probability distributions and dataset properties lecture 2 likelihood methods in forest ecology...
TRANSCRIPT
![Page 1: Probability Distributions and Dataset Properties Lecture 2 Likelihood Methods in Forest Ecology October 9 th – 20 th, 2006](https://reader036.vdocuments.site/reader036/viewer/2022062314/56649ea15503460f94ba42d2/html5/thumbnails/1.jpg)
Probability Distributions and Dataset Properties
Lecture 2
Likelihood Methods in Forest Ecology
October 9th – 20th , 2006
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Statistical Inference
Data
Scientific Model (Scientific hypothesis)
Probability Model(Statistical hypothesis)
Inference
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Parametric perspective on inference
Scientific Model (Hypothesis test)Often with linear models
Probability Model(Normal typically)
Inference
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Likelihood perspective on inference
Data
Scientific Model (hypothesis)
Probability Model
Inference
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An example...
The Data:xi = measurements of DBH on 50 treesyi = measurements of crown radius on those trees
The Scientific Model:yi = xi + (linear relationship, with 2 parameters ( and an error term () (the residuals))
The Probability Model: is normally distributed, with E[] and variance estimated from the observed variance of the residuals...
Data
Scientific Model (hypothesis)
Probability Model
Inference
Data
Scientific Model (hypothesis)
Probability Model
Inference
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The triangle of statistical inference: Model
• Models clarify our understanding of nature.• Help us understand the importance (or
unimportance) of individuals processes and mechanisms.
• Since they are not hypotheses, they can never be “correct”.
• We don’t “reject” models; we assess their validity.• Establish what’s “true” by establishing which
model the data support.
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The triangle of statistical inference:Probability distributions
• Data are never “clean”.• Most models are deterministic, they
describe the average behavior of a system but not the noise or variability. To compare models with data, we need a statistical model which describes the variability.
• We must understand the the processes giving rise to variability to select the correct probability density function (error structure) that gives rise to the variability or noise.
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DBH (cm)
0 10 20 30 40 50C
row
n ra
dius
(m
)0
1
2
3
4
5
6
The Data: xi = measurements of DBH on 50 trees yi = measurements of crown radius on those trees
The Scientific Model: yi = DBHi +
The Probability Model: is normally distributed.
Data
ScientificProbability Model
Inference
Data
Scientific Model
Probability Model
Inference
An example: Can we predict crown radius using tree diameter?
0
2
4
6
8
10
12
14
16
1.62 2.10 2.57 3.05 3.52 4.00 4.47 4.95 5.42 5.89
Crown radius
Fre
qu
en
cy
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Why do we care about probability?
• Foundation of theory of statistics.• Description of uncertainty (error).
– Measurement error
– Process error
• Needed to understand likelihood theory which is required for:Estimating model parameters.Model selection (What hypothesis do data support?).
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Error (noise, variability) is your friend!
• Classical statistics are built around the assumption that the variability is normally distributed.
• But…normality is in fact rare in ecology.
• Non-normality is an opportunity to:Represent variability in a more realistic way.Gain insights into the process of interest.
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The likelihood framework
Ask biological question
Collect data
Probability Model Model noise
Ecological Model Model signal
Estimate parameters
Estimate support regions
Answer questions
Model selection
Bolker, Notes
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Probability Concepts
• An experiment is an operation with uncertain outcome.
• A sample space is a set of all possible outcomes of an experiment.
• An event is a particular outcome of an experiment, a subset of the sample space.
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Random Variables
• A random variable is a function that assigns a numeric value to every outcome of an experiment (event) or sample. For instance
Event Random variable
Tree Growth = f (DBH, light, soil…)
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Function: formula expressing a relationship between two variables.
All pdf’s are functions BUT NOT all functions are PDF’s.
Functions and probability density functions
Functions = Scientific Model
pdf’s
Crown radius = DBHWE WILL TALK ABOUT THIS LATER
Used to model noise:Y-(DBH)
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Probability Density Functions: properties
• A function that assigns probabilities to ALL the possible values of a random variable (x).
Sx
)x(f
)x(f
1
10
x
Pro
babi
lity
den
sity
f(x)
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Probability Density Functions: Expectations
• The expectation of a random variable x is the weighted value of the possible values that x can take, each value weighted by the probability that x assumes it.
• Analogous to “center of gravity”. First moment.
0
1
)x(p:x
N
ii
)x(xpN
x
]X[E
-1 0 1 2
p(-1)=0.10 p(0)=0.25 p(1)=0.3 p(2)=0.35
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Probability Density Functions: Variance
• The variance of a random variable reflects the spread of X values around the expected value.
• Second moment of a distribution.
22
2
])X[E(]X[E
]))x(EX[(E]X[Var
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Probability Distributions
• A function that assigns probabilities to the possible values of a random variable (X).
