cispy pull request

.travis.yml

@@ -0,0 +1,9 @@
+language: r
+sudo: required
+
+r_github_packages:
+  - ./travis-tool.sh github_package jimhester/covr
+
+after_success:
+  - Rscript -e 'library(covr);coveralls()'
+

CompPack/CompPack.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

DESCRIPTION

@@ -3,28 +3,52 @@ Type: Package
 Title: Collection of Computational Statistic Tools
 Version: 1.0
 Date: 2013-04-10
-Author: Tyler Hunt
+Author: Tyler Hunt <tyler@psychoanalytix.com>, Katie Dodds <k.dodds@utah.edu>,
+    Ernest Chan <faiernest418@gmail.com>, Damon McCafferty
+    <damon.mccafferty@economics.utah.edu>
 Maintainer: Tyler Hunt <tyler@psychoanalytix.com>
 Description: The functions in this package were developed in a computational
     statistics course taught at the University of Utah.
 License: MIT
 Collate:
+    'anneal.R'
+    'around.R'
+    'cvscore.R'
+    'cvscore1.R'
+    'cvscore2.R'
+    'cvscore3.R'
+    'cvscore4.R'
+    'cvscore5.R'
+    'd1f.R'
+    'd2f.R'
     'em.mixnorm.R'
+    'f.models.R'
     'gold.sect.R'
     'grid.sect.R'
+    'jackknife.R'
+    'log.lik.R'
     'maxbound.R'
     'mc.R'
+    'mixlike.R'
     'mixnorm.R'
+    'nearest.R'
+    'nearest.loc.R'
     'newton.raph.R'
+    'onedfunction.R'
     'plot.mc.R'
+    'pois.int.R'
+    'pois.proc.R'
+    'pois.rate.R'
+    'polar.rnorm.R'
     'rand.exp.R'
     'rand.mvnorm.R'
     'rand.norm.R'
     'rand.unif.R'
+    'rand.weib.R'
     'tpower.R'
+    'twodfunction.R'
+    'twodranduphill.R'
     'update.CompPack.R'
+    'uphill.mixnorm.R'
     'wich.hill.R'
-    'pois.proc.R'
-    'nearest.R'
-    'around.R'
-    'nearest.loc.R'
+    'bootstrap.R'

NAMESPACE

@@ -1,18 +1,41 @@
+export(anneal)
 export(around)
+export(bootstrap)
+export(cvscore)
+export(cvscore1)
+export(cvscore2)
+export(cvscore3)
+export(cvscore4)
+export(cvscore5)
+export(d1f)
+export(df)
 export(em.mixnorm)
+export(f.models)
+export(gold.sect)
 export(grid.sect)
+export(jackknife)
+export(log.lik)
 export(maxbound)
 export(mc)
+export(mixlike)
 export(mixnorm)
 export(nearest)
 export(nearest.loc)
 export(newton.raph)
+export(onedfunction)
 export(plot.mc)
+export(pois.int)
 export(pois.proc)
+export(pois.rate)
+export(polar.rnorm)
 export(rand.exp)
 export(rand.mvnorm)
 export(rand.norm)
 export(rand.unif)
+export(rand.weib)
 export(tpower)
+export(twodfunction)
+export(twodranduphill)
 export(update.CompPack)
+export(uphill.mixnorm)
 export(wich.hill)

R/.gitignore

@@ -0,0 +1 @@
+rand.norm.R

R/anneal.R

@@ -0,0 +1,42 @@
+#' uphill search on fucntion
+#' 
+#' @description
+#' General anneal function, where we conduct a uphill search on the function to find maximum
+#' 
+#' @param f function to find the maximum
+#' @param mu starting point
+#' @param n number of iterations
+#' @param sig size of steps
+#' @param tt temperature
+#' @param g rate of temperature
+#' 
+#' @examples
+#' anneal(onedfunction, 2, 100, 1, 10, .99)
+#' @author Ernest Chan \email{faiernest418@@gmail.com}
+#' 
+#' @export
+
+anneal = function(f,mu,n=1000,sig=1,tt=10,g=0.999)
+{
+	m = mu
+	fm = f(m)
+	x = rep(0,n)
+	fx = rep(0,n)
+	t = tt
+	for (i in 1:n)
+	{
+		dm = m+rnorm(1,0,sig)
+		fdm = f(dm)
+
+		t = t*g
+		if (runif(1) < (fdm/fm)^(1/t))
+		{
+			m = dm
+			fm = fdm
+		}
+		x[i] = m
+		fx[i] = fm
+	}
+	ii = ((1:n)[fx==max(fx)])[1]
+	list(x = x, fx = fx, best = x[ii], fbest = fx[ii], t=t)
+}

R/around.R

@@ -2,11 +2,19 @@
 #' 
 #' @description Find the location of values around a specified value
 #' 
-#' @param x a vector, matrix, or data frame.
+#' @param x a vector.
 #' @param value specified value
 #' 
 #' @author Tyler Hunt \email{tyler@@psychoanalytix.com} 
-#' @export
+#' 
+#' @return lo the maximum value of x that is less than or equal to the value parameter.
+#' @return hi the minimum value of x that is greater than or equal to the value parameter.
+#' 
+#' @examples
+#' x = rnorm(50, 3, 7)
+#' value = 15
+#' 
+#' @export 
 
 around<-function(x, value){
   x<-sort(x)

