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R/MB.R
In MB: The Use of Marginal Distributions in Conditional Forecasting
Defines functions
ff
Documented in
ff
#' The Use of Marginal Distributions in Conditional Forecasting
#' @description A new way to predict time series using the marginal distribution table in the absence of the significance of traditional models.
#' @return the output from \code{ff()}
#' @export
#' @param dt data frame
#' @param m the number of time series
#' @param w the number of predicted values
#' @param q1 matrix independent time series values #In the case of m=2, enter the independent string values as follows(matrix(c())),In the case of m=3, enter the independent string values as follows(matrix(c(),w,m-1,byrow=T))
#' @param n number of values
#' @usage ff(dt,m,w,n,q1)
#' @examples
#' x=rnorm(17,10,1)
#' y=rnorm(17,10,1)
#' data=data.frame(x,y)
#' print("Enter independent time series values")
#' q1=list(q=matrix(c(scan(,,quiet=TRUE)),1,2-1))
#' 10.5
#'
#'
#' ff(data,2,1,17,q1)
ff=function(dt,m,w,n,q1)
{ qq=list(Number_of_categories= ceiling (2.5*(n^(0.25))),mx=matrix(nrow=m,ncol=1),mn= matrix(nrow=m,ncol=1),rn= matrix(nrow=m,ncol=1),l= matrix(nrow=m,ncol=1)
,xd=matrix(nrow=ceiling (2.5*(n^(0.25))),ncol=m),xu= matrix(nrow=ceiling (2.5*(n^(0.25))),ncol=m),xx=matrix(nrow=ceiling (2.5*(n^(0.25))),ncol=m),r=matrix(nrow=ceiling (2.5*(n^(0.25))),ncol=ceiling (2.5*(n^(0.25)))^(m-1)),
rr=matrix(nrow=1,ncol=ceiling (2.5*(n^(0.25)))^(m-1)),rr3=matrix(nrow=1,ncol=ceiling (2.5*(n^(0.25)))^(m-1)),predicted_values=matrix(nrow = w,ncol = 1))
n=nrow(dt)
if (is.null(n) || is.na(n) || is.infinite(n)) {
stop(terminatepg)
}
terminatepg = "Terminating program ... \n"
origmsg = "Here is the original message: "
dt = tryCatch(
{
as.data.frame(dt)
},
warning = function(warning_msg) {
message("dt The entered value is non-numeric, please try again")
message(origmsg)
message(paste(warning_msg, "\n"))
return(NULL)
}
)
m = tryCatch(
{
as.integer(m)
},
warning = function(warning_msg) {
message("m The entered value is non-numeric, please try again")
message(origmsg)
message(paste(warning_msg, "\n"))
return(NULL)
}
)
w = tryCatch(
{
as.integer(w)
},
warning = function(warning_msg) {
message("w The entered value is non-numeric, please try again")
message(origmsg)
message(paste(warning_msg, "\n"))
return(NULL)
}
)
nameMsg = "Number of categories format not supported! "
for(i in 1:m)
{
qq$mx[i]=max(dt[,i])
qq$mn[i]=min(dt[,i])
qq$rn[i]=qq$mx[i]-qq$mn[i]
}
if (is.null(qq$mx[i]) || is.na(qq$mx[i]) || is.infinite(qq$mx[i])) {
stop(terminatepg)
}
if (is.null(qq$mn[i]) || is.na(qq$mn[i]) || is.infinite(qq$mn[i])) {
stop(terminatepg)
}
if (is.null(qq$rn[i]) || is.na(qq$rn[i]) || is.infinite(qq$rn[i])) {
stop(terminatepg)
}
for(i in 1:m)
{
