R/show-methods.R

In MendelianRandomization: Mendelian Randomization Package

setMethod("show",
          "MRInput",
          function(object){

            exposure <- object@exposure
            outcome <- object@outcome
            snps <- object@snps

            Bx = object@betaX
            By = object@betaY
            Bxse = object@betaXse
            Byse = object@betaYse

            if("snp" %in% snps) {
              snps <- paste("snp", 1:length(Bx), sep = "_")
            } else {
              snps <- snps
            }

            betaDF <- data.frame(snps, Bx, Bxse, By, Byse)

            colnames(betaDF) <- c("SNP",
                                  paste(exposure, ".beta", sep = ""),
                                  paste(exposure, ".se", sep = ""),
                                  paste(outcome, ".beta", sep = ""),
                                  paste(outcome, ".se", sep = ""))

            print(betaDF, digits = 3)
          })

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MRMVInput",
          function(object){

            exposure <- object@exposure
            outcome <- object@outcome
            snps <- object@snps

            Bx = object@betaX
            By = object@betaY
            Bxse = object@betaXse
            Byse = object@betaYse

            betaDF <- data.frame(snps, Bx, Bxse, By, Byse)

            colnames(betaDF) <- c("SNP",
                                  paste(exposure, ".beta", sep = ""),
                                  paste(exposure, ".se", sep = ""),
                                  paste(outcome, ".beta", sep = ""),
                                  paste(outcome, ".se", sep = ""))

            print(betaDF, digits = 3)
          })

#--------------------------------------------------------------------------------------------

setMethod("show",
          "WeightedMedian",
          function(object){

            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Method", "Estimate", "Std Error", Interval_type, "", "p-value")

            Median_type <- paste(simpleCap(object@Type), " median method", sep = "")

            Value <- c(Median_type,
                       decimals(object@Estimate,3),
                       decimals(object@StdError,3),
                       paste(decimals(object@CILower, 3), ",", sep = ""),
                       decimals(object@CIUpper,3),
                       decimals(object@Pvalue, 3))

            output.table <- data.frame(matrix(Value, nrow = 1))
            colnames(output.table) <- Statistic

            cat("\n",Median_type, "\n\n")
            cat("Number of Variants :", object@SNPs, "\n")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify= "left")
            cat("------------------------------------------------------------------\n")
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "DIVW",
          function(object){
            
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Method", "Estimate", "Std Error", Interval_type, "", "p-value","Condition")
            
            Value <- c("dIVW",
                       decimals(object@Estimate,3),
                       decimals(object@StdError,3),
                       paste(decimals(object@CILower, 3), ",", sep = ""),
                       decimals(object@CIUpper,3),
                       decimals(object@Pvalue, 3),
                       decimals(object@Condition, 3))
            
            output.table <- data.frame(matrix(Value, nrow = 1))
            colnames(output.table) <- Statistic
            
            cat("\nDebiased inverse-variance weighted method\n")
            cat("(Over.dispersion:", object@Over.dispersion, ")\n\n",sep="")
            
            cat("Number of Variants :", object@SNPs, "\n")
            
            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify= "left")
            cat("------------------------------------------------------------------\n")
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "IVW",
          function(object){

            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Method", "Estimate", "Std Error", Interval_type, "", "p-value")

            Value <- c("IVW", decimals(c(object@Estimate, object@StdError),3),
                       paste(decimals(object@CILower, 3), ",", sep = ""), decimals(c(object@CIUpper, object@Pvalue), 3))

            output.table <- data.frame(matrix(Value, nrow = 1))
            colnames(output.table) <- Statistic
            correlation <- ifelse(sum(is.na(object@Correlation)) == 0,
                                  "correlated", "uncorrelated")
            penalized <- ifelse(object@Penalized == TRUE, "Weights of genetic variants with heterogeneous causal estimates have been penalized. ", "")
            robust <- ifelse(object@Robust == TRUE, "Robust regression used.", "")

            cat("\nInverse-variance weighted method\n")
            cat("(variants ", correlation, ", ", object@Model, "-effect model)\n\n" , sep = "")

            cat("Number of Variants :", object@SNPs, "\n")
            cat(penalized, robust, "\n", sep = "")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")

