I've been working on a longitudinal study using a linear mixed-effects model in R. I wonder if there is any way to visualize the results of the linear mixed-effects model. The problem in my situation is both independent and dependent variables are continuous (there is no group in independent variables)
Here, A is the continuous dependent variable and B is the continuous independent variable. There are multiple records for each patient on different dates, which makes it a longitudinal study. I want to figure out the relationship between A and B.
Is there any way to visualize it?
Edit: I fitted another model and I got this.
Linear mixed model fit by REML. t-tests use Satterthwaite's method [ lmerModLmerTest] Formula: **A ~ B + date + (1 | pNo)** Data: brain REML criterion at convergence: 7140.8 Scaled residuals: Min 1Q Median 3Q Max -2.8742 -0.5168 -0.0050 0.5118 2.5005 Random effects: Groups Name Variance Std.Dev. pNo (Intercept) 23745881 4873 Residual 21276522 4613 Number of obs: 355, groups: pNo, 150 Fixed effects: Estimate Std. Error df t value (Intercept) 26800.3 3473.9 220.1 7.715 B 22471.3 3786.1 350.2 5.935 date -390.2 50.2 246.2 -7.773 Pr(>|t|) (Intercept) 4.17e-13 *** B 7.06e-09 *** date 2.08e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: abbreviate used with non-ASCII chars (Intr) B B -0.532 date -0.865 0.057
And I plotted the qqplot, which was a straight line.