, John Fricks [ctb]| Title: | Analyze Paleontological Time-Series |
|---|---|
| Description: | Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts). |
| Authors: | Gene Hunt [aut, cre, cph]
, John Fricks [ctb] |
| Maintainer: | Gene Hunt <[email protected]> |
| License: | GPL-3 |
| Version: | 0.6.2 |
| Built: | 2024-12-10 06:31:17 UTC |
| Source: | CRAN |
Compute Akaike weights from AIC scores
akaike.wts(aa)akaike.wts(aa)
aa |
vector of AIC or AICc scores |
This function converts a vector of AIC or AICc scores to Akaike weights, a vector that summarizes proportional model support.
a vector of Akaike weights
akaike.wts(c(10, 14, 20))akaike.wts(c(10, 14, 20))
Combines information into an object of class paleoTS
as.paleoTS( mm, vv, nn, tt, MM = NULL, genpars = NULL, label = NULL, start.age = NULL, oldest = c("first", "last"), reset.time = TRUE )as.paleoTS( mm, vv, nn, tt, MM = NULL, genpars = NULL, label = NULL, start.age = NULL, oldest = c("first", "last"), reset.time = TRUE )
mm |
vector of sample means |
vv |
vector of sample variances |
nn |
vector of sample sizes |
tt |
vector of sample ages |
MM |
vector of true means (simulated data) |
genpars |
generating parameters (simulated data) |
label |
optional, label for time-series |
start.age |
optional, age of oldest sample |
oldest |
value indicating if the oldest sample is first or last in the sequence |
reset.time |
logical; if TRUE, then change time scale to start at t=0
and adjust |
This function combines data into a paleoTS object. For
empirical data it may be more convenient to use read.paleoTS.
If sample ages decrease through the sequence, as if given in millions of
years ago, tt will automatically be converted to time elapsed from
the beginning of the sequence as long as reset.time = TRUE.
a paleoTS object
All model-fitting functions estimate the contribution of sampling
noise to the observed differences between samples. They do this assuming
that the trait is represented by sample means, which have sampling
variances equal to variance divided by sample size, vv/nn. If one
is interested in analyzing statistics other than the sample mean (medians,
quantiles, or other statistics), use the the following procedure: set the
statistic in question as the mm values, replace vv with a
vector of the squared standard errors for each estimate (generated by other
means, for example bootstrapping), and set all values of nn to one.
When fitting such time-series, be sure to set the argument pool = FALSE.
x <- as.paleoTS(mm = rnorm(20), vv = rep(1, 20), nn = rep(25, 20), tt=0:19) plot(x) # easier to use sim.Stasis()x <- as.paleoTS(mm = rnorm(20), vv = rep(1, 20), nn = rep(25, 20), tt=0:19) plot(x) # easier to use sim.Stasis()
paleoTSfit objectCreate a paleoTSfit object
as.paleoTSfit( logL, parameters, modelName, method, K, n, se, convergence = NULL, logLFunction = NULL, ... )as.paleoTSfit( logL, parameters, modelName, method, K, n, se, convergence = NULL, logLFunction = NULL, ... )
logL |
model log-likelihood |
parameters |
model parameter estimates |
modelName |
model name |
method |
parameterization, either "AD" or "Joint" |
K |
number of model parameters |
n |
sample size |
se |
standard errors of parameter estimates |
convergence |
code indicating optimization convergence |
logLFunction |
name of the log-likelihood function |
... |
optional additional elements added by some functions |
a paleoTSfit object
All model-fitting functions use this function to package the resulting data-model fits. Users will not need to call this function.
# fake example; users won't need to use this unless they make their own model-fitting functions w <- as.paleoTSfit(logL = 10, parameters = 2, modelName = "StrictStasis", method = "Joint", K = 1, n = 25, se = NULL)# fake example; users won't need to use this unless they make their own model-fitting functions w <- as.paleoTSfit(logL = 10, parameters = 2, modelName = "StrictStasis", method = "Joint", K = 1, n = 25, se = NULL)
Bootstrap test to see if a complex model is significantly better than a simple model.
bootSimpleComplex( y, simpleFit, complexFit, nboot = 99, minb = 7, ret.full.distribution = FALSE, parallel = FALSE, ... )bootSimpleComplex( y, simpleFit, complexFit, nboot = 99, minb = 7, ret.full.distribution = FALSE, parallel = FALSE, ... )
y |
a |
simpleFit |
a |
complexFit |
a |
nboot |
number of replications for parametric bootstrapping |
minb |
minimum number of populations within each segment |
ret.full.distribution |
logical, indicating if the null distribution for the likelihood ratio from the parametric bootstrap should be returned |
parallel |
logical, if TRUE, the bootstrapping is done using parallel computing |
... |
further arguments, passed to optimization functions |
Simulations suggest that AICc can be overly liberal with complex models with mode shifts or punctuations (Hunt et al., 2015). This function implements an alternative of parametric boostrapping to compare the fit of a simple model with a complex model. It proceeds in five steps:
Compute the observed gain in support from the simple to complex model
as the likelihood ratio,
Simulate trait evolution under the specified simple model nboot times
Fit to each simulated sequence the specified simple and complex models
Measure the gain in support from simple to complex as the bootstrap likelihood ratio for each simulated sequence
Compute the P-value as the percentile of the bootstrap distribution corresponding to the observed LR.
Argument simpleFit should be a paleoTS object returned by the
function fitSimple or similar functions (e.g., opt.joint.GRW,
opt.GRW, etc.). Argument complexFit must be a paleoTS object
returned by fitGpunc or fitModeShift.
Calculations can be speeded up by setting parallel = TRUE, which uses
package doParallel to run the bootstrap replicates in parallel, using
one fewer than the number of detected cores.
A list of the observed likelihood ratio statistic, LRobs, the
P-value of the test, and the number of bootstrap replicates. If
ret.full.distribution = TRUE, the null distribution of likelihood
ratios generated by parametric bootstrapping is also returned.
Hunt, G., M. J. Hopkins and S. Lidgard. 2015. Simple versus complex models of trait evolution and stasis as a response to environmental change. PNAS 112(16): 4885-4890.
## Not run: x <- sim.Stasis.RW(ns = c(15, 15), omega = 0.5, ms = 1, order = "Stasis-RW") ws <- fitSimple(x) wc <- fitModeShift(x, order = "Stasis-RW", rw.model = "GRW") bootSimpleComplex(x, ws, wc, nboot = 50, minb = 7) # nboot too low for real analysis! ## End(Not run)## Not run: x <- sim.Stasis.RW(ns = c(15, 15), omega = 0.5, ms = 1, order = "Stasis-RW") ws <- fitSimple(x) wc <- fitModeShift(x, order = "Stasis-RW", rw.model = "GRW") bootSimpleComplex(x, ws, wc, nboot = 50, minb = 7) # nboot too low for real analysis! ## End(Not run)
Time-series of the length of lower first molar for the Cantius lineage
cantius_Lcantius_L
a paleoTS object with the data
Clyde, W. C. and P. D. Gingerich (1994). Rates of evolution in the dentition of early Eocene Cantius: comparison of size and shape. Paleobiology 20(4): 506-522.
Compute and (optionally) plot residuals from SSM model fit
checkSSMresiduals( y, w, show.plot = TRUE, resid.type = c("standardized", "unstandardized") )checkSSMresiduals( y, w, show.plot = TRUE, resid.type = c("standardized", "unstandardized") )
y |
a |
w |
a |
show.plot |
logical, if |
resid.type |
residual type, either "standardized" or "unstandardized" |
It is recommended that resid.type be set to the default, "standardized", which will scale residuals by their expected standard deviation
a vector of residuals, returned invisibly
y <- sim.GRW(ns = 50, ms = 0.2) w <- fitSimple(y, model = "URW", method = "SSM") # wrong model checkSSMresiduals(y, w, show.plot = TRUE) # positive residuals show model mis-fity <- sim.GRW(ns = 50, ms = 0.2) w <- fitSimple(y, model = "URW", method = "SSM") # wrong model checkSSMresiduals(y, w, show.plot = TRUE) # positive residuals show model mis-fit
Takes output from model-fitting functions and compiles model-fit information (log-likelihood, AICc, etc.) into a convenient table
compareModels(..., silent = FALSE, sort = FALSE)compareModels(..., silent = FALSE, sort = FALSE)
... |
any number of model fit ( |
silent |
if |
sort |
if |
if silent = FALSE, the table is printed and nothing is
returned. If silent = TRUE, printing is suppressed and a list of
two objects is returned: the table of model fits, modelFits, and a
list of parameter estimates, pl.
x <- sim.GRW(ns = 40, ms = 0.5, vs = 0.1) m1 <- fitSimple(x, model = "GRW") # the true model m2 <- fitSimple(x, model = "URW") plot(x, modelFit = m1) compareModels(m1, m2)x <- sim.GRW(ns = 40, ms = 0.5, vs = 0.1) m1 <- fitSimple(x, model = "GRW") # the true model m2 <- fitSimple(x, model = "URW") plot(x, modelFit = m1) compareModels(m1, m2)
Time-series of dorsal spine data from a fossil stickleback lineage
dorsal.spinesdorsal.spines
a paleoTS object of the mean number of dorsal spines (log-transformed)
Bell, M.A., M.P. Travis and D.M. Blouw 2006. Inferring natural
selection in a fossil threespine stickleback. Paleobiology 32:562-577.