            cat("Residual standard error = ", decimals(object@RSE, 3), "\n")
            if(object@Model == "fixed") { cat("Residual standard error is set to 1 in calculation of confidence interval by fixed-effect assumption.\n") }
            if(object@RSE<1) { cat("Residual standard error is set to 1 in calculation of confidence interval when its estimate is less than 1.\n") }
            if(is.na(object@Heter.Stat[1])) {
              cat("Heterogeneity is not calculated when weights are penalized, or when there is only one variant in the analysis.\n")
            } else {
            cat("Heterogeneity test statistic (Cochran's Q) = ", decimals(object@Heter.Stat[1],4), " on ", object@SNPs -1,
                    " degrees of freedom, (p-value = ", decimals(object@Heter.Stat[2], 4),"). I^2 = ", decimals(max(0, (object@Heter.Stat[1]-object@SNPs+1)/object@Heter.Stat[1]*100),1),    "%. \n", sep = "")
            }
            cat("F statistic = ", decimals(object@Fstat, 1), ". \n", sep="")
 if (sum(is.na(object@Correlation))==0) { cat("\n(Estimates with correlated variants are sensitive to the signs in the correlation matrix\n - please ensure that your correlations are expressed with respect to the same effect alleles as your summarized association estimates.) \n") }
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MaxLik",
          function(object){

            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Method", "Estimate", "Std Error", Interval_type, "", "p-value")

            Value <- c("MaxLik", decimals(c(object@Estimate, object@StdError),3),
                       paste(decimals(object@CILower, 3), ",", sep = ""), decimals(c(object@CIUpper, object@Pvalue), 3))

            output.table <- data.frame(matrix(Value, nrow = 1))
            colnames(output.table) <- Statistic
            correlation <- ifelse(sum(is.na(object@Correlation)) == 0,
                                  "correlated", "uncorrelated")

            cat("\nMaximum-likelihood method\n")
            cat("(variants ", correlation, ", ", object@Model, "-effect model)\n\n" , sep = "")

            cat("Number of Variants :", object@SNPs, "\n")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")

            cat("Residual standard error = ", decimals(object@RSE, 3), "\n")
            if(object@Model == "fixed") { cat("Residual standard error is set to 1 in calculation of confidence interval by fixed-effect assumption.\n") }
            if(object@RSE<1) { cat("Residual standard error is set to 1 in calculation of confidence interval when its estimate is less than 1.\n") }
            if(object@Heter.Stat[1] < 1e-16) {
              cat("Heterogeneity is not calculated when there is only one variant in the analysis.")
            } else {
            cat("Heterogeneity test statistic = ", decimals(object@Heter.Stat[1],4), " on ", object@SNPs -1,
                    " degrees of freedom, (p-value = ", decimals(object@Heter.Stat[2], 4),")\n", sep = "")
            }
          }
)


#--------------------------------------------------------------------------------------------

setMethod("show",
          "MRMBE",
          function(object){

            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Method", "Estimate", "Std Error", Interval_type, "", "p-value")

            Value <- c("MBE", decimals(c(object@Estimate, object@StdError),3),
                       paste(decimals(object@CILower, 3), ",", sep = ""), decimals(c(object@CIUpper, object@Pvalue), 3))

            output.table <- data.frame(matrix(Value, nrow = 1))
            colnames(output.table) <- Statistic
if(object@StdErr=="simple") { nome <- "[assuming NOME]" }
if(object@StdErr=="delta")  { nome <- "[not assuming NOME]" }

            cat("\nMode-based method of Hartwig et al\n")
            cat("(", object@Weighting, ", ", object@StdErr, " standard errors ", nome, ", bandwidth factor = ", object@Phi, ")\n\n" , sep = "")
            cat("Number of Variants :", object@SNPs, "\n")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")

