On Nov 24, 2012, at 4:58 AM, frespider wrote:
HI A.k,
I need one more question, if you can answer it please
M <- matrix(sample(1:8000),nrow=100)
colnames(M)<- paste("Col",1:ncol(M),sep="")
apply(M,2,function(x) c(Min=min(x),"1st Qu" =quantile(x,
0.25,names=FALSE),
6 59 67 47 52 66 65 69 66
#or
rowDiffs(colRanges(x))
A.K.
- Original Message -
From: frespider
To: r-help@r-project.org
Cc:
Sent: Saturday, November 24, 2012 7:58 AM
Subject: Re: [R] Summary statistics for matrix columns
HI A.k,
I need one more question, if y
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
> Behalf
> Of frespider
> Sent: Saturday, November 24, 2012 4:58 AM
> To: r-help@r-project.org
> Subject: Re: [R] Summary statistics for matrix columns
>
>
>
> HI A.k,
>
> I need
ystem elapsed
# 0.968 0.000 0.956
A.K.
____
From: Fares Said <[hidden email]>
To: arun <[hidden email]>
Cc: Pete Brecknock <[hidden email]>; R help <[hidden email]>
Sent: Friday, November 23, 2012 10:23 AM
Subject: R
mes(x)<- paste("Col",1:ncol(x1),sep="")
system.time(fun1(x1))
# user system elapsed
# 0.968 0.000 0.956
A.K.
____________
From: Fares Said
To: arun
Cc: Pete Brecknock ; R help
Sent: Friday, November 23, 2012 10:23 AM
Subject: Re: [R] Summary statistics for matrix columns
Message -
From: Pete Brecknock
To: r-help@r-project.org
Cc:
Sent: Friday, November 23, 2012 8:42 AM
Subject: Re: [R] Summary statistics for matrix columns
frespider wrote
> Hi,
>
> it is possible. but don't you think it will slow the code if you convert
> to data.fr
quot; = quantile(x,0.75,names=FALSE),
> IQR=IQR(x),
> Max = max(x))) })
> # user system elapsed
> # 0.384 0.000 0.384
>
>
> A.K.
>
>
>
> - Original Message -
> From: Pete Brecknock
> To: r
frespider wrote
> Hi,
>
> it is possible. but don't you think it will slow the code if you convert
> to data.frame?
>
> Thanks
>
> Date: Thu, 22 Nov 2012 18:31:35 -0800
> From:
> ml-node+s789695n4650500h51@.nabble
> To:
> frespider@
> Subject: RE: Summary statistics for matrix columns
>
Hi,
it is possible. but don't you think it will slow the code if you convert to
data.frame?
Thanks
Date: Thu, 22 Nov 2012 18:31:35 -0800
From: ml-node+s789695n4650500...@n4.nabble.com
To: frespi...@hotmail.com
Subject: RE: Summary statistics for matrix columns
HI,
Is it possible t
HI,
but Sd and IQR not in the order I want ,
Thanks
Date: Thu, 22 Nov 2012 18:08:57 -0800
From: ml-node+s789695n4650496...@n4.nabble.com
To: frespi...@hotmail.com
Subject: RE: Summary statistics for matrix columns
Hi,
How about this:
res<-do.call(cbind,lapply(split(x,col(x)),funct
There is still missing some statistics,
like sd and IQR and I prefer the output to be matrix
Thanks
Date: Thu, 22 Nov 2012 18:00:20 -0800
From: ml-node+s789695n4650493...@n4.nabble.com
To: frespi...@hotmail.com
Subject: Re: Summary statistics for matrix columns
HI,
You could try t
Hi peter,
but this doesn't give me them in the order I want.
Is there a better approach
Thanks
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_
Hi,
is there a way I can calculate a summary statistics for a columns matrix
let say we have this matrix
x <- matrix(sample(1:8000),nrow=100)
colnames(x)<- paste("Col",1:ncol(x),sep="")
if I used summary
summary(x)
i get the output for each column but I need the output to be in matrix with
r
I also don't like to use split function because I have like around 800 columns
Date: Thu, 22 Nov 2012 18:08:54 -0800
From: ml-node+s789695n4650496...@n4.nabble.com
To: frespi...@hotmail.com
Subject: RE: Summary statistics for matrix columns
Hi,
How about this:
res<-do.call(cbind,lapply(split(
frespider wrote
> Hi,
>
> is there a way I can calculate a summary statistics for a columns matrix
> let say we have this matrix
> x <- matrix(sample(1:8000),nrow=100)
> colnames(x)<- paste("Col",1:ncol(x),sep="")
>
> if I used summary
> summary(x)
>
> i get the output for each column but I
I've got this solved via Talks Stat
mod.1<-lm(Patents~FHouse, data=datpat)
summary(mod.1)
anova(mod.1)
xtable(mod.1)
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_
Hi, I would really appreciate all the help I can get. Unfortunately, I am
really new to statistics! I hope you guys don't mind this.
