Package 'broom.mixed'

name ordinal_tidiers

Description

the tidy method for clmm objects (from the ordinal package) lives in the broom package.

Usage

## S3 method for class 'clmm'
augment(x, data = model.frame(x), newdata, ...)

Arguments

x	An object of class merMod, such as those from lmer, glmer, or nlmer
data	original data this was fitted on; if not given this will attempt to be reconstructed
newdata	new data to be used for prediction; optional
...	Additional arguments (passed to confint.merMod for tidy; augment_columns for augment; ignored for glance)

---

Augmentation for random effects (for caterpillar plots etc.)

Description

Augmentation for random effects (for caterpillar plots etc.)

Usage

## S3 method for class 'ranef.mer'
augment(x, ci.level = 0.9, reorder = TRUE, order.var = 1, ...)

Arguments

x	ranef (conditional mode) information from an lme4 fit, using ranef(.,condVar=TRUE)
ci.level	level for confidence intervals
reorder	reorder levels by conditional mode values?
order.var	numeric or character: which variable to use for ordering levels?
...	additional arguments (unused: for generic consistency)

Examples

if (require("lme4")) {
   load(system.file("extdata","lme4_example.rda",package="broom.mixed"))
   rr <- ranef(lmm1,condVar=TRUE)
   aa <- broom::augment(rr)
   ## Q-Q plot:
   if (require(ggplot2) && require(dplyr)) {
      g0 <- ggplot(aa,aes(estimate,qq,xmin=lb,xmax=ub))+
          geom_errorbarh(height=0)+
          geom_point()+facet_wrap(~variable,scale="free_x")
      ## regular caterpillar plot:
      g1 <- ggplot(aa,aes(estimate,level,xmin=lb,xmax=ub))+
         geom_errorbarh(height=0)+
         geom_vline(xintercept=0,lty=2)+
         geom_point()+facet_wrap(~variable,scale="free_x")
      ## emphasize extreme values
      aa2 <- group_by(aa,grp,level)
      aa3 <- mutate(aa2, keep=any(estimate/std.error>2))
      ## Update caterpillar plot with extreme levels highlighted
      ##  (highlight all groups with *either* extreme intercept *or*
      ##   extreme slope)
      ggplot(aa3, aes(estimate,level,xmin=lb,xmax=ub,colour=factor(keep)))+
         geom_errorbarh(height=0)+
         geom_vline(xintercept=0,lty=2)+
         geom_point()+facet_wrap(~variable,scale="free_x")+
         scale_colour_manual(values=c("black","red"), guide=FALSE)
   }
}

---

Tidying methods for a brms model

Description

These methods tidy the estimates from brmsfit-objects (fitted model objects from the brms package) into a summary.

Usage

## S3 method for class 'brmsfit'
tidy(
  x,
  parameters = NA,
  effects = c("fixed", "ran_pars"),
  robust = FALSE,
  conf.int = TRUE,
  conf.level = 0.95,
  conf.method = c("quantile", "HPDinterval"),
  rhat = FALSE,
  ess = FALSE,
  fix.intercept = TRUE,
  exponentiate = FALSE,
  ...
)

## S3 method for class 'brmsfit'
glance(x, looic = FALSE, ...)

## S3 method for class 'brmsfit'
augment(x, data = stats::model.frame(x), newdata = NULL, se.fit = TRUE, ...)

Arguments

x	Fitted model object from the brms package. See brmsfit-class.
parameters	Names of parameters for which a summary should be returned, as given by a character vector or regular expressions. If NA (the default) summarized parameters are specified by the effects argument.
effects	A character vector including one or more of "fixed", "ran_vals", or "ran_pars". See the Value section for details.
robust	Whether to use median and median absolute deviation of the posterior distribution, rather than mean and standard deviation, to derive point estimates and uncertainty
conf.int	If TRUE columns for the lower (conf.low) and upper bounds (conf.high) of posterior uncertainty intervals are included.
conf.level	Defines the range of the posterior uncertainty conf.int, such that 100 * conf.level% of the parameter's posterior distributio lies within the corresponding interval. Only used if conf.int = TRUE.
conf.method	method for computing confidence intervals ("quantile" or "HPDinterval")
rhat	whether to calculate the *Rhat* convergence metric (FALSE by default)
ess	whether to calculate the *effective sample size* (ESS) convergence metric (FALSE by default)
fix.intercept	rename "Intercept" parameter to "(Intercept)", to match behaviour of other model types?
exponentiate	whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if TRUE, also scales the standard errors by the exponentiated coefficient, transforming them to the new scale
...	Extra arguments, not used
looic	Should the LOO Information Criterion (and related info) be included? See loo.stanfit for details. (This can be slow for models fit to large datasets.)
data	data frame
newdata	new data frame
se.fit	return standard errors of fit?

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

When parameters = NA, the effects argument is used to determine which parameters to summarize.

Generally, tidy.brmsfit returns one row for each coefficient, with at least three columns:

term	The name of the model parameter.
estimate	A point estimate of the coefficient (mean or median).
std.error	A standard error for the point estimate (sd or mad).

When effects = "fixed", only population-level effects are returned.

When effects = "ran_vals", only group-level effects are returned. In this case, two additional columns are added:

group	The name of the grouping factor.
level	The name of the level of the grouping factor.

Specifying effects = "ran_pars" selects the standard deviations and correlations of the group-level parameters.

If conf.int = TRUE, columns for the lower and upper bounds of the posterior conf.int computed.

Note

The names ‘fixed’, ‘ran_pars’, and ‘ran_vals’ (corresponding to "non-varying", "hierarchical", and "varying" respectively in previous versions of the package), while technically inappropriate in a Bayesian setting where "fixed" and "random" effects are not well-defined, are used for compatibility with other (frequentist) mixed model types.

