*_wd_here() to set and get the Working
Directory (wd) quicky.
get_wd_here() gets the working directory to the path of
the current script.set_wd_here() sets the working directory to the path of
the current script.open_wd_here() open the File Explorer at the directory
path of the current script.open_wd() open the File Explorer at the current working
directory.corr_coef() now can compute both linear and partial
correlation, controled by the argument type.network_plot() to produce a network plot
of a correlation matrix or an object computed with
corr_coef().sample_random() and
sample_systematic() for random and systematic sampling,
respectively.plot.waasb() now has new arguments to control whether
to show the percentage values within bars and the order of variables on
the x-axis.corr_coef() now handles grouped data passed from
group_by()size.varnames and
col.varnames added in corr_plot().gmd(mod, "h2"), when mod is
computed with random = "env".repel in
plot.gge().plot method for path_coeff_*()
functions.path_coeff_seq() to implement a sequential
(two chains) path analysis.prop_na() to measure the proportion of
NAs in each column.remove_cols_all_na() and
remove_rows_all_na() to remove columns and rows that have
all values as NAs.ci_mean_z() and ci_mean_t()
to compute z- and t-confidence intervals, respectively.set_wd_here() to set the working directory
to the path of the current script.rowname_to_column().mps() where stab was being rewritten with
stab_res.mgidi() example that
overwrites the function.x.lab and y.lab from
plot_scores(). Now it accepts an object from
expression()plot_waasby() now accepts objects of class
waas_means.get_model_data() now includes new options
coefs, and anova for objects computed with
ge_reg().max_overlaps in plot_scores()
to exclude text labels that overlap too many things.plot_scores() (shape, alpha, color, and size).ge_stats() to include the new stability
methods.wsmp() now accepts objects computed with
mps(), waas(), and waasb().AMMI_indexes() has been deprecated in favour of
ammi_indexes().ammi_indexes()data_ge().gmd(., "data")fai_blup() when genotypes has distance as
0.inspect() when some trait has character
values.gmd(model, "blupge")get_model_data() calling objects of class
mgidi with what = "PCA".path_coeff() when generating sequences of
direct effects depending on the constant added to the diagonal of
correlation matrix.gmd() for gge
objects.projection in gmd() for
gge objects to get the projection of each genotype in the
AEC coordinates.mtsi() with an object of class
waas.progress() and run_progress() for text
progress bar in the terminal.rbind_fill_id() To implement the common pattern of
do.call(rbind, dfs) with data frame identifier and filling
of missing values.hmgv(), rpgv(), hmrpgv(),
blup_indexes() to compute stability indexes based on a
mixed-effect model.mps() and mtmps() for uni- and
multivariate-based mean performance and stability in multi-environment
trials.ge_reg() now returns hypotesis testing for slope and
deviations from the regression. Thanks to @LeonardoBehring and @MichelSouza for
the suggestion.Resende_indexes() now remove NAs before
computing harmonic and arithmetic means.plot_scores that now has a
highlight argument to highlight genotypes or environments
by hand. Thanks to Ibrahim
Elbasyoni for his suggestions.clustering() when using with by
argument and defacult nclust argument.get_model_data() now extract BLUEs from objects
computed with gamem() and gamem_met(). Thanks
to @MdFarhad for
suggesting me this improvement.g_simula() and ge_simula() now have a
res_eff to control the residual effect.mgidi() now have an optional weights
argument to assign different weights for each trait in the selection
process. Thanks to @MichelSouza for
his suggestion.get_levels_comb() to get the combination of the levels
of a factor.g_simula() to simulate replicated genotype data.add_row_id() to add a column as the row id.remove_rownames(), column_to_rownames()
and rownames_to_column() to deal with rownames.sel.var() in corr_ci() to
filter correlations with a selected variablefill and position.fill in
plot_ci() to fill correlations by levels of a factor
variable.arrange_ggplot() and
gge().theme in arrange_ggplot() to
control the theme of the plot.by argument in gafem().mgid() now understands models of class
gafem_grouped.get_levels() to get the levels even if the
variable is not a factor.set_union(), set_difference() and
set_intersect() for set operations with many sets.venn_plot() to produce Venn diagrams.gge() now have a by argument and
understand data passed from group_by.col.stroke and size.stroke
in plot.gge()gtb and gytb now produces biplots with
lines for genotype’s vectors in type = 1.get_model_data() now understand objects of class
fai_blup and sh.get_dist() to get distance matrices from objects of
class clustering.get_corvars() to get normal, multivariate correlated
variables.get_covmat() to obtain covariance matrix based on
variances and correlation values.as_numeric(), as_integer(),
as_logical(), as_character(), and
as_factor() to coerce variables to specific formats
quickly.n_valid(), n_missing(), and
n_unique() to count valid, missing, and unique values,
respectively.tidy_colnames() to clean up column names. It is a
shortcut to tidy_strings() applied to the column names of a
data frame.env_stratification() to perform environment
stratification using factor analysis.as_*() now handles vectors.
