Fix layout of plot(fevd()) function - r

I am trying to plot FEVD (forecast error variance decomposition) for my VAR analysis. As you can see on the image, the legend screws up the graph and information. as this is an automatically created legend, I don’t know how to reposition it. I do not know much yet about plotting in R.
The only code i use to get this is :
library(vars)
var <- VAR(varTable2 , p=4 , type = "both")
plot(fevd(var, n.ahead = 10 ))
Thanks in advance


Legends do not resize well in R. You have to set your plotting window first and then chart your data.
Here's how to do it in Windows. win.graph opens a blank plotting window of the specified width. In Unix/Linux, you should look at X11() and in Mac, at quartz(). You might also consider shorter variable names.
library(vars)
data(Canada)
colnames(Canada) <-c("Long column name1","Long column name2","Long column name3","Long column name4")
var <- VAR(Canada , p=4 , type = "both")
win.graph(width=13,height=8)
plot(fevd(var, n.ahead = 10 ))

Related

Using multiple datasets for one graph

I have 2 csv data files. Each file has a "date_time" column and a "temp_c" column. I want to make the x-axis have the "date_time" from both files and then use 2 y-axes to display each "temp_c" with separate lines. I would like to use plot instead of ggplot2 if possible. I haven't been able to find any code help that works with my data and I'm not sure where to really begin. I know how to do 2 separate plots for these 2 datasets, just not combine them into one graph.
plot(grewl$temp_c ~ grewl$date_time)
and
plot(kbll$temp_c ~ kbll$date_time)
work separately but not together.

As others indicated, it is easy to add new data to a graph using points() or lines(). One thing to be careful about is how you format the axes as they will not be automatically adjusted to fit any new data you input using points() and the like.
I've included a small example below that you can copy, paste, run, and examine. Pay attention to why the first plot fails to produce what you want (axes are bad). Also note how I set this example up generally - by making fake data that showcase the same "problem" you are having. Doing this is often a better strategy than simply pasting in your data since it forces you to think about the core component of the problem you are facing.
#for same result each time
set.seed(1234)
#make data
set1<-data.frame("date1" = seq(1,10),
"temp1" = rnorm(10))
set2<-data.frame("date2" = seq(8,17),
"temp2" = rnorm(10, 1, 1))
#first attempt fails
#plot one
plot(set1$date1, set1$temp1, type = "b")
#add points - oops only three showed up bc the axes are all wrong
lines(set2$date2, set2$temp2, type = "b")
#second attempt
#adjust axes to fit everything (set to min and max of either dataset)
plot(set1$date1, set1$temp1,
xlim = c(min(set1$date1,set2$date2),max(set1$date1,set2$date2)),
ylim = c(min(set1$temp1,set2$temp2),max(set1$temp1,set2$temp2)),
type = "b")
#now add the other points
lines(set2$date2, set2$temp2, type = "b")
# we can even add regression lines
abline(reg = lm(set1$temp1 ~ set1$date1))
abline(reg = lm(set2$temp2 ~ set2$date2))

Interactive plot: Manipulate contents of a ggplot2 plot with a sliding bar

Edit: Thank you to Javier for his suggestion. I forgot to mention that I would like to incorporate this interactive plot into a report / dashboard, so something that works with a HTML document from RMarkdown would be ideal, but a dashboard solution would also be fine.
Consider the following plots; the red line represents the actual data, while the green line plots predictions generated by a model:
The predictions of two different models are displayed; one trained over the first 100 hours, and the other over the first 216 hours. Predictions are then generated for the unseen data-points, then plotted.
What I would like to do, is train n models, eg. one every 12 hours in an expanding window fashion. After having done this, I would like to present the results in an interactive fashion where the user can click/slide something to move the vertical line back and forth, thereby changing which model's predictions are displayed. The point would be to intuitively show the effect of different training lengths.
I'm new to shiny and interactive plots in R; can this be done without too much trouble?