At present, the components of parameter estimates are separated by parsing the column names of as_draws (e.g. r_patient[1,Intercept] for the random effect on the intercept for patient 1, or b_Trt1 for the fixed effect Trt1. We try to detect underscores in parameter names and warn, but detection may be imperfect.

See Also

brms, brmsfit-class

Examples

## original model
 ## Not run: 
    brms_crossedRE <- brm(mpg ~ wt + (1|cyl) + (1+wt|gear), data = mtcars,
           iter = 500, chains = 2)
 
## End(Not run)
 
   ## too slow for CRAN (>5 seconds)
   ## load stored object
   if (require("rstan") && require("brms")) {
      load(system.file("extdata", "brms_example.rda", package="broom.mixed"))

      fit <- brms_crossedRE
      tidy(fit)
      tidy(fit, parameters = "^sd_", conf.int = FALSE)
      tidy(fit, effects = "fixed", conf.method="HPDinterval")
      tidy(fit, effects = "ran_vals")
      tidy(fit, effects = "ran_pars", robust = TRUE)
      if (require("posterior")) {
      tidy(fit, effects = "ran_pars", rhat = TRUE, ess = TRUE)
   
   }
   }
   if (require("rstan") && require("brms")) {
   # glance method
   glance(fit)
   ## this example will give a warning that it should be run with
   ## reloo=TRUE; however, doing this will fail
   ## because the \code{fit} object has been stripped down to save space
   suppressWarnings(glance(fit, looic = TRUE, cores = 1))
   head(augment(fit))
  }

---

Remove NULL items in a vector or list

Description

Remove NULL items in a vector or list

Usage

compact(x)

Arguments

x	a vector or list

---

Extract fixed effects from an MCMCglmm object

Description

Function designed to extract the fixed effects from an MCMCglmm model object. Can either extract all samples from the fixed effects posteriors or return the posterior means.

Usage

## S3 method for class 'MCMCglmm'
fixef(object, use = c("all", "mean"), ...)

Arguments

object	An MCMCglmm model object to extract the effects from
use	A character string indicating whether to extract all posterior samples or the mean of the posteriors. Defaults to "all".
...	Arguments passed on to the worker function.

Value

A matrix of the fixed effects

See Also

ranef.MCMCglmm

Examples

## Not run: 
  # a simple MCMCglmm model
  data(PlodiaPO)
  m <- MCMCglmm(PO ~ 1, random= ~ FSfamily, data=PlodiaPO, verbose=FALSE)

  # only extract average fixed effects
  fixef(m, use = "mean")

  # histogram of posterior samples of fixed effects
  hist(fixef(m))
  # matches the mean
  rowMeans(fixef(m))

## End(Not run)

---

Tidying methods for gamlss objects

Description

Tidying methods for "gamlss" objects from the gamlss package.

Usage

## S3 method for class 'gamlss'
tidy(x, quick = FALSE, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x	A "gamlss" object
quick	Whether to perform a fast version, and return only the coefficients
conf.int	whether to return confidence intervals
conf.level	confidence level for CI
...	arguments passed to confint.gamlss

Value

All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.

A tibble with one row for each coefficient, containing columns:

parameter	type of coefficient being estimated: mu, sigma, nu, or tau
term	term in the model being estimated and tested
estimate	estimated coefficient
std.error	standard error
statistic	t-statistic
p.value	two-sided p-value

Examples

if (requireNamespace("gamlss", quietly = TRUE) &&
    requireNamespace("gamlss.data", quietly = TRUE)) {
    data(abdom, package="gamlss.data")
    ## Not run: 
         mod <- gamlss(y~pb(x), sigma.fo=~pb(x), family=BCT,
                       data=abdom, method=mixed(1,20))
    
## End(Not run)
    ## load stored object
    mod <- readRDS(system.file("extdata", "gamlss_example.rds",
                   package="broom.mixed"))
    tidy(mod)
}

---

Retrieve all method/class combinations currently provided by the broom.mixed package

Description

Retrieve all method/class combinations currently provided by the broom.mixed package

Usage

get_methods()

Examples

print(get_methods(), n = Inf)

---

Tidying methods for glmmADMB models

Description

These methods tidy the coefficients of glmmADMB models

Usage

## S3 method for class 'glmmadmb'
tidy(
  x,
  effects = c("fixed", "ran_pars"),
  component = "cond",
  scales = NULL,
  ran_prefix = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = "Wald",
  ...
)

## S3 method for class 'glmmadmb'
augment(x, data = stats::model.frame(x), newdata, ...)

## S3 method for class 'glmmadmb'
glance(x, ...)

Arguments

x	An object of class glmmadmb glmer, or nlmer
effects	A character vector including one or more of "fixed" (fixed-effect parameters), "ran_pars" (variances and covariances or standard deviations and correlations of random effect terms) or "ran_vals" (conditional modes/BLUPs/latent variable estimates)
component	Which component(s) to report for (e.g., conditional, zero-inflation, dispersion: at present only works for "cond")
scales	scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if scales is NULL) or ‘"varcov"’ (variances and covariances). NA means no transformation, appropriate e.g. for fixed effects; inverse-link transformations (exponentiation or logistic) are not yet implemented, but may be in the future.
ran_prefix	a length-2 character vector specifying the strings to use as prefixes for self- (variance/standard deviation) and cross- (covariance/correlation) random effects terms
conf.int	whether to include a confidence interval
conf.level	confidence level for CI
conf.method	method for computing confidence intervals (see confint.merMod)
...	extra arguments (not used)
data	original data this was fitted on; if not given this will attempt to be reconstructed
newdata	new data to be used for prediction; optional

Details

When the modeling was performed with na.action = "na.omit" (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with na.action = "na.exclude", one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to augment and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

Value

All tidying methods return a tbl_df without rownames. The structure depends on the method chosen.

