I have a data set generated as follows:
myData <- data.frame(a=1:N,b=round(rnorm(N),2),group=round(rnorm(N,4),0))
The data looks like as this
I would like to generate a stratified sample set of myData with given sample size, i.e., 50. The resulting sample set should follow the proportion allocation of the original data set in terms of "group". For instance, assume myData has 20 records belonging to group 4, then the resulting data set should have 50*20/200=5 records belonging to group 4. How to do that in R.
You can use my stratified function, specifying a value < 1 as your proportion, like this:
## Sample data. Seed for reproducibility
set.seed(1)
N <- 50
myData <- data.frame(a=1:N,b=round(rnorm(N),2),group=round(rnorm(N,4),0))
## Taking the sample
out <- stratified(myData, "group", .3)
out
# a b group
# 17 17 -0.02 2
# 8 8 0.74 3
# 25 25 0.62 3
# 49 49 -0.11 3
# 4 4 1.60 3
# 26 26 -0.06 4
# 27 27 -0.16 4
# 7 7 0.49 4
# 12 12 0.39 4
# 40 40 0.76 4
# 32 32 -0.10 4
# 9 9 0.58 5
# 42 42 -0.25 5
# 43 43 0.70 5
# 37 37 -0.39 5
# 11 11 1.51 6
Compare the counts in the final group with what we would have expected.
round(table(myData$group) * .3)
#
# 2 3 4 5 6
# 1 4 6 4 1
table(out$group)
#
# 2 3 4 5 6
# 1 4 6 4 1
You can also easily take a fixed number of samples per group, like this:
stratified(myData, "group", 2)
# a b group
# 34 34 -0.05 2
# 17 17 -0.02 2
# 49 49 -0.11 3
# 22 22 0.78 3
# 12 12 0.39 4
# 7 7 0.49 4
# 18 18 0.94 5
# 33 33 0.39 5
# 45 45 -0.69 6
# 11 11 1.51 6
Related
I want to simulate some unbalanced clustered data. The number of clusters is 20 and the average number of observations is 30. However, I would like to create an unbalanced clustered data per cluster where there are 10% more observations than specified (i.e., 33 rather than 30). I then want to randomly exclude an appropriate number of observations (i.e., 60) to arrive at the specified average number of observations per cluster (i.e., 30). The probability of excluding an observation within each cluster was not uniform (i.e., some clusters had no cases removed and others had more excluded). Therefore in the end I still have 600 observations in total. Anyone knows how to realize that in R? Here is a smaller example dataset. The number of observation per cluster doesn't follow the condition specified above though, I just used this to convey my idea.
> y <- rnorm(20)
> x <- rnorm(20)
> z <- rep(1:5, 4)
> w <- rep(1:4, each=5)
> df <- data.frame(id=z,cluster=w,x=x,y=y) #this is a balanced dataset
> df
id cluster x y
1 1 1 0.30003855 0.65325768
2 2 1 -1.00563626 -0.12270866
3 3 1 0.01925927 -0.41367651
4 4 1 -1.07742065 -2.64314895
5 5 1 0.71270333 -0.09294102
6 1 2 1.08477509 0.43028470
7 2 2 -2.22498770 0.53539884
8 3 2 1.23569346 -0.55527835
9 4 2 -1.24104450 1.77950291
10 5 2 0.45476927 0.28642442
11 1 3 0.65990264 0.12631586
12 2 3 -0.19988983 1.27226678
13 3 3 -0.64511396 -0.71846622
14 4 3 0.16532102 -0.45033862
15 5 3 0.43881870 2.39745248
16 1 4 0.88330282 0.01112919
17 2 4 -2.05233698 1.63356842
18 3 4 -1.63637927 -1.43850664
19 4 4 1.43040234 -0.19051680
20 5 4 1.04662885 0.37842390
After randomly adding and deleting some data, the unbalanced data become like this:
id cluster x y
1 1 1 0.895 -0.659
2 2 1 -0.160 -0.366
3 1 2 -0.528 -0.294
4 2 2 -0.919 0.362
5 3 2 -0.901 -0.467
6 1 3 0.275 0.134
7 2 3 0.423 0.534
8 3 3 0.929 -0.953
9 4 3 1.67 0.668
10 5 3 0.286 0.0872
11 1 4 -0.373 -0.109
12 2 4 0.289 0.299
13 3 4 -1.43 -0.677
14 4 4 -0.884 1.70
15 5 4 1.12 0.386
16 1 5 -0.723 0.247
17 2 5 0.463 -2.59
18 3 5 0.234 0.893
19 4 5 -0.313 -1.96
20 5 5 0.848 -0.0613
EDIT
This part of the problem solved (credit goes to jay.sf). Next, I want to repeat this process 1000 times and run regression on each generated dataset. However, I don't want to run regression on the whole dataset but rather on some selected clusters with the clusters being selected randomly (can use this function: df[unlist(cluster[sample.int(k, k, replace = TRUE)], use.names = TRUE), ]. In the end, I would like to get confidence intervals from those 1000 regressions. How to proceed?