Hunt, G., M. A. Bell and M. P. Travis (2008). Evolution toward a new adaptive optimum:
phenotypic evolution in a fossil stickleback lineage. Evolution 62(3): 700-710.
Computes for a specified model and duration of time the expected squared divergence (ESD), which is a useful measure of the magnitude or rate of change across different models.
ESD( y, dt, model = c("GRW", "URW", "Stasis", "allThree"), method = c("Joint", "AD"), pool = TRUE, ... )ESD( y, dt, model = c("GRW", "URW", "Stasis", "allThree"), method = c("Joint", "AD"), pool = TRUE, ... )
y |
a |
dt |
the time interval to evaluate ESD |
model |
the model of evolution to assume; see Details |
method |
Joint or AD parameterization |
pool |
logical, if TRUE, variances are averaged (pooled) across samples |
... |
other arguments to the model-fitting functions |
Hunt (2012) argued that rate metrics make sense only in the context of specific models of evolution. It is thus difficult to meaningfully compare rates across sequences generated by different evolutionary processes. ESD values can be used for a specified model and duration as a comparable measure of the amount of evolutionary change that is expected. Acceptable values for the model argument can be "GRW" for the general random walk (directional change), "URW" for the unbiased random walk, and "Stasis." In addition, one can also specify "allThree", in which case all these models will be fit and the resulting ESD will be the weighted average of them, using model support (Akaike weights) for the weighting (see Hunt [2012], p. 370)
the ESD value
Hunt, G. 2012. Measuring rates of phenotypic evolution and the inseparability of tempo and mode. Paleobiology 38:351–373.
x<- sim.GRW(ns=20) esd.urw<- ESD(x, dt=10, model="URW") esd.all<- ESD(x, dt=10, model="allThree")x<- sim.GRW(ns=20) esd.urw<- ESD(x, dt=10, model="URW") esd.all<- ESD(x, dt=10, model="allThree")
This function fits a model of punctuated change that is is protracted enough that it is captured by multiple transitional populations. Trait evolution starts in stasis, shifts to a general random walk, and then shifts back into stasis.
fit.sgs( y, minb = 7, oshare = TRUE, pool = TRUE, silent = FALSE, hess = FALSE, meth = "L-BFGS-B", model = "GRW" )fit.sgs( y, minb = 7, oshare = TRUE, pool = TRUE, silent = FALSE, hess = FALSE, meth = "L-BFGS-B", model = "GRW" )
y |
a |
minb |
minimum number of populations within each segment |
oshare |
logical, if TRUE, variance assumed to be shared (equal) across segments |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
silent |
logical, if TRUE, progress updates are suppressed |
hess |
if TRUE, standard errors computed from the Hessian matrix are returned |
meth |
optimization method, passes to |
model |
type of random walk: |
a paleoTSfit object
x <- sim.sgs(ns = c(10, 10, 10)) # default values OK w <- fit.sgs(x, minb = 8) # increase minb so example takes less time; not recommended! plot(x) abline(v = c(16, 31), lwd = 3) # actual shifts abline(v = c(w$parameters[6:7]), lwd = 2, lty = 3, col = "red") # inferred shiftsx <- sim.sgs(ns = c(10, 10, 10)) # default values OK w <- fit.sgs(x, minb = 8) # increase minb so example takes less time; not recommended! plot(x) abline(v = c(16, 31), lwd = 3) # actual shifts abline(v = c(w$parameters[6:7]), lwd = 2, lty = 3, col = "red") # inferred shifts
Fit a set of standard evolutionary models
fit3models(y, silent = FALSE, method = c("Joint", "AD", "SSM"), ...) fit4models(y, silent = FALSE, method = c("Joint", "AD", "SSM"), ...)fit3models(y, silent = FALSE, method = c("Joint", "AD", "SSM"), ...) fit4models(y, silent = FALSE, method = c("Joint", "AD", "SSM"), ...)
y |
a |
silent |
if TRUE, results are returned as a list and not printed |
method |
"Joint", "AD", or "SSM"; see |
... |
other arguments passed to model fitting functions |
Function fit3models fits the general (biased) random walk (GRW),
unbiased random walk (URW), and Stasis models. In addition to these three,
fit4models also fits the model of Strict Stasis.
if silent = FALSE, a table of model fit statistics, also printed to the screen. if silent = TRUE, a list of the model fit statistics and model parameter values.
fit4models(): add model of "Strict Stasis" to the three models
x <- sim.GRW(ns = 50, ms = 0.2) fit4models(x)x <- sim.GRW(ns = 50, ms = 0.2) fit4models(x)
This function fits nine models to a time-series following Hunt et al. (2015). These
include the simple models fit by fit4models along with mode shift and
punctuation models.
fit9models(y, silent = FALSE, method = c("Joint", "AD"), ...)fit9models(y, silent = FALSE, method = c("Joint", "AD"), ...)
y |
a |
silent |
logical, if TRUE, progress updates are suppressed |
method |
parameterization to use: |
... |
other arguments, passed to optimization functions |
if silent = FALSE, a table of model fit statistics, also printed to the screen. if silent = TRUE, a list of the model fit statistics and model parameter values.
Hunt, G., M. J. Hopkins and S. Lidgard. 2015. Simple versus complex models of trait evolution and stasis as a response to environmental change. PNAS 112(16): 4885-4890.
## Not run: x <- sim.Stasis.RW(ns = c(15, 15), omega = 0.5, ms = 1, order = "Stasis-RW") plot(x) fit9models(x) ## End(Not run)## Not run: x <- sim.Stasis.RW(ns = c(15, 15), omega = 0.5, ms = 1, order = "Stasis-RW") plot(x) fit9models(x) ## End(Not run)
Fit trait evolution model with punctuations estimated from the data
fitGpunc( y, ng = 2, minb = 7, pool = TRUE, oshare = TRUE, method = c("Joint", "AD"), silent = FALSE, hess = FALSE, parallel = FALSE, ... )fitGpunc( y, ng = 2, minb = 7, pool = TRUE, oshare = TRUE, method = c("Joint", "AD"), silent = FALSE, hess = FALSE, parallel = FALSE, ... )
y |
a |
ng |
number of groups (segments) in the sequence |
minb |
minimum number of populations within each segment |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
oshare |
logical, if TRUE, variance assumed to be shared (equal) across segments |
method |
parameterization to use: |
silent |
logical, if TRUE, progress updates are suppressed |
hess |
if TRUE, standard errors computed from the Hessian matrix are returned |
parallel |
logical, if TRUE, the analysis is done in parallel |
... |
other arguments, passed to optimization functions |
This function tests all possible shift points for punctuations, subject to the
constraint that the number of populations in each segment is always >= minb. The
shiftpoint yielding the highest log-likelihood is returned as the solution, along with
the log-likelihoods (all.logl) of all tested shift points (GG).
The function uses opt.punc (if method = "AD") or opt.joint.punc
(if method = "Joint") to do the fitting.
a paleoTSfit object with the results of the model-fitting.
