Description Usage Arguments Details Value See Also Examples

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

class

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 29 30 31 32 33 34 35 36 37 | ```
## 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

`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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ```
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
}
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.