Jesss

.Rbuildignore

@@ -0,0 +1,2 @@
+^.*\.Rproj$
+^\.Rproj\.user$

.gitignore

@@ -0,0 +1,4 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

DESCRIPTION

@@ -9,7 +9,7 @@ Description: Code to easily calculate nearly all of the CpG-based epigenetic clo
 License: What license is it under?
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.2.0
+RoxygenNote: 7.3.2
 Imports:
     readr,
     dplyr,

NAMESPACE

@@ -16,6 +16,8 @@ export(calcDunedinPoAm38)
 export(calcEpiTOC)
 export(calcEpiTOC2)
 export(calcGaragnani)
+export(calcGrimAgeV1)
+export(calcGrimAgeV2)
 export(calcHRSInChPhenoAge)
 export(calcHannum)
 export(calcHorvath1)
@@ -28,6 +30,7 @@ export(calcLeeRobust)
 export(calcLin)
 export(calcMayne)
 export(calcMiAge)
+export(calcPCClocks)
 export(calcPEDBE)
 export(calcPhenoAge)
 export(calcSmokingMcCartney)

R/calcAlcoholMcCartney.R

@@ -10,8 +10,8 @@
 #' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
 #' @export
 #'
-#' @examples calcAlcoholMcCartney(exampleBetas, examplePheno, imputation = F)
-calcAlcoholMcCartney <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = F){
+#' @examples calcAlcoholMcCartney(exampleBetas, examplePheno, imputation = T)
+calcAlcoholMcCartney <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   #######################
   ### Read in the Data###

R/calcBMIMcCartney.R

@@ -10,8 +10,8 @@
 #' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
 #' @export
 #'
-#' @examples calcBMIMcCartney(exampleBetas, examplePheno, imputation = F)
-calcBMIMcCartney <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = F){
+#' @examples calcBMIMcCartney(exampleBetas, examplePheno, imputation = T)
+calcBMIMcCartney <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   #######################
   ### Read in the Data###

R/calcBocklandt.R

@@ -10,8 +10,8 @@
 #' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
 #' @export
 #'
-#' @examples calcBocklandt(exampleBetas, examplePheno, imputation = F)
-calcBocklandt <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = F){
+#' @examples calcBocklandt(exampleBetas, examplePheno, imputation = T)
+calcBocklandt <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   #######################
   ### Read in the Data###

R/calcBohlin.R

@@ -10,8 +10,8 @@
 #' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
 #' @export
 #'
-#' @examples calcBohlin(exampleBetas, examplePheno, imputation = F)
-calcBohlin <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = F){
+#' @examples calcBohlin(exampleBetas, examplePheno, imputation = T)
+calcBohlin <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   #######################
   ### Read in the Data###

R/calcClockCategory.R

@@ -12,9 +12,9 @@
 #' @return A dataframe that has column names of the core clocks giving you multiple clocks at once in an easy to compute function. These will be appended onto the existing pheno dataframe as defined in the inputs.
 #' @export
 #'
-#' @examples calcClockCategory(exampleBetas, examplePheno, category = "chronological", imputation = F)
+#' @examples calcClockCategory(exampleBetas, examplePheno, category = "chronological", imputation = T)
 calcClockCategory <- function(DNAm, pheno , category = NULL,
-                              CpGImputation = NULL, imputation = F){
+                              CpGImputation = NULL, imputation = T){
 
   message("Please remember to cite all of the clocks you have used! Please refer to the README.md file for assistance.")
 

R/calcCoreClocks.R

@@ -10,8 +10,8 @@
 #' @return A dataframe that has column names of the core clocks giving you multiple clocks at once in an easy to compute function. These will be appended onto the existing pheno dataframe as defined in the inputs.
 #' @export
 #'
-#' @examples calcCoreClocks(exampleBetas, examplePheno, imputation = F)
-calcCoreClocks <- function(DNAm, pheno , CpGImputation = NULL, imputation = F){
+#' @examples calcCoreClocks(exampleBetas, examplePheno, imputation = T)
+calcCoreClocks <- function(DNAm, pheno , CpGImputation = NULL, imputation = T){
 
   message("Please remember to cite the core Clocks you have used! Please refer to the README.md file for assistance.")
 

