mlaib

# Suggestion plots using plotly library ####

# library(MFDFA)
# mfdfa<-MFDFA(tsx, scale, m, q) # mfdfa: contains all outputs

library(plotly)

# Supplementary functions ####
poly_fit<-function(x,y,n){
  formule<-lm(as.formula(paste('y~',paste('I(x^',1:n,')', sep='',collapse='+'))))

mlaib

library(MFDFA)
library(foreach)
library(doParallel)

MFDFA2<-function (tsx, scale, m = 1, q) {
  
  poly_fit.val<-function (x, y, n) {
    formule <- lm(as.formula(paste("y~", paste("I(x^", 1:n, ")", 
                                               sep = "", collapse = "+"))))
    res1 <- coef(formule)

mlaib

Keybase proof

I hereby claim:

• I am mlaib on github.
• I am mlaib (https://keybase.io/mlaib) on keybase.
• I have a public key ASDOIr5otLR_Kr-qXPD10q9DHh3S0q6WY8l0FLJb4NIbfgo

To claim this, I am signing this object:

mlaib

# -*- coding: utf-8 -*-
"""
Created on Tue Dec 19 12:27:25 2017

@author: mlaib
"""

import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

mlaib

##################################
## Suggestion of output plot: ####
##################################

## Supplementary functions: #####
reset <- function(){
par(mfrow=c(1, 1), oma=rep(0, 4), mar=rep(0, 4), new=TRUE)
plot(0:1, 0:1, type="n", xlab="", ylab="", axes=FALSE)}

poly_fit<-function(x,y,n){

mlaib

## MFDFA package installation: from github ####
install.packages("devtools")
devtools::install_github("mlaib/MFDFA")
library(MFDFA)
#####
a<-0.9
N<-1024
tsx<-MFsim(N,a)
scale=10:100
q<--10:10