| Title: | Multivariate Independent Comparison of Observations |
|---|---|
| Description: | A procedure for comparing multivariate samples associated with different groups. It uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. The procedure is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) <doi:10.32614/RJ-2016-055>. |
| Authors: | Nuno Fachada [aut, cre] |
| Maintainer: | Nuno Fachada <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.1.4.9000 |
| Built: | 2024-11-11 06:32:50 UTC |
| Source: | https://github.com/nunofachada/micompr |
Generic function to get the assumptions for parametric tests applied to the comparison of output observations.
assumptions(obj)assumptions(obj)
obj |
Object from which to get the assumptions. |
Assumptions for parametric tests applied to the comparison of outputs.
assumptions.cmpoutput,
assumptions.micomp
Determine two assumptions for the MANOVA test: a) multivariate normality of each group; b) homogeneity of covariance matrices.
assumptions_manova(data, factors)assumptions_manova(data, factors)
data |
Data used for the MANOVA test (rows correspond to observations, columns to dependent variables). |
factors |
Groups to which rows of |
An object of class assumptions_manova which is a list
containing two elements:
mvntestList of results from the Royston multivariate
normality test (mvn), one result per
group.
vartestResult of Box's M test for homogeneity of covariance
matrices (boxM).
This function requires the MVN and biotools packages.
# Determine the assumptions of applying MANOVA to the iris data # (i.e. multivariate normality of each group and homogeneity of covariance # matrices) a <- assumptions_manova(iris[, 1:4], iris[, 5])# Determine the assumptions of applying MANOVA to the iris data # (i.e. multivariate normality of each group and homogeneity of covariance # matrices) a <- assumptions_manova(iris[, 1:4], iris[, 5])
Determine two assumptions for the parametric comparison tests (i.e. either
t.test or aov) for each principal
component, namely: a) univariate normality of each group; b) homogeneity of
variances.
assumptions_paruv(data, factors)assumptions_paruv(data, factors)
data |
Data used in the parametric test (rows correspond to observations, columns to principal components). |
factors |
Groups to which rows of |
An object of class assumptions_paruv which is a list
containing two elements:
uvntestList of results from the Shapiro-Wilk normality test
(shapiro.test), one result per group per
principal component.
vartestResult of Bartlett test for homogeneity of variances
(bartlett.test).
# Determine the assumptions of applying ANOVA to each column (dependent # variable) of the iris data (i.e. normality of each group and homogeneity of # variances) a <- assumptions_paruv(iris[, 1:4], iris[, 5])# Determine the assumptions of applying ANOVA to each column (dependent # variable) of the iris data (i.e. normality of each group and homogeneity of # variances) a <- assumptions_paruv(iris[, 1:4], iris[, 5])
Get assumptions for parametric tests performed on output comparisons (i.e.
from objects of class cmpoutput).
## S3 method for class 'cmpoutput' assumptions(obj)## S3 method for class 'cmpoutput' assumptions(obj)
obj |
Object of class |
Object of class assumptions_cmpoutput containing the
assumptions for parametric tests performed on an output comparison.
Basically a list containing the assumptions for the MANOVA (list of objects
of class assumptions_manova, one per explained variance) and
univariate parametric tests for each principal component (object of class
assumptions_paruv).
# Create a cmpoutput object from the provided datasets cmp <- cmpoutput("All", 0.9, pphpc_ok$data[["All"]], pphpc_ok$obs_lvls) # Get the assumptions for the parametric tests performed in cmp acmp <- assumptions(cmp)# Create a cmpoutput object from the provided datasets cmp <- cmpoutput("All", 0.9, pphpc_ok$data[["All"]], pphpc_ok$obs_lvls) # Get the assumptions for the parametric tests performed in cmp acmp <- assumptions(cmp)
Get assumptions for parametric tests performed on multiple comparisons (i.e.
from objects of class micomp).
## S3 method for class 'micomp' assumptions(obj)## S3 method for class 'micomp' assumptions(obj)
obj |
Object of class |
Object of class assumptions_micomp containing the
assumptions for parametric tests performed for the multiple comparisons held
by the mcmp object. This object is a multi-dimensional list of
assumptions_cmpoutput objects. Rows are associated with individual
outputs, while columns are associated with separate comparisons.
# Create a micomp object, use provided dataset mic <- micomp(6, 0.8, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff))) # Create an object containing the statistic tests evaluating the assumptions # of the comparisons performed in the mic object a <- assumptions(mic)# Create a micomp object, use provided dataset mic <- micomp(6, 0.8, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff))) # Create an object containing the statistic tests evaluating the assumptions # of the comparisons performed in the mic object a <- assumptions(mic)
Center and scale input vector using the specified method.
centerscale(v, type)centerscale(v, type)
v |
Vector to center and scale. |
type |
Type of scaling: "center", "auto", "range", "iqrange", "vast", "pareto", "level" or "none". |
Center and scaled vector using the specified method.
