testes<-function(amostra1,amostra2=NULL,var1=NULL,var2=NULL,hipotese,erro=0.05,independent,iguais){


#TH para uma amostra#
  if(is.null(amostra2)){

#Proporcao
    if( sum(ifelse(amostra1<=1,1,0))==length(amostra1)){
z=(mean(amostra1) - hipotese)/(sqrt(hipotese*(1-hipotese)/length(amostra1)))
k=2
}else{#nao proporcoa
      if(!is.null(var1)){

        ## formula 1
        z=(mean(amostra1)-hipotese)/(sqrt(var1)/sqrt(length(amostra1)))
        k=2
      } else{

        ## formula 2
        t=(mean(amostra1)- hipotese)/(sd(amostra1)/(sqrt(length(amostra1))))
        k=3
      } #close TH mu var desconhecida
    }# close TH mu
  }  else{ #Duas amostras

    #segunda parte da arvore
    check=try(length(independent),silent=T)
    if(is(check,"try-error")) stop(paste("You must inform if the samples are independent=1 or not"))
    if(independent==0){

      #remove?
#      if( sum(ifelse(amostra1<=1,1,0))==length(amostra1) &
#          sum(ifelse(amostra2<=1,1,0))==length(amostra2)){
#        ## formula 8
#        z=(mean(amostra1)-hipotese)/(var1^2/sqrt(length(amostra1)))
#        k=6
#      }
#      else{
        ## formula 7
        d=amostra1-amostra2
        t=(mean(d)-hipotese)/(sd(d)/sqrt(length(d)))
        k=7
      }    else{
      if( sum(ifelse(amostra1<=1,1,0))==length(amostra1) &
          sum(ifelse(amostra1<=1,1,0))==length(amostra1)){
        ## formula 9
        p1=mean(amostra1); p2=mean(amostra2)
        z=(p1-p2-hipotese)/
          (sqrt((p1*(1-p1))/length(amostra1)+p2*(1-p2)/length(amostra2)))
        k=6
      }     else{
        if(!is.null(var1) & !is.null(var2)){

          ## formula 4
          z=(mean(amostra1)-mean(amostra2)-(hipotese))/
            sqrt(var1/length(amostra1)+var2/length(amostra2))
          k=6
        }     else{

    check=try(length(iguais),silent=T)
    if(is(check,"try-error")) stop(paste("You must inform if the variances are equal=1 or not"))

          if(iguais==1){
            ## formula 5

            sp=((length(amostra1)-1)*var(amostra1)+(length(amostra2)-1)*var(amostra2))/
              (length(amostra1)+length(amostra2)-2)
            t=(mean(amostra1)-mean(amostra2)-hipotese)/
              (sqrt((sp)*(1/length(amostra1) + 1/length(amostra2))))
            k=4
          }     else{
            ## formula 6
            t=(mean(amostra1)-mean(amostra2)-(hipotese)/
                 (sqrt(var(amostra1)/length(amostra1)+var(amostra2)/length(amostra2))))
            k=5
          }}}}
    }    # fim duas amostras

  if(k==2){
    limites=ifelse(z>-4 & z<4,4,z+0.5)
    r1=curve(dnorm(x),-limites,limites,ylab="Normal distribution",xlab="Z")
    # na qual o erro sera de 5%, 2.5% para cada lado
    r2=lines(c(qnorm(erro/2),qnorm(erro/2)),c(-1,0.05),col='tomato1',lwd=3)
    r3=lines(c(qnorm(1-erro/2),qnorm(1-erro/2)),c(-1,0.05),col='tomato1',lwd=3)
    r4=abline(v=z,col='orchid1',lwd=4,lty=2)

    if(z< qnorm(erro/2)){
      print(
        paste(
          'Reject the null hypothesis, that is, the true average is different from ',hipotese
        ))}  else if(z> qnorm(1-erro/2)){
          print(
            paste("Reject the null hypothesis, that is, the true average is different from ",hipotese
            ))} else
              print(
                paste("We do not have enough information to reject the null hypothesis  that the mean is equal to ",hipotese)
                )

