Recently I conducted clustering analysis in R and got this table
label data PC1 PC2 PC3
1 1 5240 64.5116 12.9746 20.8370
2 2 878 88.7790 45.3952 5.0866
3 3 3368 74.5229 14.2346 43.8447
4 4 3198 38.3777 71.6273 61.5600
5 5 3960 91.8364 38.5808 90.5126
6 6 3060 81.6686 16.1163 71.7114
7 7 6627 14.1615 72.2929 47.6329
8 8 7154 51.7948 7.2599 5.4532
9 9 5408 5.1925 96.2049 73.5915
10 10 6649 33.8615 33.0504 18.8785
11 11 946 95.4989 87.7632 3.4894
12 12 6648 19.4012 60.3144 38.1420
13 13 4657 57.8675 31.4108 28.7580
14 14 3960 72.7626 36.8543 52.3389
15 15 3811 64.1441 58.1824 61.3217
16 16 3667 93.2833 9.2812 94.4377
17 17 7120 41.8886 19.8525 10.9709
18 18 4008 46.3114 51.1215 43.8303
19 19 6588 26.2215 46.7526 28.4874
20 20 5035 89.7023 90.2512 92.8365
21 21 3199 30.6296 89.1616 76.2088
22 22 4402 78.2135 50.6542 76.3119
23 23 5463 89.9995 67.8642 90.2405
24 24 7251 8.4030 85.1949 60.1072
25 25 5266 66.3528 83.8562 81.1253
In the table, there are information about 25 clusters, containing label of cluster(label), number of data belongs to each cluster(data), cluster centroid coordinates of 1st variable(PC1), cluster centroid coordinates of 2nd variable(PC2), cluster centroid coordinates of 3rd variable(PC3).
I plotted the data on the 3d table, using 'plot3d' and 'scatterplot3d', and now want to plot 'label' and 'data' of each cluster in the table additionally. This is my current code that visualizing data clusters with distinguished by colors.
library(plot3D)
plot3d(data = clustered_results, size = 5, col = kmeans_clustered$cluster) +
text3d(x = center_kmeans$PC1, y = center_kmeans$PC2, z = center_kmeans$PC3,
family = c('serif'), cex = 2, pos = 7)
But I have no idea how to do that. How can I plot the figures in 'label', 'data' column on the centroid point using coordinates of each axis(PC1, PC2, PC3)?
Related
I have a dataset which contains the data of pairs playing a game. I have a barplot that shows the total games played by the pairs. But now I want those bars('number') to be filled with the amount of games they successfully completed('sum'). I can't get it to work. The barplot is created like this:
barplot(height = game_count$number, xlab = 'Pairs', ylim = c(0,35), ylab='Games played')
The data looks like this:
participants sum number
1 06104873220647518670 30 32
2 06105747340637377404 23 24
3 06113978630633565020 28 32
4 06121794480617858550 25 27
5 06122613960611857952 23 26
6 06123139380653583516 25 28
7 06123650620648276595 28 32
8 06124453210624910109 32 34
9 06127993700610846968 24 26
10 06128440030639764541 19 24
11 06132461300624244572 26 30
12 06137611390651588167 25 28
13 06145014400637290807 16 19
14 06163181050611257617 30 30
15 06172024240651919112 21 23
One option can be ggplot2:
library(ggplot2)
#Code
game_count$Freq <- game_count$sum/game_count$number
#Plot
ggplot(game_count,aes(x=1:nrow(game_count),y=Freq))+
geom_col(fill='cyan3',color='black')+
xlab('')
Output:
This worked for me:
barplot(t(game_correct[c('number', 'sum')]), beside=TRUE, ylim=c(0,35), col=c('black', 'green'), main='Games played and successive games by the pairs', xlab='Pairs', ylab='Games')
Result in this graph:
I have a dendrogram which I want to cut into less clusters because right know there are too many for interpretation.