Berg, R., Hoefsloot, H., Westerhuis, J., Smilde, A., and Werf, M. (2006). Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7, 142. DOI: 10.1186/1471-2164-7-142
v <- c(-100, 3, 4, 500, 10, 25, -8, -33, 321, 0, 2) centerscale(v, "center") # [1] -165.81818 -62.81818 -61.81818 434.18182 -55.81818 -40.81818 # [7] -73.81818 -98.81818 255.18182 -65.81818 -63.81818 centerscale(v, "auto") # [1] -0.9308937 -0.3526577 -0.3470437 2.4374717 -0.3133601 -0.2291509 # [7] -0.4144110 -0.5547596 1.4325760 -0.3694995 -0.3582716 centerscale(v, "range") # [1] -0.2763636 -0.1046970 -0.1030303 0.7236364 -0.0930303 -0.0680303 # [7] -0.1230303 -0.1646970 0.4253030 -0.1096970 -0.1063636 centerscale(v, "iqrange") # [1] -6.085071 -2.305254 -2.268557 15.933278 -2.048374 -1.497915 -2.708924 # [8] -3.626355 9.364470 -2.415346 -2.341952 centerscale(v, "vast") # [1] -0.34396474 -0.13030682 -0.12823247 0.90064453 -0.11578638 -0.08467115 # [7] -0.15312466 -0.20498338 0.52933609 -0.13652987 -0.13238117 centerscale(v, "pareto") # [1] -12.424134 -4.706731 -4.631804 32.531614 -4.182247 -3.058353 # [7] -5.530919 -7.404075 19.119816 -4.931509 -4.781657 centerscale(v, "level") # [1] -2.5193370 -0.9544199 -0.9392265 6.5966851 -0.8480663 -0.6201657 # [7] -1.1215470 -1.5013812 3.8770718 -1.0000000 -0.9696133 centerscale(v, "none") # [1] -100 3 4 500 10 25 -8 -33 321 0 2v <- c(-100, 3, 4, 500, 10, 25, -8, -33, 321, 0, 2) centerscale(v, "center") # [1] -165.81818 -62.81818 -61.81818 434.18182 -55.81818 -40.81818 # [7] -73.81818 -98.81818 255.18182 -65.81818 -63.81818 centerscale(v, "auto") # [1] -0.9308937 -0.3526577 -0.3470437 2.4374717 -0.3133601 -0.2291509 # [7] -0.4144110 -0.5547596 1.4325760 -0.3694995 -0.3582716 centerscale(v, "range") # [1] -0.2763636 -0.1046970 -0.1030303 0.7236364 -0.0930303 -0.0680303 # [7] -0.1230303 -0.1646970 0.4253030 -0.1096970 -0.1063636 centerscale(v, "iqrange") # [1] -6.085071 -2.305254 -2.268557 15.933278 -2.048374 -1.497915 -2.708924 # [8] -3.626355 9.364470 -2.415346 -2.341952 centerscale(v, "vast") # [1] -0.34396474 -0.13030682 -0.12823247 0.90064453 -0.11578638 -0.08467115 # [7] -0.15312466 -0.20498338 0.52933609 -0.13652987 -0.13238117 centerscale(v, "pareto") # [1] -12.424134 -4.706731 -4.631804 32.531614 -4.182247 -3.058353 # [7] -5.530919 -7.404075 19.119816 -4.931509 -4.781657 centerscale(v, "level") # [1] -2.5193370 -0.9544199 -0.9392265 6.5966851 -0.8480663 -0.6201657 # [7] -1.1215470 -1.5013812 3.8770718 -1.0000000 -0.9696133 centerscale(v, "none") # [1] -100 3 4 500 10 25 -8 -33 321 0 2
Compares output observations from two or more groups.
cmpoutput(name, ve_npcs, data, obs_lvls, lim_npcs = TRUE, mnv_test = "Pillai")cmpoutput(name, ve_npcs, data, obs_lvls, lim_npcs = TRUE, mnv_test = "Pillai")
name |
Comparison name (useful when calling this function to perform multiple comparisons). |
ve_npcs |
Percentage ( |
data |
A n x m matrix, where n is the total number of output observations (runs) and m is the number of variables (i.e. output length). |
obs_lvls |
Levels or groups associated with each observation. |
lim_npcs |
Limit number of principal components used for MANOVA to minimum number of observations per group? |
mnv_test |
The name of the test statistic to be used in MANOVA, as
described in |
Object of class cmpoutput containing the following data:
n x n matrix containing projections of output data in the principal components space. Rows correspond to observations, columns to principal components.
Levels or groups associated with each observation.
Percentage of variance explained by each principal component.
Number of principal components specified in ve_npcs OR
which explain the variance percentages given in ve_npcs.
Percentage (between 0 and 1) of variance explained by the q principal components (i.e. number of dimensions) used in MANOVA.
Comparison name (useful when calling this function to perform multiple comparisons).
P-values for the performed statistical tests, namely:
List of p-values for the MANOVA test for each
number of principal component in npcs.
Vector of p-values for the parametric test applied to groups along each principal component (t-test for 2 groups, ANOVA for more than 2 groups).
Vector of p-values for the non-parametric test applied to groups along each principal component (Mann-Whitney U test for 2 groups, Kruskal-Wallis test for more than 2 groups).
Same as field parametric, but
p-values are adjusted using weighted Bonferroni procedure.
Percentages of explained variance are used as weights.
Same as field nonparametric, but
p-values are adjusted using weighted Bonferroni procedure.
Percentages of explained variance are used as weights.
Objects returned by the manova
function for each value specified in ve_npcs.
List of objects returned by applying
t.test (two groups) or
aov (more than two groups) to each principal
component.
List of objects returned by applying
wilcox.test (two groups) or
kruskal.test (more than two groups) to each
principal component.
# Comparing the first output ("Pop.Sheep") of one the provided datasets. cmp <- cmpoutput("SheepPop", 0.8, pphpc_ok$data[["Pop.Sheep"]], pphpc_ok$obs_lvls) # Compare bogus outputs created from 2 random sources, 5 observations per # source, 20 variables each, yielding a 10 x 20 data matrix. data <- matrix(c(rnorm(100), rnorm(100, mean = 1)), nrow = 10, byrow = TRUE) olvls <- factor(c(rep("A", 5), rep("B", 5))) cmp <- cmpoutput("Bogus", 0.7, data, olvls)# Comparing the first output ("Pop.Sheep") of one the provided datasets. cmp <- cmpoutput("SheepPop", 0.8, pphpc_ok$data[["Pop.Sheep"]], pphpc_ok$obs_lvls) # Compare bogus outputs created from 2 random sources, 5 observations per # source, 20 variables each, yielding a 10 x 20 data matrix. data <- matrix(c(rnorm(100), rnorm(100, mean = 1)), nrow = 10, byrow = TRUE) olvls <- factor(c(rep("A", 5), rep("B", 5))) cmp <- cmpoutput("Bogus", 0.7, data, olvls)
Concatenate multiple outputs with multiple observations.
concat_outputs(outputlist, centscal = "none")concat_outputs(outputlist, centscal = "none")
outputlist |
List of outputs. Each output is a n x m matrix, where n is the number of observations and m is the number of variables (i.e. output length). |
centscal |
Centering and scaling method: "center", "auto", "range",
"iqrange", "vast", "pareto", "level" or "none". This task is delegated to the
|
An n x p matrix, representing the n observations of the concatenated output, each observation of length p, which is the sum of individual output lengths.