plot.corr_coef() now shows both stars or p-values
for reporting the significance of correlations.
gamem(), gamem_met(), and
waasb() now have a by argument and understand
data passed from group_by.
mtsi() and mgidi() now returns the
ranks for the contribution of each factor and understand models fitted
with gamem() and waasb() using the
by argument.
plot.mtsi() and plot.mgidi() now
returns a radar plot by default when using
type = "contribution".
get_model_data() now returns the genotypic and
phenotypic correlation matrices from objects of class waasb
and gamem.
replace_string(), replace_number(),
extract_string(), and extract_number() now
accepts tidy
evaluation in the new ... argument.
add_prefix() and
add_suffix() to add prefixes and suffixes to variable
names, respectively.select_pred() to selects a best subset of
predictor variables.acv() to compute the adjusted
coefficient of variation to account for the systematic dependence of
\(\sigma^2\) from \(\mu\).ge_acv() to compute yield stability index
based on the adjusted coefficient of variation.ge_polar() to compute yield stability
index based on Power
Law Residuals (POLAR) statistics.mantel_test() to performs a Mantel test
between two matrices.prefix and suffix in
concatenate() to add prefixes and suffixes to concatenated
values, respectively.\doi{} markup in Rd files.gytb() to generate the Genotype by
yield*trait biplot.row_col_mean() and
row_col_sum()to add a row with the mean/sum of each
variable and a column with the mean/sum for each row of a matrix or data
frame.has_zero(),
remove_cols_zero(), remove_rows_zero(),
select_cols_zero(), select_rows_zero(), and
replace_zero() to deal with 0s in a data frame.gge.gge and can_corr in
get_model_data().position in plot.gamem() and
plot.mtsi() to control the position adjustment of the bar
plot.col.by in corr_plot() to map
the color of the points by a categorical variable.use_data in functions
mgidi(), fai_blup(), and
Smith_Hazel() to control which data is used (BLUPs or
phenotypic means) to compute the index.inspect() now generate a warning if zero values are
observed in data.clip_read() and clip_write()
to read from the clipboard and write to the clipboard,
respectively.sum_by() to compute the sum by levels of
factors.mgidi() now allows using a fixed-effect model fitted
with gafem() as input data.round_cols() now can be used to round whole
matrices.plot.mgidi() can now plot the contribution for all
genotypes.plot_bars() and plot_factbars() now shows
the values with values = TRUEdplyr::across()Smith_Hazel() and print.sh() and
plot.sh() for computing the Smith and Hazel selection
index.coincidence_index() for computing the coincidence
index.get_model_data() now extracts the genotypic and
phenotypic variance-covariance matrix from objects of class
gamem and waasb.fai_blup() now returns the total genetic gain and the
list with the ideotypes’ construction.mgidi() now computes the genetic gain when a
mixed-model is used as input data.plot()
for objects of class mgidi has a new argument
type = "contribution" to plot the contribution of each
factor in the MGIDI index.plot_scores() now can produce a biplot showing other
axes besides PC1 and PC2. To change the default IPCA in each axis use
the new arguments first and second.plot_bars() and plot_factbars() now align
vertically the labels to the error bars.fai_blup() now returns the eigenvalues and explained
variance for each axis and variables into columns instead row
names.donttest{} examples. Now, the
correct data set is used in the example of fai_blup()select_rows_na() and select_cols_na() to
select rows or columns with with NA values.mgidi() to compute the multi-trait genotype-ideotype
distance index.plot_bars() to create bar plots quickly. Thanks to @MariaDiel for her
suggestion.hm_mean() and
gm_mean() removed in favour of hmean() and
gmean(), respectively.rep retired in Fox(),
ge_effects(), Huehn(),
resp_surf(), superiority(), and
Thennarasu()verbose retired in
anova_ind()region retired in
resp_surf()plot.wsmp().ge_plot()fai_blup() with gamem()inspect()plot() call on objects of class gamem,
waasb and waas now returns the variable names
automatically. Thanks to @MdFarhad for
suggesting me this change.plot.gamem() and plot.waasb() have a new
argument (type = "vcomp") to produce a plot showing the
contribution of the variance components to the phenotypic variancecv_ammi(), cv_ammif(), and
cv_blup() now check for missing values and unbalanced data
before computing the cross-validation. (#3)tibble to
version 3.0.0.get_model_data() now fills rows that don’t matches
across columns with NA. Thanks to @MdFarhad for his
report.get_model_data() called now report mean squares,
F-calculated and P-values for blocks within replicates in
anova_ind().ENV, GEN, and REP only (#2).gmd() a shortcut to get_model_data()gtb() to generate a genotype-by-trait biplot.gamem_met() to analyze genotypes in multi-environment
trials using mixed- or random-effect models allowing unbalanced data.
