# coefCI

**Class: **GeneralizedLinearMixedModel

Confidence intervals for coefficients of generalized linear mixed-effects model

## Description

returns
the confidence intervals using additional options specified by one
or more `feCI`

= coefCI(`glme`

,`Name,Value`

)`Name,Value`

pair arguments. For example,
you can specify a different confidence level or the method used to
compute the approximate degrees of freedom.

## Input Arguments

`glme`

— Generalized linear mixed-effects model

`GeneralizedLinearMixedModel`

object

Generalized linear mixed-effects model, specified as a `GeneralizedLinearMixedModel`

object.
For properties and methods of this object, see `GeneralizedLinearMixedModel`

.

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

`Alpha`

— Significance level

0.05 (default) | scalar value in the range [0,1]

Significance level, specified as the comma-separated pair consisting of
`'Alpha'`

and a scalar value in the range [0,1]. For a value α, the
confidence level is 100 × (1 – α)%.

For example, for 99% confidence intervals, you can specify the confidence level as follows.

**Example: **`'Alpha',0.01`

**Data Types: **`single`

| `double`

`DFMethod`

— Method for computing approximate degrees of freedom

`'residual'`

(default) | `'none'`

Method for computing approximate degrees of freedom, specified
as the comma-separated pair consisting of `'DFMethod'`

and
one of the following.

Value | Description |
---|---|

`'residual'` | The degrees of freedom value is assumed to be constant and equal to n –
p, where n is the number of
observations and p is the number of fixed
effects. |

`'none'` | The degrees of freedom is set to infinity. |

**Example: **`'DFMethod','none'`

## Output Arguments

`feCI`

— Fixed-effects confidence intervals

*p*-by-2 matrix

Fixed-effects confidence intervals, returned as a *p*-by-2
matrix. `feCI`

contains the confidence limits that
correspond to the *p*-by-1 fixed-effects vector returned
by the `fixedEffects`

method. The first column of `feCI`

contains
the lower confidence limits and the second column contains the upper
confidence limits.

When fitting a GLME model using `fitglme`

and
one of the maximum likelihood fit methods (`'Laplace'`

or `'ApproximateLaplace'`

):

If you specify the

`'CovarianceMethod'`

name-value pair argument as`'conditional'`

, then the confidence intervals are conditional on the estimated covariance parameters.If you specify the

`'CovarianceMethod'`

name-value pair argument as`'JointHessian'`

, then the confidence intervals account for the uncertainty in the estimated covariance parameters.

When fitting a GLME model using `fitglme`

and
one of the pseudo likelihood fit methods (`'MPL'`

or `'REMPL'`

), `coefci`

uses
the fitted linear mixed effects model from the final pseudo likelihood
iteration to compute confidence intervals on the fixed effects.

`reCI`

— Random-effects confidence intervals

*q*-by-2 matrix

Random-effects confidence intervals, returned as a *q*-by-2
matrix. `reCI`

contains the confidence limits corresponding
to the *q*-by-1 random-effects vector `B`

returned
by the `randomEffects`

method. The first column of `reCI`

contains
the lower confidence limits, and the second column contains the upper
confidence limits.

When fitting a GLME model using `fitglme`

and
one of the maximum likelihood fit methods (`'Laplace'`

or `'ApproximateLaplace'`

), `coefCI`

computes
the confidence intervals using the conditional mean squared error
of prediction (CMSEP) approach conditional on the estimated covariance
parameters and the observed response. Alternatively, you can interpret
the confidence intervals from `coefCI`

as approximate
Bayesian credible intervals conditional on the estimated covariance
parameters and the observed response.

When fitting a GLME model using `fitglme`

and
one of the pseudo likelihood fit methods (`'MPL'`

or `'REMPL'`

), `coefci`

uses
the fitted linear mixed effects model from the final pseudo likelihood
iteration to compute confidence intervals on the random effects.

## Examples

### 95% Confidence Intervals for Fixed Effects

Load the sample data.

