Mixed-effects models in s and s-plus pdf download

Stephanie Green. Lad Akins. A short summary of this paper. Download Download PDF. Translate PDF. Green2, Isabelle M. Cox1, Lad Akins3, Craig A. Layman4, William F. We examined whether biotic resistance by native predators on Caribbean coral reefs has influenced the invasion success of red lionfishes Pterois volitans and Pterois miles , piscivores from the Indo-Pacific.

Specifically, we surveyed the abundance density and biomass of lionfish and native predatory fishes that could interact with lionfish either through predation or competition on 71 reefs in three biogeographic regions of the Caribbean.

We found no relationship between the density or biomass of lionfish and that of native predators. However, lionfish densities were significantly lower on windward sites, potentially because of habitat preferences, and in marine protected areas, most likely because of ongoing removal efforts by reserve managers. Our results suggest that interactions with native predators do not influence the colonization or post-establishment population density of invasive lionfish on Caribbean reefs.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: No competing interests exist. The work performed on this manuscript by William Precht was not performed under any contract and was completed on his personal time outside of work obligations.

Introduced species may successfully invade novel areas because the biotic agents regulating their populations e. Thus, recipient communities invade and become established across the greater Caribbean with high fish biodiversity and abundance may be more likely to region [1].

The invasion has the potential to significantly alter resist establishment of invasive species than communities that are marine ecosystems through competition with native predators and less species-rich — the diversity-invasibility hypothesis [8]. Some of these In the case of introduced lionfish, healthy populations of native anticipated effects are already evident.

For example, Green et al. On more directly through predation — the biotic resistance hypothesis experimental reefs, a single lionfish was shown to reduce the [9,10]. Lionfish have been found in the stomach contents of large recruitment and richness of native fishes [3], and have significantly groupers [11], consumed by moral eels [12], and sharks in some greater impacts than that of a comparable native mesopredator areas have been taught to consume dead lionfish [13].

However, it the coney grouper, Cephalopholis fulva [4]. Lionfish are predicted to is unclear how often predation on lionfish occurs naturally. In an have substantial cascading impacts on coral reef food-webs [5] and apparent example of biotic resistance, Mumby et al. The best management strategies, in terms of reduce the post-establishment abundance of lionfish. Over three years, we surveyed 71 reefs in three regions of the Reef in Belize and Mexico, from to Table S1, Fig.

We hypothesized that if biotic resistance plays a role in Sites were selected to cover a wide range of reef fish abundance. Due to the range of habitat predatory fish. Because reefs varied not only in native predator types included and the large scale of our analysis, we modified abundance, but also in depth, habitat structure, and time since survey methods time and area to suite each reef type.

At each reef lionfish invasion, we also explored potential relationships between site, we conducted underwater visual census to survey fish lionfish abundance and these abiotic conditions. At each continuous reef site, we randomly placed Ethics Statement six to eight belt transects parallel to the spur-and-groove No endangered or protected species were involved in this field formations or along the reef slope habitat following constant study.

No vertebrates were collected in this study; surveys were isobaths. All surveys for lionfish included a thorough search of the through visual census only. Reef fish species were identified, counted, and sizes were estimated in 10 cm intervals. Fish of ,40 cm total Study Sites and Fish Abundance length TL were counted in six to eight m belt transects, We surveyed 71 coral reefs 3—15 m deep across spur-and- while fish of.

When the spurs of the spur-and-groove Figure 1. Location of survey sites. Location of surveys sites. For site abbreviations, surveys dates and coordinates refer to Table S1. Relationship between lionfish density ind m22 and abundance of native Caribbean predatory fishes density [ind m22 ] and biomass [g m22].

We used the mid- and smaller transects did not overlap. At Eleuthera, we estimated point of the 10 cm interval to calculate biomass. The variables a the area of each patch reef surveyed to calculate fish densities. The vertical arrow keys on the keyboard can be used to scroll forward and backward through a command history.

Once a command is located in this way, the cursor can be moved within the command using the horizontal arrow keys, and characters can be removed with the DEL key or added with the other keys.

More details are provided later: see The command-line editor. The recall and editing capabilities under UNIX are highly customizable. You can find out how to do this by reading the manual entry for the readline library. Next: Data permanency and removing objects , Previous: Recall and correction of previous commands , Up: Introduction and preliminaries [ Contents ][ Index ]. If commands 5 are stored in an external file, say commands.