Thanks to @EderOliveira for
his e-mail.has_class() to check if a class exists.impute_missing_val() to impute missing values in a
two-way table based on Expectation-Maximization algoritms.non_collinear_vars() to select a set of predictors with
minimal multicollinearity.replace_na() to replace NA values
quicly.random_na() to generate random NA values
based on a desired proportion.gge(), performs_ammi(),
waas(), and waasb() now handle with unbalanced
data by implementing a low-rank matrix approximation using singular
value decomposition to impute missing entires. Imputation generates a
warning message.NA values are checked and removed with a warning when
computing stability indexes. Thanks to @MdFarhad for alerting
me.plot_res in path_coeff() to
create a residual plot of the multiple regression model.verbose deprecated in functions
anova_ind() and split_factors()rep deprecated in functions
Fox(), Huehn(), superiority(),
and Thennarasu().means_by removed in functions
can_corr() and clustering().verbose removed in functions
colindiag() and split_factors().values removed in functions
desc_stat() and find_outliers().var removed in function
desc_wider().plot.resp_surf().alpha_color() To get a semi-transparent colorgafem() To analyze genotypes using fixed-effect
models.residual_plots() A helper function to create residuals
plots.stars_pval() To generate significance stars from
p-valuesdoo() An alternative to dplyr::do for
doing anythingcv_by() For computing coefficient of variation by
levels of a factor.max_by() For computing maximum values by levels of a
factor.means_by() For computing arithmetic means by levels of
a factor.min_by() For computing minimum values by levels of a
factor.n_by() For getting the length.sd_by() For computing sample standard deviation.sem_by() For computing standard error of the mean by
levels of a factor.av_dev() computes the average absolute deviation.ci_mean() computes the confidence interval for the
mean.cv() computes the coefficient of variation.hm_mean(), gm_mean() computes the harmonic
and geometric means, respectively. The harmonic mean is the reciprocal
of the arithmetic mean of the reciprocals. The geometric mean is the nth
root of n products.kurt() computes the kurtosis like used in SAS and
SPSS.range_data() Computes the range of the values.sd_amo(), sd_pop() Computes sample and
populational standard deviation, respectively.sem() computes the standard error of the mean.skew() computes the skewness like used in SAS and
SPSS.sum_dev() computes the sum of the absolute
deviations.sum_sq_dev() computes the sum of the squared
deviations.var_amo(), var_pop() computes sample and
populational variance.valid_n() Return the valid (not NA) length of a
data.colnames_to_lower(), colnames_to_upper(),
and colnames_to_title() to translate column names to lower,
upper and title cases quickly.all_lower_case(), all_upper_case(), and
all_title_case() to translate strings vectors or character
columns of a data frame to lower, upper and title cases,
respectively.tidy_strings() Tidy up characters strings, non-numeric
columns, or any selected columns in a data frame by putting all word in
upper case, replacing any space, tabulation, punctuation characters by
'_', and putting '_' between lower and upper
cases.find_text_in_num() Find text fragments in columns
assumed to be numeric. This is especially useful when
everything() is used in argument resp to
select the response variables.anova_ind(), anova_joint(),
performs_ammi(), waas() and
waasb(), now have the argument block to
analyze data from trials conducted in an alpha-lattice design. Thanks to
@myaseen208 for his suggestion
regarding multi-environment trial analysis with alpha-lattice
designs.repel included in plot_scores()
to control wheater the labels are repelled or not to avoid
overlapping.Argument means_by was deprecated in functions
can_corr() and clustering(). Use
means_by() to pass data based on means of factor to these
functions.
