t-student

# For plotting qq
qqplot.data <- function(vec){
  y <- quantile(vec[!is.na(vec)], c(0.25, 0.75))
  x <- qnorm(c(0.25, 0.75)) 
  slope <- diff(y)/diff(x)
  int <- y[1L] - slope * x[1L]
  d <- data.frame(resids = vec)
  ggplot(d, aes(sample = resids)) + 
    stat_qq() + 
    geom_abline(slope = slope, intercept = int)

t-student

rm(list = ls())
library(contrast)
taps <- c(242, 245, 244, 248, 247, 248, 
242, 244, 246, 242, 248, 246, 245, 247, 
248, 250, 247, 246, 243, 244, 246, 248, 
250, 252, 248, 250, 246, 248, 245, 250)
group <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
df1 <- data.frame(taps, group)

t-student

* Sort the table columns alphabetically ;
proc contents data=Newcoach1t1 out=col_names noprint; run; 
proc contents data=Newcoach1t1 out=col_names(keep=name ) noprint; run; 

proc sort data=col_names;  by name; run; 
data _null_;
 set col_names;
 by name;
 retain sorted_cols;
 length sorted_cols $2500.;

t-student

knee.mean <- mean(df2$kneeheel,  na.rm = T)
knee.sd <- sd(df2$kneeheel,  na.rm = T)
p <- ggplot(data = df2, aes(x=kneeheel))
p <- p + geom_density(aes(y=..density..)) 
p <- p + stat_function(fun = dnorm, args = list(mean = knee.mean, sd = knee.sd), colour = "red")
print(p)

t-student

# > head(df2)
#   id variable  cartvol time timeb
# 1  1 cartvol0 1.987862    0     0
# 2  2 cartvol0 1.694808    0     0
# 3  3 cartvol0 2.342389    0     0
# 4  4 cartvol0 1.727150    0     0
# 5  5 cartvol0 1.546601    0     0
# 6  6 cartvol0 1.425992    0     0

grid <- with(df2, seq(min(cartvol), max(cartvol), length = 100))

t-student

p <- ggplot(data = df2, aes(x = factor(time), y = cartvol  ))
p <- p + geom_boxplot()
p <- p + geom_jitter(size=1, position=position_jitter(width=0.03, height=0), colour = "blue")
#p <- p + geom_smooth(method = "lm", se = T)
p <- p + xlab("Time"  )
p <- p + ylab("Cartilage volume") 
p <- p + theme_bw()
p <- p + theme(legend.position="bottom")
print(p)

t-student

# > head(df1, 2)
#   id cartvol0 cartvol1 incr.cart interval        diff
# 1  1 1.987862 2.023709       Yes        3  0.03584695
# 2  2 1.694808 1.584858        No        2 -0.10995007

df1$diff <- df1$cartvol1 - df1$cartvol0
v <- as.numeric(quantile(df1$cartvol0, c(0.3333, 0.6667)))
df1$interval <- as.factor(findInterval(df1$cartvol0, v)+1)
ddply(df1, .(interval), summarise, diff.mean = mean(diff, na.rm = T),  n = length(diff))

t-student

---
title: "A test"
author: "t-student"
date: "January 5, 2018"
output: html_document
---

I'll be exploring the differences between these three models:

```{r, eval = FALSE}

t-student

> head(df.tmp)
# A tibble: 6 x 9
  patientid practiceid mmt.date   mmt.val int        pre        preend     post       stage
      <dbl>      <dbl> <date>       <dbl> <date>     <date>     <date>     <date>     <chr>
1     21342       3.00 2015-12-09    83.0 2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
2     19273       3.00 2015-12-11   120   2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
3     19273       3.00 2015-12-11    50.0 2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
4     19273       3.00 2015-12-11     0   2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
5     19273       3.00 2015-12-17    72.0 2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
6     19273       3.00 2015-12-17   135   2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  

t-student

# See majutils
mean_sd <- function (x, dp = 2) 
{
    my.stat <- paste0(round(mean(x, na.rm = T), dp), " (", round(sd(x, 
        na.rm = T), dp), ")")
    return(my.stat)
}

prop <- function (x, level, dp = 1, percent = T) 
{