            }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MRHetPen",
          function(object){

          if (object@CIMax%in%object@CIRange & object@CIMin%in%object@CIRange) {
   cat("Confidence interval range too narrow. Please decrease CIMin and increase CIMax and try again.") }
         else if (object@CIMax>max(object@CIRange) & object@CIMin%in%object@CIRange) {
   cat("Lower bound of confidence interval range too high. Please decrease CIMin and try again.") }
          if (object@CIMax%in%object@CIRange & object@CIMin<min(object@CIRange)) {
   cat("Upper bound of confidence interval range too low. Please increase CIMax and try again.") }
          if (object@CIMax>max(object@CIRange) & object@CIMin<min(object@CIRange)) {
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Method", "Estimate", Interval_type, "")
            dps = max(ceiling(-log10(object@CIStep)), 1)
            Ranges <- ifelse(sum(diff(object@CIRange)>1.01*object@CIStep)==0, "Single range", "Multiple ranges");

if (Ranges == "Single range") {
            Value <- c("HetPen", decimals(object@Estimate, dps), 
                       paste(decimals(min(object@CIRange), dps), ",", sep = ""), decimals(max(object@CIRange), dps))
            output.table <- data.frame(matrix(Value, nrow = 1))
            colnames(output.table) <- Statistic
            Ranges.text <- "Note: confidence interval is a single range of values.\n"
 }

if (Ranges == "Multiple ranges") {
            Value <- c("HetPen", rep("", length(object@CILower)-1),
                       decimals(object@Estimate, dps), rep("", length(object@CILower)-1), 
                       paste(decimals(object@CILower, dps), ",", sep = ""), decimals(object@CIUpper, dps))
            output.table <- data.frame(matrix(Value, nrow = length(object@CILower), byrow=FALSE))
            colnames(output.table) <- Statistic
            Ranges.text <- "Note: confidence interval contains multiple ranges of values.\n"
 }

 
            cat("\nHeterogeneity-penalized method\n")
            cat("(Prior probability of instrument validity = ", object@Prior, ")\n\n" , sep = "")
            cat("Number of Variants :", object@SNPs, "\n")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")
            cat(Ranges.text)
            }
      }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MRConMix",
          function(object){

          if (object@CIMax%in%object@CIRange & object@CIMin%in%object@CIRange) {
   cat("Confidence interval range too narrow. Please decrease CIMin and increase CIMax and try again.") }
         else if (object@CIMax>max(object@CIRange) & object@CIMin%in%object@CIRange) {
   cat("Lower bound of confidence interval range too high. Please decrease CIMin and try again.") }
          if (object@CIMax%in%object@CIRange & object@CIMin<min(object@CIRange)) {
   cat("Upper bound of confidence interval range too low. Please increase CIMax and try again.") }
          if (object@CIMax>max(object@CIRange) & object@CIMin<min(object@CIRange)) {
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")

            dps = max(ceiling(-log10(object@CIStep)), 1)
            Ranges <- ifelse(sum(diff(object@CIRange)>1.01*object@CIStep)==0, "Single range", "Multiple ranges");

if (Ranges == "Single range") {
            Statistic <- c("Method", "Estimate", Interval_type, "", "p-value")
            Value <- c("ConMix", decimals(object@Estimate, dps), 
                       paste(decimals(min(object@CIRange), dps), ",", sep = ""), decimals(max(object@CIRange), dps), signif(object@Pvalue, 3))
            output.table <- data.frame(matrix(Value, nrow = 1))
            colnames(output.table) <- Statistic
            Ranges.text <- "Note: confidence interval is a single range of values.\n"
 }

if (Ranges == "Multiple ranges") {
            Statistic <- c("Method", "Estimate", Interval_type, "")
            Value <- c("ConMix", rep("", length(object@CILower)-1),
                       decimals(object@Estimate, dps), rep("", length(object@CILower)-1), 
                       paste(decimals(object@CILower, dps), ",", sep = ""), decimals(object@CIUpper, dps))
            output.table <- data.frame(matrix(Value, nrow = length(object@CILower), byrow=FALSE))
            colnames(output.table) <- Statistic
            Ranges.text <- "Note: confidence interval contains multiple ranges of values.\n"
 }

 
            cat("\nContamination mixture method\n")
            cat("(Standard deviation of invalid estimands = ", object@Psi, ")\n\n" , sep = "")
            cat("Number of Variants :", object@SNPs, "\n")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")
            cat(Ranges.text)
            }
      }
)

#--------------------------------------------------------------------------------------------


setMethod("show",
          "Egger",
          function(object){
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Method", "Estimate", "Std Error", Interval_type, "", "p-value")

