R/can_cor.R

Defines functions print.can_cor plot.can_cor can_corr

Documented in can_corr plot.can_cor print.can_cor

#' Canonical correlation analysis
#' @description
#' `r badge('stable')`
#'
#' Performs canonical correlation analysis with collinearity diagnostic,
#' estimation of canonical loads, canonical scores, and hypothesis testing for
#' correlation pairs.
#'
#'
#'@param .data The data to be analyzed. It can be a data frame (possible with
#'  grouped data passed from [dplyr::group_by()].
#' @param FG,SG A comma-separated list of unquoted variable names that will
#'   compose the first (smallest) and second (highest) group of the correlation
#'   analysis, respectively. Select helpers are also allowed.
#'@param by One variable (factor) to compute the function by. It is a shortcut
#'  to [dplyr::group_by()]. To compute the statistics by more than
#'  one grouping variable use that function.
#' @param use The matrix to be used. Must be one of 'cor' for analysis using the
#'   correlation matrix (default) or 'cov' for analysis using the covariance
#'   matrix.
#' @param test The test of significance of the relationship between the FG and
#'   SG. Must be one of the 'Bartlett' (default) or 'Rao'.
#' @param prob The probability of error assumed. Set to 0.05.
#' @param center Should the data be centered to compute the scores?
#' @param stdscores Rescale scores to produce scores of unit variance?
#' @param verbose Logical argument. If `TRUE` (default) then the results
#'   are shown in the console.
#' @param collinearity Logical argument. If `TRUE` (default) then a
#'   collinearity diagnostic is performed for each group of variables according
#'   to Olivoto et al.(2017).
#' @return If `.data` is a grouped data passed from
#'   [dplyr::group_by()] then the results will be returned into a
#'   list-column of data frames.
#'
#' * **Matrix** The correlation (or covariance) matrix of the variables
#'
#' * **MFG, MSG** The correlation (or covariance) matrix for the variables of
#' the first group or second group, respectively.
#'
#' * **MFG_SG** The correlation (or covariance) matrix for the variables of the
#' first group with the second group.
#'
#' * **Coef_FG, Coef_SG** Matrix of the canonical coefficients of the first
#' group or second group, respectively.
#'
#' * Loads_FG, Loads_SG Matrix of the canonical loadings of the first group
#' or second group, respectively.
#'
#' * **Score_FG, Score_SG** Canonical scores for the variables in FG and SG,
#' respectively.
#'
#' * **Crossload_FG, Crossload_FG** Canonical cross-loadings for FG variables
#' on the SG scores, and cross-loadings for SG variables on the FG scores,
#' respectively.
#'
#' * **SigTest** A dataframe with the correlation of the canonical pairs and
#' hypothesis testing results.
#'
#' * **collinearity** A list with the collinearity diagnostic for each group of
#' variables.
#' @md
#' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
#' @references
#' Olivoto, T., V.Q. Souza, M. Nardino, I.R. Carvalho, M. Ferrari, A.J.
#' Pelegrin, V.J. Szareski, and D. Schmidt. 2017. Multicollinearity in path
#' analysis: a simple method to reduce its effects. Agron. J. 109:131-142.
#' \doi{10.2134/agronj2016.04.0196}
#'
#' @export
#' @examples
#' \donttest{
#' library(metan)
#'
#' cc1 <- can_corr(data_ge2,
#'                FG = c(PH, EH, EP),
#'                SG = c(EL, ED, CL, CD, CW, KW, NR))
#'
#'
#' # Canonical correlations for each environment
#' cc3 <- data_ge2 %>%
#'        can_corr(FG = c(PH, EH, EP),
#'                 SG = c(EL, ED, CL, CD, CW, KW, NR),
#'                 by = ENV,
#'                 verbose = FALSE)
#'
#'}
can_corr <- function(.data,
                     FG,
                     SG,
                     by = NULL,
                     use = "cor",
                     test = "Bartlett",
                     prob = 0.05,
                     center = TRUE,
                     stdscores = FALSE,
                     verbose = TRUE,
                     collinearity = TRUE) {
  if (!missing(by)){
    if(length(as.list(substitute(by))[-1L]) != 0){
      stop("Only one grouping variable can be used in the argument 'by'.\nUse 'group_by()' to pass '.data' grouped by more than one variable.", call. = FALSE)
    }
    .data <- group_by(.data, {{by}})
  }
  if(is_grouped_df(.data)){
    results <- .data %>%
      doo(can_corr,
          FG = {{FG}},
          SG = {{SG}},
          use = use,
          test = test,
          prob = prob,
          center = center,
          stdscores = stdscores,
          verbose = verbose,
          collinearity = collinearity)
    return(set_class(results, c("tbl_df",  "can_cor_group", "tbl",  "data.frame")))
  }
  FG <- as.data.frame(select(.data, {{FG}}) %>% select_numeric_cols())
  SG <- as.data.frame(select(.data, {{SG}}) %>% select_numeric_cols())
  if (nrow(FG) != nrow(SG)) {
    stop("The number of observations of 'FG', should be equal to 'SG'.")