tidy returns one row for each estimated effect, either with groups depending on the effects parameter. It contains the columns

group	the group within which the random effect is being estimated: NA for fixed effects
level	level within group (NA except for modes)
term	term being estimated
estimate	estimated coefficient
std.error	standard error
statistic	t- or Z-statistic (NA for modes)
p.value	P-value computed from t-statistic (may be missing/NA)

augment returns one row for each original observation, with columns (each prepended by a .) added. Included are the columns

.fitted	predicted values
.resid	residuals
.fixed	predicted values with no random effects

Also added for "merMod" objects, but not for "mer" objects, are values from the response object within the model (of type lmResp, glmResp, nlsResp, etc). These include ".mu", ".offset", ".sqrtXwt", ".sqrtrwt", ".eta".

glance returns one row with the columns

sigma	the square root of the estimated residual variance
logLik	the data's log-likelihood under the model
AIC	the Akaike Information Criterion
BIC	the Bayesian Information Criterion
deviance	deviance

See Also

na.action

Examples

if (require("glmmADMB") && require("lme4")) {
    ## original model
    ## Not run: 
        data("sleepstudy", package="lme4")
        lmm1 <- glmmadmb(Reaction ~ Days + (Days | Subject), sleepstudy,
                         family="gaussian")
    
## End(Not run)
    ## load stored object
    load(system.file("extdata","glmmADMB_example.rda",package="broom.mixed"))
    tidy(lmm1, effects = "fixed")
    tidy(lmm1, effects = "fixed", conf.int=TRUE)
    ## tidy(lmm1, effects = "fixed", conf.int=TRUE, conf.method="profile")
    ## tidy(lmm1, effects = "ran_vals", conf.int=TRUE)
    head(augment(lmm1, sleepstudy))
    glance(lmm1)

    glmm1 <- glmmadmb(cbind(incidence, size - incidence) ~ period + (1 | herd),
                  data = cbpp, family = "binomial")
    tidy(glmm1)
    tidy(glmm1, effects = "fixed")
    head(augment(glmm1, cbpp))
    glance(glmm1)

}

---

Tidying methods for glmmTMB models

Description

These methods tidy the coefficients of mixed effects models, particularly responses of the merMod class

Usage

## S3 method for class 'glmmTMB'
tidy(
  x,
  effects = c("ran_pars", "fixed"),
  component = c("cond", "zi"),
  scales = NULL,
  ran_prefix = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = "Wald",
  exponentiate = FALSE,
  ...
)

## S3 method for class 'glmmTMB'
augment(x, data = stats::model.frame(x), newdata = NULL, ...)

## S3 method for class 'glmmTMB'
glance(x, ...)

Arguments

x	An object of class merMod, such as those from lmer, glmer, or nlmer
effects	A character vector including one or more of "fixed" (fixed-effect parameters), "ran_pars" (variances and covariances or standard deviations and correlations of random effect terms) or "ran_vals" (conditional modes/BLUPs/latent variable estimates)
component	which component to extract (e.g. cond for conditional effects (i.e., traditional fixed effects); zi for zero-inflation model; disp for dispersion model
scales	scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if scales is NULL) or ‘"varcov"’ (variances and covariances). NA means no transformation, appropriate e.g. for fixed effects; inverse-link transformations (exponentiation or logistic) are not yet implemented, but may be in the future.
ran_prefix	a length-2 character vector specifying the strings to use as prefixes for self- (variance/standard deviation) and cross- (covariance/correlation) random effects terms
conf.int	whether to include a confidence interval
conf.level	confidence level for CI
conf.method	method for computing confidence intervals (see confint.merMod)
exponentiate	whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if TRUE, also scales the standard errors by the exponentiated coefficient, transforming them to the new scale
...	extra arguments (not used)
data	original data this was fitted on; if not given this will attempt to be reconstructed
newdata	new data to be used for prediction; optional

Details

When the modeling was performed with na.action = "na.omit" (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with na.action = "na.exclude", one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to augment and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

Value

All tidying methods return a tibble. The structure depends on the method chosen.

tidy returns one row for each estimated effect, either with groups depending on the effects parameter. It contains the columns

group	the group within which the random effect is being estimated: NA for fixed effects
level	level within group (NA except for modes)
term	term being estimated
estimate	estimated coefficient
std.error	standard error
statistic	t- or Z-statistic (NA for modes)
p.value	P-value computed from t-statistic (may be missing/NA)

augment returns one row for each original observation, with columns (each prepended by a .) added. Included are the columns

.fitted	predicted values
.resid	residuals
.fixed	predicted values with no random effects

glance returns one row with the columns

sigma	the square root of the estimated residual variance
logLik	the data's log-likelihood under the model
AIC	the Akaike Information Criterion
BIC	the Bayesian Information Criterion
deviance	deviance

Note

zero-inflation parameters (including the intercept) are reported on the logit scale

See Also

na.action

Examples

if (require("glmmTMB") && require("lme4")
    ## &&
    ## make sure package versions are OK
    ## checkDepPackageVersion(dep_pkg = "TMB",
    ##                       this_pkg = "glmmTMB",
    ##                        warn = FALSE) &&
    ## checkDepPackageVersion(dep_pkg = "Matrix",
    ##                       this_pkg = "TMB",
    ##                      warn = FALSE)
)
{
    data("sleepstudy",package="lme4")
    ## original model:
    ## Not run: 
        lmm1 <- glmmTMB(Reaction ~ Days + (Days | Subject), sleepstudy)
    
## End(Not run)
    ## load stored object
    L <- load(system.file("extdata","glmmTMB_example.rda",package="broom.mixed"))
    for (obj in L) {
       assign(obj, glmmTMB::up2date(get(obj)))
    }
    tidy(lmm1)
    tidy(lmm1, effects = "fixed")
    tidy(lmm1, effects = "fixed", conf.int=TRUE)
    tidy(lmm1, effects = "fixed", conf.int=TRUE, conf.method="uniroot")
    ## FIX: tidy(lmm1, effects = "ran_vals", conf.int=TRUE)
    head(augment(lmm1, sleepstudy))
    glance(lmm1)