  }

  if(k==3){limites=ifelse(t>-4 & t<4,4,t+0.5)
  r1=curve(dt(x,length(amostra1)-1),-limites,limites,ylab="T3 distribution",xlab="T")
  # na qual o erro sera de 5%, 2.5% para cada lado
  r2=lines(c(qt(erro/2,length(amostra1)-1),qt(erro/2,length(amostra1)-1)),c(-1,0.05),col='tomato1',lwd=3)
  r3=lines(c(qt(1-erro/2,length(amostra1)-1),qt(1-erro/2,length(amostra1)-1)),c(-1,0.05),col='tomato1',lwd=3)
  r4=abline(v=t,col='orchid1',lwd=4,lty=2)

  if(t< qt(erro/2,length(amostra1)-1)){
    print(
      paste(
        'Reject the null hypothesis, that is, the true average is different from ',hipotese
      ))}
  else if(t> qt(1-erro/2,length(amostra1)-1)){
    print(
      paste("Reject the null hypothesis, that is, the true average is different from ",hipotese
      ))} else
        print(
          paste("We do not have enough information to reject the null hypothesis that the mean is equal to ",hipotese)
          )
  }

  if(k==4){
    limites=ifelse(t>-4 & t<4,4,t+0.5)
    gl=length(amostra1)+length(amostra2)-2
    r1=curve(dt(x,gl),-limites,limites,ylab="T4 distribution",xlab="T")
             # na qual o erro sera de 5%, 2.5% para cada lado
             r2=lines(c(qt(erro/2,gl),qt(erro/2,gl)),c(-1,0.05),col='tomato1',lwd=3)
             r3=lines(c(qt(1-erro/2,gl),qt(1-erro/2,gl)),c(-1,0.05),col='tomato1',lwd=3)
             r4=abline(v=t,col='orchid1',lwd=4,lty=2)

             if(t< qt(erro/2,gl)){print(paste('The average of X and Y are different'))}
             if(t> qt(1-erro/2,gl)){print(
                 paste("The average of X and Y are different"))} else
                   print(
                     paste("We do not have enough information to know the average of X and Y are different")
                   )

  }

  if(k==5){
    limites=ifelse(t>-4 & t<4,4,t+0.5)
      gl=(var(amostra1)/length(amostra1)+var(amostra2)/length(amostra2))^2/
      (((var(amostra1)/length(amostra1))^2/(length(amostra1)-1))+
         (var(amostra2)/length(amostra2))^2/(length(amostra2)-1))
    r1=curve(dt(x,gl),-limites,limites,ylab="T5 distribution",xlab="T")
             # na qual o erro sera de 5%, 2.5% para cada lado
           r2=lines(c(qt(erro/2,gl),qt(erro/2,gl)),c(-1,0.05),col='tomato1',lwd=3)
             r3=lines(c(qt(1-erro/2,gl),qt(1-erro/2,gl)),c(-1,0.05),col='tomato1',lwd=3)
             r4=abline(v=t,col='orchid1',lwd=4,lty=2)

             if(t< qt(erro/2,gl)){
               print(
                 paste(
                   'The average of X and Y are different'
                 ))}
             else if(t> qt(1-erro/2,gl)){
               print(
                 paste("The average of X and Y are different"))} else
                   print(
                     paste("We do not have enough information to know the average of X and Y are different")
                   )

  }

  if(k==6){
    limites=ifelse(z>-4 & z<4,4,z+0.5)
    r1=curve(dnorm(x),-limites,limites,ylab="Normal distribution",xlab="Z")
    # na qual o erro sera de 5%, 2.5% para cada lado
    r2=lines(c(qnorm(erro/2),qnorm(erro/2)),c(-1,0.05),col='tomato1',lwd=3)
    r3=lines(c(qnorm(1-erro/2),qnorm(1-erro/2)),c(-1,0.05),col='tomato1',lwd=3)
    r4=abline(v=z,col='orchid1',lwd=4,lty=2)

    if(z< qnorm(erro/2)){
      print(
        paste(
          'The average of X and Y are different'
        ))}  else if(z> qnorm(1-erro/2)){
          print(
            paste("The average of X and Y are different"
            ))} else
              print(
                paste("We do not have enough information to know the average of X and Y are different")
              )

  }

  if(k==7){limites=ifelse(t>-4 & t<4,4,t+0.5)
  r1=curve(dt(x,length(amostra1)-1),-limites,limites,ylab="T7 distribution",xlab="T")
  # na qual o erro sera de 5%, 2.5% para cada lado
  r2=lines(c(qt(erro/2,length(amostra1)-1),qt(erro/2,length(amostra1)-1)),c(-1,0.05),col='tomato1',lwd=3)
  r3=lines(c(qt(1-erro/2,length(amostra1)-1),qt(1-erro/2,length(amostra1)-1)),c(-1,0.05),col='tomato1',lwd=3)
  r4=abline(v=t,col='orchid1',lwd=4,lty=2)

  if(t< qt(erro/2,length(amostra1)-1)){
    print(
      paste(
        'The average of X and Y are different'
      ))}
  else if(t> qt(1-erro/2,length(amostra1)-1)){
    print(
      paste("The average of X and Y are different"
      ))} else
        print(
          paste("We do not have enough information to know the average of X and Y are different")
        )
  }



  }



print('Esse pacote foi desenvolvido para fazer testes de hipotese, por Maria Gabriela, Bruno Cintra e Giovana Aleixo. Enjoy')