methods.RdMethods for object of class "MixMod" for standard generic functions.
coef(object, ...) # S3 method for MixMod coef(object, sub_model = c("main", "zero_part"), ...) fixef(object, ...) # S3 method for MixMod fixef(object, sub_model = c("main", "zero_part"), ...) ranef(object, ...) # S3 method for MixMod ranef(object, post_vars = FALSE, ...) confint(object, parm, level = 0.95, ...) # S3 method for MixMod confint(object, parm = c("fixed-effects", "var-cov","extra", "zero_part"), level = 0.95, sandwich = FALSE, ...) anova(object, ...) # S3 method for MixMod anova(object, object2, test = TRUE, L = NULL, sandwich = FALSE, ...) fitted(object, ...) # S3 method for MixMod fitted(object, type = c("mean_subject", "subject_specific", "marginal"), link_fun = NULL, ...) residuals(object, ...) # S3 method for MixMod residuals(object, type = c("mean_subject", "subject_specific", "marginal"), link_fun = NULL, tasnf_y = function (x) x, ...) predict(object, ...) # S3 method for MixMod predict(object, newdata, newdata2 = NULL, type_pred = c("response", "link"), type = c("mean_subject", "subject_specific", "marginal", "zero_part"), se.fit = FALSE, M = 300, df = 10, scale = 0.3, level = 0.95, seed = 1, return_newdata = FALSE, sandwich = FALSE, ...) simulate(object, nsim = 1, seed = NULL, ...) # S3 method for MixMod simulate(object, nsim = 1, seed = NULL, type = c("subject_specific", "mean_subject"), new_RE = FALSE, acount_MLEs_var = FALSE, sim_fun = NULL, sandwich = FALSE, ...) terms(x, ...) # S3 method for MixMod terms(x, type = c("fixed", "random", "zi_fixed", "zi_random"), ...) formula(x, ...) # S3 method for MixMod formula(x, type = c("fixed", "random", "zi_fixed", "zi_random"), ...) model.frame(formula, ...) # S3 method for MixMod model.frame(formula, type = c("fixed", "random", "zi_fixed", "zi_random"), ...) model.matrix(object, ...) # S3 method for MixMod model.matrix(object, type = c("fixed", "random", "zi_fixed", "zi_random"), ...) nobs(object, ...) # S3 method for MixMod nobs(object, level = 1, ...) VIF(object, ...) # S3 method for MixMod VIF(object, type = c("fixed", "zi_fixed"), ...) cooks.distance(model, ...) # S3 method for MixMod cooks.distance(model, cores = max(parallel::detectCores() - 1, 1), ...)
| object, object2, x, formula, model | objects inheriting from class |
|---|---|
| sub_model | character string indicating for which sub-model to extract the estimated coefficients; it is only relevant for zero-inflated models. |
| post_vars | logical; if |
| parm | character string; for which type of parameters to calculate confidence
intervals. Option |
| level | numeric scalar between 0 and 1 denoting the level of the confidence interval.
In the |
| test | logical; should a p-value be calculated. |
| L | a numeric matrix representing a contrasts matrix. This is only used when in
|
| sandwich | logical; if |
| type | character string indicating the type of fitted values / residuals / predictions
/ variance inflation factors to calculate. Option |
| link_fun | the |
| tasnf_y | a function to transform the grouped / repeated measurements outcome before calculating the residuals; for example, relevant in two-part models for semi-continuous data, in which it is assumed that the log outcome follows a normal distribution. |
| newdata, newdata2 | a data frame based on which predictions are to be calculated.
|
| type_pred | character string indicating at which scale to calculate predictions.
