version 1.2.0

DESCRIPTION

@@ -1,13 +1,13 @@
 Package: gendata
 Type: Package
 Title: Generate and Modify Synthetic Datasets
-Version: 1.1
-Date: 2015-02-27
+Version: 1.2.0
+Date: 2022-05-09
 Author: Francis Huang <flh3@hotmail.com>
 Maintainer: Francis Huang <flh3@hotmail.com>
 Description: Set of functions to create datasets using a correlation matrix. 
 License: GPL-3
-Packaged: 2015-03-01 00:27:32 UTC; huangf
 NeedsCompilation: no
+Packaged: 2022-05-09 21:40:21 UTC; flh3
 Repository: CRAN
-Date/Publication: 2015-03-01 08:20:17
+Date/Publication: 2022-05-09 21:50:02 UTC

MD5

@@ -1,11 +1,13 @@
-d24062a9da5bf317849a4f9f55c7425b *DESCRIPTION
-df390c53434517b304ac5db487184641 *NAMESPACE
-342f1ce5c35aeb3cf31f40751e358665 *R/dtrans.R
-095100e3b3da1f45b3598b24015efb31 *R/genmvnorm.R
-01ef3ab6e2a122c3c91d6870bc17a2e6 *R/recalib.R
-4cbd5ff37108a8c4cd99a19946511f89 *R/revcode.R
-ee03b3f57121283e65077055b925d2e7 *man/dtrans.Rd
+0d518d37b4fcc76ec1c6fb54d1154925 *DESCRIPTION
+901331ca84ce9a4c48f13b6f3c345713 *NAMESPACE
+030758d4fc185b2e3dbd0eec5ae9f704 *NEWS.md
+c0205abf0548202c060f77fa82e712d7 *R/dtrans.R
+be085e3c9d0854c746af9ccff06bca44 *R/genmvnorm.R
+9b91c42e2e1b13213efd59e72a597bf8 *R/recalib.R
+94e1cc2fad3a571b2bafca401160171d *R/revcode.R
+f8b23867f24b2e0d1e66038f48228cc8 *README.md
+44d1f5b47fd8044323ad2b44f8e91375 *man/dtrans.Rd
 fba3faa2ba40f50b4a7edaa14e4320ff *man/gendata-package.Rd
-2e045b7357aa5e2ed17cb27a065962a0 *man/genmvnorm.Rd
-c1b548f7156dc9fcc7e51e5d55100240 *man/recalib.Rd
+9214c09fde33d3ec4e7842541fb05ff0 *man/genmvnorm.Rd
+f95f68cdd0f0073fc7c8a7aa9101b65d *man/recalib.Rd
 a0f1851ab48c58d59a1ec40b87589faa *man/revcode.Rd

NAMESPACE

@@ -1 +1,2 @@
 exportPattern("^[[:alpha:]]+")
+importFrom("stats", "rnorm")

NEWS.md

@@ -0,0 +1,10 @@
+# 2022.05.09 v. 1.2
+- Adjusted spacing in function to make it more readable.
+- Updated package to use (based on e-mail from CRAN):
+
+Consider adding
+ importFrom("stats", "rnorm")
+ to your NAMESPACE file.
+
+# 2015-02-27 v. 1.1
+- minor update

R/dtrans.R

@@ -1,21 +1,22 @@
 #transform dataset to specified m and sd
-dtrans<-function(data,m,sd,rnd=F){
+dtrans <- function(data, m, sd, rnd = FALSE){
 #basic checking  
-x<-dim(data)[2] #getting number of vars
-cat("Number of variables in dataset:",  x,"\n")#number of vars
-cat("Number of means specified:",  length(m),"\n")
-cat("Number of standard deviations in dataset:",  length(sd),"\n")
-  if(length(m) != length(sd)){stop("Number of means/SDs should match.")}
-  if(length(m) != dim(data)[2]){stop("Incorrect number of means/SDs")}
-for (i in 1:x){  
-xrange<-range(data[,1])
-if(xrange[1]-xrange[2]==0){stop("Constant value detected")}
+  x <- dim(data)[2] #getting number of vars
+  cat("Number of variables in dataset:",  x,"\n")#number of vars
+  cat("Number of means specified:",  length(m),"\n")
+  cat("Number of standard deviations in dataset:", length(sd),"\n")
+    if(length(m) != length(sd)){stop("Number of means/SDs should match.")}
+    if(length(m) != dim(data)[2]){stop("Incorrect number of means/SDs")}
+  for (i in 1:x){  
+  xrange <- range(data[,1])
+  if(xrange[1] - xrange[2] == 0){stop("Constant value detected")}
 }
 