Calculations can be speeded up by setting parallel = TRUE, which uses
package doParallel to run the bootstrap replicates in parallel, using
one fewer than the number of detected cores.
x <- sim.punc(ns = c(15, 15), theta = c(0,3), omega = c(0.1, 0.1)) w.punc <- fitGpunc(x, oshare = TRUE) plot(x, modelFit = w.punc)x <- sim.punc(ns = c(15, 15), theta = c(0,3), omega = c(0.1, 0.1)) w.punc <- fitGpunc(x, oshare = TRUE) plot(x, modelFit = w.punc)
Trait evolution is modeled as a shift from a random walk (general or unbiased) to stasis, or vice versa.
fitModeShift( y, minb = 7, pool = TRUE, order = c("Stasis-RW", "RW-Stasis"), rw.model = c("URW", "GRW"), method = c("Joint", "AD"), silent = FALSE, hess = FALSE, ... )fitModeShift( y, minb = 7, pool = TRUE, order = c("Stasis-RW", "RW-Stasis"), rw.model = c("URW", "GRW"), method = c("Joint", "AD"), silent = FALSE, hess = FALSE, ... )
y |
|
minb |
minimum number of populations within each segment |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
order |
whether stasis or random walk come first, one of |
rw.model |
whether the random walk segment is an unbiased random walk, |
method |
parameterization to use: |
silent |
logical, if TRUE, progress updates are suppressed |
hess |
if TRUE, standard errors computed from the Hessian matrix are returned |
... |
other arguments, passed to optimization functions |
a paleoTSfit object
x <- sim.Stasis.RW(ns = c(15, 15), omega = 0.5, ms = 1, order = "Stasis-RW") plot(x) w <- fitModeShift(x, order = "Stasis-RW", rw.model = "GRW") abline(v = x$tt[15], lwd = 3) # actual shift point abline(v = x$tt[w$par["shift1"]], lty = 3, lwd = 2, col = "red") # inferred shiftx <- sim.Stasis.RW(ns = c(15, 15), omega = 0.5, ms = 1, order = "Stasis-RW") plot(x) w <- fitModeShift(x, order = "Stasis-RW", rw.model = "GRW") abline(v = x$tt[15], lwd = 3) # actual shift point abline(v = x$tt[w$par["shift1"]], lty = 3, lwd = 2, col = "red") # inferred shift
Fit the same simple model across multiple time-series
fitMult( yl, model = c("GRW", "URW", "Stasis", "covTrack"), method = c("Joint", "AD"), pool = TRUE, zl = NULL, hess = FALSE )fitMult( yl, model = c("GRW", "URW", "Stasis", "covTrack"), method = c("Joint", "AD"), pool = TRUE, zl = NULL, hess = FALSE )
yl |
a list of |
model |
the model to fit; see Details |
method |
parameterization to use: |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
zl |
for the |
hess |
if TRUE, standard errors computed from the Hessian matrix are returned |
This function fits a model with shared parameters across multiple trait time-series. The most likely application would be to model a common evolutionary dynamic across different sequences, perhaps representing time-series of the same trait and lineage from different localities or time intervals.
Four simple models are currently implemented:
GRW:
parameters mstep and vstep of the general random walk are
shared across sequences.
URW: parameter vstep of the
unbiased random walk is shared across sequences.
Stasis:
parameter omega of stasis is shared across sequences.
covTrack: parameters b0, b1, and evar of the
covariate-tracking model are shared across sequences.
Under the joint parameterization, method = "Joint", an additional parameter, anc is
fit, representing the ancestral (starting) trait value. This parameter is estimated separately
in each sequence so it is not assumed that they all start at the same trait value.
a paleoTSfit object with the results of the model-fitting
The models are described in the help for fitSimple and the functions
linked from there.
x1 <- sim.GRW(ms = 1, vs = 0.2) x2 <- sim.GRW(ms = 1, vs = 0.2) fitMult(list(x1, x2), model = "GRW")x1 <- sim.GRW(ms = 1, vs = 0.2) x2 <- sim.GRW(ms = 1, vs = 0.2) fitMult(list(x1, x2), model = "GRW")
Fit simple models of trait evolution
fitSimple( y, model = c("GRW", "URW", "Stasis", "StrictStasis", "OU", "ACDC", "covTrack"), method = c("Joint", "AD", "SSM"), pool = TRUE, z = NULL, hess = FALSE )fitSimple( y, model = c("GRW", "URW", "Stasis", "StrictStasis", "OU", "ACDC", "covTrack"), method = c("Joint", "AD", "SSM"), pool = TRUE, z = NULL, hess = FALSE )
y |
a |
model |
the model to be fit, one of |
method |
parameterization to use: |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
z |
a vector of a covariate, used only for the "covTrack" model |
hess |
if TRUE, standard errors computed from the Hessian matrix are returned |
This is a convenience function that calls the specific individual
functions for each model and parameterization, such as opt.GRW and
opt.joint.GRW. The models that this function can fit are:
GRW: General Random Walk. Under this model, evolutionary
changes, or "steps" are drawn from a distribution with a mean of mstep
and variance of vstep. mstep determines directionality and
vstep determines volatility (Hunt, 2006).
URW:
Unbiased Random Walk. Same as GRW with mstep = 0, and thus evolution
is non-directional. For a URW, vstep is the rate parameter.
Stasis: This parameterization follows Sheets & Mitchell (2001), with
a constant mean theta and variance omega (equivalent to white
noise).
Strict Stasis: Same as Stasis with omega = 0,
indicating no real evolutionary differences; all observed variation is
sampling error (Hunt et al. 2015).
OU: Ornstein-Uhlenbeck
model (Hunt et al. 2008). This model is that of a population ascending a
nearby peak in the adaptive landscape. The optimal trait value is theta,
alpha indicates the strength of attraction to that peak (= strength of
stabilizing selection around theta), vstep measures the random walk component (from genetic drift) and anc is the trait value
at the start of the sequence.
ACDC: Accelerating or decelerating evolution
model (Blomberg et al. 2003). This model is that of a population undergoing a
random walk with a step variance that increases or decreases over time. The initial step variance is vstep0,
and the parameter r controls its rate of increase (if positive) or decrease (if negative) over time.
When r < 0, the is equivalent to the "Early burst" model of Harmon et al.
covTrack: Covariate-tracking (Hunt et al. 2010). The trait tracks
a covariate with slope b1, consistent with an adaptive response. evar is the
residual variance, and, under method = "Joint", b0 is the intercept of the
relationship between trait and covariate.
model.
a paleoTSfit object with the model fitting results
For the covariate-tracking model, z should be a vector of length
n when method = "Joint" and n - 1 when method =
"AD", where n is the number of samples in y.
Method =
"Joint" is a full likelihood approach, considering each time-series as
a joint sample from a multivariate normal distribution. Method = "AD"
is a REML approach that uses the differences between successive samples.
They perform similarly, but the Joint approach does better under some
circumstances (Hunt, 2008).
Hunt, G. 2006. Fitting and comparing models of phyletic
evolution: random walks and beyond. Paleobiology 32(4): 578-601.
Hunt, G. 2008. Evolutionary patterns within fossil lineages: model-based
assessment of modes, rates, punctuations and process. p. 117-131 In
From Evolution to Geobiology: Research Questions Driving Paleontology at the
Start of a New Century. Bambach, R. and P. Kelley (Eds).
Hunt, G., M. A.
Bell and M. P. Travis. 2008. Evolution toward a new adaptive optimum:
phenotypic evolution in a fossil stickleback lineage. Evolution 62(3):
700-710.
Sheets, H. D., and C. Mitchell. 2010. Why the null matters:
statistical tests, random walks and evolution. Genetica 112–
113:105–125.
Blomberg, S. P., T. Garland, and A. R. Ives. 2003. Testing for phylogenetic signal in comparative data: behavioural traits are more labile.
Evolution 57(4):717-745.