  }
  if (ncol(FG) > ncol(SG)) {
    stop("The number of variables in 'FG' should be lesser than or equal to the number of variables in 'SG'.")
  }
  if (!test %in% c("Bartlett", "Rao")) {
    stop("The argument 'test' is incorrect, it should be 'Bartlett' or 'Rao'.")
  }
  if (!is.numeric(prob) | prob <= 0 || prob > 1) {
    stop("The argument 'prob' is incorrect. It should be numeric with values between 0 and 1.")
  }
  if (use == "cov") {
    MC <- cov(cbind(FG, SG))
    S11 <- cov(FG)
    S22 <- cov(SG)
    S12 <- cov(FG, SG)
    S21 <- cov(SG, FG)
  }
  if (use == "cor") {
    MC <- cor(cbind(FG, SG))
    S11 <- cor(FG)
    S22 <- cor(SG)
    S12 <- cor(FG, SG)
    S21 <- cor(SG, FG)
  }
  M1 <- eigen(S11)
  megval1 <- M1$values
  megvec1 <- M1$vectors
  S11_12 <- megvec1 %*% diag(1/sqrt(megval1)) %*% t(megvec1)
  S22_Inv <- solve_svd(S22)
  M2 <- eigen(S11_12 %*% S12 %*% S22_Inv %*% S21 %*% S11_12)
  megval2 <- M2$values
  megvec2 <- M2$vectors
  mtr <- megval2
  varuv <- as.data.frame(matrix(NA, length(mtr), 3))
  rownames(varuv) <- paste("U", 1:length(mtr), "V", 1:length(mtr),
                           sep = "")
  colnames(varuv) <- c("Variance", "Proportion", "Cum_proportion")
  varuv[, "Variance"] <- mtr
  varuv[, "Proportion"] <- (mtr/sum(mtr)) * 100
  varuv[, "Cum_proportion"] <- cumsum(varuv[, "Proportion"])
  coruv <- as.matrix(sqrt(mtr), ncol = length(coruv), nrow = 1)
  rownames(coruv) <- paste("U", 1:length(coruv), "V", 1:length(coruv),
                           sep = "")
  colnames(coruv) <- c("Correlation")
  Coef_FG <- S11_12 %*% megvec2
  rownames(Coef_FG) <- colnames(FG)
  colnames(Coef_FG) <- paste("U", 1:ncol(Coef_FG), sep = "")
  Coef_SG <- S22_Inv %*% S21 %*% Coef_FG %*% solve_svd(diag(sqrt(megval2)))
  colnames(Coef_SG) <- paste("V", 1:ncol(Coef_SG), sep = "")
  M3 <- eigen(diag(diag(S11)))
  megval3 <- M3$values
  megvec3 <- M3$vectors
  D11_12 <- megvec3 %*% diag(1/sqrt(megval3)) %*% t(megvec3)
  M4 <- eigen(diag(diag(S22)))
  megval4 <- M4$values
  megvec4 <- M4$vectors
  D22_12 <- megvec4 %*% diag(1/sqrt(megval4)) %*% t(megvec4)
  Rux <- t(t(Coef_FG) %*% S11 %*% D11_12)
  rownames(Rux) <- colnames(FG)
  Rvy <- t(t(Coef_SG) %*% S22 %*% D22_12)
  rownames(Rvy) <- colnames(SG)
  if (center == TRUE) {
    FG_A <- scale(FG, center = TRUE, scale = FALSE)
    SG_A <- scale(SG, center = TRUE, scale = FALSE)
  } else {
    FG_A <- FG
    SG_A <- SG
  }
  FG_A[is.na(FG_A)] <- 0
  SG_A[is.na(SG_A)] <- 0
  FG_SC <- FG_A %*% Coef_FG
  SG_SC <- SG_A %*% Coef_SG
  if (stdscores == TRUE) {
    FG_SC <- sweep(FG_SC, 2, apply(FG_SC, 2, sd), "/")
    SG_SC <- sweep(SG_SC, 2, apply(SG_SC, 2, sd), "/")
  }
  FG_CL <- cor(FG_A, SG_SC)
  SG_CL <- cor(SG_A, FG_SC)
  FG_SC = as.data.frame(FG_SC)
  SG_SC = as.data.frame(SG_SC)
  if (test == "Bartlett") {
    n <- nrow(FG)
    p <- ncol(FG)
    q <- ncol(SG)
    QtdF <- length(coruv)
    Bartlett <- as.data.frame(matrix(NA, QtdF, 5))
    colnames(Bartlett) <- c("Canonical_pairs", "Lambda_Wilks",
                            "Chi_square", "DF", "p_value")
    Bartlett[, 1] <- paste("U", 1:QtdF, "V", 1:QtdF, sep = "")
    i <- 1
    for (i in 1:QtdF) {
      Lambda <- prod(1 - coruv[i:QtdF]^2)