You can with the manipulate package for quick interactive plots. Shiny requires more fine-tuning and it is more time-consuming.
Here is a reproducible example for you to test out:
This creates the slider bar:
library(manipulate)
manipulate(plot(1:x), x = slider(1, 100))
Put your code here for the creation of the interactive plot:
manipulate(
plot(cars, xlim = c(0, x.max), type = type, ann = label),
x.max = slider(10, 25, step=5, initial = 25),
type = picker("Points" = "p", "Line" = "l", "Step" = "s"),
label = checkbox(TRUE, "Draw Labels"))
Check out the CRAN manipulate package for more information:
https://cran.r-project.org/web/packages/manipulate/index.html

I was able to do this with the example at the bottom of this link.
library(shiny)
sliderInput("n", "Training length:", 100, min=24, max= 11*24)
renderPlot({
plotPredictCurve(data= df, trainLength= input$n)
})

R figure being cropped

Here's a bit of a basic question, you'd think R would automate this always. I'm making a heatmap (basic stuff, png() into heatmap.2() into dev.off()), and my row labels get cleaved off on the right hand side, along with a tiny part of the dendrogram on the left:
Fiddling with the margins manually would eventually lead to a sensible output, but the problem is that this is part of an automated pipeline with biologist users in mind. Surely there's some automated way to do this, right? I mean, in RGui on OSX, a quick reshape of the figure window fixes the layout automatically, so it has to be possible. Thanks, and sorry to bother you.
EDIT: Procedure to reproduce this visualisation:
Input data:
Time,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48
CATMA1A23000,0.21129,0.48322,0.54234,0.93714,0.51816,0.11103,0.38304,0.97183,0.61381,-0.13775,0.32575,0.85221,0.1921,1.8692,0.4117,0.51977,0.74019,0.98932,0.023536,0.8849,0.75141,0.42846,0.03839,0.50295,0.38849,-0.95087,0.55017,-0.14813,0.79036,0.40978,-0.33048,0.81173,0.60585,0.014413,0.86586,0.063947,1.1195,0.25425,-0.090325,1.3035,0.90609,0.62866,0.83168,0.88655,0.4843,0.60098,0.26669,0.66605,0.78468,-0.76709,0.29615,-0.8911,0.5022,-0.93834,-0.065904,0.58003,0.41217,-0.99012,-0.032054,0.69119,1.1791,-0.0059458,0.81002,0.76421,1.302,0.77637,0.7824,1.192,-0.27005,-0.24698,1.8706,0.33513,0.38592,1.1987,0.48569,0.7104,2.4307,0.68148,0.11407,0.65619,0.94468,1.245,0.44146,0.71048,0.96389,1.7647,0.22729,0.96875,-0.2738,1.2975,2.0503,0.52507,2.282,1.0773,0.44489,0.22504
CATMA1A30870,3.9909,5.1844,5.2522,5.5482,5.665,5.4666,5.4456,5.5456,5.0014,5.5408,3.9324,3.9288,4.3624,3.0828,5.4821,5.1446,4.5638,4.6449,5.2604,4.2607,4.0942,3.9892,5.1361,3.8625,3.9454,5.3148,5.188,6.088,5.2085,5.3073,5.5652,4.8358,4.6111,4.9911,3.1497,4.3796,4.2456,3.9712,5.0998,5.4383,5.0908,4.5155,4.6486,5.1233,4.4259,4.7218,3.7928,4.0384,4.1085,6.2787,5.4937,5.9637,5.4085,5.188,5.3057,5.1963,4.3807,4.8352,3.9584,3.5859,3.8535,3.9755,4.5199,5.1093,5.3526,4.1214,4.5787,3.4499,4.1962,4.6929,4.3379,4.6376,4.4774,5.2341,5.3061,5.6769,3.6244,5.1073,4.543,4.9929,4.9341,3.9829,4.4304,4.0021,3.7498,4.2972,5.3338,4.8094,4.5766,5.5171,3.1207,5.0926,3.0253,4.351,4.351,4.5253
