Dear Stata users,
Can anyone please help with the following?
I have a rather small but complex dataset consisting having these variables of interest
Var1 = A continuous variable from 0 to approximately 7
Var2 = A categorical variable from 1 to 3, where 1 acts as the reference for the value 2 and 3
Var3 = A continuous variable from 5 to approximately 45
Var4 = ID of the included patients goes from 1 to 10
It is a paired and little unbalanced dataset without any missing data.
Can anyone please inform me
xtmixed c.var1 c.var3 i.var2|| var4: || var2:
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -186.78944
Iteration 1: log likelihood = -186.29506
Iteration 2: log likelihood = -186.29315
Iteration 3: log likelihood = -186.29313
Computing standard errors:
Mixed-effects ML regression Number of obs = 109
-------------------------------------------------------------
| No. of Observations per Group
Group Variable | Groups Minimum Average Maximum
----------------+--------------------------------------------
Var4 | 10 5 10.9 19
Var2 | 28 1 3.9 8
-------------------------------------------------------------
Wald chi2(3) = 100.09
Log likelihood = -186.29313 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
Var1 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Var3 | .0451614 .014722 3.07 0.002 .0163068 .074016
|
Var2 |
2 | -1.765215 .3077596 -5.74 0.000 -2.368413 -1.162018
3 | -2.999844 .3132985 -9.58 0.000 -3.613898 -2.38579
|
_cons | 4.157045 .3764994 11.04 0.000 3.41912 4.894971
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Var4: Identity |
sd(_cons) | .1781771 .248025 .0116404 2.727322
-----------------------------+------------------------------------------------
Var2: Identity |
sd(_cons) | 1.67e-06 7.87e-06 1.62e-10 .0171642
-----------------------------+------------------------------------------------
sd(Residual) | 1.325808 .0939577 1.153871 1.523364
------------------------------------------------------------------------------
LR test vs. linear model: chi2(2) = 0.16 Prob > chi2 = 0.9226
Note: LR test is conservative and provided only for reference.
Can anyone please help with the following?
I have a rather small but complex dataset consisting having these variables of interest
Var1 = A continuous variable from 0 to approximately 7
Var2 = A categorical variable from 1 to 3, where 1 acts as the reference for the value 2 and 3
Var3 = A continuous variable from 5 to approximately 45
Var4 = ID of the included patients goes from 1 to 10
It is a paired and little unbalanced dataset without any missing data.
Can anyone please inform me
- how to recode the xtmixed so I can have both random slope and intercept in the model in which var2 is nested within var4?
- how to calculate the mixed linear regression equation with 95 % confidence interval for the slope and the corresponding p-value for these three equations (I know the random effects are not listed in equations but it is rather because I don’t know how to calculate them)?
- Var1 = intercept + var3 if var2 == 1
- Var1 = intercept + var3 if var2 == 2
- Var1 = intercept + var3 if var2 == 3
- how to interpret the random effects parameters box?
xtmixed c.var1 c.var3 i.var2|| var4: || var2:
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -186.78944
Iteration 1: log likelihood = -186.29506
Iteration 2: log likelihood = -186.29315
Iteration 3: log likelihood = -186.29313
Computing standard errors:
Mixed-effects ML regression Number of obs = 109
-------------------------------------------------------------
| No. of Observations per Group
Group Variable | Groups Minimum Average Maximum
----------------+--------------------------------------------
Var4 | 10 5 10.9 19
Var2 | 28 1 3.9 8
-------------------------------------------------------------
Wald chi2(3) = 100.09
Log likelihood = -186.29313 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
Var1 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Var3 | .0451614 .014722 3.07 0.002 .0163068 .074016
|
Var2 |
2 | -1.765215 .3077596 -5.74 0.000 -2.368413 -1.162018
3 | -2.999844 .3132985 -9.58 0.000 -3.613898 -2.38579
|
_cons | 4.157045 .3764994 11.04 0.000 3.41912 4.894971
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Var4: Identity |
sd(_cons) | .1781771 .248025 .0116404 2.727322
-----------------------------+------------------------------------------------
Var2: Identity |
sd(_cons) | 1.67e-06 7.87e-06 1.62e-10 .0171642
-----------------------------+------------------------------------------------
sd(Residual) | 1.325808 .0939577 1.153871 1.523364
------------------------------------------------------------------------------
LR test vs. linear model: chi2(2) = 0.16 Prob > chi2 = 0.9226
Note: LR test is conservative and provided only for reference.
