nlme_tidiers | R Documentation |
These methods tidy the coefficients of mixed effects models
of the lme
class from functions of the nlme
package.
## S3 method for class 'lme'
tidy(
x,
effects = c("var_model", "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 "var_model", "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 |
estimated |
This column is only included if some parameters are fixed. TRUE if the residual error is estimated and FALSE if the residual error is fixed. |
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 |
If effects="var_model"
(the weights
argument to the model),
tidy
returns the columns defined in the help for tidy.varFunc
.
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
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, conf.int = TRUE, conf.level = 0.8)
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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