R/Delta.R
In toni-1991/Delta: Measure of Agreement Between Two Raters
Defines functions
Delta
Documented in
Delta
#' Delta coefficient function
#'
#' This function provides an analysis of the matrix provided, returning all all the parameters estimations
#' and SE calculations that have sense with the fixedrows and gstandard provided.
#'
#' This function study the matrix provided by the user. This function modify the matrix deleting missing rows and columns and
#' if it is needed for the estimation, adding 0.5 to each cell.
#'
#' Also calculate Cohen's Kappa coefficient and the goodness of fit for the Delta model.
#' @param datatable Matrix. Expected to be square matrix with at least 2 rows (columns), non negative values and at least an element different of zero.
#' @param fixedrows Boolean. Indicate if sample rows are fixed beforehand. Default is TRUE.
#' @param gstandard Text. Indicate if there are a Gold Standard by Rows or columns. Only first letter matter without Case sensitivity. Options are: "N" for None, "R" for in Rows and "C" for in Columns. Default is "N".
#' @param maxits Whole number. Indicate the maximum number of iterations of the numeric method to calculate B. Expected to be 100 <= maxits <= 5000. Default is 1000.
#' @param tol Double number. Indicate the precision of the numeric method to calculate B. Expected to be 1e-6 <= tol <= 1e-15.Default is 1e-12.
#' @param dplaces Whole number. Decimal placed to be shown in the result. Expected to be 1 <= dplaces <0 6. Default 4.
#' @param showall Boolean. Indicate if all output should be shown. If TRUE also shown hidden results. If FALSE shown only main output. By default is FALSE.
#' @keywords Delta datatable fixedrows gstandard maxits tol dplaces
#' @export
#' @examples
#' Delta(matrix(c(1,2,3,4),2,2))
#' Delta(matrix(c(65,5,10,20),2,2),fixedrows=TRUE,gstandard="Row")
Delta <- function(datatable,fixedrows = FALSE, gstandard = "No",
maxits=1000,tol=1e-12,dplaces=4,showall = FALSE){
# Check input variables
delta.samplingtype = CheckInput(datatable,fixedrows ,gstandard ,maxits,tol,dplaces,showall)
#Keep original size
k_0 = dim(datatable)[1]
#Calculate matrix deleting Unnecessary rows
res = GetM1(datatable)
M1 = res$M1
k = res$k
Del_rows = res$Deleted
#For type 1, no extra matrix are needed
if (k >= 2){
Mxs = GetMx(M1)
M2 = Mxs$M2
M3 = Mxs$M3
M4 = Mxs$M4
#Get type of problem
ktp = GetKappaProblemType(M1)
if (k > 2) {
dtp = GetDeltaProblemType(M1)
}
else {
dtp = GetDeltaProblemType(M2)
dtp2 = GetDeltaProblemType(M1)
}
#mat will be the matrix to be used for calcs of covariance
if (k > 2){
mat = M1
if (dtp == "DN0" || dtp == "DN1"){
mat.SE = M3
}
else if (dtp == "DN2"){
mat.SE = M1
}
}
else if (k == 2){
mat = M2
mat.SE = M2
}
#NO asintotics calculation
#Calculations: Part 2, derivate Kappa and SE
kappa.res = GetKappa(M1)
# Kappa estimation done with M1
kappa.val = kappa.res$kappa
if (ktp == "K0"){
kappa.res = GetKappa(M3)
}
# Kappa SE done with M1 if K1 or M3 if K0
kappa.SE = kappa.res$SE
#Calculations: Part 2, calculate pi_i, delta_i
res.Pi = GetDeltaPi(mat,dtp,tol,maxits)
Pi = res.Pi$Pi
Delta = res.Pi$Delta
B = res.Pi$B
#Also Delta_cov, Pi_cov and B_cov
if (dtp == "DN0" || dtp == "DN1"){
