Effect + Residual Modelling
Effect + Residual Modelling
ER(formula, data)
## S3 method for class 'ER'
print(x, ...)
## S3 method for class 'ER'
plot(
x,
y = 1,
what = "raw",
col = NULL,
pch = NULL,
model.line = (what %in% c("raw")),
ylim = NULL,
ylab = "",
xlab = "",
main = NULL,
...
)
tableER(object, variable)formula |
a model formula specifying features and effects. |
data |
a |
x |
Object of class |
... |
Additional arguments to |
y |
Response name or number. |
what |
What part of ER to plot; |
col |
Color of points, defaults to grouping. Usually set to a factor name. |
pch |
Plot character of points, defaults to 1. Usually set to a factor name. |
model.line |
Include line indicating estimates, default = TRUE. Can be an effect name. |
ylim |
Y axis limits ( |
ylab |
Y label ( |
xlab |
X label ( |
main |
Main title, defaults to |
object |
ER object. |
variable |
Numeric for selecting a variable for extraction. |
ER returns an object of class ER containing effects, ER values,
fitted values, residuals, features, coefficients, dummy design, symbolic design, dimensions,
highest level interaction and feature names.
* Mosleth et al. (2021) Cerebrospinal fluid proteome shows disrupted neuronal development in multiple sclerosis. Scientific Report, 11,4087. <doi:10.1038/s41598-021-82388-w>
* E.F. Mosleth et al. (2020). Comprehensive Chemometrics, 2nd edition; Brown, S., Tauler, R., & Walczak, B. (Eds.). Chapter 4.22. Analysis of Megavariate Data in Functional Omics. Elsevier. <doi:10.1016/B978-0-12-409547-2.14882-6>
## Multiple Sclerosis
data(MS, package = "ER")
er <- ER(proteins ~ MS * cluster, data = MS)
print(er)
plot(er) # Raw data, first feature
plot(er,2) # Raw data, numbered feature
plot(er,'Q76L83', col='MS', pch='cluster') # Selected colour and plot character
plot(er,'Q76L83', what='effect MS',
model.line='effect cluster') # Comparison of factors (points and lines)
# Example compound plot
old.par <- par(c("mfrow", "mar"))
# on.exit(par(old.par))
par(mfrow = c(3,3), mar = c(2,4,4,1))
plot(er,'Q76L83') # Raw data, named feature
plot(er,'Q76L83', what='fits') # Fitted values
plot(er,'Q76L83', what='residuals') # Residuals
plot(er,'Q76L83', what='effect MS') # Effect levels
plot(er,'Q76L83', what='effect cluster') # ----||----
plot(er,'Q76L83', what='effect MS:cluster') # ----||----
plot(er,'Q76L83', what='MS') # ER values
plot(er,'Q76L83', what='cluster') # --------||---------
plot(er,'Q76L83', what='MS:cluster') # --------||---------
par(old.par)
# Complete overview of ER
tab <- tableER(er, 1)
# In general there can be more than two, effects, more than two levels, and continuous effects:
# MS$three <- factor(c(rep(1:3,33),1:2))
# er3 <- ER(proteins ~ MS * cluster + three, data = MS)
## Lactobacillus
data(Lactobacillus, package = "ER")
erLac <- ER(proteome ~ strain * growthrate, data = Lactobacillus)
print(erLac)
plot(erLac) # Raw data, first feature
plot(erLac,2) # Raw data, numbered feature
plot(erLac,'P.LSA0316', col='strain',
pch='growthrate') # Selected colour and plot character
plot(erLac,'P.LSA0316', what='strain',
model.line='growthrate') # Selected model.line
## Diabetes
data(Diabetes, package = "ER")
erDia <- ER(transcriptome ~ surgery * T2D, data = Diabetes)
print(erDia)
plot(erDia) # Raw data, first feature
plot(erDia,2) # Raw data, numbered feature
plot(erDia,'ILMN_1720829', col='surgery',
pch='T2D') # Selected colour and plot characterPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.