My dataframe looks like this:
> head(alpha)
locs 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
1 A1 12 14 15 15 14 21 10 18 18 20
2 A2 11 11 12 13 9 16 20 18 18 11
3 B1 12 13 20 17 21 20 27 14 22 25
4 B2 15 18 18 25 21 17 27 23 28 23
5 B3 22 22 26 24 28 23 31 25 32 25
6 B4 18 21 25 20 20 14 23 22 20 26
library("ggplot2") #for the heatmap
library("ggdendro") #for the dendrogram
library("reshape2") #for data wrangling
library("grid") #to combine the two plots heatmap and dendrogram
# Read in data
setwd("C:/Users/data")
alpha <- read.csv2("alpha.csv", header=TRUE, check.names=FALSE)
str(alpha) #structure of the dataset: locations (locs) = factor, values = integer
#scale the data variables (columns 4-9)
alpha.scaled <- alpha
alpha.scaled[, c(2:11)] <- scale(alpha.scaled[, 2:11])
alpha.scaled[, c(2:11)] <- scale(alpha.scaled[, 2:11])
# run clustering
alpha.matrix <- as.matrix(alpha.scaled[, -c(1)])
rownames(alpha.matrix) <- alpha.scaled$locs
alpha.dendro <- as.dendrogram(hclust(d = dist(x = alpha.matrix), method="complete" ))
# Create dendrogram (=cluster)
dendro.plot <- ggdendrogram(data = alpha.dendro, rotate = TRUE)
alphacut <- cutree(alpha.dendro, h=3)
alphacut <- cutree(alpha.dendro, h=3)
Error in cutree(alpha.dendro, h = 3) :
'tree' incorrect (composante 'merge')
alphacut <- cutree(as.dendrogram(hclust(d = dist(x = alpha.matrix), method="complete" )),k=5)
alphacut <- cutree(as.dendrogram(hclust(d = dist(x = alpha.matrix), method="complete" )), k=5)
Error in cutree(as.dendrogram(hclust(d = dist(x = alpha.matrix), method = "complete")), :
'tree' incorrect (composante 'merge')
I haven't found a solution to this. When I look at 'alpha.dendro' there is a list of 2 but no merge component, so this seems to be the problem. Does somebody know what to do?
So I can take points and use the R libraries deldir or spatstat::dirichlet to find the dirichlet tesselation of those points.
Now I have a point not in the set, and I want to know the indices of the points forming the dirichlet tile which my not-in-set-point is interior to. I can get there by knowing the tile label (or index).
Are there any libraries or methods to do this? I'm thinking spatstat, but not finding something there yet.
The function cut.ppp() can take a point pattern and find which tesselation
tile each point in the pattern belongs to. Below is the code for a simple
example of a point pattern that only contains a single point (0.5, 0.5).
library(spatstat)
dd <- dirichlet(cells)
plot.tess(dd, do.labels = TRUE)
xx <- ppp(.5, .5, window = Window(dd))
plot(xx, add = TRUE, col = "red", cex = 2, pch = 20)
yy <- cut(xx, dd)
yy
#> Marked planar point pattern: 1 point
#> Multitype, with levels =
#> 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 36 37 38 39 40 41 42
#> window: rectangle = [0, 1] x [0, 1] units
marks(yy)
#> [1] 18
#> 42 Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 ... 42
Created on 2018-12-03 by the reprex package (v0.2.1)
If X is a point pattern and B is a tessellation, then
M <- marks(cut(X, B))
returns a factor (vector of categorical values) identifying which tile contains each of the points of X. Alternatively,
M <- tileindex(X$x, X$y, B)
or
f <- as.function(B)
M <- f(X)
I am trying to cluster several amino acid sequences of a fixed length (13) into K clusters based on the Atchley factors (5 numbers which represent each amino acid.
For example, I have an input vector of strings like the following:
key <- HDMD::AAMetric.Atchley
sequences <- sapply(1:10000, function(x) paste(sapply(1:13, function (X) sample(rownames(key), 1)), collapse = ""))
However, my actual list of sequences is over 10^5 (specifying for need for computational efficiency).
I then convert these sequences into numeric vectors by the following:
key <- HDMD::AAMetric.Atchley
m1 <- key[strsplit(paste(sequences, collapse = ""), "")[[1]], ]
p = 13
output <-
do.call(cbind, lapply(1:p, function(i)
m1[seq(i, nrow(m1), by = p), ]))
I want to output (which is now 65 dimensional vectors) in an efficient way.
I was originally using Mini-batch kmeans, but I noticed the results were very inconsistent when I repeated. I need a consistent clustering approach.
I also was concerned about the curse of dimensionality, considering at 65 dimensions, Euclidean distance doesn't work.
Many high dimensional clustering algorithms I saw assume that outliers and noise exists in the data, but as these are biological sequences converted to numeric values, there is no noise or outlier.
In addition to this, feature selection will not work, as each of the properties of each amino acid and each amino acid are relevant in the biological context.
How would you recommend clustering these vectors?