res.Pi.M3 = GetDeltaPi(M3,dtp,tol,maxits, original.mx = FALSE)
Pi_cov = res.Pi.M3$Pi
Delta_cov = res.Pi.M3$Delta
B_cov = res.Pi.M3$B
}
else {
Pi_cov = res.Pi$Pi
Delta_cov = res.Pi$Delta
B_cov = res.Pi$B
}
#Goodnes of fit to the model if possible
if (dtp == "DN0" ){
res = GetGoodness(M3,Pi_cov,Delta_cov)
}
else {
res = GetGoodness(mat,Pi_cov,Delta_cov)
}
X.sq = res$X.squared
df = res$df
p.val = res$p.value
E.matrix = res$E.matrix
Covar = GetCovariance(mat.SE,Delta_cov,Pi_cov,B_cov)
Cov_Delta = Covar$Cov_Delta
Cov_mix = Covar$Cov_mix
Cov_Pi = Covar$Cov_Pi
E = Covar$E
#Update Delta and Pi to correct size in cases k = 2
if (k == 2){
Pi = Pi[-3]
Pi_cov= Pi_cov[-3]
Delta = Delta[-3]
Delta_cov = Delta_cov[-3]
Cov_Delta = Cov_Delta[-3,-3]
Cov_mix = Cov_mix[-3,-3]
Cov_Pi = Cov_Pi[-3,-3]
}
res.Params = GetDeltaParams(mat, Delta, Pi, k)
res.Params2 = GetDeltaParams(mat.SE, Delta_cov, Pi_cov, k)
res.ParamsVar = GetDeltaParamsVar(mat.SE,fixedrows, Delta_cov, Pi_cov, k, Cov_Delta, E)
#Calculations Part 3: Asintotic calculations
if (k == 2){
res.M1 = GetAsinDeltaParams(M1,fixedrows)
if (dtp2 == "DA0") {
res.M3 = GetAsinDeltaParams(M3,fixedrows)
}
res.M4 = GetAsinDeltaParams(M4,fixedrows)
kappa.res.asin = GetKappa(M4)
}
}
if (k > 2){
res = list("Raw.matrix" = datatable, "M1" = M1,"M3" = M3)
}
else {
res = list("Raw.matrix" = datatable, "M1" = M1,"M2" = M2,"M3" = M3,"M4" = M4)
}
res$Del_rows = Del_rows
res$dtp = dtp
if (k == 2){
res$dtp2 = dtp2
}
res$ktp = ktp
res$k = k
res$k0 = k_0
res$showall = showall
res$samplingtype = delta.samplingtype
if (is.null(res.ParamsVar$P.cov)){
res.ParamsVar$P.cov = rep(NaN,k)
}
if (is.null(res.ParamsVar$S.cov)){
res.ParamsVar$S.cov = rep(NaN,k)
}
#First table 1
if (dtp != "DN1" && dtp != "DN0") {
Agreement = paste(round(res.Params$A,dplaces)," \u00b1 ",round(res.ParamsVar$A.cov,dplaces))
Conformity = paste(round(res.Params$F,dplaces)," \u00b1 ",round(res.ParamsVar$F.cov,dplaces))
Predictivity = paste(round(res.Params$P,dplaces)," \u00b1 ",round(res.ParamsVar$P.cov,dplaces))
Consistency = paste(round(res.Params$S,dplaces)," \u00b1 ",round(res.ParamsVar$S.cov,dplaces))
}
else{
Agreement = paste(round(res.Params$A,dplaces))
Conformity = paste(round(res.Params$F,dplaces))
Predictivity = paste(round(res.Params$P,dplaces))
Consistency = paste(round(res.Params$S,dplaces))
}
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
if (is.null(Pi)){
Pi = rep(NaN,k)
}
if (dtp != "DN1" && dtp != "DN0"){
Delta_tab = paste(round(Delta,dplaces), "(" ,round(Delta_cov,dplaces) , ")")
Pi_tab = paste(round(Pi,dplaces), "(" , round(Pi_cov,dplaces), ")")
Table = data.frame(1:k,Delta_tab,Pi_tab,Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
}
else {
Table = data.frame(1:k,round(Delta,dplaces),round(Pi,dplaces),Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
}
res$Fullparamstable = Table
res$Deltaoverall = round(res.Params$Delta.total,dplaces)
res$Deltaoverall_SE = round(res.ParamsVar$Delta.total.cov,dplaces)
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable = Table
if (dtp == "DN1" || dtp == "DN0"){
#First table with M3
Agreement = paste(round(res.Params2$A,dplaces)," \u00b1 ",round(res.ParamsVar$A.cov,dplaces))
Conformity = paste(round(res.Params2$F,dplaces)," \u00b1 ",round(res.ParamsVar$F.cov,dplaces))