# Collect 20 observations of 3 outputs with different scales and lengths # Output 1, length 100 out1 <- matrix(rnorm(2000, mean = 0, sd = 1), nrow = 20) # Output 2, length 200 out2 <- matrix(rnorm(4000, mean = 100, sd = 200), nrow = 20) # Output 1, length 50 out3 <- matrix(rnorm(1000, mean = -1000, sd = 10), nrow = 20) # Concatenate and range scale outputs, resulting matrix dimensions will be # 20 x 350 outconcat <- concat_outputs(list(out1, out2, out3), "range")# Collect 20 observations of 3 outputs with different scales and lengths # Output 1, length 100 out1 <- matrix(rnorm(2000, mean = 0, sd = 1), nrow = 20) # Output 2, length 200 out2 <- matrix(rnorm(4000, mean = 100, sd = 200), nrow = 20) # Output 1, length 50 out3 <- matrix(rnorm(1000, mean = -1000, sd = 10), nrow = 20) # Concatenate and range scale outputs, resulting matrix dimensions will be # 20 x 350 outconcat <- concat_outputs(list(out1, out2, out3), "range")
Load and group outputs from files containing multiple observations of the groups to be compared.
grpoutputs( outputs, folders, files, lvls = NULL, concat = F, centscal = "range", ... )grpoutputs( outputs, folders, files, lvls = NULL, concat = F, centscal = "range", ... )
outputs |
A vector with the labels of each output, or an integer with
the number of outputs (in which case output labels will be assigned
automatically). In either case, the number of outputs should account for
an additional concatenated output, as specified in the |
folders |
Vector of folder names where to read files from. These are
recycled if |
files |
Vector of filenames or file sets to load in each folder. File
sets can be given as regular expressions, or as wildcards by
wrapping them with |
lvls |
Vector of factor levels (groups). Must be the same length as
|
concat |
If TRUE add an additional output which corresponds to the concatenation of all outputs, properly centered and scaled. |
centscal |
Method for centering and scaling outputs if |
... |
Options passed to |
Each file corresponds to an observation, and should have a tabular format
where columns correspond to outputs and rows to variables or dimensions.
Observations (files) are grouped by factor levels which correspond to the
file groups given in the files parameter. Factor levels differentiate
observations from distinct groups.
Object of class grpoutputs containing the following data:
List of all outputs, each one grouped into a n x m matrix, where n is the total number of output observations and m is the number of variables or dimensions (i.e. output length).
Vector containing number of observations for each level or group.
Factor vector of levels or groups associated with each observation.
Vector of factor levels in the order they occur (as given in parameter with the same name).
Boolean indicating if this object was created with an additional concatenated output.
# Determine paths for data folders, each containing outputs for 10 runs of # the PPHPC model dir_nl_ok <- system.file("extdata", "nl_ok", package = "micompr") dir_jex_ok <- system.file("extdata", "j_ex_ok", package = "micompr") files <- glob2rx("stats400v1*.tsv") # Create a grouped outputs object using outputs from NetLogo and Java # implementations of the PPHPC model go <- grpoutputs(7, c(dir_nl_ok, dir_jex_ok), c(files, files), lvls = c("NL", "JEX"), concat = TRUE) # Do the same, but specify output names and don't specify levels go <- grpoutputs(c("a", "b", "c", "d", "e", "f"), c(dir_nl_ok, dir_jex_ok), c(files, files))# Determine paths for data folders, each containing outputs for 10 runs of # the PPHPC model dir_nl_ok <- system.file("extdata", "nl_ok", package = "micompr") dir_jex_ok <- system.file("extdata", "j_ex_ok", package = "micompr") files <- glob2rx("stats400v1*.tsv") # Create a grouped outputs object using outputs from NetLogo and Java # implementations of the PPHPC model go <- grpoutputs(7, c(dir_nl_ok, dir_jex_ok), c(files, files), lvls = c("NL", "JEX"), concat = TRUE) # Do the same, but specify output names and don't specify levels go <- grpoutputs(c("a", "b", "c", "d", "e", "f"), c(dir_nl_ok, dir_jex_ok), c(files, files))
Performs multiple independent comparisons of output observations.
micomp( outputs, ve_npcs, comps, concat = F, centscal = "range", lim_npcs = TRUE, mnv_test = "Pillai", ... )micomp( outputs, ve_npcs, comps, concat = F, centscal = "range", lim_npcs = TRUE, mnv_test = "Pillai", ... )
outputs |
A vector with the labels of each output, or an integer with the number of outputs (in which case output labels will be assigned automatically). |
ve_npcs |
Percentage ( |
comps |
A list of lists, where each list contains information regarding an individual comparison. Each list can have one of two configurations:
|
concat |
Create an additional, concatenated output? Ignored for sublists
passed in the |
centscal |
Method for centering and scaling outputs if |
lim_npcs |
Limit number of principal components used for MANOVA to minimum number of observations per group? |
mnv_test |
The name of the test statistic to be used in MANOVA, as
described in |
... |
Options passed to |
An object of class micomp, which is a two-dimensional
list of cmpoutput objects. Rows are associated with individual
outputs, while columns are associated with separate comparisons.