    ## original model:
    ##  glmm1 <- glmmTMB(incidence/size ~ period + (1 | herd),
    ##                  data = cbpp, family = binomial, weights=size)
    tidy(glmm1)
    tidy(glmm1, effects = "fixed")
    tidy(glmm1, effects = "fixed", exponentiate=TRUE)
    tidy(glmm1, effects = "fixed", conf.int=TRUE, exponentiate=TRUE)
    head(augment(glmm1, cbpp))
    head(augment(glmm1, cbpp, type.residuals="pearson"))
    glance(glmm1)
## Not run: 
    ## profile CIs - a little bit slower but more accurate
    tidy(glmm1, effects = "fixed", conf.int=TRUE, exponentiate=TRUE, conf.method="profile")

## End(Not run)
}

---

insert a row of NAs into a data frame wherever another data frame has NAs

Description

insert a row of NAs into a data frame wherever another data frame has NAs

Usage

insert_NAs(x, original)

Arguments

x	data frame that has one row for each non-NA row in original
original	data frame with NAs

---

Tidying methods for mixed effects models

Description

These methods tidy the coefficients of lme4::lmer and lme4::glmer models (i.e., merMod objects). Methods are also provided for allFit objects.

Usage

## S3 method for class 'merMod'
tidy(
  x,
  effects = c("ran_pars", "fixed"),
  scales = NULL,
  exponentiate = FALSE,
  exponentiate_ran_coefs = FALSE,
  ran_prefix = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = "Wald",
  ddf.method = NULL,
  profile = NULL,
  debug = FALSE,
  ...
)

## S3 method for class 'rlmerMod'
tidy(
  x,
  effects = c("ran_pars", "fixed"),
  scales = NULL,
  exponentiate = FALSE,
  exponentiate_ran_coefs = FALSE,
  ran_prefix = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = "Wald",
  ddf.method = NULL,
  profile = NULL,
  debug = FALSE,
  ...
)

## S3 method for class 'merMod'
augment(x, data = stats::model.frame(x), newdata, ...)

## S3 method for class 'merMod'
glance(x, ...)

Arguments

x	An object of class merMod, such as those from lmer, glmer, or nlmer
effects	A character vector including one or more of "fixed" (fixed-effect parameters); "ran_pars" (variances and covariances or standard deviations and correlations of random effect terms); "ran_vals" (conditional modes/BLUPs/latent variable estimates); or "ran_coefs" (predicted parameter values for each group, as returned by coef.merMod)
scales	scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if scales is NULL) or ‘"vcov"’ (variances and covariances). NA means no transformation, appropriate e.g. for fixed effects.
exponentiate	whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if TRUE, also scales the standard errors by the exponentiated coefficient, transforming them to the new scale
exponentiate_ran_coefs	whether to exponentiate the predicted paramater values for each group
ran_prefix	a length-2 character vector specifying the strings to use as prefixes for self- (variance/standard deviation) and cross- (covariance/correlation) random effects terms
conf.int	whether to include a confidence interval
conf.level	confidence level for CI
conf.method	method for computing confidence intervals (see lme4::confint.merMod)
ddf.method	the method for computing the degrees of freedom and t-statistics (only applicable when using the lmerTest package: see summary.lmerModLmerTest
profile	pre-computed profile object, for speed when using conf.method="profile"
debug	print debugging output?
...	Additional arguments (passed to confint.merMod for tidy; augment_columns for augment; ignored for glance)
data	original data this was fitted on; if not given this will attempt to be reconstructed
newdata	new data to be used for prediction; optional

Details

When the modeling was performed with na.action = "na.omit" (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with na.action = "na.exclude", one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to augment and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

tidy returns one row for each estimated effect, either with groups depending on the effects parameter. It contains the columns

group	the group within which the random effect is being estimated: "fixed" for fixed effects
level	level within group (NA except for modes)
term	term being estimated
estimate	estimated coefficient
std.error	standard error
statistic	t- or Z-statistic (NA for modes)
p.value	P-value computed from t-statistic (may be missing/NA)

augment returns one row for each original observation, with columns (each prepended by a .) added. Included are the columns

.fitted	predicted values
.resid	residuals
.fixed	predicted values with no random effects

Also added for "merMod" objects, but not for "mer" objects, are values from the response object within the model (of type lmResp, glmResp, nlsResp, etc). These include ".mu", ".offset", ".sqrtXwt", ".sqrtrwt", ".eta".

glance returns one row with the columns

nobs	the number of observations
sigma	the square root of the estimated residual variance
logLik	the data's log-likelihood under the model
AIC	the Akaike Information Criterion
BIC	the Bayesian Information Criterion
deviance	deviance

See Also

na.action

Examples

if (require("lme4")) {
    ## original model
    ## Not run: 
        lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
    
## End(Not run)
    ## load stored object
    load(system.file("extdata", "lme4_example.rda", package="broom.mixed"))
    (tt <- tidy(lmm1))
    tidy(lmm1, effects = "fixed")
    tidy(lmm1, effects = "fixed", conf.int=TRUE)
    tidy(lmm1, effects = "fixed", conf.int=TRUE, conf.method="profile")
    ## lmm1_prof <- profile(lmm1) # generated by extdata/runexamples
    tidy(lmm1, conf.int=TRUE, conf.method="profile", profile=lmm1_prof)
    ## conditional modes (group-level deviations from population-level estimate)
    tidy(lmm1, effects = "ran_vals", conf.int=TRUE)
    ## coefficients (group-level estimates)
    (rcoef1 <- tidy(lmm1, effects = "ran_coefs"))
    if (require(tidyr) && require(dplyr)) {
       ## reconstitute standard coefficient-by-level table
       spread(rcoef1,key=term,value=estimate)
       ## split ran_pars into type + term; sort fixed/sd/cor
       (tt %>% separate(term,c("type","term"),sep="__",fill="left")
           %>% arrange(!is.na(type),desc(type)))
    }
    head(augment(lmm1, sleepstudy))
    glance(lmm1)