Predictivity = paste(round(res.Params2$P,dplaces)," \u00b1 ",round(res.ParamsVar$P.cov,dplaces))
Consistency = paste(round(res.Params2$S,dplaces)," \u00b1 ",round(res.ParamsVar$S.cov,dplaces))
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
Delta_tab = paste(round(Delta_cov,dplaces))
Pi_tab = paste(round(Pi_cov,dplaces))
Table = data.frame(1:k,Delta_tab,Pi_tab,Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
res$Fullparamstable2 = Table
res$Deltaoverall2 = round(res.Params2$Delta.total,dplaces)
res$Deltaoverall_SE2 = round(res.ParamsVar$Delta.total.cov,dplaces)
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable2 = Table
}
res$Cov_Delta = round(Cov_Delta,dplaces)
res$Cov_mix = round(Cov_mix,dplaces)
res$Cov_Pi = round(Cov_Pi,dplaces)
if (k >= 2){
res$E_matrix = round(E.matrix,dplaces)
Chisq = paste("Chi-squared =", round(X.sq,dplaces), "(d.f.=",df,"); p=", round(p.val,dplaces))
if (ktp == "K0") {
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.val,dplaces), " \u00b1 ",round(kappa.SE,dplaces), "*")
}
else {
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.val,dplaces), " \u00b1 ",round(kappa.SE,dplaces))
}
if (dtp == "DN0" || dtp == "DN1") {
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.Params2$Delta.total,dplaces), " *\u00b1 ",round(res.ParamsVar$Delta.total.cov,dplaces), "*")
}
else {
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.Params$Delta.total,dplaces), " \u00b1 ",round(res.ParamsVar$Delta.total.cov,dplaces))
}
Encoding(Chisq) = "UTF-8"
Encoding(Kappa.results) = "UTF-8"
Encoding(Delta.results) = "UTF-8"
Summary1 = data.frame(c(Chisq,Kappa.results,Delta.results))
names(Summary1) = "Summary"
res$Summary = Summary1
#Asintotic solutions
# res.m4 and res.M5
if (k == 2){
if (ktp == "K0" ) {
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.val,dplaces), " \u00b1 ",round(kappa.SE,dplaces), "*")
}
else {
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.val,dplaces), " \u00b1 ",round(kappa.SE,dplaces))
}
if (dtp2 == "DA0" ) {
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.M1$Delta.total,dplaces), " \u00b1 ",round(res.M3$Delta.total.cov,dplaces),"*")
}
else {
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.M1$Delta.total,dplaces), " \u00b1 ",round(res.M1$Delta.total.cov,dplaces))
}
Encoding(Kappa.results) = "UTF-8"
Encoding(Delta.results) = "UTF-8"
Summary2 = data.frame(c(Kappa.results,Delta.results))
names(Summary2) = "Summary"
res$Summary_AN = Summary2
if (is.null(res.M1$P.cov)){
res.M1$P.cov = rep(NaN,k)
}
if (is.null(res.M1$S.cov)){
res.M1$S.cov = rep(NaN,k)
}
if (dtp2 == "DA0"){
Agreement = paste(round(res.M1$A,dplaces))
Conformity = paste(round(res.M1$F,dplaces))
Predictivity = paste(round(res.M1$P,dplaces))
Consistency = paste(round(res.M1$S,dplaces))
}
else {
Agreement = paste(round(res.M1$A,dplaces)," \u00b1 ",round(res.M1$A.cov,dplaces))
Conformity = paste(round(res.M1$F,dplaces)," \u00b1 ",round(res.M1$F.cov,dplaces))
Predictivity = paste(round(res.M1$P,dplaces)," \u00b1 ",round(res.M1$P.cov,dplaces))
Consistency = paste(round(res.M1$S,dplaces)," \u00b1 ",round(res.M1$S.cov,dplaces))
}
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