# Create a micomp object from existing files and folders dir_nl_ok <- system.file("extdata", "nl_ok", package = "micompr") dir_jex_ok <- system.file("extdata", "j_ex_ok", package = "micompr") dir_jex_noshuff <- system.file("extdata", "j_ex_noshuff", package = "micompr") dir_jex_diff <- system.file("extdata", "j_ex_diff", package = "micompr") files <- glob2rx("stats400v1*.tsv") mic <- micomp(7, 0.8, list(list(name = "NLOKvsJEXOK", folders = c(dir_nl_ok, dir_jex_ok), files = c(files, files), lvls = c("NLOK", "JEXOK")), list(name = "NLOKvsJEXNOSHUFF", folders = c(dir_nl_ok, dir_jex_noshuff), files = c(files, files), lvls = c("NLOK", "JEXNOSHUFF")), list(name = "NLOKvsJEXDIFF", folders = c(dir_nl_ok, dir_jex_diff), files = c(files, files), lvls = c("NLOK", "JEXDIFF"))), concat = TRUE) # Create a micomp object from package datasets (i.e. grpoutputs objects) # directly mic <- micomp(c("o1", "o2", "o3", "o4"), 0.9, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff))) # Create a micomp object using manually inserted data mic <- micomp(6, 0.5, list( list(name = "NLOKvsJEXOK", grpout = list(data = pphpc_ok$data, obs_lvls = pphpc_ok$obs_lvls)), list(name = "NLOKvsJEXNOSHUFF", grpout = list(data = pphpc_noshuff$data, obs_lvls = pphpc_noshuff$obs_lvls)), list(name = "NLOKvsJEXDIFF", grpout = list(data = pphpc_diff$data, obs_lvls = pphpc_diff$obs_lvls))))# Create a micomp object from existing files and folders dir_nl_ok <- system.file("extdata", "nl_ok", package = "micompr") dir_jex_ok <- system.file("extdata", "j_ex_ok", package = "micompr") dir_jex_noshuff <- system.file("extdata", "j_ex_noshuff", package = "micompr") dir_jex_diff <- system.file("extdata", "j_ex_diff", package = "micompr") files <- glob2rx("stats400v1*.tsv") mic <- micomp(7, 0.8, list(list(name = "NLOKvsJEXOK", folders = c(dir_nl_ok, dir_jex_ok), files = c(files, files), lvls = c("NLOK", "JEXOK")), list(name = "NLOKvsJEXNOSHUFF", folders = c(dir_nl_ok, dir_jex_noshuff), files = c(files, files), lvls = c("NLOK", "JEXNOSHUFF")), list(name = "NLOKvsJEXDIFF", folders = c(dir_nl_ok, dir_jex_diff), files = c(files, files), lvls = c("NLOK", "JEXDIFF"))), concat = TRUE) # Create a micomp object from package datasets (i.e. grpoutputs objects) # directly mic <- micomp(c("o1", "o2", "o3", "o4"), 0.9, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff))) # Create a micomp object using manually inserted data mic <- micomp(6, 0.5, list( list(name = "NLOKvsJEXOK", grpout = list(data = pphpc_ok$data, obs_lvls = pphpc_ok$obs_lvls)), list(name = "NLOKvsJEXNOSHUFF", grpout = list(data = pphpc_noshuff$data, obs_lvls = pphpc_noshuff$obs_lvls)), list(name = "NLOKvsJEXDIFF", grpout = list(data = pphpc_diff$data, obs_lvls = pphpc_diff$obs_lvls))))
Plot method for objects of class assumptions_cmpoutput
containing p-values produced by testing the assumptions of the
parametric tests used for comparing an output.
## S3 method for class 'assumptions_cmpoutput' plot(x, ...)## S3 method for class 'assumptions_cmpoutput' plot(x, ...)
x |
Objects of class |
... |
Extra options passed to |
Several bar plots are presented, showing the p-values yielded by the
Shapiro-Wilk (shapiro.test) and Royston tests
(mvn) for univariate and multivariate normality,
respectively, and for the Bartlett (bartlett.test) and
Box's M (boxM) for testing homogeneity of variances
and of covariance matrices, respectively. The following bar plots are shown:
One bar plot for the p-values of the Bartlett test, one bar (p-value) per individual principal component.
s bar plots for p-values of the Shapiro-Wilk test, where s is the number of groups being compared. Individual bars in each plot are associated with a principal component.
t bar plot for the p-values of the Royston test with s bars each, where t is the number of unique MANOVA tests performed (one per requested explained variances) and s is the number of groups being compared. These plots will not show if there is only one principal component being considered.
One plot for the p-values of the Box's M test, one bar (p-value) per unique MANOVA tests performed (one per requested explained variances).
None.
# Create a cmpoutput object from the provided datasets cmp <- cmpoutput("All", 0.9, pphpc_ok$data[["All"]], pphpc_ok$obs_lvls) # Display a bar plot with the p-values of the assumptions for the parametric # tests performed in cmp plot(assumptions(cmp))# Create a cmpoutput object from the provided datasets cmp <- cmpoutput("All", 0.9, pphpc_ok$data[["All"]], pphpc_ok$obs_lvls) # Display a bar plot with the p-values of the assumptions for the parametric # tests performed in cmp plot(assumptions(cmp))
Plot method for objects of class assumptions_manova which
presents a bar plot containing the p-values produced by the Royston
multivariate normality test (mvn) for each group
being compared.
## S3 method for class 'assumptions_manova' plot(x, ...)## S3 method for class 'assumptions_manova' plot(x, ...)
x |
Objects of class |
... |
Extra options passed to |
None.
# Plot the Royston test p-value for multivariate normality of each group # (species) of the iris data plot(assumptions_manova(iris[, 1:4], iris[, 5])) # Plot the same data with logarithmic scale for p-values plot(assumptions_manova(iris[, 1:4], iris[, 5]), log = "y")# Plot the Royston test p-value for multivariate normality of each group # (species) of the iris data plot(assumptions_manova(iris[, 1:4], iris[, 5])) # Plot the same data with logarithmic scale for p-values plot(assumptions_manova(iris[, 1:4], iris[, 5]), log = "y")
Plot method for objects of class assumptions_cmpoutput
containing p-values produced by testing the assumptions of the
parametric tests used for multiple output comparisons.
## S3 method for class 'assumptions_micomp' plot(x, ...)## S3 method for class 'assumptions_micomp' plot(x, ...)
x |
Object of class |
... |
Extra options passed to |
Several bar plots are presented, one for each comparison and output combination, showing the several statistical tests employed to verify the assumptions of the parametric tests.
None.
# Create a micomp object, use provided dataset mic <- micomp(6, 0.65, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff))) # Plot the p-values of the statistic tests evaluating the assumptions of the # comparisons performed in the mic object plot(assumptions(mic))# Create a micomp object, use provided dataset mic <- micomp(6, 0.65, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff))) # Plot the p-values of the statistic tests evaluating the assumptions of the # comparisons performed in the mic object plot(assumptions(mic))
Plot method for objects of class assumptions_paruv containing
p-values produced by testing the assumptions of the parametric tests
used for comparing outputs.
## S3 method for class 'assumptions_paruv' plot(x, ...)## S3 method for class 'assumptions_paruv' plot(x, ...)
x |
Objects of class |
... |
Extra options passed to |
One bar plot is presented for the Bartlett test
(bartlett.test), showing the respective p-values
along principal component. s bar plots are presented for the
Shapiro-Wilk (shapiro.test), where s is the
number of groups being compared; individual bars in each plot represent the
p-values associated with each principal component.
None.
# Plot the Shapiro-Wilk and Bartlett test p-values for each dependent # variable of the iris data plot(assumptions_paruv(iris[, 1:4], iris[, 5])) # Plot the same data with logarithmic scale for p-values plot(assumptions_paruv(iris[, 1:4], iris[, 5]), log = "y")# Plot the Shapiro-Wilk and Bartlett test p-values for each dependent # variable of the iris data plot(assumptions_paruv(iris[, 1:4], iris[, 5])) # Plot the same data with logarithmic scale for p-values plot(assumptions_paruv(iris[, 1:4], iris[, 5]), log = "y")
Plot objects of class cmpoutput.
