is it possible to run an ANOVA in r with only means, standard deviation and n-value? Here is my data frame:
q2data.mean <- c(90,85,92,100,102,106)
q2data.sd <- c(9.035613,11.479667,9.760268,7.662572,9.830258,9.111457)
q2data.n <- c(9,9,9,9,9,9)
q2data.frame <- data.frame(q2data.mean,q2data.sq,q2data.n)
I am trying to find the means square residual, so I want to take a look at the ANOVA table.
Any help would be really appreciated! :)
Here you go, using ind.oneway.second from the rspychi package:
library(rpsychi)
with(q2data.frame, ind.oneway.second(q2data.mean,q2data.sd,q2data.n) )
#$anova.table
# SS df MS F
#Between (A) 2923.5 5 584.70 6.413
#Within 4376.4 48 91.18
#Total 7299.9 53
# etc etc
Update: the rpsychi package was archived in March 2022 but the function is still available here: http://github.com/cran/rpsychi/blob/master/R/ind.oneway.second.R (hat-tip to #jrcalabrese in the comments)
As an unrelated side note, your data could do with some renaming. q2data.frame is a data.frame, no need to put it in the title. Also, no need to specify q2data.mean inside q2data.frame - surely mean would suffice. It just means you end up with complex code like:
q2data.frame$q2data.mean
when:
q2$mean
would give you all the info you need.
Related
Very new to R here, also very new to the idea of coding and computer stuff.
Second week of class and I need to find some summary statistics from a set of data my professor provided. I downloaded the chart of data and tried to follow along with his verbal instructions during class, but I am one of the only non-computer science backgrounds in my degree program (I am an RN going for degree in Health Informatics), so he went way too fast for me.
I was hoping for some input on just where to start with his list of tasks for me to complete. I downloaded his data into an excel file, and then uploaded it into R and it is now a matrix. However, everything I try for getting the mean and standard deviation of the columns he wants comes up with an error. I am understanding that I need to convert these column names into some sort of vector, but online every website tells me to do these tasks differently. I don't even know where to start with this assignment.
Any help on how to get myself started would be greatly appreciated. Ive included a screenshot of his instructions and of my matrix. and please, excuse my ignorance/lack of familiarity compared to most of you here... this is my second week into my masters I am hoping I begin to pick this up soon I am just not there yet.
the instructions include:
# * Import the dataset
# * summarize the dataset,Compute the mean and standard deviation for the three variables (columns): age, height, weight
# * Tabulate smokers and age.level data with the variable and its frequency. How many smokers in each age category ?
# * Subset dataset by the mothers that smoke and weigh less than 100kg,how many mothers meet this requirements?
# * Compute the mean and standard deviation for the three variables (columns): age, height, weight
# * Plot a histogram
Stack Overflow is not a place for homeworks, but I feel your pain. Let's get piece by piece.
First let's use a package that helps us do those tasks:
library(data.table) # if not installed, install it with install.packages("data.table")
Then, let's load the data:
library(readxl) #again, install it if not installed
dt = setDT(read_excel("path/to/your/file/here.xlsx"))
Now to the calculations:
1 summarize the dataset. Here you'll see the ranges, means, medians and other interesting data of your table.
summary(dt)
1A mean and standard deviation of age, height and weight (replace age with the column name of height and weight to get those)
dt[, .(meanValue = mean(age, na.rm = TRUE), stdDev = sd(age, na.rm = TRUE))]
2 tabulate smokers and age.level. get the counts for each combination:
dt[, .N, by = .(smoke, age.level)]
3 subset smoker mothers with wt < 100 (I'm asuming non-pregnant mothers have NA in the gestation field. Adjust as necessary):
dt[smoke == 1 & weight < 100 & !is.na(gestation), .N]
4 Is the same as 1A.
5 Plot a histogram (but you don't specify of what variable, so let's say it's age):
hist(dt$age)
Keep on studying R, it's not that difficult. The book recommended in the comments is a very good start.