I think self organizing maps can be of help here - at least the implementation is quite fast so you will know soon enough if it is helpful or not:
using the data from the op along with:
rownames(output) <- 1:nrow(output)
colnames(output) <- make.names(colnames(output), unique = TRUE)
library(SOMbrero)
you define the number of cluster in advance
fit <- trainSOM(x.data=output , dimension = c(5, 5), nb.save = 10, maxit = 2000,
scaling="none", radius.type = "gaussian")
the nb.save is used as intermediate steps for further exploration how the training developed during the iterations:
plot(fit, what ="energy")
seems like more iterations is in order
check the frequency of clusters:
table(my.som$clustering)
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
428 417 439 393 505 458 382 406 271 299 390 303 336 358 365 372 332 268 437 464 541 381 569 419 467
predict clusters based on new data:
predict(my.som, output[1:20,])
#output
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
19 12 11 8 9 1 11 13 14 5 18 2 22 21 23 22 4 14 24 12
check which variables were important for clustering:
summary(fit)
#part of output
Summary
Class : somRes
Self-Organizing Map object...
online learning, type: numeric
5 x 5 grid with square topology
neighbourhood type: gaussian
distance type: euclidean
Final energy : 44.93509
Topographic error: 0.0053
ANOVA :
Degrees of freedom : 24
F pvalue significativity
pah 1.343 0.12156074
pss 1.300 0.14868987
ms 16.401 0.00000000 ***
cc 1.695 0.01827619 *
ec 17.853 0.00000000 ***
find optimal number of clusters:
plot(superClass(fit))
fit1 <- superClass(fit, k = 4)
summary(fit1)
#part of output
SOM Super Classes
Initial number of clusters : 25
Number of super clusters : 4
Frequency table
1 2 3 4
6 9 4 6
Clustering
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
1 1 2 2 2 1 1 2 2 2 1 1 2 2 2 3 3 4 4 4 3 3 4 4 4
ANOVA
Degrees of freedom : 3
F pvalue significativity
pah 1.393 0.24277933
pss 3.071 0.02664661 *
ms 19.007 0.00000000 ***
cc 2.906 0.03332672 *
ec 23.103 0.00000000 ***
Much more in this vignette
I'm trying to add error bars to a second curve (using dataset "pmfprofbs01"), but I'm having problems and I couldn't fix this.
There are a few threads on this error, but unfortunately it looks like every other answer is case specific, and I'm not able to overcome this error in my code. I am able to plot a first smoothed curve (stat_smooth) and overlapping errorbars (using geom_errobar). The problem rises when I try to add a second curve to the same graph, for comparison purposes.
With following code, I get the following error: "Error: Aesthetics must be either length 1 or the same as the data (35): ymin, ymax, x, y, colour"
I am looking to add additional errorbars to the second smoothed curve (corresponding to datasets pmfprof01 and pmfprofbs01).
Could someone explain why I keep getting this error? The code works until using the second call of geom_errorbar().
These are my 4 datasets (all used as data frames):
- pmfprof1 and pmfprof01 are the two datasets used for applying the smoothing method.
- pmfprofbs1 and pmfprofbs01 contain additional information based on an error analysis for plotting error bars.
