I am trying to get a more meaningful version of the data plotted when a categorical predictor appears in the output of a tree function.
The values are airport codes: FLR, FUE, GOA, HER etc,
If I use tree() and
plot(Simulate.tree2); text(Simulate.tree2, pretty=1)
I get:
Which is not bad, but the codes are abbreviated and not clear.
If I use maptree() and
draw.tree(Simulate.tree2)
I get:
which is not at all helpful, since the letters just indicate the position of the value in a vector (I assume)
Is there a way in either package (or both) to get the actual values printed?
Have you tried this?
plot(Simulate.tree2)
text(Simulate.tree1, pretty = 3)
From the documentation, it looks like passing an integer to pretty sets the minimum length of the labels at that integer value. So for airport codes, you'd want 3.
Related
I found a sample code with iris data set in R language.
I want to use the same code but with other data set(heart disease dataset) which has only numerical values.will that work?
Make sure your data doesn't contain missing values. If values are missing, Compiler will throw an error, while model building stage. So, if some data points are missing, probably you should try for imputing them.
Also Make sure your output Variable/class variable is Categorical in nature.
Also, if its binary classification problem and labels are 0,1
make sure you encode those 0's and 1's to proper text labels and then convert them into factor's.
Example for encoding numbers into categorical's
data$class <- ifelse(data$class==0,"not_found","found")
data$class <- as.factor(data$class,levels=c("found","not_found))
I know that has been asked quite frequently. However, by applying the previous advice I'm still confused about two things.
How to convert from multinomial values to integers?
How to get the integer back to the factor/character after the analysis?
library(car)
data(Prestige)
View(Prestige)
# here I convert directly from character which seems quite useless
Prestige$TYPE<-as.numeric(levels(Prestige$type))
# here I generate factors
Prestige$type<-as.factor(Prestige$type)
# and try to convert afterwards. doesnt work either
Prestige$TYPE<-as.numeric(levels(Prestige$type))
Basically, I would like to extract the three levels in type without renaming it manually.
A vector with class factor has an attributes called levels. The levels function acts on that attributes and not on the vector itself.
library(car)
data(Prestige)
length(Prestige$type) # 102
levels(Prestige$type) # Notice that this has length 3.
If you want the numeric values for the vector, use
as.numeric(Prestige$type)
What was bc is not 1, what was prof is now 2, and what was wc is now 3.
if you need to reconstitute the factor, use
factor(Prestige$type, 1:3, c("bc", "prof", "wc"))
But as a general rule, it's better not to alter your factors unless you need to alter the categories. If you need the numerical codes under the data, make a new variable
Prestige$type_numeric <- as.numeric(Prestige$type)
I am trying to use a custom function inside 'ddply' in order to create a new variable (NormViability) in my data frame, based on values of a pre-existing variable (CelltiterGLO).
The function is meant to create a rescaled (%) value of 'CelltiterGLO' based on the mean 'CelltiterGLO' values at a specific sub-level of the variable 'Concentration_nM' (0.01).
So if the mean of 'CelltiterGLO' at 'Concentration_nM'==0.01 is set as 100, I want to rescale all other values of 'CelltiterGLO' over the levels of other variables ('CTSC', 'Time_h' and 'ExpType').
The normalization function is the following:
normalize.fun = function(CelltiterGLO) {
idx = Concentration_nM==0.01
jnk = mean(CelltiterGLO[idx], na.rm = T)
out = 100*(CelltiterGLO/jnk)
return(out)
}
and this is the code I try to apply to my dataframe:
library("plyr")
df.bis=ddply(df,
.(CTSC, Time_h, ExpType),
transform,
NormViability = normalize.fun(CelltiterGLO))
The code runs, but when I try to double check (aggregate or tapply) if the mean of 'NormViability' equals '100' at 'Concentration_nM'==0.01, I do not get 100, but different numbers. The fact is that, if I try to subset my df by the two levels of the variable 'ExpType', the code returns the correct numbers on each separated subset. I tried to make 'ExpType' either character or factor but I got similar results. 'ExpType has two levels/values which are "Combinations" and "DoseResponse", respectively. I can't figure out why the code is not working on the entire df, I wonder if this is due to the fact that the two levels of 'ExpType' do not contain the same number of levels for all the other variables, e.g. one of the levels of 'Time_h' is missing for the level "Combinations" of 'ExpType'.
