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I am doing Wavelet Analysis in R using Biwavelet. However, I receive the error message:
Error in check.datum(y) :
The step size must be constant (see approx function to interpolate)
When I run the following code:
wtc.AB = wtc(t1, t2, nrands = nrands)
Please share your help here. Complete Code is:
# Import your data
Data <- read.csv("https://dl.dropboxusercontent.com/u/18255955/Tutorials/Commodities.csv")
# Attach your data so that you can access variables directly using their
# names
attach(Data)
# Define two sets of variables with time stamps
t1 = cbind(DATE, ISLX)
t2 = cbind(DATE, GOLD)
# Specify the number of iterations. The more, the better (>1000). For the
# purpose of this tutorial, we just set it = 10
nrands = 10
wtc.AB = wtc(t1, t2, nrands = nrands)
# Plotting a graph
par(oma = c(0, 0, 0, 1), mar = c(5, 4, 5, 5) + 0.1)
plot(wtc.AB, plot.phase = TRUE, lty.coi = 1, col.coi = "grey", lwd.coi = 2,
lwd.sig = 2, arrow.lwd = 0.03, arrow.len = 0.12, ylab = "Scale", xlab = "Period",
plot.cb = TRUE, main = "Wavelet Coherence: A vs B")```
The tutorial for catboost with R says this:
library(catboost)
countries = c('RUS','USA','SUI')
years = c(1900,1896,1896)
phone_codes = c(7,1,41)
domains = c('ru','us','ch')
dataset = data.frame(countries, years, phone_codes, domains)
label_values = c(0,1,1)
fit_params <- list(iterations = 100,
loss_function = 'Logloss',
ignored_features = c(4,9),
border_count = 32,
depth = 5,
learning_rate = 0.03,
l2_leaf_reg = 3.5)
pool = catboost.load_pool(dataset, label = label_values, cat_features = c(0,3))
model <- catboost.train(pool, params = fit_params)
However, this results in:
Error in catboost.from_data_frame(data, label, pairs, weight, group_id, :
Unsupported column type: character
Many thanks,
how can I impose constraints on the coefficient matrix of a var model in r.
Some of my code is followed
library(readxl)
dat_pc_log_d <- read_excel("C:/Users/Desktop/dat_pc_log_d.xlsx")
attach(dat_pc_log_d)
dat_pc_log_d$itcrm = NULL
dat_pc_log_d$...1 = NULL
data = ts(dat_pc_log_d,start = c(2004,1),end = c(2019,1),frequency = 4)
VAR_modelo = VAR(data,p=2)
VAR_modelo_restriccion = restrict(VAR_modelo,method = "ser",thresh = 2.0)
ir_pib = irf(VAR_modelo_restriccion, impulse = "pbipc_log_d", response = c("pbipc_log_d", "expopc_log_d", "pbiagr_log_d"),
boot = TRUE, ci = 0.95)
I need to ensure exogeneity of a variable, for it I have to impose zero in some lags coefficients of the independent variable. How can I do it ?
thanks
library(readxl)
dat_pc_log_d <- read_excel("C:/Users//dat_pc_log_d.xlsx")
attach(dat_pc_log_d)
dat_pc_log_d$...1 = NULL
data = ts(dat_pc_log_d,start = c(2004,1),end = c(2019,1),frequency = 4)
VAR_modelo = VAR(data,p=2)
restriccion = matrix(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1),
nrow=8, ncol=17, byrow = TRUE)
VAR_modelo_restriccion = restrict(VAR_modelo,method = "man", resmat = restriccion)
ir_pib = irf(VAR_modelo_restriccion, impulse = "itcrm", response = c("pbipc_log_d", "expopc_log_d", "inverpc_log_d" , "pbiagr_log_d"),
boot = TRUE, nhead=20 ,ci = 0.68)
I have a problem forecasting this non stationery data(https://drive.google.com/file/d/14o5hHe8zxR0onRWq0mZNcYqI101O0Dkw/view?usp=sharing) using Auto Arima.
Please review my code.