`load mfr`

This simulated data is from a manufacturing company that operates 50 factories across the world, with each factory running a batch process to create a finished product. The company wants to decrease the number of defects in each batch, so it developed a new manufacturing process. To test the effectiveness of the new process, the company selected 20 of its factories at random to participate in an experiment: Ten factories implemented the new process, while the other ten continued to run the old process. In each of the 20 factories, the company ran five batches (for a total of 100 batches) and recorded the following data:

Flag to indicate whether the batch used the new process (

`newprocess`

)Processing time for each batch, in hours (

`time`

)Temperature of the batch, in degrees Celsius (

`temp`

)Categorical variable indicating the supplier (

`A`

,`B`

, or`C`

) of the chemical used in the batch (`supplier`

)Number of defects in the batch (

`defects`

)

The data also includes `time_dev`

and `temp_dev`

, which represent the absolute deviation of time and temperature, respectively, from the process standard of 3 hours at 20 degrees Celsius.

Fit a generalized linear mixed-effects model using `newprocess`

, `time_dev`

, `temp_dev`

, and `supplier`

as fixed-effects predictors. Include a random-effects term for intercept grouped by `factory`

, to account for quality differences that might exist due to factory-specific variations. The response variable `defects`

has a Poisson distribution, and the appropriate link function for this model is log. Use the Laplace fit method to estimate the coefficients. Specify the dummy variable encoding as `'effects'`

, so the dummy variable coefficients sum to 0.

The number of defects can be modeled using a Poisson distribution

$${\text{defects}}_{ij}\sim \text{Poisson}({\mu}_{ij})$$

This corresponds to the generalized linear mixed-effects model

$$\mathrm{log}({\mu}_{ij})={\beta}_{0}+{\beta}_{1}{\text{newprocess}}_{ij}+{\beta}_{2}{\text{time}\text{\_}\text{dev}}_{ij}+{\beta}_{3}{\text{temp}\text{\_}\text{dev}}_{ij}+{\beta}_{4}{\text{supplier}\text{\_}\text{C}}_{ij}+{\beta}_{5}{\text{supplier}\text{\_}\text{B}}_{ij}+{b}_{i},$$

where

$${\text{defects}}_{ij}$$ is the number of defects observed in the batch produced by factory $$i$$ during batch $$j$$.

$${\mu}_{ij}$$ is the mean number of defects corresponding to factory $$i$$ (where $$i=1,2,...,20$$) during batch $$j$$ (where $$j=1,2,...,5$$).

$${\text{newprocess}}_{ij}$$, $${\text{time}\text{\_}\text{dev}}_{ij}$$, and $${\text{temp}\text{\_}\text{dev}}_{ij}$$ are the measurements for each variable that correspond to factory $$i$$ during batch $$j$$. For example, $${\text{newprocess}}_{ij}$$ indicates whether the batch produced by factory $$i$$ during batch $$j$$ used the new process.

$${\text{supplier}\text{\_}\text{C}}_{ij}$$ and $${\text{supplier}\text{\_}\text{B}}_{ij}$$ are dummy variables that use effects (sum-to-zero) coding to indicate whether company

`C`

or`B`

, respectively, supplied the process chemicals for the batch produced by factory $$i$$ during batch $$j$$.$${b}_{i}\sim N(0,{\sigma}_{b}^{2})$$ is a random-effects intercept for each factory $$i$$ that accounts for factory-specific variation in quality.

glme = fitglme(mfr,'defects ~ 1 + newprocess + time_dev + temp_dev + supplier + (1|factory)','Distribution','Poisson','Link','log','FitMethod','Laplace','DummyVarCoding','effects');

Use `fixedEffects`

to display the estimates and names of the fixed-effects coefficients in `glme`

.

[beta,betanames] = fixedEffects(glme)

`beta = `*6×1*
1.4689
-0.3677
-0.0945
-0.2832
-0.0719
0.0711

`betanames=`*6×1 table*
Name
_______________
{'(Intercept)'}
{'newprocess' }
{'time_dev' }
{'temp_dev' }
{'supplier_C' }
{'supplier_B' }

Each row of `beta`

contains the estimated value for the coefficient named in the corresponding row of `betanames`

. For example, the value –0.0945 in row 3 of `beta`

is the estimated coefficient for the predictor variable `time_dev`

.