      chisq <- -((n - 1) - (p + q + 1)/2) * log(Lambda)
      gl <- (p - i + 1) * (q - i + 1)
      pValor <- pchisq(chisq, gl, ncp = 0, lower.tail = F)
      Bartlett[i, 2] <- round(Lambda, 5)
      Bartlett[i, 3] <- round(chisq, 5)
      Bartlett[i, 4] <- gl
      Bartlett[i, 5] <- round(pValor, 5)
    }
    teste <- Bartlett
  }
  if (test == "Rao") {
    n <- nrow(FG)
    p1 <- ncol(FG)
    q1 <- ncol(SG)
    QtdF <- length(coruv)
    Rao <- as.data.frame(matrix(NA, QtdF, 6))
    colnames(Rao) <- c("Canonical pairs", "Lambda_Wilks",
                       "F_value", "DF1", "DF2", "p_value")
    Rao[, 1] <- paste("U", 1:QtdF, "V", 1:QtdF, sep = "")
    for (i in 1:QtdF) {
      p <- p1 - i + 1
      q <- q1 - i + 1
      t <- (n - 1) - (p + q + 1)/2
      s <- ifelse((p^2 + q^2) <= 5, 1, sqrt((p^2 * q^2 -
                                               4)/(p^2 + q^2 - 5)))
      Lambda <- prod(1 - coruv[i:QtdF]^2)
      gl1 <- p * q
      gl2 <- (1 + t * s - p * q/2)
      FVAL <- ((1 - Lambda^(1/s))/Lambda^(1/s)) * gl2/gl1
      pValor <- pf(FVAL, gl1, gl2, ncp = 0, lower.tail = FALSE)
      Rao[i, 2] <- round(Lambda, 5)
      Rao[i, 3] <- round(FVAL, 5)
      Rao[i, 4] <- gl1
      Rao[i, 5] <- round(gl2, 5)
      Rao[i, 6] <- round(pValor, 5)
    }
    teste <- Rao
  }
  results <- data.frame(cbind(cbind(varuv, coruv), teste[-1]))
  names(results) <- c("Var", "Percent", "Sum", "Corr", "Lambda",
                      "Chisq", "DF", "p_val")
  if (collinearity == TRUE) {
    colin <- list(FG = colindiag(FG),
                  SG = colindiag(SG))
  } else {
    colin <- NULL
  }
  if (verbose == TRUE) {
    cat("---------------------------------------------------------------------------\n")
    cat("Matrix (correlation/covariance) between variables of first group (FG)\n")
    cat("---------------------------------------------------------------------------\n")
    print(S11)
    if (collinearity == TRUE) {
      cat("---------------------------------------------------------------------------\n")
      cat("Collinearity within first group \n")
      cat("---------------------------------------------------------------------------\n")
      print(colindiag(FG))
    }
    cat("---------------------------------------------------------------------------\n")
    cat("Matrix (correlation/covariance) between variables of second group (SG)\n")
    cat("---------------------------------------------------------------------------\n")
    print(S22)
    if (collinearity == TRUE) {
      cat("---------------------------------------------------------------------------\n")
      cat("Collinearity within second group \n")
      cat("---------------------------------------------------------------------------\n")
      print(colindiag(SG))
    }
    cat("---------------------------------------------------------------------------\n")
    cat("Matrix (correlation/covariance) between FG and SG\n")
    cat("---------------------------------------------------------------------------\n")
    print(S12)
    cat("---------------------------------------------------------------------------\n")
    cat("Correlation of the canonical pairs and hypothesis testing \n")