Harmon, L. J. et al. 2010. Early bursts of body size and shape evolution are rare in comparative data. Evolution 64(8):2385-2396.
opt.GRW, opt.joint.GRW,
opt.joint.OU, opt.covTrack
y <- sim.Stasis(ns = 20, omega = 2) w1 <- fitSimple(y, model = "GRW") w2 <- fitSimple(y, model = "URW") w3 <- fitSimple(y, model = "Stasis") compareModels(w1, w2, w3)y <- sim.Stasis(ns = 20, omega = 2) w1 <- fitSimple(y, model = "GRW") w2 <- fitSimple(y, model = "URW") w3 <- fitSimple(y, model = "Stasis") compareModels(w1, w2, w3)
Compute Information Criteria
IC(logL, K, n = NULL, method = c("AICc", "AIC", "BIC"))IC(logL, K, n = NULL, method = c("AICc", "AIC", "BIC"))
logL |
log-likelihood |
K |
number of parameters |
n |
sample size |
method |
either "AIC", "AICc", or "BIC" |
the value of the specified information criterion
This function is used internally by the model-fitting functions. It will not generally be called directly by the user.
ic1 <- IC(logL = 0, K = 2, method = "AIC") # plain AIC ic2 <- IC(logL = 0, K = 2, n = 10, method = "AICc") ic3 <- IC(logL = 0, K = 2, n = 1000, method = "AICc") # converges to AIC with increasing n print(rbind(ic1, ic2, ic3))ic1 <- IC(logL = 0, K = 2, method = "AIC") # plain AIC ic2 <- IC(logL = 0, K = 2, n = 10, method = "AICc") ic3 <- IC(logL = 0, K = 2, n = 1000, method = "AICc") # converges to AIC with increasing n print(rbind(ic1, ic2, ic3))
Time-varying Kalman filter calculations
Kfiltertv(num, y, Atv, mu0, Sigma0, Phitv, Ups, Gam, Qtv, Rtv, input)Kfiltertv(num, y, Atv, mu0, Sigma0, Phitv, Ups, Gam, Qtv, Rtv, input)
num |
the number of samples in the time-series |
y |
values of the time-series |
Atv |
q x p x n observation array |
mu0 |
p x 1 vector setting the mean of the system at time zero |
Sigma0 |
p x p variance matrix of the system at time zero |
Phitv |
p x p x n array reflecting autoregression of the state variables |
Ups |
p x r matrix with the coefficients/parameters relating the inputs to the system equation |
Gam |
q x r matrix with the coefficients/parameters relating the inputs to the observation equation |
Qtv |
p x p x n array of system stochastic variance; user needs to ensure positive definite |
Rtv |
q x q x n array observation stochastic variance; user needs to ensure positive definite |
input |
n x r array of the exogenous variables/covariates |
For the dimensions of the argument arrays, n is the length of the
time-series, q is the dimension of the observation variable(s),
p is the dimension of the state variable(s), and r isthe
dimension of the input variable(s).
This function is based on the
Kfilter function of the astsa package, modified modified to
allow for time-varying terms for the Kalman filter. This modification
facilitates fitting a broader array of models and handling non-uniform
temporal spacing of samples. See the documentation for that function, and
the reference below for additional information.
A list of the following elements:
xp one-step-ahead prediction of the state
Pp mean square prediction error
xf filter value of the state
Pf mean square filter error
like log-likelihood
innov innovation series
sig innovation covariances
Kn last value of the gain, needed for smoothing
This function is used in the internal SSM log-likelihood functions for the models. The user will not need to use this they create their own model-fitting functions.
John Fricks ([email protected])
Shumway, R. H., and D. S. Stoffer. 2017. Time Series Analysis and its Applications (4th Ed.) Springer International.
y <- sim.GRW(ms = 0, vs = 1, vp = 0) n <- length(y) kf <- Kfiltertv(n ,y = y$mm, Atv = array(1, dim = c(1,1,n)), mu0 = y$mm[1], Sigma0 = y$vv[1]/y$nn[1], Phitv = array(1, dim = c(1,1,n)), Ups = NULL, Gam = NULL, Qtv = array(1, dim = c(1,1,n)), Rtv = array(0, dim = c(1,1,n)), input = NULL)y <- sim.GRW(ms = 0, vs = 1, vp = 0) n <- length(y) kf <- Kfiltertv(n ,y = y$mm, Atv = array(1, dim = c(1,1,n)), mu0 = y$mm[1], Sigma0 = y$vv[1]/y$nn[1], Phitv = array(1, dim = c(1,1,n)), Ups = NULL, Gam = NULL, Qtv = array(1, dim = c(1,1,n)), Rtv = array(0, dim = c(1,1,n)), input = NULL)
Approximate log-transformation of time-series data
ln.paleoTS(y)ln.paleoTS(y)
y |
a |
For a random variable x, its approximate mean on a natural log scale is the log of its untransformed mean. The approximate variance on a log scale is equal to the squared coefficient of variation.
the converted paleoTS object
This transformation only makes sense for variables with dimension and a true zero point, such as lengths and areas.
Hunt, G. 2006. Fitting and comparing models of phyletic
evolution: random walks and beyond. Paleobiology 32:578-601.
Lewontin, R. 1966. On the measurement of relative variability.
Systematic Zoology 15:141-142.
x <- sim.Stasis(ns = 10, theta = 20, omega = 1) plot(x) xl <- ln.paleoTS(x) plot(xl)x <- sim.Stasis(ns = 10, theta = 20, omega = 1) plot(x) xl <- ln.paleoTS(x) plot(xl)
Gingerich (1993) introduced a method that plots on log-log scale, the rate and interval for each pair of samples in an evolutionary sequence. On this plot, the slope is interpreted as an indicator of evolutionary mode (-1 for stasis, 0.5 for random walk, 0 for directional), and the intercept is interpreted as a measure of the rate of evolution over one generation.
LRI(y, gen.per.t = 1e+06, draw = TRUE)LRI(y, gen.per.t = 1e+06, draw = TRUE)
y |
a |
gen.per.t |
the number of generations per unit time |
draw |
logical, if TRUE, a plot is produced |
Following Gingerich (1993), a robust line is fit through the
points by minimizing the sum of absolute deviations. If generations are one
year long and time is measured in Myr, gen.per.t= 1e6.
A named vector with three elements: Intercept, slope, and
GenerationalRate
This method was important in early attempts to disentangle evolutionary tempo and mode. Likelihood-based methods have a more sound statistical basis, and in particular the estimation of 'Generational Rates' using LRI is compromised by sampling error; see Hunt (2012) and the example below.
Gingerich, P.D. 1993. Quantification and comparison of
evolutionary rates. American Journal of Science 293-A:453–478.
Hunt, G. 2012. Measuring rates of phenotypic evolution and the
inseparability of tempo and mode. Paleobiology 38:351–373.
set.seed(1) xFast <- sim.GRW(ns = 20, ms = 0.5, vs = 0.2) # fast evolution xSlow <- sim.Stasis(ns = 20, omega = 0) # strict stasis (zero rates) lri.Fast <- LRI(xFast, draw = FALSE) lri.Slow <- LRI(xSlow, draw = FALSE) print(lri.Fast[3], 4) print(lri.Slow[3], 4) # LRI thinks strict stasis rates are MUCH faster!set.seed(1) xFast <- sim.GRW(ns = 20, ms = 0.5, vs = 0.2) # fast evolution xSlow <- sim.Stasis(ns = 20, omega = 0) # strict stasis (zero rates) lri.Fast <- LRI(xFast, draw = FALSE) lri.Slow <- LRI(xSlow, draw = FALSE) print(lri.Fast[3], 4) print(lri.Slow[3], 4) # LRI thinks strict stasis rates are MUCH faster!
This function computes D, the rate metric proposed by Lynch
(1990). This metric derives from the random walk model, with D =
Vstep/(2Vp), where Vstep is the step variance of the unbiased
random walk, and Vp is the within sample variance, pooled among
samples. Under mutation - drift equilibrium, D is expected to range
approximately between 5e-5 and 5e-3.
lynchD(y, gen.per.t = 1e+06, pool = TRUE, method = c("Joint", "AD"), ...)lynchD(y, gen.per.t = 1e+06, pool = TRUE, method = c("Joint", "AD"), ...)
y |
a |
gen.per.t |
the number of generations per unit time |
pool |
logical, whether variances should be pooled over samples |
method |
parameterization to use: based on ancestor-descendant (AD) differences, or Joint consideration of all samples |
... |
further arguments, passed to |
D |
value of rate metric |
pooled.var |
value of pooled within-sample variance |
gen.per.t |
number of generations per unit time |
vstep |
computed |
drift.range |
expected minimum and maximum values
of |
result |
conclusion reached about the plausibility of neutral evolution |
Lynch (1990). The rate of morphological evolution in mammals from the standpoint of the neutral expectation. The American Naturalist 136:727-741. Hunt, G. 2012. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology 38:351-373.