Compute the 95% confidence intervals for the fixed-effects coefficients.

feCI = coefCI(glme)

`feCI = `*6×2*
1.1515 1.7864
-0.7202 -0.0151
-1.7395 1.5505
-2.1926 1.6263
-0.2268 0.0831
-0.0826 0.2247

Column 1 of `feCI`

contains the lower bound of the 95% confidence interval. Column 2 contains the upper bound. Row 1 corresponds to the intercept term. Rows 2, 3, and 4 correspond to `newprocess`

, `time_dev`

, and `temp_dev`

, respectively. Rows 5 and 6 correspond to the indicator variables `supplier_C`

and `supplier_B`

, respectively. For example, the 95% confidence interval for the coefficient for `time_dev`

is [-1.7395 , 1.5505]. Some of the confidence intervals include 0, which indicates that those predictors are not significant at the 5% significance level. To obtain specific $$p$$-values for each fixed-effects term, use `fixedEffects`

. To test significance for entire terms, use `anova`

.

### 99% Confidence Intervals for Random Effects

Load the sample data.

`load mfr`

This simulated data is from a manufacturing company that operates 50 factories across the world, with each factory running a batch process to create a finished product. The company wants to decrease the number of defects in each batch, so it developed a new manufacturing process. To test the effectiveness of the new process, the company selected 20 of its factories at random to participate in an experiment: Ten factories implemented the new process, while the other ten continued to run the old process. In each of the 20 factories, the company ran five batches (for a total of 100 batches) and recorded the following data:

Flag to indicate whether the batch used the new process (

`newprocess`

)Processing time for each batch, in hours (

`time`

)Temperature of the batch, in degrees Celsius (

`temp`

)Categorical variable indicating the supplier (

`A`

,`B`

, or`C`

) of the chemical used in the batch (`supplier`

)Number of defects in the batch (

`defects`

)

The data also includes `time_dev`

and `temp_dev`

, which represent the absolute deviation of time and temperature, respectively, from the process standard of 3 hours at 20 degrees Celsius.

Fit a generalized linear mixed-effects model using `newprocess`

, `time_dev`

, `temp_dev`

, and `supplier`

as fixed-effects predictors. Include a random-effects intercept grouped by `factory`

, to account for quality differences that might exist due to factory-specific variations. The response variable `defects`

has a Poisson distribution, and the appropriate link function for this model is log. Use the Laplace fit method to estimate the coefficients.

The number of defects can be modeled using a Poisson distribution

$${\text{defects}}_{ij}\sim \text{Poisson}({\mu}_{ij})$$

This corresponds to the generalized linear mixed-effects model

$$\mathrm{log}({\mu}_{ij})={\beta}_{0}+{\beta}_{1}{\text{newprocess}}_{ij}+{\beta}_{2}{\text{time}\text{\_}\text{dev}}_{ij}+{\beta}_{3}{\text{temp}\text{\_}\text{dev}}_{ij}+{\beta}_{4}{\text{supplier}\text{\_}\text{C}}_{ij}+{\beta}_{5}{\text{supplier}\text{\_}\text{B}}_{ij}+{b}_{i},$$

where

$${\text{defects}}_{ij}$$ is the number of defects observed in the batch produced by factory $$i$$ during batch $$j$$.

$${\mu}_{ij}$$ is the mean number of defects corresponding to factory $$i$$ (where $$i=1,2,...,20$$) during batch $$j$$ (where $$j=1,2,...,5$$).

$${\text{newprocess}}_{ij}$$, $${\text{time}\text{\_}\text{dev}}_{ij}$$, and $${\text{temp}\text{\_}\text{dev}}_{ij}$$ are the measurements for each variable that correspond to factory $$i$$ during batch $$j$$. For example, $${\text{newprocess}}_{ij}$$ indicates whether the batch produced by factory $$i$$ during batch $$j$$ used the new process.