> pmfprof1
Z correctedpmfprof1
1 -1.1023900 -8.025386e-22
2 -1.0570000 6.257110e-02
3 -1.0116000 1.251420e-01
4 -0.9662020 2.143170e-01
5 -0.9208040 3.300960e-01
6 -0.8754060 4.658550e-01
7 -0.8300090 6.113410e-01
8 -0.7846110 4.902430e-01
9 -0.7392140 3.344200e-01
10 -0.6938160 4.002040e-01
11 -0.6484190 1.215460e-01
12 -0.6030210 -1.724360e-01
13 -0.5576240 -6.077170e-01
14 -0.5122260 -1.513420e+00
15 -0.4668290 -2.075330e+00
16 -0.4214310 -2.617160e+00
17 -0.3760340 -3.350500e+00
18 -0.3306360 -4.076220e+00
19 -0.2852380 -4.926540e+00
20 -0.2398410 -5.826390e+00
21 -0.1944430 -6.761300e+00
22 -0.1490460 -7.301530e+00
23 -0.1036480 -7.303880e+00
24 -0.0582507 -7.026800e+00
25 -0.0128532 -6.627960e+00
26 0.0325444 -6.651490e+00
27 0.0779419 -6.919830e+00
28 0.1233390 -6.686490e+00
29 0.1687370 -6.129060e+00
30 0.2141350 -6.120890e+00
31 0.2595320 -6.455160e+00
32 0.3049300 -6.554560e+00
33 0.3503270 -6.983390e+00
34 0.3957250 -7.413500e+00
35 0.4411220 -6.697370e+00
36 0.4865200 -5.477230e+00
37 0.5319170 -4.552890e+00
38 0.5773150 -3.393060e+00
39 0.6227120 -2.449930e+00
40 0.6681100 -2.183190e+00
41 0.7135080 -1.673980e+00
42 0.7589050 -8.003740e-01
43 0.8043030 -2.918780e-01
44 0.8497000 -1.159710e-01
45 0.8950980 9.123767e-22
> pmfprof01
Z correctedpmfprof01
1 -1.25634000 -1.878749e-21
2 -1.20387000 -1.750190e-01
3 -1.15141000 -3.500380e-01
4 -1.09894000 -6.005650e-01
5 -1.04647000 -7.935110e-01
6 -0.99400600 -8.626150e-01
7 -0.94153900 -1.313880e+00
8 -0.88907200 -2.067770e+00
9 -0.83660500 -2.662440e+00
10 -0.78413800 -4.514190e+00
11 -0.73167100 -7.989510e+00
12 -0.67920400 -1.186870e+01
13 -0.62673800 -1.535970e+01
14 -0.57427100 -1.829150e+01
15 -0.52180400 -2.067170e+01
16 -0.46933700 -2.167890e+01
17 -0.41687000 -2.069820e+01
18 -0.36440300 -1.662640e+01
19 -0.31193600 -1.265950e+01
20 -0.25946900 -1.182580e+01
21 -0.20700200 -1.213370e+01
22 -0.15453500 -1.233680e+01
23 -0.10206800 -1.235160e+01
24 -0.04960160 -1.123630e+01
25 0.00286531 -9.086940e+00
26 0.05533220 -6.562710e+00
27 0.10779900 -4.185860e+00
28 0.16026600 -3.087430e+00
29 0.21273300 -2.005150e+00
30 0.26520000 -9.295540e-02
31 0.31766700 1.450360e+00
32 0.37013400 1.123910e+00
33 0.42260100 2.426750e-01
34 0.47506700 1.213370e-01
35 0.52753400 5.265226e-21
> pmfprofbs1
Z correctedpmfprof01 bsmean bssd bsse bsci
1 -1.1023900 -8.025386e-22 0.00000000 0.0000000 0.00000000 0.0000000
2 -1.0570000 6.257110e-02 1.46519200 0.6691245 0.09974719 0.2010273
3 -1.0116000 1.251420e-01 1.62453300 0.6368053 0.09492933 0.1913175
4 -0.9662020 2.143170e-01 1.62111600 0.7200497 0.10733867 0.2163269
5 -0.9208040 3.300960e-01 1.44754700 0.7236743 0.10787900 0.2174158
6 -0.8754060 4.658550e-01 1.67509800 0.7148755 0.10656735 0.2147724
7 -0.8300090 6.113410e-01 1.78144200 0.7374481 0.10993227 0.2215539
8 -0.7846110 4.902430e-01 1.73058700 0.7701354 0.11480501 0.2313743
9 -0.7392140 3.344200e-01 0.97430090 0.7809477 0.11641681 0.2346227
10 -0.6938160 4.002040e-01 1.26812000 0.8033838 0.11976139 0.2413632
11 -0.6484190 1.215460e-01 0.93601510 0.7927926 0.11818254 0.2381813
12 -0.6030210 -1.724360e-01 0.63201080 0.8210839 0.12239996 0.2466809
13 -0.5576240 -6.077170e-01 0.05952252 0.8653050 0.12899205 0.2599664
14 -0.5122260 -1.513420e+00 0.57893690 0.8858471 0.13205429 0.2661379