qq$l[i]=(qq$rn[i]/(qq$Number_of_categories))
}
if (is.null(qq$l[i]) || is.na(qq$l[i]) || is.infinite(qq$l[i])) {
stop(terminatepg)
}
for(i in 1:m)
{
qq$xd[1,i]=qq$mn[i]
qq$xu[1,i]=qq$mn[i]+qq$l[i]
qq$xx[1,i]=(qq$xd[1,i]+qq$xu[1,i])/2
for(j in 2:qq$Number_of_categories)
{
qq$xd[j,i]=qq$xd[j-1,i]+qq$l[i]
qq$xu[j,i]=qq$xu[j-1,i]+qq$l[i]
qq$xx[j,i]=(qq$xd[j,i]+qq$xu[j,i])/2
}}
for(i in 1:m)
{qq$xu[qq$Number_of_categories,i]=qq$xu[qq$Number_of_categories,i]+0.5}
if (is.null(qq$xd[i]) || is.na(qq$xd[i]) || is.infinite(qq$xd[i])) {
stop(terminatepg)
}
if (is.null(qq$xu[i]) || is.na(qq$xu[i]) || is.infinite(qq$xu[i])) {
stop(terminatepg)
}
if (is.null(qq$xx[i]) || is.na(qq$xx[i]) || is.infinite(qq$xx[i])) {
stop(terminatepg)
}
if(m==2)
{
for(i in 1:qq$Number_of_categories)
{
for(s in 1:qq$Number_of_categories)
{
qq$r[i,s]=0
for(j in 1:n)
{
ss1= dt[j,1]>=qq$xd[s,1] & dt[j,1]<qq$xu[s,1]
ss2= dt[j,2]>=qq$xd[i,2] & dt[j,2]<qq$xu[i,2]
ss=(ss1 & ss2)
if(ss)
{qq$r[i,s]=qq$r[i,s]+1}
}}}
qq$r=round(qq$r/n,2)
qq$rr=apply(qq$r,2,sum)
qq$rr3=apply(qq$r,1,sum)
if (is.null(qq$r[i]) || is.na(qq$r[i]) || is.infinite(qq$r[i])) {
stop(terminatepg)
}
if (is.null(qq$rr[i]) || is.na(qq$rr[i]) || is.infinite(qq$rr[i])) {
stop(terminatepg)
}
if (is.null(qq$rr3[i]) || is.na(qq$rr3[i]) || is.infinite(qq$rr3[i])) {
stop(terminatepg)
}
for(i in 1:w)
{
y=0
sss=0
for(s in 1:qq$Number_of_categories)
{
sss=sss+1
if(q1[i]>=qq$xd[s,1] & q1[i]<qq$xu[s,1])
{if(qq$rr[sss]!=0)
{
y=y+(qq$r[,sss]*(qq$xx[,2]))/qq$rr[sss]}
else
{for(sss in 1:qq$Number_of_categories)
y=y+(qq$rr3[sss]*(qq$xx[sss,2]))}}
qq$predicted_values[i]=sum(y)
}}
return(qq)
if (is.null(qq$predicted_values[i]) || is.na(qq$predicted_values[i]) || is.infinite(qq$predicted_values[i])) {
stop(terminatepg)
}}
else
{
sss=0
for(i in 1:qq$Number_of_categories)
{
for(s in 1:qq$Number_of_categories)
{
sss=sss+1
for(h in 1:qq$Number_of_categories)
{
qq$r[h,sss]=0
for(j in 1:n)
{
ss1= (dt[j,1]>=qq$xd[i,1] & dt[j,1]<qq$xu[i,1])
ss2= (dt[j,2]>=qq$xd[s,2] & dt[j,2]<qq$xu[s,2])
ss3= (dt[j,3]>=qq$xd[h,3] & dt[j,3]<qq$xu[h,3])
ss=(ss1 & ss2 & ss3)
if(ss)
{qq$r[h,sss]=qq$r[h,sss]+1}
}}}}
qq$r=round(qq$r/n,2)
qq$rr=apply(qq$r,2,sum)
qq$rr3=apply(qq$r,1,sum)
for(i in 1:w)
{
y=0
sss=0
for(s in 1:qq$Number_of_categories)
{
for(h in 1:qq$Number_of_categories)
{
sss=sss+1
if(q1[i,1]>=qq$xd[s,1] & q1[i,1]<qq$xu[s,1])
{
if(q1[i,2]>=qq$xd[h,2] & q1[i,2]<qq$xu[h,2])
{
if(qq$rr[sss]!=0)
{
y=y+(qq$r[,sss]*qq$xx[,3])/qq$rr[sss]}
else
{for(sss in 1:qq$Number_of_categories)
y=y+(qq$rr3[sss]*qq$xx[sss,3])}}
qq$predicted_values[i]=sum(y)
}}}}
return(qq)
if (is.null(qq$predicted_values[i]) || is.na(qq$predicted_values[i]) || is.infinite(qq$predicted_values[i])) {
stop(terminatepg)}
}}
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MB documentation built on Jan. 7, 2023, 1:27 a.m.