## S3 method for class 'cmpoutput' plot(x, ...)## S3 method for class 'cmpoutput' plot(x, ...)
x |
Object of class |
... |
Extra options passed to |
This method produces four sub-plots, namely:
Scatter plot containing the projection of output observations on the first two dimensions of the principal components space.
Bar plot of the percentage of variance explain per principal component.
Bar plot of p-values for the parametric test for each principal component.
Bar plot of p-values for the non-parametric test for each principal component.
None.
# Comparing the concatenated output of the pphpc_ok dataset, which # contains simulation output data from two similar implementations of the # PPHPC model. plot(cmpoutput("All", 0.95, pphpc_ok$data[["All"]], pphpc_ok$obs_lvls))# Comparing the concatenated output of the pphpc_ok dataset, which # contains simulation output data from two similar implementations of the # PPHPC model. plot(cmpoutput("All", 0.95, pphpc_ok$data[["All"]], pphpc_ok$obs_lvls))
Plot objects of class grpoutputs.
## S3 method for class 'grpoutputs' plot(x, ...)## S3 method for class 'grpoutputs' plot(x, ...)
x |
Object of class |
... |
Extra options passed to |
Each output is plotted individually, and observations are plotted on top of
each other. Observations from different groups are plotted with different
colors (which can be controlled through the col parameter given in
...).
This function can be very slow for a large number of observations.
None.
# Determine paths for the data folder containing outputs of different # lengths dir_na <- system.file("extdata", "testdata", "NA", package = "micompr") # Sets of files A and B have 3 files each filesA <- glob2rx("stats400v1*n20A.tsv") filesB <- glob2rx("stats400v1*n20B.tsv") # Instantiate grpoutputs object go <- grpoutputs(7, dir_na, c(filesA, filesB), lvls = c("A", "B"), concat = TRUE) # Plot grpoutputs object plot(go)# Determine paths for the data folder containing outputs of different # lengths dir_na <- system.file("extdata", "testdata", "NA", package = "micompr") # Sets of files A and B have 3 files each filesA <- glob2rx("stats400v1*n20A.tsv") filesB <- glob2rx("stats400v1*n20B.tsv") # Instantiate grpoutputs object go <- grpoutputs(7, dir_na, c(filesA, filesB), lvls = c("A", "B"), concat = TRUE) # Plot grpoutputs object plot(go)
For each comparison and output combination, draw a scatter plot containing the projection of output observations on the first two dimensions of the principal components space.
## S3 method for class 'micomp' plot(x, ...)## S3 method for class 'micomp' plot(x, ...)
x |
An object of class |
... |
Extra options passed to |
None.
plot(micomp(c("SheepPop", "WolfPop", "GrassQty"), 0.95, list(list(name = "I", grpout = pphpc_ok), list(name = "II", grpout = pphpc_noshuff), list(name = "III", grpout = pphpc_diff))))plot(micomp(c("SheepPop", "WolfPop", "GrassQty"), 0.95, list(list(name = "I", grpout = pphpc_ok), list(name = "II", grpout = pphpc_noshuff), list(name = "III", grpout = pphpc_diff))))
A dataset containing simulation output data from two implementations of the PPHPC model, one of which setup with a different parameter.
pphpc_diffpphpc_diff
A grpoutputs object containing simulation output data
from 20 runs of the PPHPC model, 10 runs from each implementation. The model
has six outputs, but the object contains a seventh output corresponding to
the concatenation of the six outputs
Runs are obtained from the NetLogo and Java (EX with 8 threads)
implementations of the PPHPC model available at
https://github.com/nunofachada/pphpc. The config400v1.txt
configuration was used in both cases, with the exception of restart
parameter, , in the Java implementation, which was set to 9 instead
of 10.
A dataset containing simulation output data from two implementations of the PPHPC model, one of which has agent list shuffling deactivated.
pphpc_noshuffpphpc_noshuff
A grpoutputs object containing simulation output data
from 20 runs of the PPHPC model, 10 runs from each implementation. The model
has six outputs, but the object contains a seventh output corresponding to
the concatenation of the six outputs
Runs are obtained from the NetLogo and Java (EX with 8 threads)
implementations of the PPHPC model available at
https://github.com/nunofachada/pphpc. The config400v1.txt
configuration was used in both cases. Runs with the Java implementation
were performed with the '-u' option, i.e. with agent list shuffling turned
off.
A dataset containing simulation output data from two implementations of the PPHPC model.
pphpc_okpphpc_ok
A grpoutputs object containing simulation output data
from 20 runs of the PPHPC model, 10 runs from each implementation. The model
has six outputs, but the object contains a seventh output corresponding to
the concatenation of the six outputs
Runs are obtained from the NetLogo and Java (EX with 8 threads)
implementations of the PPHPC model available at
https://github.com/nunofachada/pphpc. The config400v1.txt
configuration was used in both cases.
Print method for objects of class assumptions_cmpoutput, which
contain the assumptions for the parametric tests used in a comparison of an
output.
## S3 method for class 'assumptions_cmpoutput' print(x, ...)## S3 method for class 'assumptions_cmpoutput' print(x, ...)
x |
Object of class |
... |
Currently ignored. |
None.
# Create a cmpoutput object from the provided datasets cmp <- cmpoutput("All", c(0.7, 0.8, 0.9), pphpc_diff$data[["All"]], pphpc_diff$obs_lvls)# Create a cmpoutput object from the provided datasets cmp <- cmpoutput("All", c(0.7, 0.8, 0.9), pphpc_diff$data[["All"]], pphpc_diff$obs_lvls)
Print information about objects of class assumptions_manova, which
represent the assumptions of the MANOVA test performed on a comparison of
outputs.
## S3 method for class 'assumptions_manova' print(x, ...)## S3 method for class 'assumptions_manova' print(x, ...)
x |
Object of class |
... |
Currently ignored. |
The argument x, invisibly, as for all print
methods.