R in the working directory work , they may be executed at any time in an R session with the command. For Windows Source is also available on the File menu.

The function sink ,. The command. Previous: Executing commands from or diverting output to a file , Up: Introduction and preliminaries [ Contents ][ Index ]. The entities that R creates and manipulates are known as objects. These may be variables, arrays of numbers, character strings, functions, or more general structures built from such components.

During an R session, objects are created and stored by name we discuss this process in the next section. The R command. The collection of objects currently stored is called the workspace. All objects created during an R session can be stored permanently in a file for use in future R sessions. At the end of each R session you are given the opportunity to save all the currently available objects.

If you indicate that you want to do this, the objects are written to a file called. RData 6 in the current directory, and the command lines used in the session are saved to a file called.

When R is started at later time from the same directory it reloads the workspace from this file. At the same time the associated commands history is reloaded. It is recommended that you should use separate working directories for analyses conducted with R. It is quite common for objects with names x and y to be created during an analysis. Names like this are often meaningful in the context of a single analysis, but it can be quite hard to decide what they might be when the several analyses have been conducted in the same directory.

Next: Vector arithmetic , Previous: Simple manipulations; numbers and vectors , Up: Simple manipulations; numbers and vectors [ Contents ][ Index ]. R operates on named data structures. The simplest such structure is the numeric vector , which is a single entity consisting of an ordered collection of numbers.

To set up a vector named x , say, consisting of five numbers, namely This is an assignment statement using the function c which in this context can take an arbitrary number of vector arguments and whose value is a vector got by concatenating its arguments end to end. Assignment can also be made using the function assign. An equivalent way of making the same assignment as above is with:.

Assignments can also be made in the other direction, using the obvious change in the assignment operator. So the same assignment could be made using. If an expression is used as a complete command, the value is printed and lost 8. So now if we were to use the command. Next: Generating regular sequences , Previous: Vectors and assignment , Up: Simple manipulations; numbers and vectors [ Contents ][ Index ]. Vectors can be used in arithmetic expressions, in which case the operations are performed element by element.

Vectors occurring in the same expression need not all be of the same length. If they are not, the value of the expression is a vector with the same length as the longest vector which occurs in the expression.

Shorter vectors in the expression are recycled as often as need be perhaps fractionally until they match the length of the longest vector. In particular a constant is simply repeated. So with the above assignments the command. In addition all of the common arithmetic functions are available. If the argument to var is an n -by- p matrix the value is a p -by- p sample covariance matrix got by regarding the rows as independent p -variate sample vectors.

Note that max and min select the largest and smallest values in their arguments, even if they are given several vectors. The parallel maximum and minimum functions pmax and pmin return a vector of length equal to their longest argument that contains in each element the largest smallest element in that position in any of the input vectors. Internally calculations are done as double precision real numbers, or double precision complex numbers if the input data are complex.

R has a number of facilities for generating commonly used sequences of numbers. For example is the vector c 1, 2, …, 29, The function seq is a more general facility for generating sequences. It has five arguments, only some of which may be specified in any one call.

The first two arguments, if given, specify the beginning and end of the sequence, and if these are the only two arguments given the result is the same as the colon operator. That is seq 2,10 is the same vector as Arguments to seq , and to many other R functions, can also be given in named form, in which case the order in which they appear is irrelevant.

A related function is rep which can be used for replicating an object in various complicated ways. The simplest form is. Another useful version is. As well as numerical vectors, R allows manipulation of logical quantities. The first two are often abbreviated as T and F , respectively. However there are situations where logical vectors and their coerced numeric counterparts are not equivalent, for example see the next subsection.

In some cases the components of a vector may not be completely known. In general any operation on an NA becomes an NA. The motivation for this rule is simply that if the specification of an operation is incomplete, the result cannot be known and hence is not available. The function is. Examples are. In summary, is. To differentiate these, is. Next: Index vectors; selecting and modifying subsets of a data set , Previous: Missing values , Up: Simple manipulations; numbers and vectors [ Contents ][ Index ].

Character quantities and character vectors are used frequently in R, for example as plot labels. Where needed they are denoted by a sequence of characters delimited by the double quote character, e. Character strings are entered using either matching double " or single ' quotes, but are printed using double quotes or sometimes without quotes.