R/calcDNAmClockCortical.R

@@ -10,9 +10,9 @@
 #' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
 #' @export
 #'
-#' @examples calcDNAmClockCortical(exampleBetas, examplePheno, imputation = F)
+#' @examples calcDNAmClockCortical(exampleBetas, examplePheno, imputation = T)
 #' @examples calcDNAmClockCortical(exampleBetas, examplePheno, CpGImputation = DNAmClockCortical_imputeRef, imputation = T) # Imputation in the case that there are missing CpGs using the original Brain Imputation from authors
-calcDNAmClockCortical <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = F){
+calcDNAmClockCortical <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   ###################################################
   ### Check if all necessary CpGs are in the data ###

R/calcDNAmTL.R

@@ -10,8 +10,8 @@
 #' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
 #' @export
 #'
-#' @examples calcDNAmTL(exampleBetas, examplePheno, imputation = F)
-calcDNAmTL <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = F){
+#' @examples calcDNAmTL(exampleBetas, examplePheno, imputation = T)
+calcDNAmTL <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   #######################
   ### Read in the Data###

R/calcDunedinPoAm38.R

@@ -11,7 +11,7 @@
 #' @export
 #' @source <https://doi.org/10.7554/eLife.54870>
 #'
-#' @examples calcDunedinPoAm38(exampleBetas, examplePheno, imputation = F)
+#' @examples calcDunedinPoAm38(exampleBetas, examplePheno, imputation = T)
 calcDunedinPoAm38 <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   ###########################

R/calcEpiTOC.R

@@ -11,7 +11,7 @@
 #' @export
 #' @source <https://zenodo.org/record/2632938#.YfGA3S-B2Cg>
 #'
-#' @examples calcEpiTOC(exampleBetas, examplePheno, imputation = F)
+#' @examples calcEpiTOC(exampleBetas, examplePheno, imputation = T)
 calcEpiTOC <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   #######################

R/calcEpiTOC2.R

@@ -11,7 +11,7 @@
 #' @export
 #' @source <https://zenodo.org/record/2632938#.YfGA3S-B2Cg>
 #'
-#' @examples calcEpiTOC2(exampleBetas, examplePheno, imputation = F)
+#' @examples calcEpiTOC2(exampleBetas, examplePheno, imputation = T)
 calcEpiTOC2 <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T, approximated = F){
 
   #######################

R/calcGaragnani.R

@@ -10,8 +10,8 @@
 #' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
 #' @export
 #'
-#' @examples calcGaragnani(exampleBetas, examplePheno, imputation = F)
-calcGaragnani <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = F){
+#' @examples calcGaragnani(exampleBetas, examplePheno, imputation = T)
+calcGaragnani <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   #######################
   ### Read in the Data###