# Print information concerning the assumptions of applying MANOVA to the iris # data (i.e. multivariate normality of each group and homogeneity of # covariance matrices) assumptions_manova(iris[, 1:4], iris[, 5])# Print information concerning the assumptions of applying MANOVA to the iris # data (i.e. multivariate normality of each group and homogeneity of # covariance matrices) assumptions_manova(iris[, 1:4], iris[, 5])
Print information about objects of class assumptions_micomp, which
represent the assumptions concerning the parametric tests performed on
multiple comparisons of outputs.
## S3 method for class 'assumptions_micomp' print(x, ...)## S3 method for class 'assumptions_micomp' print(x, ...)
x |
Object of class |
... |
Currently ignored. |
The argument x, invisibly, as for all print
methods.
# Create a micomp object, use provided dataset mic <- micomp(c("SheepPop", "WolfPop", "GrassQty"), 0.7, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff))) # Print the results (p-values) of the statistic tests evaluating the # assumptions of the comparisons performed in the mic object assumptions(mic)# Create a micomp object, use provided dataset mic <- micomp(c("SheepPop", "WolfPop", "GrassQty"), 0.7, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff))) # Print the results (p-values) of the statistic tests evaluating the # assumptions of the comparisons performed in the mic object assumptions(mic)
Print information about objects of class assumptions_paruv, which
represent the assumptions of the parametric test (i.e. either
t.test or aov) performed on a
comparison of outputs.
## S3 method for class 'assumptions_paruv' print(x, ...)## S3 method for class 'assumptions_paruv' print(x, ...)
x |
Object of class |
... |
Currently ignored. |
The argument x, invisibly, as for all print
methods.
# Print information about the assumptions of applying ANOVA to each column # (dependent variable) of the iris data (i.e. normality of each group and # homogeneity of variances) assumptions_paruv(iris[, 1:4], iris[, 5])# Print information about the assumptions of applying ANOVA to each column # (dependent variable) of the iris data (i.e. normality of each group and # homogeneity of variances) assumptions_paruv(iris[, 1:4], iris[, 5])
Print information about objects of class cmpoutput.
## S3 method for class 'cmpoutput' print(x, ...)## S3 method for class 'cmpoutput' print(x, ...)
x |
Object of class |
... |
Currently ignored. |
The argument x, invisibly, as for all print
methods.
# Comparing the fifth output of the pphpc_diff dataset, which contains # simulation output data from two implementations of the PPHPC model executed # with a different parameter. cmpoutput("WolfPop", 0.7, pphpc_diff$data[[5]], pphpc_diff$obs_lvls)# Comparing the fifth output of the pphpc_diff dataset, which contains # simulation output data from two implementations of the PPHPC model executed # with a different parameter. cmpoutput("WolfPop", 0.7, pphpc_diff$data[[5]], pphpc_diff$obs_lvls)
Print information about objects of class grpoutputs.
## S3 method for class 'grpoutputs' print(x, ...)## S3 method for class 'grpoutputs' print(x, ...)
x |
Object of class |
... |
Currently ignored. |
The argument x, invisibly, as for all print
methods.
# Determine paths for data folders, each containing outputs for 10 runs of # the PPHPC model dir_nl_ok <- system.file("extdata", "nl_ok", package = "micompr") dir_jex_diff <- system.file("extdata", "j_ex_diff", package = "micompr") files <- glob2rx("stats400v1*.tsv") # Create a grpoutputs object go <- grpoutputs(6, c(dir_nl_ok, dir_jex_diff), c(files, files)) # Print information about object (could just type "go" instead) print(go)# Determine paths for data folders, each containing outputs for 10 runs of # the PPHPC model dir_nl_ok <- system.file("extdata", "nl_ok", package = "micompr") dir_jex_diff <- system.file("extdata", "j_ex_diff", package = "micompr") files <- glob2rx("stats400v1*.tsv") # Create a grpoutputs object go <- grpoutputs(6, c(dir_nl_ok, dir_jex_diff), c(files, files)) # Print information about object (could just type "go" instead) print(go)
Print information about objects of class micomp.
## S3 method for class 'micomp' print(x, ...)## S3 method for class 'micomp' print(x, ...)
x |
Object of class |
... |
Currently ignored. |
The argument x, invisibly, as for all print.
methods.
# A micomp object from package datasets (i.e. grpoutputs objects) directly micomp(c("outA", "outB", "outC", "outD"), 0.9, list(list(name = "Comp1", grpout = pphpc_ok), list(name = "Comp2", grpout = pphpc_noshuff), list(name = "Comp3", grpout = pphpc_diff)))# A micomp object from package datasets (i.e. grpoutputs objects) directly micomp(c("outA", "outB", "outC", "outD"), 0.9, list(list(name = "Comp1", grpout = pphpc_ok), list(name = "Comp2", grpout = pphpc_noshuff), list(name = "Comp3", grpout = pphpc_diff)))
Generic function to format p-values.
pvalf(pval, params)pvalf(pval, params)
pval |
Numeric p-value to format (between 0 and 1). |
params |
A list of method-dependent options. |
A string representing the formatted p-value.
Format a p-value for printing in a LaTeX table. Requires the
ulem LaTeX package for underlining the p-values.
## Default S3 method: pvalf(pval, params = list())## Default S3 method: pvalf(pval, params = list())
pval |
Numeric value between 0 and 1. |
params |
A list of options. This function accepts the following options:
|
A string representing the formatted pval.
pvalf(0.1) pvalf(0.000001) pvalf(c(0.06, 0.04, 0.005, 0.00001), list(minval = 0.0001))pvalf(0.1) pvalf(0.000001) pvalf(c(0.06, 0.04, 0.005, 0.00001), list(minval = 0.0001))
Summary method for objects of class assumptions_cmpoutput, which
contain the assumptions for the parametric tests used in a comparison of an
output.
## S3 method for class 'assumptions_cmpoutput' summary(object, ...)## S3 method for class 'assumptions_cmpoutput' summary(object, ...)
object |
Object of class |
... |
Currently ignored. |
A list with the following items:
A matrix of p-values for the MANOVA assumptions. All rows, expect the last one, correspond to the Royston test for multivariate normality for each group; the last row corresponds to Box's M test for homogeneity of covariance matrices. Columns correspond to number of principal components required to explain the percentage of user-specified variance.
A matrix of p-values for the t-test assumptions. All rows, expect the last one, correspond to the Shapiro-Wilk normality test for each group; the last row corresponds to Bartlett's for equality of variances. Columns correspond to the principal components on which the t-test was applied.
