Takes the output of dist_calc function. Or use with the result of the permanova function to ensure the results correspond to exactly the same input data. Runs betadisper for all categorical variables in variables argument. See help('betadisper', package = 'vegan').
dist_bdisp(
data,
variables,
method = c("centroid", "median")[[1]],
complete_cases = TRUE,
verbose = TRUE
)Arguments
- data
psExtra output from dist_calc
- variables
list of variables to use as group
- method
centroid or median
- complete_cases
drop samples with NAs in any of the variables listed
- verbose
sends messages about progress if true
Value
psExtra containing betadisper results
Examples
library(phyloseq)
library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.6-4
data("dietswap", package = "microbiome")
# add some missings to demonstrate automated removal
sample_data(dietswap)$sex[3:6] <- NA
# create a numeric variable to show it will be skipped with a warning
dietswap <- ps_mutate(dietswap, timepoint = as.numeric(timepoint))
# straight to the betadisp
bd1 <- dietswap %>%
tax_agg("Genus") %>%
dist_calc("aitchison") %>%
dist_bdisp(variables = c("sex", "bmi_group", "timepoint")) %>%
bdisp_get()
#> Dropping samples with missings: 4
#> Warning: Variable 'timepoint' is skipped as it cannot be used for grouping (class = 'numeric')
bd1$sex
#> $model
#>
#> Homogeneity of multivariate dispersions
#>
#> Call: vegan::betadisper(d = distMat, group = meta[[V]], type = method)
#>
#> No. of Positive Eigenvalues: 123
#> No. of Negative Eigenvalues: 0
#>
#> Average distance to centroid:
#> female male
#> 10.022 9.059
#>
#> Eigenvalues for PCoA axes:
#> (Showing 8 of 123 eigenvalues)
#> PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
#> 5106.7 2075.2 1360.1 1281.4 1061.7 890.1 665.4 567.5
#>
#> $anova
#> Analysis of Variance Table
#>
#> Response: Distances
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Groups 1 50.14 50.144 20.976 7.853e-06 ***
#> Residuals 216 516.35 2.391
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> $tukeyHSD
#> Tukey multiple comparisons of means
#> 95% family-wise confidence level
#>
#> Fit: aov(formula = distances ~ group, data = df)
#>
#> $group
#> diff lwr upr p adj
#> male-female -0.9632687 -1.377813 -0.5487242 7.9e-06
#>
#>
# quick vegan plotting methods
plot(bd1$sex$model, label.cex = 0.5)
boxplot(bd1$sex$model)
# compute distance and use for both permanova and dist_bdisp
testDist <- dietswap %>%
tax_agg("Genus") %>%
dist_calc("bray")
PERM <- testDist %>%
dist_permanova(
variables = c("sex", "bmi_group"),
n_processes = 1, n_perms = 99
)
#> Dropping samples with missings: 4
#> 2024-04-03 12:20:16.206291 - Starting PERMANOVA with 99 perms with 1 processes
#> 2024-04-03 12:20:16.358622 - Finished PERMANOVA
str(PERM, max.level = 1)
#> Formal class 'psExtra' [package "microViz"] with 15 slots
bd <- PERM %>% dist_bdisp(variables = c("sex", "bmi_group"))
bd
#> psExtra object - a phyloseq object with extra slots:
#>
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 130 taxa and 218 samples ]
#> sample_data() Sample Data: [ 218 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 130 taxa by 3 taxonomic ranks ]
#>
#> psExtra info:
#> tax_agg = "Genus"
#>
#> bray distance matrix of size 218
#> 0.7639533 0.731024 0.7283254 0.6637252 0.7437293 ...
#>
#> permanova:
#> Permutation test for adonis under reduced model
#> Marginal effects of terms
#> Permutation: free
#> Number of permutations: 99
#>
#> vegan::adonis2(formula = formula, data = metadata, permutations = n_perms, by = by, parallel = parall)
#> Df SumOfSqs R2 F Pr(>F)
#> sex 1 0.361 0.00933 2.1539 0.17
#> bmi_group 2 2.377 0.06143 7.0888 0.01 **
#> Residual 214 35.874 0.92720
#> Total 217 38.691 1.00000
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> betadisper:
#> sex bmi_group