I'm preparing my data for a PCA, for which I need to standardize it. I've been following someone else's code in vegan but am not getting a mean of zero and SD of 1, as I should be.
I'm using a data set called musci which has 13 variables, three of which are labels to identify my data.
log.musci<-log(musci[,4:13],10)
stand.musci<-decostand(log.musci,method="standardize",MARGIN=2)
When I then check for mean=0 and SD=1...
colMeans(stand.musci)
sapply(stand.musci,sd)
I get mean values ranging from -8.9 to 3.8 and SD values are just listed as NA (for every data point in my data set rather than for each variable). If I leave out the last variable in my standardization, i.e.
log.musci<-log(musci[,4:12],10)
the means don't change, but the SDs now all have a value of 1.
Any ideas of where I've gone wrong?
Cheers!
You data is likely a matrix.
## Sample data
dat <- as.matrix(data.frame(a=rnorm(100, 10, 4), b=rexp(100, 0.4)))
So, either convert to a data.frame and use sapply to operate on columns
dat <- data.frame(dat)
scaled <- sapply(dat, scale)
colMeans(scaled)
# a b
# -2.307095e-16 2.164935e-17
apply(scaled, 2, sd)
# a b
# 1 1
or use apply to do columnwise operations
scaled <- apply(dat, 2, scale)
A z-transformation is quite easy to do manually.
See below using a random string of data.
data <- c(1,2,3,4,5,6,7,8,9,10)
data
mean(data)
sd(data)
z <- ((data - mean(data))/(sd(data)))
z
mean(z) == 0
sd(z) == 1
The logarithm transformation (assuming you mean a natural logarithm) is done using the log() function.
log(data)
Hope this helps!
Related
I'm trying to assess the feasibility of an instrumental variable in my project with a variable I havent seen before. The variable essentially is an interaction between the mean and standard deviation of a sample drawn from a gaussian, and im trying to see what this distribution might look like. Below is what im trying to do, any help is much appreciated.
Generate a set of 1000 individuals with a variable x following the gaussian distribution, draw 50 random samples of 5 individuals from this distribution with replacement, calculate the means and standard deviation of x for each sample, create an interaction variable named y which is calculated by multiplying the mean and standard deviation of x for each sample, plot the distribution of y.
Beginners version
There might be more efficient ways to code this, but this is easy to follow, I guess:
stat_pop <- rnorm(1000, mean = 0, sd = 1)
N = 50
# As Ben suggested, we create a data.frame filled with NA values
samples <- data.frame(mean = rep(NA, N), sd = rep(NA, N))
# Now we use a loop to populate the data.frame
for(i in 1:N){
# draw 5 samples from population (without replacement)
# I assume you want to replace for each turn of taking 5
# If you want to replace between drawing each of the 5,
# I think it should be obvious how to adapt the following code
smpl <- sample(stat_pop, size = 5, replace = FALSE)
# the data.frame currently has two columns. In each row i, we put mean and sd
samples[i, ] <- c(mean(smpl), sd(smpl))
}
# $ is used to get a certain column of the data.frame by the column name.
# Here, we create a new column y based on the existing two columns.
samples$y <- samples$mean * samples$sd
# plot a histogram
hist(samples$y)
Most functions here use positional arguments, i.e., you are not required to name every parameter. E.g., rnorm(1000, mean = 0, sd = 1) is the same as rnorm(1000, 0, 1) and even the same as rnorm(1000), since 0 and 1 are the default values.
Somewhat more efficient version
In R, loops are very inefficient and, thus, ought to be avoided. In case of your question, it does not make any noticeable difference. However, for large data sets, performance should be kept in mind. The following might be a bit harder to follow:
stat_pop <- rnorm(1000, mean = 0, sd = 1)
N = 50
n = 5
# again, I set replace = FALSE here; if you meant to replace each individual
# (so the same individual can be drawn more than once in each "draw 5"),
# set replace = TRUE
# replicate repeats the "draw 5" action N times
smpls <- replicate(N, sample(stat_pop, n, replace = FALSE))
# we transform the output and turn it into a data.frame to make it
# more convenient to work with
samples <- data.frame(t(smpls))
samples$mean <- rowMeans(samples)
samples$sd <- apply(samples[, c(1:n)], 1, sd)
samples$y <- samples$mean * samples$sd
hist(samples$y)
General note
Usually, you should do some research on the problem before posting here. Then, you either find out how it works by yourself, or you can provide an example of what you tried. To this end, you can simply google each of the steps you outlined (e.g., google "generate random standard distribution R" in order to find out about the function rnorm().
Run ?rnorm to get help on the function in RStudio.