y <- sim.GRW(ns = 20, ms = 0, vs = 1e-4, tt=seq(0, 1e6, length.out=20)) # per-year simulation lynchD(y, gen.per.t = 1)y <- sim.GRW(ns = 20, ms = 0, vs = 1e-4, tt=seq(0, 1e6, length.out=20)) # per-year simulation lynchD(y, gen.per.t = 1)
Analytical ML estimator for random walk and stasis models
mle.GRW(y) mle.URW(y) mle.Stasis(y)mle.GRW(y) mle.URW(y) mle.Stasis(y)
y |
a |
a vector of mstep and vstep for mle.GRW,
vstep for mle.URW, and theta and omega for
mle.Stasis
mle.URW(): ML parameter estimates for URW model
mle.Stasis(): ML parameter estimates for Stasis model
These analytical solutions assume even spacing of samples and equal sampling variance in each, which will usually be violated in real data. They are used here mostly to generate initial parameter estimates for numerical optimization; they not likely to be called directly by the user.
y <- sim.GRW(ms = 1, vs = 1) w <- mle.GRW(y) print(w)y <- sim.GRW(ms = 1, vs = 1) w <- mle.GRW(y) print(w)
Fit a model in which a trait tracks a covariate
opt.covTrack( y, z, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.joint.covTrack( y, z, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE )opt.covTrack( y, z, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.joint.covTrack( y, z, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE )
y |
a |
z |
a vector of covariate values |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
cl |
optional control list, passed to |
meth |
optimization algorithm, passed to |
hess |
if TRUE, return standard errors of parameter estimates from the hessian matrix |
In this model, changes in a trait are linearly related to changes in
a covariate with a slope of b and residual variance evar:
dx = b * dz + eps, where eps ~ N(0, evar). This model was
described, and applied to an example in which body size changes tracked
changes in temperature, by Hunt et al. (2010).
For the AD version (opt.covTrack), a trait sequence of
length ns, the covariate, z, can be of length ns - 1,
interpreted as the vector of changes, dx. If z is
of length ns, differences are taken and these are used as the
dx's, with a warning issued.
The Joint version
(opt.joint.covTrack), z should be of length ns and
there is an additional parameter that is the intercept of the linear
relationship between trait and covariate. See warning below about using the
Joint version.
a paleoTSfit object with the results of the model fitting
opt.joint.covTrack(): fits the covTrack model using the joint parameterization
The Joint parameterization of this model can be fooled by temporal autocorrelation and, especially, trends in the trait and the covariate. The latter is tested for, but the AD parameterization is generally safer for this model.
Hunt, G, S. Wicaksono, J. E. Brown, and K. G. Macleod. 2010. Climate-driven body size trends in the ostracod fauna of the deep Indian Ocean. Palaeontology 53(6): 1255-1268.
set.seed(13) z <- c(1, 2, 2, 4, 0, 8, 2, 3, 1, 9, 4, 3) x <- sim.covTrack(ns = 12, z = z, b = 0.5, evar = 0.2) w.urw <- opt.URW(x) w.cov <- opt.covTrack(x, z) compareModels(w.urw, w.cov)set.seed(13) z <- c(1, 2, 2, 4, 0, 8, 2, 3, 1, 9, 4, 3) x <- sim.covTrack(ns = 12, z = z, b = 0.5, evar = 0.2) w.urw <- opt.URW(x) w.cov <- opt.covTrack(x, z) compareModels(w.urw, w.cov)
Fit evolutionary model using "AD" parameterization
opt.GRW( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.URW( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.Stasis( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.StrictStasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE)opt.GRW( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.URW( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.Stasis( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.StrictStasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE)
y |
a |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
cl |
optional control list, passed to |
meth |
optimization algorithm, passed to |
hess |
if TRUE, return standard errors of parameter estimates from the hessian matrix |
These functions use differences between consecutive populations in the time series in order to remove temporal autocorrelation. This is referred to as the "Ancestor-Descendant" or "AD" parameterization by Hunt [2008], and it is a REML approach (like phylogenetic independent contrasts). A full ML approach, called "Joint" was found to have generally better performance (Hunt, 2008) and generally should be used instead.
a paleoTSfit object with the model fitting results
opt.URW(): fit the URW model by the AD parameterization
opt.Stasis(): fit the Stasis model by the AD parameterization
opt.StrictStasis(): fit the Strict Stasis model by the AD parameterization
It is easier to use the convenience function fitSimple.
Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology 32(4): 578-601.
x <- sim.GRW(ns = 20, ms = 1) # strong trend plot(x) w.grw <- opt.GRW(x) w.urw <- opt.URW(x) compareModels(w.grw, w.urw)x <- sim.GRW(ns = 20, ms = 1) # strong trend plot(x) w.grw <- opt.GRW(x) w.urw <- opt.URW(x) compareModels(w.grw, w.urw)
Fit random walk model with shift(s) in generating parameters
opt.GRW.shift(y, ng = 2, minb = 7, model = 1, pool = TRUE, silent = FALSE)opt.GRW.shift(y, ng = 2, minb = 7, model = 1, pool = TRUE, silent = FALSE)
y |
a |
ng |
number of segments in the sequence |
minb |
minimum number of populations in each segment |
model |
numeric, specifies exact evolutionary model; see Details |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
silent |
logical, if TRUE, progress updates are suppressed |
Fits a model in which a sequence is divided into two or more segments and
trait evolution proceeds as a general random walk, with each segment (potentially)
getting its own generating parameters (mstep, vstep).
This function tests for shifts after each population, subject to the
constraint that the number of populations in each segment is always >= minb. The
shiftpoint yielding the highest log-likelihood is returned as the solution, along with
the log-likelihoods (all.logl of all tested shift points (GG).
Different variants of the model can be specified by the model argument:
model = 1: mstep is separate across segments; vstep is shared
model = 2: mstep is shared across segments; vstep is separate
model = 3: mstep is set to zero (unbiased random walk); vstep
is separate across segments
model = 4: mstep and vstep are both separate across segments
a paleoTSfit object
x <- sim.GRW.shift(ns = c(15,15), ms = c(0, 1), vs = c(0.1,0.1)) w.sep <- opt.GRW.shift(x, ng = 2, model = 4) w.sameVs <- opt.GRW.shift(x, ng = 2, model = 1) compareModels(w.sep, w.sameVs) plot(x) abline(v = x$tt[16], lwd = 3) # actual shift point abline(v = x$tt[w.sameVs$par["shift1"]], lty = 3, col = "red", lwd = 2) # inferred shift pointx <- sim.GRW.shift(ns = c(15,15), ms = c(0, 1), vs = c(0.1,0.1)) w.sep <- opt.GRW.shift(x, ng = 2, model = 4) w.sameVs <- opt.GRW.shift(x, ng = 2, model = 1) compareModels(w.sep, w.sameVs) plot(x) abline(v = x$tt[16], lwd = 3) # actual shift point abline(v = x$tt[w.sameVs$par["shift1"]], lty = 3, col = "red", lwd = 2) # inferred shift point
Fit evolutionary models using the "Joint" parameterization
opt.joint.GRW( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.joint.URW( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.joint.Stasis( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.joint.StrictStasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE)opt.joint.GRW( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.joint.URW( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.joint.Stasis( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE ) opt.joint.StrictStasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE)
y |
a |
pool |
if |
cl |
optional control list, passed to |
meth |
optimization algorithm, passed to |
hess |
if TRUE, return standard errors of parameter estimates from the hessian matrix |
These functions use the joint distribution of population means to fit models using a full maximum-likelihood approach. This approach was found to have somewhat better performance than the "AD" approach, especially for noisy trends (Hunt, 2008).
a paleoTSfit object with the model fitting results
opt.joint.URW(): fit the URW model by the Joint parameterization
opt.joint.Stasis(): fit the Stasis model by the Joint parameterization
opt.joint.StrictStasis(): fit the Strict Stasis model by the Joint parameterization
It is easier to use the convenience function fitSimple.
Hunt, G., M. J. Hopkins and S. Lidgard. 2015. Simple versus complex models of trait evolution and stasis as a response to environmental change. Proc. Natl. Acad. Sci. USA 112(16): 4885-4890.
x <- sim.GRW(ns = 20, ms = 1) # strong trend plot(x) w.grw <- opt.joint.GRW(x) w.urw <- opt.joint.URW(x) compareModels(w.grw, w.urw)x <- sim.GRW(ns = 20, ms = 1) # strong trend plot(x) w.grw <- opt.joint.GRW(x) w.urw <- opt.joint.URW(x) compareModels(w.grw, w.urw)
Fit Ornstein-Uhlenbeck model using the "Joint" parameterization
opt.joint.OU( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE )opt.joint.OU( y, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE )
y |
a |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
cl |
optional control list, passed to |
meth |
optimization algorithm, passed to |
hess |
if TRUE, return standard errors of parameter estimates from the hessian matrix |
This function fits an Ornstein-Uhlenbeck (OU) model to time-series data. The OU
model has four generating parameters: an ancestral trait value (anc), an optimum
value (theta), the strength of attraction to the optimum (alpha), and a
parameter that reflects the tendency of traits to diffuse (vstep). In a
microevolutionary context, these parameters can be related to natural selection and
genetic drift; see Hunt et al. (2008).
a paleoTSfit object with the model fitting results
It is easier to use the convenience function fitSimple. Note also that
preliminary work found that the "AD" parameterization did not perform as well for the OU
model and thus it is not implemented here.
