search for: data100

...combining them using the cbind statement. The code for that is given below. for (i in 1:100) { y <- rpois(i,lambda=10) X0 <- seq(1,1,length=i) X1 <- rnorm(i,mean=5,sd=10) X2 <- rnorm(i,mean=17,sd=12) X3 <- rnorm(i,mean=3, sd=24) ind <- rep(1:5,20) } data100 <- cbind(y,X0,X1,X2,X3,ind) but when i look at the data100 table, the y values now take the observation count. (ie) the data under Y is not the poisson random generates but the observation number. Hence the last vector (ind) does not have a header. Is there any way i can drop the number of obse...

...urse, according to the value of each structure, we can see which one has a higher value than the others (ex: structure CV11 has a value of 15, structure IN12 has a value of 4). But what I want to know is, if we take all the trees having a final value higher than 100 (we create a new dataframe "data100"), and we compare with the trees having a final value under 100 (we create another dataframe "data0"), can we find a significant difference in the number and occurrence of structures found on these trees? And which structure is related to trees with a higher value than 100?? For now,...

...urse, according to the value of each structure, we can see which one has a higher value than the others (ex: structure CV11 has a value of 15, structure IN12 has a value of 4). But what I want to know is, if we take all the trees having a final value higher than 100 (we create a new dataframe "data100"), and we compare with the trees having a final value under 100 (we create another dataframe "data0"), can we find a significant difference in the number and occurrence of structures found on these trees? And which structure is related to trees with a higher value than 100 ? > For...

Yes of course, I can share this short view of the datas. Here is the head() of data100, containing all the trees with a final value higher than 100?: CV11 CV12 CV13 CV14 CV15 CV21 CV22 CV23 CV24 CV25 CV26 CV31 CV32 CV33 CV41 CV42 CV43 CV44 CV51 CV52 IN11 IN12 IN13 1291 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1083 0 4 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3919 0 0 0 0 0 0 0...

The code given below worked well. However, the problem is that when I typed dir1 to see the results I found that R order the files as: [1] "data1.flt" "data10.flt" "data100.flt" "data101.flt" [5] "data102.flt" "data103.flt" "data104.flt" "data105.flt" [9] "data106.flt" "data107.flt" "data108.flt" "data109.flt" [13] "data11.flt" "data110.flt" "d...

...-project.org/web/packages/compare/index.html). Best, -m PS: Data is already plural :) datas does not exist. On 12 September 2017 at 13:57, C?line L?scher <c-luescher at hispeed.ch> wrote: > Yes of course, I can share this short view of the datas. > > > > Here is the head() of data100, containing all the trees with a final value > higher than 100 : > > CV11 > > CV12 > > CV13 > > CV14 > > CV15 > > CV21 > > CV22 > > CV23 > > CV24 > > CV25 > > CV26 > > CV31 > > CV32 > > CV33 > > CV41 > &...

Dear R-user I have 100 lists. Each list has several components. For example, >data1 $a [1] 1 2 $b [1] 3 4 $c [1] 5 There are data1, data2,...., data100. All lists have the same number and the same name of components. Is there any function I can use for applying to only a specific component across 100 lists? (e.g., taking mean of $c acorss 100 lists) or do I need to write my own function for that? Thank you. Taka,

Hi, I am using kmeans to cluster a dataset. I test this example: > data<-matrix(scan("data100.txt"),100,37,byrow=T) (my dataset is 100 rows and 37 columns--clustering rows) > c1<-kmeans(data,3,20) > c1 $cluster [1] 1 1 1 1 1 1 1 3 3 3 1 3 1 3 3 1 1 1 1 3 1 3 3 1 1 1 3 3 1 1 3 1 1 1 1 3 3 [38] 3 1 1 1 3 1 1 1 1 3 3 3 1 1 1 1 1 1 3 1 3 1 1 3 1 1 1 1 3 1 1 1 1 1 1 3 1 [75...