How can I adapt the size of the following plots with regard to their length of the x-axis?
The width of the plots should refer to the length of their respective section of the x-axis. The height should be the same for all plots.
The function you want is base graphics function help("layout").
First I will make up a dataset, since you have not posted one. I will not draw the regression lines, just the points.
Data creation code.
fun <- function(X, A) {
apply(X, 1, function(.x){
xx <- seq(.x[1], .x[2], length.out = 100)
y <- A[1]*xx + A[2] + rnorm(100, 0, 25)
list(xx, y)
})}
Coef <- matrix(c(0.24, 0.54,
0.75, 0.54,
0.33, 2.17,
0.29, 3.3,
0.29, 4.41), byrow = TRUE, ncol = 2)
X <- matrix(c(0.1, 0.49,
0.5, 2.49,
2.5, 3.9,
4.0, 5.9,
6.0, 12.0), byrow = TRUE, ncol = 2)
set.seed(1234)
res <- fun(X, Coef)
The problem.
Define a layout matrix with each plot in a sequence from first to 5th. And the widths given by the X ranges.
layout_mat <- matrix(c(1, 2, 3, 4, 5), 1, 5, byrow = TRUE)
w <- apply(X, 1, diff)
l <- layout(layout_mat, widths = w)
layout.show(l)
Now make some room for the axis annotation, saving the default graphics parameters, and plot the 5 graphs.
om <- par(mar = c(3, 0.1, 0.1, 0.1),
oma = c(3, 2, 0.1, 0.1))
for(i in 1:5) plot(res[[i]][[1]], res[[i]][[2]])
par(om)
Related
I have a dataframe with 3 columns, example like this (purely hypothetical):
id <- c("Muller", "Muller", "Ter Stegen", "Musiala", "Musiala", "Musiala", "Pavard")
tid <- c("Davies", "De Ligt", "Muller", "Kimmich", "Pavard", "Lewandowski", "De Ligt")
Passes <- c(14, 5, 1, 10, 23, 4, 1)
Passes <- data.frame(id, tid, Passes)
dput(Passes)
And I have been wanting to plot this so that the vertices appear at specific coordinates in the output graph .
So far my codes are like this:
g <- graph.data.frame(Passes, directed = TRUE)
set_edge_attr(g, "weight", value= E(g)$Passes)
coords <- data.frame(id = c("Ter Stegen", "Musiala", "Davies", "Kimmich", 'De Ligt', "Lewandowski", "Muller", "Pavard"),
x= c(0.5, 1, 1, 1, 2, 3, 3, 3.5),
y= c(1, 1.8, 1.4, 1, 0.6, 1.8, 1.6, 1.2))
plot(g, vertex.size= 2, edge.arrow.size = 0.3, vertex.label.cex = 0.8,
edge.curved=.2, asp = 0, vertex.label.dist=0.7,
layout=coords, xlim = c(0, 4), ylim = c(0, 2))
But then I keep getting errors like 'Error in norm_coords(layout, -1, 1, -1, 1) : `layout' not a matrix''
Anyone know what is wrong with my code, or can propose a better method? Thank you! It's just my actual dataframe has 32 unique ids and together there are 252 rows, I want to find an efficient way to give each unique id a position.
Thanks,
Emmy
try
library(tidyverse)
new.coords <- coords %>% arrange(factor(id, levels = V(g))) %>% select(x,y) %>% as.matrix()
plot(g, vertex.size= 2, edge.arrow.size = 0.3, vertex.label.cex = 0.8,
edge.curved=.2, asp = 0, vertex.label.dist=0.7,
layout = new.coords)
I am learning to work with bnlearn and I keep running into the following error in the last line of my code below:
Error in custom.fit(dag, cpt) : wrong number of conditional probability distributions
What am I doing wrong?