As per Ben Bolker's request, I am posting my solution but see jay.sf for a more generalizable answer.
#First create an oversampled dataset:
y <- rnorm(24)
x <- rnorm(24)
z <- rep(1:6, 4)
w <- rep(1:4, each=6)
df <- data.frame(id=z,cluster=w,x=x,y=y)
#Then just slice_sample to arrive at the sample size as desired
df %>% slice_sample(n = 20) %>%
arrange(cluster)
#Or just use base R
a <- df[sample(nrow(df), 20), ]
df2 <- a[order(a$cluster), ]
Let ncl be the desired number of clusters. We may generate a sampling space S which is a sequence of tolerance tol around mean observations per cluster mnobs. From that we draw repeatetly a random sample of size 1 to obtain a list of clusters CL. If the sum of cluster lengths meets ncl*mnobs we break the loop, add random data to the clusters and rbind the result.
FUN <- function(ncl=20, mnobs=30, tol=.1) {
S <- do.call(seq.int, as.list(mnobs*(1 + tol*c(-1, 1))))
repeat({
CL <- lapply(1:ncl, function(x) rep(x, sample(S, 1, replace=T)))
if (sum(lengths(CL)) == ncl*mnobs) break
})
L <- lapply(seq.int(CL), function(i) {
id <- seq.int(CL[[i]])
cbind(id, cluster=i,
matrix(rnorm(max(id)*2),,2, dimnames=list(NULL, c("x", "y"))))
})
do.call(rbind.data.frame, L)
}
Usage
set.seed(42)
res <- FUN() ## using defined `arg` defaults
dim(res)
# [1] 600 4
(res.tab <- table(res$cluster))
# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# 29 29 31 31 30 32 31 30 32 28 28 27 28 31 32 33 31 30 27 30
table(res.tab)
# 27 28 29 30 31 32 33
# 2 3 2 4 5 3 1
sapply(c("mean", "sd"), function(x) do.call(x, list(res.tab)))
# mean sd
# 30.000000 1.747178
Displayable example
set.seed(42)
FUN(4, 5, tol=.3) ## tol needs to be adjusted for smaller samples
# id cluster x y
# 1 1 1 1.51152200 -0.0627141
# 2 2 1 -0.09465904 1.3048697
# 3 3 1 2.01842371 2.2866454
# 4 1 2 -1.38886070 -2.4404669
# 5 2 2 -0.27878877 1.3201133
# 6 3 2 -0.13332134 -0.3066386
# 7 4 2 0.63595040 -1.7813084
# 8 5 2 -0.28425292 -0.1719174
# 9 6 2 -2.65645542 1.2146747
# 10 1 3 1.89519346 -0.6399949
# 11 2 3 -0.43046913 0.4554501
# 12 3 3 -0.25726938 0.7048373
# 13 4 3 -1.76316309 1.0351035
# 14 5 3 0.46009735 -0.6089264
# 15 1 4 0.50495512 0.2059986
# 16 2 4 -1.71700868 -0.3610573
# 17 3 4 -0.78445901 0.7581632
# 18 4 4 -0.85090759 -0.7267048
# 19 5 4 -2.41420765 -1.3682810
# 20 6 4 0.03612261 0.4328180
I have two different factorial experiments. Let's say that in experiment one there is a treatment column that splits all reps into treatments 1 and 2. Then there is a second treatment where columns are split again, and a third column splitting them again. There is also a code for each treatment (8, if you're following). I need to do t tests between 2 opposing treatments.