    cat("---------------------------------------------------------------------------\n")
    print(results)
    cat("---------------------------------------------------------------------------\n")
    cat("Canonical coefficients of the first group \n")
    cat("---------------------------------------------------------------------------\n")
    print(Coef_FG)
    cat("---------------------------------------------------------------------------\n")
    cat("Canonical coefficients of the second group \n")
    cat("---------------------------------------------------------------------------\n")
    print(Coef_SG)
    cat("---------------------------------------------------------------------------\n")
    cat("Canonical loads of the first group \n")
    cat("---------------------------------------------------------------------------\n")
    print(Rux)
    cat("---------------------------------------------------------------------------\n")
    cat("Canonical loads of the second group \n")
    cat("---------------------------------------------------------------------------\n")
    print(Rvy)
  }

  out <- list(Matrix = MC, MFG = S11, MSG = S22,
              MFG_SG = S12, Coef_FG = Coef_FG, Coef_SG = Coef_SG, Loads_FG = Rux,
              Loads_SG = Rvy, Score_FG = FG_SC, Score_SG = SG_SC, Crossload_FG = FG_CL,
              Crossload_SG = SG_CL, Sigtest = results, collinearity = colin) %>%
    add_class(class = "can_cor")
  invisible(out)
}




#' Plots an object of class can_cor
#'
#' Graphs of the Canonical Correlation Analysis
#'
#' @param x The `waasb object`
#' @param type The type of the plot. Defaults to `type = 1` (Scree-plot of
#'   the correlations of the canonical loadings). Use `type = 2`, to
#'   produce a plot with the scores of the variables in the first group,
#'   `type = 3` to produce a plot with the scores of the variables in the
#'   second group, or `type = 4` to produce a circle of correlations.
#' @param plot_theme The graphical theme of the plot. Default is
#'   `plot_theme = theme_metan()`. For more details,see
#'   [ggplot2::theme()].
#' @param size.tex.pa The size of the text of the plot area. Default is
#'   `3.5`.
#' @param size.tex.lab The size of the text in axis text and labels.
#' @param x.lab The label of x-axis. Each plot has a default value. New
#'   arguments can be inserted as `x.lab = 'my label'`.
#' @param x.lim The range of x-axis. Default is `NULL` (maximum and minimum
#'   values of the data set). New arguments can be inserted as `x.lim =
#'   c(x.min, x.max)`.
#' @param x.breaks The breaks to be plotted in the x-axis. Default is
#'   `authomatic breaks`. New arguments can be inserted as `x.breaks =
#'   c(breaks)`
#' @param y.lab The label of y-axis. Each plot has a default value. New
#'   arguments can be inserted as `y.lab = 'my label'`.
#' @param y.lim The range of y-axis. Default is `NULL`. The same arguments
#'   than `x.lim` can be used.
#' @param y.breaks The breaks to be plotted in the x-axis. Default is
#'   `authomatic breaks`. The same arguments than `x.breaks` can be
#'   used.
#' @param axis.expand Multiplication factor to expand the axis limits by to
#'   enable fitting of labels. Default is `1.1`.
