During the previous lectures we got familiar with basic statistics by using the binomial distribution that models the number of “successes” in a sample of \(n\) trials from a population where the success probability is \(p\). It is a discrete distribution where the outcome variable can only take discrete values \(0,1,2,\ldots,n\).
Now we move to outcome variables, such as BMI or LDL cholesterol levels, that can take any value in a continuous interval, such as, for example, in the interval (10.0, 150.0) for BMI measured in kg/m\(^2\).
The most prevalent continuous distribution is the Normal distribution (also called the Gaussian distribution after German mathematician Carl F. Gauss, 1777-1855). A reason for its wide applicability is that, in numerous settings, the distribution of sum of independent random variables tends towards a Normal distribution. (This deep mathematical fact called the Central Limit Theorem will be demonstrated a bit at the end of the lecture.) A consequence is that complex properties, such as variation between individuals in IQ or height, or susceptibility to coronary artery disease, that all result from an interplay between a huge number of individual factors, both from the genome and from the environment, follow approximately a Normal distribution in the population. Read more: http://www.bmj.com/content/310/6975/298.full.
Normal distribution is defined by 2 parameters: mean (mu, \(\mu\)) and standard deviation (sigma, \(\sigma\)). Often (outside R) variance
(sigma^2, \(\sigma^2\)) is used in
place of standard deviation to define the second parameter. Always pay
attention to which one is in question since mixing up numerical values
of these parameters badly mixes up all the statistics! For now, remember
that the basic R functions take in standard deviation
sigma
.
The standard normal distribution, N(0,1), has mean = 0 and sd = 1.
Let’s plot the density function dnorm(,0, 1)
and the
cumulative distribution function pnorm(, 0, 1)
of N(0,1)
next to each other. We use par(mfrow = c(1,2))
to set the
plotting parameter mfrow
to split the plotting region into
1 row and 2 columns, and the two plots will be shown next to each
other.
x = seq(-3, 3, 0.001) #range of x-values where we evaluate dnorm() and pnorm()
d = dnorm(x, 0, 1) #values of density function of N(0,1)
p = pnorm(x, 0, 1) #values of cumulative distribution function of N(0,1)
par(mfrow = c(1,2)) #plotting region split to 1 x 2 area to show two plots next to each other
plot(x, d, xlab = "", ylab = "Density function of N(0,1)",
main = "Standard Normal Distribution",
t = "l", lwd = 1.3, col = "limegreen")
plot(x, p, xlab = "", ylab = "Cumulative distribution function of N(0,1)",
main = "mean = 0 and sd = 1",
t = "l", lwd = 1.3, col = "darkgreen")
The density plot shows that the peak is at the mean of the distribution, and the density drops from there symmetrically making a bell-shaped curve characteristic to a Normal distribution.
The cumulative distribution plot shows how the probability mass accumulates as we move from small values (here starting from -3) to larger values (here up to 3). We know that 95% of the probability mass of the N(0,1) distribution is between values -1.96 and 1.96:
qnorm(c(0.025, 0.975), 0, 1) #95% of mass of N(0,1) is between these two points
## [1] -1.959964 1.959964
(This fact was actually the motivation to use 1.96 as the multiplier of the SE in Lecture 3 when we derived approximate 95% confidence intervals for a proportion parameter.)
Let’s generate samples from a Normal distribution, say with mean = 4
and sd = 2, using rnorm()
and plot the data using a
histogram. Standard hist()
shows on y-axis the counts of
observations in each bin, but by setting prob = TRUE
we can
make the y-axis to scale to the values of a density function (making the
total area of the histogram = 1). Then we can also show the theoretical
density function dnorm(,4,2)
in the same plot and compare
the two.
n.samples = 5000 #samples from distribution
mu = 4 #mean
sigma = 2 #standard deviation
x = rnorm(n.samples, mu, sigma) #random sample from normal distribution
c(mean = mean(x), sd = sd(x)) #show empirical mean and sd of data
## mean sd
## 3.985483 1.994015
hist(x, breaks = 40, col = "gray", prob = TRUE, main = "N(4,2)")
x.grid = seq(min(x), max(x), length.out = 1000) #grid of x-values to evaluate dnorm(, mu, sigma)
lines(x.grid, dnorm(x.grid, mu, sigma), col = "red", lwd = 1.5, lty = 2) #add dashed line to the current plot
#Let's make a legend text to appear in the top right corner
#pch is plotting symbol (15 square; -1 no symbol); lty is line type (0 no line; 2 dashed line)
legend("topright", col = c("gray","red"), legend = c("empirical","theoretical"),
lwd = 1.5, pch = c(15,-1), lty = c(0,2))
We see that the histogram of 5000 random samples from N(4,2) matches quite well with the theoretical density function of N(4,2), as it should. With more samples, the match would get even tighter.