I've tried factor, mydata and subset and get error messages each time, especially since then a t test has 80 variables in the independent variable. Here are the examples (except the factor one)
myvars <- c("SH1RUC", "SH1RC")
newdata <- mydata[myvars]
newdata <- subset(december, shadehouse=="1" & system=="open" & media=="coir")
I'd like to be able to grab either shadehouse, either system and either media for doing t tests. Otherwise I'd like to grab the name, i.e. "SH1RUC" or "SH1RC," grouped together, to run a t test.
Based on the comment, here is a sample dataset:
Dep1 Dep2 Dep3 Ind1 Ind2 Ind3
1 1 3 5 4.63 65 21
2 1 3 5 5.25 64 22
3 1 3 6 4.76 67 23
4 1 3 6 5.87 65 24
5 1 4 5 4.65 87 25
6 1 4 5 5.76 67 21
7 1 4 6 3.99 75 22
8 1 4 6 4.09 46 23
9 2 3 5 5.98 68 24
10 2 3 5 3.67 79 25
11 2 3 6 5.43 75 22
12 2 3 6 4.56 57 23
13 2 4 5 5.43 65 24
14 2 4 5 2.99 68 25
15 2 4 6 4.09 58 26
16 2 4 6 5.70 56 23
I'm trying to perform a t test between two specific dependent variable sets, for example rows 1 & 2 and rows 9 & 10, or rows 5 & 6 and rows 7 & 8. In the actual data there are 10 data points for each set, and I want to compare the means. I can't seems to group columns together effectively.
My initial goal was to set ylim for data plotted by barplot. When I started to dig deeper I've found several things that I do not understand. Let me explain my research:
I have 1D vector:
> str(vectorName)
num [1:999] 1 1 1 1 1 1 1 1 1 1 ...
> dim(vectorName)
NULL
> length(vectorName)
[1] 999
If I want to count the particular elements of this vector I do:
> vectorNameTable = table(vectorName)
> vectorNameTable
vectorName
0 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2 0.225 0.25 0.275 0.3 0.325 0.35 0.375 0.4
563 72 35 22 14 21 14 10 5 3 7 3 6 5 3 1 3
0.425 0.45 0.475 0.5 0.525 0.55 0.575 0.6 0.625 0.65 0.675 0.7 0.725 0.75 0.775 0.8 0.825
1 3 3 5 7 11 3 4 3 11 5 9 5 7 8 5 3
0.85 0.875 0.9 0.925 0.975 1
3 4 2 1 1 108
This is how I display those data more elegant way (in R-studio):
> View(vectorNameTable)
Which gives me output like this:
vectorName Freq
1 0 563
2 0.025 72
3 0.05 35
4 0.075 22
5 0.1 14
6 0.125 21
7 0.15 14
8 0.175 10
9 0.2 5
10 0.225 3
11 0.25 7
12 0.275 3
13 0.3 6
14 0.325 5
15 0.35 3
16 0.375 1
17 0.4 3
18 0.425 1
19 0.45 3
20 0.475 3
21 0.5 5
22 0.525 7
23 0.55 11
24 0.575 3
25 0.6 4
26 0.625 3
27 0.65 11
28 0.675 5
29 0.7 9
30 0.725 5
31 0.75 7
32 0.775 8
33 0.8 5
34 0.825 3
35 0.85 3
36 0.875 4
37 0.9 2
38 0.925 1
39 0.975 1
40 1 108
If I want to plot this data I do:
> barplot(vectorNameTable)
Which gives me this plot:
As you can see 0 is occurring more times than is y-axis size. So what I want is to set the size of y-axis using:
barplot(table(vectorNameTable), ylim=c(0,MAX_VALUE_IN_FREQ_COLUMN))