transparent_color()desc_stat() now handles grouped data passed from
dplyr::group_by()plot_scores() now support objects of class
waas_mean.citation("metan").plot_scores(type = 2) (#1)get_model_data() now support models of class
anova_joint and gafem and extract random
effects of models fitted with waasb() and
gamem().plot.waasb() and plot.gamem() to
show distribution of random effects.inspect(), cv_blup(),
cv_ammif(), and cv_ammi() now generate a
warning message saying that is not possible to compute cross-validation
procedures in experiments with two replicates only. Thanks to @Vlatko for his
email.plot.wsmp() now returns heatmaps created with ggplot2.
Thus, we removed dependency on gplots.corr_plot() now don’t write a warning message to the
console by default.select_numeric_cols() now is used as a helper function
in metan.metan now reexports mutate() from
dplyr package.get_model_data() now set default values for each class
of models.by that calls internally
split_factors() included to facilitate the application of
the functions to each level of one grouping variable.add_cols(), and add_rows() for adding
columns and rows, respectively.remove_cols(), and remove_rows() for
removing columns and rows, respectively.select_cols() and select_rows() for
selecting columns and rows, respectively.select_numeric_cols(), and
select_non_numeric_cols() for selecting numeric and
non-numeric variables quickly.round_cols() for rounding a whole data frame to
significant figures.all_lower_case(), and all_upper_case() for
handling with cases.extract_number(), extract_string(),
remove_strings(), replace_number(), and
replace_string(), for handling with numbers and
strings.get_level_size(), and get_levels() for
getting size of levels and levels of a factor.means_by() for computing means by one or more factors
quickly.ge_means() for computing genotype-environment
meansge_winners() for getting winner genotypes or ranking
genotypes within environments.env_dissimilarity() for computing dissimilarity between
test environments.starts_with(),
ends_with(), contains(),
contains(), num_range(),
one_of(), everything(), and
last_col().resp (response variable(s) now
support select helpers.sem() for computing standard error
of mean.remove_rows_na() and
remove_cols_na() for removing rows or columns with
NA values quickly.difference_var(),
intersect_var(), and union_var() for selecting
variables that match an expression.Schmildt() for stability analysis.ge_reg.get_model_data() to support objects of class
Schmildtand Annicchiarico.on.exit() is used in S3 generic functions
print() to ensure that the settings are reset when a
function is excited.I’m very pleased to announce the release of metan 1.1.0,
This is a minor release with bug fixes and new functions. The most
important changes are described below.
corr_stab_ind() for computing Spearman’s
rank correlation between stability indexes;corr_coef() for computing correlation
coefficients and p-values;plot.corr_coef() for creating correlation
heat maps;print.corr_coef() for printing
correlation and p-values;make_lower_tri() and
make_upper_tri() for creating lower and upper triangular
matrices, respectively.reorder_cormat() for reordering a
correlation matrix according to the correlation coefficients;get_model_data() by supporting new
classes of models. Now, get_model_data() can be used to get
all statistics or ranks computed with the wrapper function
ge_stats().arrange_ggplot() now support objects of class
ggmatrix.theme_metan()arrange_ggplot() for arranging ggplot2
graphics;ge_effects() for computing
genotype-environment effects;gai() for computing the geometric
adaptability index;gm_mean() for computing geometric
mean;hm_mean() for computing harmonic
mean;Huehn() for computing Huehn’s
stability statistic;Thennasaru() for computing
Thennasaru’s stability statistic;get_model_data() by supporting new
classes of models;gamem() for analyzing genotypes in one-way
trials using mixed-effect models;desc_wider() to convert an output of the
function desc_stat() to a ‘wide’ format;Fox() for Stability analysis;Shukla() for stability analysis;to_factor() to quickly convert variables
to factors;get_model_data() function;The changes in this version were made based on suggestions received when metan was submitted to CRAN for the first time.
