Nothing
summary.UnifixedContCont <- summary.UnimixedContCont <- function(object, ..., Object){
if (missing(Object)){Object <- object}
cat("\nFunction call:\n\n")
print(Object$Call)
cat("\n\n# Data summary and descriptives")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
cat("\n\nTotal number of trials: ", nrow(Object$Obs.Per.Trial))
cat("\nTotal number of patients: ", dim(Object$Data.Analyze)[1])
cat("\nM(SD) patients per trial: ", format(round(mean((Object$Obs.Per.Trial$Obs.per.trial)), 4), nsmall = 4), " (", format(round(sd((Object$Obs.Per.Trial$Obs.per.trial)), 4), nsmall = 4), ")",
" [min: ", min((Object$Obs.Per.Trial$Obs.per.trial)), "; max: ", max((Object$Obs.Per.Trial$Obs.per.trial)), "]", sep="")
cat("\nTotal number of patients in experimental treatment group: ", length(Object$Data.Analyze$Treat[Object$Data.Analyze$Treat==1]),
"\nTotal number of patients in control treatment group: ", length(Object$Data.Analyze$Treat[Object$Data.Analyze$Treat!=1]))
means_table <- rbind(tapply(Object$Data.Analyze$Surr, list(Object$Data.Analyze$Treat), mean), tapply(Object$Data.Analyze$True, list(Object$Data.Analyze$Treat), mean))
colnames(means_table) <- c("Control Treatment", "Experimental treatment")
rownames(means_table) <- c("Surrogate", "True endpoint")
cat("\n\nMean surrogate and true endpoint values in each treatment group: \n\n")
print(format(round(data.frame(means_table, stringsAsFactors = TRUE), 4), nsmall = 4))
Var_table <- rbind(tapply(Object$Data.Analyze$Surr, list(Object$Data.Analyze$Treat), var), tapply(Object$Data.Analyze$True, list(Object$Data.Analyze$Treat), var))
colnames(Var_table) <- c("Control Treatment", "Experimental treatment")
rownames(Var_table) <- c("Surrogate", "True endpoint")
cat("\n\nVar surrogate and true endpoint values in each treatment group: \n\n")
print(format(round(data.frame(Var_table, stringsAsFactors = TRUE), 4), nsmall = 4))
cat("\n\nCorrelations between the true and surrogate endpoints in the control (r_T0S0)")
cat("\nand the experimental treatment groups (r_T1S1):\n\n")
print(round(Object$Cor.Endpoints, 4), nsmall = 4)
cat("\n\n\n# Meta-analytic results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
cat("\n\n")
print(format(round(Object$Trial.R2, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Indiv.R2, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Trial.R, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Indiv.R, 4), nsmall = 4))
#ICA
mode <- function(data) {
x <- data
z <- density(x)
mode_val <- z$x[which.max(z$y)]
fit <- list(mode_val= mode_val)
}
cat("\n\n\n# ICA results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Mean (SD) ICA: ", format(round(mean(Object$ICA$ICA), 4), nsmall = 4), " (", format(round(sd(Object$ICA$ICA), 4), nsmall = 4), ")",
" [min: ", format(round(min(Object$ICA$ICA), 4), nsmall = 4), "; max: ", format(round(max(Object$ICA$ICA), 4), nsmall = 4), "]", sep="")
cat("\nMode ICA: ", format(round(mode(Object$ICA$ICA)$mode_val, 4), nsmall = 4))
cat("\n\nQuantiles of the ICA distribution: \n\n")
quant <- quantile(Object$ICA$ICA, probs = c(.05, .10, .20, .50, .80, .90, .95))
print(quant)
# Max ent results
Fit <- MaxEntContCont(x=Object$ICA, T0T0 = Object$T0T0, T1T1 = Object$T1T1, S0S0 = Object$S0S0, S1S1 = Object$S1S1)
cat("\n\nMaximum entropy ICA: ")
cat(Fit$ICA.Max.Ent) #, " (with entropy = ", Object$Max.Ent, ")\n", sep = "")
cat("\n\nObtained under correlation structure:\n")
print(Fit$Table.ICA.Entropy[1,3:8], row.names = "")
cat("\n")
