Displaying 3 results from an estimated 3 matches for "tt_pvalu".
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tt_pvalue
2011 Aug 01
2
Errors, driving me nuts
...list.files (pattern = "kegg.combine")
> for (i in 1:length (files_to_test)) {
+ raw_data <- read.table (files_to_test[i], header=TRUE, sep=" ")
+ tmpA <- raw_data[,compareA]
+ tmpB <- raw_data[,compareB]
+ tt <- t.test (tmpA, tmpB, var.equal=TRUE)
+ tt_pvalue[i] <- tt$p.value
+ }
Error in tt_pvalue[i] <- tt$p.value : object 'tt_pvalue' not found
# I tried setting up a vector...
# as.vector(tt_pvalue, mode="any") ### but NO GO
> file.name = paste("ttest.results.", compareA, compareB, "")
> setwd(save_to)...
2011 Aug 05
2
Which is more efficient?
Greetings all,
I am curious to know if either of these two sets of code is more efficient?
Example1:
## t-test ##
colA <- temp [ , j ]
colB <- temp [ , k ]
ttr <- t.test ( colA, colB, var.equal=TRUE)
tt_pvalue [ i ] <- ttr$p.value
or
Example2:
tt_pvalue [ i ] <- t.test ( temp[ , j ], temp[ , k ], var.equal=TRUE)
-------------
I have three loops, i, j, k.
One to test the all of <i> files in a directory. One to tease out
column <j> and compare it by means of t-test to column <k>...
2011 Aug 03
2
Error message for MCC
...="numeric", length = vl)
> tt <- vector(mode="numeric", length = vl)
>
>
> ########################
> ## Calculate P-values ##
> for (i in 1:3){
+ temp1 <- read.table(files_to_test[i], header=TRUE, sep=" ")
+ numrows <- nrow(temp1)
+ tt_pvalue <- matrix(data=temp, nrow=numrows, ncol=vl)
+ colA <- temp[,compareA]
+ colB <- temp[,compareB]
+ tt <- t.test(colA, colB, var.equal=TRUE)
+ tt_pvalue <- tt$p.value
+ }
Error in temp[, compareA] : incorrect number of dimensions
--
Matt Curcio
M: 401-316-5358
E: matt....