modelstring(dag)= "[s][r][nblw|r][nblg|nblw][mlw|s:r][f|s:r:mlw][mlg|mlw:f]
[mlgr|mlg:nblg]"
###View DAG Specifics
dag
arcs(dag)
nodes(dag)
# Create Levels
State <- c("State0", "State1")
##Create probability distributions given; these are all 2d b/c they have 1 or 2 nodes
cptS <- matrix(c(0.6, 0.4), ncol=2, dimnames=list(NULL, State))
cptR <- matrix(c(0.7, 0.3), ncol=2, dimnames=list(NULL, State))
cptNBLW <- matrix(c(0.95, 0.05, 0.05, 0.95), ncol=2, dimnames=list(NULL, "r"= State))
cptNBLG <- matrix(c(0.9, 0.099999999999999998, 0.2, 0.8), ncol=2, dimnames=list(NULL,
"nblw"=State))
cptMLG <- matrix(c(0.95, 0.05, 0.4, 0.6, 0.2, 0.8, 0.05, 0.95),ncol=2,nrow = 2,
dimnames=list("mlw"= State, "f"=State))
cptMLGR <- matrix(c(0.6,0.4,0.95,0.05,0.2,0.8,0.55,0.45),ncol=2,nrow = 2,
dimnames=list("mlg"= State, "nblg"=State))
cptMLW <-matrix(c(0.95, 0.05, 0.1, 0.9, 0.2, 0.8, 0.01, 0.99), ncol=2,nrow = 2,byrow = TRUE,
dimnames=list("r"= State, "s"=State))
# Build 3-d matrices( becuase you have 3 nodes, you can't use the matrix function; you
have to build it from scratch)
cptF <- c(0.05, 0.95, 0.4, 0.6, 0.9, 0.1, 0.99, 0.01, 0.9, 0.1, 0.95, 0.05, 0.95, 0.05, 0.99,
0.01)
dim(cptF) <- c(2, 2, 2, 2)
dimnames(cptF) <- list("s"=State, "r"=State, "mlw"=State)
###Create CPT Table
cpt <- list(s = cptS, r = cptR, mlw = cptMLW,nblw= cptNBLW,
mlg= cptMLG, nblg= cptNBLG, mlgr= cptMLGR)
# Construct BN network with Conditional Probability Table
S.net <- custom.fit(dag,cpt)
Reference: https://rpubs.com/sarataheri/bnlearnCGM
You have several errors in your CPT definitions. Primarily, you need to make sure that:
the number of probabilities supplied are equal to the product of the number of states in the child and parent nodes,
that the number of dimensions of the matrix/array is equal to the number of parent nodes plus one, for the child node,
the child node should be given in the first dimension when the node dimension is greater than one.
the names given in the dimnames arguments (e.g. the names in dimnames=list(ThisName = ...)) should match the names that were defined in the DAG, in your case with modelstring and in my answer with model2network. (So my earlier suggestion of using dimnames=list(cptNBLW = ...) should be dimnames=list(nblw = ...) to match how node nblw was declared in the model string)
You also did not add node f into your cpt list.