# Create a cmpoutput object from the provided datasets cmp <- cmpoutput("All", c(0.5, 0.6, 0.7), pphpc_ok$data[["All"]], pphpc_ok$obs_lvls) # Obtain the summary of the assumptions of the cmpoutput object summary(assumptions(cmp))# Create a cmpoutput object from the provided datasets cmp <- cmpoutput("All", c(0.5, 0.6, 0.7), pphpc_ok$data[["All"]], pphpc_ok$obs_lvls) # Obtain the summary of the assumptions of the cmpoutput object summary(assumptions(cmp))
Summary method for objects of class assumptions_micomp, which
contain the assumptions for the parametric tests used in multiple comparisons
of outputs.
## S3 method for class 'assumptions_micomp' summary(object, ...)## S3 method for class 'assumptions_micomp' summary(object, ...)
object |
Object of class |
... |
Currently ignored. |
A list in which each component is associated with a distinct comparison. Each component contains a matrix, in which columns represent individual outputs and rows correspond to the statistical tests evaluating the assumptions of the parametric tests used in each output. More specifically, each matrix has rows with the following information:
One row per group per
variance to explain / number of PCs, with the p-value yielded
by the Royston test (mvn) for the
respective group and variance/npcs combination.
One row per variance to explain with the
p-value yielded by Box's M test
(boxM).
One row per group, with the
p-value yielded by the Shapiro-Wilk test
(shapiro.test) for the respective group.
One row with the p-value yielded by Bartlett's test
(bartlett.test).
# Create a micomp object, use provided dataset mic <- micomp(5, c(0.7, 0.8, 0.9), list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff)), concat = TRUE) # Get the assumptions summary sam <- summary(assumptions(mic))# Create a micomp object, use provided dataset mic <- micomp(5, c(0.7, 0.8, 0.9), list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff)), concat = TRUE) # Get the assumptions summary sam <- summary(assumptions(mic))
Summary method for objects of class cmpoutput.
## S3 method for class 'cmpoutput' summary(object, ...)## S3 method for class 'cmpoutput' summary(object, ...)
object |
Object of class |
... |
Currently ignored. |
A list with the following components:
Output name.
Number of principal components which explain var.exp
percentage of variance.
Minimum percentage of variance which must be explained by the number of principal components used for the MANOVA test.
P-value of the MANOVA test.
Name of the used parametric test.
Vector of $p$-values returned by applying the parametric test to each principal component.
Vector of $p$-values returned by applying the parametric test to each principal component, adjusted with the weighted Bonferroni procedure, percentage of explained variance used as weight.
Name of the used non-parametric test.
Vector of $p$-values returned by applying the non-parametric test to each principal component.
Vector of $p$-values returned by applying the non-parametric test to each principal component, adjusted with the weighted Bonferroni procedure, percentage of explained variance used as weight.
# Comparing the concatenated output of the pphpc_noshuff dataset, which # contains simulation output data from two implementations of the PPHPC model # executed with a minor implementation difference. summary( cmpoutput("All", 0.6, pphpc_noshuff$data[["All"]], pphpc_noshuff$obs_lvls) )# Comparing the concatenated output of the pphpc_noshuff dataset, which # contains simulation output data from two implementations of the PPHPC model # executed with a minor implementation difference. summary( cmpoutput("All", 0.6, pphpc_noshuff$data[["All"]], pphpc_noshuff$obs_lvls) )
Summary method for objects of class grpoutputs.
## S3 method for class 'grpoutputs' summary(object, ...)## S3 method for class 'grpoutputs' summary(object, ...)
object |
Object of class |
... |
Currently ignored. |
A list with the following components:
Dimensions for each output, i.e. number of observations and number of variables (i.e. output length).
Number of output observations in each group.
# Determine paths for data folders, each containing outputs for 10 runs of # the PPHPC model dir_nl_ok <- system.file("extdata", "nl_ok", package = "micompr") dir_jex_noshuff <- system.file("extdata", "j_ex_noshuff", package = "micompr") files <- glob2rx("stats400v1*.tsv") # Create a grpoutputs object go <- grpoutputs(c("o1", "o2"), c(dir_nl_ok, dir_jex_noshuff), c(files, files))# Determine paths for data folders, each containing outputs for 10 runs of # the PPHPC model dir_nl_ok <- system.file("extdata", "nl_ok", package = "micompr") dir_jex_noshuff <- system.file("extdata", "j_ex_noshuff", package = "micompr") files <- glob2rx("stats400v1*.tsv") # Create a grpoutputs object go <- grpoutputs(c("o1", "o2"), c(dir_nl_ok, dir_jex_noshuff), c(files, files))
Summary method for objects of class micomp.
## S3 method for class 'micomp' summary(object, ...)## S3 method for class 'micomp' summary(object, ...)
object |
Object of class |
... |
Currently ignored. |
A list in which each component is associated with a distinct comparison. Each component contains a matrix, in which columns represent individual outputs and rows have information about the outputs. More specifically, each matrix has the following rows:
Number of principal components required to explain the
specified percentage of variance. There is one row of this kind for
each percentage of variance specified when creating the
micomp object.
P-value for the MANOVA test applied to the #PCs
required to explain the specified percentage of variance. There is one
row of this kind for each percentage of variance specified when
creating the micomp object.
P-value for the parametric test (first principal component).
P-value for the non-parametric test (first principal component).
P-value for the parametric test (first principal component), adjusted with the weighted Bonferroni procedure, percentage of explained variance used as weight.
P-value for the non-parametric test (first principal component), adjusted with the weighted Bonferroni procedure, percentage of explained variance used as weight.
# A micomp object from package datasets (i.e. grpoutputs objects) directly summary(micomp(5, 0.85, list(list(name = "CompEq", grpout = pphpc_ok), list(name = "CompNoShuf", grpout = pphpc_noshuff), list(name = "CompDiff", grpout = pphpc_diff))))# A micomp object from package datasets (i.e. grpoutputs objects) directly summary(micomp(5, 0.85, list(list(name = "CompEq", grpout = pphpc_ok), list(name = "CompNoShuf", grpout = pphpc_noshuff), list(name = "CompDiff", grpout = pphpc_diff))))
TikZ scatter plotCreate a simple 2D TikZ scatter plot, useful for plotting PCA data.
tikzscat(data, obs_lvls, marks, tscale, axes_color = "gray")tikzscat(data, obs_lvls, marks, tscale, axes_color = "gray")
data |
Data to plot, m x 2 numeric matrix, where m is the number of observations or points to plot. |
obs_lvls |
Levels or groups associated with each observation. |
marks |
Character vector determining how to draw the points in
|
tscale |
The |
axes_color |
Axes color (must be a |
This function creates a simple TikZ 2D scatter plot within a
tikzpicture environment. The points are plotted on a normalized
figure with x and y axes bounded between [-1, 1]. To render
adequately, the final LaTeX document should load the plotmarks
TikZ library.