Table = data.frame(1:k,round(res.M1$Delta,dplaces),round(res.M1$Pi,dplaces),Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
res$Fullparamstable_AN = Table
res$Deltaoverall_AN = round(res.M1$Delta.total,dplaces)
if (dtp2 == "DA1"){
res$Deltaoverall_SE_AN = round(res.M1$Delta.total.cov,dplaces)
}
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable_AN = Table
if (dtp2 == "DA0") {
if (is.null(res.M3$P.cov)){
res.M3$P.cov = rep(NaN,k)
}
if (is.null(res.M3$S.cov)){
res.M3$S.cov = rep(NaN,k)
}
Agreement = paste(round(res.M3$A,dplaces)," \u00b1 ",round(res.M3$A.cov,dplaces))
Conformity = paste(round(res.M3$F,dplaces)," \u00b1 ",round(res.M3$F.cov,dplaces))
Predictivity = paste(round(res.M3$P,dplaces)," \u00b1 ",round(res.M3$P.cov,dplaces))
Consistency = paste(round(res.M3$S,dplaces)," \u00b1 ",round(res.M3$S.cov,dplaces))
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
Table = data.frame(1:k,round(res.M3$Delta,dplaces),round(res.M3$Pi,dplaces),Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
res$Fullparamstable_AN2 = Table
res$Deltaoverall_AN2 = round(res.M3$Delta.total,dplaces)
res$Deltaoverall_SE_AN2 = round(res.M3$Delta.total.cov,dplaces)
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable_AN2 = Table
}
#Extra
## Adding 1
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.res.asin$kappa,dplaces), " \u00b1 ",round(kappa.res.asin$SE,dplaces))
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.M4$Delta.total,dplaces), " \u00b1 ",round(res.M4$Delta.total.cov,dplaces))
Encoding(Kappa.results) = "UTF-8"
Encoding(Delta.results) = "UTF-8"
Summary2 = data.frame(c(Kappa.results,Delta.results))
names(Summary2) = "Summary"
res$Summary_AE = Summary2
if (is.null(res.M4$P.cov)){
res.M4$P.cov = rep(NaN,k)
}
if (is.null(res.M4$S.cov)){
res.M4$S.cov = rep(NaN,k)
}
Agreement = paste(round(res.M4$A,dplaces)," \u00b1 ",round(res.M4$A.cov,dplaces))
Conformity = paste(round(res.M4$F,dplaces)," \u00b1 ",round(res.M4$F.cov,dplaces))
Predictivity = paste(round(res.M4$P,dplaces)," \u00b1 ",round(res.M4$P.cov,dplaces))
Consistency = paste(round(res.M4$S,dplaces)," \u00b1 ",round(res.M4$S.cov,dplaces))
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
Table = data.frame(1:k,round(res.M4$Delta,dplaces),round(res.M4$Pi,dplaces),Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
res$Fullparamstable_AE = Table
res$Deltaoverall_AE = round(res.M4$Delta.total,dplaces)
res$Deltaoverall_SE_AE = round(res.M4$Delta.total.cov,dplaces)
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable_AE = Table
}
}
class(res) <- "Delta"
return(res)
}
#Delta(matrix(c(10,0,0,0,10,0,0,0,10),3,3),fixedrows=FALSE,gstandard="No",maxits=100,tol=1e-12,dplaces=4)
toni-1991/Delta documentation built on Dec. 9, 2019, 8:54 a.m.
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Defines functions Delta
Documented in Delta
#' Delta coefficient function
#'
#' This function provides an analysis of the matrix provided, returning all all the parameters estimations
#' and SE calculations that have sense with the fixedrows and gstandard provided.
#'
#' This function study the matrix provided by the user. This function modify the matrix deleting missing rows and columns and
#' if it is needed for the estimation, adding 0.5 to each cell.
