`nlme`

tidiers are deprecated.

# S3 method for lme tidy(x, effects = "random", ...) # S3 method for lme augment(x, data = x$data, newdata, ...) # S3 method for lme glance(x, ...)

x | An object of class |
---|---|

effects | Either "random" (default) or "fixed" |

... | 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
random or fixed depending on the `effects`

parameter. If
`effects = "random"`

, it contains the columns

the group within which the random effect is being estimated

level within group

term being estimated

estimated coefficient

fixed term being estimated

estimate of fixed effect

standard error

t-statistic

P-value computed from t-statistic

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

returned as NA. To quote Brian Ripley on R-help: McCullagh & Nelder (1989) would be the authoritative reference, but the 1982 first edition manages to use 'deviance' in three separate senses on one page.

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

class from functions of the `nlme`

package.

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("nlme") & require("lme4")) { # example regressions are from lme4 documentation, but used for nlme lmm1 <- lme(Reaction ~ Days, random=~ Days|Subject, sleepstudy) tidy(lmm1) tidy(lmm1, effects = "fixed") 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) } # }