Quotes for a full list. Character vectors may be concatenated into a vector by the c function; examples of their use will emerge frequently. The paste function takes an arbitrary number of arguments and concatenates them one by one into character strings. Any numbers given among the arguments are coerced into character strings in the evident way, that is, in the same way they would be if they were printed. Note particularly that recycling of short lists takes place here too; thus c "X", "Y" is repeated 5 times to match the sequence Subsets of the elements of a vector may be selected by appending to the name of the vector an index vector in square brackets.

More generally any expression that evaluates to a vector may have subsets of its elements similarly selected by appending an index vector in square brackets immediately after the expression. Note that if x has missing values, y will be shorter than x.

The advantage is that alphanumeric names are often easier to remember than numeric indices. This option is particularly useful in connection with data frames, as we shall see later. An indexed expression can also appear on the receiving end of an assignment, in which case the assignment operation is performed only on those elements of the vector. Previous: Index vectors; selecting and modifying subsets of a data set , Up: Simple manipulations; numbers and vectors [ Contents ][ Index ].

Vectors are the most important type of object in R, but there are several others which we will meet more formally in later sections. Next: Changing the length of an object , Previous: Objects, their modes and attributes , Up: Objects, their modes and attributes [ Contents ][ Index ].

The entities R operates on are technically known as objects. Examples are vectors of numeric real or complex values, vectors of logical values and vectors of character strings. Vectors must have their values all of the same mode. Thus any given vector must be unambiguously either logical , numeric , complex , character or raw.

Note that a vector can be empty and still have a mode. For example the empty character string vector is listed as character 0 and the empty numeric vector as numeric 0. R also operates on objects called lists , which are of mode list. These are ordered sequences of objects which individually can be of any mode. The other recursive structures are those of mode function and expression. Functions are the objects that form part of the R system along with similar user written functions, which we discuss in some detail later.

Expressions as objects form an advanced part of R which will not be discussed in this guide, except indirectly when we discuss formulae used with modeling in R. By the mode of an object we mean the basic type of its fundamental constituents. Another property of every object is its length. The functions mode object and length object can be used to find out the mode and length of any defined structure Further properties of an object are usually provided by attributes object , see Getting and setting attributes.

For example, if z is a complex vector of length , then in an expression mode z is the character string "complex" and length z is R caters for changes of mode almost anywhere it could be considered sensible to do so, and a few where it might not be. For example with. A further coercion , or change of mode, reconstructs the numerical vector again:. Now d and z are the same. The reader should consult the different help files to become familiar with them. Next: Getting and setting attributes , Previous: Intrinsic attributes: mode and length , Up: Objects, their modes and attributes [ Contents ][ Index ].

Similarly character is a empty character vector, and so on. Once an object of any size has been created, new components may be added to it simply by giving it an index value outside its previous range.

This applies to any structure at all, provided the mode of the additional component s agrees with the mode of the object in the first place. This automatic adjustment of lengths of an object is used often, for example in the scan function for input. Conversely to truncate the size of an object requires only an assignment to do so.

Hence if alpha is an object of length 10, then. The old indices are not retained, of course. We can then retain just the first three values by. Next: The class of an object , Previous: Changing the length of an object , Up: Objects, their modes and attributes [ Contents ][ Index ].

The function attributes object returns a list of all the non-intrinsic attributes currently defined for that object. The function attr object , name can be used to select a specific attribute. These functions are rarely used, except in rather special circumstances when some new attribute is being created for some particular purpose, for example to associate a creation date or an operator with an R object. The concept, however, is very important. Some care should be exercised when assigning or deleting attributes since they are an integral part of the object system used in R.

When it is used on the left hand side of an assignment it can be used either to associate a new attribute with object or to change an existing one.

For example. Previous: Getting and setting attributes , Up: Objects, their modes and attributes [ Contents ][ Index ]. All objects in R have a class , reported by the function class. For simple vectors this is just the mode, for example "numeric" , "logical" , "character" or "list" , but "matrix" , "array" , "factor" and "data. A special attribute known as the class of the object is used to allow for an object-oriented style 13 of programming in R. For example if an object has class "data.

To remove temporarily the effects of class, use the function unclass. For example if winter has the class "data. Only in rather special situations do you need to use this facility, but one is when you are learning to come to terms with the idea of class and generic functions. Generic functions and classes will be discussed further in Classes, generic functions and object orientation , but only briefly.