    glmm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
                  data = cbpp, family = binomial)
    tidy(glmm1)
    tidy(glmm1,exponentiate=TRUE)
    tidy(glmm1, effects = "fixed")
    ## suppress warning about influence.merMod
    head(suppressWarnings(augment(glmm1, cbpp)))
    glance(glmm1)

    startvec <- c(Asym = 200, xmid = 725, scal = 350)
    nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
                  Orange, start = startvec)
    ## suppress warnings about var-cov matrix ...
    op <- options(warn=-1)
    tidy(nm1)
    tidy(nm1, effects = "fixed")
    options(op)
    head(augment(nm1, Orange))
    glance(nm1)
    detach("package:lme4")
}
if (require("lmerTest")) {
   lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
   tidy(lmm1)
   glance(lmm1)
   detach("package:lmerTest")  # clean up
}

---

Tidying methods for mediation analyses involving mixed effects models

Description

These methods tidy the coefficients of mediation::mediate output (i.e., mediate.mer objects) when lme4::lmer and lme4::glmer models (i.e., merMod objects) provide the input.

Usage

## S3 method for class 'mediate.mer'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x	an object of class mediate.mer, as from mediate using lmer, glmer, or nlmer models
conf.int	whether to include a confidence interval
conf.level	confidence level for CI
...	additional arguments (unused: for generic consistency)

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

tidy returns one row for each estimated effect: first the mediated effect in the control and treatment groups, respectively, then the direct effect in each group. It contains the columns

term	term being estimated
estimate	estimated coefficient
std.error	standard error
p.value	P-value computed from t-statistic (may be missing/NA)

See Also

mediate, tidy.mediate

Examples

if (require("lme4") && require("mediation")) {
    ## Borrowed from \code{help(mediation::mediate)}:
    ## Not run: 
        ## Varying intercept for mediator 
        mod_m <- glmer(job_dich ~ treat + econ_hard + (1 | educ), 
                       family = binomial(link = "probit"), data = jobs)
        ## Varying intercept and slope for outcome
        mod_y <- glmer(work1 ~ treat + job_dich + econ_hard + (1 + treat | occp),
                       family = binomial(link = "probit"), data = jobs)
        ## Output based on mediator group ("educ")
        mod_med <- mediate(mod_m, mod_y, treat = "treat", 
                           mediator = "job_dich", sims=50, group.out="educ")
    
## End(Not run)
    ## Load stored objects
    load(system.file("extdata", "mediation_example.rda", package="broom.mixed"))
    ## Tidy outputs
    tidy(mod_m)
    tidy(mod_y)
    tidy(mod_med)
}

---

Tidying methods for mixed effects models

Description

These methods tidy the coefficients of mixed effects models of the lme class from functions of the nlme package.

Usage

## S3 method for class 'lme'
tidy(
  x,
  effects = c("var_model", "ran_pars", "fixed"),
  scales = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  ...
)

## S3 method for class 'lme'
augment(x, data = x$data, newdata, ...)

## S3 method for class 'lme'
glance(x, ...)

## S3 method for class 'gls'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

## S3 method for class 'gls'
augment(x, data = nlme::getData(x), newdata, ...)

Arguments

x	An object of class lme, such as those from lme or nlme
effects	One or more of "var_model", "ran_pars", "fixed", "ran_vals", and/or "ran_coefs".
scales	scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if scales is NULL) or ‘"vcov"’ (variances and covariances). NA means no transformation, appropriate e.g. for fixed effects.
conf.int	whether to include a confidence interval
conf.level	confidence level for CI
...	extra arguments (not used)
data	original data this was fitted on; if not given this will attempt to be reconstructed
newdata	new data to be used for prediction; optional

Details

When the modeling was performed with na.action = "na.omit" (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with na.action = "na.exclude", one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to augment and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

tidy returns one row for each estimated effect, either random or fixed depending on the effects parameter. If effects = "ran_vals" (or "ran_pars"), it contains the columns

group	the group within which the random effect is being estimated
level	level within group
term	term being estimated
estimate	estimated coefficient
estimated	This column is only included if some parameters are fixed. TRUE if the residual error is estimated and FALSE if the residual error is fixed.

If effects="fixed", tidy returns the columns

term	fixed term being estimated
estimate	estimate of fixed effect
std.error	standard error
statistic	t-statistic
p.value	P-value computed from t-statistic

If effects="var_model" (the weights argument to the model), tidy returns the columns defined in the help for tidy.varFunc.

augment returns one row for each original observation, with columns (each prepended by a .) added. Included are the columns

.fitted	predicted values
.resid	residuals
.fixed	predicted values with no random effects

glance returns one row with the columns

sigma	the square root of the estimated residual variance
logLik	the data's log-likelihood under the model
AIC	the Akaike Information Criterion
BIC	the Bayesian Information Criterion
deviance	returned as NA. To quote Brian Ripley on R-help https://stat.ethz.ch/pipermail/r-help/2006-May/104744.html, "McCullagh & Nelder (1989) would be the authorative [sic] reference, but the 1982 first edition manages to use 'deviance' in three separate senses on one page."