# Read Data
r = read.csv('../Amazon/Amazon1.csv', header = TRUE, stringsAsFactors = FALSE)
# Time Series construction
ts = ts(t(r[,1:25]), frequency = 12, start = c(2016,01) )
# plotting Time series
ts.plot(ts[,1:2],type = 'b', xlab = 'Monthly Cycle', ylab = 'Number of Sales', main = "(TIME SERIES) Amazon Sales Cycle of multiple products for 24 months",col=c(rep("black",1),rep("red",2)))
legend("topleft",cex=.65,legend = ts[1,1:2], col = 1:ncol(ts), lty = 1)
set1 = ts[2:20,1]
#set2 = ts[15:20,1]
set1 = as.numeric(set1)
#set2 = as.numeric(set2)
# Building Forecasting models
mf = meanf(set1,h=4,level=c(90,95),fan=FALSE,lambda=NULL)
plot(mf)
mn = naive(set1,h=4,level=c(90,95),fan=FALSE,lambda=NULL)
plot(mn)
md = rwf(set1,h=4,drift=T,level=c(90,95),fan=FALSE,lambda=NULL)
plot(md)
# Checking Accuracy
accuracy(mf)
accuracy(mn)
accuracy(md)
# Identifying Stationarity/Non-Stationarity(unit Root testing)
adf = adf.test(set1)
adf
kpss = kpss.test(set1)
kpss
ndiffs(set1)
diff_data = diff(set1)
adf.test(diff_data) # Rerunning unit test on differenced data
# Identifying Seasonality/Trend
Stl = stl(set1,s.window='periodic')
# ARIMA modelling
ar_set1 = forecast::auto.arima(diff(diff_data), approximation=FALSE,trace=FALSE, stationary = TRUE)
forecast(ar_set1, h = 5)
ar_set2 = forecast::ets(diff(diff_data))
ar_set3 = forecast::nnetar(diff(diff_data), approximation=FALSE,trace=FALSE, stationary = TRUE)
# Prediction
predict(ar_set1, n.ahead = 5,se.fit = TRUE)
predict(ar_set2, n.ahead = 5,se.fit = TRUE)
predict(ar_set3, n.ahead = 5,se.fit = TRUE)
plot(forecast(ar_set1,h=6))
points(1:length(diff(diff_data)),fitted(ar_set1),type="l",col="green")
plot(forecast(ar_set2,h=6))
points(1:length(diff(diff_data)),fitted(ar_set2),type="l",col="green")
plot(forecast(ar_set3,h=6))
points(1:length(diff(diff_data)),fitted(ar_set3),type="l",col="green")
accuracy(ar_set1, test = diff_data)
accuracy(ar_set2, test = diff_data)
accuracy(ar_set3, test = diff_data)
I'm unable to get the forecast values using either of those 3 methods. Where am i going wrong?
so I am in dire need of help. I have finally managed to construct my R-INLA model and get it to graph as needed. via the code below:
First I create the stacks (note this is the very end of my INLA process, the mesh etc has already been done)
stk.abdu = inla.stack(data = list(y = 1, e = 0), A = list(abdu.mat, 1),tag = 'abdu', effects = list(list(i = 1:sc.mesh.5$n), data.frame(Intercept = 1,dwater=winter.abdu$dwater,elev=winter.abdu$elev,forest=winter.abdu$forest,developed=winter.abdu$developed,openwater=winter.abdu$OpenWater,barren=winter.abdu$barren,shrubland=winter.abdu$shrubland,herb=winter.abdu$herb,planted=winter.abdu$planted,wetland=winter.abdu$wetland,dist=winter.abdu$dwater)))
stk.quad = inla.stack(data = list(y = 0, e = 0.1), A = list(quad.mat, 1),tag = 'quad', effects = list(list(i = 1:sc.mesh.5$n), data.frame(Intercept = 1,dwater=dummy$dwater,elev=dummy$elev,forest=dummy$forest,developed=dummy$developed,openwater=dummy$openwater,barren=dummy$barren,shrubland=dummy$shrubland,herb=dummy$herb,planted=dummy$planted,wetland=dummy$wetland,dist=dummy$dwater)))
stk.prd<-inla.stack(data = list(y = NA), A = list(Aprd, 1),tag = 'prd', effects = list(list(i = 1:sc.mesh.5$n), data.frame(Intercept = 1,dwater=prddf2$dwater,elev=prddf2$elev,forest=prddf2$forest,developed=prddf2$developed,openwater=prddf2$openwater,barren=prddf2$barren,shrubland=prddf2$shrubland,herb=prddf2$herb,planted=prddf2$planted,wetland=prddf2$wetland,dist=prddf2$dwater)))
stk.all.prd = inla.stack(stk.abdu,stk.quad,stk.prd)
Next I fit my model
ft.inla.prd<-inla(y ~ 0 + Intercept + elev + dwater + forest+ developed + f(inla.group(dist,n=50,method="quantile"),model="rw1",scale.model=TRUE)+f(i,model=sc.spde),family="binomial",data=inla.stack.data(stk.all.prd),control.predictor = list(A = inla.stack.A(stk.all.prd),compute=TRUE),E=inla.stack.data(stk.all.prd)$e,control.compute=list(dic = TRUE),control.fixed=list(expand.factor.strategy="INLA"))
Then I change the predicted values from logit to probabilities
ft.inla.prd$newfield <- exp(ft.inla.prd$summary.random$i$mean)/(1 + exp(ft.inla.prd$summary.random$i$mean))
And finally I use inla.mesh.project and levelplot to create my image
xmean <- inla.mesh.project(projgrid,ft.inla.prd$newfield)
levelplot(xmean, col.regions=topo.colors(99), main='Probability of Presence',xlab='', ylab='', scales=list(draw=FALSE))
So my problem is that I now want to export this data (what is projected as the graph) as a raster so that I can work with it in ArcGIS. However, I have not been able to find a way to do so.
Any input is greatly appreciated