#' Interpret of CFA / SEM Indices of Goodness of Fit
#'
#' Interpretation of indices of fit found in confirmatory analysis or structural
#' equation modelling, such as RMSEA, CFI, NFI, IFI, etc.
#'
#' @param x vector of values, or an object of class `lavaan`.
#' @param rules Can be the name of a set of rules (see below) or custom set of
#'   [rules()].
#' @inheritParams interpret
#'
#' @inherit performance::model_performance.lavaan details
#' @inherit performance::model_performance.lavaan references
#'
#' @details
#' ## Indices of fit
#' - **Chisq**: The model Chi-squared assesses overall fit and the discrepancy
#' between the sample and fitted covariance matrices. Its p-value should be >
#' .05 (i.e., the hypothesis of a perfect fit cannot be rejected). However, it
#' is quite sensitive to sample size.
#'
#' - **GFI/AGFI**: The (Adjusted) Goodness of Fit is the proportion of variance
#' accounted for by the estimated population covariance. Analogous to R2. The
#' GFI and the AGFI should be > .95 and > .90, respectively (Byrne, 1994;
#' `"byrne1994"`).
#'
#' - **NFI/NNFI/TLI**: The (Non) Normed Fit Index. An NFI of 0.95, indicates the
#' model of interest improves the fit by 95\% relative to the null model. The
#' NNFI (also called the Tucker Lewis index; TLI) is preferable for smaller
#' samples. They should be > .90 (Byrne, 1994; `"byrne1994"`) or > .95
#' (Schumacker & Lomax, 2004; `"schumacker2004"`).
#'
#' - **CFI**: The Comparative Fit Index is a revised form of NFI. Not very
#' sensitive to sample size (Fan, Thompson, & Wang, 1999). Compares the fit of a
#' target model to the fit of an independent, or null, model. It should be > .96
#' (Hu & Bentler, 1999; `"hu&bentler1999"`) or .90 (Byrne, 1994; `"byrne1994"`).
#'
#' - **RFI**: the Relative Fit Index, also known as RHO1, is not guaranteed to
#' vary from 0 to 1. However, RFI close to 1 indicates a good fit.
#'
#' - **IFI**: the Incremental Fit Index (IFI) adjusts the Normed Fit Index (NFI)
#' for sample size and degrees of freedom (Bollen's, 1989). Over 0.90 is a good
#' fit, but the index can exceed 1.
#'
#' - **PNFI**: the Parsimony-Adjusted Measures Index. There is no commonly
#' agreed-upon cutoff value for an acceptable model for this index. Should be >
#' 0.50.
#'
#' - **RMSEA**: The Root Mean Square Error of Approximation is a
#' parsimony-adjusted index. Values closer to 0 represent a good fit. It should
#' be < .08 (Awang, 2012; `"awang2012"`) or < .05 (Byrne, 1994; `"byrne1994"`).
#' The p-value printed with it tests the hypothesis that RMSEA is less than or
#' equal to .05 (a cutoff sometimes used for good fit), and thus should be not
#' significant.
#'
#' - **RMR/SRMR**: the (Standardized) Root Mean Square Residual represents the
#' square-root of the difference between the residuals of the sample covariance
#' matrix and the hypothesized model. As the RMR can be sometimes hard to
#' interpret, better to use SRMR. Should be < .08 (Byrne, 1994; `"byrne1994"`).
#'
#' See the documentation for \code{\link[lavaan:fitmeasures]{fitmeasures()}}.
#'
#'
#' ## What to report
#' For structural equation models (SEM), Kline (2015) suggests that at a minimum
#' the following indices should be reported: The model **chi-square**, the
#' **RMSEA**, the **CFI** and the **SRMR**.
#'
#' @note When possible, it is recommended to report dynamic cutoffs of fit
#'   indices. See https://dynamicfit.app/cfa/.
#'
#'
#' @examples
#' interpret_gfi(c(.5, .99))
#' interpret_agfi(c(.5, .99))
#' interpret_nfi(c(.5, .99))
#' interpret_nnfi(c(.5, .99))
#' interpret_cfi(c(.5, .99))
#' interpret_rmsea(c(.07, .04))
#' interpret_srmr(c(.5, .99))
#' interpret_rfi(c(.5, .99))
#' interpret_ifi(c(.5, .99))
#' interpret_pnfi(c(.5, .99))
#'
#' @examplesIf require("lavaan") && interactive()
#' # Structural Equation Models (SEM)
#' structure <- " ind60 =~ x1 + x2 + x3
#'                dem60 =~ y1 + y2 + y3
#'                dem60 ~ ind60 "
#'
#' model <- lavaan::sem(structure, data = lavaan::PoliticalDemocracy)
#'
#' interpret(model)
#'
#' @references
#' - Awang, Z. (2012). A handbook on SEM. Structural equation modeling.
#'
#' - Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/Windows.
#'   Thousand Oaks, CA: Sage Publications.
#'
#' - Fan, X., B. Thompson, and L. Wang (1999). Effects of sample size,
#'   estimation method, and model specification on structural equation modeling
#'   fit indexes. Structural Equation Modeling, 6, 56-83.
#'
#' - Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in
#'   covariance structure analysis: Conventional criteria versus new
#'   alternatives. Structural equation modeling: a multidisciplinary journal,
#'   6(1), 1-55.
#'
#' - Kline, R. B. (2015). Principles and practice of structural equation
#'   modeling. Guilford publications.
#'
#' - Schumacker, R. E., and Lomax, R. G. (2004). A beginner's guide to
#'   structural equation modeling, Second edition. Mahwah, NJ: Lawrence Erlbaum
#'   Associates.
#'
#' - Tucker, L. R., and Lewis, C. (1973). The reliability coefficient for
#'   maximum likelihood factor analysis. Psychometrika, 38, 1-10.
#'
#'
#' @keywords interpreters
#' @export
interpret_gfi <- function(x, rules = "byrne1994") {
  rules <- .match.rules(
    rules,
    list(
      byrne1994 = rules(c(0.95), c("poor", "satisfactory"), name = "byrne1994", right = FALSE)
    )
  )

