Description Usage Arguments Details Value See Also Examples

`lme4`

tidiers are deprecated.

1 2 3 4 5 6 7 8 9 10 | ```
## S3 method for class 'merMod'
tidy(x, effects = c("ran_pars", "fixed"),
scales = NULL, ran_prefix = NULL, conf.int = FALSE,
conf.level = 0.95, conf.method = "Wald", ...)
## 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) or "ran_modes" (conditional modes/BLUPs/latent variable estimates) |

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

`...` |
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 |

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

class

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

`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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
## Not run:
library(lme4)
# example regressions are from lme4 documentation
lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
tidy(lmm1)
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_modes", conf.int=TRUE)
head(augment(lmm1, sleepstudy))
glance(lmm1)
glmm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
tidy(glmm1)
tidy(glmm1, effects = "fixed")
head(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)
tidy(nm1)
tidy(nm1, effects = "fixed")
head(augment(nm1, Orange))
glance(nm1)
## End(Not run)
``` |

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