Below is your code with comments where things have been changed. (I have commented out the offending lines and added ones straight after)
library(bnlearn)
dag <- model2network("[s][r][nblw|r][nblg|nblw][mlw|s:r][mlg|mlw:f][mlgr|mlg:nblg][f|s:r:mlw]")
State <- c("State0", "State1")
cptS <- matrix(c(0.6, 0.4), ncol=2, dimnames=list(NULL, State))
cptR <- matrix(c(0.7, 0.3), ncol=2, dimnames=list(NULL, State))
# add child node into first slot of dimnames
cptNBLW <- matrix(c(0.95, 0.05, 0.05, 0.95), ncol=2, dimnames=list(nblw=State, "r"= State))
cptNBLG <- matrix(c(0.9, 0.099999999999999998, 0.2, 0.8), ncol=2, dimnames=list(nblg=State,"nblw"=State))
# Use a 3d array and not matrix, and add child node into dimnames
# cptMLG <- matrix(c(0.95, 0.05, 0.4, 0.6, 0.2, 0.8, 0.05, 0.95),ncol=2,nrow = 2, dimnames=list("mlw"= State, "f"=State))
cptMLG <- array(c(0.95, 0.05, 0.4, 0.6, 0.2, 0.8, 0.05, 0.95),dim=c(2,2,2), dimnames=list(mlg = State, "mlw"= State, "f"=State))
# cptMLGR <- matrix(c(0.6,0.4,0.95,0.05,0.2,0.8,0.55,0.45),ncol=2,nrow = 2, dimnames=list("mlg"= State, "nblg"=State))
cptMLGR <- array(c(0.6,0.4,0.95,0.05,0.2,0.8,0.55,0.45), dim=c(2,2,2), dimnames=list(mlgr=State, "mlg"= State, "nblg"=State))
# cptMLW <-matrix(c(0.95, 0.05, 0.1, 0.9, 0.2, 0.8, 0.01, 0.99), ncol=2,nrow = 2,byrow = TRUE, dimnames=list("r"= State, "s"=State))
cptMLW <-array(c(0.95, 0.05, 0.1, 0.9, 0.2, 0.8, 0.01, 0.99), dim=c(2,2,2), dimnames=list(mlw=State, "r"= State, "s"=State))
# add child into first slot of dimnames
cptF <- c(0.05, 0.95, 0.4, 0.6, 0.9, 0.1, 0.99, 0.01, 0.9, 0.1, 0.95, 0.05, 0.95, 0.05, 0.99, 0.01)
dim(cptF) <- c(2, 2, 2, 2)
dimnames(cptF) <- list("f" = State, "s"=State, "r"=State, "mlw"=State)
# add missing node f into list
cpt <- list(s = cptS, r = cptR, mlw = cptMLW,nblw= cptNBLW, mlg= cptMLG, nblg= cptNBLG, mlgr= cptMLGR, f=cptF)
# Construct BN network with Conditional Probability Table
S.net <- custom.fit(dag, dist=cpt)
I am using the below R code to simulate time series data, moving average of order 1 to be precise. I am varying 3 variables which are:
N = Number of elements in the series c(15L, 20L, 30L, 50L, 100L)
SD = standard deviation c(1, 2, 3, 4, 5) ^ 2
theta = the \theta value c(0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99)
I have 5 data.frames which you will see as .csv files in your R working directory. Each data.frame has 35 columns that I want properly labeled.
MWE
N <- c(15L, 20L, 30L, 50L, 100L)
SD = c(1, 2, 3, 4, 5) ^ 2
theta = c(0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99)
res <- vector('list', length(N))
names(res) <- paste('N', N, sep = '_')
set.seed(123L)
for (i in seq_along(N)){
res[[i]] <- vector('list', length(SD))
names(res[[i]]) <- paste('SD', SD, sep = '_')
ma <- matrix(NA_real_, nrow = N[i], ncol = length(theta))
for (j in seq_along(SD)){
wn <- rnorm(N[i], mean = 0, sd = SD[j])
ma[1:2, ] <- wn[1:2]
for (k in 3:N[i]){
ma[k, ] <- wn[k - 1L] * theta + wn[k]
}
colnames(ma) <- paste('ma_theta', theta, sep = '_')
res[[i]][[j]] <- ma
}
}
res1 <- lapply(res, function(dat) do.call(cbind, dat))
sapply(names(res1), function(nm) write.csv(res1[[nm]],
file = paste0(nm, ".csv"), row.names = FALSE, quote = FALSE))
I want columnname to be label not only with respect to theta alone but also with SD.