x = rnorm(10000, 176, 7)
hist(x, main = "", xlab = "", col = "black", breaks = 50)
Theoretical variance is sd squared, i.e., \(7^2=49\). Let’s validate:
c(theoretical = 7^2, empirical = var(x))
## theoretical empirical
## 49.00000 48.88782
pnorm()
to estimate the expected proportion of
Finnish men that are \(\leq 165\) cm.
Compare to the empirical estimate of the same proportion in the
simulated data set.pnorm(165, 176, 7) #theoretical proportion of values of N(176, var = 7^2) that are <= 165
## [1] 0.05804157
mean(x <= 165) #empirical proportion of values in x that are <= 165
## [1] 0.0571
qnorm()
for theoretical estimate and quantile()
for an empirical
estimate from the simulated sample.qnorm(0.99, 176, 7) #theoretical cut-point with 99% in the left tail so 1% in the right tail
## [1] 192.2844
quantile(x, 0.99) #empirical cut-point that leaves 99% of the values on the left side
## 99%
## 192.2204
Does my data set follow a Normal distribution?
Let’s generate two data sets of size \(n=1000\). First from the uniform distribution in (0,1) and the second from N(0,1). Uniform distribution has “equal probability to pick any value in the interval”, and therefore its density function is a flat line. Let’s plot the two distributions using histograms.
n.samples = 1000
x.uni = runif(n.samples, 0, 1) #runif takes in left (0) and right (1) endpoints of interval
x.norm = rnorm(n.samples, 0, 1) #standard normal N(0,1)
par(mfrow = c(1,2))
#we can scale histogram to probability density by prob = TRUE
hist(x.uni, prob = TRUE, main = "Uniform(0,1)", col = "orange")
hist(x.norm, prob = TRUE, main = "N(0,1)", col = "limegreen")
We can see that the left-hand plot does not look like a “bell-shaped” Normal distribution, while the right-hand plot does look roughly as Normal.
Let’s run Shapiro-Wilk test for normality for both samples. It returns a P-value under the null hypothesis that the data are Normally distributed.
shapiro.test(x.uni)
##
## Shapiro-Wilk normality test
##
## data: x.uni
## W = 0.94784, p-value < 2.2e-16
shapiro.test(x.norm)
##
## Shapiro-Wilk normality test
##
## data: x.norm
## W = 0.99806, p-value = 0.3073
A low P-value for dataset x.uni
means that it is
unlikely to come from a Normal distribution, while a high P-value for
x.norm
does not indicate deviation from the Normal
distribution.
The quantitative tests (like Shapiro-Wilk above) may have little discriminatory power for small samples (that is, they do not detect deviation from Normality with statistical significance) and too much for large samples (that is, they detect tiny deviations with statistical significance). It is always good to also visually inspect Normality of data since that can inform what kind of deviations from Normality we may have and whether these deviations might be important in practice. QQ-plot is a tool for that.
A visual checking for Normality of a data set is often done with a
quantile-quantile plot (QQ-plot) using first qqnorm( )
and
then qqline( )
to add a reference line to the plot, as
below.
par(pty = "s") #make plots exact squares
par(mfrow = c(1,2)) #split plotting area into 1 x 2 panels
qqnorm(x.uni, main = "QQ-plot for Uniform(0,1)", cex = 0.5, col = "orange")
qqline(x.uni) #adds line to the existing QQ-plot
qqnorm(x.norm, main = "QQ-plot for N(0,1)", cex = 0.5, col = "limegreen")
qqline(x.norm) #adds line to the existing QQ-plot
In QQ-plots, each data point from the observed data (“Sample
Quantiles”) is plotted against the corresponding Theoretical Quantile
from the Normal distribution. That is, there is one point whose
x-coordinate is the minimum from the Normal distribution and
y-coordinate is the minimum of the observed values. Similarly, for every
\(i\), there is one point whose
x-coordinate is the \(i\)th largest
value from the theoretical Normal distribution (of a sample of this
size), and y-coordinate is the \(i\)th
largest value of the observed data. If the shapes of the two
distributions (observed data and theoretical distribution) match each
other, then these points appear approximately on a line. The reference
line is put there by qqline()
command. We see that on the
right side the observed data seem to follow a Normal distribution
because points are approximately on the line whereas on the left side
the points are not on the line and hence the data are unlikely to come
from a Normal distribution.