R/calcGrimAgeV1.R

@@ -0,0 +1,91 @@
+#' CalcGrimAgeV1
+#'
+#' @description A function to calculate GrimAgeV1
+#'
+#' @param DNAm a matrix of methylation beta values. Needs to be rows = samples and columns = CpGs, with rownames and colnames.
+#' @param pheno Optional: The sample phenotype data (also with samples as rows) that the clock will be appended to.
+#' @param CpGImputation An optional namesd vector for the mean value of each CpG that will be input from another dataset if such values are missing here (from sample cleaning)
+#' @param imputation Logical value that will allows you to perform (T)/ skip (F) imputation of mean values for missing CpGs. Warning: when imputation = F if there are missing CpGs, it will automatically ignore these CpGs during calculation, making the clock values less accurate.
+#'
+#' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
+#' @export
+#'
+#' @examples CalcGrimAgeV1(exampleBetas, examplePheno, imputation = F)
+#'
+#'
+#'
+#'
+#'
+
+
+calcGrimAgeV1 <- function(datMeth, datPheno){
+
+  if(!("Age" %in% variable.names(datPheno))){
+    stop("Error: datPheno must have a column named Age")
+  }
+  if(!("Female" %in% variable.names(datPheno))){
+    stop("Error: datPheno must have a column named Female")
+  }
+  if(sum(startsWith(colnames(datMeth),"cg")) == 0){
+    warning("Warning: It looks like you may need to format datMeth using t(datMeth) to get samples as rows!")
+  }
+
+  #In datPheno, rows are samples and columns are phenotypic variables.
+  #One of the phenotypic variables must be "Age", and another one "Female" (coded as Female = 1, Male = 0; should be a numeric variable as this will be included in PCGrimAge calculation)
+  #Also ensure that the order of datMeth sample IDs matches your phenotype data sample IDs, otherwise your data will be scrambled
+
+  #load(file = paste(path_to_GrimAgeV1_directory,"CalcGrimAgeV1.RData", sep = ""))
+  #print("Loaded GrimAgeV1 data.")
+
+  #If needed: Fill in missing CpGs needed for calculation of PCs; use mean values from GSE40279 (Hannum 2013; blood)- note that for other tissues you might prefer to use a different one
+  datMeth <- as.data.frame(datMeth)
+  if(length(c(CpGs_GrimAge1[!(CpGs_GrimAge1 %in% colnames(datMeth))],CpGs_GrimAge1[apply(datMeth[,colnames(datMeth) %in% CpGs_GrimAge1], 2, function(x)all(is.na(x)))])) == 0){
+    message("GrimAgeV1 - No CpGs were NA for all samples")
+  } else{
+    missingCpGs_GrimAge1 <- c(CpGs_GrimAge1[!(CpGs_GrimAge1 %in% colnames(datMeth))])
+    datMeth[,missingCpGs_GrimAge1] <- NA
+    datMeth = datMeth[,CpGs_GrimAge1]
+    missingCpGs_GrimAge1 <- CpGs_GrimAge1[apply(datMeth[,CpGs_GrimAge1], 2, function(x)all(is.na(x)))]
+    for(i in 1:length(missingCpGs_GrimAge1)){
