Dear Statalisters,

I am using a mixed-effect linear model and I want to plot the coefficient of the main independent variable against the outcome variable. Below is the model that I ran. This is one regression model out of many other models for different outcome variables and this construct of covariates seems the most parsimonious one (I have tested the model fitting for all the models). Now I want to create a regression coefficient plot that would be a two-way graph with a scatter plot along with the coefficient plot. I have checked the [coefplot] command but I am preferring a line diagram instead of a forest plot. I would be grateful if you could assist me. below is the result of my analysis.

HTML Code:
Performing EM optimization:

Performing gradient-based optimization:

Iteration 0: log likelihood = -610.4117
Iteration 1: log likelihood = -609.88061
Iteration 2: log likelihood = -609.88034
Iteration 3: log likelihood = -609.88034

Computing standard errors:

Mixed-effects ML regression Number of obs = 216
Group variable: Interval Number of groups = 78

Obs per group:
min = 1
avg = 2.8
max = 12

Wald chi2(6) = 80.96
Log likelihood = -609.88034 Prob > chi2 = 0.0000


rv_tlc Coef. Std. Err. z P>|z| [95% Conf. Interval]

PC .3898496 .2800982 1.39 0.164 -.1591329 .938832

gender
Female 1.119768 .6264817 1.79 0.074 -.1081135 2.347649
age .2764968 .0330719 8.36 0.000 .2116771 .3413165
bmi -.0892654 .0533956 -1.67 0.095 -.1939188 .015388

race
Others -.8500946 1.398114 -0.61 0.543 -3.590347 1.890158
pack_years -.0717252 .0866186 -0.83 0.408 -.2414946 .0980442
_cons 14.27193 2.021221 7.06 0.000 10.31041 18.23345



Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

Interval: Identity
var(_cons) .6608308 .9796505 .0361596 12.07693

var(Residual) 15.98341 1.754463 12.88947 19.82001

LR test vs. linear model: chibar2(01) = 0.59 Prob >= chibar2 = 0.2211
I created this plot, but I am not sure if this fitted plot actually represents that linear prediction obtained from the mixed effect model.

Graph.gph