fitted
Fitted responses from a linear mixed-effects model
Description
returns
the fitted conditional
response from the linear mixed-effects model yfit
= fitted(lme
)lme
.
returns
the fitted response from the linear mixed-effects model yfit
= fitted(lme
,Name,Value
)lme
with
additional options specified by one or more Name,Value
pair
arguments.
For example, you can specify if you want to compute the fitted marginal response.
Examples
Compute Fitted Conditional and Marginal Responses
Load the sample data.
load flu
The flu
dataset array has a Date
variable, and 10 variables containing estimated influenza rates (in 9 different regions, estimated from Google® searches, plus a nationwide estimate from the Center for Disease Control and Prevention, CDC).
To fit a linear-mixed effects model, your data must be in a properly formatted dataset array. To fit a linear mixed-effects model with the influenza rates as the responses and region as the predictor variable, combine the nine columns corresponding to the regions into an array. The new dataset array, flu2
, must have the response variable, FluRate
, the nominal variable, Region
, that shows which region each estimate is from, and the grouping variable Date
.
flu2 = stack(flu,2:10,'NewDataVarName','FluRate','IndVarName','Region'); flu2.Date = nominal(flu2.Date);
Fit a linear mixed-effects model with fixed effects for region and a random intercept that varies by Date
.
Region is a categorical variable. You can specify the contrasts for categorical variables using the DummyVarCoding
name-value pair argument when fitting the model. When you do not specify the contrasts, fitlme
uses the 'reference'
contrast by default. Because the model has an intercept, fitlme
takes the first region, NE
, as the reference and creates eight dummy variables representing the other eight regions. For example, is the dummy variable representing the region MidAtl
. For details, see Dummy Variables.
The corresponding model is
where is the observation for level of grouping variable Date
, , = 0, 1, ..., 8, are the fixed-effects coefficients, with being the coefficient for region NE
. is the random effect for level of the grouping variable Date
, and is the observation error for observation . The random effect has the prior distribution, and the error term has the distribution, .
lme = fitlme(flu2,'FluRate ~ 1 + Region + (1|Date)')
lme = Linear mixed-effects model fit by ML Model information: Number of observations 468 Fixed effects coefficients 9 Random effects coefficients 52 Covariance parameters 2 Formula: FluRate ~ 1 + Region + (1 | Date) Model fit statistics: AIC BIC LogLikelihood Deviance 318.71 364.35 -148.36 296.71 Fixed effects coefficients (95% CIs): Name Estimate SE tStat DF pValue Lower Upper {'(Intercept)' } 1.2233 0.096678 12.654 459 1.085e-31 1.0334 1.4133 {'Region_MidAtl' } 0.010192 0.052221 0.19518 459 0.84534 -0.092429 0.11281 {'Region_ENCentral'} 0.051923 0.052221 0.9943 459 0.3206 -0.050698 0.15454 {'Region_WNCentral'} 0.23687 0.052221 4.5359 459 7.3324e-06 0.13424 0.33949 {'Region_SAtl' } 0.075481 0.052221 1.4454 459 0.14902 -0.02714 0.1781 {'Region_ESCentral'} 0.33917 0.052221 6.495 459 2.1623e-10 0.23655 0.44179 {'Region_WSCentral'} 0.069 0.052221 1.3213 459 0.18705 -0.033621 0.17162 {'Region_Mtn' } 0.046673 0.052221 0.89377 459 0.37191 -0.055948 0.14929 {'Region_Pac' } -0.16013 0.052221 -3.0665 459 0.0022936 -0.26276 -0.057514 Random effects covariance parameters (95% CIs): Group: Date (52 Levels) Name1 Name2 Type Estimate Lower Upper {'(Intercept)'} {'(Intercept)'} {'std'} 0.6443 0.5297 0.78368 Group: Error Name Estimate Lower Upper {'Res Std'} 0.26627 0.24878 0.285
The -values 7.3324e-06 and 2.1623e-10 respectively show that the fixed effects of the flu rates in regions WNCentral
and ESCentral
are significantly different relative to the flu rates in region NE
.
