`lme4`

tidiers are deprecated.

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

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

the group within which the random effect is being estimated: `"fixed"`

for fixed effects

level within group (`NA`

except for modes)

term being estimated

estimated coefficient

standard error

t- or Z-statistic (`NA`

for modes)

P-value computed from t-statistic (may be missing/NA)

predicted values

residuals

predicted values with no random effects

the square root of the estimated residual variance

the data's log-likelihood under the model

the Akaike Information Criterion

the Bayesian Information Criterion

deviance

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.

na.action

# NOT RUN { if (require("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) } # }