similar to: Multiple histograms one graphic device & groupings

Hello, I am trying to export the results from symnum() while maintain their readability. I tried using sink to text file and also copying and pasting but the results end up looking like this: > symnum(c5.s) bC bED bEN bLP bLS bPA bPD bPR p bbContag 1 bbED + 1 bbENN_MN + B 1 bbLPI , , , 1 bbLSI + B B , 1 bbPAFRAC , * * , * 1 bbPD , B B

Hello, Below are the first two rows from my dataset and the header. This dataset has 5749 rows and I want to select only certain rows to be used based on existing grouping values. I am trying to group the data based on the values under 'ex_bin'. (e.g a group for 250, 251, 252, 500, 501, 502) I would then like to perform a lm for each grouping. My data: > all[1:2,] year extent scape

Hello, I used the aggregate function with success on my data frame "all". > aggregate(all, list(sc_recov), mean) I then made a subset of this data frame: >s.all <-subset(all, select= c(3,4,5,6,7,8,9,10,11,23)) but when I tried to to run the aggregate function on this subset: > aggregate(s.all, list(sc_recov), mean) The result is garbage (i.e. its not grouping the data).

Hello, I am trying to plot multiple histograms with the same scales, etc into one plot. The commands below produce a 3 page PDF with each histogram occupying the upper right quadrant. And use slightly different scales on the X and Y axes. > s21 <- dat[dat$sc_recov=="21",] > s21.ED <- subset(s21, select=(bbED)) > s31 <- all[all$sc_recov=="31",] > s31.ED

Hello, this code below was from a helpful R-help user. dat <- read.csv("Resid_fix2.csv", sep="," , header=T) dat11 <- dat[1:413,] # convert ambiguous columns to factors: dat11$Pri_No <- factor(dat11$Pri_No) dat11$RecovUnit <- factor(dat11$RecovUnit) # plot: require(lattice) bwplot(bbED~ Pri_No | RecovUnit, data=dat11, as.table=TRUE, layout=c(4,1)) This works,

https://bugzilla.netfilter.org/bugzilla/show_bug.cgi?id=488 Summary: Chain/Groupings of networks don't total pkts and bytes correctly Product: iptables Version: 1.2.11 Platform: i386 OS/Version: RedHat Linux Status: NEW Severity: normal Priority: P2 Component: iptables

Quick question, as I attempt to learn R. For post-hoc tests 1) Is there an easy function that will take, say the results of tukeyHSD and create a grouping table. e.g., if I have treatments 1, 2, and 3, with 1 and 2 being statistically the same and 3 being different from both Group Treatment A 1 A 2 B 3 2) I've been stumbling over the proper syntax for simple effects for a tukeyHSD

https://bugzilla.netfilter.org/bugzilla/show_bug.cgi?id=488 netfilter@linuxace.com changed: What |Removed |Added ---------------------------------------------------------------------------- CC| |netfilter@linuxace.com ------- Additional Comments From netfilter@linuxace.com 2006-06-24 19:10 MET ------- Not

Hal, On 6/11/2012 12:48 PM, Andrew Trick wrote: > Ignoring compile time for a moment, I think an advantage of a DFA is modeling a situation where the hardware can assign resources to best fit the entire group rather then one instruction at a time. For example, if InstA requires either Unit0 or Unit1, and InstB requires Unit0, is {InstA, InstB} a valid group? Depending on your cpu, a DFA could

R community, I would like to know how to extract rows from a data frame (DF) such that each row in the new data frame (D.F) represents the first instance of a unique variable pairing in the original dataframe (ordered first by variable V1 then by variable V2). The unique function does not seem to be able to accomplish this. I imagine there must be a simple solution for this, but I can't

On Jun 11, 2012, at 12:07 PM, Hal Finkel <hfinkel at anl.gov> wrote: > Looking at VLIWPacketizerList::PacketizeMIs, it seems like the > instructions are first scheduled (via some external scheme?), and then > packetized 'in order'. Is that correct? Anshu? > In the PowerPC grouping scheme, resources are assigned on a group > basis (by the instruction dispatching

I'm trying to figure out how to use lmer to fit models with factors that have some nesting and some non-nested groupings. For example, in this paper: http://www.stat.columbia.edu/~gelman/research/published/parkgelmanbafumi.pdf we have a logistic regression of survey respondents' political preferences (1=Republican, 0=Democrat), regressing on sex, ethnicity, state (51 states within 5

Dear List, I have a problem I'm finding it difficult to make headway with. Say I have 6 ordered observations, and I want to find all combinations of splitting these 6 ordered observations in g groups, where g = 1, ..., 6. Groups can only be formed by adjacent observations, so observations 1 and 4 can't be in a group on their own, only if 1,2,3&4 are all in the group. For example,

On Jun 11, 2012, at 9:30 AM, Hal Finkel <hfinkel at anl.gov> wrote: > I'm considering writing more-detailed itineraries for some PowerPC CPUs > that use the 'traditional' instruction grouping scheme. In essence, > this means that multiple instructions will stall in some pipeline stage > until a complete group is formed, then all will continue. > > I expect to

I'm considering writing more-detailed itineraries for some PowerPC CPUs that use the 'traditional' instruction grouping scheme. In essence, this means that multiple instructions will stall in some pipeline stage until a complete group is formed, then all will continue. I expect to provide CPU-specific code to help determine when the currently-waiting instructions would form a group.

On Mon, 11 Jun 2012 10:48:18 -0700 Andrew Trick <atrick at apple.com> wrote: > On Jun 11, 2012, at 9:30 AM, Hal Finkel <hfinkel at anl.gov> wrote: > > > I'm considering writing more-detailed itineraries for some PowerPC > > CPUs that use the 'traditional' instruction grouping scheme. In > > essence, this means that multiple instructions will stall

Dear R-ers, my task is to simple: to assign cases to desired groupings based on the combined values on 2 variables. I can think of 3 methods of doing it. Method 1 seems to me pretty r-like, but it requires a lot of lines of code - onerous. Method 2 is a loop, so not very good - as it loops through all rows of mydata. Method 3 is a loop but loops through fewer lines, so it seems to me more