# summarize

Distribution summary statistics of standard Bayesian linear regression model

## Syntax

``summarize(Mdl)``
``SummaryStatistics = summarize(Mdl)``

## Description

To obtain a summary of a Bayesian linear regression model for predictor selection, see `summarize`.

example

````summarize(Mdl)` displays a tabular summary of the random regression coefficients and disturbance variance of the standard Bayesian linear regression model `Mdl` at the command line. For each parameter, the summary includes the: Standard deviation (square root of the variance)95% equitailed credible intervalsProbability that the parameter is greater than 0Description of the distributions, if known ```

example

````SummaryStatistics = summarize(Mdl)` returns a structure array that stores a: Table containing the summary of the regression coefficients and disturbance varianceTable containing the covariances between variablesDescription of the joint distribution of the parameters ```

## Examples

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Consider the multiple linear regression model that predicts the US real gross national product (`GNPR`) using a linear combination of industrial production index (`IPI`), total employment (`E`), and real wages (`WR`).

`${\text{GNPR}}_{t}={\beta }_{0}+{\beta }_{1}{\text{IPI}}_{t}+{\beta }_{2}{\text{E}}_{t}+{\beta }_{3}{\text{WR}}_{t}+{\epsilon }_{t}.$`

For all $t$ time points, ${\epsilon }_{t}$ is a series of independent Gaussian disturbances with a mean of 0 and variance ${\sigma }^{2}$.

Assume these prior distributions:

• $\beta |{\sigma }^{2}\sim {N}_{4}\left(M,{\sigma }^{2}V\right)$. $M$ is a 4-by-1 vector of means, and $V$ is a scaled 4-by-4 positive definite covariance matrix.

• ${\sigma }^{2}\sim IG\left(A,B\right)$. $A$ and $B$ are the shape and scale, respectively, of an inverse gamma distribution.

These assumptions and the data likelihood imply a normal-inverse-gamma conjugate model.

Create a normal-inverse-gamma conjugate prior model for the linear regression parameters. Specify the number of predictors `p` and the variable names.

```p = 3; VarNames = ["IPI" "E" "WR"]; PriorMdl = bayeslm(p,'ModelType','conjugate','VarNames',VarNames);```

`PriorMdl` is a `conjugateblm` Bayesian linear regression model object representing the prior distribution of the regression coefficients and disturbance variance.

Summarize the prior distribution.

`summarize(PriorMdl)`
``` | Mean Std CI95 Positive Distribution ----------------------------------------------------------------------------------- Intercept | 0 70.7107 [-141.273, 141.273] 0.500 t (0.00, 57.74^2, 6) IPI | 0 70.7107 [-141.273, 141.273] 0.500 t (0.00, 57.74^2, 6) E | 0 70.7107 [-141.273, 141.273] 0.500 t (0.00, 57.74^2, 6) WR | 0 70.7107 [-141.273, 141.273] 0.500 t (0.00, 57.74^2, 6) Sigma2 | 0.5000 0.5000 [ 0.138, 1.616] 1.000 IG(3.00, 1) ```

The function displays a table of summary statistics and other information about the prior distribution at the command line.

