R/hypTestScores.R
hypTestScores.RdThis function provides a convenient interface for carrying out manova using
the scores from PCA and the factors (groups) stored in a
Spectra object. The function will do anova as well, if you
only provide one vector of scores, though this is probably of limited use.
A Spectra object contains group information stored in its
spectra$groups element, but you can also use
splitSpectraGroups to generate additional groups/factors that
might be more useful than the original.
hypTestScores(spectra, pca, pcs = 1:3, fac = NULL, ...)An object of S3 class Spectra().
An object of class prcomp.
An integer vector giving the PCA scores to use as the response in the manova analysis.
A character vector giving the factors to be used in the manova.
They will be searched for within the Spectra object.
Additional arguments to be passed downstream, in this case to
aov. Untested.
The results of the analysis print to the console unless assigned.
If assigned, the object class is one of several described in
aov depending upon the data passed to it.
This function is an extraordinarily thin wrapper which helps the user to
avoid writing a very tedious formula specification.
splitSpectraGroups which can be used to create
additional factor elements in the Spectra object, which can then be
used with this function. Additional documentation at
https://bryanhanson.github.io/ChemoSpec/
data(metMUD2)
# Original factor encoding:
levels(metMUD2$groups)
#> [1] "AbcD" "AbCD" "ABcD" "ABCD"
# Split those original levels into 2 new ones (re-code them)
new.grps <- list(geneBb = c("B", "b"), geneCc = c("C", "c"))
mM3 <- splitSpectraGroups(metMUD2, new.grps)
#> Additional data was found: geneBb
#> Additional data was found: geneCc
# Now do the PCA and anova, with 3 ways to see the results
pca <- c_pcaSpectra(mM3)
#> Additional data was found: geneBb
#> Additional data was found: geneCc
res <- hypTestScores(mM3, pca, fac = c("geneBb", "geneCc"))
#> Additional data was found: geneBb
#> Additional data was found: geneCc
#> [1] "geneBb" "geneCc"
#> [1] "geneBb*geneCc"
res
#> Call:
#> manova(formula = form)
#>
#> Terms:
#> geneBb geneCc geneBb:geneCc Residuals
#> PC1 476.0737 0.0814 2.1953 3.6544
#> PC2 0.0457 19.8592 3.6427 4.0036
#> PC3 0.0000 0.0749 0.1187 3.7585
#> Deg. of Freedom 1 1 1 16
#>
#> Residual standard errors: 0.4779147 0.5002239 0.4846729
#> Estimated effects may be unbalanced
summary(res)
#> Df Pillai approx F num Df den Df Pr(>F)
#> geneBb 1 0.99270 634.87 3 14 3.450e-15 ***
#> geneCc 1 0.83976 24.46 3 14 7.902e-06 ***
#> geneBb:geneCc 1 0.57216 6.24 3 14 0.006517 **
#> Residuals 16
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary.aov(res)
#> Response PC1 :
#> Df Sum Sq Mean Sq F value Pr(>F)
#> geneBb 1 476.07 476.07 2084.3630 < 2.2e-16 ***
#> geneCc 1 0.08 0.08 0.3565 0.558820
#> geneBb:geneCc 1 2.20 2.20 9.6117 0.006876 **
#> Residuals 16 3.65 0.23
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Response PC2 :
#> Df Sum Sq Mean Sq F value Pr(>F)
#> geneBb 1 0.0457 0.0457 0.1826 0.674845
#> geneCc 1 19.8592 19.8592 79.3659 1.337e-07 ***
#> geneBb:geneCc 1 3.6427 3.6427 14.5576 0.001522 **
#> Residuals 16 4.0036 0.2502
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Response PC3 :
#> Df Sum Sq Mean Sq F value Pr(>F)
#> geneBb 1 0.0000 0.000001 0.0000 0.9981
#> geneCc 1 0.0749 0.074882 0.3188 0.5802
#> geneBb:geneCc 1 0.1187 0.118667 0.5052 0.4875
#> Residuals 16 3.7585 0.234908
#>
# You can also call this function on the existing groups:
res <- hypTestScores(metMUD2, pca, fac = "groups")
#> [1] "groups"
#> [1] "groups"