search for: q25

...(34L, 57L, 94L, 152L, 160L, 160L), Incorrect = c(126L, 103L, 66L, 8L, 0L, 0L)), .Names = c("Contr", "Correct", "Incorrect" ), row.names = c("13", "15", "17", "19", "21", "23"), class = "data.frame") q25 <- rep( qlogis( 0.25 ), nrow(c1) ) # offset defined in arguments c1.glm <- glm( cbind(Correct, Incorrect) ~ Contr - 1, binomial, c1, offset = q25 ) # offset defined in formula c1f.glm <- glm( cbind(Correct, Incorrect) ~ Contr + offset(q25) -1, binomial, c1 ) cc <- seq( 0, 1, len = 10...

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) ) So I got the mean, median std dev, quantiles etc. IS there any way I can add the mode to the mixt. Thanks ahead for any suggest...

...0.0e+00 Q20 <--- G param21 0.64 0.068 9.3 0.0e+00 Q21 <--- G param22 0.63 0.065 9.7 0.0e+00 Q22 <--- G param23 0.71 0.065 10.9 0.0e+00 Q23 <--- G param24 0.70 0.052 13.7 0.0e+00 Q24 <--- G param25 0.41 0.066 6.3 3.4e-10 Q25 <--- G param26 0.98 0.091 10.8 0.0e+00 Q1 <--> Q1 param27 0.72 0.068 10.6 0.0e+00 Q2 <--> Q2 param28 1.09 0.099 11.0 0.0e+00 Q3 <--> Q3 param29 0.77 0.072 10.7 0.0e+00 Q4 <--> Q4 param31 0.79 0.075 10.6 0.0e+00 Q...

Hi, Seeking suggestions to compute uncentered (pearson correlation) correlation efficiently. corr from stats library works on x and y columns. dist from amap library works on x and y rows. My data layout is slightly different such that row(i) of matrix x is compared to row(i) of matrix y. Thanks [[alternative HTML version deleted]]

...1213 **@uark.edu ## 10295236 9/3/2008 Xattempts Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 1 1 1 1 0 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 1 2 1 1 1 1 1 1 0 1 0 0 1 1 0 0 1 0 0 0 0 1 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Score Form CRSE_GRADE_OFF 1 0 0 0 0 0 0 0 0 0 1 0 0 0 9 E D 2 0 0 0 0 0 0 0 0 0 0 1 1 0 13 G D Each student took a pre- and post- test indicated by the date under "Start&quot...

...m contributed packages. At least to those. summary_anno1148ft <- anno1148ft %>% tidyr::pivot_longer(c(t_depth, t_alt_count, t_alt_ratio), names_to = "measure") %>% dplyr::group_by(dat, measure) %>% dplyr::summarize(minimum = min(value,na.rm=T), q25 = quantile(value, probs = 0.25,na.rm=T), med = median(value,na.rm=T), q75 = quantile(value, probs = 0.75,na.rm=T), maximum = max(value,na.rm=T), average = mean(value,na.rm=T), #standard_deviation = s...

...", "Selenium", "Silicon", "Sodium", "Sulfate", "TDS", "TIC", "TKN", "TOC", "TSS", "TVSS", "Zinc"), row.names = 54:80, class = "data.frame") library(prettyR) library(moments) Q25 <- function(x, na.rm=TRUE){quantile(as.numeric(x), 0.25, na.rm = na.rm, names = FALSE)} Q75 <- function(x, na.rm=TRUE){quantile(as.numeric(x), 0.75, na.rm = na.rm, names = FALSE)} describe(x.f, num.desc=c("mean", "median", "sd", "min", "max",...