Options are |
| se.fit | logical, if |
| M | numeric scalar denoting the number of Monte Carlo samples; see Details for more information. |
| df | numeric scalar denoting the degrees of freedom for the Student's t proposal distribution; see Details for more information. |
| scale | numeric scalar or vector denoting the scaling applied to the subject-specific covariance matrices of the random effects; see Details for more information. |
| seed | numerical scalar giving the seed to be used in the Monte Carlo scheme. |
| return_newdata | logical; if |
| nsim | numeric scalar giving the number of times to simulate the response variable. |
| new_RE | logical; if |
| acount_MLEs_var | logical; if |
| sim_fun | a function based on which to simulate the response variable. This is
relevant for non-standard models. The |
| cores | the number of cores to use in the computation. |
| ... | further arguments; currently none is used. |
In generic terms, we assume that the mean of the outcome \(y_i\) (\(i = 1, ..., n\) denotes the subjects) conditional on the random effects is given by the equation: $$g{E(y_i | b_i)} = \eta_i = X_i \beta + Z_i b_i,$$ where \(g(.)\) denotes the link function, \(b_i\) the vector of random effects, \(\beta\) the vector of fixed effects, and \(X_i\) and \(Z_i\) the design matrices for the fixed and random effects, respectively.
Argument type_pred of predict() specifies whether predictions will be
calculated in the link / linear predictor scale, i.e., \(\eta_i\) or in the response
scale, i.e., \(g{E(y_i | b_i)}\).
When type = "mean_subject", predictions are calculated using only the fixed
effects, i.e., the \(X_i \beta\) part, where \(X_i\) is evaluated in newdata.
This corresponds to predictions for the 'mean' subjects, i.e., subjects who have
random effects value equal to 0. Note, that in the case of nonlinear link functions this
does not correspond to the averaged over the population predictions (i.e., marginal
predictions).
When type = "marginal", predictions are calculated using only the fixed
effects, i.e., the \(X_i \beta\) part, where \(X_i\) is evaluated in newdata,
but with \(\beta\) coefficients the marginalized coefficients obtain from
marginal_coefs. These predictions will be marginal / population averaged
predictions.
When type = "zero_part", predictions are calculated for the logistic regression of
the extra zero-part of the model (i.e., applicable for zero-inflated and hurdle models).
When type = "subject_specific", predictions are calculated using both the fixed-
and random-effects parts, i.e., \(X_i \beta + Z_i b_i\), where \(X_i\) and \(Z_i\)
are evaluated in newdata. Estimates for the random effects of each subject are
obtained as modes from the posterior distribution \([b_i | y_i; \theta]\) evaluated in
newdata and with \(theta\) (denoting the parameters of the model, fixed effects
and variance components) replaced by their maximum likelihood estimates.
Notes: (i) When se.fit = TRUE and type_pred = "response", the
standard errors returned are on the linear predictor scale, not the response scale.
(ii) When se.fit = TRUE and the model contains an extra zero-part, no standard
errors are computed when type = "mean_subject". (iii) When the model contains an
extra zero-part, type = "marginal" predictions are not yet implemented.
When se.fit = TRUE and type = "subject_specific", standard errors and
confidence intervals for the subject-specific predictions are obtained by a Monte Carlo
scheme entailing three steps repeated M times, namely
Account for the variability of maximum likelihood estimates (MLES) by simulating a new value \(\theta^*\) for the parameters \(\theta\) from a multivariate normal distribution with mean the MLEs and covariance matrix the covariance matrix of the MLEs.
Account for the variability in the random effects estimates by
simulating a new value \(b_i^*\) for the random effects \(b_i\) from the posterior
distribution \([b_i | y_i; \theta^*]\). Because the posterior distribution does not
have a closed-form, a Metropolis-Hastings algorithm is used to sample the new value
\(b_i^*\) using as proposal distribution a multivariate Student's-t distribution
with degrees of freedom df, centered at the mode of the posterior distribution
\([b_i | y_i; \theta]\) with \(\theta\) the MLEs, and scale matrix the inverse
Hessian matrix of the log density of \([b_i | y_i; \theta]\) evaluated at the modes,
but multiplied by scale. The scale and df parameters can be used
to adjust the acceptance rate.