#'
#' Also calculate Cohen's Kappa coefficient and the goodness of fit for the Delta model.
#' @param datatable Matrix. Expected to be square matrix with at least 2 rows (columns), non negative values and at least an element different of zero.
#' @param fixedrows Boolean. Indicate if sample rows are fixed beforehand. Default is TRUE.
#' @param gstandard Text. Indicate if there are a Gold Standard by Rows or columns. Only first letter matter without Case sensitivity. Options are: "N" for None, "R" for in Rows and "C" for in Columns. Default is "N".
#' @param maxits Whole number. Indicate the maximum number of iterations of the numeric method to calculate B. Expected to be 100 <= maxits <= 5000. Default is 1000.
#' @param tol Double number. Indicate the precision of the numeric method to calculate B. Expected to be 1e-6 <= tol <= 1e-15.Default is 1e-12.
#' @param dplaces Whole number. Decimal placed to be shown in the result. Expected to be 1 <= dplaces <0 6. Default 4.
#' @param showall Boolean. Indicate if all output should be shown. If TRUE also shown hidden results. If FALSE shown only main output. By default is FALSE.
#' @keywords Delta datatable fixedrows gstandard maxits tol dplaces
#' @export
#' @examples
#' Delta(matrix(c(1,2,3,4),2,2))
#' Delta(matrix(c(65,5,10,20),2,2),fixedrows=TRUE,gstandard="Row")
Delta <- function(datatable,fixedrows = FALSE, gstandard = "No",
maxits=1000,tol=1e-12,dplaces=4,showall = FALSE){
# Check input variables
delta.samplingtype = CheckInput(datatable,fixedrows ,gstandard ,maxits,tol,dplaces,showall)
#Keep original size
k_0 = dim(datatable)[1]
#Calculate matrix deleting Unnecessary rows
res = GetM1(datatable)
M1 = res$M1
k = res$k
Del_rows = res$Deleted
#For type 1, no extra matrix are needed
if (k >= 2){
Mxs = GetMx(M1)
M2 = Mxs$M2
M3 = Mxs$M3
M4 = Mxs$M4
#Get type of problem
ktp = GetKappaProblemType(M1)
if (k > 2) {
dtp = GetDeltaProblemType(M1)
}
else {
dtp = GetDeltaProblemType(M2)
dtp2 = GetDeltaProblemType(M1)
}
#mat will be the matrix to be used for calcs of covariance
if (k > 2){
mat = M1
if (dtp == "DN0" || dtp == "DN1"){
mat.SE = M3
}
else if (dtp == "DN2"){
mat.SE = M1
}
}
else if (k == 2){
mat = M2
mat.SE = M2
}
#NO asintotics calculation
#Calculations: Part 2, derivate Kappa and SE
kappa.res = GetKappa(M1)
# Kappa estimation done with M1
kappa.val = kappa.res$kappa
if (ktp == "K0"){
kappa.res = GetKappa(M3)
}
# Kappa SE done with M1 if K1 or M3 if K0
kappa.SE = kappa.res$SE
#Calculations: Part 2, calculate pi_i, delta_i
res.Pi = GetDeltaPi(mat,dtp,tol,maxits)
Pi = res.Pi$Pi
Delta = res.Pi$Delta
B = res.Pi$B
#Also Delta_cov, Pi_cov and B_cov
if (dtp == "DN0" || dtp == "DN1"){
res.Pi.M3 = GetDeltaPi(M3,dtp,tol,maxits, original.mx = FALSE)
Pi_cov = res.Pi.M3$Pi
Delta_cov = res.Pi.M3$Delta
B_cov = res.Pi.M3$B
}
else {
Pi_cov = res.Pi$Pi
Delta_cov = res.Pi$Delta
B_cov = res.Pi$B
}
#Goodnes of fit to the model if possible
if (dtp == "DN0" ){
res = GetGoodness(M3,Pi_cov,Delta_cov)
}
else {
res = GetGoodness(mat,Pi_cov,Delta_cov)
}
X.sq = res$X.squared
df = res$df