CATMA1A65800,0.65615,0.54186,0.93488,0.2205,0.38134,0.95061,0.60157,0.66508,0.83971,1.3625,0.9037,1.1606,1.0143,0.91896,1.3587,1.2468,1.0147,0.44113,1.5196,0.60232,0.52456,0.82557,0.43697,1.3253,0.78614,0.73697,1.0593,0.010271,0.59042,0.58544,0.38307,1.0372,1.071,0.21915,1.1779,0.97341,0.95672,0.92057,1.3866,1.3884,0.99241,1.0546,1.0215,1.0144,1.1606,1.2421,0.89549,0.56747,0.99347,1.0706,1.3403,0.48719,0.30759,1.0215,0.76474,0.87313,0.47597,1.5028,1.2201,1.7923,0.64299,1.3612,1.0079,0.64145,1.0969,0.90254,0.9109,0.85213,0.55241,0.63797,1.5373,0.45586,0.59624,0.5717,0.13919,-0.012128,2.7367,0.83577,0.13422,0.13908,0.32377,0.013345,-0.017209,0.47044,1.034,1.4932,1.0067,0.57515,1.1405,0.95364,3.0214,0.14601,2.1645,1.1704,0.45205,0.83406
CATMA1C71184,4.2879,2.7256,3.306,2.2375,2.6575,4.6438,4.3899,3.8518,5.3494,3.8049,5.4095,4.5437,3.4362,4.0983,2.5059,2.7275,3.9432,3.5177,4.266,4.6446,4.9338,3.5188,3.7903,4.4867,3.9692,3.3382,3.2898,3.4103,3.3025,4.1279,4.3532,5.0625,5.756,4.7799,5.0303,4.8974,3.8698,2.887,2.7541,2.7837,3.0665,4.0168,3.7507,4.6273,5.2384,3.7049,4.5843,3.97,2.7142,3.6205,3.5394,3.3561,3.2672,4.4467,5.2044,5.2177,3.8496,5.7881,4.7594,5.0395,2.8281,2.8751,2.8322,2.6873,1.8326,3.042,3.834,4.7844,3.1947,3.2571,4.0826,4.8398,3.8147,2.7439,2.846,2.64,2.8055,3.421,3.8495,4.9367,4.6413,3.5281,4.0636,3.7144,3.4468,2.3466,1.7452,2.3151,4.022,2.9211,3.89,3.7213,4.1856,3.3613,4.0891,4.2135
CATMA1C71776,-2.0266,-0.77692,-1.3279,-0.40422,-0.38614,-0.34387,-0.44277,0.99775,1.0748,0.17831,-0.14209,-0.98905,-0.88619,0.40548,-0.37731,0.18112,-0.80473,-0.26957,0.49049,0.25413,0.30341,0.20203,-1.0343,-0.17877,-0.46107,-1.202,-0.58841,-1.0995,-1.1079,-0.10977,0.29428,0.33363,0.35537,0.55571,-0.11074,-0.69584,-0.052035,-0.18207,-0.45321,-0.2778,-0.81074,-0.27129,0.087758,0.52214,0.33791,0.51601,-0.30863,0.14386,-0.2922,-0.21013,-0.73268,-1.1732,-0.36599,0.4731,1.0357,0.62377,0.8219,0.6029,-0.27195,1.0677,-0.48423,-0.57601,-0.43393,-2.0519,-0.67433,-0.44234,0.0078619,0.40677,-0.47102,-0.59971,0.30058,-0.4604,-0.56523,-3.3913,-0.52856,-0.54137,1.0418,-0.26115,0.21246,0.37858,0.38716,-0.86708,-0.41297,-0.21536,-0.06004,-0.48526,-1.2651,-0.52592,0.23757,-0.73376,1.0272,0.59383,0.65704,-0.017658,-0.44128,-0.30226
CATMA2A41800,1.5057,1.8357,2.7851,2.9125,2.6421,2.3881,2.4772,2.3367,2.2988,3.0073,2.7659,2.8454,2.5939,2.6327,2.6184,2.3415,3.3473,2.7683,1.1123,1.9614,1.8795,2.2335,2.9227,3.1536,2.6255,2.5264,2.3874,3.3196,1.8572,2.3446,2.3358,1.683,2.1742,2.8082,2.8634,2.8607,2.6755,3.2567,2.0726,3.0746,2.8984,2.5235,2.9399,2.5731,1.9196,2.9845,2.965,2.8055,1.1878,-0.6181,2.9552,2.7814,3.0701,-0.42372,2.5897,2.6331,1.7218,-0.70788,2.9578,1.1506,3.3772,1.6571,3.3166,3.5001,2.6541,2.5367,2.8643,2.1034,2.6389,1.8915,2.4861,3.2271,1.7718,2.7359,2.391,2.7223,1.1594,2.4025,1.1524,2.1405,2.253,2.4368,2.8053,2.2955,2.2877,2.7792,3.4115,3.2464,-0.37629,3.0582,1.682,2.0598,1.8747,2.0158,2.4714,2.1816