#' @param shape The shape of points in the plot. Default is `21` (circle).
#'   Values must be between `21-25`: `21` (circle), `22`
#'   (square), `23` (diamond), `24` (up triangle), and `25` (low
#'   triangle).
#' @param col.shape A vector of length 2 that contains the color of shapes for
#'   genotypes above and below of the mean, respectively. Defaults to
#'   `"orange"`. `c("blue", "red")`.
#' @param col.alpha The alpha value for the color. Default is `0.9`. Values
#'   must be between `0` (full transparency) to `1` (full color).
#' @param size.shape The size of the shape in the plot. Default is `3.5`.
#' @param size.bor.tick The size of tick of shape. Default is `0.3`. The
#'   size of the shape will be `size.shape + size.bor.tick`
#' @param labels Logical arguments. If `TRUE` then the points in the plot
#'   will have labels.
#' @param main The title of the plot. Defaults to `NULL`, in which each
#'   plot will have a default title. Use a string text to create an own title or
#'   set to `main = FALSE` to omit the plot title.
#' @param ... Currently not used.
#' @return An object of class `gg, ggplot`.
#' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
#' @method plot can_cor
#' @importFrom ggforce geom_circle
#' @export
#' @examples
#' \donttest{
#' library(metan)
#' cc1 = can_corr(data_ge2,
#'                FG = c(PH, EH, EP),
#'                SG = c(EL, ED, CL, CD, CW, KW, NR))
#' plot(cc1, 2)
#'
#' cc2 <-
#' data_ge2 %>%
#' means_by(GEN) %>%
#' column_to_rownames("GEN") %>%
#' can_corr(FG = c(PH, EH, EP),
#'                SG = c(EL, ED, CL, CD, CW, KW, NR))
#' plot(cc2, 2, labels = TRUE)
#'
#'}
#'
plot.can_cor <- function(x,
                         type = 1,
                         plot_theme = theme_metan(),
                         size.tex.lab = 12,
                         size.tex.pa = 3.5,
                         x.lab = NULL,
                         x.lim = NULL,
                         x.breaks = waiver(),
                         y.lab = NULL,
                         y.lim = NULL,
                         y.breaks = waiver(),
                         axis.expand = 1.1,
                         shape = 21,
                         col.shape = "orange",
                         col.alpha = 0.9,
                         size.shape = 3.5,
                         size.bor.tick = 0.3,
                         labels = FALSE,
                         main = NULL, ...) {
  if(has_class(x, "can_cor_group")){
    stop("The object 'x' must be of class 'can_cor'.")
  }
  if(type == 1){
    data = x$Sigtest %>% mutate(CCP = 1:n())
    y.lab = ifelse(!missing(y.lab), y.lab, paste0("Explained variance"))
    x.lab = ifelse(!missing(x.lab), x.lab, paste0("Order of the canonical pairs"))
    if(!missing(main)){
      if(main == FALSE){
        main = ""
      } else{
        main = ifelse(!missing(main), main, paste0("Scree-plot of the correlations of the canonical loadings"))
      }
    }
    if (!missing(y.lim)) {
      y.lim <- y.lim
    } else {
      y.lim <- c(min(data$Var) - (min(data$Var) * axis.expand - min(data$Var)),
                 max(data$Var) + (max(data$Var) * axis.expand - max(data$Var)))
    }
    p = ggplot(data, aes(CCP, Var))+
      geom_point(size = size.shape, stroke = size.bor.tick, alpha = col.alpha) +
      scale_shape_manual(labels = "", values = shape)+
      geom_point(size = size.shape)+
      geom_line()+
      labs(x = x.lab, y = y.lab)+
      scale_y_continuous(limits = y.lim, breaks = y.breaks) +
      ggtitle(main)+
      plot_theme
  }
  if(type == 2){
    data = x$Score_FG
    y.lab = ifelse(!missing(y.lab), y.lab, paste0("Second canonical pair"))
    x.lab = ifelse(!missing(x.lab), x.lab, paste0("First canonical pair"))
    main = ifelse(!missing(main), main, paste0("Scores of the variables in the first group"))
    if(!missing(main)){
      if(main == FALSE){
        main = ""
      }
    }
    if (!missing(x.lim)) {
      x.lim <- x.lim
    } else {

      x.lim <- c(min(data$U1 * axis.expand), max(data$U1 * axis.expand))
    }
    if (!missing(y.lim)) {
      y.lim <- y.lim
    } else {
      y.lim <- c(min(data$U2 * axis.expand), max(data$U2 * axis.expand))
    }
    p = ggplot(data, aes(U1, U2, label = rownames(data)))+