p.val = res$p.value
E.matrix = res$E.matrix
Covar = GetCovariance(mat.SE,Delta_cov,Pi_cov,B_cov)
Cov_Delta = Covar$Cov_Delta
Cov_mix = Covar$Cov_mix
Cov_Pi = Covar$Cov_Pi
E = Covar$E
#Update Delta and Pi to correct size in cases k = 2
if (k == 2){
Pi = Pi[-3]
Pi_cov= Pi_cov[-3]
Delta = Delta[-3]
Delta_cov = Delta_cov[-3]
Cov_Delta = Cov_Delta[-3,-3]
Cov_mix = Cov_mix[-3,-3]
Cov_Pi = Cov_Pi[-3,-3]
}
res.Params = GetDeltaParams(mat, Delta, Pi, k)
res.Params2 = GetDeltaParams(mat.SE, Delta_cov, Pi_cov, k)
res.ParamsVar = GetDeltaParamsVar(mat.SE,fixedrows, Delta_cov, Pi_cov, k, Cov_Delta, E)
#Calculations Part 3: Asintotic calculations
if (k == 2){
res.M1 = GetAsinDeltaParams(M1,fixedrows)
if (dtp2 == "DA0") {
res.M3 = GetAsinDeltaParams(M3,fixedrows)
}
res.M4 = GetAsinDeltaParams(M4,fixedrows)
kappa.res.asin = GetKappa(M4)
}
}
if (k > 2){
res = list("Raw.matrix" = datatable, "M1" = M1,"M3" = M3)
}
else {
res = list("Raw.matrix" = datatable, "M1" = M1,"M2" = M2,"M3" = M3,"M4" = M4)
}
res$Del_rows = Del_rows
res$dtp = dtp
if (k == 2){
res$dtp2 = dtp2
}
res$ktp = ktp
res$k = k
res$k0 = k_0
res$showall = showall
res$samplingtype = delta.samplingtype
if (is.null(res.ParamsVar$P.cov)){
res.ParamsVar$P.cov = rep(NaN,k)
}
if (is.null(res.ParamsVar$S.cov)){
res.ParamsVar$S.cov = rep(NaN,k)
}
#First table 1
if (dtp != "DN1" && dtp != "DN0") {
Agreement = paste(round(res.Params$A,dplaces)," \u00b1 ",round(res.ParamsVar$A.cov,dplaces))
Conformity = paste(round(res.Params$F,dplaces)," \u00b1 ",round(res.ParamsVar$F.cov,dplaces))
Predictivity = paste(round(res.Params$P,dplaces)," \u00b1 ",round(res.ParamsVar$P.cov,dplaces))
Consistency = paste(round(res.Params$S,dplaces)," \u00b1 ",round(res.ParamsVar$S.cov,dplaces))
}
else{
Agreement = paste(round(res.Params$A,dplaces))
Conformity = paste(round(res.Params$F,dplaces))
Predictivity = paste(round(res.Params$P,dplaces))
Consistency = paste(round(res.Params$S,dplaces))
}
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
if (is.null(Pi)){
Pi = rep(NaN,k)
}
if (dtp != "DN1" && dtp != "DN0"){
Delta_tab = paste(round(Delta,dplaces), "(" ,round(Delta_cov,dplaces) , ")")
Pi_tab = paste(round(Pi,dplaces), "(" , round(Pi_cov,dplaces), ")")
Table = data.frame(1:k,Delta_tab,Pi_tab,Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
}
else {
Table = data.frame(1:k,round(Delta,dplaces),round(Pi,dplaces),Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
}
res$Fullparamstable = Table
res$Deltaoverall = round(res.Params$Delta.total,dplaces)
res$Deltaoverall_SE = round(res.ParamsVar$Delta.total.cov,dplaces)
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable = Table
if (dtp == "DN1" || dtp == "DN0"){
#First table with M3
Agreement = paste(round(res.Params2$A,dplaces)," \u00b1 ",round(res.ParamsVar$A.cov,dplaces))
Conformity = paste(round(res.Params2$F,dplaces)," \u00b1 ",round(res.ParamsVar$F.cov,dplaces))
Predictivity = paste(round(res.Params2$P,dplaces)," \u00b1 ",round(res.ParamsVar$P.cov,dplaces))
Consistency = paste(round(res.Params2$S,dplaces)," \u00b1 ",round(res.ParamsVar$S.cov,dplaces))
