## ----pphpc1, eval = FALSE-----------------------------------------------------
# # Load library
# library(micompr)
# 
# # Output names
# outputs <- c("$P^s$", "$P^w$", "$P^c$", "$\\mean{E}^s$",
#              "$\\overline{E}^w$", "$\\overline{C}$",
#              "$\\widetilde{A}$")
# 
# # Outputs from the NetLogo implementation
# dir_nl_ok <- paste0(dir_data, "nl_ok")
# # Outputs from the Java implementation, first configuration
# dir_jex_ok <- paste0(dir_data, "j_ex_ok")
# # Outputs from the Java implementation, second configuration
# dir_jex_noshuff <- paste0(dir_data, "j_ex_noshuff")
# # Outputs from the Java implementation, third configuration
# dir_jex_diff <- paste0(dir_data, "j_ex_diff")
# 
# # Files for model size 400, parameter set 1
# filez <- glob2rx("stats400v1*.txt")
# 
# # Perform the three comparison cases
# mic <- micomp(outputs,
#               ve_npcs = 0.75,
#               list(list(name = "I",
#                         folders = c(dir_nl_ok, dir_jex_ok),
#                         files = c(filez, filez),
#                         lvls = c("NLOK", "JEXOK")),
#                    list(name = "II",
#                         folders = c(dir_nl_ok, dir_jex_noshuff),
#                         files = c(filez, filez),
#                         lvls = c("NLOK", "JEXNS")),
#                    list(name = "III",
#                         folders = c(dir_nl_ok, dir_jex_diff),
#                         files = c(filez, filez),
#                         lvls = c("NLOK","JEXDIF"))),
#               concat = T)

## ----pphpc2, eval = FALSE-----------------------------------------------------
# toLatex(mic,
#         booktabs = T,
#         data_show = c("npcs-1", "mnvp-1", "parp-1", "scoreplot"),
#         data_labels = c("$\\#$PCs", "MNV", "$t$-test", "PCS"),
#         col_width = T,
#         pvalf_params = list(minval = 1e-8, na_str = "*"),
#         label = "tab:pphpc",
#         caption = paste("Comparison of a NetLogo implementation of",
#                         "the PPHPC model against three configurations",
#                         "of a parallel Java implementation."))

## ----sunspot1, results = 'hide', warning = FALSE------------------------------
# Load library
library(micompr)

# Months in the 1749-1859 interval (110 years)
# Months in the 1902-2012 interval (110 years)
m <- sunspot.month[c(1:1320, 1837:3156)]
m <- matrix(m, nrow = 20)

# Factor vector, two levels:
# a) ten 11-year cycles from 1749 to 1859
# b) ten 11-year cycles from 1902 to 2012
groups <- factor(c(rep("A", 10), rep("B", 10)))

# Compare the two groups, use 9 PCs for MANOVA
cmp <- cmpoutput("SunSpots", 9, m, groups)

## ----sunspot2, results = 'markup', warning = FALSE----------------------------
cmp

## ----sunspot3, results = 'markup', warning = FALSE----------------------------
assumptions(cmp)

## ----sunspot4, fig.show = 'asis', fig.env = 'figure', fig.cap = 'Plots produced by sunspots example.'----
plot(cmp)

## ----derma1, eval = FALSE-----------------------------------------------------
# # Load libraries
# library(bmp)
# library(micompr)
# 
# # Image definitions
# imgs <- dir(imgfolder)
# nimgs <- length(imgs)
# npixels <- 760 * 570
# 
# # Specify image groups (Common nevi, atypical nevi,
# # melanomas).
# f <- read.table(grpsfile, row.names = 1)
# grps <- f[order(row.names(f)), ]
# 
# # Read images from disk
# # Use different color channels as outputs, and also
# # use a concatenated output
# rimgs <- matrix(nrow = nimgs, ncol = npixels)
# gimgs <- matrix(nrow = nimgs, ncol = npixels)
# bimgs <- matrix(nrow = nimgs, ncol = npixels)
# rgbimgs <- matrix(nrow = nimgs, ncol = npixels * 3)
# 
# for (i in 1:nimgs) {
# 
#   cimg <- read.bmp(paste0(imgfolder, imgs[i]))
#   rimgs[i, ] <- c(cimg[ , , 1])
#   gimgs[i, ] <- c(cimg[ , , 2])
#   bimgs[i, ] <- c(cimg[ , , 3])
#   rgbimgs[i, ] <- c(cimg[ , , 1], cimg[ , , 2], cimg[ , , 3])
# 
# }
# 
# # Perform multivariate independent comparison of images
# mic <-
#   micomp(outputs = c("R", "G", "B", "RGB"),
#          ve_npcs = 0.9,
#          comps = list(
#            list(name = "1v2",
#                 grpout = list(
#                   data = list(R = rimgs[grps != 3, ],
#                               G = gimgs[grps != 3, ],
#                               B = bimgs[grps != 3, ],
#                               RGB = rgbimgs[grps != 3, ]),
#                   obs_lvls = factor(grps[grps != 3]))),
#            list(name = "1v3",
#                 grpout = list(
#                   data = list(R = rimgs[grps != 2, ],
#                               G = gimgs[grps != 2, ],
#                               B = bimgs[grps != 2, ],
#                               RGB = rgbimgs[grps != 2, ]),
#                   obs_lvls = factor(grps[grps != 2]))),
#            list(name = "2v3",
#                 grpout = list(
#                   data = list(R = rimgs[grps != 1, ],
#                               G = gimgs[grps != 1, ],
#                               B = bimgs[grps != 1, ],
#                               RGB = rgbimgs[grps != 1, ]),
#                   obs_lvls = factor(grps[grps != 1])))))

## ----derma2, eval = FALSE-----------------------------------------------------
# toLatex(mic,
#         booktabs = T,
#         data_show = c("parp-1", "nparp-1", "scoreplot"),
#         data_labels = c("$t$-test", "$U$ test", "PCS"),
#         pvalf_params = list(minval = 1e-8, na_str = "*"),
#         label = "tab:ph2",
#         caption = paste("Comparison of PH$^2$ dataset images",
#                         "grouped by lesion type."))