x = rnorm(1000, -1, 2) #NOTE: var = 4 --> sd = sqrt(4) = 2
shapiro.test(x) #Look at the P-value
##
## Shapiro-Wilk normality test
##
## data: x
## W = 0.99833, p-value = 0.4502
par(mfrow = c(1,2))
qqnorm(x, cex = 0.5)
qqline(x)
hist(x, col = "khaki", xlab = "x", breaks = 25, prob = TRUE)
x.seq = seq(min(x), max(x), length = 1000) #evaluate density function at these points
lines(x.seq, dnorm(x.seq, mean(x), sd(x)), col = "red", lwd = 1.5, lty = 2) #use empirical mean and sd
We see that x
looks like Normally distributed (P-value
is not small, QQ-plot is on the line, histogram looks Normal).
y = x^2
shapiro.test(y)
##
## Shapiro-Wilk normality test
##
## data: y
## W = 0.71674, p-value < 2.2e-16
par(mfrow = c(1,2))
qqnorm(y, cex = 0.5)
qqline(y)
hist(y, col = "khaki", xlab = "y", breaks = 25, prob = TRUE)
x.seq = seq(min(y), max(y), length = 1000) #evaluate density function at these points
lines(x.seq, dnorm(x.seq, mean(y), sd(y)), col = "red", lwd = 1.5, lty = 2) #use empirical mean and sd
We see that y=x^2
is not at all Normally distributed
(P-value is small, QQ-plot is not on the line, histogram does not look
Normal).
When data are Normally distributed, a set of tests called t-tests are available http://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/7-t-tests. These are used to compare
In the last two situations, it is important to make a difference between independent data sets and correlated data sets when choosing a t-test. Two samples are independent when each sampled individual from each population can be assumed chosen at random without any dependence on which other values have been sampled from the population. Contrast this to, say, situation where same set of individuals have each been measured two times, say, first before a treatment and second after the treatment. When we are interested in the treatment effect from such a design, we should treat the data as paired, each individual’s two measurements forming a pair. In particular, the sets of measurements under the two conditions are not independent because there is likely to be factors that an individual possess under both conditions (such as body-mass, pain tolerance, genetics etc.) that can affect the measurement (e.g. how much drug is needed for an individual to feel painless). Thus we would need to use the paired t-test.
The t-tests analyze means of continuous measurements and can
therefore be seen as continuous version of prop.test( )
that we used previously to analyze proportions that are means of binary
outcomes.
By using the function t.test()
we not only do the test,
but also, and often more importantly, get estimates for the means /
differences between the means and their confidence intervals. Comparing
the means using t-tests is quite robust to the distributional assumption
and in large samples works also for non-normal data.
One-sample t-test: (Try ?t.test
.)
n.samples = 100
x = rnorm(n.samples, 3, 1)
#test whether mean is 2.5
t.test(x, mu = 2.5)
##
## One Sample t-test
##
## data: x
## t = 6.1481, df = 99, p-value = 1.658e-08
## alternative hypothesis: true mean is not equal to 2.5
## 95 percent confidence interval:
## 2.913969 3.308503
## sample estimates:
## mean of x
## 3.111236
We can read from the output the (two-sided) P-value under the null hypothesis that the mean is 2.5, which here is very small (2e-8) indicating that the population mean is unlikely to be 2.5. We also have the empirical mean from the data and a 95%CI for the population mean. From this 95%CI, we see that 2.5 is quite far from it, which also says that the population mean is unlikely to be 2.5.