A factor is a vector object used to specify a discrete classification grouping of the components of other vectors of the same length. R provides both ordered and unordered factors. Suppose, for example, we have a sample of 30 tax accountants from all the states and territories of Australia 14 and their individual state of origin is specified by a character vector of state mnemonics as. A factor is similarly created using the factor function:. To find out the levels of a factor the function levels can be used.

To continue the previous example, suppose we have the incomes of the same tax accountants in another vector in suitably large units of money. To calculate the sample mean income for each state we can now use the special function tapply :. The function tapply is used to apply a function, here mean , to each group of components of the first argument, here incomes , defined by the levels of the second component, here statef 15 , as if they were separate vector structures.

The result is a structure of the same length as the levels attribute of the factor containing the results. The reader should consult the help document for more details. Suppose further we needed to calculate the standard errors of the state income means.

To do this we need to write an R function to calculate the standard error for any given vector. Since there is an builtin function var to calculate the sample variance, such a function is a very simple one liner, specified by the assignment:.

Writing functions will be considered later in Writing your own functions. After this assignment, the standard errors are calculated by. To do this you could use tapply once more with the length function to find the sample sizes, and the qt function to find the percentage points of the appropriate t -distributions. The function tapply can also be used to handle more complicated indexing of a vector by multiple categories. For example, we might wish to split the tax accountants by both state and sex.

However in this simple instance just one factor what happens can be thought of as follows. The values in the vector are collected into groups corresponding to the distinct entries in the factor. The function is then applied to each of these groups individually. The value is a vector of function results, labelled by the levels attribute of the factor.

The combination of a vector and a labelling factor is an example of what is sometimes called a ragged array , since the subclass sizes are possibly irregular. When the subclass sizes are all the same the indexing may be done implicitly and much more efficiently, as we see in the next section. Previous: The function tapply and ragged arrays , Up: Ordered and unordered factors [ Contents ][ Index ].

The levels of factors are stored in alphabetical order, or in the order they were specified to factor if they were specified explicitly.

Sometimes the levels will have a natural ordering that we want to record and want our statistical analysis to make use of. The ordered function creates such ordered factors but is otherwise identical to factor. For most purposes the only difference between ordered and unordered factors is that the former are printed showing the ordering of the levels, but the contrasts generated for them in fitting linear models are different. Next: Array indexing. An array can be considered as a multiply subscripted collection of data entries, for example numeric.

R allows simple facilities for creating and handling arrays, and in particular the special case of matrices. A dimension vector is a vector of non-negative integers. If its length is k then the array is k -dimensional, e. The dimensions are indexed from one up to the values given in the dimension vector. A vector can be used by R as an array only if it has a dimension vector as its dim attribute. Suppose, for example, z is a vector of elements.

The assignment. Other functions such as matrix and array are available for simpler and more natural looking assignments, as we shall see in The array function. Arrays can be one-dimensional: such arrays are usually treated in the same way as vectors including when printing , but the exceptions can cause confusion.

Individual elements of an array may be referenced by giving the name of the array followed by the subscripts in square brackets, separated by commas. More generally, subsections of an array may be specified by giving a sequence of index vectors in place of subscripts; however if any index position is given an empty index vector, then the full range of that subscript is taken.

For any array, say Z , the dimension vector may be referenced explicitly as dim Z on either side of an assignment. Also, if an array name is given with just one subscript or index vector , then the corresponding values of the data vector only are used; in this case the dimension vector is ignored.

This is not the case, however, if the single index is not a vector but itself an array, as we next discuss. Next: The array function , Previous: Array indexing. Subsections of an array , Up: Arrays and matrices [ Contents ][ Index ]. As well as an index vector in any subscript position, a matrix may be used with a single index matrix in order either to assign a vector of quantities to an irregular collection of elements in the array, or to extract an irregular collection as a vector.

A matrix example makes the process clear. In the case of a doubly indexed array, an index matrix may be given consisting of two columns and as many rows as desired. The entries in the index matrix are the row and column indices for the doubly indexed array.

Suppose for example we have a 4 by 5 array X and we wish to do the following:. In this case we need a 3 by 2 subscript array, as in the following example. Negative indices are not allowed in index matrices. NA and zero values are allowed: rows in the index matrix containing a zero are ignored, and rows containing an NA produce an NA in the result.

As a less trivial example, suppose we wish to generate an unreduced design matrix for a block design defined by factors blocks b levels and varieties v levels.