}
summary.BifixedContCont <- function(object, ..., Object){
if (missing(Object)){Object <- object}
cat("\nFunction call:\n\n")
print(Object$Call)
cat("\n\n# Data summary and descriptives")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
cat("\n\nTotal number of trials: ", nrow(Object$Obs.Per.Trial))
cat("\nTotal number of patients: ", dim(Object$Data.Analyze)[1])
cat("\nM(SD) patients per trial: ", format(round(mean((Object$Obs.Per.Trial$Obs.per.trial)), 4), nsmall = 4), " (", format(round(sd((Object$Obs.Per.Trial$Obs.per.trial)), 4), nsmall = 4), ")",
" [min: ", min((Object$Obs.Per.Trial$Obs.per.trial)), "; max: ", max((Object$Obs.Per.Trial$Obs.per.trial)), "]", sep="")
cat("\nTotal number of patients in experimental treatment group: ", length(Object$Data.Analyze$Treat[Object$Data.Analyze$Treat==1]),
"\nTotal number of patients in control treatment group: ", length(Object$Data.Analyze$Treat[Object$Data.Analyze$Treat!=1]))
means_table <- rbind(tapply(Object$Data.Analyze$Surr, list(Object$Data.Analyze$Treat), mean), tapply(Object$Data.Analyze$True, list(Object$Data.Analyze$Treat), mean))
colnames(means_table) <- c("Control Treatment", "Experimental treatment")
rownames(means_table) <- c("Surrogate", "True endpoint")
cat("\n\nMean surrogate and true endpoint values in each treatment group: \n\n")
print(format(round(data.frame(means_table, stringsAsFactors = TRUE), 4), nsmall = 4))
Var_table <- rbind(tapply(Object$Data.Analyze$Surr, list(Object$Data.Analyze$Treat), var), tapply(Object$Data.Analyze$True, list(Object$Data.Analyze$Treat), var))
colnames(Var_table) <- c("Control Treatment", "Experimental treatment")
rownames(Var_table) <- c("Surrogate", "True endpoint")
cat("\n\nVar surrogate and true endpoint values in each treatment group: \n\n")
print(format(round(data.frame(Var_table, stringsAsFactors = TRUE), 4), nsmall = 4))
cat("\n\nCorrelations between the true and surrogate endpoints in the control (r_T0S0)")
cat("\nand the experimental treatment groups (r_T1S1):\n\n")
print(round(Object$Cor.Endpoints, 4), nsmall = 4)
cat("\n\n\n# Meta-analytic results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
cat("\n\n")
print(format(round(Object$Trial.R2, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Indiv.R2, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Trial.R, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Indiv.R, 4), nsmall = 4))
#ICA
mode <- function(data) {
x <- data
z <- density(x)
mode_val <- z$x[which.max(z$y)]
fit <- list(mode_val= mode_val)
}
cat("\n\n\n# ICA results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Mean (SD) ICA: ", format(round(mean(Object$ICA$ICA), 4), nsmall = 4), " (", format(round(sd(Object$ICA$ICA), 4), nsmall = 4), ")",
" [min: ", format(round(min(Object$ICA$ICA), 4), nsmall = 4), "; max: ", format(round(max(Object$ICA$ICA), 4), nsmall = 4), "]", sep="")
cat("\nMode ICA: ", format(round(mode(Object$ICA$ICA)$mode_val, 4), nsmall = 4))
cat("\n\nQuantiles of the ICA distribution: \n\n")
quant <- quantile(Object$ICA$ICA, probs = c(.05, .10, .20, .50, .80, .90, .95))
print(quant)
# Max ent results
Fit <- MaxEntContCont(x=Object$ICA, T0T0 = Object$T0T0, T1T1 = Object$T1T1, S0S0 = Object$S0S0, S1S1 = Object$S1S1)
cat("\n\nMaximum entropy ICA: ")
cat(Fit$ICA.Max.Ent) #, " (with entropy = ", Object$Max.Ent, ")\n", sep = "")
cat("\n\nObtained under correlation structure:\n")
print(Fit$Table.ICA.Entropy[1,3:8], row.names = "")
cat("\n")
}
