### Function Reference: `fitlm`

statistics: tab = fitlm (X, y)
statistics: tab = fitlm (X, y, name, value)
statistics: tab = fitlm (X, y, modelspec)
statistics: tab = fitlm (X, y, modelspec, name, value)
statistics: [tab] = fitlm (…)
statistics: [tab, stats] = fitlm (…)
statistics: [tab, stats] = fitlm (…)

Regress the continuous outcome (i.e. dependent variable) y on continuous or categorical predictors (i.e. independent variables) X by minimizing the sum-of-squared residuals. Unless requested otherwise, `fitlm` prints the model formula, the regression coefficients (i.e. parameters/contrasts) and an ANOVA table. Note that unlike `anovan`, `fitlm` treats all factors as continuous by default. A bootstrap resampling variant of this function, `bootlm`, is available in the statistics-resampling package and has similar usage.

X must be a column major matrix or cell array consisting of the predictors. A constant term (intercept) should not be included in X - it is automatically added to the model. y must be a column vector corresponding to the outcome variable. modelspec can specified as one of the following:

- "constant" : model contains only a constant (intercept) term.
- "linear" (default) : model contains an intercept and linear term for each predictor.
- "interactions" : model contains an intercept, linear term for each predictor and all products of pairs of distinct predictors.
- "full" : model contains an intercept, linear term for each predictor and all combinations of the predictors.
- a matrix of term definitions : an t-by-(N+1) matrix specifying terms in a model, where t is the number of terms, N is the number of predictor variables, and +1 accounts for the outcome variable. The outcome variable is the last column in the terms matrix and must be a column of zeros. An intercept must be specified in the first row of the terms matrix and must be a row of zeros.

`fitlm` can take a number of optional parameters as name-value pairs.

`[…] = fitlm (..., "CategoricalVars", categorical)`

- categorical is a vector of indices indicating which of the columns (i.e. variables) in X should be treated as categorical predictors rather than as continuous predictors.

`fitlm` also accepts optional `anovan` parameters as name-value pairs (except for the "model" parameter). The accepted parameter names from `anovan` and their default values in `fitlm` are:

- CONTRASTS : "treatment"
- SSTYPE: 2
- ALPHA: 0.05
- DISPLAY: "on"
- WEIGHTS: [] (empty)
- RANDOM: [] (empty)
- CONTINUOUS: [1:N]
- VARNAMES: [] (empty)

Type ’`help anovan`’ to find out more about what these options do.

`fitlm` can return up to two output arguments:

[tab] = fitlm (…) returns a cell array containing a table of model parameters

[tab, stats] = fitlm (…) returns a structure containing additional statistics, including degrees of freedom and effect sizes for each term in the linear model, the design matrix, the variance-covariance matrix, (weighted) model residuals, and the mean squared error. The columns of stats.coeffs (from left-to-right) report the model coefficients, standard errors, lower and upper 100*(1-alpha)% confidence interval bounds, t-statistics, and p-values relating to the contrasts. The number appended to each term name in stats.coeffnames corresponds to the column number in the relevant contrast matrix for that factor. The stats structure can be used as input for `multcompare`. Note that if the model contains a continuous variable and you wish to use the STATS output as input to `multcompare`, then the model needs to be refit with the "contrast" parameter set to a sum-to-zero contrast coding scheme, e.g."simple".

See also: anovan, multcompare

Source Code: fitlm

### Example: 1

     y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
            22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
            22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
            22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
            25.694 ]';
     X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

     [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");

    MODEL FORMULA (based on Wilkinson's notation):

    Y ~ 1 + X1

    MODEL PARAMETERS (contrasts for the fixed effects)

    Parameter               Estimate        SE  Lower.CI  Upper.CI        t Prob>|t|
    --------------------------------------------------------------------------------
    (Intercept)                   10      1.02      7.93      12.1     9.83    <.001
    X1_1                           8      1.64      4.66      11.3     4.87    <.001
    X1_2                           9      1.44      6.07      11.9     6.25    <.001
    X1_3                          11      1.49      7.97        14     7.38    <.001
    X1_4                          19       1.4      16.2      21.8    13.58    <.001

    ANOVA TABLE (Type II sums-of-squares):

    Source                   Sum Sq.    d.f.    Mean Sq.  R Sq.            F  Prob>F
    --------------------------------------------------------------------------------
    X1                        1561.3       4      390.33  0.855        47.10   <.001
    Error                     265.17      32      8.2866
    Total                     1826.5      36

### Example: 2

     popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
                6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
     brands = {'Gourmet', 'National', 'Generic'; ...
               'Gourmet', 'National', 'Generic'; ...
               'Gourmet', 'National', 'Generic'; ...
               'Gourmet', 'National', 'Generic'; ...
               'Gourmet', 'National', 'Generic'; ...
               'Gourmet', 'National', 'Generic'};
     popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
               'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};

     [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                              "CategoricalVars",[1,2],"display","on");

    MODEL FORMULA (based on Wilkinson's notation):

    Y ~ 1 + X1 + X2 + X1:X2

    MODEL PARAMETERS (contrasts for the fixed effects)

    Parameter               Estimate        SE  Lower.CI  Upper.CI        t Prob>|t|
    --------------------------------------------------------------------------------
    (Intercept)                 5.67     0.215       5.2      6.14    26.34    <.001
    X1_1                       -1.33     0.304        -2     -0.67    -4.38    <.001
    X1_2                       -2.17     0.304     -2.83      -1.5    -7.12    <.001
    X2_1                        1.17     0.304     0.504      1.83     3.83     .002
    X1:X2_1                   -0.333      0.43     -1.27     0.604    -0.77     .454
    X1:X2_2                   -0.167      0.43      -1.1     0.771    -0.39     .705

    ANOVA TABLE (Type II sums-of-squares):

    Source                   Sum Sq.    d.f.    Mean Sq.  R Sq.            F  Prob>F
    --------------------------------------------------------------------------------
    X1                         15.75       2       7.875  0.904        56.70   <.001
    X2                           4.5       1         4.5  0.730        32.40   <.001
    X1*X2                   0.083333       2    0.041667  0.048         0.30    .746
    Error                     1.6667      12     0.13889
    Total                         22      17