CATMA2C47274,2.2048,1.6574,0.96502,0.15092,0.5251,0.40351,0.90106,-0.12415,0.48846,0.03794,0.86292,0.27027,0.71848,1.8535,0.47188,-0.067504,0.68578,0.28113,0.17559,0.29659,0.067618,0.79368,-0.36732,0.80945,2.4297,1.3272,1.3741,0.005024,0.57369,0.6195,0.17845,1.3991,0.71594,-0.76358,0.74421,0.078431,0.97596,0.14151,-0.77085,0.64926,-0.24602,1.3478,0.49879,0.90549,0.78024,0.94685,0.023589,0.07446,1.3597,0.19311,1.1883,-0.20756,0.59042,0.031659,0.3591,1.1757,0.97314,-1.0133,0.92409,1.2264,-0.0095851,-0.47315,1.3107,1.3967,0.80332,0.5494,1.0394,1.4519,0.50253,-0.65282,1.5689,0.17996,1.5938,0.28124,0.93511,0.10344,3.6906,0.56448,-0.40754,0.75218,0.10346,-0.37737,-0.53789,0.57109,0.29602,-0.64821,-0.52805,0.34899,0.50146,0.61456,2.1785,-0.41,2.135,0.7426,0.3307,0.64542
CATMA2C47710,3.9021,3.7272,3.709,3.494,3.7557,3.7883,3.8697,3.4952,3.6365,3.6948,4.0954,3.7629,4.1282,3.6108,4.2769,3.3689,3.5232,3.7488,3.6237,4.0805,3.855,3.8724,4.2706,3.5584,3.9576,4.1324,3.857,3.6508,3.7609,3.7885,3.6256,3.8022,3.7189,3.7417,3.6357,3.6448,4.2841,3.8731,3.6047,3.7792,3.5703,3.8284,3.8098,3.8824,4.2441,3.6127,3.8253,3.529,3.6078,4.0917,3.6794,3.7977,3.8231,3.9369,3.6087,3.7634,3.6419,4.0052,3.7083,4.0191,3.3211,3.9133,3.74,4.0801,4.0999,3.6312,3.7747,3.7768,3.7977,3.9409,3.9423,3.6708,4.1773,3.666,3.8267,3.8832,4.0462,3.8007,3.3932,4.0945,3.9901,4.1036,4.1064,3.8178,3.7936,3.8638,4.3395,3.5859,3.5698,3.7885,4.4263,4.0292,3.6857,4.1197,4.0262,3.7753
CATMA3A13690,5.2022,5.6481,5.7765,4.8253,6.0581,6.2026,5.2711,6.4481,6.0929,6.3395,5.3806,4.9156,5.3634,4.5556,5.3547,5.5214,5.6947,5.9289,5.6269,5.5406,6.0508,5.8498,5.6275,5.4422,5.3293,5.7543,5.1577,6.3626,5.429,5.7245,5.7554,5.4575,5.2244,6.2312,6.0425,5.0317,5.4494,5.5252,5.5124,5.6588,5.0015,5.7454,5.6711,6.132,5.4789,6.1668,5.6978,5.113,4.3572,5.6177,5.9557,6.0099,5.2522,5.5598,6.2427,5.4703,5.4275,5.9159,5.3029,4.1094,5.927,5.9223,5.1257,5.3835,5.6579,5.5849,5.8674,4.9425,5.2144,5.5382,4.7165,5.5688,5.2766,6.4298,5.9438,5.7105,4.757,5.9097,5.3665,5.8336,5.8393,5.6104,5.4702,5.4028,5.0756,5.632,5.9963,5.7889,5.7521,5.5561,4.0685,6.3026,4.9628,6.176,5.7256,4.8529
CATMA3A15560,0.23073,0.32331,0.37953,0.081038,-0.33446,0.18495,0.86901,0.71363,0.33531,1.4741,0.32824,0.18869,-0.34772,0.97628,0.62455,-0.14645,0.42603,-0.00031328,0.92441,0.14199,0.25522,0.55583,0.10067,1.2818,0.64424,-0.24454,0.33039,0.25975,1.1023,-0.5464,0.62174,-0.15397,0.78478,0.2999,-0.063974,0.55156,0.44076,0.17798,0.61707,0.9308,0.19809,1.2378,0.26328,0.19767,0.069484,0.76748,0.45386,0.4495,0.050625,0.99311,-0.10988,-0.070507,0.46274,0.19475,0.36696,-0.16537,1.0566,0.94446,0.85272,1.194,-0.41335,0.17202,0.38729,0.65495,-0.32063,0.60186,0.24444,1.3128,0.48551,0.55998,1.8149,0.36494,0.10693,-0.19085,0.25082,-0.10628,-0.38432,0.50117,0.97923,-0.31536,-0.2634,-0.82767,0.70017,-0.0093473,0.27653,0.67773,0.40978,0.3315,0.5179,0.32044,2.9356,-0.51529,2.7438,0.4153,0.32022,0.74311