$${\text{supplier}\text{\_}\text{C}}_{ij}$$ and $${\text{supplier}\text{\_}\text{B}}_{ij}$$ are dummy variables that use effects (sum-to-zero) coding to indicate whether company

`C`

or`B`

, respectively, supplied the process chemicals for the batch produced by factory $$i$$ during batch $$j$$.$${b}_{i}\sim N(0,{\sigma}_{b}^{2})$$ is a random-effects intercept for each factory $$i$$ that accounts for factory-specific variation in quality.

glme = fitglme(mfr,'defects ~ 1 + newprocess + time_dev + temp_dev + supplier + (1|factory)','Distribution','Poisson','Link','log','FitMethod','Laplace','DummyVarCoding','effects');

Use `randomEffects`

to compute and display the estimates of the empirical Bayes predictors (EBPs) for the random effects associated with `factory`

.

[B,Bnames] = randomEffects(glme)

`B = `*20×1*
0.2913
0.1542
-0.2633
-0.4257
0.5453
-0.1069
0.3040
-0.1653
-0.1458
-0.0816
⋮

`Bnames=`*20×3 table*
Group Level Name
___________ ______ _______________
{'factory'} {'1' } {'(Intercept)'}
{'factory'} {'2' } {'(Intercept)'}
{'factory'} {'3' } {'(Intercept)'}
{'factory'} {'4' } {'(Intercept)'}
{'factory'} {'5' } {'(Intercept)'}
{'factory'} {'6' } {'(Intercept)'}
{'factory'} {'7' } {'(Intercept)'}
{'factory'} {'8' } {'(Intercept)'}
{'factory'} {'9' } {'(Intercept)'}
{'factory'} {'10'} {'(Intercept)'}
{'factory'} {'11'} {'(Intercept)'}
{'factory'} {'12'} {'(Intercept)'}
{'factory'} {'13'} {'(Intercept)'}
{'factory'} {'14'} {'(Intercept)'}
{'factory'} {'15'} {'(Intercept)'}
{'factory'} {'16'} {'(Intercept)'}
⋮

Each row of `B`

contains the estimated EBPs for the random-effects coefficient named in the corresponding row of `Bnames`

. For example, the value -0.2633 in row 3 of `B`

is the estimated coefficient of `'(Intercept)'`

for level `'3'`

of `factory`

.

Compute the 99% confidence intervals of the EBPs for the random effects.

```
[feCI,reCI] = coefCI(glme,'Alpha',0.01);
reCI
```

`reCI = `*20×2*
-0.2125 0.7951
-0.3510 0.6595
-0.8219 0.2954
-0.9953 0.1440
0.0730 1.0176
-0.6362 0.4224
-0.1796 0.7877
-0.7044 0.3738
-0.6795 0.3880
-0.6142 0.4509
⋮

Column 1 of `reCI`

contains the lower bound of the 99% confidence interval. Column 2 contains the upper bound. Each row corresponds to a level of `factory`

, in the order shown in `Bnames`

. For example, row 3 corresponds to the coefficient of `'(Intercept)'`

for level `'3'`

of `factory`

, which has a 99% confidence interval of [-0.8219 , 0.2954]. For additional statistics related to each random-effects term, use `randomEffects`

.

## References

[1] Booth, J.G., and J.P. Hobert. “Standard Errors
of Prediction in Generalized Linear Mixed Models.” *Journal
of the American Statistical Association*. Vol. 93, 1998,
pp. 262–272.

## See Also

`GeneralizedLinearMixedModel`

| `anova`

| `coefTest`

| `covarianceParameters`

| `fixedEffects`

| `randomEffects`

## MATLAB 명령

다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.

명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.

Select a Web Site

Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

You can also select a web site from the following list:

## How to Get Best Site Performance

Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.

### Americas

- América Latina (Español)
- Canada (English)
- United States (English)

### Europe

- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)

- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)