27, 0.75, 4.25,
>>> 9, 0.1, 1.14, 4.17, 6.73, 3.81, 3.27,
>>> 97.44, 9.74, 0.45, 8.14, 5.91, 13.1, 98.22, 8.92, 72.62, 70.26,
>>> 59.46, 29.89, 56.35, 91.25, 49.94, 20.65, 62.04,
>>> 95.13, 35.89, 99.64, 29.44, 33.12, 45.91, 96.69, 9.05, 38.56,
>>> 3.09, 0.6, 8.69, 16.95, 74.03, 84.05, 39.87, 15.52,
>>> 27.92, 35.72, 80.26, 71.93, 66.73, 87.8, 5.43, 98.3, 7.41, 9.86,
>>> 63.64, 0.36, 5.84, 1.58, 20.1, 4.21, 82.12,
>>> 19.29, 9.02, 22.12, 54.08, 74.95, 3.24, 9.67, 67.98,
>>> 9.92, 40.69,
>>> 6.24, 8.76, 74.25, 46.34, 25.69, 90.63, 83.71,
>>> 73.53, 57.88, 15.84, 82.07, 67.45, 47.39, 98.77, 75.1,
>>> 64.9, 3.71,
>>> 87.44, 61.06, 4.77, 57.54, 7.68, 4.54, 6.15,
>>> 3.32, 60.39, 33.78, 66.22, 18.67, 76.53, 63.54, 47.06, 38.47,
>>> 88.15, 18.25, 4.26, 67.19, 88.87, 29.65, 7.33, 68.18,
>>> 28.03, 6.91, 77.82, 22.23, 73.23, 95.21, 27.11, 37.01, 34.88,
>>> 28.15, 11.27, 15.67, 96.08, 89.52, 28.6, 8.22, 23.55,
>>> 59.2, 36.38, 41.38, 0.4, 56.82, 32.35, 20.6, 18.13, 8.15, 1.08,
>>> 9.85, 1.07, 37.75, 97.6, 7.16, 8.51, 4.42,
>>> 0.15, 1.28, 7.42, 71.15, 9.39)
>>> ll <- c(70.65, 42.69, 37.58, 31.96, 34.49, 38.96, 56.57, 27.22, 35.45,
>>> 37.28, 20.17, 19.08, 54.41, 52, 120.04, 31.5, 40.94, 37.34,
>>> 10.06, 63.21, 18.55, 34.21, 51.45, 54.58, 1.88, 70.21, 145.86,
>>> 52.85, 14.38, 25.96, -25.57, 63.65, 86.72, 41.04, 41.94, 28.03,
>>> 26.85, 26.23, 19.41, 15.73, 29.25, 36.75, 26, 22.9, 36.86, 38.83,
>>> 8.27, 17.19, 41.73, -45.94, 24.26, 25.55, 34.86, 26.59, 45.9,
>>> -39.72, 52.08, -10.12, -12.26, 0.0399999999999991, 30.61, 3.65,
>>> -27.25, 60.06, 34.35, 3.96, 101.87, 47.61, 55.36, 46.56, 91.88, 44.09,
>>> -23.69, 74.95, 56.94, 58.91, 81.4, 129.31, 86.55, -17.03, 53.95,
>>> 109.63, 41.48, 26.08, 209.78, 110.74, 59.07, 29.27, 88.2, 39.57,
>>> -22.3, 25.59, 27.14, -12.64, 43.64, 44.16, 52.42, 45.9, 44.79, 7.88,
>>> 47.21, 33.48, 44.88, 1.92, -20.95, 46.76, 35.33, 31.02,
>>> 40.08, 10.81, 205.76, 31.24, -6.25, 74.66, 54.31, -33.63,
>>> -2.20999999999999, 54.47, 19.12, 103.66, 43.93, 116.55, 53.61, 4.23,
>>> 12.9, 35.1, 136.29, 98.56, 235.94, 27.23, 126.46, 28.32, 40.46,
>>> 38.85, 40.68, 25.61, 31.22, -5.22, 57.33, -14.53, 72.46, 36.94,
>>> 41.53, -32.15, 90.75, 111.74, -13.19, -29.87, 49.35, 26.67,
>>> 6.31999999999999, 25.97, 42.09, -22.82, 33.77, -14.23, -39.21, 28.89,
>>> 19.99, 32.12, 36.85, 51.73, 36.33, -38.08, -30.52, 27.4, 45.78,
>>> 42.45, 32.8, 50.62, 17.62, 32.6, 1.18, 18.65, 33.4, 33.87, 38.85,
>>> 43.92, 32.15, 50.93, 19.25, -18.6, 34.84, 36.99, 42.58, 46.85,