 #transforming
 for (i in 1:x){
-  data[i]=m[i]+sd[i]*data[i]
+  data[i] = m[i] + sd[i] * data[i]
  }
-if(rnd==T){return(round(data,0))
-} else {return(data)}
+if(rnd == TRUE){return(round(data, 0))
+} else {
+  return(data)}
 }

R/genmvnorm.R

@@ -1,49 +1,49 @@
 
-genmvnorm<-function(cor,k,n,seed=F){
+genmvnorm <- function(cor, k, n, seed = FALSE){
 
 
-  if(is.matrix(cor)==F){
-    x<-length(cor)
-    if(x != (k*(k-1)/2) ){stop("STOP: wrong correlation table")}
+  if(is.matrix(cor) == FALSE){
+    x <- length(cor)
+    if(x != (k * (k - 1) / 2)){stop("STOP: wrong correlation table")}
 
-    cr.cor<-matrix(NA,k,k)
-    diag(cr.cor)<-1
-    cr.cor[lower.tri(cr.cor)]<-cor
-    cr.cor[upper.tri(cr.cor)]<-t(cr.cor)[upper.tri(cr.cor)]
+    cr.cor <- matrix(NA, k, k)
+    diag(cr.cor) <- 1
+    cr.cor[lower.tri(cr.cor)] <- cor
+    cr.cor[upper.tri(cr.cor)] <- t(cr.cor)[upper.tri(cr.cor)]
 
-    e<-eigen(cr.cor)
-    L<-e$values #placing the eigenvalues in V
-    Vm<-matrix(0,nrow=k,ncol=k) #creating a k x k matrix.
-    diag(Vm)<-L #putting the eigenvalues on the diagonals
+    e <- eigen(cr.cor)
+    L <- e$values #placing the eigenvalues in V
+    Vm <- matrix(0, nrow = k, ncol = k) #creating a k x k matrix.
+    diag(Vm) <- L #putting the eigenvalues on the diagonals
     Vm #check-- matrix with eigenvalues
     e$vectors #these are the eigenvectors
-    l<-e$vectors %*% sqrt(Vm) #these are the loadings
+    l <- e$vectors %*% sqrt(Vm) #these are the loadings
   }
 
-  if(is.matrix(cor)==T){ #if a correlation matrix was used
+  if(is.matrix(cor) == TRUE){ #if a correlation matrix was used
 
-    e<-eigen(cor)
-    L<-e$values #placing the eigenvalues in V
-    Vm<-matrix(0,nrow=k,ncol=k) #creating a k x k matrix.
-    diag(Vm)<-L #putting the eigenvalues on the diagonals
+    e <- eigen(cor)
+    L <- e$values #placing the eigenvalues in V
+    Vm <- matrix(0, nrow = k, ncol = k) #creating a k x k matrix.
+    diag(Vm) <- L #putting the eigenvalues on the diagonals
     Vm #check-- matrix with eigenvalues
     e$vectors #these are the eigenvectors
-    l<-e$vectors %*% sqrt(Vm) #these are the loadings
+    l <- e$vectors %*% sqrt(Vm) #these are the loadings
 
   }
 
-  if(seed != F){set.seed(seed)} 
-  f<-matrix(nrow=k,l)
+  if(seed != FALSE){set.seed(seed)} 
+  f <- matrix(nrow = k, l)
   #dim(f)
-  ma<-matrix(nrow=n,ncol=k)
+  ma <- matrix(nrow = n, ncol = k)
   for (i in 1:k){
-    ma[,i]<-rnorm(n)
+    ma[,i] <- rnorm(n)
   }
-  tma<-t(ma)
-  sol<-f %*% tma
-  sol<-t(sol)
+  tma <- t(ma)
+  sol <- f %*% tma
+  sol <- t(sol)
 
-  data<-data.frame(sol)
+  data <- data.frame(sol)
   return(data)
 
 }

R/recalib.R

@@ -1,13 +1,13 @@
-recalib<-function(data,var,low,high){
-  nmin<-low
-  nmax<-high
-  if(low>high){
-    nmax<-low
-    nmin<-high
+recalib<-function(data, var, low, high){
+  nmin <- low
+  nmax <- high
+  if(low > high){
+    nmax <- low
+    nmin <- high
   }
-  cmin<-min(data[,var])
-  cmax<-max(data[,var])
+  cmin <- min(data[,var])
+  cmax <- max(data[,var])
 