      geom_hline(yintercept = 0, linetype = "dashed")+
      geom_vline(xintercept = 0, linetype = "dashed")+
      geom_point(size = size.shape, shape = shape, stroke = size.bor.tick, fill = col.shape,  alpha = col.alpha) +
      scale_y_continuous(limits = y.lim, breaks = y.breaks) +
      scale_x_continuous(limits = x.lim, breaks = x.breaks) +
      ggtitle(main)+
      labs(x = x.lab, y = y.lab)+
      plot_theme %+replace% theme(aspect.ratio = 1,
                                  axis.text = element_text(size = size.tex.lab, colour = "black"),
                                  axis.title = element_text(size = size.tex.lab, colour = "black"))
    if(labels == TRUE){
      p = p + geom_text_repel(size = size.tex.pa)
    }
  }
  if(type == 3){
    data = x$Score_SG
    y.lab = ifelse(!missing(y.lab), y.lab, paste0("Second canonical pair"))
    x.lab = ifelse(!missing(x.lab), x.lab, paste0("First canonical pair"))
    main = ifelse(!missing(main), main, paste0("Scores of the variables in the second group"))
    if(!missing(main)){
      if(main == FALSE){
        main = ""
      }
    }
    if (!missing(x.lim)) {
      x.lim <- x.lim
    } else {
      x.lim <- c(min(data$V1 * axis.expand), max(data$V1 * axis.expand))
    }
    if (!missing(y.lim)) {
      y.lim <- y.lim
    } else {
      y.lim <- c(min(data$V2 * axis.expand), max(data$V2 * axis.expand))
    }
    p = ggplot(data, aes(V1, V2, label = rownames(data)))+
      geom_hline(yintercept = 0, linetype = "dashed")+
      geom_vline(xintercept = 0, linetype = "dashed")+
      geom_point(size = size.shape, shape = shape, stroke = size.bor.tick, fill = col.shape, alpha = col.alpha) +
      scale_y_continuous(limits = y.lim, breaks = y.breaks) +
      scale_x_continuous(limits = x.lim, breaks = x.breaks) +
      ggtitle(main)+
      labs(x = x.lab, y = y.lab)+
      plot_theme %+replace% theme(aspect.ratio = 1,
                                  axis.text = element_text(size = size.tex.lab, colour = "black"),
                                  axis.title = element_text(size = size.tex.lab, colour = "black"))
    if(labels == TRUE){
      p = p + geom_text_repel(size = size.tex.pa)
    }
  }
  if(type == 4){
    y.lab = ifelse(!missing(y.lab), y.lab, paste0("Second canonical pair"))
    x.lab = ifelse(!missing(x.lab), x.lab, paste0("First canonical pair"))
    main = ifelse(!missing(main), main, paste0("Circle of correlations"))
    if(!missing(main)){
      if(main == FALSE){
        main = ""
      }
    }
    FGV = x$Loads_FG %>% as.data.frame() %>% select(1:2) %>%
      setNames(c("x", "y")) %>%
      rownames_to_column("VAR") %>%
      mutate(GROUP = "First Group")
    SGV = x$Loads_SG %>% as.data.frame() %>% select(1:2) %>%
      setNames(c("x", "y")) %>%
      rownames_to_column("VAR") %>%
      mutate(GROUP = "Second Group")
    datplot = rbind(FGV, SGV)
    p =   ggplot(datplot, aes(x, y, label = VAR))+
      geom_point(aes(color = GROUP), show.legend = FALSE)+
      geom_hline(yintercept = 0, linetype = "dashed")+
      geom_vline(xintercept = 0, linetype = "dashed")+
      scale_x_continuous(limits = c(-1, 1))+
      scale_y_continuous(limits = c(-1, 1))+
      geom_text_repel(aes(color = GROUP), show.legend = FALSE)+
      theme(aspect.ratio = 1,
            legend.position = "bottom", legend.title = element_blank())+
      geom_circle(aes(x0 = 0, y0 = 0, r = 1), inherit.aes = FALSE)+
      geom_segment(aes(x = 0, y = 0, xend = x, yend = y, color = GROUP),
                   arrow = arrow(length = unit(0.3, "cm")))+
      ggtitle(main)+
      labs(x = x.lab, y = y.lab)+
      plot_theme %+replace% theme(aspect.ratio = 1,
                                  legend.position = c(0.85, 0.06),
                                  legend.key.size = unit(1, "lines"),
                                  legend.title = element_blank(),
                                  axis.text = element_text(size = size.tex.lab, colour = "black"),
                                  axis.title = element_text(size = size.tex.lab, colour = "black"))
  }
  return(p)
}







#' Print an object of class can_cor
#'
#' Print an object of class `can_cor` object in two ways. By default, the
#' results are shown in the R console. The results can also be exported to the
#' directory.
