lme4_tidiers | R Documentation |

These methods tidy the coefficients of `lme4::lmer`

and `lme4::glmer`

models (i.e., `merMod`

objects). Methods are also provided for `allFit`

objects.

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

`x` |
An object of class |

`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 |

`scales` |
scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if |

`exponentiate` |
whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if |

`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 |

`ddf.method` |
the method for computing the degrees of freedom and t-statistics (only applicable when using the lmerTest package: see |

`profile` |
pre-computed profile object, for speed when using |

`debug` |
print debugging output? |

`...` |
Additional arguments (passed to |

`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 |

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.

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: |

`level` |
level within group ( |

`term` |
term being estimated |

`estimate` |
estimated coefficient |

`std.error` |
standard error |

`statistic` |
t- or Z-statistic ( |

`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 |

na.action

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 }

broom.mixed documentation built on April 18, 2022, 1:06 a.m.

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