CATMA3A21390,-0.54603,-0.32262,-0.29966,-0.22463,-0.49654,-1.0552,-1.0705,-0.93928,-0.71247,-2.7228,-0.67849,-0.24868,-1.5015,-0.22486,-0.26215,-0.34298,-0.73168,-0.33699,-0.3455,-0.5349,-0.68784,-0.47506,-1.2541,0.86942,-0.14503,-2.0603,-0.35647,-0.67731,0.20243,-1.1881,-1.3828,-1.0471,-0.20157,-2.5492,-0.7862,-0.21715,-0.20583,-0.067758,-0.86589,-0.75509,-0.62777,-0.57176,-2.0996,-0.72894,-0.56391,-1.1054,-0.94172,-0.98805,-0.20763,-0.42764,-2.0142,-1.4079,-0.66326,-0.10354,-1.6284,-1.9413,-0.77001,-0.58492,-0.99846,-0.013285,-0.31205,-0.10665,-1.2396,-1.7556,-0.40454,-0.26631,-1.2694,-0.32263,-1.2293,-0.93683,0.97994,-1.8696,-0.26859,-0.25661,-0.33193,-0.83889,-0.25944,-0.63826,0.3387,-1.4443,-0.93123,-0.84915,-0.24606,-0.63109,-0.78621,-0.40802,-0.6463,-0.84319,-1.0379,-1.1471,1.9932,-1.3029,1.5286,-0.19754,0.018446,-0.55479
CATMA3A53880,3.917,3.6689,3.7213,3.8812,3.8253,4.4861,4.258,4.7926,4.9737,4.0869,3.2183,3.5812,3.408,3.3128,3.3915,4.1923,3.9122,4.0586,4.2707,4.3077,4.8926,4.1982,3.4817,3.5267,3.6952,3.6632,3.9304,4.0793,3.9499,4.2175,4.8303,4.9599,4.6336,3.8998,3.7932,3.8572,3.2167,3.7749,4.3971,4.0662,3.9241,4.6407,4.2837,4.9644,4.972,4.2565,3.8796,3.383,3.2382,4.1017,4.058,4.0423,3.6033,4.0774,5.141,4.9736,4.4372,3.8969,3.7386,3.3618,3.539,4.2588,3.4726,3.7424,3.5818,4.9159,4.3567,4.1291,4.1108,4.4035,3.1374,3.4675,3.762,3.9264,3.7319,4.1205,2.0915,4.4192,4.5049,4.5427,4.6205,3.4653,3.6017,3.3376,3.266,3.5561,3.4538,3.761,4.2013,4.1787,4.017,4.7773,3.748,4.4977,3.8685,3.7464
CATMA5A09500,6.2838,5.1076,6.6209,5.7153,5.9395,5.3601,4.2366,5.189,3.3074,4.8701,5.0357,5.0951,6.2843,5.691,5.5054,5.9949,5.0834,5.1484,4.5884,4.805,4.458,5.0816,4.7264,5.3661,5.0645,5.8293,5.2877,5.6602,5.3336,4.7329,4.843,5.1901,3.8431,4.683,5.7106,4.0774,6.5689,5.8061,5.3445,5.2905,3.837,4.6271,4.2277,4.6165,4.0081,4.5176,4.259,4.6363,4.8557,3.9828,5.9728,5.4698,5.4102,4.5802,5.0247,4.8967,4.0116,2.6636,5.4684,4.3454,6.6893,6.5624,6.1464,5.4444,5.3309,5.6682,5.5012,4.3162,2.9028,3.3906,4.6684,4.914,5.3601,5.5138,6.2191,5.8043,5.8165,5.1155,3.8681,4.9268,4.0232,6.1699,4.6327,5.7364,6.1934,6.7188,5.0755,5.7058,5.3703,4.3921,3.2108,4.9503,4.1003,4.8225,4.6701,4.7166
CATMA5A12680,1.7891,1.362,1.4303,0.61973,0.54124,1.2733,0.7775,0.66788,1.7338,0.63125,1.5852,0.91616,1.0712,0.81919,0.90152,0.77799,1.0835,0.32735,2.0868,0.5895,1.1445,0.64545,0.71776,0.33929,1.42,1.0729,1.4879,0.86379,0.95453,1.2928,0.97753,1.1738,0.79397,0.31563,0.86345,0.67595,1.3501,1.079,1.2034,1.2432,1.3696,1.1473,0.72528,0.57199,0.85965,0.65339,0.47614,0.43219,1.363,2.4026,0.98947,0.85204,1.2591,2.3151,1.6775,1.6555,1.0371,2.5586,-0.097071,0.91744,1.068,1.6819,1.0122,0.99911,0.75267,1.2195,0.42175,0.2554,0.4819,0.80025,0.44517,0.32442,1.9863,1.5019,1.2893,1.0669,1.6881,1.7581,1.6505,1.943,2.141,1.018,0.68207,0.84392,1.1452,1.009,1.1376,1.0572,1.9258,1.7039,0.10261,1.4549,0.94693,0.77945,0.049658,0.84178