The predictions are calculated using \(X_i \beta^* + Z_i b_i^*\).
Argument newdata2 can be used to calculate dynamic subject-specific predictions.
I.e., using the observed responses \(y_i\) in newdata, estimates of the random
effects of each subject are obtained. For the same subjects we want to obtain predictions
in new covariates settings for which no response data are yet available. For example,
in a longitudinal study, for a subject we have responses up to a follow-up \(t\)
(newdata) and we want the prediction at \(t + \Delta t\) (newdata2).
The estimated fixed and random effects, coefficients (this is similar as in package nlme), confidence intervals fitted values (on the scale on the response) and residuals.
# \donttest{ # simulate some data set.seed(123L) n <- 500 K <- 15 t.max <- 25 betas <- c(-2.13, -0.25, 0.24, -0.05) D <- matrix(0, 2, 2) D[1:2, 1:2] <- c(0.48, -0.08, -0.08, 0.18) times <- c(replicate(n, c(0, sort(runif(K-1, 0, t.max))))) group <- sample(rep(0:1, each = n/2)) DF <- data.frame(year = times, group = factor(rep(group, each = K))) X <- model.matrix(~ group * year, data = DF) Z <- model.matrix(~ year, data = DF) b <- cbind(rnorm(n, sd = sqrt(D[1, 1])), rnorm(n, sd = sqrt(D[2, 2]))) id <- rep(1:n, each = K) eta.y <- as.vector(X %*% betas + rowSums(Z * b[id, ])) DF$y <- rbinom(n * K, 1, plogis(eta.y)) DF$id <- factor(id) ################################################ fm1 <- mixed_model(fixed = y ~ year + group, random = ~ year | id, data = DF, family = binomial()) head(coef(fm1))#> (Intercept) year group1 #> 1 -2.305583 0.54222964 -0.265744 #> 2 -1.905620 -0.12525835 -0.265744 #> 3 -2.395862 -0.14287663 -0.265744 #> 4 -2.379605 0.42518796 -0.265744 #> 5 -2.348230 0.01936688 -0.265744 #> 6 -2.056225 0.69488984 -0.265744fixef(fm1)#> (Intercept) year group1 #> -2.2391690 0.1968593 -0.2657440#> (Intercept) year #> 1 -0.06641381 0.3453703 #> 2 0.33354914 -0.3221176 #> 3 -0.15669315 -0.3397359 #> 4 -0.14043607 0.2283287 #> 5 -0.10906060 -0.1774924 #> 6 0.18294398 0.4980305confint(fm1)#> 2.5 % Estimate 97.5 % #> (Intercept) -2.4728845 -2.2391690 -2.00545354 #> year 0.1529535 0.1968593 0.24076514 #> group1 -0.5673166 -0.2657440 0.03582867confint(fm1, "var-cov")#> 2.5 % Estimate 97.5 % #> var.(Intercept) 0.22607344 0.50791169 0.06600001 #> cov.(Int)_year -0.08018454 -0.03554827 0.06888253 #> var.year 0.15508238 0.16033127 -0.81295806#> 1 2 3 4 5 6 #> 0.09066165 0.15603499 0.83100175 0.96225413 0.97895242 0.97984947#> 1 2 3 4 5 6 #> -0.1805435 -0.1980669 0.6881459 0.6194025 0.5928527 0.5908654fm2 <- mixed_model(fixed = y ~ year * group, random = ~ year | id, data = DF, family = binomial()) # likelihood ratio test between fm1 and fm2 anova(fm1, fm2)#> #> AIC BIC log.Lik LRT df p.value #> fm1 5203.04 5228.33 -2595.52 #> fm2 5201.62 5231.12 -2593.81 3.43 1 0.0642 #>#> #> Marginal Wald Tests Table #> #> User-defined contrasts matrix: #> (Intr) year group1 yr:gr1 #> 0 0 0 1 #> #> Chisq df Pr(>|Chi|) #> 3.7927 1 0.0515 #># }