Hunt, G., M. A. Bell and M. P. Travis. 2008. Evolution toward a new adaptive optimum: phenotypic evolution in a fossil stickleback lineage. Evolution 62(3): 700-710.
x <- sim.OU(vs = 0.5) # most defaults OK w <- opt.joint.OU(x) plot(x, modelFit = w)x <- sim.OU(vs = 0.5) # most defaults OK w <- opt.joint.OU(x) plot(x, modelFit = w)
Fit a model of trait evolution with specified punctuation(s)
opt.punc( y, gg, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE, oshare ) opt.joint.punc( y, gg, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE, oshare )opt.punc( y, gg, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE, oshare ) opt.joint.punc( y, gg, pool = TRUE, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE, oshare )
y |
a |
gg |
vector of indices indicating different segments |
pool |
if TRUE, sample variances are substituted with their pooled estimate |
cl |
optional control list, passed to |
meth |
optimization algorithm, passed to |
hess |
if TRUE, return standard errors of parameter estimates from the |
oshare |
logical, if TRUE, variance assumed to be shared (equal) across segments |
The sequence is divided into segments, which are separated by punctuations. Means for
each segment are given by the vector theta with variances given by the vector
omega (or a single value if oshare = TRUE). This function calls optim to numerically fit this model to a time-series, y.
a paleoTSfit object with the results of the model fitting
opt.joint.punc(): fits the punctuation model using the joint parameterization
These functions would be used in the uncommon situation in which there
is a prior hypothesis as to where the punctuation(s) take place. Normally
users will instead use the function fitGpunc, which uses these
functions to fit a range of possible timings for the punctuations.
x <- sim.punc(ns = c(15, 15), theta = c(0,3), omega = c(0.1, 0.1)) w.sta <- fitSimple(x, model = "Stasis", method = "Joint") w.punc <- opt.joint.punc(x, gg = rep(1:2, each = 15), oshare = TRUE) compareModels(w.sta, w.punc)x <- sim.punc(ns = c(15, 15), theta = c(0,3), omega = c(0.1, 0.1)) w.sta <- fitSimple(x, model = "Stasis", method = "Joint") w.punc <- opt.joint.punc(x, gg = rep(1:2, each = 15), oshare = TRUE) compareModels(w.sta, w.punc)
Fit evolutionary models using state-space models (SSM)
opt.ssm.GRW(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.URW(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.Stasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.StrictStasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.OU(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.ACDC(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.covOU(y, z, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.URWshift(y, gg, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.covOU_vshift( y, z, gg, pool = TRUE, cl = list(fnscale = -1), hess = FALSE )opt.ssm.GRW(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.URW(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.Stasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.StrictStasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.OU(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.ACDC(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.covOU(y, z, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.URWshift(y, gg, pool = TRUE, cl = list(fnscale = -1), hess = FALSE) opt.ssm.covOU_vshift( y, z, gg, pool = TRUE, cl = list(fnscale = -1), hess = FALSE )
y |
a |
pool |
if |
cl |
optional control list, passed to |
hess |
if |
z |
a covariate vector, used only for the covOU models |
gg |
a grouping vector, used only for the URWshift and covOU_vshift models |
These functions use a state space model formulation to compute likelihoods and fit models.
Functions to fit the OU covariate tracking models (covOU, covOU_vshift) require a covariate argument, z.
At present, only the OU covariate tracking with a shift in the step variance (covOU_vshift) requires the grouping vector argument (gg).
a paleoTSfit object with the model fitting results
For GRW, URW, Stasis, StrictStasis, ACDC and OU models, it will likely be easier to use the convenience function fitSimple with argument method = "SSM".
The grouping vector, gg, is a vector of length equal to the number of samples. It has one element for each sample and takes
integer value from 1 to the number of sample groups separated by shiftpoints. See the example below.
y <- sim.GRW(ns = 30, vs = 2) w1 <- opt.ssm.URW(y) gg <- rep(1:2, each = 15) # shift occurs immediately after sample 15 w2 <- opt.ssm.URWshift(y, gg = gg) # test model in which the step variance shifts compareModels(w1, w2)y <- sim.GRW(ns = 30, vs = 2) w1 <- opt.ssm.URW(y) gg <- rep(1:2, each = 15) # shift occurs immediately after sample 15 w2 <- opt.ssm.URWshift(y, gg = gg) # test model in which the step variance shifts compareModels(w1, w2)
Plot a paleoTS object
## S3 method for class 'paleoTS' plot( x, nse = 1, pool = FALSE, add = FALSE, modelFit = NULL, pch = 21, pt.bg = "white", lwd = 1.5, ylim = NULL, xlab = NULL, ylab = NULL, add.label = TRUE, ... )## S3 method for class 'paleoTS' plot( x, nse = 1, pool = FALSE, add = FALSE, modelFit = NULL, pch = 21, pt.bg = "white", lwd = 1.5, ylim = NULL, xlab = NULL, ylab = NULL, add.label = TRUE, ... )
x |
a |
nse |
the number of standard errors represented by the error bars on the plot; defaults to 1 |
pool |
logical indicating if variances should be pooled across samples
for the purposes of displaying error bars; defaults to |
add |
logical, if |
modelFit |
optional model fit from fitting functions |
pch |
plotting symbol, defaults to 19 |
pt.bg |
color fill for points, defaults to white |
lwd |
line width, defaults to 1.5 |
ylim |
optional, y-limits of the plot |
xlab |
optional, label for x-axis |
ylab |
optional, label for y-axis |
add.label |
logical, if |
... |
other arguments passed to plotting functions |
none.
x <- sim.GRW(ns = 30) w <- fitSimple(x, model = "GRW", method = "Joint") plot(x, modelFit = w) plot(x, xlab = "Time [Myr]", ylab = "Body length [mm]", pch = 22, pt.bg = "blue")x <- sim.GRW(ns = 30) w <- fitSimple(x, model = "GRW", method = "Joint") plot(x, modelFit = w) plot(x, xlab = "Time [Myr]", ylab = "Body length [mm]", pch = 22, pt.bg = "blue")
Computes a pooled variance from samples in a paleontological time-series
pool.var(y, nn = NULL, minN = NULL, ret.paleoTS = FALSE)pool.var(y, nn = NULL, minN = NULL, ret.paleoTS = FALSE)
y |
either a |
nn |
a vector of sample sizes |
minN |
sample size below which variances are replaced with pooled variances. See Details. |
ret.paleoTS |
if TRUE, a |
A pooled variance of a set of populations is the weighted average
of the individual variances of the populations, with the weight for each
population equal to its sample size minus one.