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
Delta_tab = paste(round(Delta_cov,dplaces))
Pi_tab = paste(round(Pi_cov,dplaces))
Table = data.frame(1:k,Delta_tab,Pi_tab,Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
res$Fullparamstable2 = Table
res$Deltaoverall2 = round(res.Params2$Delta.total,dplaces)
res$Deltaoverall_SE2 = round(res.ParamsVar$Delta.total.cov,dplaces)
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable2 = Table
}
res$Cov_Delta = round(Cov_Delta,dplaces)
res$Cov_mix = round(Cov_mix,dplaces)
res$Cov_Pi = round(Cov_Pi,dplaces)
if (k >= 2){
res$E_matrix = round(E.matrix,dplaces)
Chisq = paste("Chi-squared =", round(X.sq,dplaces), "(d.f.=",df,"); p=", round(p.val,dplaces))
if (ktp == "K0") {
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.val,dplaces), " \u00b1 ",round(kappa.SE,dplaces), "*")
}
else {
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.val,dplaces), " \u00b1 ",round(kappa.SE,dplaces))
}
if (dtp == "DN0" || dtp == "DN1") {
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.Params2$Delta.total,dplaces), " *\u00b1 ",round(res.ParamsVar$Delta.total.cov,dplaces), "*")
}
else {
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.Params$Delta.total,dplaces), " \u00b1 ",round(res.ParamsVar$Delta.total.cov,dplaces))
}
Encoding(Chisq) = "UTF-8"
Encoding(Kappa.results) = "UTF-8"
Encoding(Delta.results) = "UTF-8"
Summary1 = data.frame(c(Chisq,Kappa.results,Delta.results))
names(Summary1) = "Summary"
res$Summary = Summary1
#Asintotic solutions
# res.m4 and res.M5
if (k == 2){
if (ktp == "K0" ) {
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.val,dplaces), " \u00b1 ",round(kappa.SE,dplaces), "*")
}
else {
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.val,dplaces), " \u00b1 ",round(kappa.SE,dplaces))
}
if (dtp2 == "DA0" ) {
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.M1$Delta.total,dplaces), " \u00b1 ",round(res.M3$Delta.total.cov,dplaces),"*")
}
else {
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.M1$Delta.total,dplaces), " \u00b1 ",round(res.M1$Delta.total.cov,dplaces))
}
Encoding(Kappa.results) = "UTF-8"
Encoding(Delta.results) = "UTF-8"
Summary2 = data.frame(c(Kappa.results,Delta.results))
names(Summary2) = "Summary"
res$Summary_AN = Summary2
if (is.null(res.M1$P.cov)){
res.M1$P.cov = rep(NaN,k)
}
if (is.null(res.M1$S.cov)){
res.M1$S.cov = rep(NaN,k)
}
if (dtp2 == "DA0"){
Agreement = paste(round(res.M1$A,dplaces))
Conformity = paste(round(res.M1$F,dplaces))
Predictivity = paste(round(res.M1$P,dplaces))
Consistency = paste(round(res.M1$S,dplaces))
}
else {
Agreement = paste(round(res.M1$A,dplaces)," \u00b1 ",round(res.M1$A.cov,dplaces))
Conformity = paste(round(res.M1$F,dplaces)," \u00b1 ",round(res.M1$F.cov,dplaces))
Predictivity = paste(round(res.M1$P,dplaces)," \u00b1 ",round(res.M1$P.cov,dplaces))
Consistency = paste(round(res.M1$S,dplaces)," \u00b1 ",round(res.M1$S.cov,dplaces))
}
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
Table = data.frame(1:k,round(res.M1$Delta,dplaces),round(res.M1$Pi,dplaces),Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
