View source: R/apa_print_nlme.R
apa_print.lme | R Documentation |
These methods take mixed-effects models fitted with the nlme package and create formatted character strings report the results in accordance with APA manuscript guidelines.
## S3 method for class 'lme'
apa_print(x, conf.int = 0.95, in_paren = FALSE, est_name = NULL, ...)
## S3 method for class 'anova.lme'
apa_print(x, in_paren = FALSE, ...)
x |
A (non-)linear mixed-effects model fitted with |
conf.int |
Numeric specifying the required confidence level or a named list
of additional arguments that are passed to |
in_paren |
Logical. Whether the formatted string is to be reported in
parentheses. If |
est_name |
An optional character. The label to be used for fixed-effects coefficients. |
... |
Further arguments that may be passed to |
apa_print()
-methods return a named list of class apa_results
containing the following elements:
estimate |
One or more character strings giving point estimates, confidence intervals, and confidence level. A single string is returned in a vector; multiple strings are returned as a named list. If no estimate is available the element is |
statistic |
One or more character strings giving the test statistic, parameters (e.g., degrees of freedom), and p-value. A single string is returned in a vector; multiple strings are returned as a named list. If no estimate is available the element is |
full_result |
One or more character strings comprised 'estimate' and 'statistic'. A single string is returned in a vector; multiple strings are returned as a named list. |
table |
A |
Column names in apa_results_table
are standardized following the broom glossary (e.g., term
, estimate
conf.int
, statistic
, df
, df.residual
, p.value
). Additionally, each column is labelled (e.g., $\hat{\eta}^2_G$
or $t$
) using the tinylabels package and these labels are used as column names when an apa_results_table
is passed to apa_table()
.
Other apa_print:
apa_print()
,
apa_print.BFBayesFactor()
,
apa_print.aov()
,
apa_print.emmGrid()
,
apa_print.glht()
,
apa_print.htest()
,
apa_print.list()
,
apa_print.lm()
,
apa_print.merMod()
library(nlme)
fm1 <- lme(distance ~ age, data = Orthodont, method = "ML") # random is ~ age
apa_print(fm1, conf.int = .9)
# ANOVA-like tables
single_anova <- anova(fm1)
apa_print(single_anova)
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