15 -0.4668290 -2.075330e+00 -0.08164613 0.8921298 0.13299086 0.2680255
16 -0.4214310 -2.617160e+00 -1.08074600 0.8906925 0.13277660 0.2675937
17 -0.3760340 -3.350500e+00 -1.67279700 0.9081813 0.13538367 0.2728479
18 -0.3306360 -4.076220e+00 -2.50074900 1.0641550 0.15863486 0.3197076
19 -0.2852380 -4.926540e+00 -3.12062200 1.0639080 0.15859804 0.3196333
20 -0.2398410 -5.826390e+00 -4.47060100 1.1320770 0.16876008 0.3401136
21 -0.1944430 -6.761300e+00 -5.40812700 1.1471780 0.17101120 0.3446504
22 -0.1490460 -7.301530e+00 -6.42419100 1.1685490 0.17419700 0.3510710
23 -0.1036480 -7.303880e+00 -5.79613500 1.1935850 0.17792915 0.3585926
24 -0.0582507 -7.026800e+00 -5.85496900 1.2117630 0.18063896 0.3640539
25 -0.0128532 -6.627960e+00 -6.70480400 1.1961400 0.17831002 0.3593602
26 0.0325444 -6.651490e+00 -8.27106200 1.3376870 0.19941060 0.4018857
27 0.0779419 -6.919830e+00 -8.79402900 1.3582760 0.20247983 0.4080713
28 0.1233390 -6.686490e+00 -8.35947700 1.3673080 0.20382624 0.4107848
29 0.1687370 -6.129060e+00 -8.04437600 1.3921620 0.20753126 0.4182518
30 0.2141350 -6.120890e+00 -8.18588300 1.5220550 0.22689456 0.4572759
31 0.2595320 -6.455160e+00 -8.37217600 1.5436800 0.23011823 0.4637728
32 0.3049300 -6.554560e+00 -8.59346400 1.6276880 0.24264140 0.4890116
33 0.3503270 -6.983390e+00 -8.88378700 1.6557140 0.24681927 0.4974316
34 0.3957250 -7.413500e+00 -9.72709800 1.6569390 0.24700188 0.4977996
35 0.4411220 -6.697370e+00 -9.46033400 1.6378470 0.24415582 0.4920637
36 0.4865200 -5.477230e+00 -8.37590600 1.6262700 0.24243002 0.4885856
37 0.5319170 -4.552890e+00 -7.52867000 1.6617010 0.24771176 0.4992302
38 0.5773150 -3.393060e+00 -6.89192300 1.6667330 0.24846189 0.5007420
39 0.6227120 -2.449930e+00 -6.25115300 1.6670390 0.24850750 0.5008340
40 0.6681100 -2.183190e+00 -6.05373800 1.6720180 0.24924973 0.5023298
41 0.7135080 -1.673980e+00 -5.10526700 1.6668400 0.24847784 0.5007742
42 0.7589050 -8.003740e-01 -4.42001600 1.6561830 0.24688918 0.4975725
43 0.8043030 -2.918780e-01 -4.26640200 1.6588970 0.24729376 0.4983878
44 0.8497000 -1.159710e-01 -4.46318500 1.6533830 0.24647179 0.4967312
45 0.8950980 9.123767e-22 -5.17173200 1.6557990 0.24683194 0.4974571
> pmfprofbs01
Z correctedpmfprof01 bsmean bssd bsse bsci
1 -1.25634000 -1.878749e-21 0.000000 0.0000000 0.00000000 0.0000000
2 -1.20387000 -1.750190e-01 2.316589 0.4646486 0.07853995 0.1596124
3 -1.15141000 -3.500380e-01 2.320647 0.4619668 0.07808664 0.1586911
4 -1.09894000 -6.005650e-01 2.635883 0.6519826 0.11020517 0.2239639
5 -1.04647000 -7.935110e-01 2.814679 0.6789875 0.11476983 0.2332404
6 -0.99400600 -8.626150e-01 2.588038 0.7324196 0.12380151 0.2515949
7 -0.94153900 -1.313880e+00 2.033736 0.7635401 0.12906183 0.2622852
8 -0.88907200 -2.067770e+00 2.394285 0.8120181 0.13725611 0.2789380
9 -0.83660500 -2.662440e+00 2.465425 0.9485307 0.16033095 0.3258317
10 -0.78413800 -4.514190e+00 0.998115 1.0177400 0.17202946 0.3496059
11 -0.73167100 -7.989510e+00 -1.585430 1.0502190 0.17751941 0.3607628
12 -0.67920400 -1.186870e+01 -5.740894 1.2281430 0.20759406 0.4218819
13 -0.62673800 -1.535970e+01 -9.325951 1.3289330 0.22463068 0.4565045
14 -0.57427100 -1.829150e+01 -12.010540 1.3279860 0.22447060 0.4561792
15 -0.52180400 -2.067170e+01 -14.672770 1.3296720 0.22475559 0.4567583