The t-test is based on the fact that if \(x_i\) for each \(i=1,...,n\) comes from \(N(\mu, \sigma^2)\), then the empirical mean \(\overline{x}=\frac{1}{n}\sum_i x_i\) is distributed as \(N(\mu, \frac{\sigma^2}{n})\), and thus the test statistic \[z=\frac{\overline{x} - \mu}{(\sigma/\sqrt{n})} \text{ follows } N(0,1), \] and this can be used to derive P-values. Idea is that if the observed mean \(\overline{x}\) is far from the hypothesized mean \(\mu\), then \(z\)-score has so large magnitude (either positive or negative) that it is a very unlikely observation under the standard Normal distribution, thus giving a small P-value under the hypothesis that the mean is \(\mu\).
However, usually we do not know the exact value of \(\sigma\) but first have to estimate it from the data using sample standard deviation \[ \widehat{\sigma} = \sqrt{\frac{\sum_i (x_i - \overline{x})^2}{n-1}}, \] then the test statistic \[t=\frac{\overline{x} - \mu}{(\widehat{\sigma}/\sqrt{n})} \] follows t-distribution with \(n-1\) degrees of freedom. When \(n>30\) or so, this distribution is very close to the Standard Normal distribution but for smaller sample sizes there are some differences between the two. This test statistic and its distribution is where the t-test has got its name.
Let’s test empirically that this is indeed the t-statistic reported by R above.
mu = 2.5
(mean(x) - mu)/(sd(x) / sqrt(n.samples)) #should be t-statistic given by t.test above
## [1] 6.148132
Two-sample t-test for independent data sets:
Let’s then compare two independent Normal samples for their
similarity in means. Let’s generate two data sets x
and
y
and display them with a boxplot.
n.samples = 100
x = rnorm(n.samples, 3, 1)
y = rnorm(n.samples, 4, 2)
boxplot(x, y, names=c("x","y"), col = c("steelblue","lightblue") )
What do you see in the boxplot? For each data set, the box shows the
interquartile range, i.e., the middle 50% of the values, starting from
the first quartile (25%) and extending to the 3rd quartile (75%). In the
middle of the box, the median is marked with a black stripe. The lines
outside box extend up to 1.5 times the interquartile range and the
remaining outlier observations would be shown as individual points.
Boxplot starts to be useful when there are more than about 20
observations. If there are less, stripchart()
, that shows
every observation, may be more informative than the boxplot.
#Do Welch t-test to test whether mean of x and y are equal:
t.test(x, y)
##
## Welch Two Sample t-test
##
## data: x and y
## t = -4.2017, df = 154.55, p-value = 4.465e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.3905990 -0.5011722
## sample estimates:
## mean of x mean of y
## 3.087692 4.033578
A smallish P-value says that the means are unlikely to be the same. Welch t-test can be used in cases where the variances of the two distributions need not be the same (like in our case here). The test statistic for Welch t-test is \[ w = \frac{\overline{x} - \overline{y}}{\sqrt{\frac{\widehat{\sigma}_x^2}{n_x} + \frac{\widehat{\sigma}_y^2}{n_y}}} \] and it is compared to a t-distribution with certain degrees of freedom (not explained here).
In addition to Welch t-test, there is also a version of 2-sample
t-test called Student’s t-test which is specifically
for cases where the variance is known to be the same between the groups.
Since usually we do not know this, the Welch t-test should be our
default option. Indeed, t.test()
has its default parameter
var.equal = FALSE
, meaning that it uses Welch test by
default. However, in cases where variances are the same, Student’s
t-test is more powerful than Welch test and that is why it is
kept as an option in t.test()
. (We will talk about what
power means in the next lecture.)
stripchart()
. Use a
t-test to test whether the mean of the patient population seems to be
\(\mu\) based on these data. What is
the 95% confidence interval for the mean?x = rnorm(10, 3, 2)
stripchart(x, col = "red")
t.test(x, mu = 6) #'mu' is t.test parameter for null hypothesis mean
##
## One Sample t-test
##
## data: x
## t = -3.881, df = 9, p-value = 0.003726
## alternative hypothesis: true mean is not equal to 6
## 95 percent confidence interval:
## 1.214385 4.738885
## sample estimates:
## mean of x
## 2.976635
From the chart we see that nearly all values are below the reference value of \(\mu = 6\) and hence it is not surprising that the P-value is small indicating a deviation of the sample mean from the reference value.
