# plotResiduals

**Class: **GeneralizedLinearMixedModel

Plot residuals of generalized linear mixed-effects model

## Syntax

## Description

`plotResiduals(`

plots the conditional residuals of `glme`

,`plottype`

,`Name,Value`

)`glme`

using additional
options specified by one or more `Name,Value`

pair arguments.
For example, you can specify the residual type and the graphical properties of
residual data points.

returns
a handle, `h`

= plotResiduals(___)`h`

, to the lines or patches in the plot
of residuals.

## 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`

.

`plottype`

— Type of residual plot

`"histogram"`

(default) | `"caseorder"`

| `"fitted"`

| `"lagged"`

| `"probability"`

| `"observed"`

| `"symmetry"`

Type of residual plot, specified as one of the following.

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

`"histogram"` | Histogram of residuals |

`"caseorder"` | Residuals versus case order. Case order is the same as the
row order used in the input data `tbl` when fitting
the model using `fitglme` . |

`"fitted"` | Residuals versus fitted values |

`"lagged"` | Residuals versus lagged residual (r(t)
versus r(t – 1)) |

`"probability"` | Normal probability plot |

`"observed"` | Observed vs. fitted values. This plot includes a dotted
reference line of y =
x. Each residual is represented by
the vertical distance from the corresponding observed value
to the reference line. |

`"symmetry"` | Symmetry plot |

**Example: **`plotResiduals(glme,"lagged")`

`ax`

— Target axes

`Axes`

object

*Since R2024a*

Target axes, specified as an Axes object. If you do not specify the axes,
then `plotResiduals`

uses the current axes (`gca`

).

### 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.*

`ResidualType`

— Residual type

`'raw'`

(default) | `'Pearson'`

Residual type, specified by the comma-separated pair consisting
of `ResidualType`

and one of the following.

Residual Type | Formula |
---|---|

`'raw'` |
$${r}_{ci}={y}_{i}-{g}^{-1}\left({x}_{i}^{T}\widehat{\beta}+{z}_{i}^{T}\widehat{b}+{\delta}_{i}\right)$$ |

`'Pearson'` |
$${r}_{ci}^{pearson}=\frac{{r}_{ci}}{\sqrt{\frac{\widehat{{\sigma}^{2}}}{{w}_{i}}{v}_{i}\left({\mu}_{i}\left(\widehat{\beta},\widehat{b}\right)\right)}}$$ |

In each of these equations:

*y*is the_{i}*i*th element of the*n*-by-1 response vector,*y*, where*i*= 1, ...,*n*.*g*^{-1}is the inverse link function for the model.*x*_{i}^{T}is the*i*th row of the fixed-effects design matrix*X*.*z*_{i}^{T}is the*i*th row of the random-effects design matrix*Z*.*δ*is the_{i}*i*th offset value.*σ*^{2}is the dispersion parameter.*w*is the_{i}*i*th observation weight.*v*is the variance term for the_{i}*i*th observation.*μ*is the mean of the response for the_{i}*i*th observation.$$\widehat{\beta}$$ and $$\widehat{b}$$ are estimated values of

*β*and*b*.

Raw residuals from a generalized linear mixed-effects model have nonconstant variance. Pearson residuals are expected to have an approximately constant variance, and are generally used for analysis.

**Example: **`'ResidualType','Pearson'`

## Output Arguments

`h`

— Handle to residual plot

graphics object

Handle to the residual plot, returned as a graphics object. You can use dot notation to change certain property values of the object, including face color for a histogram, and marker style and color for a scatterplot. For more information, see Access Property Values.

## Examples

### Create Plots of Residuals

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');

Create diagnostic plots using Pearson residuals to test the model assumptions.

Plot a histogram to visually confirm that the mean of the Pearson residuals is equal to 0. If the model is correct, we expect the Pearson residuals to be centered at 0.

plotResiduals(glme,'histogram','ResidualType','Pearson')

The histogram shows that the Pearson residuals are centered at 0.

Plot the Pearson residuals versus the fitted values, to check for signs of nonconstant variance among the residuals (heteroscedasticity). We expect the conditional Pearson residuals to have a constant variance. Therefore, a plot of conditional Pearson residuals versus conditional fitted values should not reveal any systematic dependence on the conditional fitted values.

plotResiduals(glme,'fitted','ResidualType','Pearson')

The plot does not show a systematic dependence on the fitted values, so there are no signs of nonconstant variance among the residuals.

Plot the Pearson residuals versus lagged residuals, to check for correlation among the residuals. The conditional independence assumption in GLME implies that the conditional Pearson residuals are approximately uncorrelated.

plotResiduals(glme,'lagged','ResidualType','Pearson')

There is no pattern to the plot, so there are no signs of correlation among the residuals.

## Version History

### R2024b: Plot observed versus fitted values

You can now plot observed versus fitted values by specifying the
`plottype`

input argument as
`"observed"`

.

### R2024a: Specify target axes

Specify the target axes for the plot by using the `ax`

input
argument.

## See Also

`GeneralizedLinearMixedModel`

| `fitglme`

| `fitted`

| `plot`

| `residuals`

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