+      if (!is.na(imputeMissingCpGs_GrimAge1[missingCpGs_GrimAge1[i]])){
+        datMeth[,missingCpGs_GrimAge1[i]] <- imputeMissingCpGs_GrimAge1[missingCpGs_GrimAge1[i]]
+        #print(imputeMissingCpGs_GrimAge1[missingCpGs_GrimAge1[i]])
+      }
+      else{
+        datMeth[,missingCpGs_GrimAge1[i]] <- 0
+
+      }
+    }
+    message(paste0("GrimAgeV1 needed to fill in ", length(missingCpGs_GrimAge1), " CpGs..."))
+  }
+
+  #Prepare methylation data for calculation of PC Clocks (subset to 78,464 CpGs and perform imputation if needed)
+  #datMeth <- datMeth[,CpGs]
+  #meanimpute <- function(x) ifelse(is.na(x),mean(x,na.rm=T),x)
+  #datMeth <- apply(datMeth,2,meanimpute)
+  #Note: you may substitute another imputation method of your choice (e.g. KNN), but we have not found the method makes a significant difference.
+  #message("Mean imputation successfully completed for any missing CpG values")
+
+  #Initialize a data frame for PC clocks
+  DNAmAge <- datPheno
+
+  # Calculate GrimAge Clocks
+  temp <- as.matrix(cbind(datMeth, Age = as.numeric(datPheno$Age, Female=datPheno$Female)))
+  DNAmAge$DNAmPACKYRS <- as.numeric(temp[,names(CalcGrimAgeV1$PACKYRS.model)] %*% CalcGrimAgeV1$PACKYRS.model + CalcGrimAgeV1$PACKYRS.intercept)
+  DNAmAge$DNAmADM <- as.numeric(temp[,names(CalcGrimAgeV1$ADM.model)] %*% CalcGrimAgeV1$ADM.model + CalcGrimAgeV1$ADM.intercept)
+  DNAmAge$DNAmB2M <- as.numeric(temp[,names(CalcGrimAgeV1$B2M.model)] %*% CalcGrimAgeV1$B2M.model + CalcGrimAgeV1$B2M.intercept)
+  DNAmAge$DNAmCystatinC <- as.numeric(temp[,names(CalcGrimAgeV1$CystatinC.model)] %*% CalcGrimAgeV1$CystatinC.model + CalcGrimAgeV1$CystatinC.intercept)
+  DNAmAge$DNAmGDF15 <- as.numeric(temp[,names(CalcGrimAgeV1$GDF15.model)] %*% CalcGrimAgeV1$GDF15.model + CalcGrimAgeV1$GDF15.intercept)
+  DNAmAge$DNAmLeptin <- as.numeric(temp[,names(CalcGrimAgeV1$Leptin.model)] %*% CalcGrimAgeV1$Leptin.model + CalcGrimAgeV1$Leptin.intercept)
+  DNAmAge$DNAmPAI1 <- as.numeric(temp[,names(CalcGrimAgeV1$PAI1.model)] %*% CalcGrimAgeV1$PAI1.model + CalcGrimAgeV1$PAI1.intercept)
+  DNAmAge$DNAmTIMP1 <- as.numeric(temp[,names(CalcGrimAgeV1$TIMP1.model)] %*% CalcGrimAgeV1$TIMP1.model + CalcGrimAgeV1$TIMP1.intercept)
+  #print("Components Calculated.Calculating GrimAGE...")
+  DNAmAge$Age<-DNAmAge$Age
+  DNAmAge$Female<-DNAmAge$Female
+  DNAmAge$GrimAgeV1 <- as.numeric(as.matrix(DNAmAge[,CalcGrimAgeV1$components]) %*% CalcGrimAgeV1$GrimAge.model ) #+ CalcGrimAgeV1$GrimAge.intercept)
+  y<-DNAmAge$GrimAgeV1
+  DNAmAge$GrimAgeV1 <- (((y- CalcGrimAgeV1$GrimAge.transform[3])/CalcGrimAgeV1$GrimAge.transform[4])*CalcGrimAgeV1$GrimAge.transform[2]) + CalcGrimAgeV1$GrimAge.transform[1]
+
+  return(DNAmAge)
+
+}