In the one-sample t-test, the 95% CI is given for the mean of the distribution. Here, the interval is wide because the sample size is so small, but it is still quite far from the reference value of \(\mu = 6\).
y = rnorm(40, 3, 1)
boxplot(x,y, names = c("x","y"), col = c("yellow","gold"))
t.test(x, y)
##
## Welch Two Sample t-test
##
## data: x and y
## t = -0.22407, df = 9.9011, p-value = 0.8273
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.959101 1.601522
## sample estimates:
## mean of x mean of y
## 2.976635 3.155424
In the two-sample t-test, the 95% CI is for the difference between
the means of x
and y
. Here it is
(-1.96,..,1.60), and there is no evidence that the population means
would be different from each other because value 0 is well within this
interval. Note also that the P-value is large (0.83), which says that we
do not detect any clear deviation from the null hypothesis that the
means are equal. This result is not surprising since the means were
indeed the same in the data generation, only the standard deviations
differed, which is not tested by the t-test.
Paired sample t-test.
Consider the default example data set sleep
in R’s
datasets
library. It shows how much two soporific drugs
(here 1 and 2) increase in hours of sleep compared to control on 10
patients. (Write ?sleep
for info and sleep
to
see the data.)
sleep
## extra group ID
## 1 0.7 1 1
## 2 -1.6 1 2
## 3 -0.2 1 3
## 4 -1.2 1 4
## 5 -0.1 1 5
## 6 3.4 1 6
## 7 3.7 1 7
## 8 0.8 1 8
## 9 0.0 1 9
## 10 2.0 1 10
## 11 1.9 2 1
## 12 0.8 2 2
## 13 1.1 2 3
## 14 0.1 2 4
## 15 -0.1 2 5
## 16 4.4 2 6
## 17 5.5 2 7
## 18 1.6 2 8
## 19 4.6 2 9
## 20 3.4 2 10
So we have 20 observations (extra
) on 10 individuals
(ID
), each measured once with each of the two drugs
(group
). The question is whether there is a difference in
extra sleep individuals get depending on which drug they are given. We
should use the paired t-test because the same
individuals are measured for both drugs.
We can do this paired t-test by making two vectors of the extra sleep
times corresponding to the two drugs and calling
t.test(, paired = TRUE)
.
drug.1 = sleep$extra[sleep$group == 1]
drug.2 = sleep$extra[sleep$group == 2]
#Check that we got the data correct
rbind(drug.1, drug.2)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## drug.1 0.7 -1.6 -0.2 -1.2 -0.1 3.4 3.7 0.8 0.0 2.0
## drug.2 1.9 0.8 1.1 0.1 -0.1 4.4 5.5 1.6 4.6 3.4
t.test(drug.1, drug.2, paired = TRUE)
##
## Paired t-test
##
## data: drug.1 and drug.2
## t = -4.0621, df = 9, p-value = 0.002833
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -2.4598858 -0.7001142
## sample estimates:
## mean difference
## -1.58
It seems that with drug 2 people sleep longer. The mean of the differences is -1.58 (-2.46, -0.70) and the P-value is 0.003 (under the null hypothesis that the mean difference was 0).
NOTE: To avoid writing complicated expressions like
sleep$extra[sleep$group == 1]
, we could use
with(sleep,)
structure which means that all the individual
variables (here extra
and group
) are taken
from the sleep
data set. So the paired t-test can be done
like this:
with(sleep, t.test(extra[group == 1], extra[group == 2], paired = TRUE))
##
## Paired t-test
##
## data: extra[group == 1] and extra[group == 2]
## t = -4.0621, df = 9, p-value = 0.002833
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -2.4598858 -0.7001142
## sample estimates:
## mean difference
## -1.58
Let’s visualize the differences between 2 and 1 using both a stripchart and a boxplot.
sleep.d = drug.2 - drug.1
stripchart(sleep.d, method = "stack", xlab = "hours", col = "blue",
main = "Sleep prolongation with 2 vs 1 (n = 10)")
#Let's add a horizontal boxplot to the current plot (add = TRUE), at y = 0.6 (at = .6)
boxplot(sleep.d, horizontal = TRUE, add = TRUE, at = .6, col = "steelblue")
Actually, the paired t-test is nothing but a one-sample t-test ran on the set of within sample differences. Let’s confirm.
t.test(sleep.d) #One-sample t-test testing whether mean of within pair differences is 0
##
## One Sample t-test
##
## data: sleep.d
## t = 4.0621, df = 9, p-value = 0.002833
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.7001142 2.4598858
## sample estimates:
## mean of x
## 1.58
The group of men are independent of the group of women (or at least no links between sampling strategies of the men and women have been described). Therefore, use the independent sample t-test.