            Value <- c("MR-Egger", decimals(c(object@Estimate,
                                              object@StdError.Est), 3),
                               paste(decimals(object@CILower.Est, 3), ",", sep = ""),
                                   decimals(c(object@CIUpper.Est,
                                              object@Pvalue.Est), 3),

                       "(intercept)", decimals(c(object@Intercept,
                                                 object@StdError.Int), 3),
paste(decimals(object@CILower.Int, 3), ",", sep = ""),
                                                 
                                      decimals(c(object@CIUpper.Int,
                                                 object@Pvalue.Int), 3))

            output.table <- data.frame(matrix(Value, nrow = 2, byrow = T))
            colnames(output.table) <- Statistic

            correlation <- ifelse(sum(is.na(object@Correlation)) == 0, "correlated", "uncorrelated")
            penalized <- ifelse(object@Penalized == TRUE, "Weights of genetic variants with heterogeneous causal estimates have been penalized. ", "")
            robust <- ifelse(object@Robust == TRUE, "Robust regression used.", "")

            cat("\nMR-Egger method\n")
            cat("(variants ", correlation, ", ", object@Model, "-effect model)\n\n" , sep = "")

            cat("Number of Variants = ", object@SNPs, "\n")
            cat(penalized, robust, "\n", sep = "")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify= "left")
            cat("------------------------------------------------------------------\n")

            cat("Residual Standard Error : ", decimals(object@RSE, 3), "\n")
            if(object@RSE<1) { cat("Residual standard error is set to 1 in calculation of confidence interval when its estimate is less than 1.\n") }
            if(is.na(object@Heter.Stat[1])) {
              cat("Heterogeneity not calculated when weights are penalized.\n")
            } else {
              cat("Heterogeneity test statistic = ", decimals(object@Heter.Stat[1],4), " on ", object@SNPs - 2,
                  " degrees of freedom, (p-value = ", decimals(object@Heter.Stat[2], 4),")\n", sep = "")
            }
              if(!is.nan(object@I.sq)) {
cat("I^2_GX statistic: ", decimals(object@I.sq*100, 1), "%\n", sep="") }
          }
)
#--------------------------------------------------------------------------------------------

setMethod("show",
          "MRAll",
          function(object){

            df <- slot(object, "Values")
            df[,2:6] <- decimals(df[,2:6], 3)

            space <- rep("", 6)

            if(object@Method == "all"){
              df <- rbind(df[1:3,],
                          space,
                          df[4:7,],
                          space,
                          df[8:15,])
            } else {
              df <- df
            }

            print(df, justify = "left", row.names = FALSE)
          }
)

#--------------------------------------------------------------------------------------------

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MVIVW",
          function(object){

            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            
            if(sum(is.na(object@CondFstat)) == 0){
            Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value", "Cond F-stat")
            
            Value <- cbind(object@Exposure, decimals(object@Estimate, 3), decimals(object@StdError,3),
                            paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                            decimals(object@Pvalue, 3), decimals(object@CondFstat, 1))
            }
            
            if(sum(is.na(object@CondFstat)) > 0){
            Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value")

            Value <- cbind(object@Exposure, decimals(object@Estimate, 3), decimals(object@StdError,3),
                       paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                             decimals(object@Pvalue, 3))
            }
            
            output.table <- data.frame(matrix(Value, nrow = length(object@Exposure)))
            colnames(output.table) <- Statistic
            
            correlation <- ifelse(sum(is.na(object@Correlation)) == 0,
                                  "correlated", "uncorrelated")
            robust <- ifelse(object@Robust == TRUE, "Robust regression used.", "")


            cat("\nMultivariable inverse-variance weighted method\n")
            cat("(variants ", correlation, ", ", object@Model, "-effect model)\n\n" , sep = "")

            cat("Number of Variants :", object@SNPs, "\n")
            cat(robust, "\n", sep = "")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")

            cat("Residual standard error = ", decimals(object@RSE, 3), "\n")
            if(object@Model == "fixed") { cat("Residual standard error is set to 1 in calculation of confidence interval by fixed-effect assumption.\n") }
            if(object@RSE<1) { cat("Residual standard error is set to 1 in calculation of confidence interval when its estimate is less than 1.\n") }
            if(is.na(object@Heter.Stat[1])) {
              cat("Heterogeneity is not calculated when weights are penalized, or when there is only one variant in the analysis.")
            } else {
            cat("Heterogeneity test statistic = ", decimals(object@Heter.Stat[1],4), " on ", object@SNPs-length(object@Exposure),
                    " degrees of freedom, (p-value = ", decimals(object@Heter.Stat[2], 4),")\n", sep = "")
            }
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MVEgger",
          function(object){