I am a newbie in R programming and seek help in analyzing the Metabolomics data - 118 metabolites with 4 conditions (3 replicates per condition). I would like to know, for each metabolite, which condition(s) is significantly different from which. Here is part of my data
> head(mydata)
Conditions HMDB03331 HMDB00699 HMDB00606 HMDB00707 HMDB00725 HMDB00017 HMDB01173
1 DMSO_BASAL 0.001289121 0.001578235 0.001612297 0.0007772231 3.475837e-06 0.0001221674 0.02691318
2 DMSO_BASAL 0.001158363 0.001413287 0.001541713 0.0007278363 3.345166e-04 0.0001037669 0.03471329
3 DMSO_BASAL 0.001043537 0.002380287 0.001240891 0.0008595932 4.007387e-04 0.0002033625 0.07426482
4 DMSO_G30 0.001195253 0.002338346 0.002133992 0.0007924157 4.189224e-06 0.0002131131 0.05000778
5 DMSO_G30 0.001511538 0.002264779 0.002535853 0.0011580857 3.639661e-06 0.0001700157 0.02657079
6 DMSO_G30 0.001554804 0.001262859 0.002047611 0.0008419137 6.350990e-04 0.0000851638 0.04752020
This is what I have so far.
I learned the first line from this post
kwtest_pvl = apply(mydata[,-1], 2, function(x) kruskal.test(x,as.factor(mydata$Conditions))$p.value)
and this is where I loop through the metabolite that past KW test
tCol = colnames(mydata[,-1])[kwtest_pvl <= 0.05]
for (k in tCol){
output = posthoc.kruskal.dunn.test(mydata[,k],as.factor(mydata$Conditions),p.adjust.method = "BH")
}
I am not sure how to manage my output such that it is easier to manage for all the metabolites that passed KW test. Perhaps saving the output from each iteration appending to excel? I also tried dunn.test package since it has an option of table or list output. However, it still leaves me at the same point. Kinda stuck here.
Moreover, should I also perform some kind of adjusted p-value, i.e FWER, FDR, BH right after KW test - before performing the posthoc test?
Any suggestion(s) would be greatly appreciated.
I'm wondering how I would utilize the depmixs4 package for R to run HMM on a dataset. What functions would I use so I get a classification of a testing data set?
I have a file of training data, a file of label data, and a test data.
Training data consists of 4620 rows. Each row has 1079 values. These values are 83 windows with 13 values per window so in otherwords the 1079 is data that is made up of 83 states and each category has 13 observations. Each of these rows with 1079 values is a spoken word so it have 4620 utterances. But in total the data only has 7 distinct words. each of these distinct words have 660 different utterances hence the 4620 rows of words.
So we have words (0-6)
The label file is a list where each row is labeled 0-6 corresponding to what word they are. For example row 300 is labeled 2, row 450 is labeled 6 and 520 is labeled 0.
The test file contains about 5000 rows structured exactly like the training data except there are no labels assocaiated with it.
I want to use HMM to using the training data to classify the test data.
How would I use depmixs4 to output a classification of my test data?
I'm looking at :
depmix(response, data=NULL, nstates, transition=~1, family=gaussian(),
prior=~1, initdata=NULL, respstart=NULL, trstart=NULL, instart=NULL,
ntimes=NULL,...)
but I don't know what response refers to or any of the other parameters.
Here's a quick, albeit incomplete, test to get you started, if only to familiarize you with the basic outline. Please note that this is a toy example and it merely scratches the surface for HMM design/analysis. The vignette for the depmixs4 package, for instance, offers quite a lot of context and examples. Meanwhile, here's a brief intro.
Let's say that you wanted to investigate if industrial production offers clues about economic recessions. First, let's load the relevant packages and then download the data from the St. Louis Fed:
library(quantmod)
library(depmixS4)
library(TTR)
fred.tickers <-c("INDPRO")
getSymbols(fred.tickers,src="FRED")
Next, transform the data into rolling 1-year percentage changes to minimize noise in the data and convert data into data.frame format for analysis in depmixs4:
indpro.1yr <-na.omit(ROC(INDPRO,12))
indpro.1yr.df <-data.frame(indpro.1yr)
Now, let's run a simple HMM model and choose just 2 states--growth and contraction. Note that we're only using industrial production to search for signals:
model <- depmix(response=INDPRO ~ 1,
family = gaussian(),
nstates = 2,
data = indpro.1yr.df ,
transition=~1)
Now let's fit the resulting model, generate posterior states
for analysis, and estimate probabilities of recession. Also, we'll bind the data with dates in an xts format for easier viewing/analysis. (Note the use of set.seed(1), which is used to create a replicable starting value to launch the modeling.)
set.seed(1)
model.fit <- fit(model, verbose = FALSE)
model.prob <- posterior(model.fit)
prob.rec <-model.prob[,2]
prob.rec.dates <-xts(prob.rec,as.Date(index(indpro.1yr)),
order.by=as.Date(index(indpro.1yr)))
Finally, let's review and ideally plot the data:
head(prob.rec.dates)
[,1]
1920-01-01 1.0000000
1920-02-01 1.0000000
1920-03-01 1.0000000
1920-04-01 0.9991880
1920-05-01 0.9999549
1920-06-01 0.9739622
High values (>0.80 ??) indicate/suggest that the economy is in recession/contraction.