CATMA5A14990,0.41247,0.51378,0.375,-0.24526,0.027658,-0.015345,-0.028278,-0.2643,0.3905,0.12299,0.94334,0.11482,0.50109,-0.55162,0.10768,-0.26433,-0.31064,-0.76526,1.0147,-0.73178,0.3323,-0.13657,-0.14013,-0.17878,0.31259,0.36723,0.55499,-0.2664,0.31862,0.60225,0.078919,0.18282,0.23014,-0.27835,0.16392,0.58131,1.052,0.062394,0.064929,-0.11918,-0.2167,-0.26018,-0.21649,-0.032523,-0.05763,-0.62775,-0.29148,0.13633,0.72715,1.535,0.08501,0.76137,0.54934,1.5163,-0.26612,0.58127,0.30339,1.8464,-0.17161,0.30309,-0.070279,0.28016,-0.036628,0.67464,0.34753,0.26499,-0.17856,-0.61162,-0.17941,-0.01017,0.21112,0.026389,0.64566,-0.39685,0.31403,-0.21637,1.1253,0.41434,0.77759,0.20421,0.70657,0.53273,-0.67276,-0.65656,0.6997,-0.06428,-0.13546,-0.15055,0.99804,1.4673,-0.47155,-0.056436,0.84609,-0.23068,-0.55114,0.68975
CATMA5A25890,-0.23072,0.82252,0.32512,0.13284,0.14406,0.005071,0.40953,-0.11252,-0.6367,-0.048073,0.21325,-0.046316,-0.35078,1.1023,-0.019882,-0.55825,-0.34621,0.1114,0.030302,-0.08142,0.19157,0.5152,-0.034952,0.20984,1.0547,0.4816,0.31078,0.14731,0.31811,-0.27275,-0.47217,-0.043207,0.29109,0.41192,-1.069,0.23357,-0.17453,0.015661,-0.26869,0.14587,0.19336,0.5926,-0.1583,0.61242,0.18635,0.01207,-0.21772,0.13986,1.9305,0.3349,0.62954,-0.04455,1.121,-0.14155,-0.33772,-0.42633,0.41828,-0.29794,-0.7107,1.4786,-0.37442,-0.45371,0.16175,-0.057764,-0.11803,-0.037838,0.25868,0.88605,0.33225,-0.24331,0.93084,-0.33266,0.9762,0.24755,0.30857,0.14661,1.8351,0.40313,0.45084,-0.52105,-0.31271,-0.37122,-0.28615,-0.0023692,-0.43072,-0.24417,-0.5184,0.13874,-0.32606,-0.1705,2.1396,-0.77657,2.3056,0.32096,0.29337,-0.034746
CATMA5A31940,4.4307,4.8735,4.7911,5.8201,5.9587,5.576,5.9084,5.849,5.2832,5.5423,5.0275,4.5254,4.5054,3.0696,5.6512,4.3986,4.9856,4.9222,4.951,4.5506,4.2185,4.3184,5.4783,4.4515,3.9397,5.6269,5.0988,6.1254,5.7446,5.6146,5.452,5.2047,5.3527,5.93,4.8931,5.5529,3.9876,4.1072,4.0583,5.4324,5.4681,4.392,4.5189,5.0629,4.9347,5.2165,4.6681,4.7428,4.1539,6.3376,5.353,6.2896,5.5844,5.1654,5.4391,5.3933,5.261,5.6974,4.5142,3.9851,3.569,4.0946,4.6662,5.1085,5.0319,4.4598,4.8672,3.7326,5.3032,5.0448,4.7771,4.2209,4.1886,4.284,5.4681,5.8331,4.7683,5.6614,5.3313,5.2017,5.1002,5.0642,4.4867,4.8576,4.1454,4.0971,5.0368,4.845,4.753,5.5496,2.9838,5.1394,3.342,4.1246,5.0121,4.618
Code proper (BHC is from Bioconductor):
library(argparse)
library(RColorBrewer)
library(BHC)
library(parallel)
library(gplots)
#data prep and clustering, to get dendrograms etc as shown in plot
data = read.csv('input.csv',header=TRUE,row.names=1,check.names=FALSE)
genes = rownames(data)
samples = colnames(data)
data = data.matrix(data)
standardisedData = (data-mean(data))/sd(data)
samples = as.numeric(samples)
hc = bhc(standardisedData, genes, timePoints=samples, dataType='time-course', verbose=TRUE)
#the plotting proper
png('heatmap.png',width=6,height=6,units='in',res=300)
heatmap.2(standardisedData, Colv=NA, Rowv=hc, tracecol=NA, scale="none", col=brewer.pal(11,'RdBu'))
#heatmap.2(standardisedData, Colv=NA, Rowv=hc, tracecol=NA, scale="none", col=brewer.pal(11,'RdBu'), margins=c(3,10)) <- this fixes it on a single-case basis
dev.off()