            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value")

            Value <- cbind(c(object@Exposure, "(intercept)"), decimals(c(object@Estimate, object@Intercept), 3), decimals(c(object@StdError.Est, object@StdError.Int),3),
                       paste(decimals(c(object@CILower.Est, object@CILower.Int), 3), ",", sep = ""), decimals(c(object@CIUpper.Est, object@CIUpper.Int),3),
                             decimals(c(object@Pvalue.Est, object@Pvalue.Int), 3))

            output.table <- data.frame(matrix(Value, nrow = length(object@Exposure)+1))
            colnames(output.table) <- Statistic
            correlation <- ifelse(sum(is.na(object@Correlation)) == 0,
                                  "correlated", "uncorrelated")

            cat("\nMultivariable MR-Egger method\n")
            cat("(variants ", correlation, ", ", object@Model, "-effect model)\n\n" , sep = "")
            cat("Orientated to exposure :", object@Orientate, "\n")
            cat("Number of Variants :", object@SNPs, "\n")

            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")

            cat("Residual standard error = ", decimals(object@RSE, 3), "\n")
            if(object@RSE<1) { cat("Residual standard error is set to 1 in calculation of confidence interval when its estimate is less than 1.\n") }
            cat("Heterogeneity test statistic = ", decimals(object@Heter.Stat[1],4), " on ", object@SNPs-length(object@Exposure)-1,
                    " degrees of freedom, (p-value = ", decimals(object@Heter.Stat[2], 4),")\n", sep = "")
          }
)


#--------------------------------------------------------------------------------------------

setMethod("show",
          "MVMedian",
          function(object){
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value")
            
            Value <- cbind(object@Exposure, decimals(object@Estimate, 3), decimals(object@StdError,3),
                           paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                           decimals(object@Pvalue, 3))
            output.table <- data.frame(matrix(Value, nrow = length(object@Exposure)))
            colnames(output.table) <- Statistic
            
            cat("\nMultivariable median method \n\n")
            cat("Number of variants :", object@SNPs, "\n")
            
            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")
            
          }
         )

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MVLasso",
          function(object){
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value")
            
            Value <- cbind(object@Exposure, decimals(object@Estimate, 3), decimals(object@StdError,3),
                           paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                           decimals(object@Pvalue, 3))
            output.table <- data.frame(matrix(Value, nrow = length(object@Exposure)))
            colnames(output.table) <- Statistic
            
            cat("\nMultivariable MR-Lasso method \n\n")
            cat("Orientated to exposure :", object@Orientate, "\n")
            cat("Number of variants :", object@SNPs, "\n")
            cat("Number of valid instruments :", object@Valid, "\n")
            cat("Tuning parameter :", object@Lambda, "\n")            
            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")
            
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MRLasso",
          function(object){
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value")
            
            Value <- cbind(object@Exposure, decimals(object@Estimate, 3), decimals(object@StdError,3),
                           paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                           decimals(object@Pvalue, 3))
            output.table <- data.frame(matrix(Value, nrow = length(object@Exposure)))
            colnames(output.table) <- Statistic
            
            cat("\nMR-Lasso method \n\n")
            cat("Number of variants :", object@SNPs, "\n")
            cat("Number of valid instruments :", object@Valid, "\n")
            cat("Tuning parameter :", object@Lambda, "\n")            
            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")
            
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MRcML",
          function(object){
            
            if(object@MA)
            {
              if(object@DP)
              {
                Method = "cML-MA-BIC-DP"
              } else{Method = "cML-MA-BIC"}
            } else{
              if(object@DP)
              {
                Method = "cML-BIC-DP"
              } else{Method = "cML-BIC"}
            }
            