I am using the describe function in prettyR. I would like to add the 25% 75% quartiles to the summary table how do I do this I have tried describe(x.f, num.desc=c("mean", "median", "sd", "min", "max", "skewness", "quantile(x.f, na.rm=T, probs=seq(0.25, 0.75))", "valid.n")) help -- Let's not spend our time

...ibuted packages. At least to those. summary_anno1148ft <- anno1148ft %>% tidyr::pivot_longer(c(t_depth, t_alt_count, t_alt_ratio), names_to = "measure") %>% dplyr::group_by(dat, measure) %>% dplyr::summarize(minimum = min(value,na.rm=T), q25 = quantile(value, probs = 0.25,na.rm=T), med = median(value,na.rm=T), q75 = quantile(value, probs = 0.75,na.rm=T), maximum = max(value,na.rm=T), average = mean(value,na.rm=T), #standard_deviatio...

...generated one column of summary. Do you know why? Thank you, Yuan Chun Ding summary_anno1148ft <- anno1148ft %>% pivot_longer(c(t_depth, t_alt_count, t_alt_ratio), names_to = "measure") %>% group_by(dat, measure) %>% summarize(minimum = min(value,na.rm=T), q25 = quantile(value, probs = 0.25,na.rm=T), med = median(value,na.rm=T), q75 = quantile(value, probs = 0.75,na.rm=T), maximum = max(value,na.rm=T), average = mean(value,na.rm=T), #standard_deviation = sd(value), .groups = "dr...

...generated one column of summary. Do you know why? Thank you, Yuan Chun Ding summary_anno1148ft <- anno1148ft %>% pivot_longer(c(t_depth, t_alt_count, t_alt_ratio), names_to = "measure") %>% group_by(dat, measure) %>% summarize(minimum = min(value,na.rm=T), q25 = quantile(value, probs = 0.25,na.rm=T), med = median(value,na.rm=T), q75 = quantile(value, probs = 0.75,na.rm=T), maximum = max(value,na.rm=T), average = mean(value,na.rm=T), #standard_deviation = sd(value), .groups = "dr...

...library(plyr) library(reshape2) library(ggplot2) library(lattice) library(latticeExtra) # User functions Factor <- function(...) factor(..., levels = ...) PAN <- function(x1, y1, x2, y2, txt, ...) { panel.rect(x1, y1, x2, y2) panel.text(mean(c(x1, x2)), mean(c(y1, y2)), txt, ...) } q25 <- function(.x) quantile(.x, 0.25) q75 <- function(.x) quantile(.x, 0.75) # Create a dummy dataset ecg <- expand.grid( trt = Factor(c('Placebo', '100mg', '200mg', '400mg')), subject = 1:50, time = Factor(c('Screening', 'Pre-dos...

Hi, Here i have an matrix like this, ABC PQR XYZ MNO ------ ------- ------ -------- 3 6 7 15 2 12 24 15 20 5 1 2 25 50 15 35 i need to get the "MODE" - for each column-wise "VARIANCE" - for

...t; > Thank you, > > Yuan Chun Ding > > summary_anno1148ft <- anno1148ft %>% > pivot_longer(c(t_depth, t_alt_count, t_alt_ratio), names_to = "measure") > %>% > group_by(dat, measure) %>% > summarize(minimum = min(value,na.rm=T), > q25 = quantile(value, probs = 0.25,na.rm=T), > med = median(value,na.rm=T), > q75 = quantile(value, probs = 0.75,na.rm=T), > maximum = max(value,na.rm=T), > average = mean(value,na.rm=T), > #standard_deviation = sd(value), >...

...------------------- > > 3) Two-pass-encoding with varying quantizer can lead to better overall > quality than fixed quantizer encoding. > > E.g.: Encode Barcelona with Quant 25, but Suzie with quant 8. > Total size will be similar: > > Suzie-Q8: 275442 + Barcelona-Q25: 347980 = Total 623422 > Suzie-Q20: 115378 + Barcelona-Q20: 550760 = Total 666138 > > But visual quality makes a real difference as you can see > from th other > attached pictures: Barcelona-Q25 isn't too much worse than Q20. > Suzie-Q8 is _much_ better than Q2...