linear.kernel: Integration with respect to locally weighted kernel

Description Usage Arguments Value Functions See Also

View source: R/tweetfunctions.R

Description

Integration with respect to locally weighted kernel

Usage

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linear.kernel(t1, t2, ptime, slope, c = 0.0006265725)

power.kernel(t1, t2, ptime, share.time, slope, theta = 0.2314843,
  cutoff = 300, c = 0.0006265725)

integral.memory.kernel(p.time, share.time, slope, window, theta = 0.2314843,
  cutoff = 300, c = 0.0006265725)

Arguments

t1

a vector of integral lower limit

t2

a vector of integral upper limit

ptime

the time (a scalar) to estimate infectiousness and predict for popularity

slope

slope of the linear kernel

c

the constant density when t is less than the cutoff

share.time

observed resharing times, sorted, share.time[1] =0

theta

exponent of the power law

cutoff

the cutoff value where the density changes from constant to power law

p.time

equally spaced vector of time to estimate the infectiousness, p.time[1]=0

window

size of the linear kernel

Value

linear.kernel returns the integral from vector t1 to vector t2 of c*[slope(t-ptime) + 1]; power.kernel returns the integral from vector t1 to vector 2 of c*((t-share.time)/cutoff)^(-(1+theta))[slope(t-ptime) + 1]; integral.memory.kernel returns the vector with ith entry being integral_-inf^inf phi_share.time[i]*kernel(t-p.time)

Functions

See Also

memory.pdf


seismic documentation built on May 2, 2019, 1:07 p.m.