- Minor bug fix related to 1.0.16
- Bug fix: Plots show wrong number of variables when compared to
n_limit
for odd numbers- Solves issue [https://github.com/andjar/ALASCA/issues/12](Plot function prnts output as showing x number of variables, but not actually)
- New function:
predict_scores()
. It accepts a data table with columnsvariable
andvalue
, and returns a new data table with a score column
- New features:
optimize_PCs = TRUE
(default:FALSE
) will check if significant principal components have to be re-ordered during bootstrapping. This may happen if PCs are close in explanatory value so that they are shuffled during bootstrap. If this happens, it will trigger a warningwaterfall = TRUE
(default:FALSE
) inplot()
will replace points with bars for loadings. This can be very nice in combination withloading_group_column
- New feature:
plot()
now accepts the argumentsort_loadings
to control the order of the loading variablessort_loadings = "loading"
sorts the variables by loading (default)sort_loadings = "alpha"
sorts the variables alphabeticallysort_loadings = c(...)
sorts the variables in the same order asc(...)
, where...
is the variables of interest. Note that it may be required to increasen_limit
(or usen_limit = 0
) to ensure that all variables are shown. It is recommended to usen_limit = 0
, i.e.,plot(..., sort_loadings = c(...), n_limit = 0)
- Fix: Error when running LMMs without scaling
- Fix: Error when running linear models with Rfast (
Error in crossprod(x, y) : requires numeric/complex matrix/vector arguments
)
- Fix: Error for small datasets
- Fix: Error for custom stratification columns
- New feature: Black-and-white mode for more plot types
- Fix: Crash when combining
use_Rfast = FALSE
and another random intercept thanID
- Fix: Crash when trying to use only a three-way interaction as effect. Still a but unstable
- New feature: Plot effects in gray scale/black-and-white with symbols instead of colors. Can be tested with
plot(..., bw = TRUE)
(orgrayscale = TRUE
orgreyscale = TRUE
) or similarly withALASCA(..., plot.bw = TRUE)
- Fix: Prediction plot with single-variable effect (e.g.,
time
) did not color the groups correctly
- Fix: Crash when some participants are missing certain measurements
- New feature: Permutation testing (
validation_method = "permutation"
)- Simple permutation testing where data labels are shuffled at two levels: either the participant is re-assigned (e.g., a participant is randomly moved to a new group (or not)), or labels are shifted within participant (e.g. the time labels for a participant are shuffled)
- By default, the first effect is assumed to be shuffled within participant and the others shuffled across participant. The default can be overwritten by specifying
permutation_within_participants
andpermutation_across_participants
, e.g.permutation_within_participants = c("time")
- The participants should only belong to one group for each of the variables in
permutation_across_participants
and samples will be be reassigned as a block forpermutation_within_participants
(i.e., if a participant has two samples in group A and one sample in group B, then the two former samples will be reassigned together and not individually)
- Improved performance:
df["value"][ rowNumers ]
is somewhat faster thandf[ rowNumers, value ]
- Fix: Error when using another column name than
ID
for ID
- First release with new framework