  interpret(x, rules)
}


#' @rdname interpret_gfi
#' @export
interpret_agfi <- function(x, rules = "byrne1994") {
  rules <- .match.rules(
    rules,
    list(
      byrne1994 = rules(c(0.90), c("poor", "satisfactory"), name = "byrne1994", right = FALSE)
    )
  )

  interpret(x, rules)
}


#' @rdname interpret_gfi
#' @export
interpret_nfi <- function(x, rules = "byrne1994") {
  rules <- .match.rules(
    rules,
    list(
      byrne1994 = rules(c(0.90), c("poor", "satisfactory"), name = "byrne1994", right = FALSE),
      schumacker2004 = rules(c(0.95), c("poor", "satisfactory"), name = "schumacker2004", right = FALSE)
    )
  )

  interpret(x, rules)
}

#' @rdname interpret_gfi
#' @export
interpret_nnfi <- interpret_nfi


#' @rdname interpret_gfi
#' @export
interpret_cfi <- function(x, rules = "byrne1994") {
  rules <- .match.rules(
    rules,
    list(
      "hu&bentler1999" = rules(c(0.96), c("poor", "satisfactory"), name = "hu&bentler1999", right = FALSE),
      "byrne1994" = rules(c(0.90), c("poor", "satisfactory"), name = "byrne1994", right = FALSE)
    )
  )

  interpret(x, rules)
}

#' @rdname interpret_gfi
#' @export
interpret_rfi <- function(x, rules = "default") {
  rules <- .match.rules(
    rules,
    list(
      default = rules(c(0.90), c("poor", "satisfactory"), name = "default", right = FALSE)
    )
  )

  interpret(x, rules)
}

#' @rdname interpret_gfi
#' @export
interpret_ifi <- function(x, rules = "default") {
  rules <- .match.rules(
    rules,
    list(
      default = rules(c(0.90), c("poor", "satisfactory"), name = "default", right = FALSE)
    )
  )

  interpret(x, rules)
}

#' @rdname interpret_gfi
#' @export
interpret_pnfi <- function(x, rules = "default") {
  rules <- .match.rules(
    rules,
    list(
      default = rules(c(0.50), c("poor", "satisfactory"), name = "default")
    )
  )

  interpret(x, rules)
}

#' @rdname interpret_gfi
#' @export
interpret_rmsea <- function(x, rules = "byrne1994") {
  rules <- .match.rules(
    rules,
    list(
      byrne1994 = rules(c(0.05), c("satisfactory", "poor"), name = "byrne1994"),
      awang2012 = rules(c(0.05, 0.08), c("good", "satisfactory", "poor"), name = "awang2012")
    )
  )

  interpret(x, rules)
}


#' @rdname interpret_gfi
#' @export
interpret_srmr <- function(x, rules = "byrne1994") {
  rules <- .match.rules(
    rules,
    list(
      byrne1994 = rules(c(0.08), c("satisfactory", "poor"), name = "byrne1994")
    )
  )

  interpret(x, rules)
}

# lavaan ------------------------------------------------------------------

#' @rdname interpret_gfi
#' @export
interpret.lavaan <- function(x, ...) {
  interpret(performance::model_performance(x, ...), ...)
}

#' @rdname interpret_gfi
#' @export
interpret.performance_lavaan <- function(x, ...) {
  mfits <- c(
    "GFI", "AGFI", "NFI", "NNFI",
    "CFI", "RMSEA", "SRMR", "RFI",
    "IFI", "PNFI"
  )
  mfits <- intersect(names(x), mfits)

  table <- lapply(mfits, function(ind_name) {
    .interpret_ind <- eval(parse(text = paste0("interpret_", tolower(ind_name))))
    interp <- .interpret_ind(x[[ind_name]])
    rules <- attr(interp, "rules")
    data.frame(
      Name = ind_name,
      Value = x[[ind_name]],
      Threshold = rules$values,
      Interpretation = interp,
      stringsAsFactors = FALSE
    )
  })

  do.call(rbind, table)
}