The documentation of the following functions was updated by including/updating the \value section of .Rd files.
AMMI_indexes()Annichiarico()anova_ind()as.lpcor()as.split_factors()bind_cv()can_cor()comb_vars()corr_ci()corr_plot()covcor_design()cv_ammi()cv_ammif()cv_blup()desc_stat()ecovalence()fai_blup()ge_factanal()ge_plot()ge_reg()ge_stats()get_model_data()is.lpcorr()is.split_factors()mahala()mahala_design()make_mat()make_sym()mtsi()pairs_mantel()plot.*() and plot_*() functionsrbind_fill()resca()resp_surf()waas()wsmp()waasb()To allow automatic testing, the examples of the following functions were unwrapped by deleting \dontrun{}.
bind_cv()clustering()comb_vars()corr_ci()corr_plot()covcor_design()desc_stat()ecovalence()path_coefff()plot.fai_blup()plot.mtsi()plot.wsmp()plot_ci()wsmp()In the examples of the functions for cross-validation \dontrun{} was
changed with \donttest{} * cv_ammi() *
cv_ammif() * cv_blup() *
plot.cv_ammif()
This is the first version that will be submitted to CRAN. In this version, deprecated functions in the last versions were defunct. Some new features were implemented.
fai_blup() computes the FAI-BLUP index
(https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12443)gge() computes the genotype plus
genotype-vs-environment model.plot_factbars() and plot_factlines() are
used to create bar and line plots, respectively, considering an one- or
two-way experimental design.desc_stat() computes several descriptive
statistics.can_corr()computes canonical correlation
coefficients.resp_surf() computes response surface model using two
quantitative factors.make_mat() is used to create a two-way table using two
columns (factors) and one response variable.make_sym() is used to create a symmetric matrix using a
upper- or lower-diagonal matrix.AMMI_indexes() and fai_blup() and
desc_stat(). For example, to indicate the statistics to be
computed in desc_stat() you must use now
stats = c("mean, SE.mean, CV, max, min")) instead
stats = c("mean", "SE.mean", "CV", "max", "min"))In the latest development version, the package METAAB was renamed to metan (multi-environment trials analysis). Aiming at a cleaner coding, in this version, some functions were deprecated and will be defunct in the near future. Alternative functions were implemented.
WAAS.AMMI(), use waas().WAASBYratio(), use wsmp().WAASratio.AMMI(), use wsmp().autoplot.WAAS.AMMI(), use
autoplot.waas().plot.WAASBYratio(), use
plot.wsmp().plot.WAASratio.AMMI(), use
plot.wsmp().predict.WAAS.AMMI(), use
predict.waas().summary.WAAS.AMMI(), use
summary.waas()Widely-known parametric and nonparametric methods were implemented, using the following functions.
Annicchiarico() to compute the genotypic confidence
index.ecovalence() to compute the Wricke’s ecovalence.ge_factanal() to compute to compute the stability and
environmental.ge_reg() to compute the joint-regression analysis.
stratification using factor analysis.superiority() to compute the nonparametric superiority
index.In the latest development version, some useful functions were included. One of the most interesting features included in this version was allowing the functions to receive data from the forward-pipe operator %>%. Bellow are the functions included in this version.
anova_ind() to perform a within-environment analysis of
variance easily;colindiag() to perform a collinearity diagnostic of a
set of predictors;afind_outliers() to easily find possible outliers in the
dataset;group_factors() to split a dataset into a list of
subsets using one or more grouping factors. This function may be used
befor some functions, e.g., find_outliers(),
colindiag(), path_coeff() to compute the
statistics for each level of the factor (or combination of levels of
factors).lpcor() to compute linear and partial correlation
coefficients.pairs_mantel() to compute a graphic pairwise Mantel’s
test using a set of correlation matrices;path_coeff() to compute path coefficients with minimal
multicollinearity;The following S3 Methods were also implemented:
is.group_factors() and as.group_factors()
to check or easily coerce a dataframe that has one or more factor
columns to an object of group_factors;is.lpcorr() and as.lpcorr() to check or
easily coerce a list of correlation matrices to an object of
lpcorr;plot(), predict(),
summary() implemented.