I am trying to find significance levels, beta, R, R squared, adjusted R
squared, standard error and t test.
FILE http://r.789695.n4.nabble.com/file/n4541923/datpat
] På vegne af
jose romero [jlauren...@yahoo.com]
Sendt: 12. februar 2010 18:23
Til: r-help@r-project.org
Emne: [R] summary statistics for grouped data
Hello list:
Is there an easy way (preferably through one of
the standard R packages) of obtaining summary
statistics for grouped data? I could
: [R] summary statistics for grouped data
Hello list:
Is there an easy way (preferably through one of the standard R packages) of
obtaining summary statistics for grouped data? I could split the data into
classes by hist, and then progressively calculate all the "columns" i need to
obtai
; From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
> project.org] On Behalf Of jose romero
> Sent: Friday, February 12, 2010 10:24 AM
> To: r-help@r-project.org
> Subject: [R] summary statistics for grouped data
>
> Hello list:
>
> Is there an easy way (preferably
Hello list:
Is there an easy way (preferably through one of the standard R packages) of
obtaining summary statistics for grouped data? I could split the data into
classes by hist, and then progressively calculate all the "columns" i need to
obtain the mean and standard deviation, but i was look
Thanks a bunch! They all are helpful :)
On 2/10/09, Jim Lemon wrote:
> William Revelle wrote:
>> At 6:41 PM -0500 2/9/09, David Winsemius wrote:
>>> describe() in Hmisc provides much of the rest of what you asked for:
>>>
describe(pref900$TCHDL)
>>> pref900$TCHDL
>>> n missing unique
William Revelle wrote:
At 6:41 PM -0500 2/9/09, David Winsemius wrote:
describe() in Hmisc provides much of the rest of what you asked for:
describe(pref900$TCHDL)
pref900$TCHDL
n missing uniqueMean .05 .10 .25 .50
.75 .90 .95
9061904469 16051 4.1
At 6:41 PM -0500 2/9/09, David Winsemius wrote:
describe() in Hmisc provides much of the rest of what you asked for:
describe(pref900$TCHDL)
pref900$TCHDL
n missing uniqueMean .05 .10 .25 .50
.75 .90 .95
9061904469 16051 4.123 2.320 2.557 3.0
Just to play a little:
mystats<- function(x)
{
xvalid<-x[!is.na(x)]
XNtotal<-length(x)
XNnull<-length(xvalid)
XNnotNull<-XNtotal-XNnull
Xmean<-mean(xvalid)
Xmedian<-median(xvalid)
Xsd<-sd(xvalid)
Xrange<-range(xvalid)
my.results<-cbind(XNtotal, XNnull, XNnotNull, Xmean, Xmedian, Xsd,
Xmin=Xrange[1
Dear SY,
Also take a look at basicStats in the fBasics package.
# fBasics package
install.packages('fBasics')
require(fBasics)
# For reproducibility
set.seed(123)
# Some data
x<-c(NA,rnorm(10),NA)
basicStats(x)
HTH,
Jorge
On Mon, Feb 9, 2009 at 6:04 PM, phoebe kong wrote:
> Hi all,
>
> I
describe() in Hmisc provides much of the rest of what you asked for:
> describe(pref900$TCHDL)
pref900$TCHDL
n missing uniqueMean .05 .10 .25 .50 .
75 .90 .95
9061904469 16051 4.123 2.320 2.557 3.061 3.841
4.886 6.054 6.867
lowest
Hi all,
I'm wondering if there is a function that can return summary statistics:
N=total number of observation, # missing, mean, median, range, standard
deviation.
As I know, summary() returns some of info I've mentioned above.
Thanks,
SY
[[alternative HTML version deleted]]
__
On Fri, Nov 21, 2008 at 5:50 AM, Gerit Offermann <[EMAIL PROTECTED]> wrote:
> Dear list,
>
> thanks to your help I managed to find means of analysing my data.
>
> However, the whole data set contains 264 variables. Of which some are
> factors, others are not. The factors tend to be grouped, e.g.
>
Hi
[EMAIL PROTECTED] napsal dne 21.11.2008 11:50:52:
> Dear list,
>
> thanks to your help I managed to find means of analysing my data.
>
> However, the whole data set contains 264 variables. Of which some are
> factors, others are not. The factors tend to be grouped, e.g.
> data$f1304 to data
Dear list,
thanks to your help I managed to find means of analysing my data.
