search for: fctr

...065e-05 *** factor(sex)m 0.581713 0.296229 1.9637 0.0495616 * ResCondCorp -0.176251 0.020263 -8.6982 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) fct()1 fctr(g) fctr(s) factr(pct)1 -0.334 fctr(gcmp)j -0.417 0.066 factor(sx)m -0.505 -0.002 -0.173 ResCondCorp -0.309 -0.010 0.302 -0.032 ### Here is the error message: ### Warning message: Estimated variance for factor 'factor(cdrgsaou2$ids)' is e...

...ample1_condition1_place1 sample2_condition1_place1 sample3_condition1_place1 . . . sample3_condition3_place3 I want to turn it into three separate factor columns "sample", "condition" and "place". This is what I did so far: # generate a factor column for the example fctr<- factor(c("sample1_condition1_place1", "sample2_condition1_place1", "sample3_condition1_place1")) splitfctr <- strsplit(as.character(fctr),"_") > splitfctr [[1]] [1] "sample1" "condition1" "place1" [[2]] [1] "...

...hrsperpatday -0.025376 0.043556 -0.5826 0.5602 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) timpnt as.()2 as.()3 skllmx timepoint -0.606 as.fctr(h)2 -0.382 0.132 as.fctr(h)3 -0.734 0.453 0.526 skillmixpc -0.996 0.596 0.335 0.715 nrshrsprptd 0.001 -0.297 0.204 -0.130 -0.056 Can anyone suggest another way that I can do this analysis please? Or a work around for this method? Any help...

...0785 0.48040 Number of obs: 60, groups: trtpair, 3 Fixed effects: Estimate Std. Error t value (Intercept) 0.3752 0.3563 1.053 as.factor(t2)1 -0.1677 0.2909 -0.576 as.factor(t3)1 0.1796 0.2909 0.617 Correlation of Fixed Effects: (Intr) a.(2)1 as.fctr(2)1 -0.816 as.fctr(3)1 -0.816 0.500 =============================== Dr. Jim Maas University of East Anglia

...with(input, Map(seq, from, to)) res0 <- with(input, Map(data.frame, station=station, year=year, record=record)) as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 resulting in > as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 station year record <fctr> <int> <fctr> 1 07EA001 1960 QMS 2 07EA001 1961 QMC 3 07EA001 1962 QMC 4 07EA001 1963 QMC 5 07EA001 1964 QMC 6 07EA001 1965 QMC 7 07EA001 1966 QMC 8 07EA001 1967 QMC 9 07EA001 1968 QMC 10 07EA001 1969 QMC # ... with 14 more ro...

...res0 <- with(input, Map(data.frame, station=station, year=year, > record=record)) > as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 > > resulting in > > > as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 > station year record > <fctr> <int> <fctr> > 1 07EA001 1960 QMS > 2 07EA001 1961 QMC > 3 07EA001 1962 QMC > 4 07EA001 1963 QMC > 5 07EA001 1964 QMC > 6 07EA001 1965 QMC > 7 07EA001 1966 QMC > 8 07EA001 1967 QMC > 9 07EA001 1968 QMC > 10 0...

try this: ########################################## library(dplyr) input <- tribble( ~station, ~from, ~to, ~record, "07EA001" , 1960 , 1960 , "QMS", "07EA001" , 1961 , 1970 , "QMC", "07EA001" , 1971 , 1971 , "QMM", "07EA001" , 1972 , 1976 , "QMC", "07EA001" , 1977 ,

int he example below I run facter looking for the fact "sp_serial_number". as you can see, the command facter sp_serial_number returns nothing, but the fact is there and is returned when just running facter and grepping for the fact. Some fact seem to work and others do not. any ideas? marcuspro:~ allan$ facter --version 1.5.7 marcuspro:~ allan$ facter sp_serial_number marcuspro:~

...rame, station=station, year=year, >> record=record)) >> as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 >> >> resulting in >> >>> as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 >> station year record >> <fctr> <int> <fctr> >> 1 07EA001 1960 QMS >> 2 07EA001 1961 QMC >> 3 07EA001 1962 QMC >> 4 07EA001 1963 QMC >> 5 07EA001 1964 QMC >> 6 07EA001 1965 QMC >> 7 07EA001 1966 QMC >> 8 07EA001 1967 QM...

..., >>> ?????? record=record)) >>> ??? as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 >>> >>> resulting in >>> >>>> as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 >>> ??? station? year record >>> ???? <fctr> <int> <fctr> >>> ? 1 07EA001? 1960??? QMS >>> ? 2 07EA001? 1961??? QMC >>> ? 3 07EA001? 1962??? QMC >>> ? 4 07EA001? 1963??? QMC >>> ? 5 07EA001? 1964??? QMC >>> ? 6 07EA001? 1965??? QMC >>> ? 7 07EA001? 1966??? QMC >...

...=record)) >>>> as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 >>>> >>>> resulting in >>>> >>>>> as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3 >>>> station year record >>>> <fctr> <int> <fctr> >>>> 1 07EA001 1960 QMS >>>> 2 07EA001 1961 QMC >>>> 3 07EA001 1962 QMC >>>> 4 07EA001 1963 QMC >>>> 5 07EA001 1964 QMC >>>> 6 07EA001 1965 QMC >>>>...

...ves you averages > The result of this calculation is: car light_duty heavy_duty motorcycle 36.54109 36.54109 36.54109 36.54109 But this doesn't give the same result as the dplyr method which is: date_time type vel <dttm> <fctr> <dbl> 1 2017-10-17 13:00:00 car 36.39029 2 2017-10-17 13:00:00 light_duty 38.56522 3 2017-10-17 13:00:00 heavy_duty 37.53333 4 2017-10-17 13:00:00 motorcycle 36.08696 The base R way of getting the result should be modified slightly into sapply(split(mydf, mydf$type), function(...

Hi Thanks for working example. you could use split/ lapply approach, however it is probably not much better than dplyr method. sapply(split(mydf, mydf$type), function(speed, n_vehicles) sum(mydf$speed*mydf$n_vehicles)/sum(mydf$n_vehicles)) gives you averages aggregate(mydf$n_vehicles, list(mydf$type), sum)$x gives you sums Cheers Petr > -----Original Message----- > From: R-help