Description Usage Arguments Details Value See Also Examples

These methods tidy the coefficients of mixed effects models
of the `lme`

class from functions of the `nlme`

package.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## S3 method for class 'lme'
tidy(
x,
effects = c("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, ...)
``` |

`x` |
An object of class |

`effects` |
One or more of "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 |

`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 |

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
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 |

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 |

`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." |

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 | ```
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, 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))
}
``` |

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