I have just started my basic statistic course using R and we're studying using R for paired t-tests. I have come across questions where we're given two sets of data and we're asked to find whether the difference in mean is equal to 0 or greater than 0 so on so forth. The function we use for two samples x and y with an unknown variance is similar to the one below;
t.test(x, y, var.equal=TRUE, alternative="greater")
My question is, how would we to do this if we wanted to test the difference in mean is more than or equal to a specified number against the alternative that its less than a specific number and not 0.
For example, say we're given two datas for before and after weights of 10 people. How do we test that the mean difference in weight is more than or equal to say 3kg against the alternative where the mean difference in weight is less than 3kg. Is there a way to do this? Would really appreciate any guidance on this matter.
It might be worthwhile posting on https://stats.stackexchange.com/ as well if you're in need of more theoretical proof. Is it ok to add/subtract the 3kg from either x or y and then use the t-test to check for similarity? I think this would tell you at least which outcome is more likely, if that's the end goal. It would be good to get feedback on this
# number of obs, and rnorm dist for simulating
N <- 10
mu <- 70
sd <- 10
set.seed(1)
x <- round(rnorm(N, mu, sd), 1)
# three outcomes
# (1) no change
y_same <- x + round(rnorm(N, 0, 5), 1)
# (2) average increase of 3
y_imp <- x + rnorm(N, 3, 5)
# (3) average decrease of 3
y_dec <- x + rnorm(N, -3, 5)
# say y_imp is true
y_act <- y_imp
# can we test whether we're closer to the output by altering
# the original data? or conversely, altering y_imp
t_inc <- t.test(x+3, y_act, var.equal=TRUE, alternative="two.sided")
t_dec <- t.test(x-3, y_act, var.equal=TRUE, alternative="two.sided")
t_inc$p.value
[1] 0.8279801
t_dec$p.value
[1] 0.0956033
# one with the highest p.value has the closest distribution, so
# +3 kg more likely than -3kg
You can set mu=3 to change the null hypothesis from 0 to 3 assuming your x variables are in the units you describe above.
t.test(x, y, mu=3, alternative="greater", paired=TRUE)
More (general) information on Stack Exchange [here].(https://stats.stackexchange.com/questions/206316/can-a-paired-or-two-group-t-test-test-if-the-difference-between-two-means-is-l/206317#206317)
I need to simulate 1000 sets of normal distribution(each 60 subgroups, n=5) by using r programming. Each set of normal distribution is contaiminated with 4 outliers(more than 1.5 IQR). can anyone help?
Thanks in advance
A very simple approach to create a data.frame with a few outliers :
# Create a vector with normally distributed values and a few outliers
# N - Number of random values
# n.out - number of outliers
my.rnorm <- function(N, num.out, mean=0, sd=1){
x <- rnorm(N, mean = mean, sd = sd)
ind <- sample(1:N, num.out, replace=FALSE )
x[ind] <- (abs(x[ind]) + 3*sd) * sign(x[ind])
x
}
N=60
num.out = 4
df <- data.frame( col1 = my.rnorm(N, num.out),
col2 = my.rnorm(N, num.out),
col3 = my.rnorm(N, num.out),
col4 = my.rnorm(N, num.out),
col5 = my.rnorm(N, num.out))
Please note that I used mean=0 and sd=1 as values mean=1, sd=0 that you provided in the comments do not make much sense.
The above approach does not guarantee that there will be exactly 4 outliers. There will be at least 4, but in some rare cases there could be more as rnorm() function does not guarantee that it never produces outliers.
Another note is that data.frames might not be the best objects to store numeric values. If all your 1000 data.frames are numeric, it is better to store them in matrices.
Depending on the final goal and the type of the object you store your data in (list, data.frame or matrix) there are faster ways to create 1000 objects filled with random values.
For a science project, I am looking for a way to generate random data in a certain range (e.g. min=0, max=100000) with a certain correlation with another variable which already exists in R. The goal is to enrich the dataset a little so I can produce some more meaningful graphs (no worries, I am working with fictional data).
For example, I want to generate random values correlating with r=-.78 with the following data:
var1 <- rnorm(100, 50, 10)
I already came across some pretty good solutions (i.e. https://stats.stackexchange.com/questions/15011/generate-a-random-variable-with-a-defined-correlation-to-an-existing-variable), but only get very small values, which I cannot transform so the make sense in the context of the other, original values.