The confidence limits for the standard deviation of the random-effects term, , do not include 0 (0.5297, 0.78368), which indicates that the random-effects term is significant. You can also test the significance of the random-effects terms using the compare
method.
The conditional fitted response from the model at a given observation includes contributions from fixed and random effects. For example, the estimated best linear unbiased predictor (BLUP) of the flu rate for region WNCentral
in week 10/9/2005 is
This is the fitted conditional response, since it includes contributions to the estimate from both the fixed and random effects. You can compute this value as follows.
beta = fixedEffects(lme); [~,~,STATS] = randomEffects(lme); % Compute the random-effects statistics (STATS) STATS.Level = nominal(STATS.Level); y_hat = beta(1) + beta(4) + STATS.Estimate(STATS.Level=='10/9/2005')
y_hat = 1.2884
In the previous calculation, beta(1)
corresponds to the estimate for and beta(4)
corresponds to the estimate for . You can simply display the fitted value using the fitted
method.
F = fitted(lme); F(flu2.Date == '10/9/2005' & flu2.Region == 'WNCentral')
ans = 1.2884
The estimated marginal response for region WNCentral
in week 10/9/2005 is
Compute the fitted marginal response.
F = fitted(lme,'Conditional',false); F(flu2.Date == '10/9/2005' & flu2.Region == 'WNCentral')
ans = 1.4602
Plot Residuals vs. Fitted Values
Load the sample data.
load('weight.mat');
weight
contains data from a longitudinal study, where 20 subjects are randomly assigned to 4 exercise programs, and their weight loss is recorded over six 2-week time periods. This is simulated data.
Store the data in a table. Define Subject
and Program
as categorical variables.
tbl = table(InitialWeight,Program,Subject,Week,y); tbl.Subject = nominal(tbl.Subject); tbl.Program = nominal(tbl.Program);
Fit a linear mixed-effects model where the initial weight, type of program, week, and the interaction between the week and type of program are the fixed effects. The intercept and week vary by subject.
lme = fitlme(tbl,'y ~ InitialWeight + Program*Week + (Week|Subject)');
Compute the fitted values and raw residuals.
F = fitted(lme); R = residuals(lme);
Plot the residuals versus the fitted values.
plot(F,R,'bx') xlabel('Fitted Values') ylabel('Residuals')
Now, plot the residuals versus the fitted values, grouped by program.
figure() gscatter(F,R,Program)
Input Arguments
lme
— Linear mixed-effects model
LinearMixedModel
object
Linear mixed-effects model, specified as a LinearMixedModel
object constructed using fitlme
or fitlmematrix
.
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.
Example: yfit = fitted(lme,'Conditional',false)
Conditional
— Indicator for conditional response
true
(default) | false
Indicator for conditional response, specified as the comma-separated
pair consisting of 'Conditional'
and either of
the following.
true | Contribution from both fixed effects and random effects (conditional) |
false | Contribution from only fixed effects (marginal) |
Example: 'Conditional',false
Data Types: logical
Output Arguments
yfit
— Fitted response values
n-by-1 vector
Fitted response values, returned as an n-by-1 vector, where n is the number of observations.
More About
Fitted Conditional and Marginal Response
A conditional response includes contributions from both fixed and random effects, whereas a marginal response includes contribution from only fixed effects.
Suppose the linear mixed-effects model, lme
,
has an n-by-p fixed-effects
design matrix X and an n-by-q random-effects
design matrix Z. Also, suppose the p-by-1
estimated fixed-effects vector is , and the q-by-1
estimated best linear unbiased predictor (BLUP) vector of random effects
is . The fitted conditional response
is
and the fitted marginal response is
Version History
Introduced in R2013b
See Also
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