Stata 15 help for fp predict

[R] fp postestimation -- Postestimation tools for fp

Postestimation commands

The following postestimation commands are of special interest after fp:

Command Description ------------------------------------------------------------------------- fp plot component-plus-residual plot from most recently fit fractional polynomial model fp predict create variable containing prediction or SEs of fractional polynomials -------------------------------------------------------------------------

The following standard postestimation commands are also available if available after est_cmd:

Command Description ------------------------------------------------------------------------- contrast contrasts and ANOVA-style joint tests of estimates estat ic Akaike's and Schwarz's Bayesian information criteria (AIC and BIC) estat summarize summary statistics for the estimation sample estat vce variance-covariance matrix of the estimators (VCE) estimates cataloging estimation results forecast dynamic forecasts and simulations hausman Hausman's specification test lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients linktest link test for model specification lrtest likelihood-ratio test margins marginal means, predictive margins, marginal effects, and average marginal effects marginsplot graph the results from margins (profile plots, interaction plots, etc.) nlcom point estimates, standard errors, testing, and inference for nonlinear combinations of coefficients predict predictions, residuals, influence statistics, and other diagnostic measures predictnl point estimates, standard errors, testing, and inference for generalized predictions pwcompare pairwise comparisons of estimates suest seemingly unrelated estimation test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses -------------------------------------------------------------------------

predict

The behavior of predict following fp is determined by est_cmd. See the corresponding est_cmd postestimation entry for available predict options.

Also see information on fp predict below.

margins

The behavior of margins following fp is determined by est_cmd. See the corresponding est_cmd postestimation entry for available margins options.

Syntax for fp plot and fp predict

Component-plus-residual plot for most recently fit fractional polynomial model

fp plot [if] [in], residuals(res_option) [graph_options]

Create variable containing the prediction or SEs of fractional polynomials

fp predict [type] newvar [if] [in] [, predict_options]

graph_options Description ------------------------------------------------------------------------- Main * residuals(res_option) residual option name to use in predict after est_cmd, or residuals(none) if residuals are not to be graphed equation(eqno) specify equation level(#) set confidence level; default is level(95)

Plot plotopts(scatter_options) affect rendition of the component-plus-residual scatter points

Fitted line lineopts(cline_options) affect rendition of the fitted line

CI plot ciopts(area_options) affect rendition of the confidence bands

Add plots addplot(plot) add other plots to the generated graph

Y axis, X axis, Titles, Legend, Overall twoway_options any options other than by() documented in [G-3] twoway_options ------------------------------------------------------------------------- * residuals(res_option) is required.

predict_options Description ------------------------------------------------------------------------- Main fp calculate the fractional polynomial; the default stdp calculate the standard error of the fractional polynomial equation(eqno) specify equation -------------------------------------------------------------------------

Menu for fp plot and fp predict

fp plot

Statistics > Linear models and related > Fractional polynomials > Component-plus-residual plot

fp predict

Statistics > Linear models and related > Fractional polynomials > Fractional polynomial prediction

Description for fp plot and fp predict

fp plot produces a component-plus-residual plot. The fractional polynomial comprises the component, and the residual is specified by the user in residuals(). The component-plus-residuals are plotted against the fractional polynomial variable. If you only want to plot the component fit, without residuals, you would specify residuals(none).

fp predict generates the fractional polynomial or the standard error of the fractional polynomial. The fractional polynomial prediction is equivalent to the fitted values prediction given by predict, xb, with the covariates other than the fractional polynomial variable set to zero. The standard error may be quite large if the range of the other covariates is far from zero. In this situation, the covariates would be centered and their range would include, or come close to including, zero.

These postestimation commands can be used only when the fractional polynomial variables do not interact with other variables in the specification of est_cmd. See fvvarlist for more information about interactions.

Options for fp plot

+------+ ----+ Main +-------------------------------------------------------------

residuals(res_option) specifies what type of residuals to plot in the component-plus-residual plot. res_option is the same option that would be specified to predict after est_cmd. Residuals can be omitted from the plot by specifying residuals(none). residuals() is required.

equation(eqno) is relevant only when you have previously fit a multiple-equation model in est_cmd. It specifies the equation to which you are referring.

equation(#1) would mean that the calculation is to be made for the first equation, equation(#2) would mean the second, and so on. You could also refer to the equations by their names: equation(income) would refer to the equation name income, and equation(hours) would refer to the equation named hours.

If you do not specify equation(), the results are the same as if you specified equation(#1).

level(#); see [R] estimation options.

+------+ ----+ Plot +-------------------------------------------------------------

plotopts(scatter_options) affects the rendition of the component-plus-residual scatter points; see [G-2] graph twoway scatter.

+-------------+ ----+ Fitted line +------------------------------------------------------

lineopts(cline_options) affects the rendition of the fitted line; see [G-3] cline_options.

+---------+ ----+ CI plot +----------------------------------------------------------

ciopts(area_options) affects the rendition of the confidence bands; see [G-3] area_options.

+-----------+ ----+ Add plots +--------------------------------------------------------

addplot(plot) provides a way to add other plots to the generated graph. See [G-3] addplot_option.

+-----------------------------------------+ ----+ Y axis, X axis, Titles, Legend, Overall +--------------------------

twoway_options are any of the options documented in [G-3] twoway_options, excluding by(). These include options for titling the graph (see [G-3] title_options) and for saving the graph to disk (see [G-3] saving_option).

Options for fp predict

+------+ ----+ Main +-------------------------------------------------------------

fp calculates the fractional polynomial, the linear prediction with other variables set to zero. This is the default.

stdp calculates the standard error of the fractional polynomial.

equation(eqno) is relevant only when you have previously fit a multiple-equation model in est_cmd. It specifies the equation to which you are referring.

equation(#1) would mean that the calculation is to be made for the first equation, equation(#2) would mean the second, and so on. You could also refer to the equations by their names: equation(income) would refer to the equation name income, and equation(hours) would refer to the equation named hours.

If you do not specify equation(), the results are the same as if you specified equation(#1).

Examples

Setup . webuse igg

Fit the optimal second-degree fractional polynomial regression model . fp <age>: regress sqrtigg <age>

Produce a component-plus-residual plot to evaluate the fit of the model . fp plot, r(residuals)

Predict the standard errors of the fractional polynomial . fp predict se, stdp


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