Following the example:
var1 <- rnorm(100, 50, 10)
n <- length(var1)
rho <- -0.78
theta <- acos(rho)
x1 <- var1
x2 <- rnorm(n, 50, 50)
X <- cbind(x1, x2)
Xctr <- scale(X, center=TRUE, scale=FALSE)
Id <- diag(n)
Q <- qr.Q(qr(Xctr[ , 1, drop=FALSE]))
P <- tcrossprod(Q) # = Q Q'
x2o <- (Id-P) %*% Xctr[ , 2]
Xc2 <- cbind(Xctr[ , 1], x2o)
Y <- Xc2 %*% diag(1/sqrt(colSums(Xc2^2)))
var2 <- Y[ , 2] + (1 / tan(theta)) * Y[ , 1]
cor(var1, var2)
What I get for var2 are values ranging between -0.5 and 0.5. with a mean of 0. I would like to have much more distributed data, so I could simply transform it by adding 50 and have a quite simililar range compared to my first variable.
Does anyone of you know a way to generate this kind of - more or less -meaningful data?
Thanks a lot in advance!
Starting with var1, renamed to A, and using 10,000 points:
set.seed(1)
A <- rnorm(10000,50,10) # Mean of 50
First convert values in A to have the new desired mean 50,000 and have an inverse relationship (ie subtract):
B <- 1e5 - (A*1e3) # Note that { mean(A) * 1000 = 50,000 }
This only results in r = -1. Add some noise to achieve the desired r:
B <- B + rnorm(10000,0,8.15e3) # Note this noise has mean = 0
# the amount of noise, 8.15e3, was found through parameter-search
This has your desired correlation:
cor(A,B)
[1] -0.7805972
View with:
plot(A,B)
Caution
Your B values might fall outside your range 0 100,000. You might need to filter for values outside your range if you use a different seed or generate more numbers.
That said, the current range is fine:
range(B)
[1] 1668.733 95604.457
If you're happy with the correlation and the marginal distribution (ie, shape) of the generated values, multiply the values (that fall between (-.5, +.5) by 100,000 and add 50,000.
> c(-0.5, 0.5) * 100000 + 50000
[1] 0e+00 1e+05
edit: this approach, or any thing else where 100,000 & 50,000 are exchanged for different numbers, will be an example of a 'linear transformation' recommended by #gregor-de-cillia.
I have got a text file containing 200 models all compared to eachother and a molecular distance for each 2 models compared. It looks like this:
1 2 1.2323
1 3 6.4862
1 4 4.4789
1 5 3.6476
.
.
All the way down to 200, where the first number is the first model, the second number is the second model, and the third number the corresponding molecular distance when these two models are compared.
I can think of a way to import this into R and create a nice 200x200 matrix to perform some clustering analyses on. I am still new to Stack and R but thanks in advance!
Since you don't have the distance between model1 and itself, you would need to insert that yourself, using the answer from this question:
(you can ignore the wrong numbering of the models compared to your input data, it doesn't serve a purpose, really)
# Create some dummy data that has the same shape as your data:
df <- expand.grid(model1 = 1:120, model2 = 2:120)
df$distance <- runif(n = 119*120, min = 1, max = 10)
head(df)
# model1 model2 distance
# 1 2 7.958746
# 2 2 1.083700
# 3 2 9.211113
# 4 2 5.544380
# 5 2 5.498215
# 6 2 1.520450
inds <- seq(0, 200*119, by = 200)
val <- c(df$distance, rep(0, length(inds)))
inds <- c(seq_along(df$distance), inds + 0.5)
val <- val[order(inds)]
Once that's in place, you can use matrix() with the ncol and nrow to "reshape" your vector of distance in the appropriate way:
matrix(val, ncol = 200, nrow = 200)
Edit:
When your data only contains the distance for one direction, so only between e.g. model1 - model5 and not model5 - model1 , you will have to fill the values in the upper triangular part of a matrix, like they do here. Forget about the data I generated in the first part of this answer. Also, forget about adding the ones to your distance column.
dist_mat <- diag(200)
dist_mat[upper.tri(dist_mat)] <- your_data$distance
To copy the upper-triangular entries to below the diagonal, use:
dist_mat[lower.tri(dist_mat)] <- t(dist_mat)[lower.tri(dist_mat)]
As I do not know from your question what format is your file in, I will assume the most general file format, i.e., CSV.
Then you should look at the reading files, read.csv, or fread.
Example code:
dt <- read.csv(file, sep = "", header = TRUE)
I suggest using data.table package. Then:
setDT(dt)
dt[, id := paste0(as.character(col1), "-", as.character(col2))]
This creates a new variable out of the first and the second model and serves as a unique id.
What I do is then removing this id and scale the numerical input.
After scaling, run clustering algorithms.
Merge the result with the id to analyse your results.
Is that what you are looking for?