Marima R library in CoCalc

R.version

library(marima)
data(austr)
series<-t(austr)[,1:90]
# Define marima model
Model5 <- define.model(kvar=7,ar=1,ma=1,rem.var=1,reg.var=6:7)
# Estimate marima model
Marima5 <- marima(series,Model5$ar.pattern,Model5$ma.pattern,penalty=1)
# Calculate residuals by filtering
Resid <- arma.filter(series, Marima5$ar.estimates,
Marima5$ma.estimates)
# Compare residuals
plot(Marima5$residuals[2, 5:90], Resid$residuals[2, 5:90], xlab='marima residuals', ylab='arma.filter residuals')

library(marima)
data(austr)
series<-austr
Model5 <- define.model(kvar=7, ar=1, ma=1, rem.var=1, reg.var=6:7)
Marima5 <- marima(ts(series[1:90, ]), Model5$ar.pattern, Model5$ma.pattern,
penalty=1)
nstart <- 90
nstep <- 10
cat("Calling arma.forecast.\n")
cat("In the example the input series is dim(length,kvar).\n")
cat("and of type ts() (timeseries) for illustration. \n")
Forecasts <- arma.forecast(series=ts(series), marima=Marima5,
nstart=nstart, nstep=nstep )
Year<-series[91:100,1]
One.step <- Forecasts$forecasts[, (nstart+1)]
One.step
Predict <- Forecasts$forecasts[ 2, 91:100]
Predict
stdv<-sqrt(Forecasts$pred.var[2, 2, ])
upper.lim=Predict+stdv*1.645
lower.lim=Predict-stdv*1.645
Out<-rbind(Year, Predict, upper.lim, lower.lim)
print(Out)
# plot results:
plot(series[1:100, 1], Forecasts$forecasts[2, ], type='l', xlab='Year',
ylab='Rate of armed suicides', main='Prediction of suicides by firearms',
ylim=c(0.0, 4.1))
lines(series[1:90, 1], series[1:90, 2], type='p')
grid(lty=2, lwd=1, col='black')
Years<-2005:2014
lines(Years, Predict, type='l')
lines(Years, upper.lim, type='l')
lines(Years, lower.lim, type='l')
lines(c(2004.5, 2004.5), c(0.0, 2.0), lty = 2)

# Generate Y=series with 4 variables for illustration:
set.seed(4711)
Y<-matrix(round(100*rnorm(40)+10), nrow=4)
# Example 1: use of difference parameter: If
difference=c(2, 1, 2, 1, 3, 12)
difference
# the variable 2 is differenced
# twice, and variable 3 is differenced once with lag=12.
# Example 2:
poly <- define.dif(series=Y, difference=c(2, 1, 3, 1, 3, 1))
poly
# Generates a (4-variate) polynomial differencing array (with a leading
# unity matrix corresponding to lag=0, and (in the example) differencing
# of variable 2 for lag 1 and variable 3 for lag 1 but twice. Afterwards
# the series Y is differenced accordingly. Results in poly$series and
# poly$dif.poly .
# Example 3: Generation and application of multivariate differencing
# polynomial. Re-use the 4-variate time series and use the
# differencing polynomial (ar-form):
# var=1, dif=1, var=2, dif=6, and var=3 and 4, no differencing.
dif.y <-define.dif(Y, c(1, 1, 2, 6, 3, 0, 4, 0))
# Now dif.y contains the differenced series and the differencing
# polynomial. Print the generated polynomial in short form:
short.form(dif.y$dif.poly)
# Specifying no differencing (3, 0 and 4, 0) may be omitted:
dif.y <-define.dif(Y, c(1, 1, 2, 6))
dif.y
# Example 4:
y<-matrix(round(rnorm(1200)*100+50), nrow=6)
library(marima)
difference<-c(3, 2, 4, 0, 5, 0, 6, 7)
matrix(difference, nrow=2)
Y<-define.dif(y, difference=difference)
round(rowMeans(Y$dif.series), 2)
round(Y$averages, 2)