plot_coefficients: Plot the coefficients, the standard errors and the p-values...
In PSTR: Panel Smooth Transition Regression Modelling

Description Usage Arguments Details Value Author(s) See Also Examples

This function plots the curves of the coefficients, the standard errors and the p-values against the transition variable.

1 2	plot_coefficients(obj, vars, length.out = 100, color = "blue", size = 1.5)

`obj`	an object of the class PSTR returned from some functions in the package. Note that the corresponding PSTR model must be estimated first.
`vars`	a vector of column numbers or names (character strings) specifying which variables in the nonlinear part to use.
`length.out`	a scalar of desired length (number of points) for building the x-axis. 100 by default.
`color`	the color of the lines.
`size`	the size of the lines.

The curves of the coefficients, the standard errors and the p-values against the transition variable are functions

f_1(x) = β_{0j} + β_{1j}g(x ; γ, c)

f_2(x) = se(f_1(x))

f_3(x) = 1 - Prob\{ X < [f_1(x)/f_2(x)]^2 \}

where x is a variable taking the position of the transition variable, se stands for the cluster-robust and heteroskedasticity-consistent standard error of the estimate f_1(x) at x, X is a random variable following chi-square distribution with degrees of freedom one.

More than one variable can be put in vars.

The return value is a list of the same length as vars, whose elements are plottable objects.

A list of plottable objects from the ggplot2 package.

Yukai Yang, yukai.yang@statistik.uu.se

Functions which return an object of the class PSTR can be input into this function

EstPSTR

pstr = NewPSTR(Hansen99, dep='inva', indep=4:20, indep_k=c('vala','debta','cfa','sales'),
    tvars=c('vala','debta','cfa','sales'), iT=14) # create a new PSTR object

# estimate the PSTR model first
pstr = EstPSTR(use=pstr, im=1, iq=1, useDelta=TRUE, par=c(.63,0), method='CG')

# plot the curve and surfaces
ret = plot_coefficients(pstr, vars=1:4, length.out=100, color="dodgerblue4", size=2)
ret[[1]]
ret[[1]] + ggplot2::scale_x_log10()