I'm trying to visualise some PCA results using ggfortify as instructed here https://cran.r-project.org/web/packages/ggfortify/vignettes/plot_pca.html.
Here is my dataset:
sample1 <- structure(list(labels = c(rep("Condition1",3),rep("Condition2",3),rep("Condition3",3),
rep("Condition4",3),rep("Condition5",3),rep("Condition6",3),
rep("Condition7",3),rep("Condition8",3),rep("Condition9",3),
rep("Condition10",3)),
species1 = c(0.8854078,1.1426745,1.1997603,1.0752697,1.9146937,1.9551159,
0.9922772,1.8622131,1.9098490,1.0333174,0.8282050,0.8441316,
0.7967572,0.8551591,0.9468684,0.8860703,0.7994813,0.8080075,
1.2594811,1.1314699,1.0391089,1.0937708,1.0020137,0.9798906,
1.1896010,0.9886625,0.9184887,0.7433927,0.9162975,0.9110867),
species2 = c(0.8901513,1.0258955,1.1474593,1.0235203,1.3896777,1.5973619,
1.1878031,1.6099871,1.7580490,1.1427220,0.7071613,0.7560707,
0.7514282,0.7191674,0.8902374,1.3310765,0.6761733,0.7420234,
1.0251943,1.1350485,0.8723263,1.2321523,0.8916545,0.8315078,
0.8729525,1.0623186,0.8274900,0.9045540,0.7310989,0.7677916),
species3 = c(0.9662067,1.2001695,1.3431823,1.0595119,1.3833918,1.7351321,
0.6916019,1.1917492,1.5819428,1.0543243,0.9598088,0.9540960,
0.9418114,1.1564554,1.2758472,1.1501074,0.9732534,0.9465542,
1.0501864,0.9820822,0.7656833,0.9566891,0.7713283,0.7583800,
1.0911549,0.9461998,0.7428917,1.0001292,0.9719712,0.9313093),
species4 = c(0.7683807,1.4180243,1.4913914,1.3634394,1.6938784,2.0093107,
0.9411017,2.2384420,2.3109009,1.1387285,0.9596912,0.9513277,
0.6880320,1.1848339,1.3143804,1.4067813,0.8328677,0.8561612,
0.8263480,1.0818343,0.7703761,0.9124809,0.6062723,0.6545570,
1.1832932,1.0339432,0.7443652,1.1154476,0.8497842,0.8511975),
species5 = c(0.9572177,1.3932815,1.4020281,1.1314849,1.6915749,1.7423292,
1.1445479,1.4396891,1.5233912,0.9904151,0.9439300,0.9472164,
1.0387161,0.9559629,1.0126704,1.1433313,0.9523364,0.9439938,
1.2334351,1.0288614,0.9527279,0.9237914,0.6949624,0.7079906,
1.5397641,0.8460837,0.7982519,0.9132740,0.8411369,0.8439991),
species6 = c(1.0439714,0.9893081,1.0110028,0.9781595,1.0104842,1.0536184,
0.9939584,0.8124520,0.8565823,1.0299807,0.9819601,0.9848036,
1.0614300,1.0318185,1.0549166,0.9659700,0.9615660,0.9546094,
0.9888312,1.0165082,0.9852863,0.9685218,0.9886145,0.9875676,
1.0036011,1.0101036,0.9799294,1.0237522,0.9902889,0.9855792),
species7 = c(0.9328656,1.2854305,1.2898100,1.0467648,1.3192825,1.3513350,
0.7220414,1.1949623,1.2363336,1.1368031,0.9352001,0.9396318,
0.9466253,1.0874090,1.1093370,1.0653638,0.9490317,0.9428856,
1.0399305,0.9240539,0.9036315,0.9486899,0.7473155,0.7584183,
1.1038823,0.9570963,0.9351377,0.9462613,0.9032550,0.9029622),
species8 = c(0.9604464,1.2623905,1.2728665,1.0226677,1.3284123,1.3723773,
0.7151207,1.2286889,1.2838473,1.1727388,0.9447701,0.9486432,
0.9946795,1.1421753,1.1651877,1.1121437,0.9121262,0.9078907,
1.0222506,0.9264043,0.8983589,0.9461709,0.7986562,0.8063740,
1.0669461,0.9087593,0.8825160,0.9909126,0.9382247,0.9353111),
species9 = c(1.1268171,1.7182203,1.6682739,1.3667400,1.4472609,1.7220027,
0.8512170,1.5833162,1.8001507,1.5587438,1.0149041,0.9957001,
0.9458149,1.1636174,1.2586902,1.5092807,0.9218130,0.9146309,
0.9146585,0.7764501,0.6896659,0.9008603,0.8169017,0.7983043,
1.3871795,0.9755146,0.8183833,1.0444352,0.8010848,0.8139960),
species10 = c(0.9632305,1.2843560,1.2843606,1.0453979,1.4559130,1.4700216,
0.7202460,1.2099570,1.2369601,1.1035078,1.0026660,1.0051063,
0.9473880,1.1149870,1.1305114,1.0628961,0.9140461,0.9098227,
1.0242460,0.8829375,0.8721637,0.9532801,0.8127208,0.8228729,
1.1044777,0.9628386,0.9491688,0.9659567,0.9734319,0.9714403),
species11 = c(0.9216739,1.2844749,1.3348376,0.9948908,1.3635805,1.5213323,
0.6891492,1.1190134,1.3050141,1.0902293,0.9565687,0.9569387,
0.9892187,1.1349498,1.2010546,1.1020109,0.9419127,0.9320829,
1.0274009,0.8933980,0.8079352,0.8902054,0.7705972,0.7713996,
1.0949460,0.8986867,0.8107097,0.9517565,1.0002236,0.9768961)),
.Names = c("labels", "16:1/22:0","16:1/23:2","16:1/25:0","18:0/15:2",
"18:0/17:2","18:1/16:2","18:2/18:1","18:2/24:0","19:0/16:1",
"20:1/24:0","20:1/26:0"),
row.names = c(NA, 30L), class = "data.frame")
I could plot the results using autoplot() and lfda() using the following code:
library(ggfortify)
library(lfda)
autoplot(prcomp(sample1[,-1]), center = TRUE, scale. = TRUE)
model <- lfda(sample1[,-1], sample1[,1], knn=2, r = 2, metric="plain")
autoplot(model, data = sample1, frame = TRUE, frame.colour = 'labels')
But I encountered the following error messages when using the cluster library:
library(cluster)
autoplot(pam(sample1[,-1],3), frame = TRUE, frame.type = 'norm')
The error messages are:
Error in combine_vars(vars, ind_list) : Each argument must yield either positive or negative integers In addition: Warning messages: 1: In 14:0/16:1 : longer object length is not a multiple of shorter object length 2: In 14:0/18:1 : longer object length is not a multiple of shorter object length 3: In 14:0/20:3 : longer object length is not a multiple of shorter object length ...
The cluster package worked with one of my other datasets, so I'm guessing that something might be off in this particular dataset itself (like unlucky numeric values)...? I have been unable to figure out what these messages mean or how to fix them on my own...Any help would be much appreciated!
Thank you very much!