See Also

na.action

Examples

if (require("nlme") && require("lme4")) {
    data("sleepstudy", package="lme4")
    ## original model
    ## Not run: 
         lmm1 <- lme(Reaction ~ Days, random=~ Days|Subject, sleepstudy)
    
## End(Not run)
    ## load stored object
    load(system.file("extdata","nlme_example.rda", package="broom.mixed"))
    tidy(lmm1)
    tidy(lmm1, effects = "fixed")
    tidy(lmm1, conf.int = TRUE)
    tidy(lmm1, conf.int = TRUE, conf.level = 0.8)
    tidy(lmm1, effects = "ran_pars")
    tidy(lmm1, effects = "ran_vals")
    tidy(lmm1, effects = "ran_coefs")
    head(augment(lmm1, sleepstudy))
    glance(lmm1)

    startvec <- c(Asym = 200, xmid = 725, scal = 350)
    nm1 <- nlme(circumference ~ SSlogis(age, Asym, xmid, scal),
                  data = Orange,
                  fixed = Asym + xmid + scal ~1,
                  random = Asym ~1,
                  start = startvec)
    tidy(nm1)
    tidy(nm1, effects = "fixed")
    head(augment(nm1, Orange))
    glance(nm1)

    gls1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
                         correlation = corAR1(form = ~ 1 | Mare))
    tidy(gls1)
    glance(gls1)
    head(augment(gls1))
}

---

Extract random effects from an MCMCglmm object

Description

Function designed to extract the random effects from an MCMCglmm model object. Can either extract all samples from the random effects posteriors or return the posterior means.

Usage

## S3 method for class 'MCMCglmm'
ranef(object, use = c("all", "mean"), ...)

Arguments

object	An MCMCglmm model object to extract the effects from
use	A character string indicating whether to extract all posterior samples or the mean of the posteriors. Defaults to "all".
...	Arguments passed on to the worker function.

Value

A matrix of the fixed effects

See Also

fixef.MCMCglmm

Examples

## Not run: 
  # a simple MCMCglmm model
  data(PlodiaPO)
  m <- MCMCglmm(PO ~ 1, random= ~ FSfamily, data=PlodiaPO, pr=TRUE, verbose=FALSE)

  # only extract average fixed effects
  head(ranef(m, use = "mean"))

  # histogram of posterior samples of fixed effects
  hist(ranef(m)[1, ])
  # matches the mean
  rowMeans(ranef(m)[1:6, ])

## End(Not run)

---

Extract the levels of factors used for random effects in MCMCglmm objects

Description

Extract the levels of factors used for random effects in MCMCglmm objects

Usage

ranefLevels(object, data, ...)

Arguments

object	An MCMCglmm model object
data	The dataset used for the model
...	Not currently used

See Also

paramNamesMCMCglmm, ranef.MCMCglmm

Examples

## Not run: 
  # a simple MCMCglmm model
  data(PlodiaPO)
  m <- MCMCglmm(PO ~ 1, random = ~ FSfamily, data = PlodiaPO, verbose=FALSE)

  # extract the random effects levels
  ranefLevels(m, PlodiaPO)

## End(Not run)

---

Tidying methods for an rstanarm model

Description

These methods tidy the estimates from rstanarm fits (stan_glm, stan_glmer, etc.) into a summary.

Usage

## S3 method for class 'stanreg'
tidy(
  x,
  effects = c("fixed", "ran_pars"),
  conf.int = FALSE,
  conf.level = 0.9,
  conf.method = c("quantile", "HPDinterval"),
  exponentiate = FALSE,
  ...
)

## S3 method for class 'stanreg'
glance(x, looic = FALSE, ...)

Arguments

x	Fitted model object from the rstanarm package. See stanreg-objects.
effects	A character vector including one or more of "fixed", "ran_vals", or "ran_pars". See the Value section for details.
conf.int	If TRUE columns for the lower (conf.low) and upper (conf.high) bounds of the 100*prob% posterior uncertainty intervals are included. See posterior_interval for details.
conf.level	See posterior_interval.
conf.method	method for computing confidence intervals ("quantile" or "HPDinterval")
exponentiate	whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if TRUE, also scales the standard errors by the exponentiated coefficient, transforming them to the new scale
...	For glance, if looic=TRUE, optional arguments to loo.stanfit.
looic	Should the LOO Information Criterion (and related info) be included? See loo.stanfit for details. (This can be slow for models fit to large datasets.)

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

When effects="fixed" (the default), tidy.stanreg returns one row for each coefficient, with three columns:

term	The name of the corresponding term in the model.
estimate	A point estimate of the coefficient (posterior median).
std.error	A standard error for the point estimate based on mad. See the Uncertainty estimates section in print.stanreg for more details.

For models with group-specific parameters (e.g., models fit with stan_glmer), setting effects="ran_vals" selects the group-level parameters instead of the non-varying regression coefficients. Addtional columns are added indicating the level and group. Specifying effects="ran_pars" selects the standard deviations and (for certain models) correlations of the group-level parameters.

Setting effects="auxiliary" will select parameters other than those included by the other options. The particular parameters depend on which rstanarm modeling function was used to fit the model. For example, for models fit using stan_glm the overdispersion parameter is included if effects="aux", for stan_lm the auxiliary parameters include the residual SD, R^2, and log(fit_ratio), etc.

glance returns one row with the columns

algorithm	The algorithm used to fit the model.
pss	The posterior sample size (except for models fit using optimization).
nobs	The number of observations used to fit the model.
sigma	The square root of the estimated residual variance, if applicable. If not applicable (e.g., for binomial GLMs), sigma will be given the value 1 in the returned object.

If looic=TRUE, then the following additional columns are also included:

looic	The LOO Information Criterion.
elpd_loo	The expected log predictive density (elpd_loo = -2 * looic).
p_loo	The effective number of parameters.