Now the data come in as 40 father-mother pairs. Within families, the parents share many environmental factors, and may share some factors also because they have paired up in the first place. Therefore, use the paired t-test to account for the shared background within families.
What if continuous data are not normally distributed and you are unsure whether the methods you would like to use are robust enough for the non-normality? There are non-parametric methods available for these cases. Non-parametric means that we do not assume any specific form or shape about the underlying population distribution. It other words, the method is statistically valid whether the data follow Normal or Uniform or Binomial distribution, for example. Under Normality, non-parametric methods typically lose some statistical power (topic of next lecture) compared to methods that assume Normality, but the obvious advantage of non-parametric tests is that they are robust to deviations from the exact distributional assumptions.
Consider the question whether the values (of some biomarker) in population X (patients) tend to be smaller or larger than in another population Y (healthy). Collect \(n\) samples from X and \(m\) samples from Y and order the \(n+m\) values in ascending order. If distributions of X and Y are similar then the sum of ranks of values from X should come from the distribution of sum of ranks of \(n\) randomly chosen values from among all possible ranks \(1,...,n+m\). If the sum of ranks of X is clearly lower than under the null distribution, then values from X tend to be smaller than those from Y. If sum of ranks from X is clearly larger than under the null, then X tends to be larger than Y. This can be tested by Wilcoxon rank sum test (also known as Mann-Whitney U-test). http://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/10-rank-score-tests
As an example, let’s make samples of \(n=10\) and \(m=13\) samples from two uniform distributions that have ranges (0,1) and (0.5, 1.5), respectively, and apply Wilcoxon test.
n = 10 #samples from X
m = 13 #samples from Y
# Sample X from U(0,1) and Y from U(0.5,1.5)
# Distributions are not the same but on average Y>X
X = runif(n, 0, 1)
Y = runif(m, 0.5, 1.5)
# Visualize X and Y together with different colors
stripchart(list(X,Y), col = c("blue","red"), group.names = c("X","Y"))
# We want to test whether X and Y come from the same distribution
# We use Wilcoxon rank sum test.
wilcox.test(X,Y)
##
## Wilcoxon rank sum exact test
##
## data: X and Y
## W = 8, p-value = 0.0001171
## alternative hypothesis: true location shift is not equal to 0
We see from the plot that X tends to be smaller than Y and Wilcoxon test P-value (0.0001) indicates a possible difference as well.
Generate \(n=100\) values from
Uniform(-0.5, 0.5) distribution and also from Uniform(-1, 1)
distribution. Use stripchart( )
to visualize the observed
data using different colors for the two samples. Which one seems to have
higher values? Apply Wilcoxon rank sum test to data? What is
P-value?
n = 100
X = runif(n, -0.5, 0.5)
Y = runif(n, -1, 1)
# Visualize X and Y together with different colors
stripchart(list(X,Y), col = c("blue","red"), group.names = c("X","Y"))
wilcox.test(X,Y)
##
## Wilcoxon rank sum test with continuity correction
##
## data: X and Y
## W = 4655, p-value = 0.3999
## alternative hypothesis: true location shift is not equal to 0
Here the ranges of the distributions are clearly different (X is concentrated on a subrange of Y), but their median values are similar (actually 0), and there is no tendency of either of them being smaller than the other. Hence Wilcoxon P-value is not significant.
Let’s finish with a summary of what we have done in this lecture.