I would suggest just giving it a fairly wide default margin, such that it should be clipped even with longer labels, e.g.:
heatmap.2(standardisedData, Colv=NA, Rowv=hc, tracecol=NA, scale="none", col=brewer.pal(11,'RdBu'), margins=c(14, 14))
The default margin size is (5,5). The above increases the both the column and row margins, but you can increase just one or the other as well.
If you want to be more precise, you could try using max(strwidth(rownames(standardisedData))) to determine the amount of space needed by the largest label, and then convert that into the units expected by par(mar=*) (what heatmap.2() uses to specify plot margins.)
I think this should do the trick:
# determine margins to use
png('/tmp/trash', width=6,height=6,units='in',res=300)
plot.new()
margin_width = max(strwidth(rownames(standardisedData), units='inches')) * par('fin')[1]
dev.off()
#the plotting proper
png('heatmap.png',width=6,height=6,units='in',res=300)
heatmap.2(standardisedData, Colv=NA, Rowv=hc, tracecol=NA, scale="none", col=brewer.pal(11,'RdBu'), margins=c(5, margin_width))
dev.off()

How to control plot layout for lmerTest output results?

I am using lme4 and lmerTest to run a mixed model and then use backward variable elimination (step) for my model. This seems to work well. After running the 'step' function in lmerTest, I plot the final model. The 'plot' results appear similar to ggplot2 output.
I would like to change the layout of the plot. The obvious answer is to do it manually myself creating an original plot(s) with ggplot2. If possible, I would like to simply change the layout of of the output, so that each plot (i.e. plotted dependent variable in the final model) are in their own rows.
See below code and plot to see my results. Note plot has three columns and I would like three rows. Further, I have not provided sample data (let me know if I need too!).
library(lme4)
library(lmerTest)
# Full model
Female.Survival.model.1 <- lmer(Survival.Female ~ Location + Substrate + Location:Substrate + (1|Replicate), data = Transplant.Survival, REML = TRUE)
# lmerTest - backward stepwise elimination of dependent variables
Female.Survival.model.ST <- step(Female.Survival.model.1, reduce.fixed = TRUE, reduce.random = FALSE, ddf = "Kenward-Roger" )
Female.Survival.model.ST
plot(Female.Survival.model.ST)