R/calcGrimAgeV2.R

@@ -0,0 +1,101 @@
+#' calcGrimAge2V2
+#'
+#' @description A function to calculate GrimAgeV2
+#'
+#' @param DNAm a matrix of methylation beta values. Needs to be rows = samples and columns = CpGs, with rownames and colnames.
+#' @param pheno Optional: The sample phenotype data (also with samples as rows) that the clock will be appended to.
+#' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
+#' @export
+#'
+#' @examples calcPhenoAge(exampleBetas, examplePheno)
+#'
+#'
+
+calcGrimAgeV2 <- function(datMeth, datPheno){
+  if(!("Age" %in% variable.names(datPheno))){
+    stop("Error: datPheno must have a column named Age")
+  }
+  if(!("Female" %in% variable.names(datPheno))){
+    stop("Error: datPheno must have a column named Female")
+  }
+  if(sum(startsWith(colnames(datMeth),"cg")) == 0){
+    warning("Warning: It looks like you may need to format datMeth using t(datMeth) to get samples as rows!")
+  }
+
+  #In datPheno, rows are samples and columns are phenotypic variables.
+  #One of the phenotypic variables must be "Age", and another one "Female" (coded as Female = 1, Male = 0; should be a numeric variable as this will be included in PCGrimAge calculation)
+  #Also ensure that the order of datMeth sample IDs matches your phenotype data sample IDs, otherwise your data will be scrambled
+
+  #load(file = paste(path_to_GrimAgeV2_directory,"CalcGrimAgeV2.RData", sep = ""))
+  #print("Loaded GrimAgeV2 data.")
+
+  #If needed: Fill in missing CpGs needed for calculation of PCs; use mean values from GSE40279 (Hannum 2013; blood)- note that for other tissues you might prefer to use a different one
+  # datMeth <- as.data.frame(datMeth)
+  # if(length(c(CpGs[!(CpGs %in% colnames(datMeth))],CpGs[apply(datMeth[,colnames(datMeth) %in% CpGs], 2, function(x)all(is.na(x)))])) == 0){
+  #   message("No CpGs were NA for all samples")
+  # } else{
+  #   missingCpGs <- c(CpGs[!(CpGs %in% colnames(datMeth))])
+  #   datMeth[,missingCpGs] <- NA
+  #   datMeth = datMeth[,CpGs]
+  #   missingCpGs <- CpGs[apply(datMeth[,CpGs], 2, function(x)all(is.na(x)))]
+  #   for(i in 1:length(missingCpGs)){
+  #     datMeth[,missingCpGs[i]] <- imputeMissingCpGs[missingCpGs[i]]
+  #   }
+  #   message("Any missing CpGs successfully filled in (see function for more details)")
+  # }
+
+  datMeth <- as.data.frame(datMeth)
+  if(length(c(CpGs_GrimAge2[!(CpGs_GrimAge2 %in% colnames(datMeth))],CpGs_GrimAge2[apply(datMeth[,colnames(datMeth) %in% CpGs_GrimAge2], 2, function(x)all(is.na(x)))])) == 0){
+    message("GrimAgeV2 - No CpGs were NA for all samples")
+  } else{
+    missingCpGs_GrimAge2 <- c(CpGs_GrimAge2[!(CpGs_GrimAge2 %in% colnames(datMeth))])
+    datMeth[,missingCpGs_GrimAge2] <- NA
+    datMeth = datMeth[,CpGs_GrimAge2]
+    missingCpGs_GrimAge2 <- CpGs_GrimAge2[apply(datMeth[,CpGs_GrimAge2], 2, function(x)all(is.na(x)))]
+    for(i in 1:length(missingCpGs_GrimAge2)){
+      if (!is.na(imputeMissingCpGs_GrimAge2[missingCpGs_GrimAge2[i]])){
+        datMeth[,missingCpGs_GrimAge2[i]] <- imputeMissingCpGs_GrimAge2[missingCpGs_GrimAge2[i]]
+        #print(imputeMissingCpGs_GrimAge2[missingCpGs_GrimAge2[i]])
+      }
+      else{
+        datMeth[,missingCpGs_GrimAge2[i]] <- 0
+      }
+    }