The problem is that I cannot find the largest value in Freq column. To be more precise I cannot even access the Freq column. I've tried:
> vectorNameTable[,1]
Error in vectorNameTable[, 1] : incorrect number of dimensions
and several other attempts, but seems that the only thing that I am able to obtain is whole row:
> vectorNameTable[1]
0
563
> vectorNameTable[2]
0.025
72
Or even the Freq value in given row:
> vectorNameTable[[1]]
[1] 563
> vectorNameTable[[2]]
[1] 72
The one possible workaround that is working is converting the data to matrix:
vectorNameDF = data.frame(vectorNameTable)
val = vectorNameDF[[1]]
frq = vectorNameDF[[2]]
val = as.numeric(levels(val))
vectorNameMTX = matrix(c(val, frq), nrow=length(val))
Then I cand do something like this:
barplot(vectorNameTable, ylim=c(0,max(vectorNameMTX[,2])+50))
Which will return:
But as you can see it is extreme overkill. Another mysterious thing that I've found is that plotting the graph this way (same as barplot(vectorNameMTX, beside=FALSE)):
> barplot(vectorNameMTX)
Will return this:
This command > barplot(vectorNameMTX, beside=TRUE) will return this:
Why this is happening? I mean what is this "line" on the left? And where is x-axis? If I do View(vectorNameMTX) it returns very similar table to View(vectorNameTable). The documentation for barplot says (only important things):
Bar Plots
Description
Creates a bar plot with vertical or horizontal bars.
Usage
barplot(height, ...)
height
either a vector or matrix of values describing the bars which make up the plot. If height is a vector, the plot consists of a sequence of rectangular bars with heights given by the values in the vector. If height is a matrix and beside is FALSE then each bar of the plot corresponds to a column of height, with the values in the column giving the heights of stacked sub-bars making up the bar. If height is a matrix and beside is TRUE, then the values in each column are juxtaposed rather than stacked.
I'm passing the matrix, but it does not working as expected:
> class(vectorNameMTX)
[1] "matrix"
On the other hand this one is not mentioned as supported type but it is working:
> class(vectorNameTable)
[1] "table"
Why I can't access columns of vectorNameTable? Why is passing the table object working while passing an matrix is not? What I'm missing here and what is the best way to achieve my goal?
Thank you
Table of a 1d vector is a 1d vector, so there is no columns. You can do something like
> a <- rbinom(1000, 25, 0.5)
> tb <- table(a)
> tb
a
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
8 20 31 71 96 155 141 146 136 94 46 33 15 7 1
> dim(tb)
[1] 15 # 1 dimension of 15
> tb[which.max(tb)]
11
155
So you can feed this max value to barplot.