Thanks very much for your help and I apologize in advance if the answer is already present in Stackoverflow and I was not able to find it.
Michele
I (the OP) found out that the function was missing one variable in the arguments, that was used in the statements. Simply adding the variable Concentration_nM to the custom function solved the problem.
THANKS
m.
I have a very large data set that I have binned, and stored each bin (subset) as a list so that I can easily call any given subset. My problem is in calling for a specific column within a subset.
For example my data (which has diameters and strengths as the columns), is broken up into 20 bins, by diameter. I manually binned the data, like so:
subset.1 <- subset(mydata, Diameter <= 0.01)
Similar commands were used, to make 20 bins. Then I stored the names (subset.1 through subset.20) into a list:
diameter.bin<-list(subset.1, ... , subset.20)
I can successfully call each diameter bin using:
diameter.bin[x]
Now, if I only want to see the strength values for a given diameter bin, I can use the original name (that is store in the list):
subset.x$Strength
But I cannot get this information using the list call:
diameter.bin[x]$Strength
This command returns NULL
Note that when I call any subset (either by diameter.bin[x], subset.x or even subset.x$Strength) my column headers do show up. When I use:
names(subset.1)
This returns "Diameter" and "Strength"
But when I use:
names(diameter.bin[1])
This returns NULL.
I'm assuming that the column header is part of the problem, but I'm not sure how to fix it, other than take the headers off of the original data file. I would prefer not to do this if at all possible.
The end goal is to look at the distribution of strength values for each diameter bin, so I will be doing things like drawing histograms, calculating parameters etc. I was hoping to do something along these lines to produce the histograms:
n=length(diameter.bin)
for(i in (1:n))
{
hist(diameter.bin[i]$Strength)
}
And do something similar to this to store median values for each bin in a new vector.
Any tips are greatly appreciated, as right now I'm doing it all 1 bin at a time, and I know a loop (or something similar) would really speed up my analysis.
You need two square brackets. Here is a reproducible example demonstrating the issue:
> diam <- data.frame(x=rnorm(5), y=rnorm(5))
>
> diam.l <- list(diam, diam)
> diam.l[1]$x
NULL
> diam.l[[1]]$x
[1] -0.5389441 -0.5155441 -1.2437108 -2.0044323 -0.6914124
New to R and having problem with a very simple task! I have read a few columns of .csv data into R, the contents of which contains of variables that are in the natural numbers plus zero, and have missing values. After trying to use the non-parametric package, I have two problems: first, if I use the simple command bw=npregbw(ydat=y, xdat=x, na.omit), where x and y are column vectors, I get the error that "number of regression data and response data do not match". Why do I get this, as I have the same number of elements in each vector?
Second, I would like to call the data ordered and tell npregbw this, using the command bw=npregbw(ydat=y, xdat=ordered(x)). When I do that, I get the error that x must be atomic for sort.list. But how is x not atomic, it is just a vector with natural numbers and NA's?
Any clarifications would be greatly appreciated!
1) You probably have a different number of NA's in y and x.
2) Can't be sure about this, since there is no example. If it is of following type:
x <- c(3,4,NA,2)
Then ordered(x) should work fine. Please provide an example of your case.
EDIT: You of course tried bw=npregbw(ydat=y, xdat=x)? ordered() makes your vector an ordered factor (see ?ordered), which is not an atomic vector (see 2.1.1 link and ?factor)
EDIT2: So the problem was the way of subsetting data. Note the difference in various ways of subsetting. data$x and data[,i] (where i = column number of column x) give you vectors, while data[c("x")] and data[i] give a data frame. Functions expect vectors, unless they call for data = (your data). In that case they work with column names