Let’s read in the blood pressure / cholesterol data from a file
systbp_ldlc.txt
.
x = read.table("systbp_ldlc.txt", as.is = TRUE, header = TRUE)
str(x) #Show structure of the data
## 'data.frame': 965 obs. of 3 variables:
## $ sex : int 2 2 2 2 2 2 2 2 2 2 ...
## $ systbp: int 123 150 160 113 139 145 124 126 127 135 ...
## $ ldlc : num 3.4 4.2 2.7 2.3 3.5 3.4 3.1 4 3.6 3.3 ...
head(x) #Show the first few lines
## sex systbp ldlc
## 1 2 123 3.4
## 2 2 150 4.2
## 3 2 160 2.7
## 4 2 113 2.3
## 5 2 139 3.5
## 6 2 145 3.4
#Sex is 1=Male, 2=Female, systolic blood pressure in mmHg, ldl-cholesterol in mmol/l
summary(x) #summary of the columns
## sex systbp ldlc
## Min. :1.000 Min. : 93.0 Min. :1.400
## 1st Qu.:1.000 1st Qu.:122.0 1st Qu.:2.900
## Median :2.000 Median :132.0 Median :3.500
## Mean :1.544 Mean :134.4 Mean :3.571
## 3rd Qu.:2.000 3rd Qu.:145.0 3rd Qu.:4.100
## Max. :2.000 Max. :208.0 Max. :7.500
#How many males and females?
table(x$sex) # '$' denotes variables of data frame.
##
## 1 2
## 440 525
#We can also do things to the rows that match the condition 'sex == 1' (i.e. are males)
summary(x[x$sex == 1,]) #summary for males
## sex systbp ldlc
## Min. :1 Min. : 97.0 Min. :1.500
## 1st Qu.:1 1st Qu.:125.0 1st Qu.:3.100
## Median :1 Median :134.0 Median :3.700
## Mean :1 Mean :137.1 Mean :3.765
## 3rd Qu.:1 3rd Qu.:148.0 3rd Qu.:4.325
## Max. :1 Max. :208.0 Max. :7.300
#Or we can enclose the command in "with(x, )" and then R knows to pick variable 'sex' automatically from 'x'
with(x, summary(x[sex == 2, ])) #summary for females
## sex systbp ldlc
## Min. :2 Min. : 93.0 Min. :1.400
## 1st Qu.:2 1st Qu.:119.0 1st Qu.:2.800
## Median :2 Median :130.0 Median :3.300
## Mean :2 Mean :132.2 Mean :3.408
## 3rd Qu.:2 3rd Qu.:143.0 3rd Qu.:3.900
## Max. :2 Max. :196.0 Max. :7.500
#Above we saw mean and quantiles but let's plot the whole distribution.
#Use systbp in females.
y = x[x$sex == 2,"systbp"] #now 'y' is shorthand for our data
hist(y, breaks = 30, xlab = "mmHG", main = "SystBP Females", col = "red")
It is not symmetric around the mode but has skew to the right, towards the high values. This is common in positively valued measurements. Often logarithm of the values is much more symmetric and closer to a Normal distribution.
hist(log(y), breaks = 30, xlab = "log(mmHG)", main = "SystBP Females", col = "tomato")
Let’s make QQ-plots to assess Normality of both raw values and the log-transformed values.
par(mfrow = c(1,2)) #2 plots next to each other (plotting area divided into 1 row, 2 columns)
par(pty = "s") #make plots to exact squares
qqnorm(y, main = "SystBP Females", cex = 0.5)
qqline(y)
qqnorm(log(y), main = "log(SystBP) Females", cex = 0.5)
qqline(log(y))
We see that the original data have more higher values than a Normal distribution due to the skew to right whereas the log-transformed data look like Normally distributed.
Are the means in males and females different? Even though the
distributions were not perfectly normal, the sample sizes (~500 for
males and females) are large enough that the confidence interval from
t.test()
is a good approximation for the difference in
means.
y.m = x[x$sex == 1, "systbp"]
y.f = x[x$sex == 2, "systbp"]
t.test(y.m, y.f)
##
## Welch Two Sample t-test
##
## data: y.m and y.f
## t = 4.2583, df = 944.71, p-value = 2.266e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.621554 7.103381
## sample estimates:
## mean of x mean of y
## 137.0682 132.2057
#With log-transformed data we have more perfectly normally distributed data:
logy.m = log(y.m)
logy.f = log(y.f)
t.test(logy.m, logy.f)
##
## Welch Two Sample t-test
##
## data: logy.m and logy.f
## t = 4.4962, df = 954.69, p-value = 7.769e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02111718 0.05382917
## sample estimates:
## mean of x mean of y
## 4.912726 4.875253
#What about non-parametric test?
wilcox.test(y.m, y.f)
##
## Wilcoxon rank sum test with continuity correction
##
## data: y.m and y.f
## W = 133548, p-value = 2.84e-05
## alternative hypothesis: true location shift is not equal to 0
#Note that non-parametric test is immune to any monotonic transformation.