See Also

summary,stanfit-method

Examples

if (require("rstanarm")) {
## Not run: 
#'     ## original models
  fit <- stan_glmer(mpg ~ wt + (1|cyl) + (1+wt|gear), data = mtcars,
                      iter = 500, chains = 2)
  fit2 <- stan_glmer((mpg>20) ~ wt + (1 | cyl) + (1 + wt | gear),
                    data = mtcars,
                    family = binomial,
                    iter = 500, chains = 2
  
## End(Not run)
## load example data
  load(system.file("extdata", "rstanarm_example.rda", package="broom.mixed"))

  # non-varying ("population") parameters
  tidy(fit, conf.int = TRUE, conf.level = 0.5)
  tidy(fit, conf.int = TRUE, conf.method = "HPDinterval", conf.level = 0.5)

  #  exponentiating (in this case, from log-odds to odds ratios)
  (tidy(fit2, conf.int = TRUE, conf.level = 0.5)
          |> dplyr::filter(term != "(Intercept)")
  )
  (tidy(fit2, conf.int = TRUE, conf.level = 0.5, exponentiate = TRUE)
          |> dplyr::filter(term != "(Intercept)")
  )

  # hierarchical sd & correlation parameters
  tidy(fit, effects = "ran_pars")

  # group-specific deviations from "population" parameters
  tidy(fit, effects = "ran_vals")

  # glance method
   glance(fit)
  ## Not run: 
     glance(fit, looic = TRUE, cores = 1)
  
## End(Not run)
} ## if require("rstanarm")

---

Extract standard deviation of "random" effects from an MCMCglmm object

Description

Function designed to extract the standard deviation of the random effects from an MCMCglmm model object. Note that this is not the same as the posterior distribution of (co)variance matrices. It is based on the posterior distribution of the random effects. This also means it requires pr=TRUE to be set in the model for the information to be saved. Can optionally return standard deviation of random effects after back transforming to the response metric. Currently probabilities, but only for ordinal family models (family="ordinal").

Usage

stdranef(object, which, type = c("lp", "response"), ...)

Arguments

object	An MCMCglmm model object to extract the effects from
which	A list of random effects to extract or their numeric positions If there are two numbers in a list, effects are simulataneous.
type	A character string indicating whether to calculate the standard deviation on the linear predictor metric, ‘lp’ or response, ‘response’.
...	Not currently used.

Value

A list of class postMCMCglmmRE with means (M) and individual estimates (Data)

Examples

## Not run: 
  # a simple MCMCglmm model
  data(PlodiaPO)
  PlodiaPO <- within(PlodiaPO, {
    PO2 <- cut(PO, quantile(PO, c(0, .33, .66, 1)))
    plate <- factor(plate)
  })

  m <- MCMCglmm(PO2 ~ 1, random = ~ FSfamily + plate,
    family = "ordinal", data = PlodiaPO,
    prior = list(
      R = list(V = 1, fix = 1),
      G = list(
        G1 = list(V = 1, nu = .002),
        G2 = list(V = 1, nu = .002)
      )
    ), verbose=FALSE, thin=1, pr=TRUE)

  # summary of the model
  summary(m)

  # examples of extracting standard deviations of
  # different random effects on the linear predictor metric
  # or after transformation to probabilities (only for ordinal)
  stdranef(m, which = list(1), type = "lp")
  stdranef(m, which = list(2), type = "lp")
  stdranef(m, which = list(1, 2, c(1, 2)), type = "lp")
  stdranef(m, type = "lp")

  ## error because no 3rd random effect
  #stdranef(m, which = list(1, 2, 3), type = "lp")

  stdranef(m, which = list("FSfamily", "plate"), type = "lp")

  # mean standard deviations on the probability metric
  # also the full distributions, if desired in the Data slot.
  res <- stdranef(m, type = "response")
  res$M # means
  hist(res$Data$FSfamily[, 1]) # histogram

## End(Not run)

---

Tidying methods for lqmm models (EXPERIMENTAL)

Description

These methods, suggested by David Luke Thiessen on Stack Exchange, provide support for linear quantile mixed models. They have not been carefully tested - please check output carefully and report problems!

Usage

## S3 method for class 'lqmm'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

## S3 method for class 'lqmm'
glance(x, ...)

Arguments

x	An object of class merMod, such as those from lmer, glmer, or nlmer
conf.int	whether to include a confidence interval
conf.level	confidence level for CI
...	Additional arguments (passed to confint.merMod for tidy; augment_columns for augment; ignored for glance)

---

Tidying methods for MCMC (Stan, JAGS, etc.) fits

Description

Tidying methods for MCMC (Stan, JAGS, etc.) fits

Usage

## S3 method for class 'MCMCglmm'
tidy(x, effects = c("fixed", "ran_pars"), scales = NULL, ...)

tidyMCMC(
  x,
  pars,
  robust = FALSE,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = c("quantile", "HPDinterval"),
  drop.pars = c("lp__", "deviance"),
  rhat = FALSE,
  ess = FALSE,
  index = FALSE,
  ...
)

## S3 method for class 'rjags'
tidy(
  x,
  robust = FALSE,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = "quantile",
  ...
)

## S3 method for class 'stanfit'
tidy(
  x,
  pars,
  robust = FALSE,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = c("quantile", "HPDinterval"),
  drop.pars = c("lp__", "deviance"),
  rhat = FALSE,
  ess = FALSE,
  index = FALSE,
  ...
)

## S3 method for class 'mcmc'
tidy(
  x,
  pars,
  robust = FALSE,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = c("quantile", "HPDinterval"),
  drop.pars = c("lp__", "deviance"),
  rhat = FALSE,
  ess = FALSE,
  index = FALSE,
  ...
)

## S3 method for class 'mcmc.list'
tidy(
  x,
  pars,
  robust = FALSE,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = c("quantile", "HPDinterval"),
  drop.pars = c("lp__", "deviance"),
  rhat = FALSE,
  ess = FALSE,
  index = FALSE,
  ...
)

Arguments

x	a model fit to be converted to a data frame
effects	which effects (fixed, random, etc.) to return
scales	scales on which to report results
...	mostly unused; for tidy.MCMCglmm, these represent options passed through to tidy.mcmc (e.g. robust, conf.int, conf.method, ...)
pars	(character) specification of which parameters to include
robust	use mean and standard deviation (if FALSE) or median and mean absolute deviation (if TRUE) to compute point estimates and uncertainty?
conf.int	(logical) include confidence interval?
conf.level	probability level for CI
conf.method	method for computing confidence intervals ("quantile" or "HPDinterval")
drop.pars	Parameters not to include in the output (such as log-probability information)
rhat, ess	(logical) include Rhat and/or effective sample size estimates?
index	Add index column, remove index from term. For example, term a[13] becomes term a and index 13.