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            
            output.table = 
              data.frame(Method = Method,
                         Estimate = decimals(object@Estimate,3),
                         SE = decimals(object@StdError,3),
                         Pvalue = decimals(object@Pvalue,3),
                         CI = paste("[",decimals(object@CILower,3),
                                    ",",decimals(object@CIUpper,3),"]",sep="")
                         )
            colnames(output.table)[5] = Interval_type

            
            cat("\nConstrained maximum likelihood method (MRcML) \n")
            cat("Number of Variants: ", object@SNPs, "\n")
            cat("Results for: ",output.table$Method, "\n")
            
            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "PIVW",
          function(object){

             Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
             Statistic <- c("Method", "Estimate", "Std Error", Interval_type, "", "p-value","Condition")

             blk = rep("", length(object@CILower)-1)
             Value <- c("pIVW", blk,
                        decimals(object@Estimate, 3), blk,
                        decimals(object@StdError,3), blk,
                        paste(decimals(object@CILower, 3), ",", sep = ""),
                        decimals(object@CIUpper, 3),
                        decimals(object@Pvalue, 3), blk,
                        decimals(object@Condition, 3), blk)

             output.table <- data.frame(matrix(Value, nrow = length(object@CILower), byrow=FALSE))
             colnames(output.table) <- Statistic

             cat("\nPenalized inverse-variance weighted method\n\n")
             cat("Over dispersion:", object@Over.dispersion, "\n")
             cat("Bootstrapping Fieller:", object@Boot.Fieller, "\n")
             cat("Penalty parameter (lambda):", object@Lambda, "\n")
             cat("IV selection threshold (delta):", round(object@Delta,3), "\n")
             cat("Number of variants :", object@SNPs, "\n")


             cat("------------------------------------------------------------------\n")
             print(output.table, quote = F, row.names = FALSE, justify= "left")
             cat("------------------------------------------------------------------\n")
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MVMRcML",
          function(object){
            if(object@DP){
              Method = 'MVMRcML-DP'
              n_eff_B = object@eff_DP_B
            }else{
              Method = 'MVMRcML-BIC'
            }
            Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
            Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value")
            
            Value <- cbind(object@Exposure, decimals(object@Estimate, 3), decimals(object@StdError,3),
                           paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                           decimals(object@Pvalue, 3))
            output.table <- data.frame(matrix(Value, nrow = length(object@Exposure)))
            colnames(output.table) <- Statistic
            
            cat("\nMultivariable MRcML method \n\n")
            cat("Number of variants :", object@SNPs, "\n")
            cat("Results for: ",Method, "\n")
            if(object@DP){
              cat("Number of data perturbations with successful convergence: ",n_eff_B, "\n")
            }
            cat("------------------------------------------------------------------\n")
            print(output.table, quote = F, row.names = FALSE, justify = "left")
            cat("------------------------------------------------------------------\n")
            
          }
         )

#--------------------------------------------------------------------------------------------

setMethod("show",
          "PCGMM",
          function(object){
             
             Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
             Statistic <- c("Method", "Estimate", "Std Error", Interval_type, "", "p-value", "F-stat")
             
             Value <- c("PC-GMM", decimals(object@Estimate, 3), decimals(object@StdError,3),
                            paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                            decimals(object@Pvalue, 3), decimals(object@Fstat, 1))
             
             output.table <- data.frame(matrix(Value, nrow = 1))
             colnames(output.table) <- Statistic
             robust1 <- ifelse(object@robust == TRUE, "\nRobust model with overdispersion heterogeneity.", "\nNon-robust model with no overdispersion heterogeneity.")
             
             
             cat("\nUnivariable principal components generalized method of moments (PC-GMM) method\n")
             
             cat("\nNumber of principal components used :", object@PCs, "\n")
             cat(robust1, "\n", sep = "")
             
             cat("\n------------------------------------------------------------------\n")
             print(output.table, quote = F, row.names = FALSE, justify = "left")
             cat("------------------------------------------------------------------\n")
             
             if(object@robust == TRUE) { cat("\nOverdispersion heterogeneity parameter estimate =", object@Overdispersion, "\n") }
             if(object@robust == FALSE) { cat("\nHeterogeneity test statistic = ", decimals(object@Heter.Stat[1],4), " on ", object@PCs-length(object@Exposure),
                                              " degrees of freedom, (p-value = ", decimals(object@Heter.Stat[2], 4),")\n", sep = "")}
             if(object@robust == TRUE) { cat("\nHeterogeneity test statistic = ", decimals(object@Heter.Stat[1],4),"\n", sep = "")}
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MVPCGMM",
          function(object){
             
             Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
             Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value", "Cond F-stat")
             
             Value <- cbind(object@Exposure, decimals(object@Estimate, 3), decimals(object@StdError,3),
                            paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                            decimals(object@Pvalue, 3), decimals(object@CondFstat, 1))
             
             output.table <- data.frame(matrix(Value, nrow = length(object@Exposure)))
             colnames(output.table) <- Statistic
             correlation <- ifelse(sum(is.na(object@ExpCorrelation)) == 0,
                                   "correlated exposures", "uncorrelated exposures")
             robust1 <- ifelse(object@robust == TRUE, "\nRobust model with overdispersion heterogeneity.", "\nNon-robust model with no overdispersion heterogeneity.")
             
             
             cat("\nMultivariable principal components generalized method of moments (PC-GMM) method\n")
             
             if(object@ExpCorrelation == FALSE){cat("\nExposure correlation matrix not specified. Exposures are assumed to be uncorrelated.\n")}
             
             cat("\nNumber of principal components used :", object@PCs, "\n")
             cat(robust1, "\n", sep = "")
             
             cat("\n------------------------------------------------------------------\n")
             print(output.table, quote = F, row.names = FALSE, justify = "left")
             cat("------------------------------------------------------------------\n")
             
             if(object@robust == TRUE) { cat("\nOverdispersion heterogeneity parameter estimate =", decimals(object@Overdispersion,4), "\n") }
             if(object@robust == FALSE) { cat("\nHeterogeneity test statistic = ", decimals(object@Heter.Stat[1],4), " on ", object@PCs-length(object@Exposure),
                                                            " degrees of freedom, (p-value = ", decimals(object@Heter.Stat[2], 4),")\n", sep = "")}
             if(object@robust == TRUE) { cat("\nHeterogeneity test statistic = ", decimals(object@Heter.Stat[1],4),"\n", sep = "")}
          }
)

#--------------------------------------------------------------------------------------------

setMethod("show",
          "MVGMM",
          function(object){
             
             Interval_type <- paste(100*(1-object@Alpha), "% CI", sep = "")
             Statistic <- c("Exposure", "Estimate", "Std Error", Interval_type, "", "p-value", "Cond F-stat")
             
             Value <- cbind(object@Exposure, decimals(object@Estimate, 3), decimals(object@StdError,3),
                            paste(decimals(object@CILower, 3), ",", sep = ""), decimals(object@CIUpper,3),
                            decimals(object@Pvalue, 3), decimals(object@CondFstat, 1))
             
             output.table <- data.frame(matrix(Value, nrow = length(object@Exposure)))
             colnames(output.table) <- Statistic
             correlation <- ifelse(sum(is.na(object@ExpCorrelation)) == 0,
                                   "correlated exposures", "uncorrelated exposures")
             robust1 <- ifelse(object@robust == TRUE, "\nRobust model with overdispersion heterogeneity.", "\nNon-robust model with no overdispersion heterogeneity.")
             
             
             cat("\nMultivariable generalized method of moments (GMM) method\n")
             
             if(object@ExpCorrelation == FALSE){cat("\nExposure correlation matrix not specified. Exposures are assumed to be uncorrelated.\n")}
             
             cat(robust1, "\n", sep = "")
             
             cat("\n------------------------------------------------------------------\n")
             print(output.table, quote = F, row.names = FALSE, justify = "left")
             cat("------------------------------------------------------------------\n")
             
             if(object@robust == TRUE) { cat("\nOverdispersion heterogeneity parameter estimate =", object@Overdispersion, "\n") }
             if(object@robust == FALSE) { cat("\nHeterogeneity test statistic = ", decimals(object@Heter.Stat[1],4), " (p-value = ", decimals(object@Heter.Stat[2], 4),")\n", sep = "")}
             if(object@robust == TRUE) { cat("\nHeterogeneity test statistic = ", decimals(object@Heter.Stat[1],4),"\n", sep = "")}
          }
)