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# dat has to be a data.frame or data.table
# varnames should bea vector with the variable names you are looking for, e.g., c("hc", "hv"), or regular expressions
# the result would be a vector with variables names

lookvar  <- function(dat, varnames) {
  n  <- names(dat)
  
  nn  <- list()
    for (i in 1:length(varnames)) {
      nn[[i]]  <- grep(varnames[i],n)

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# AVERAGE PREDICTVE COMPARISON

# loading libraries
library(arm)

# data
dat <- read.csv("http://dl.getdropbox.com/u/18116710/example.csv")

# id: individual identifier
# wave: 1 to 7

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mycoefplot <- function(coef) {

# reverse coefficients
coef <- apply(coef, 2, rev)
nvar <- length(rownames(coef))

# dotplot
dotplot(1:nvar~coef[,1], 
	xlim=c(min(coef[,1]-2*coef[,2])-.02, max(coef[,1]+2*coef[,2])+.02), 
	xlab='Average predictive comparison', ylab=" ",

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# SAMPLING ERROR
 
serr <- function(n, deff=1, rr=1, N=NULL, cl=.95, p=0.5, relative=FALSE)  {

# validation

if (sum(n==0)>=1) { 
  stop("n vector contains 0 values")
}

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######################################################
# SIMULATION FUNCTION TERM PAPER SOC 952, SPRING 2014
######################################################

# RETURN STANDARDIZED BIAS AND COVERAGE RATES

# These libraries have to be installed

library(data.table)
library(lavvan)

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# MY R FUNCTIONS
# AUTHOR: SEBASTIAN DAZA
# Jun 02, 2014, 10:35 AM

# LOOKING FOR VARIABLES ####
lookvar  <- function(dat, varnames) {
  n  <- names(dat)
  
  nn  <- list()
    for (i in 1:length(varnames)) {

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###########
# Example
##########

library(igraph)

actors <- data.frame(name=c("Alice", "Bob", "Cecil", "David",
                            "Esmeralda"),
                     age=c(48,33,45,34,21),
                     gender=c("F","M","F","M","F"))

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library(acs)
rm(list=ls(all=TRUE))

variables <- c("C17002_002", "C17002_003", "C17002_004", "C17002_005","C17002_006", "C17002_001")
dat <- acs.fetch(geo = geo.make(state = "WI", county = "*", tract = "*"), variable = variables)
p_output <- divide.acs(dat[,1] + dat[,2] + dat[,3] + dat[,4] + dat[,5], dat[,6], method = "proportion")
r_output <- divide.acs(dat[,1] + dat[,2] + dat[,3] + dat[,4] + dat[,5], dat[,6], method = "ratio")

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# EXAMPLES USING PACKAGE ACSR
# AUTHOR: SEBASTIAN DAZA
# March 23, 2015, 8:19 AM

# INSTALL LAST VERSION OF DATA.TABLE AND ACS PACKAGE (YOU HAVE TO INSTALL DEVTOOLS BEFORE!)
# I ASSUME ACS IS ALREADY INSTALLED!
install.packages("devtools")
devtools::install_github("Rdatatable/data.table", build_vignettes = FALSE)

# INSTALL MY PACKAGE

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# load data
load("ex.Rdata")

###################################
# example only using mice package
###################################

library(mice)

imp <- mice(ex, maxit = 0)