I want the columnname to be labelled like the below. I do not want 2 or more columns to have the same label. I want ma_SD_1... to (with theta=(0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99)) be exhausted before ma_SD_4... (with theta=(0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99)) before ma_SD_9... (with theta=(0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99)) before ma_SD_16... (with theta=(0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99)) before ma_SD_25... (with theta=(0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 0.99)).
ma_SD_1_theta_0.2, ma_SD_1_theta_0.4, ma_SD_1_theta_0.6, ma_SD_1_theta_0.8, ma_SD_1_theta_0.9, ma_SD_1_theta_0.95, ma_SD_1_theta_0.99
ma_SD_4_theta_0.2, ma_SD_4_theta_0.4, ma_SD_4_theta_0.6, ma_SD_4_theta_0.8, ma_SD_4_theta_0.9, ma_SD_4_theta_0.95, ma_SD_4_theta_0.99
ma_SD_9_theta_0.2, ma_SD_9_theta_0.4, ma_SD_9_theta_0.6, ma_SD_9_theta_0.8, ma_SD_9_theta_0.9, ma_SD_9_theta_0.95, ma_SD_9_theta_0.99
ma_SD_1_theta_0.2, ma_SD_16_theta_0.4, ma_SD_16_theta_0.6, ma_SD_16_theta_0.8, ma_SD_16_theta_0.9, ma_SD_16_theta_0.95, ma_SD_16_theta_0.99
This should do it as you are iterating (using j) over the SD:
colnames(ma) <- paste('ma_SD',SD[j],'theta', theta, sep = '_')
When i use code from example:
library(deepnet)
Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2))
Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1))
x <- matrix(c(Var1, Var2), nrow = 100, ncol = 2)
y <- c(rep(1, 50), rep(0, 50))
nn <- dbn.dnn.train(x, y, hidden = c(5))
it works. But when i use this code:
Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2))
Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1))
x <- matrix(c(Var1, Var2), nrow = 100, ncol = 2)
**y <- c(rep("1", 50), rep("0", 50))**
nn <- dbn.dnn.train(x, y, hidden = c(5))
i receive error:
Error in batch_y - nn$post[[i]] : non-numeric argument to binary operator
How can i use deepnet package for classification problem?
y1 <- c(rep("1", 50), rep("0", 50))
lead you to character vector which is not acceptable by the package. so that you get error
class(y)
#[1] "character"
The right y should be numeric as follows
y <- c(rep(1, 50), rep(0, 50))
class(y)
#[1] "numeric"
if you see inside your y , you can find that you have 1 or 0 which is a binary values for classification
> table(y)
#y
# 0 1
#50 50
If you want to train as it is mentioned in the manual, you can do the following to train and predict a test set
Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2))
Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1))
x <- matrix(c(Var1, Var2), nrow = 100, ncol = 2)
y <- c(rep(1, 50), rep(0, 50))
If you now look at your x and y by str just simply write str(x) or str(y) you can see that they are numeric (to make sure, you can check them by class(x) and class(y).
After having your X and y , then you can build your model
dnn <- dbn.dnn.train(x, y, hidden = c(5, 5))
If you have a test set to predict, then you can predict it using for example as is mentioned in the manual
test_Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2))
test_Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1))
test_x <- matrix(c(test_Var1, test_Var2), nrow = 100, ncol = 2)
nn.test(dnn, test_x, y)
#[1] 0.25
Again your test_x must be numeric. If your problem is that you have the values as character, then you can convert it to numeric by mydata<- as.numeric()
I would like to put in each box from the ploted contingency table, the value obtained by the table.
The following image represent the contingency table
Te following code is how to display the contingency table:
> svm.video.table2<-table(pred=svm.video.pred2, true= filteredDataFinal$rate)
> svm.video.table2
An this one is how to plot that table
plot(svm.video.table2)
And adhoc approach would be:
text(x = 0.23, y = 0.55, "10")
text(x = 0.23, y = 0.67, "2")
text(x = 0.64, y = 0.94, "1")
text(x = 0.64, y = 0.45, "9")
text(x = 0.92, y = 0.44, "4")
PS: I generated the data to make your example reproducible with svm.video.table2 <- as.table(matrix(c(10, 1, 0, 2, 9, 0, 0, 0, 4), ncol = 3))