I am trying to plot a cohort trace of 15 health states in R but I dont really know how to do it in the right way.
My data looks like this:
dput(m_M_ad)
structure(c(0.27, 0.256305347853185, 0.242951797354611, 0.230338299921634,
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), dim = c(36L, 15L), dimnames = list(c("0", "1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35"), c("Healthy_noAC",
"Healthy_AC", "IS_mild", "IS_mod", "IS_sev", "IS_dead", "IH_mild",
"IH_mod", "IH_sev", "IH_dead", "MI", "MI_dead", "SE", "MB", "Dead"
)))
I tried using plot_trace(m_M_ad)
The problem is that the plot shows the last two health states as white. How can I change the color?
The trace plotted with plot_trace(m_M_ad)
Does anyone know how to add different colors to that plot? Or maybe how to write a code for plotting that cohort trace another way?
Very thankful for any help!
I am using metaMDS() from Vegan to make an ordination. My data is samples (rows) times variables (columns). In my case the columns are analytes measured i blood, i.e. not 'species' per se. Due to the different scales for my variables I have transformed the data using scale(center = T, scale = T).
It is a numeric matrix with no missing values and no binary variables.
But the function does not calculate scores for 'species' (i.e. my analytes).
And I get the message 'species scores not available'.
I can't find anywhere that these scores should not be calculated when using 'euclidean' distance. I normally get the species score when I use Bray-Curtis distance on a regular community dataset.
plotdf <- structure(c(-0.698904932895234, 0.30704395903274, 1.24113935868014,
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0.422600855847354), .Dim = c(30L, 30L), .Dimnames = list(c("2",
"7", "10", "11", "15", "16", "17", "18", "19", "28", "29", "32",
"33", "35", "37", "39", "42", "43", "44", "46", "61", "62", "63",
"64", "66", "67", "68", "69", "71", "72"), c("C0_S", "C2_S",
"C3_S", "C12.DC_S", "C12.1_S", "Ala_S", "Arg_S", "Asn_S", "Cit_S",
"Gln_S", "Gly_S", "His_S", "Ile_S", "Leu_S", "Lys_S", "Orn_S",
"Phe_S", "Pro_S", "Ser_S", "Thr_S", "Trp_S", "Tyr_S", "Val_S",
"Creatinine_S", "Spermidine_S", "t4.OH.Pro_S", "Taurine_S", "total.DMA_S",
"lysoPC.a.C14.0_S", "lysoPC.a.C16.0_S")))
I have tried these to version:
NMDS=metaMDS(plotdf,k=2,trymax=500, distance = "euclidean")
NMDS=metaMDS(daisy(plotdf, metric ="euclidean", stand = F ))
And I get this message:
plot(NMDS)
species scores not available
Is there something I haven't understood for this function, e.g. the negative values when scaling or the choice of distance.. which is causing this problem?
metaMDS finds species scores after the analysis using weighted averages, and these are not well-defined for negative input (as these would imply negative weights). However, if you have used scaled data and Euclidean distances, you can find species scores similarly as in PCA: as fitted vectors. In vegan this would go like this with your example:
NMDS=metaMDS(plotdf,k=2,trymax=500, distance = "euclidean")
envfit(NMDS, plotdf) # species scores