PSTR (version 1.2.4)

plot_coefficients: Plot the coefficients, the standard errors and the p-values against the transition variable.

Description

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

Usage

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

Arguments

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.

Value

A list of plottable objects from the ggplot2 package.

Details

The curves of the coefficients, the standard errors and the p-values against the transition variable are functions $$f_1(x) = \beta_{0j} + \beta_{1j}g(x ; \gamma, c)$$ $$f_2(x) = se(f_1(x))$$ $$f_3(x) = 1 - Prob\{ X < [f_1(x)/f_2(x)]^2 \} $$ 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.

See Also

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

EstPSTR

Examples

# NOT RUN {
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()
# }
# NOT RUN {
# }