However, the whole data set contains 264 variables. Of which some are
factors, others are not. The factors tend to be grouped, e.g.
data$f1304 to data$f1484 and data$f3204 to data$5408.
But there are other types of va
Dear Gerit,
Here is a start using a data set which first column is numeric and the rest
are factors 'f1', 'f2',,'f1381' (I'm using only 3):
# Data set
x <- c(1,4,2,6,8,3,4,2,4,5,1,3)
y <- as.factor(c(2,2,1,1,1,2,2,1,1,2,1,2))
z <- as.factor(c(1,2,2,1,1,2,2,3,3,3,3,3))
mydata=data.frame(x,y,z)
Look at summaryBy in the doBy package.
On Thu, Nov 20, 2008 at 9:16 AM, Gerit Offermann <[EMAIL PROTECTED]> wrote:
> Dear list,
>
> I reduced my data to the following:
>
> x <- c(1,4,2,6,8,3,4,2,4,5,1,3)
> y <- as.factor(c(2,2,1,1,1,2,2,1,1,2,1,2))
> z <- as.factor(c(1,2,2,1,1,2,2,3,3,3,3,3))
>
>
Dear list,
I reduced my data to the following:
x <- c(1,4,2,6,8,3,4,2,4,5,1,3)
y <- as.factor(c(2,2,1,1,1,2,2,1,1,2,1,2))
z <- as.factor(c(1,2,2,1,1,2,2,3,3,3,3,3))
I can produce the statistical summary just fine.
s1 <- tapply(x, y, summary)
d1 <- tapply(x, y, sd)
s2 <- tapply(x, z, summary)
d2
Try this:
sek <- seq(1, nrow(Indometh), 9)
Indometh$time[sek] <- NA
Indometh$timeclass <- factor(cut(Indometh$time, breaks=c(0,2,4,6,8,10)))
x <- summary(conc ~ Subject + timeclass, method="cross", data=Indometh)
vec <- x$S
dim(vec) <- attr(x, "out.attrs")$dim
dimnames(vec) <- attr(x, "out.attrs")
Lauri Nikkinen wrote:
R users,
I intention is to calculate some summary statistics across factor
levels. I know that in Hmisc package there is a summary function which
produces neat summary statistics when using "cross" option. I would
like to produce similar output with N and Missing columns bu
R users,
I intention is to calculate some summary statistics across factor
levels. I know that in Hmisc package there is a summary function which
produces neat summary statistics when using "cross" option. I would
like to produce similar output with N and Missing columns but produce
a data.frame.
Hi
one of the good starting points is Paul Johnsons StatsRus (the first hit
in Google and I believe it is in Rwiki too). It helped me when I started
with R about 10 years ago. For me usually the best way to arrange data is
in "database form". It means each column is a variable (numerical,
cate
Jim,
prettyR looks like it will work, but with the way that my data frame
is set up I still can not get what I want out of it. I am lacking in
my knowledge on manipulating data frames, and general R programing.
Is there a reference that will give me all of these wonderful data
manipulation tools t
stephen sefick wrote:
> below is my data frame. I would like to compute summary statistics
> for mgl for each river mile (mean, median, mode). My apologies in
> advance- I would like to get something like the SAS print out of PROC
> Univariate. I have performed an ANOVA and a tukey LSD and I wo
Mode <- function(var)rownames(table(var))[which.max(table(var))]
as.data.frame(sapply(c("mean", "median", "Mode"),
function(fun)tapply(x$mgl, x$RM, fun, na.rm=T)))
On 12/02/2008, stephen sefick <[EMAIL PROTECTED]> wrote:
> below is my data frame. I would like to compute summary statistics
> for
Hi Stephen,
Try
tapply(DATA$mgl,DATA$RM,summary) # DATA is a data.frame
I hope this helps.
Jorge
On 2/12/08, stephen sefick <[EMAIL PROTECTED]> wrote:
>
> below is my data frame. I would like to compute summary statistics
> for mgl for each river mile (mean, median, mode). My apologies in
Here is one way of doing it: (no exactly sure if 'mode' makes sense
with your data)
> x <- read.table(textConnection("RM mgl
+ 1 215 0.9285714
+ 2 215 0.7352941
+ 3 215 1.6455696
+ 4 215 0.600
+ 5 sc 1.833
+ 6 sc 0.833
+ 7 sc 2.5438596
+ 8 sc 0.250
+ 9 202
below is my data frame. I would like to compute summary statistics
for mgl for each river mile (mean, median, mode). My apologies in
advance- I would like to get something like the SAS print out of PROC
Univariate. I have performed an ANOVA and a tukey LSD and I would
just like the summary stat
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