I have a data frame in R of the following form:
BC solvopt istrng tSolv EPB
1 10 1 0 0.10 -78.1450
2 10 1 1 0.15 -78.7174
3 10 1 10 0.14 -78.7175
4 10 1 100 0.12 -78.7184
5 10 1 1000 0.09 -78.7232
6 10 1 2 0.15 -78.7175
7 10 1 20 0.14 -78.7176
8 10 1 200 0.12 -78.7192
30 10 2 0 0.10 -78.1450
31 10 2 1 0.11 -78.7174
32 10 2 10 0.11 -78.7175
33 10 2 100 0.10 -78.7184
34 10 2 1000 0.13 -78.7232
35 10 2 2 0.11 -78.7174
36 10 2 20 0.10 -78.7176
37 10 2 200 0.10 -78.7192
59 10 3 0 0.16 -78.1450
60 10 3 1 0.23 -78.7174
61 10 3 10 0.21 -78.7175
62 10 3 100 0.19 -78.7184
63 10 3 1000 0.17 -78.7232
64 10 3 2 0.22 -78.7175
65 10 3 20 0.21 -78.7176
66 10 3 200 0.18 -78.7192
88 10 4 0 0.44 -78.1450
89 10 4 1 14.48 -78.7162
90 10 4 10 12.27 -78.7175
91 10 4 100 1.23 -78.7184
92 10 4 1000 0.44 -78.7232
93 10 4 2 14.52 -78.7172
94 10 4 20 6.16 -78.7176
95 10 4 200 0.62 -78.7192
I want to add a column to this frame which shows the relative error in the EPB for each value of BC and istrng relative to solvopt=3.
For example, to compute the relative difference in EPB at each row I would subtract the EPB value of the corresponding row with the same value of BC and istrng but with solvopt=3.
Is there an easy way to do this short of splitting this into multiple data frames (for each solvopt) and then remunging it together?
The end goal is to generate plots of relative error vs istrng for each value of BC using qplot.
If you merge the subset where solvopt==3 against the main data on both BC and istrong, and subtract the difference, you should get the result you want, e.g.:
newdat <- merge(dat,dat[dat$solvopt==3,c("BC","istrng","EPB")], by=c("BC","istrng"))
newdat$diff <- with(newdat, EPB.x - EPB.y)
...or do it all in one fell swoop using match and interaction:
dat$diff <- dat$EPB - dat[dat$solvopt==3,"EPB"][match(
with(dat, interaction(BC,istrng) ),
with(dat[dat$solvopt==3,], interaction(BC,istrng) )
)]
A similar option with data.table
library(data.table)
res <- setkey(setDT(dat), BC,istrng)[dat[solvopt==3, c(1,3,5),
with=FALSE]][, diff:= EPB- i.EPB][]
I have one variable A
0
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
which is an input into the following function
NoBeta <- function(A)
{
return(1-(1- B * (1-4000))/EXP(0.007*A)
}
The variable B is the result of this function how do I feed the result back into the function to calculate my next result?
Here is B
0
0.07
0.10
0.13
0.16
0.19
0.22
0.24
0.27
0.30
0.32
0.34
0.37
0.39
0.41
0.43
0.45
0.47
So the function needs to return the values of B but also using B e.g. if we using A 10 as input then the input for B is 0, when the input for A is 15 the input for B is the result from the previous calculation 0.07
B is calculated with the following formula in Excel
=1-(1-B1*(1-4000))/EXP(0.007*$A2)
How do I implement this formula in R?
If I understand your question correctly you wish to reference a previous row in a calculation for the current row.
You can adapt a function that was provided in another SO question here.
rowShift <- function(x, shiftLen = 1L) {
r <- (1L + shiftLen):(length(x) + shiftLen)
r[r<1] <- NA
return(x[r])
}
test <- data.frame(x = c(1:10), y = c(2:11))
test$z <- rowShift(test$x, -1) + rowShift(test$y, -1)
> test
x y z
1 1 2 NA
2 2 3 3
3 3 4 5
4 4 5 7
5 5 6 9
6 6 7 11
7 7 8 13
8 8 9 15
9 9 10 17
10 10 11 19
Then what you want to achieve becomes
test$z2 <- 1- (1-rowShift(test$x, -1)*(1-4000))/exp(0.007*rowShift(test$y, -1))
> head(test)
x y z z2
1 1 2 NA NA
2 2 3 3 -3943.390
3 3 4 5 -7831.772
4 4 5 7 -11665.716
5 5 6 9 -15445.790
6 6 7 11 -19172.560