Yes. https://lmgtfy.com/?q=R+ancova
--
Sent from my phone. Please excuse my brevity.
On November 21, 2016 9:09:58 PM PST, li li wrote:
>Hi all,
>Is there an R function which can handles dependent response in Analysis
>of covariance model. The dependence structure is known and is there to
>accoun
Cut-and-paste is whatever you cut.. cut from a text editor and you will get
text. Notepad++ is one such program.
Recommendation: set your mail program to text and put your question on the body
of the email. Attachments sometimes get through, but they really are not the
preferred way to communic
Hopefully the attached is text and not html. I have not found a text option in
firefox.
I have also been informed that a windows cut and paste is not truly text, which
is the reason for the attachment from notepad++
If there is any way I can improve the question, please inform of the problem
Hi all,
Is there an R function which can handles dependent response in Analysis
of covariance model. The dependence structure is known and is there to
account for it in ANCOVA analysis in R?
Thanks.
Hanna
[[alternative HTML version deleted]]
_
Hello, there,
R document for heatmap says that Rowv could be a vector of values to specify
the row order. However, I couldn't figure out how to apply it. A simple example
here:> b=as.data.frame(matrix(c(3,4,5,8,9,10,13,14,15,27,19,20),3,4))
> b
V1 V2 V3 V4
1 3 8 13 27
2 4 9 14 19
3 5 10 15
R-Help,
Please help me understand why these models and predictions are different:
library(gbm)
set.seed(32321)
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1])
X4 <- factor(sample(letters[1:6],N,replace=TRUE))
Hello, there,
R document for heatmap says that Rowv could be a vector of values to specify
the row order. However, I couldn't figure out how to apply it. A simple example
here:> b=as.data.frame(matrix(c(3,4,5,8,9,10,13,14,15,27,19,20),3,4))
> b
V1 V2 V3 V4
1 3 8 13 27
2 4 9 14 19
3 5 10 15
Thank you Jim and Don.
On Monday, November 21, 2016 4:54 PM, Jim Lemon
wrote:
Hi Olu,
If you always have only one non-NA value in the first three columns:
veg_df<-data.frame(col1=c(NA,"cassava","yam",NA,NA,NA,"maize"),
col2=c("pumpkin",NA,NA,"cherry",NA,NA,NA),
col3=c(NA,NA,NA,NA,"pe
Something along the lines of
tmpfun <- function(chvec) chvec[!is.na(chvec)]
apply( mydata, 1, tmpfun)
Assuming every row has only one non-NA entry.
--
Don MacQueen
Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062
On 11/21/16, 1:26 PM, "R-hel
Hi Olu,
If you always have only one non-NA value in the first three columns:
veg_df<-data.frame(col1=c(NA,"cassava","yam",NA,NA,NA,"maize"),
col2=c("pumpkin",NA,NA,"cherry",NA,NA,NA),
col3=c(NA,NA,NA,NA,"pepper","mango",NA))
veg_df$col4<-apply(as.matrix(veg_df),1,function(x) x[!is.na(x)])
Jim
Hello,
Try the following.
dat <- read.table(text = "
| colA | colB | colC | colD |
| NA | pumpkin | NA | Pumpkin |
| Cassava | NA | NA | Cassava |
| yam | NA | NA | yam |
| NA | Cherry | NA | Cherry |
| NA | NA | Pepper | Pepper |
| NA | NA | Mango | Mango |
| maize | NA | NA | maize |
", header
Hi Ramnik,
Bert's answer is correct, and an easy way to see why is to look at:
c(1,F,"b")
[1] "1" "FALSE" "b"
The reason that "F" is translated to "FALSE" is that is its default
value when R is started. If you change that value:
F<-"foo"
c(1,F,"b")
[1] "1" "foo" "b"
as.logical(c(1,F,"b"))
Hello,I have the following data
| colA | colB | colC | colD |
| NA | pumpkin | NA | Pumpkin |
| Cassava | NA | NA | Cassava |
| yam | NA | NA | yam |
| NA | Cherry | NA | Cherry |
| NA | NA | Pepper | Pepper |
| NA | NA | Mango | Mango |
| maize | NA | NA | maize |
All I want to do is to combine
> On Nov 21, 2016, at 4:21 AM, Stuart Patterson
> wrote:
>
> Dear David,
> Thank you for your reply. Your suggestions on how better to write the command
> are very useful, and I can see how the simplification would help. I hadn't
> realised that the lower order variables would be included if
Dear Sir/Madam,
I want to report a problem of 'predict' function in the 'markovchain' package
and I will use to examples to explain the problem.
Problem:
-It only follows the path with transition probability greater or equal than 0.5.
Here are two examples.
Example (1)
I created an MC with th
To go further with your teacher's code, I would start by finding out what
kind of object "map" is, in R terms. For example, can your teacher give
you the output from the commands:
class(map)
str(map)
?
Without that information, I don't think anyone here can give you effective
help.
Also, as o
Hi,
I was wondering if I could get some advice on the following question please:
I have a time-dependent cox model with three variables, each of which interacts
with the other two. So my final model is:
fit12<-coxph(formula = Surv(data$TimeIn, data$Timeout, data$Status) ~
data$Year+data$Life_S
Not an answer, but note that your vectors are all first (silently)
coerced to character, as vectors must be all of one type.
I would hazard a guess that the answer is: it's simply an arbitrary
inconsistency (different folks wrote the functions at different
times). Note that AFAICS, the difference
Dear David,
Thank you for your reply. Your suggestions on how better to write the
command are very useful, and I can see how the simplification would help. I
hadn't realised that the lower order variables would be included if I
simply wrote in teh interaction terms. Thank you
The table below is ho
Hello,
I'm trying to use R for solving PDEs ( heat and Poisson equations) in R.
I found fdaPDE as a package implementing FEMs, has any1 used it before ? I find
it severely lacks documentation and examples on its usage.
Any feedback is welcome!
Thanks in advance,
Marios Barlas
PhD Candidate
Hi,
I am trying to understand under which specific conditions does explicit
coercion produce warnings.
> as.numeric(c(1, F, "b"))
[1] 1 NA NA
Warning message:
NAs introduced by coercion
> as.logical(c(1, F, "b"))
[1]NA FALSENA
In above examples, as.numeric produces warning but as.logi
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