summary.BimixedContCont <- function(object, ..., Object){
if (missing(Object)){Object <- object}
cat("\nFunction call:\n\n")
print(Object$Call)
cat("\n\n# Data summary and descriptives")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
cat("\n\nTotal number of trials: ", nrow(Object$Obs.Per.Trial))
cat("\nTotal number of patients: ", dim(Object$Data.Analyze)[1])
cat("\nM(SD) patients per trial: ", format(round(mean((Object$Obs.Per.Trial$Obs.per.trial)), 4), nsmall = 4), " (", format(round(sd((Object$Obs.Per.Trial$Obs.per.trial)), 4), nsmall = 4), ")",
" [min: ", min((Object$Obs.Per.Trial$Obs.per.trial)), "; max: ", max((Object$Obs.Per.Trial$Obs.per.trial)), "]", sep="")
cat("\nTotal number of patients in experimental treatment group: ", length(Object$Data.Analyze$Treat[Object$Data.Analyze$Treat==1]),
"\nTotal number of patients in control treatment group: ", length(Object$Data.Analyze$Treat[Object$Data.Analyze$Treat!=1]))
means_table <- rbind(tapply(Object$Data.Analyze$Surr, list(Object$Data.Analyze$Treat), mean), tapply(Object$Data.Analyze$True, list(Object$Data.Analyze$Treat), mean))
colnames(means_table) <- c("Control Treatment", "Experimental treatment")
rownames(means_table) <- c("Surrogate", "True endpoint")
cat("\n\nMean surrogate and true endpoint values in each treatment group: \n\n")
print(format(round(data.frame(means_table, stringsAsFactors = TRUE), 4), nsmall = 4))
Var_table <- rbind(tapply(Object$Data.Analyze$Surr, list(Object$Data.Analyze$Treat), var), tapply(Object$Data.Analyze$True, list(Object$Data.Analyze$Treat), var))
colnames(Var_table) <- c("Control Treatment", "Experimental treatment")
rownames(Var_table) <- c("Surrogate", "True endpoint")
cat("\n\nVar surrogate and true endpoint values in each treatment group: \n\n")
print(format(round(data.frame(Var_table, stringsAsFactors = TRUE), 4), nsmall = 4))
cat("\n\nCorrelations between the true and surrogate endpoints in the control (r_T0S0)")
cat("\nand the experimental treatment groups (r_T1S1):\n\n")
print(round(Object$Cor.Endpoints, 4), nsmall = 4)
cat("\n\n\n# Meta-analytic results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
cat("\n\n")
print(format(round(Object$Trial.R2, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Indiv.R2, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Trial.R, 4), nsmall = 4))
cat("\n")
print(format(round(Object$Indiv.R, 4), nsmall = 4))
#ICA
mode <- function(data) {
x <- data
z <- density(x)
mode_val <- z$x[which.max(z$y)]
fit <- list(mode_val= mode_val)
}
cat("\n\n\n# ICA results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Mean (SD) ICA: ", format(round(mean(Object$ICA$ICA), 4), nsmall = 4), " (", format(round(sd(Object$ICA$ICA), 4), nsmall = 4), ")",
" [min: ", format(round(min(Object$ICA$ICA), 4), nsmall = 4), "; max: ", format(round(max(Object$ICA$ICA), 4), nsmall = 4), "]", sep="")
cat("\nMode ICA: ", format(round(mode(Object$ICA$ICA)$mode_val, 4), nsmall = 4))
cat("\n\nQuantiles of the ICA distribution: \n\n")
quant <- quantile(Object$ICA$ICA, probs = c(.05, .10, .20, .50, .80, .90, .95))
print(quant)
# Max ent results
Fit <- MaxEntContCont(x=Object$ICA, T0T0 = Object$T0T0, T1T1 = Object$T1T1, S0S0 = Object$S0S0, S1S1 = Object$S1S1)
cat("\n\nMaximum entropy ICA: ")
cat(Fit$ICA.Max.Ent) #, " (with entropy = ", Object$Max.Ent, ")\n", sep = "")
cat("\n\nObtained under correlation structure:\n")
print(Fit$Table.ICA.Entropy[1,3:8], row.names = "")
cat("\n")
}
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