res$Fullparamstable_AN = Table
res$Deltaoverall_AN = round(res.M1$Delta.total,dplaces)
if (dtp2 == "DA1"){
res$Deltaoverall_SE_AN = round(res.M1$Delta.total.cov,dplaces)
}
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable_AN = Table
if (dtp2 == "DA0") {
if (is.null(res.M3$P.cov)){
res.M3$P.cov = rep(NaN,k)
}
if (is.null(res.M3$S.cov)){
res.M3$S.cov = rep(NaN,k)
}
Agreement = paste(round(res.M3$A,dplaces)," \u00b1 ",round(res.M3$A.cov,dplaces))
Conformity = paste(round(res.M3$F,dplaces)," \u00b1 ",round(res.M3$F.cov,dplaces))
Predictivity = paste(round(res.M3$P,dplaces)," \u00b1 ",round(res.M3$P.cov,dplaces))
Consistency = paste(round(res.M3$S,dplaces)," \u00b1 ",round(res.M3$S.cov,dplaces))
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
Table = data.frame(1:k,round(res.M3$Delta,dplaces),round(res.M3$Pi,dplaces),Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
res$Fullparamstable_AN2 = Table
res$Deltaoverall_AN2 = round(res.M3$Delta.total,dplaces)
res$Deltaoverall_SE_AN2 = round(res.M3$Delta.total.cov,dplaces)
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable_AN2 = Table
}
#Extra
## Adding 1
Kappa.results = paste("Kappa \u00b1 S.E. = ",round(kappa.res.asin$kappa,dplaces), " \u00b1 ",round(kappa.res.asin$SE,dplaces))
Delta.results = paste("Delta \u00b1 S.E. = ",round(res.M4$Delta.total,dplaces), " \u00b1 ",round(res.M4$Delta.total.cov,dplaces))
Encoding(Kappa.results) = "UTF-8"
Encoding(Delta.results) = "UTF-8"
Summary2 = data.frame(c(Kappa.results,Delta.results))
names(Summary2) = "Summary"
res$Summary_AE = Summary2
if (is.null(res.M4$P.cov)){
res.M4$P.cov = rep(NaN,k)
}
if (is.null(res.M4$S.cov)){
res.M4$S.cov = rep(NaN,k)
}
Agreement = paste(round(res.M4$A,dplaces)," \u00b1 ",round(res.M4$A.cov,dplaces))
Conformity = paste(round(res.M4$F,dplaces)," \u00b1 ",round(res.M4$F.cov,dplaces))
Predictivity = paste(round(res.M4$P,dplaces)," \u00b1 ",round(res.M4$P.cov,dplaces))
Consistency = paste(round(res.M4$S,dplaces)," \u00b1 ",round(res.M4$S.cov,dplaces))
Encoding(Agreement) = "UTF-8"
Encoding(Conformity) = "UTF-8"
Encoding(Predictivity) = "UTF-8"
Encoding(Consistency) = "UTF-8"
Table = data.frame(1:k,round(res.M4$Delta,dplaces),round(res.M4$Pi,dplaces),Agreement,Conformity,Predictivity,Consistency)
colnames(Table) = c("Class","Delta","Pi","Agreement","Conformity","Predictivity","Consistency")
res$Fullparamstable_AE = Table
res$Deltaoverall_AE = round(res.M4$Delta.total,dplaces)
res$Deltaoverall_SE_AE = round(res.M4$Delta.total.cov,dplaces)
if (delta.samplingtype == 1) {
Table = Table[,c(-6,-7)]
}
else if (delta.samplingtype == 2) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 3) {
Table = Table[,c(-5,-6,-7)]
}
else if (delta.samplingtype == 4) {
Table = Table[,-7]
}
else if (delta.samplingtype == 5) {
Table = Table[,c(-5,-6)]
}
res$Paramstable_AE = Table
}
}
class(res) <- "Delta"
return(res)
}
#Delta(matrix(c(10,0,0,0,10,0,0,0,10),3,3),fixedrows=FALSE,gstandard="No",maxits=100,tol=1e-12,dplaces=4)
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