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Defines functions ff
Documented in ff
#' The Use of Marginal Distributions in Conditional Forecasting
#' @description A new way to predict time series using the marginal distribution table in the absence of the significance of traditional models.
#' @return the output from \code{ff()}
#' @export
#' @param dt data frame
#' @param m the number of time series
#' @param w the number of predicted values
#' @param q1 matrix independent time series values #In the case of m=2, enter the independent string values as follows(matrix(c())),In the case of m=3, enter the independent string values as follows(matrix(c(),w,m-1,byrow=T))
#' @param n number of values
#' @usage ff(dt,m,w,n,q1)
#' @examples
#' x=rnorm(17,10,1)
#' y=rnorm(17,10,1)
#' data=data.frame(x,y)
#' print("Enter independent time series values")
#' q1=list(q=matrix(c(scan(,,quiet=TRUE)),1,2-1))
#' 10.5
#'
#'
#' ff(data,2,1,17,q1)
ff=function(dt,m,w,n,q1)
{ qq=list(Number_of_categories= ceiling (2.5*(n^(0.25))),mx=matrix(nrow=m,ncol=1),mn= matrix(nrow=m,ncol=1),rn= matrix(nrow=m,ncol=1),l= matrix(nrow=m,ncol=1)
,xd=matrix(nrow=ceiling (2.5*(n^(0.25))),ncol=m),xu= matrix(nrow=ceiling (2.5*(n^(0.25))),ncol=m),xx=matrix(nrow=ceiling (2.5*(n^(0.25))),ncol=m),r=matrix(nrow=ceiling (2.5*(n^(0.25))),ncol=ceiling (2.5*(n^(0.25)))^(m-1)),
rr=matrix(nrow=1,ncol=ceiling (2.5*(n^(0.25)))^(m-1)),rr3=matrix(nrow=1,ncol=ceiling (2.5*(n^(0.25)))^(m-1)),predicted_values=matrix(nrow = w,ncol = 1))
n=nrow(dt)
if (is.null(n) || is.na(n) || is.infinite(n)) {
stop(terminatepg)
}
terminatepg = "Terminating program ... \n"
origmsg = "Here is the original message: "
dt = tryCatch(
{
as.data.frame(dt)
},
warning = function(warning_msg) {
message("dt The entered value is non-numeric, please try again")
message(origmsg)
message(paste(warning_msg, "\n"))
return(NULL)
}
)
m = tryCatch(
{
as.integer(m)
},
warning = function(warning_msg) {
message("m The entered value is non-numeric, please try again")
message(origmsg)
message(paste(warning_msg, "\n"))
return(NULL)
}
)
w = tryCatch(
{
as.integer(w)
},
warning = function(warning_msg) {
message("w The entered value is non-numeric, please try again")
message(origmsg)
message(paste(warning_msg, "\n"))
return(NULL)
}
)
nameMsg = "Number of categories format not supported! "
for(i in 1:m)
{
qq$mx[i]=max(dt[,i])
qq$mn[i]=min(dt[,i])
qq$rn[i]=qq$mx[i]-qq$mn[i]
}
if (is.null(qq$mx[i]) || is.na(qq$mx[i]) || is.infinite(qq$mx[i])) {
stop(terminatepg)
}
if (is.null(qq$mn[i]) || is.na(qq$mn[i]) || is.infinite(qq$mn[i])) {
stop(terminatepg)
}
if (is.null(qq$rn[i]) || is.na(qq$rn[i]) || is.infinite(qq$rn[i])) {
stop(terminatepg)
}
for(i in 1:m)
{
qq$l[i]=(qq$rn[i]/(qq$Number_of_categories))
}