#'
#'
#' @param x An object of class `can_cor`.
#' @param export A logical argument. If `TRUE|T`, a *.txt file is exported
#'   to the working directory
#' @param file.name The name of the file if `export = TRUE`
#' @param digits The significant digits to be shown.
#' @param ... Options used by the tibble package to format the output. See
#'   [`tibble::print()`][tibble::formatting] for more details.
#' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
#' @method print can_cor
#' @export
#' @examples
#' \donttest{
#' library(metan)
#' cc <- can_corr(data_ge2,
#'                FG = c(PH, EH, EP),
#'                SG = c(EL, CL, CD, CW, KW, NR, TKW),
#'                verbose = FALSE)
#' print(cc)
#' }
print.can_cor <- function(x, export = FALSE, file.name = NULL, digits = 3, ...) {
  if (has_class(x, "can_cor_group")) {
    stop("The object must be of class 'can_cor'")
  }
  if (export == TRUE) {
    file.name <- ifelse(is.null(file.name) == TRUE, "Canonical print", file.name)
    sink(paste0(file.name, ".txt"))
  }
  cat("---------------------------------------------------------------------------\n")
  cat("Matrix (correlation/covariance) between variables of first group (FG)\n")
  cat("---------------------------------------------------------------------------\n")
  print(x$MFG, digits = digits)
  cat("\n---------------------------------------------------------------------------\n")
  cat("Collinearity diagnostic between first group\n")
  cat("---------------------------------------------------------------------------\n")
  print(colindiag(x$MFG, n = nrow(x$Score_FG)))
  cat("\n---------------------------------------------------------------------------\n")
  cat("Matrix (correlation/covariance) between variables of second group (SG)\n")
  cat("---------------------------------------------------------------------------\n")
  print(x$MSG, digits = digits)
  cat("\n---------------------------------------------------------------------------\n")
  cat("Collinearity diagnostic between second group\n")
  cat("---------------------------------------------------------------------------\n")
  print(colindiag(x$MSG, n = nrow(x$Score_SG)))
  cat("\n---------------------------------------------------------------------------\n")
  cat("Matrix (correlation/covariance) between FG and SG)\n")
  cat("---------------------------------------------------------------------------\n")
  print(x$MFG_SG, digits = digits)
  cat("\n---------------------------------------------------------------------------\n")
  cat("Correlation of the canonical pairs and hypothesis testing \n")
  cat("---------------------------------------------------------------------------\n")
  print(x$Sigtest, digits = digits)
  cat("\n---------------------------------------------------------------------------\n")
  cat("Canonical coefficients of the first group \n")
  cat("---------------------------------------------------------------------------\n")
  print(x$Coef_FG, digits = digits)
  cat("\n---------------------------------------------------------------------------\n")
  cat("Canonical coefficients of the second group \n")
  cat("---------------------------------------------------------------------------\n")
  print(x$Coef_SG, digits = digits)
  if (export == TRUE) {
    sink()
  }
}

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metan documentation built on Nov. 10, 2021, 9:11 a.m.