# NOT RUN {
# Example 1: Species packing model:
n <- 100; p <- 5; S <- 5
mydata <- rcqo(n, p, S, es.opt = TRUE, eq.max = TRUE)
names(mydata)
(myform <- attr(mydata, "formula"))
fit <- cqo(myform, poissonff, mydata, Bestof = 3) # eq.tol = TRUE
matplot(attr(mydata, "latvar"), mydata[,-(1:(p-1))], col = 1:S)
persp(fit, col = 1:S, add = TRUE)
lvplot(fit, lcol = 1:S, y = TRUE, pcol = 1:S) # The same plot as above
# Compare the fitted model with the 'truth'
concoef(fit) # The fitted model
attr(mydata, "concoefficients") # The 'truth'
c(apply(attr(mydata, "latvar"), 2, sd),
apply(latvar(fit), 2, sd)) # Both values should be approx equal
# Example 2: negative binomial data fitted using a Poisson model:
n <- 200; p <- 5; S <- 5
mydata <- rcqo(n, p, S, fam = "negbin", sqrt = TRUE)
myform <- attr(mydata, "formula")
fit <- cqo(myform, fam = poissonff, dat = mydata) # I.tol = TRUE,
lvplot(fit, lcol = 1:S, y = TRUE, pcol = 1:S)
# Compare the fitted model with the 'truth'
concoef(fit) # The fitted model
attr(mydata, "concoefficients") # The 'truth'
# Example 3: gamma2 data fitted using a Gaussian model:
n <- 200; p <- 5; S <- 3
mydata <- rcqo(n, p, S, fam = "gamma2", log.arg = TRUE)
fit <- cqo(attr(mydata, "formula"),
fam = gaussianff, data = mydata) # I.tol = TRUE,
matplot(attr(mydata, "latvar"),
exp(mydata[, -(1:(p-1))]), col = 1:S) # 'raw' data
# Fitted model to transformed data:
lvplot(fit, lcol = 1:S, y = TRUE, pcol = 1:S)
# Compare the fitted model with the 'truth'
concoef(fit) # The fitted model
attr(mydata, "concoefficients") # The 'truth'
# }
Run the code above in your browser using DataLab