Again, a very, very basic introduction, perhaps too basic. Hope it helps.
I am completely new to R. I tried reading the reference and a couple of good introductions, but I am still quite confused.
I am hoping to do the following:
I have produced a .txt file that looks like the following:
area,energy
1.41155882174e-05,1.0914586287e-11
1.46893363946e-05,5.25011714434e-11
1.39244046855e-05,1.57904991488e-10
1.64155121046e-05,9.0815757601e-12
1.85202830392e-05,8.3207522281e-11
1.5256036289e-05,4.24756620609e-10
1.82107587343e-05,0.0
I have the following command to read the file in R:
tbl <- read.csv("foo.txt",header=TRUE).
producing:
> tbl
area energy
1 1.411559e-05 1.091459e-11
2 1.468934e-05 5.250117e-11
3 1.392440e-05 1.579050e-10
4 1.641551e-05 9.081576e-12
5 1.852028e-05 8.320752e-11
6 1.525604e-05 4.247566e-10
7 1.821076e-05 0.000000e+00
Now I want to store each column in two different vectors, respectively area and energy.
I tried:
area <- c(tbl$first)
energy <- c(tbl$second)
but it does not seem to work.
I need to different vectors (which must include only the numerical data of each column) in order to do so:
> prob(energy, given = area), i.e. the conditional probability P(energy|area).
And then plot it. Can you help me please?
As #Ananda Mahto alluded to, the problem is in the way you are referring to columns.
To 'get' a column of a data frame in R, you have several options:
DataFrameName$ColumnName
DataFrameName[,ColumnNumber]
DataFrameName[["ColumnName"]]
So to get area, you would do:
tbl$area #or
tbl[,1] #or
tbl[["area"]]
With the first option generally being preferred (from what I've seen).
Incidentally, for your 'end goal', you don't need to do any of this:
with(tbl, prob(energy, given = area))
does the trick.
Does anyone have any good thoughts on how to code complex tabulations in R?
I am afraid I might be a little vague on this, but I want to set up a script to create a bunch of tables of a complexity analogous to the stat abstract of the united states.
e.g.: http://www.census.gov/compendia/statab/tables/09s0015.pdf
And I would like to avoid a whole bunch of rbind and hbind statements.
In SAS, I have heard, there is a table creation specification language; I was wondering if there was something of similar power for R?
Thanks!
It looks like you want to apply a number of different calculations to some data, grouping it by one field (in the example, by state)?
There are many ways to do this. See this related question.
You could use Hadley Wickham's reshape package (see reshape homepage). For instance, if you wanted the mean, sum, and count functions applied to some data grouped by a value (this is meaningless, but it uses the airquality data from reshape):
> library(reshape)
> names(airquality) <- tolower(names(airquality))
> # melt the data to just include month and temp
> aqm <- melt(airquality, id="month", measure="temp", na.rm=TRUE)
> # cast by month with the various relevant functions
> cast(aqm, month ~ ., function(x) c(mean(x),sum(x),length(x)))
month X1 X2 X3
1 5 66 2032 31
2 6 79 2373 30
3 7 84 2601 31
4 8 84 2603 31
5 9 77 2307 30
Or you can use the by() function. Where the index will represent the states. In your case, rather than apply one function (e.g. mean), you can apply your own function that will do multiple tasks (depending upon your needs): for instance, function(x) { c(mean(x), length(x)) }. Then run do.call("rbind" (for instance) on the output.
Also, you might give some consideration to using a reporting package such as Sweave (with xtable) or Jeffrey Horner's brew package. There is a great post on the learnr blog about creating repetitive reports that shows how to use it.
Another options is the plyr package.
library(plyr)
names(airquality) <- tolower(names(airquality))
ddply(airquality, "month", function(x){
with(x, c(meantemp = mean(temp), maxtemp = max(temp), nonsense = max(temp) - min(solar.r)))
})
Here is an interesting blog posting on this topic. The author tries to create a report analogous to the United Nation's World Population Prospects: The 2008 Revision report.
Hope that helps,
Charlie