• They come in two flavors:
DISCRETE: outcomes are a set of discrete possibilities such as integers (e.g, counting).
CONTINUOUS: A probability distribution over a continuous range (real numbers or the non-negative real numbers).
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0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Event (x)
Pro
ba
bil
ity
Probability Mass Functions
For a discrete random variable, X, the probability that x takes ona value x is a discrete density function, f(x) also known as probability mass or distribution function.
Sx
)x(f
)x(f
}xX{f)x(f
1
10
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Pro
babi
lity
den
sity
f(x)
Probability Density Functions: Continuous variables
A probability density function (f(x)) gives the probability that a random variable X takes on values within a range.
1
0
dx)x(f
)x(f
}bXa{Pdx)x(fb
a
a b
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Some rules of probability
)A(obPr)A|B(obPr)BA(obPr
)A(obPr
)BA(obPr)A|B(obPr
)B(obPr
)BA(obPr)B|A(obPr
)B(obPr)*A(obPr)BA(obPr
)BA(obPr)B(obPr)A(obPr)BA(obPr
assuming independence
A B
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Real data: Histograms
-5 -4 -3 -2 -1 0 1 2 3TEN
0
1
2
3
4
Cou
nt
0.0
0.1
0.2
0.3
0.4
Proportion per B
ar
-10 -5 0 5FIFTY
0
5
10
15
Cou
nt
0.0
0.1
0.2
0.3
Proportion per B
ar
-10 -5 0 5 10HUNDRED
0
10
20
30
40
Cou
nt
0.0
0.1
0.2
0.3
0.4
Proportion per B
ar
-10 -5 0 5 10FIVEHUND
0
20
40
60
80
100
120
Cou
nt
0.0
0.1
0.2
Proportion per B
ar
-10 -5 0 5 10THOUS
0
50
100
150
Cou
nt
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
Proportion per B
ar
n = 10 n = 50 n = 100
n = 500 n = 1000
VARIABLE VARIABLE
VARIABLE
VARIABLE
VARIABLE
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Histograms and PDF’s
Probability density functions approximate the distribution of finite data sets.
VARIABLE
-10 -5 0 5 100
50
100
150
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14n = 1000
Pro
ba
bili
ty
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Uses of Frequency Distributions
• Empirical (frequentist):Make predictions about the frequency of a particular
event.Judge whether an observation belongs to a
population.
• Theoretical:Predictions about the distribution of the data based
on some basic assumptions about the nature of the forces acting on a particular biological system.
Describe the randomness in the data.
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Some useful distributions
1. Discrete Binomial : Two possible outcomes. Poisson: Counts. Negative binomial: Counts. Multinomial: Multiple categorical outcomes.
2. Continuous Normal. Lognormal. Exponential Gamma Beta
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An example: Seed predation
)N(
V)V(obPrx
110
VedVisitProbPr x =no seeds taken
0 to N
Assume each seed has equal probability (p)of being taken. Then:
01
011
1
xif)p(p)!xN(!x
!NV)x(prob
xif)p(V)V()x(prob
)p()takennotseeds)xN((prob
p)takenseedsx(prob
xNx
N
xN
x
Normalization constant
t1 t2 ( )
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Zero-inflated binomial
Histogram of rzibinom(n = 1000, prob = 0.6, size = 12, zprob = 0.3)
rzibinom(n = 1000, prob = 0.6, size = 12, zprob = 0.3)
Fre
quen
cy
0 2 4 6 8 10
010
020
030
0
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Binomial distribution: Discrete events that can take one of two values
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Event (x)
Pro
bab
ilit
y)!xn(!x
!n
x
n
)p(px
n)xX(P xnx
1
E[x] = npVariance =np(1-p)n = number of sitesp = prob. of survival
Example: Probability of survival derived from pop data
n =20p = 0.5
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Binomial distribution
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Poisson Distribution: Counts (or getting hit in the head by a horse)
Variance
]X[E!k
)(e)kX(P
k
k = number of seedlings
λ= arrival rate
500 0.5
0 1 2 3 4 5 6 7POISSON
0
100
200
300
400
Cou
nt
0.0
0.1
0.2
0.3
0.4 Proportion per B
ar
Number of Seedlings/quadrat**Alt param= λ=rt
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Poisson distribution
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Example: Number of seedlings in census quad.
0 10 20 30 40 50 60 70 80 90 100
Number of seedlings/trap
0
10
20
30
40
50
60C
ou
nt
0.0
0.1
0.2
0.3
0.4P
ropo
rtion pe
r Bar
Alchornea latifolia
(Data from LFDP, Puerto Rico)
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Clustering in space or time
Poisson processE[X]=Variance[X]
Poisson processE[X]<Variance[X]Overdispersed Clumped or patchy
Negative binomial?