For many kinds of
traits, variation levels tend to be similar among closely related
populations. When this is true and sample sizes are low, much of the
observed differences in variance among samples will be due to the high
noise of estimated the variances. Replacing the observed variances of all
populations (or only those with nn < minN) with the estimated pooled
variance can reduce this noise.
if ret.paleoTS = TRUE a paleoTS object with all (or
some) variances replaced with the pooled variance; otherwise the pooled
variance
data(cantius_L) cant_all <- pool.var(cantius_L, ret.paleoTS = TRUE) # replace all variances with pooled variance cant_n5 <- pool.var(cantius_L, minN = 5, ret.paleoTS = TRUE) # replace only pops with n < 5data(cantius_L) cant_all <- pool.var(cantius_L, ret.paleoTS = TRUE) # replace all variances with pooled variance cant_n5 <- pool.var(cantius_L, minN = 5, ret.paleoTS = TRUE) # replace only pops with n < 5
Print a paleoTSfit object
## S3 method for class 'paleoTSfit' print(x, ...)## S3 method for class 'paleoTSfit' print(x, ...)
x |
a paleoTSfit object |
... |
other arguments for other print methods |
None; this function is used only to print
y <- sim.punc(theta = c(0, 2), omega = c(0.1, 0.1)) wg <- fitGpunc(y) print(wg)y <- sim.punc(theta = c(0, 2), omega = c(0.1, 0.1)) wg <- fitGpunc(y) print(wg)
Read a text-file with data from a paleontological time-series
read.paleoTS(file = NULL, oldest = "first", reset.time = TRUE, ...)read.paleoTS(file = NULL, oldest = "first", reset.time = TRUE, ...)
file |
file name; if not supplied, an interactive window prompts the user to navigate to the text file |
oldest |
"first" if samples are in order from oldest to youngest, "last" if the opposite |
reset.time |
logical; see |
... |
other arguments, passed to |
This function reads a text file with a specified format and converts
it into a paleoTS object. It will often be the easiest way for users
to import their own data. The text file should have four columns without
headers, in this order: sample size, sample means, sample variances, sample
ages.
a paleoTS object
Simulate trait evolution that tracks a covariate
sim.covTrack( ns = 20, b = 1, evar = 0.1, z, nn = rep(20, times = ns), tt = 0:(ns - 1), vp = 1 )sim.covTrack( ns = 20, b = 1, evar = 0.1, z, nn = rep(20, times = ns), tt = 0:(ns - 1), vp = 1 )
ns |
number of populations in a sequence |
b |
slope of the relationship between the change in the covariate and the change in the trait |
evar |
residual variance of the same relationship |
z |
vector of covariate that the trait tracks |
nn |
vector of sample sizes for populations |
tt |
vector of times (ages) for populations |
vp |
phenotypic trait variance within each population |
In this model, changes in a trait are linearly related to changes in
a covariate with a slope of b and residual variance evar:
dx = b * dz + eps, where eps ~ N(0, evar). This model was
described, and applied to an example in which body size changes tracked
changes in temperature, by Hunt et al. (2010).
a paleoTS object
For a trait sequence of length ns, the covariate, z, can
be of length ns - 1,in which case it is interpreted as the vector of
changes, dz. If z is of length ns,
differences are taken and these are used as the dz's.
Hunt, G, S. Wicaksono, J. E. Brown, and K. G. Macleod. 2010. Climate-driven body size trends in the ostracod fauna of the deep Indian Ocean. Palaeontology 53(6): 1255-1268.
set.seed(13) z <- c(1, 2, 2, 4, 0, 8, 2, 3, 1, 9, 4, 3) x <- sim.covTrack(ns = 12, z = z, b = 0.5, evar = 0.2) plot(x, ylim = c(-1, 10)) lines(x$tt, z, col = "blue")set.seed(13) z <- c(1, 2, 2, 4, 0, 8, 2, 3, 1, 9, 4, 3) x <- sim.covTrack(ns = 12, z = z, b = 0.5, evar = 0.2) plot(x, ylim = c(-1, 10)) lines(x$tt, z, col = "blue")
Simulate random walk or directional time-series for trait evolution
sim.GRW(ns = 20, ms = 0, vs = 0.1, vp = 1, nn = rep(20, ns), tt = 0:(ns - 1))sim.GRW(ns = 20, ms = 0, vs = 0.1, vp = 1, nn = rep(20, ns), tt = 0:(ns - 1))
ns |
number of populations in the sequence |
ms |
mean of evolutionary "steps" |
vs |
variance of evolutionary "steps" |
vp |
phenotypic variance within populations |
nn |
vector of population sample sizes |
tt |
vector of population times (ages) |
The general random walk model considers time in discrete steps.
At each time step, an evolutionary change is drawn at random from a distribution of
possible evolutionary "steps." It turns out that the long-term dynamics of an evolving
lineage depend only on the mean and variance of this step distribution. The former,
mstep, determined the directionality in a sequence and the latter, vstep,
determines its volatility.
a paleoTS object
This function simulates an unbiased random walk if ms is equal to zero and
a general (or biased) random walk otherwise.
sim.Stasis, sim.OU, as.paleoTS
x.grw <- sim.GRW(ms = 0.5) x.urw <- sim.GRW(ms = 0) plot(x.grw, ylim = range(c(x.grw$mm, x.urw$mm))) plot(x.urw, add = TRUE, col = "blue") legend(x = "topleft", c("GRW", "URW"), col = c("black", "blue"), lty = 1)x.grw <- sim.GRW(ms = 0.5) x.urw <- sim.GRW(ms = 0) plot(x.grw, ylim = range(c(x.grw$mm, x.urw$mm))) plot(x.urw, add = TRUE, col = "blue") legend(x = "topleft", c("GRW", "URW"), col = c("black", "blue"), lty = 1)
Simulate (general) random walk with shift(s) in generating parameters
sim.GRW.shift( ns = c(10, 10), ms = c(0, 1), vs = c(0.5, 0.5), nn = rep(30, sum(ns)), tt = 0:(sum(ns) - 1), vp = 1 )sim.GRW.shift( ns = c(10, 10), ms = c(0, 1), vs = c(0.5, 0.5), nn = rep(30, sum(ns)), tt = 0:(sum(ns) - 1), vp = 1 )
ns |
vector of the number of samples in each segment |
ms |
vector of mean step parameter in each segment |
vs |
vector of step variance parameter in each segment |
nn |
vector of sample sizes, one for each population |
tt |
vector of samples times (ages) |
vp |
phenotypic variance in each sample |
Simulates under a model in which a sequence is divided into two or more segments.
Trait evolution proceeds as a general random walk, with each segment getting its own
generating parameters (mstep, vstep).
a paleoTS object with the simulated time-series
sim.GRW, sim.sgs, opt.GRW.shift
x <- sim.GRW.shift(ns = c(10,10,10), ms = c(0, 1, 0), vs = c(0.1,0.1,0.1)) plot(x) abline(v = c(9.5, 19.5), lty = 3, lwd = 2, col = "blue") # shows where dynamics shift text (c(5, 15, 25), c(2,2,2), paste("segement", 1:3, sep =" "), col = "blue")x <- sim.GRW.shift(ns = c(10,10,10), ms = c(0, 1, 0), vs = c(0.1,0.1,0.1)) plot(x) abline(v = c(9.5, 19.5), lty = 3, lwd = 2, col = "blue") # shows where dynamics shift text (c(5, 15, 25), c(2,2,2), paste("segement", 1:3, sep =" "), col = "blue")
Simulate an Ornstein-Uhlenbeck time-series
sim.OU( ns = 20, anc = 0, theta = 10, alpha = 0.3, vstep = 0.1, vp = 1, nn = rep(20, ns), tt = 0:(ns - 1) )sim.OU( ns = 20, anc = 0, theta = 10, alpha = 0.3, vstep = 0.1, vp = 1, nn = rep(20, ns), tt = 0:(ns - 1) )
ns |
number of populations in the sequence |
anc |
ancestral phenotype |
theta |
OU optimum (long-term mean) |
alpha |
strength of attraction to the optimum |
vstep |
step variance |
vp |
phenotypic variance of each sample |
nn |
vector of sample sizes |
tt |
vector of sample times (ages) |
This function simulates an Ornstein-Uhlenbeck (OU) process. In
microevolutionary terms, this models a population ascending a nearby peak
in the adaptive landscape. The optimal trait value is theta,
alpha indicates the strength of attraction to that peak (= strength
of stabilizing selection around theta), vstep measures the
random walk component (from genetic drift) and anc is the trait
value at the start of the sequence.
a paleoTS object
Hunt, G., M. A. Bell and M. P. Travis. 2008. Evolution toward a new adaptive optimum: phenotypic evolution in a fossil stickleback lineage. Evolution 62(3): 700-710.
x1 <- sim.OU(alpha = 0.8) # strong alpha x2 <- sim.OU(alpha = 0.1) # weaker alpha plot(x1) plot(x2, add = TRUE, col = "blue")x1 <- sim.OU(alpha = 0.8) # strong alpha x2 <- sim.OU(alpha = 0.1) # weaker alpha plot(x1) plot(x2, add = TRUE, col = "blue")
Simulates punctuated trait evolution with punctuations that are rapid relative to the spacing of samples. In practice, the time-series is divided into two or more segments, each of which has its own mean and variance.