R/bootstrap.R

@@ -0,0 +1,24 @@
+#' bootstrap
+#' 
+#' @description
+#' This function is used for estimating standard errors when the distribution is not know.
+#' 
+#' @param x a vector.
+#' @param boots The number of bootstraps.
+#' @param fn the function you want to bootstrap, ie., mean, var, cov, etc.
+#' 
+#' @author Tyler Hunt \email{tyler@@psychoanalytix.com}
+#' 
+#' @examples
+#' x = runif(10, 0, 1)
+#' bootstrap(x,fn=mean)
+#' 
+#' @export
+
+
+bootstrap<-function(x, boots=100, fn){
+	n=length(x)
+	lings<-replicate(boots, fn(sample(x,n, replace=TRUE)))
+
+	list(se=sd(lings), lings=lings)
+}

R/cvscore.R

@@ -0,0 +1,96 @@
+#' Cross Validation
+#'
+#' @description Uses cross-validation to score how accurately a a linear, quadratic, cubic, and 4th and 5th degree polynomial models will perform when used in prediction.
+#'
+#' @param x is the independent variable
+#' @param y is the dependent variable
+#'
+#' @author Katie Dodds \email{k.dodds@@utah.edu}
+#'
+#' @examples
+#' attach(mtcars)
+#' x=wt
+#' y=mpg
+#' cvscore(x,y)
+#'
+#' @export
+cvscore<-
+function(x,y){
+{
+	n=length(x)
+	yhat=rep(0,n)
+
+	for (i in 1:n)
+	{
+		yi=y[-i]
+		xi=x[-i]
+		zi=xi*yi
+		param=glm(yi~xi)$coeff
+		yhat[i]=param[1]+x[i]*param[2]
+	}
+	linear=sum((yhat-y)^2)
+}
+{
+	n=length(x)
+	yhat=rep(0,n)
+
+	for (i in 1:n)
+	{
+		yi=y[-i]
+		xi=x[-i]
+		zi=xi*yi
+		param=glm(yi~xi+zi)$coeff
+		yhat[i]=param[1]+x[i]*param[2]+x[i]^2*param[3]		
+	}
+	quadratic=sum((yhat-y)^2)
+}
+{
+	n=length(x)
+	yhat=rep(0,n)
+
+	for (i in 1:n)
+	{
+		yi=y[-i]
+		xi=x[-i]
+		zi=xi*xi
+		wi=xi*xi*xi
+		param=glm(yi~xi+zi+wi)$coeff
+		yhat[i]=param[1]+x[i]*param[2]+x[i]^2*param[3]	+x[i]^3*param[4]	
+	}
+	cubic=sum((yhat-y)^2)
+} 
+{
+	n=length(x)
+	yhat=rep(0,n)
+
+	for (i in 1:n)
+	{
+		yi=y[-i]
+		xi=x[-i]
+		zi=xi*xi
+		wi=xi*xi*xi
+		vi=xi*xi*xi*xi
+		param=glm(yi~xi+zi+wi+vi)$coeff
+		yhat[i]=param[1]+x[i]*param[2]+x[i]^2*param[3]	+x[i]^3*param[4]	+x[i]^4*param[5]
+	}
+	fourth.degree=sum((yhat-y)^2)
+}
+{
+	n=length(x)
+	yhat=rep(0,n)
+
+	for (i in 1:n)
+	{
+		yi=y[-i]
+		xi=x[-i]
+		zi=xi*xi
+		wi=xi*xi*xi
+		vi=xi*xi*xi*xi
+		ui=xi^5
+		param=glm(yi~xi+zi+wi+vi+ui)$coeff
+		yhat[i]=param[1]+x[i]*param[2]+x[i]^2*param[3]	+x[i]^3*param[4]	+x[i]^4*param[5]+x[i]^5*param[6]
+	}
+	fifth.degree=sum((yhat-y)^2)
+}
+list(linear=linear, quadratic=quadratic, cubic=cubic, fourth.degree, fifth.degree)
+}

R/cvscore1.R

@@ -0,0 +1,34 @@
+#' Cross Validation-Linear Model
+#'
+#' @description Uses cross-validation to score how accurately a linear model will perform when used in prediction. Score can be compared to that of cvscore2,...,cvscore5.
+#'
+#' @param x is the independent variable
+#' @param y is the dependent variable
+#'
+#' @author Katie Dodds \email{k.dodds@@utah.edu}
+#'
+#' @examples
+#'
+#' attach(mtcars)
+#' x=wt
+#' y=mpg
+#' cvscore1(x,y)
+#'
+#' @export
+
+cvscore1<-
+function (x,y) 
+{
+	n=length(x)
+	yhat=rep(0,n)
+
+	for (i in 1:n)
+	{
+		yi=y[-i]
+		xi=x[-i]
+		zi=xi*yi
+		param=glm(yi~xi)$coeff
+		yhat[i]=param[1]+x[i]*param[2]
+	}
+	sum((yhat-y)^2)
+}

R/cvscore2.R

@@ -0,0 +1,33 @@
+#' Cross Validation-Quadratic Model
+#'
+#' @description  Uses cross-validation to score how accurately a quadratic model will perform when used in prediction. Score can be compared to that of cvscore1,...,cvscore5.
+#'
+#' @param x is the independent variable
+#' @param y is the dependent variable
+#'
+#' @author Katie Dodds \email{k.dodds@@utah.edu}
+#'
+#' @examples
+#'
+#' attach(mtcars)
+#' x=wt
+#' y=mpg
+#' cvscore2(x,y)
+#'
+#' @export
+cvscore2<-
+function (x,y) 
+{
+	n=length(x)
+	yhat=rep(0,n)
+
+	for (i in 1:n)
+	{
+		yi=y[-i]
+		xi=x[-i]
+		zi=xi*yi
+		param=glm(yi~xi+zi)$coeff
+		yhat[i]=param[1]+x[i]*param[2]+x[i]^2*param[3]		
+	}
+	sum((yhat-y)^2)
+}