-  data[,var]<-(nmax-nmin)/(cmax-cmin)*(data[,var]-cmin)+nmin
+  data[,var] <- (nmax - nmin) / (cmax - cmin) * (data[,var] - cmin) + nmin
   return(data)
 }

R/revcode.R

@@ -1,10 +1,10 @@
 #reverse coding
-revcode<-function(data,vars){
-x<-length(vars)
+revcode <- function(data, vars){
+ x <- length(vars)
  for (i in 1:x){
- mx<-max(data[,vars[i]])
- mn<-min(data[,vars[i]])
- data[,vars[i]]<-(mx)-data[,vars[i]]+(mn)
+ mx <- max(data[,vars[i]])
+ mn <- min(data[,vars[i]])
+ data[,vars[i]] <- (mx) - data[,vars[i]] + (mn)
 }
 return(data)
 }

README.md

@@ -0,0 +1,2 @@
+# gendata
+

man/dtrans.Rd

@@ -8,21 +8,21 @@ Data Transform
 Transforms variables in a dataset with a specified mean and standard deviation.
 }
 \usage{
-dtrans(data, m, sd, rnd = F)
+dtrans(data, m, sd, rnd = FALSE)
 }
 %- maybe also 'usage' for other objects documented here.
 \arguments{
   \item{data}{
-name of your dataset
+name of your dataset.
 }
   \item{m}{
-indicate a vector of desired means
+indicate a vector of desired means.
 }
   \item{sd}{
-indicate a vector of desired standard deviations
+indicate a vector of desired standard deviations.
 }
   \item{rnd}{
-Indicates if you want to round the numbers (no decimals). T or F.
+indicates if you want to round the numbers (no decimals). \code{TRUE} or \code{FALSE}.
 }
 }
 \author{
@@ -31,23 +31,22 @@ Francis Huang
 
 \examples{
 
-sdata<-genmvnorm(cor=c(.7,.2,.3),k=3,n=500,seed=12345)
+sdata <- genmvnorm(cor = c(.7, .2, .3), k = 3, n = 500, seed = 12345)
 cor(sdata)
 summary(sdata)
 #note: data are in z scores
 
-s2<-dtrans(sdata,c(0,100,50),c(1,15,10),rnd=FALSE)
+s2 <- dtrans(sdata, c(0, 100, 50), c(1, 15, 10), rnd = FALSE)
 summary(s2)
 sd(s2[,2])
 sd(s2[,3])
 #note: variables X2 and X3 are now rescaled with the appropriate means and standard deviations.
 head(s2)
 
-s2<-dtrans(sdata,c(0,100,50),c(1,15,10),rnd=TRUE)
-#at times, you may want a dataset to not have decimals. use rnd=T.
+s2 <- dtrans(sdata, c(0, 100, 50), c(1, 15, 10), rnd = TRUE)
+#at times, you may want a dataset to not have decimals. use \code{rnd= TRUE}.
 head(s2)
 }
 % Add one or more standard keywords, see file 'KEYWORDS' in the
 % R documentation directory.
-\keyword{ transform }
-\keyword{ data }% __ONLY ONE__ keyword per line
+\keyword{transform}

man/genmvnorm.Rd

@@ -8,7 +8,7 @@ Genmvnorm
 Generates a multivariate normal dataset based on a specified correlation matrix. 
 }
 \usage{
-genmvnorm(cor, k, n, seed = F)
+genmvnorm(cor, k, n, seed = FALSE)
 }
 %- maybe also 'usage' for other objects documented here.
 \arguments{

man/recalib.Rd

@@ -39,20 +39,17 @@ Francis Huang
 \link[gendata:dtrans]{dtrans}
 }
 \examples{
-sdata<-genmvnorm(cor=c(.7,.2,.3),k=3,n=500,seed=12345)
+sdata <- genmvnorm(cor = c(.7, .2, .3), k = 3, n = 500, seed = 12345)
 cor(sdata)
 summary(sdata[,1])
 #note the min and max of variable X1
 #changes variable one to have a minimum of 10 and a maximum of 50
 #correlations remain the same
 
-s2<-recalib(sdata,1,10,50)
+s2 <- recalib(sdata, 1, 10, 50)
 cor(s2)
 summary(s2[,1])
 #note revised values of variable X1
 
 }
-% Add one or more standard keywords, see file 'KEYWORDS' in the
-% R documentation directory.
-\keyword{ ~kwd1 }
-\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
+