sim.punc( ns = c(10, 10), theta = c(0, 1), omega = rep(0, length(theta)), nn = rep(30, sum(ns)), tt = 0:(sum(ns) - 1), vp = 1 )sim.punc( ns = c(10, 10), theta = c(0, 1), omega = rep(0, length(theta)), nn = rep(30, sum(ns)), tt = 0:(sum(ns) - 1), vp = 1 )
ns |
vector of the number of samples in each segment |
theta |
vector of means, one for each segment |
omega |
vector of variances, one for each segment. |
nn |
vector of sample sizes, one for each population |
tt |
vector of times (ages), one for each population |
vp |
phenotypic variance within each population |
Segments are separated by punctuations. Population means in the ith segment are
drawn randomly from a normal distribution with a mean equal to ith element of theta
and variance equal to the ith element of omega. The magnitudes of punctuations are
determined by the differences in adjacent theta values.
a paleoTS object with the simulated time-series.
x <- sim.punc(ns = c(15, 15), theta = c(0,3), omega = c(0.1, 0.1)) plot(x)x <- sim.punc(ns = c(15, 15), theta = c(0,3), omega = c(0.1, 0.1)) plot(x)
This function simulates a punctuated change that is is protracted enough that it is captured by multiple transitional populations. Trait evolution starts in stasis, shifts to a general random walk, and then shifts back into stasis.
sim.sgs( ns = c(20, 20, 20), theta = 0, omega = 1, ms = 1, vs = 0.1, nn = rep(30, sum(ns)), tt = 0:(sum(ns) - 1), vp = 1 )sim.sgs( ns = c(20, 20, 20), theta = 0, omega = 1, ms = 1, vs = 0.1, nn = rep(30, sum(ns)), tt = 0:(sum(ns) - 1), vp = 1 )
ns |
vector with the number of samples in each segment |
theta |
trait mean for initial stasis segment |
omega |
trait variance for stasis segments |
ms |
step mean during random walk segment |
vs |
step variance during random walk segment |
nn |
vector of sample sizes for each population |
tt |
vector of times (ages) for each population |
vp |
phenotypic trait variance for each population |
Trait evolution proceeds in three segments: Stasis, General random walk, stasis (sgs).
The initial stasis segment has a mean of theta and variance omega before
shifting in the second segment to a general random walk with parameters ms and
vs. Finally, the third segment is a return to stasis, centered around the trait value
of the last population of the random walk.
a paleoTS object
x <- sim.sgs() # default values OK plot(x)x <- sim.sgs() # default values OK plot(x)
Simulate Stasis time-series for trait evolution
sim.Stasis( ns = 20, theta = 0, omega = 0, vp = 1, nn = rep(20, ns), tt = 0:(ns - 1) )sim.Stasis( ns = 20, theta = 0, omega = 0, vp = 1, nn = rep(20, ns), tt = 0:(ns - 1) )
ns |
number of populations in the sequence |
theta |
mean of populations |
omega |
variance among populations |
vp |
phenotypic variance within populations |
nn |
vector of population sample sizes |
tt |
vector of population times (ages) |
a paleoTS object
x <- sim.Stasis(omega = 0.5, vp = 0.1) w.sta <- fitSimple(x, model = "Stasis") w.ss <- fitSimple(x, model = "StrictStasis") compareModels(w.sta, w.ss)x <- sim.Stasis(omega = 0.5, vp = 0.1) w.sta <- fitSimple(x, model = "Stasis") w.ss <- fitSimple(x, model = "StrictStasis") compareModels(w.sta, w.ss)
Trait evolution is modeled as a shift from a random walk (general or unbiased) to stasis, or vice versa.
sim.Stasis.RW( ns = c(20, 20), order = c("Stasis-RW", "RW-Stasis"), anc = 0, omega = 1, ms = 0, vs = 1, vp = 1, nn = 30, tt = NULL )sim.Stasis.RW( ns = c(20, 20), order = c("Stasis-RW", "RW-Stasis"), anc = 0, omega = 1, ms = 0, vs = 1, vp = 1, nn = 30, tt = NULL )
ns |
vector of the number of samples in each segment |
order |
whether stasis or random walk come first, one of |
anc |
starting trait value |
omega |
variance of stasis segment |
ms |
step mean during random walk segment |
vs |
step variance during random walk segment |
vp |
phenotypic trait variance for each population |
nn |
vector of sample sizes for each population |
tt |
vector of times (ages) for each population |
The anc argument is the starting trait value, and if the
first segment is stasis, this is also the value of the stasis mean. When the first segment
is a random walk, the stasis mean in the second segment is equal to the true trait mean at
the end of the initial random walk.
a paleoTSfit object
x1 <- sim.Stasis.RW(omega = 0.1, ms = 5, order = "Stasis-RW") x2 <- sim.Stasis.RW(omega = 0.1, ms = 5, order = "RW-Stasis") plot(x1) plot(x2, add = TRUE, col = "blue") abline(v = 19, lty=3)x1 <- sim.Stasis.RW(omega = 0.1, ms = 5, order = "Stasis-RW") x2 <- sim.Stasis.RW(omega = 0.1, ms = 5, order = "RW-Stasis") plot(x1) plot(x2, add = TRUE, col = "blue") abline(v = 19, lty=3)
Convert time-series to standard deviation units
std.paleoTS(y, center = c("mean", "start"))std.paleoTS(y, center = c("mean", "start"))
y |
a |
center |
optional translation of time-series according to "mean" or "start"; see Details |
The standardization expresses each sample mean as the deviation
from the overall mean, divided by the pooled within-sample standard deviation. Sample
variances are also divided by the pooled sample variance.
Essentially,
this converts paleontological time-series data into standard deviation
units, similar to the computation of evolutionary rates in haldanes. This
operation does not change the relative fit of models, but it does
facilitate the comparison of parameter estimates across time-series of
traits measured in different units.
If argument center = "start"
the time-series is translated such that the trait mean of the first sample
is zero.
the converted paleoTS object
x <- sim.Stasis(ns = 8, theta = 1, omega = 4, vp = 2) xs <- std.paleoTS(x, center = "start") plot(x, ylim = range(c(x$mm, xs$mm))) plot(xs, col = "red", add = TRUE) legend(x = "topright", c("unstandardized", "standardized"), lty=1, col=c("black", "red"), cex=0.7)x <- sim.Stasis(ns = 8, theta = 1, omega = 4, vp = 2) xs <- std.paleoTS(x, center = "start") plot(x, ylim = range(c(x$mm, xs$mm))) plot(xs, col = "red", add = TRUE) legend(x = "topright", c("unstandardized", "standardized"), lty=1, col=c("black", "red"), cex=0.7)
Subsampling is done according to the supplied logical vector or, if none is supplied, as a proportion of samples, randomly chosen.
sub.paleoTS(y, ok = NULL, k = 0.1, reset.time = TRUE)sub.paleoTS(y, ok = NULL, k = 0.1, reset.time = TRUE)
y |
a |
ok |
a logical vector, |
k |
proportion of samples to retain, with the samples chosen randomly |
reset.time |
if TRUE, resets the time so that the first population time is zero |
the sub-sampled paleoTS object
x <- sim.GRW(ns=20) plot(x) xs1 <- sub.paleoTS(x, k = 0.5) plot(xs1, add = TRUE, col="green") keep <- rep(c(TRUE, FALSE), 10) xs2 <- sub.paleoTS(x, ok = keep) plot(xs2, add = TRUE, col = "red")x <- sim.GRW(ns=20) plot(x) xs1 <- sub.paleoTS(x, k = 0.5) plot(xs1, add = TRUE, col="green") keep <- rep(c(TRUE, FALSE), 10) xs2 <- sub.paleoTS(x, ok = keep) plot(xs2, add = TRUE, col = "red")
Test for heterogeneity of variances among samples in a time-series
test.var.het(y, method = "Bartlett")test.var.het(y, method = "Bartlett")
y |
a |
method |
test to use; currently only |
a list with the test statistic, degrees of freedom, and p-value
Most often, this function will be used to assess if it is reasonable to
pool variances across samples using pool.var. A significant result means
that the null hypothesis of equal variances across samples is rejected. Even in
this case, however, it may still be preferable to pool variances, at least for
some populations, if sample sizes are quite low.
Sokal, R. R and F. J. Rohlf. 1995. Biometry 3rd Ed.
data(cantius_L) test.var.het(cantius_L) # significant, but still may want to pool variancesdata(cantius_L) test.var.het(cantius_L) # significant, but still may want to pool variances