Load the Nelson-Plosser data set and create variables for the predictor and response data.

```load Data_NelsonPlosser X = DataTable{:,PriorMdl.VarNames(2:end)}; y = DataTable.GNPR;```

Estimate the posterior distributions. Suppress the estimation display.

`PosteriorMdl = estimate(PriorMdl,X,y,'Display',false);`

`PosteriorMdl` is a `conjugateblm` model object that contains the posterior distributions of $\beta$ and ${\sigma }^{2}$.

Obtain summary statistics from the posterior distribution.

`summary = summarize(PosteriorMdl);`

`summary` is a structure array containing three fields: `MarginalDistributions`, `Covariances`, and `JointDistribution`.

Display the marginal distribution summary and covariances by using dot notation.

`summary.MarginalDistributions`
```ans=5×5 table Mean Std CI95 Positive Distribution _________ __________ ________________________ _________ __________________________ Intercept -24.249 8.7821 -41.514 -6.9847 0.0032977 {'t (-24.25, 8.65^2, 68)'} IPI 4.3913 0.1414 4.1134 4.6693 1 {'t (4.39, 0.14^2, 68)' } E 0.0011202 0.00032931 0.00047284 0.0017676 0.99952 {'t (0.00, 0.00^2, 68)' } WR 2.4683 0.34895 1.7822 3.1543 1 {'t (2.47, 0.34^2, 68)' } Sigma2 44.135 7.802 31.427 61.855 1 {'IG(34.00, 0.00069)' } ```
`summary.Covariances`
```ans=5×5 table Intercept IPI E WR Sigma2 __________ ___________ ___________ ___________ ______ Intercept 77.125 0.77133 -0.0023655 0.5311 0 IPI 0.77133 0.019994 -6.5001e-06 -0.02948 0 E -0.0023655 -6.5001e-06 1.0844e-07 -8.0013e-05 0 WR 0.5311 -0.02948 -8.0013e-05 0.12177 0 Sigma2 0 0 0 0 60.871 ```

The `MarginalDistributions` field is a table of summary statistics and other information about the posterior distribution. `Covariances` is a table containing the covariance matrix of the parameters.

## Input Arguments

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Standard Bayesian linear regression model, specified as a model object in this table.

Model ObjectDescription
`conjugateblm`Dependent, normal-inverse-gamma conjugate prior or posterior model returned by `bayeslm` or `estimate`
`semiconjugateblm`Independent, normal-inverse-gamma semiconjugate prior model returned by `bayeslm`
`diffuseblm`Diffuse prior model returned by `bayeslm`
`empiricalblm`Prior or posterior model characterized by random draws from respective distributions, returned by `bayeslm` or `estimate`
`customblm`Prior distribution function that you declare returned by `bayeslm`

## Output Arguments

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Parameter distribution summary, returned as a structure array containing the information in this table.

Structure FieldDescription
`MarginalDistributions`

Table containing a summary of the parameter distributions. Rows correspond to parameters. Columns correspond to the:

• Estimated posterior mean (`Mean`)

• Standard deviation (`Std`)

• 95% equitailed credible interval (`CI95`)

• Posterior probability that the parameter is greater than 0 (`Positive`)

• Description of the marginal or conditional posterior distribution of the parameter (`Distribution`)

Row names are the names in `Mdl.VarNames`, and the name of the last row is `Sigma2`.

`Covariances`

Table containing covariances between parameters. Rows and columns correspond to the intercept (if one exists) the regression coefficients, and disturbance variance. Row and column names are the same as the row names in `MarginalDistributions`.

`JointDistribution`

A string scalar that describes the distributions of the regression coefficients (`Beta`) and the disturbance variance (`Sigma2`) when known.

For distribution descriptions:

• `N(Mu,V)` denotes the normal distribution with mean `Mu` and variance matrix `V`. This distribution can be multivariate.

• `IG(A,B)` denotes the inverse gamma distribution with shape `A` and scale `B`.

• `t(Mu,V,DoF)` denotes the Student’s t distribution with mean `Mu`, variance `V`, and degrees of freedom `DoF`.

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### Bayesian Linear Regression Model

A Bayesian linear regression model treats the parameters β and σ2 in the multiple linear regression (MLR) model yt = xtβ + εt as random variables.

For times t = 1,...,T:

• yt is the observed response.

• xt is a 1-by-(p + 1) row vector of observed values of p predictors. To accommodate a model intercept, x1t = 1 for all t.

• β is a (p + 1)-by-1 column vector of regression coefficients corresponding to the variables that compose the columns of xt.

• εt is the random disturbance with a mean of zero and Cov(ε) = σ2IT×T, while ε is a T-by-1 vector containing all disturbances. These assumptions imply that the data likelihood is

`$\ell \left(\beta ,{\sigma }^{2}|y,x\right)=\prod _{t=1}^{T}\varphi \left({y}_{t};{x}_{t}\beta ,{\sigma }^{2}\right).$`

ϕ(yt;xtβ,σ2) is the Gaussian probability density with mean xtβ and variance σ2 evaluated at yt;.

Before considering the data, you impose a joint prior distribution assumption on (β,σ2). In a Bayesian analysis, you update the distribution of the parameters by using information about the parameters obtained from the likelihood of the data. The result is the joint posterior distribution of (β,σ2) or the conditional posterior distributions of the parameters.

## Version History

Introduced in R2017a