A string containing the TikZ figure code for plotting the
specified data.
tikzscat(rbind(c(1.5, 2), c(0.5, 1)), factor(c(1,2)), c("mark=square*,mark options={color=red},mark size=0.8pt", "mark=diamond*,mark options={color=black},mark size=1pt"), 6)tikzscat(rbind(c(1.5, 2), c(0.5, 1)), factor(c(1,2)), c("mark=square*,mark options={color=red},mark size=0.8pt", "mark=diamond*,mark options={color=black},mark size=1pt"), 6)
cmpoutput object to LaTeX tableThis method converts cmpoutput objects to character vectors
representing LaTeX tables.
## S3 method for class 'cmpoutput' toLatex(object, cmp_name = "Comp. 1", ...)## S3 method for class 'cmpoutput' toLatex(object, cmp_name = "Comp. 1", ...)
object |
A |
cmp_name |
Comparison name (to appear in table). |
... |
Any options accepted by the |
This method simply wraps the cmpoutput object into a
micomp object, and invokes toLatex.micomp on the
wrapped object.
A character vector where each element holds one line of the
corresponding LaTeX table.
# Create a cmpoutput object by comparing the first output ("Pop.Sheep") of # one the provided datasets. cmp <- cmpoutput("SheepPop", 0.9, pphpc_ok$data[["Pop.Sheep"]], pphpc_ok$obs_lvls) # Print latex table source to screen toLatex(cmp)# Create a cmpoutput object by comparing the first output ("Pop.Sheep") of # one the provided datasets. cmp <- cmpoutput("SheepPop", 0.9, pphpc_ok$data[["Pop.Sheep"]], pphpc_ok$obs_lvls) # Print latex table source to screen toLatex(cmp)
micomp object to LaTeX tableThis method converts micomp objects to character vectors
representing LaTeX tables.
## S3 method for class 'micomp' toLatex( object, ..., orientation = T, data_show = c("npcs-1", "mnvp-1", "parp-1", "nparp-1", "scoreplot"), data_labels = NULL, labels_cmp_show = T, labels_col_show = T, label_row_show = T, tag_comp = "Comp.", tag_data = "Data", tag_outputs = "Outputs", table_placement = "ht", latex_envs = c("center"), booktabs = F, booktabs_cmalign = "l", caption = NULL, caption_cmd = "\\caption", label = NULL, col_width = F, pvalf_f = pvalf.default, pvalf_params = list(), scoreplot_marks = c("mark=square*,mark options={color=red},mark size=0.8pt", "mark=diamond*,mark options={color=black},mark size=1pt", "mark=triangle*,mark options={color=green},mark size=1pt"), scoreplot_scale = 6, scoreplot_before = "\\raisebox{-.5\\height}{\\resizebox {1.2cm} {1.2cm} {", scoreplot_after = "}}" )## S3 method for class 'micomp' toLatex( object, ..., orientation = T, data_show = c("npcs-1", "mnvp-1", "parp-1", "nparp-1", "scoreplot"), data_labels = NULL, labels_cmp_show = T, labels_col_show = T, label_row_show = T, tag_comp = "Comp.", tag_data = "Data", tag_outputs = "Outputs", table_placement = "ht", latex_envs = c("center"), booktabs = F, booktabs_cmalign = "l", caption = NULL, caption_cmd = "\\caption", label = NULL, col_width = F, pvalf_f = pvalf.default, pvalf_params = list(), scoreplot_marks = c("mark=square*,mark options={color=red},mark size=0.8pt", "mark=diamond*,mark options={color=black},mark size=1pt", "mark=triangle*,mark options={color=green},mark size=1pt"), scoreplot_scale = 6, scoreplot_before = "\\raisebox{-.5\\height}{\\resizebox {1.2cm} {1.2cm} {", scoreplot_after = "}}" )
object |
A |
... |
Currently ignored. |
orientation |
If TRUE, outputs are placed along columns, while data is placed along rows. If FALSE, outputs are placed along rows, while data is placed along columns. |
data_show |
Vector of strings specifying what data to show. Available options are:
|
data_labels |
Vector of strings specifying the labels of the data to show. If NULL, default labels are used for all elements. If individual elements are set to NA, default labels will be used for those elements. |
labels_cmp_show |
Show the column containing the comparison labels? |
labels_col_show |
Show the column containing the data labels
( |
label_row_show |
Show the |
tag_comp |
Tag identifying comparison labels. |
tag_data |
Tag identifying data labels. |
tag_outputs |
Tag identifying outputs. |
table_placement |
|
latex_envs |
Wrap table in the specified |
booktabs |
Use |
booktabs_cmalign |
How to align |
caption |
Table caption. |
caption_cmd |
Command used for table caption. |
label |
Table label for cross-referencing. |
col_width |
Resize table to page column width? |
pvalf_f |
P-value formatter function, which receives a numeric
value between 0 and 1 and returns a string containing the formatted value.
Default is |
pvalf_params |
Parameters for |
scoreplot_marks |
Vector of strings specifying how |
scoreplot_scale |
|
scoreplot_before |
|
scoreplot_after |
|
This method is inspired by the functionality provided by the xtable
and print.xtable functions (from the
xtable
package), but follows the standard behavior of the
toLatex generic.
A character vector where each element holds one line of the
corresponding LaTeX table.
# Create a micomp object, use provided dataset, three first outputs, plus # a fourth concatenated output mic <- micomp(4, 0.8, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff)), concat = TRUE) # Print latex table source to screen toLatex(mic)# Create a micomp object, use provided dataset, three first outputs, plus # a fourth concatenated output mic <- micomp(4, 0.8, list(list(name = "NLOKvsJEXOK", grpout = pphpc_ok), list(name = "NLOKvsJEXNOSHUFF", grpout = pphpc_noshuff), list(name = "NLOKvsJEXDIFF", grpout = pphpc_diff)), concat = TRUE) # Print latex table source to screen toLatex(mic)