Examples

if (require("MCMCglmm")) {
  ## original model
  ## Not run: 
      mm0 <- MCMCglmm(Reaction ~ Days,
                 random = ~Subject, data = sleepstudy,
                 nitt=4000,
                 pr = TRUE
             )
   
## End(Not run)
   ## load stored object
   load(system.file("extdata","MCMCglmm_example.rda",
                                     package="broom.mixed"))
   tidy(mm0)
   tidy(mm1)
   tidy(mm2)
   tail(tidy(mm0,effects="ran_vals"))
}

# Using example from "RStan Getting Started"
# https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started

model_file <- system.file("extdata", "8schools.stan", package = "broom.mixed")
schools_dat <- list(J = 8,
                    y = c(28,  8, -3,  7, -1,  1, 18, 12),
                    sigma = c(15, 10, 16, 11,  9, 11, 10, 18))
## original model
## Not run: 
    set.seed(2015)
    rstan_example <- rstan::stan(file = model_file, data = schools_dat,
                         iter = 1000, chains = 2, save_dso = FALSE)

## End(Not run)
if (require(rstan)) {
   ## load stored object
   rstan_example <- readRDS(system.file("extdata", "rstan_example.rds", package = "broom.mixed"))
   tidy(rstan_example)
   tidy(rstan_example, conf.int = TRUE, pars = "theta")
   td_mean <- tidy(rstan_example, conf.int = TRUE)
   td_median <- tidy(rstan_example, conf.int = TRUE, robust = TRUE)
   
   if (require(dplyr) && require(ggplot2)) {
       tds <- (dplyr::bind_rows(list(mean=td_mean, median=td_median), .id="method")
          %>% mutate(type=ifelse(grepl("^theta",term),"theta",
            ifelse(grepl("^eta",term),"eta",
                  "other")))
      )

     ggplot(tds, aes(estimate, term)) +
      geom_errorbarh(aes(xmin = conf.low, xmax = conf.high),height=0) +
      geom_point(aes(color = method))+
      facet_wrap(~type,scale="free",ncol=1)
 } ## require(dplyr,ggplot2)
} ## require(rstan)
if (require(R2jags)) {
   ## see help("jags",package="R2jags")
   ## and  example("jags",package="R2jags")
   ## for details
   ## load stored object
   R2jags_example <- readRDS(system.file("extdata", "R2jags_example.rds", package = "broom.mixed"))
   tidy(R2jags_example)
   tidy(R2jags_example, conf.int=TRUE, conf.method="quantile")
}

---

Tidying methods for TMB models

Description

Tidying methods for TMB models

Usage

## S3 method for class 'TMB'
tidy(
  x,
  effects = c("fixed", "random"),
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = c("wald", "uniroot", "profile"),
  ...
)

Arguments

x	An object of class TMB (you may need to use class(obj) <- "TMB" on your results from TMB)
effects	which effects should be returned?
conf.int	whether to include a confidence interval
conf.level	confidence level for CI
conf.method	method for computing confidence intervals
...	additional arguments passed to confint function (tmbroot, tmbprofile)

Examples

if (require("TMB")) {

    ## Not run: 
       runExample("simple",thisR=TRUE)
       class(obj) <- "TMB"
       tidy(obj,conf.int=TRUE,conf.method="wald")
    
## End(Not run)
    ## Not run: tidy(obj,conf.int=TRUE,conf.method="uniroot")
    ## Not run: tidy(obj,conf.int=TRUE,conf.method="profile")
}

---

Tidying VarCorr of Mixed Effect Models

Description

Tidying VarCorr of Mixed Effect Models

Usage

## S3 method for class 'VarCorr.lme'
tidy(x, scales = c("sdcor", "vcov"), conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x	a VarCorr object
scales	see tidy.merMod
conf.int	whether to include a confidence interval
conf.level	confidence level for CI
...	Additional arguments (passed to confint.merMod for tidy; augment_columns for augment; ignored for glance)

---

Tidy variance structure for the nlme package.

Description

Returns a tibble with the following columns:

group

type of varFunc, along with the right hand side of the formula in parentheses e.g. "varExp(age | Sex)".

term

terms included in the formula of the variance model, specifically the names of the coefficients. If the value is fixed, it will be appended with " ; fixed".

estimate

estimated coefficient

estimated

This column is only included if some parameters are fixed. TRUE if the parameter is estimated and FALSE if the parameter is fixed.

Usage

## S3 method for class 'varFunc'
tidy(x, ...)

## S3 method for class 'varComb'
tidy(x, ...)

Arguments

x	An object of class varFunc, such as those used as the weights argument from the nlme package
...	Ignored

Value

If the varFunc is uninitialized or has no parameters, the function will return an empty tibble. Otherwise, it will return a tibble with names described in the details section.

Examples

## Not run: 
if (require("nlme")) {
ChickWeight_arbitrary_group <- datasets::ChickWeight
ChickWeight_arbitrary_group$group_arb_n <-
  1 + (
    as.integer(ChickWeight_arbitrary_group$Chick) >
    median(as.integer(ChickWeight_arbitrary_group$Chick))
  )
ChickWeight_arbitrary_group$group_arb <- c("low", "high")[ChickWeight_arbitrary_group$group_arb_n]

fit_with_fixed <-
  lme(
    weight ~ Diet * Time,
    random = ~Time | Chick,
    data =ChickWeight_arbitrary_group,
    weights=varIdent(fixed=c("low"=5), form=~1|group_arb)
  )
# Show all parameters
tidy(fit_with_fixed)
# Exclude fixed parameters
tidy(fit_with_fixed) %>%
  filter(across(any_of("estimated"), ~.x))
}

## End(Not run)

---

strip rownames from an object

Description

strip rownames from an object

Usage

unrowname(x)

Arguments

x	a data frame

---