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#TEMPLATE DE ANALISE - contexto_study_3.R
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#
# Glossário
# $ = Pertence a = data$sexo, que dizer variável sexo pertencente ao banco data
#
#
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#ORGANIZANDO O AMBIENTE DO R

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Methods

[insert information on the definition of the outcomes modeled]. Heterogeneity across studies was first examined using quantitative and qualitative criteria and clinical judgment. Qualitative factors included the type of [criteria used to evaluate methodological homogeneity]. While quantitative indicators were Cochrane's Q (considering p-values lower than 0.05 to indicators of heterogeneity), H and I statistics (Higgins and Thompson, 2002). High Iˆ2 values indicate high heterogeneity, with a proposed categorization of 25% (low), 45% (moderate) and 75% (high) (Higgins et al., 2003). In our study, we adopted the interpretation of the [method of estimation] because our main goal is to [random-effect-summarize the estimates of XXXX, across a variety of studies, being applicable to different scenarios and populations as comprehended in the systematic revire OR fixed-effect-find the true-effect of of XXXX along different populations sized reported in the literature]. Also, as a form of sensibility for the

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# bootstrapped standard errors and confidence intervals for sem
# original sem code from sem:::bootSem.sem
# original post: http://r.789695.n4.nabble.com/Bootstrap-bootSem-causes-R-to-crash-tp4661900p4677999.html


# commented out lines referencing tcltk

bootSem2 <- function (model, R = 100, Cov = cov, data = model$data, max.failures = 10, 
          ...) 
{

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#KMO
kmo = function( data ){
	
	library(MASS)
	X <- cor(as.matrix(data))
	iX <- ginv(X)
	S2 <- diag(diag((iX^-1)))
	AIS <- S2%*%iX%*%S2                      # anti-image covariance matrix
	IS <- X+AIS-2*S2                         # image covariance matrix
	Dai <- sqrt(diag(diag(AIS)))

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#####################################################################################
#R TEMPLATE FOR NETWORK METANALYSIS
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#SETTING ENVIRONMENT

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#######################################################################################
#example_metanalysis.R is licensed under a Creative Commons Attribution - Non commercial 3.0 Unported License. see full license at the end of this file.
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#this script follows a combination of the guidelines proposed by Hadley Wickham http://goo.gl/c04kq as well as using the formatR package http://goo.gl/ri6ky
#if this is the first time you are conducting an analysis using this protocol, please watch http://goo.gl/DajIN while following step by step

#link to manuscript

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#SETTING ENVIRONMENT

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# script stolen and adapted from http://goo.gl/kUzaov


if (!require('sem')) install.packages('sem')
if (!require('lavaan')) install.packages('lavaan')
if (!require('Rcmdr')) install.packages('Rcmdr')


# creating a dataset for g + 3 factors, all orthogonal

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######################################################################
#BASIC R STATISTICS TEMPLATE
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#
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#SETTING ENVIRONMENT

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# Mayo Clinic Lung Cancer Data
library(survival)

# learn about the dataset
help(lung)

# create a Surv object 
survobj <- with(lung, Surv(time,status))

# Plot survival distribution of the total sample

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#
# Functions to make ggplot KM survivor curves made with survfit() in library(survival)
#
# code written by Ramon Saccilotto
# and included in his ggplot2 tutorial
# 2010-12-08

# define custom function to create a survival data.frame
createSurvivalFrame <- function(f.survfit){
  # initialise frame variable