>>> 34.72, 42.58, -18.15, 39.61)
>>> ul <- c(106.35, 55.31, 44.42, 34.04, 35.51, 51.04, 133.43, 32.78, 46.55,
>>> 90.72, 23.83, 38.92, 63.59, 90, 135.96, 46.5, 43.06, 56.66,
>>> 161.94, 136.79, 119.45, 53.79, 54.55, 77.42, 130.12, 71.79, 176.14,
>>> 85.15, 30.62, 34.04, 159.57, 134.35, 171.28, 146.96, 56.06, 37.97,
>>> 29.15, 35.77, 32.59, 30.27, 30.75, 45.25, 44, 23.1, 39.14, 47.17,
>>> 21.73, 24.81, 48.27, 148.94, 43.74, 26.45, 51.14, 38.41, 72.1,
>>> 156.72, 69.92, 135.12, 128.26, 118.96, 90.39, 116.35, 155.25,
>>> 159.94, 75.65, 128.04, 292.13, 119.39, 254.64, 105.44, 158.12, 135.91,
>>> 169.69,
>>> 93.05, 134.06, 65.09, 82.6, 146.69, 120.45, 131.03, 222.05, 189.37,
>>> 72.52, 81.92, 281.22, 271.26, 202.93, 162.73, 263.8, 50.43, 174.3,
>>> 40.41, 46.86, 114.64, 44.36, 55.84, 55.58, 86.1, 53.21, 172.12,
>>> 85.79, 51.52, 89.12, 110.08, 128.95, 53.24, 54.67, 166.98, 59.92,
>>> 92.19, 218.24, 48.76, 142.25, 167.34, 105.69, 147.63, 165.21,
>>> 201.53, 134.88, 135.34, 208.07, 251.45, 148.39, 201.77, 163.1, 164.9,
>>> 143.71,
>>> 273.44, 358.06, 36.77, 241.54, 43.68, 49.54, 51.15, 47.32, 146.39,
>>> 98.78, 127.22, 94.67, 138.53, 199.54, 131.06, 118.47, 144.15, 127.25,
>>> 120.26, 121.19, 147.87, 108.65, 41.33, 142.68, 82.03, 55.91, 132.82,
>>> 78.23, 132.23, 151.21, 83.11, 94.01, 101.88, 93.15, 74.27, 67.67,
>>> 154.08, 148.52, 84.6, 62.22, 89.55, 151.2, 123.38, 100.38, 33.4,
>>> 114.82, 83.35, 74.6, 70.13, 55.15, 46.08, 51.85, 53.07, 94.75, 176.6,
>>> 49.16, 54.01, 51.42, 47.15, 37.28, 57.42, 124.15, 58.39)
>>> my.data <- data.frame(cluster, type, target, sample, average, stdev, ll,
>>> ul, stringsAsFactors = FALSE)
>>>
>>> library(lattice)
>>> library(latticeExtra)
>>> useOuterStrips(
>>> strip = strip.custom(par.strip.text = list(cex = 0.75)),
>>> strip.left = strip.custom(par.strip.text = list(cex = 0.75)),
>>> stripplot(
>>> average ~ type|target+cluster,
>>> my.data,
>>> groups = type,
>>> pch=1,
>>> jitter.data = TRUE,
>>> main = "Group-wise",
>>> xlab = expression(bold("Target")), ylab = expression(bold("Reading")),
>>> col = c("grey", "green", "red"),
>>> par.settings = list(strip.background = list(col=c("paleturquoise",
>>> "grey"))),
>>> scales = list(alternating = FALSE, x=list(draw=FALSE)),
>>> key = list(
>>> space = "top",
>>> columns = 3,
>>> text = list(c("Blank", "Negative", "Positive"), col="black"),
>>> rectangles = list(col=c("grey", "green", "red"))
>>> )
>>> )
>>> )
>>>
>>> [[alternative HTML version deleted]]
>>>
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