+    message(paste0("GrimAgeV2 needed to fill in ", length(missingCpGs_GrimAge2), " CpGs..."))
+  }
+
+  #Prepare methylation data for calculation of PC Clocks (subset to 78,464 CpGs and perform imputation if needed)
+  #datMeth <- datMeth[,CpGs]
+  #meanimpute <- function(x) ifelse(is.na(x),mean(x,na.rm=T),x)
+  #datMeth <- apply(datMeth,2,meanimpute)
+  #Note: you may substitute another imputation method of your choice (e.g. KNN), but we have not found the method makes a significant difference.
+  #message("Mean imputation successfully completed for any missing CpG values")
+
+  #Initialize a data frame for PC clocks
+  DNAmAge <- datPheno
+
+  # Calculate GrimAge Clocks
+  temp <- as.matrix(cbind(datMeth, Age = as.numeric(datPheno$Age, Female=datPheno$Female)))
+  DNAmAge$DNAmPACKYRS <- as.numeric(temp[,names(CalcGrimAge2$PACKYRS.model)] %*% CalcGrimAge2$PACKYRS.model + CalcGrimAge2$PACKYRS.intercept)
+  DNAmAge$DNAmADM <- as.numeric(temp[,names(CalcGrimAge2$ADM.model)] %*% CalcGrimAge2$ADM.model + CalcGrimAge2$ADM.intercept)
+  DNAmAge$DNAmB2M <- as.numeric(temp[,names(CalcGrimAge2$B2M.model)] %*% CalcGrimAge2$B2M.model + CalcGrimAge2$B2M.intercept)
+  DNAmAge$DNAmCystatinC <- as.numeric(temp[,names(CalcGrimAge2$CystatinC.model)] %*% CalcGrimAge2$CystatinC.model + CalcGrimAge2$CystatinC.intercept)
+  DNAmAge$DNAmGDF15 <- as.numeric(temp[,names(CalcGrimAge2$GDF15.model)] %*% CalcGrimAge2$GDF15.model + CalcGrimAge2$GDF15.intercept)
+  DNAmAge$DNAmLeptin <- as.numeric(temp[,names(CalcGrimAge2$Leptin.model)] %*% CalcGrimAge2$Leptin.model + CalcGrimAge2$Leptin.intercept)
+  DNAmAge$DNAmPAI1 <- as.numeric(temp[,names(CalcGrimAge2$PAI1.model)] %*% CalcGrimAge2$PAI1.model + CalcGrimAge2$PAI1.intercept)
+  DNAmAge$DNAmTIMP1 <- as.numeric(temp[,names(CalcGrimAge2$TIMP1.model)] %*% CalcGrimAge2$TIMP1.model + CalcGrimAge2$TIMP1.intercept)
+  #New Proteins
+  DNAmAge$DNAmlogA1C <- as.numeric(temp[,names(CalcGrimAge2$logA1C.model)] %*% CalcGrimAge2$logA1C.model + CalcGrimAge2$logA1C.intercept)
+  DNAmAge$DNAmlogCRP <- as.numeric(temp[,names(CalcGrimAge2$logCRP.model)] %*% CalcGrimAge2$logCRP.model + CalcGrimAge2$logCRP.intercept)
+
+  # DNAmAge$Age<-DNAmAge$cAGE
+  # DNAmAge$Female<-DNAmAge$cFEMALE
+
+  DNAmAge$GrimAgeV2 <- as.numeric(as.matrix(DNAmAge[,CalcGrimAge2$components]) %*% CalcGrimAge2$GrimAge.model ) #+ CalcGrimAge2$GrimAge.intercept)
+  y<-DNAmAge$GrimAgeV2
+  #print(y)
+  DNAmAge$GrimAgeV2 <- (((y- CalcGrimAge2$GrimAge.transform[3])/CalcGrimAge2$GrimAge.transform[4])*CalcGrimAge2$GrimAge.transform[2]) + CalcGrimAge2$GrimAge.transform[1]
+
+  return(DNAmAge)
+
+}

R/calcHRSInChPhenoAge.R

@@ -10,8 +10,8 @@
 #' @return If you added the optional pheno input (preferred) the function appends a column with the clock calculation and returns the dataframe. Otherwise, it will return a vector of calculated clock values in order of the
 #' @export
 #'
-#' @examples calcHRSInChPhenoAge(exampleBetas, examplePheno, imputation = F)
-calcHRSInChPhenoAge <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = F){
+#' @examples calcHRSInChPhenoAge(exampleBetas, examplePheno, imputation = T)
+calcHRSInChPhenoAge <- function(DNAm, pheno = NULL, CpGImputation = NULL, imputation = T){
 
   #######################
   ### Read in the Data###