#Thus it gives exactly the same P-value for log-transformed data as it gave for the original data:
wilcox.test(logy.m, logy.f)
##
## Wilcoxon rank sum test with continuity correction
##
## data: logy.m and logy.f
## W = 133548, p-value = 2.84e-05
## alternative hypothesis: true location shift is not equal to 0
Conclusions: Means of systbp
are
statistically higher in males than in females. (Same conclusion by all 3
tests with P-values < 1e-4.) The difference in means is 4.9 mmHG
(95%CI 2.6,…,7.1).
Read more about logarithms http://www.bmj.com/content/312/7032/700.full and transformations http://www.bmj.com/content/312/7033/770.full. Note that in conclusion, we gave a 95%CI for the difference in means in the original scale (mmHG) even though the distributions were not perfectly normal. This is based on a large sample size and Central Limit Theorem but would not be a good practice for small data sets. For that, read about CI of the difference in means of transformed data http://www.bmj.com/content/312/7039/1153.full.
Assume that dataset \(X\) contains \(n\) (independent) samples from some distribution with mean=\(\mu\) and standard deviation=\(\sigma\) but \(X\) does not necessarily need to follow Normal, binomial or any other distribution we have ever heard about. CLT says that the distribution of the point estimate of the mean of \(X\) is approximately Normal(mean=\(\mu\),sd= \(\sigma/\sqrt{n}\)) in LARGE samples. This result can be used to derive confidence intervals for the estimated mean. For example, a 95%CI is the observed mean \(\pm 1.96\,s/\sqrt{n}\), where \(s\) is the observed standard deviation of \(X\). The importance of this result is its complete generality with respect to the shape of the underlying distribution. However, it requires a large sample to work in practice: Rather hundreds than a few dozens.
Let’s try it out with Uniformly distributed values. Let’s compute a mean of \(n=500\) U(0,1) distributed values, and repeat this computation 1000 times. We’ll do that by making a big data matrix where rows are 1000 repetitions and columns are 500 observations. According to the theory, the mean of U(0,1) is 0.5 and its sd is \(1/\sqrt{12}\approx 0.289\). Thus, we expect that the mean of 500 samples from U(0,1) has a mean of 0.5 and sd of \(1/\sqrt{12\cdot 500} \approx 0.0129.\) Our main interest is whether the distribution of the mean over the data sets is Normal as CLT claims.
n = 500 #sample size in each repetition
m = 1000 #number of repetitions
X = matrix(runif(n*m), nrow = m, ncol = n) #data matrix
means = rowSums(X) / n #collect 1000 means here
c(mean(means), sd(means)) #what is the mean and sd of the estimates of the mean
## [1] 0.49958043 0.01287575
qqnorm(means) #see whether means seem Normally distributed
qqline(means)
Indeed, the distributions of means look like Normal according to the QQ-plot. Let’s see the histogram of the means, and let’s also show a histogram of one of the individual data sets to see that it indeed looked like uniform (and is far from Normal!).
par(mfrow = c(1,2))
hist(means, xlim = c(0.4,0.6), breaks = 15, col = "limegreen", main="Means of 1000 data sets follow a Normal")
#plot the first data set just to make sure that the original data look like U(0,1)
hist(X[1,], breaks = 10, col = "gray", main="One data set follows Uniform(0,1)")
From the histograms we see how a single data set looks uniformly distributed on [0,1] (right) but how the means of 1000 such data sets is tightly concentrated around 0.5 and looks like Normally distributed. This is CLT in action.
From this result, we can derive the standard Normal approximation to
the confidence interval for the mean of values from any distribution.
Suppose that we have \(n\) values from
a distribution \(D\). The endpoints of
the 95% confidence interval for the mean of \(D\) are \[\overline{x}\pm 1.96 \times
\widehat{s}/\sqrt{n},\] where \(\overline{x}\) is the empirical mean of the
\(n\) values, \(\widehat{s}\) is their empirical standard
deviation and \(1.96\)
=qnorm(1-0.05/2)
is the quantile point from the standard
Normal distribution below which probability mass \(0.975 = 1-0.05/2\) lies.