fit.to.data.set.wrapper {RFlocalfdr}

fit.to.data.set.wrapper

Description

This function allows you to express your love of cats.

Usage

fit.to.data.set.wrapper(
  df,
  imp,
  debug.flag = 0,
  plot.string = "",
  temp.dir = temp.dir,
  return.all = TRUE,
  try.counter = 3
)

Arguments

df

contains x and y, midpoints and counts from a histogram of imp

imp

importances

debug.flag

debug flag

plot.string

file name for a debugging plot, passed on to fit.to.data.set

temp.dir

directory for debugging output, passed on to fit.to.data.set

return.all

passed to fit.to.data.set. If TRUE then the full output of minpack.lm::nlsLM is returned. Otherwise just the matrix of coefficients and t-values is returned.

try.counter

passed on to fit.to.data.set try.counter=1 my.dsn xi= 1 try.counter=2 xi= mean(x) try.counter=3 start xi, omega, lambda from the parameters retuned by fitdistrplus::fitdist

Value

If the skew-normal fitting routine is succesful, then the matrix of parmaters and standard errors is returned. – othewise a "try-error" message is returned

Examples

data(imp20000)                                      
imp<-log(imp20000$importances)                               
t2<-imp20000$counts
temp<-imp[t2 > 1]   #see                          
temp<-temp[temp != -Inf]                         
temp <- temp - min(temp) + .Machine$double.eps   
f_fit <- f.fit(temp)                             
y <- f_fit$zh$density                            
x <- f_fit$midpoints                             
df <- data.frame(x, y)                           
fitted_parameters <- fit.to.data.set.wrapper(df, temp, try.counter = 3,return.all=FALSE)           
fitted_parameters

hist(temp, breaks = 200, freq = FALSE)
lines(df$x, df$y, type = "l", col = "green", lwd = 2, 
      xlim = c(0, max(df$x) + 0.5))
curve(sn::dsn(x, xi = fitted_parameters$Estimate[1], omega = fitted_parameters$Estimate[2], 
              alpha = fitted_parameters$Estimate[3]), add = TRUE, 
                col = "purple", lwd = 3, xlim = c(0, 16))
curve(my.dsn(x, xi = fitted_parameters$Estimate[1], omega = fitted_parameters$Estimate[2],  
                lambda = fitted_parameters$Estimate[3]), add = TRUE, 
                col = "orange", lwd = 3)


library(RFlocalfdr.data)
data(ch22)
t2 <-ch22$C
imp<-log(ch22$imp)
imp<-imp[t2 > 30]
imp <- imp[imp != -Inf]
imp <- imp - min(imp) + .Machine$double.eps
f_fit <- f.fit(imp )
y <- f_fit$zh$density
x <- f_fit$midpoints
C <- quantile(imp,probs=0.75)
df2 <- data.frame(x[x < C], y[x < C])
initial.estimates <- fit.to.data.set.wrapper(df2, imp)
#Nonlinear regression model                                            
#  model: y ~ my.dsn(x, xi = xi, omega = omega, lambda = lambda)       
#   data: df                                                           
#       xi.xi  omega.omega lambda.alpha                                
#       1.163        1.193        1.574                                
# residual sum-of-squares: 0.06269                                     
#                                                                      
#Number of iterations to convergence: 23                               
#Achieved convergence tolerance: 1.49e-08