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Negative binomial:Table 4.2 & 4.3 in H&M Bycatch Data
E[X]=0.279Variance[X]=1.56
Suggests temporal or spatial aggregationin the data!!
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Negative Binomial: Counts
2
1
11
1
p
)p(rVariance
p
r]X[E
)p(pr
n)nX(P rnr
0 10 20 30 40 50NEGBIN
0
10
20
30
40
50
60
70
80
90
100
Cou
nt
0.0
0.1
0.2
Proportion per B
ar
Number of Seeds
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Negative Binomial: Counts
large k
Poisson; k
:variance to related kk
mmVariance
m]X[E
km
m
k
m
!n)k(
)nk()nXPr(
nk
0
1
2
0 10 20 30 40 50NEGBIN
0
10
20
30
40
50
60
70
80
90
100
Cou
nt
0.0
0.1
0.2
Proportion per B
ar
Number of Seeds
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Negative binomial
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Negative Binomial: Count data
0 10 20 30 40 50 60 70 80 90 100
No seedlings/quad.
0
10
20
30
Cou
nt
0.0
0.1
0.2P
roportion per Bar
Prestoea acuminata
(Data from LFDP, Puerto Rico)
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Normal PDF with mean = 0
X
0
0.2
0.4
0.6
0.8
1
-5 -4 -3 -2 -1 0 1 2 3 4 5
Pro
b(x
)
Var = 0.25
Var = 0.5
Var = 1
Var = 2
Var = 5
Var = 10
Normal Distribution
2
2
2
2 22
1
Variance
mMean
))mx(
exp()x(f E[x] = mVariance = δ2
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Normal Distribution with increasing variance
dcxVariance
mMean
))mx(
exp()x(f
2
2
2 22
1
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Lognormal: One tail and no negative values
)e(meiancevarme]x[Eemmedian
),(Y,eX)xln(
expx
)x(f Y
1
2
1
2
1
22
2
2
22
2
2
0.8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 10 20 30 40 50 60 70
x is always positive
f(x)
x
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Lognormal: Radial growth data
0 1 2 3 4HEMLOCK
0
50
100
150
Cou
nt
0.0
0.1
0.2 Prop
ortion per Bar
0 1 2 3REDCEDAR
0
10
20
30
40
Cou
nt0.0
0.1
0.2
Prop
ortion per Bar
Growth (cm/yr) Growth (cm/yr)
Red cedarHemlock
(Data from Date Creek, British Columbia)
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Exponential
2
1
1
Variance
]x[E
e)x(f x
Variable
Co
unt
0 1 2 3 4 5 60
10
20
30
40
50
60
70
80
0.0
0.1
0.2
0.3
0.4
Pro
portion
per B
ar
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Exponential: Growth data (negatives assumed 0)
0 1 2 3 4 5 6 7 8Growth (mm/yr)
0
200
400
600
800
1000
1200C
ount
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7P
roportion per Bar
Beilschemedia pendula
(Data from BCI, Panama)
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Gamma: One tail and flexibility
parameter scales
parameter shapea
as]X[Var
as]x[E
ex)n(s
)x(f s/xaa
2
11
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Gamma: “raw” growth data
0 1 2 3 4 5 6 7 8 9Growth (mm/yr)
0
200
400
600
800
1000
Cou
nt
Alseis blackiana
(Data from BCI, Panama)
0 10 20 300
50
100
150
200
Cordia bicolor
Growth (mm/yr)
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Beta distribution
.otherwise;xfor)x(x)b,a(
)b,a|x(f ib
ia
ii 01011 11
)ba(
)b()a()b,a(
)ba()ba(
ba)x(Var
x of value expected ba
a)x(E
12
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Beta: Light interception by crown trees
(Data from Luquillo, PR)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0GLI
0
100
200
300
400
500
600
Co
unt
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Pro
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er B
ar
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Mixture models
• What do you do when your data don’t fit any known distribution?– Add covariates– Mixture models
• Discrete
• Continuous
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Discrete mixtures
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Discrete mixture:Zero-inflated binomial
0
001
xif)x(prob*V)x(prob
xif)(prob)V()x(prob
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Continuous (compounded) mixtures
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The Method of Moments
• You can match up the sample values of the moments of the distributions and match them up with the theoretical moments.
• Recall that:
• The MOM is a good way to get a first (but biased) estimate of the parameters of a distribution. ML estimators are more reliable.
0)x(p:x
)x(xp]X[E
22 ])X[E(]X[E]X[Var
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MOM: Negative binomial
mu)x(xp]X[E)x(p:x
0
k
mumu])X[E(]X[E]X[Var
222