16 -0.46933700 -2.167890e+01 -14.912250 1.3192610 0.22299581 0.4531820
17 -0.41687000 -2.069820e+01 -12.850570 1.3288470 0.22461614 0.4564749
18 -0.36440300 -1.662640e+01 -6.093746 1.3497100 0.22814263 0.4636416
19 -0.31193600 -1.265950e+01 -5.210692 1.3602240 0.22991982 0.4672533
20 -0.25946900 -1.182580e+01 -6.041660 1.3818700 0.23357866 0.4746890
21 -0.20700200 -1.213370e+01 -5.765808 1.3854680 0.23418683 0.4759249
22 -0.15453500 -1.233680e+01 -6.985883 1.4025360 0.23707185 0.4817880
23 -0.10206800 -1.235160e+01 -7.152865 1.4224030 0.24042999 0.4886125
24 -0.04960160 -1.123630e+01 -3.600538 1.4122650 0.23871635 0.4851300
25 0.00286531 -9.086940e+00 -0.751673 1.5764920 0.26647578 0.5415439
26 0.05533220 -6.562710e+00 2.852910 1.5535620 0.26259991 0.5336672
27 0.10779900 -4.185860e+00 5.398850 1.5915640 0.26902342 0.5467214
28 0.16026600 -3.087430e+00 6.262459 1.6137360 0.27277117 0.5543377
29 0.21273300 -2.005150e+00 8.047920 1.6283340 0.27523868 0.5593523
30 0.26520000 -9.295540e-02 11.168640 1.6267620 0.27497297 0.5588123
31 0.31766700 1.450360e+00 12.345900 1.6363310 0.27659042 0.5620994
32 0.37013400 1.123910e+00 12.124650 1.6289230 0.27533824 0.5595546
33 0.42260100 2.426750e-01 11.279890 1.6137100 0.27276677 0.5543288
34 0.47506700 1.213370e-01 11.531670 1.6311490 0.27571450 0.5603193
35 0.52753400 5.265226e-21 11.284980 1.6662890 0.28165425 0.5723903
The code for plotting both curves is:
deltamean01<-pmfprofbs01[,"bsmean"]-
pmfprofbs01[,"correctedpmfprof01"]
correctmean01<-pmfprofbs01[,"bsmean"]-deltamean01
deltamean1<-pmfprofbs1[,"bsmean"]-
pmfprofbs1[,"correctedpmfprof1"]
correctmean1<-pmfprofbs1[,"bsmean"]-deltamean1
pl<- ggplot(pmfprof1, aes(x=pmfprof1[,1], y=pmfprof1[,2],
colour="red")) +
list(
stat_smooth(method = "gam", formula = y ~ s(x), size = 1,
colour="chartreuse3",fill="chartreuse3", alpha = 0.3),
geom_line(data=pmfprof1,linetype=4, size=0.5,colour="chartreuse3"),
geom_errorbar(aes(ymin=correctmean1-pmfprofbs1[,"bsci"],
ymax=correctmean1+pmfprofbs1[,"bsci"]),
data=pmfprofbs1,colour="chartreuse3",
width=0.02,size=0.9),
geom_point(data=pmfprof1,size=1,colour="chartreuse3"),
xlab(expression(xi*(nm))),
ylab("PMF (KJ/mol)"),
## GCD
geom_errorbar(aes(ymin=correctmean01-pmfprofbs01[,"bsci"],
ymax=correctmean01+pmfprofbs01[,"bsci"]),
data=pmfprofbs01,
width=0.02,size=0.9),
geom_line(data=pmfprof01,aes(x=pmfprof01[,1],y=pmfprof01[,2]),
linetype=4, size=0.5,colour="darkgreen"),
stat_smooth(data=pmfprof01,method = "gam",aes(x=pmfprof01[,1],pmfprof01[,2]),
formula = y ~ s(x), size = 1,
colour="darkgreen",fill="darkgreen", alpha = 0.3),
theme(text = element_text(size=20),
axis.text.x = element_text(size=20,colour="black"),
axis.text.y = element_text(size=20,colour="black")),
scale_x_continuous(breaks=number_ticks(8)),
scale_y_continuous(breaks=number_ticks(8)),
theme(panel.background = element_rect(fill ='white',
colour='gray')),
theme(plot.background = element_rect(fill='white',
colour='white')),
theme(legend.position="none"),
theme(legend.key = element_blank()),
theme(legend.title = element_text(colour='gray', size=20)),
NULL
)
pl
This is the result of using pl,
[enter image description here][1]
[1]: https://i.stack.imgur.com/x8FjY.png
Thanks in advance for any suggestion,