The function that creates these plots is called plotLSMEANS. You can look at the code for the function via lmerTest:::plotLSMEANS. The reason to look at the code is 1) to verify that, indeed, the plots are based on ggplot2 code and 2) to see if you can figure out what needs to be changed to get what you want.
In this case, it sounds like you'd want facet_wrap to have one column instead of three. I tested with the example from the **lmerTest* function step help page, and it looks like you can simply add a new facet_wrap layer to the plot.
library(ggplot2)
plot(Female.Survival.model.ST) +
facet_wrap(~namesforplots, scales = "free", ncol = 1)

Try this: plot(difflsmeans(Female.Survival.model.ST$model, test.effs = "Location "))

Error plotting Kohonen maps in R?

I was reading through this blog post on R-bloggers and I'm confused by the last section of the code and can't figure it out.
http://www.r-bloggers.com/self-organising-maps-for-customer-segmentation-using-r/
I've attempted to recreate this with my own data. I have 5 variables that follow an exponential distribution with 2755 points.
I am fine with and can plot the map that it generates:
plot(som_model, type="codes")
The section of the code I don't understand is the:
var <- 1
var_unscaled <- aggregate(as.numeric(training[,var]),by=list(som_model$unit.classif),FUN = mean, simplify=TRUE)[,2]
plot(som_model, type = "property", property=var_unscaled, main = names(training)[var], palette.name=coolBlueHotRed)
As I understand it, this section of the code is suppose to be plotting one of the variables over the map to see what it looks like but this is where I run into problems. When I run this section of the code I get the warning:
Warning message:
In bgcolors[!is.na(showcolors)] <- bgcol[showcolors[!is.na(showcolors)]] :
number of items to replace is not a multiple of replacement length
and it produces the plot:
Which just some how doesn't look right...
Now what I think it has come down to is the way the aggregate function has re-ordered the data. The length of var_unscaled is 789 and the length of som_model$data, training[,var] and unit.classif are all of length 2755. I tried plotting the aggregated data, the result was no warning but an unintelligible graph (as expected).
Now I think it has done this because unit.classif has a lot of repeated numbers inside it and that's why it has reduced in size.
The question is, do I worry about the warning? Is it producing an accurate graph? What exactly is the "Property"'s section looking for in the plot command? Is there a different way I could "Aggregate" the data?

I think that you have to create the palette color. If you put the argument
coolBlueHotRed <- function(n, alpha = 1) {rainbow(n, end=4/6, alpha=alpha)[n:1]}
and then try to get a plot, for example
plot(som_model, type = "count", palette.name = coolBlueHotRed)
the end is succesful.
This link can help you: http://rgm3.lab.nig.ac.jp/RGM/R_rdfile?f=kohonen/man/plot.kohonen.Rd&d=R_CC

I think that not all of the cells on your map have points inside.
You have 30 by 30 map and about 2700 points. In average it's about 3 points per cell. With high probability some cells have more than 3 points and some cells are empty.
The code in the post on R-bloggers works well when all of the cells have points inside.
To make it work on your data try change this part:
var <- 1
var_unscaled <- aggregate(as.numeric(training[, var]), by = list(som_model$unit.classif), FUN = mean, simplify = TRUE)[, 2]
plot(som_model, type = "property", property = var_unscaled, main = names(training)[var], palette.name = coolBlueHotRed)
with this one:
var <- 1
var_unscaled <- aggregate(as.numeric(data.temp[, data.classes][, var]),
by = list(som_model$unit.classif),
FUN = mean,
simplify = T)
v_u <- rep(0, max(var_unscaled$Group.1))
v_u[var_unscaled$Group.1] <- var_unscaled$x
plot(som_model,
type = "property",
property = v_u,
main = colnames(data.temp[, data.classes])[var],
palette.name = coolBlueHotRed)
Hope it helps.

Just add these functions to your script:
coolBlueHotRed <- function(n, alpha = 1) {rainbow(n, end=4/6, alpha=alpha)[n:1]}
pretty_palette <- c("#1f77b4","#ff7f0e","#2ca02c", "#d62728","#9467bd","#8c564b","#e377c2")

Resources