if (is.null(qq$l[i]) || is.na(qq$l[i]) || is.infinite(qq$l[i])) {
stop(terminatepg)
}
for(i in 1:m)
{
qq$xd[1,i]=qq$mn[i]
qq$xu[1,i]=qq$mn[i]+qq$l[i]
qq$xx[1,i]=(qq$xd[1,i]+qq$xu[1,i])/2
for(j in 2:qq$Number_of_categories)
{
qq$xd[j,i]=qq$xd[j-1,i]+qq$l[i]
qq$xu[j,i]=qq$xu[j-1,i]+qq$l[i]
qq$xx[j,i]=(qq$xd[j,i]+qq$xu[j,i])/2
}}
for(i in 1:m)
{qq$xu[qq$Number_of_categories,i]=qq$xu[qq$Number_of_categories,i]+0.5}
if (is.null(qq$xd[i]) || is.na(qq$xd[i]) || is.infinite(qq$xd[i])) {
stop(terminatepg)
}
if (is.null(qq$xu[i]) || is.na(qq$xu[i]) || is.infinite(qq$xu[i])) {
stop(terminatepg)
}
if (is.null(qq$xx[i]) || is.na(qq$xx[i]) || is.infinite(qq$xx[i])) {
stop(terminatepg)
}
if(m==2)
{
for(i in 1:qq$Number_of_categories)
{
for(s in 1:qq$Number_of_categories)
{
qq$r[i,s]=0
for(j in 1:n)
{
ss1= dt[j,1]>=qq$xd[s,1] & dt[j,1]<qq$xu[s,1]
ss2= dt[j,2]>=qq$xd[i,2] & dt[j,2]<qq$xu[i,2]
ss=(ss1 & ss2)
if(ss)
{qq$r[i,s]=qq$r[i,s]+1}
}}}
qq$r=round(qq$r/n,2)
qq$rr=apply(qq$r,2,sum)
qq$rr3=apply(qq$r,1,sum)
if (is.null(qq$r[i]) || is.na(qq$r[i]) || is.infinite(qq$r[i])) {
stop(terminatepg)
}
if (is.null(qq$rr[i]) || is.na(qq$rr[i]) || is.infinite(qq$rr[i])) {
stop(terminatepg)
}
if (is.null(qq$rr3[i]) || is.na(qq$rr3[i]) || is.infinite(qq$rr3[i])) {
stop(terminatepg)
}
for(i in 1:w)
{
y=0
sss=0
for(s in 1:qq$Number_of_categories)
{
sss=sss+1
if(q1[i]>=qq$xd[s,1] & q1[i]<qq$xu[s,1])
{if(qq$rr[sss]!=0)
{
y=y+(qq$r[,sss]*(qq$xx[,2]))/qq$rr[sss]}
else
{for(sss in 1:qq$Number_of_categories)
y=y+(qq$rr3[sss]*(qq$xx[sss,2]))}}
qq$predicted_values[i]=sum(y)
}}
return(qq)
if (is.null(qq$predicted_values[i]) || is.na(qq$predicted_values[i]) || is.infinite(qq$predicted_values[i])) {
stop(terminatepg)
}}
else
{
sss=0
for(i in 1:qq$Number_of_categories)
{
for(s in 1:qq$Number_of_categories)
{
sss=sss+1
for(h in 1:qq$Number_of_categories)
{
qq$r[h,sss]=0
for(j in 1:n)
{
ss1= (dt[j,1]>=qq$xd[i,1] & dt[j,1]<qq$xu[i,1])
ss2= (dt[j,2]>=qq$xd[s,2] & dt[j,2]<qq$xu[s,2])
ss3= (dt[j,3]>=qq$xd[h,3] & dt[j,3]<qq$xu[h,3])
ss=(ss1 & ss2 & ss3)
if(ss)
{qq$r[h,sss]=qq$r[h,sss]+1}
}}}}
qq$r=round(qq$r/n,2)
qq$rr=apply(qq$r,2,sum)
qq$rr3=apply(qq$r,1,sum)
for(i in 1:w)
{
y=0
sss=0
for(s in 1:qq$Number_of_categories)
{
for(h in 1:qq$Number_of_categories)
{
sss=sss+1
if(q1[i,1]>=qq$xd[s,1] & q1[i,1]<qq$xu[s,1])
{
if(q1[i,2]>=qq$xd[h,2] & q1[i,2]<qq$xu[h,2])
{
if(qq$rr[sss]!=0)
{
y=y+(qq$r[,sss]*qq$xx[,3])/qq$rr[sss]}
else
{for(sss in 1:qq$Number_of_categories)
y=y+(qq$rr3[sss]*qq$xx[sss,3])}}
qq$predicted_values[i]=sum(y)
}}}}
return(qq)
if (is.null(qq$predicted_values[i]) || is.na(qq$predicted_values[i]) || is.infinite(qq$predicted_values[i])) {
stop(terminatepg)}
}}
Try the MB package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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