Further suppose there are n plots in the experiment. We could proceed as follows:. However a simpler direct way of producing this matrix is to use table :. Index matrices must be numerical: any other form of matrix e. As well as giving a vector structure a dim attribute, arrays can be constructed from vectors by the array function, which has the form. If the size of h is exactly 24 the result is the same as.

However if h is shorter than 24, its values are recycled from the beginning again to make it up to size 24 see Mixed vector and array arithmetic. As an extreme but common example.

At this point dim Z stands for the dimension vector c 3,4,2 , and Z[] stands for the data vector as it was in h , and Z[] with an empty subscript or Z with no subscript stands for the entire array as an array. Arrays may be used in arithmetic expressions and the result is an array formed by element-by-element operations on the data vector. The dim attributes of operands generally need to be the same, and this becomes the dimension vector of the result.

So if A , B and C are all similar arrays, then. However the precise rule concerning mixed array and vector calculations has to be considered a little more carefully. The precise rule affecting element by element mixed calculations with vectors and arrays is somewhat quirky and hard to find in the references. From experience we have found the following to be a reliable guide. An important operation on arrays is the outer product. If a and b are two numeric arrays, their outer product is an array whose dimension vector is obtained by concatenating their two dimension vectors order is important , and whose data vector is got by forming all possible products of elements of the data vector of a with those of b.

The multiplication function can be replaced by an arbitrary function of two variables. In particular the outer product of two ordinary vectors is a doubly subscripted array that is a matrix, of rank at most 1. Notice that the outer product operator is of course non-commutative. Defining your own R functions will be considered further in Writing your own functions.

As an artificial but cute example, consider the determinants of 2 by 2 matrices [a, b; c, d] where each entry is a non-negative integer in the range 0, 1, …, 9 , that is a digit.

The problem is to find the determinants, ad - bc , of all possible matrices of this form and represent the frequency with which each value occurs as a high density plot. This amounts to finding the probability distribution of the determinant if each digit is chosen independently and uniformly at random.

The function aperm a, perm may be used to permute an array, a. The result of the function is an array of the same size as a but with old dimension given by perm[j] becoming the new j -th dimension. The easiest way to think of this operation is as a generalization of transposition for matrices.

Indeed if A is a matrix, that is, a doubly subscripted array then B given by. Next: Forming partitioned matrices, cbind and rbind , Previous: Generalized transpose of an array , Up: Arrays and matrices [ Contents ][ Index ]. As noted above, a matrix is just an array with two subscripts. However it is such an important special case it needs a separate discussion.

R contains many operators and functions that are available only for matrices. For example t X is the matrix transpose function, as noted above. The functions nrow A and ncol A give the number of rows and columns in the matrix A respectively. An n by 1 or 1 by n matrix may of course be used as an n -vector if in the context such is appropriate. Conversely, vectors which occur in matrix multiplication expressions are automatically promoted either to row or column vectors, whichever is multiplicatively coherent, if possible, although this is not always unambiguously possible, as we see later.

If the second argument to crossprod is omitted it is taken to be the same as the first. The meaning of diag depends on its argument. On the other hand diag M , where M is a matrix, gives the vector of main diagonal entries of M. Also, somewhat confusingly, if k is a single numeric value then diag k is the k by k identity matrix! In R,. Next: Singular value decomposition and determinants , Previous: Linear equations and inversion , Up: Matrix facilities [ Contents ][ Index ].

The function eigen Sm calculates the eigenvalues and eigenvectors of a symmetric matrix Sm. The result of this function is a list of two components named values and vectors. Had we only needed the eigenvalues we could have used the assignment:.

If the expression. For large matrices it is better to avoid computing the eigenvectors if they are not needed by using the expression.

The function svd M takes an arbitrary matrix argument, M , and calculates the singular value decomposition of M. D is actually returned as a vector of the diagonal elements. The result of svd M is actually a list of three components named d , u and v , with evident meanings. If this calculation were needed often with a variety of matrices it could be defined as an R function. As a further trivial but potentially useful example, you might like to consider writing a function, say tr , to calculate the trace of a square matrix.

Look again at the diag function. R has a builtin function det to calculate a determinant, including the sign, and another, determinant , to give the sign and modulus optionally on log scale ,. Previous: Singular value decomposition and determinants , Up: Matrix facilities [ Contents ][ Index ]. The function lsfit returns a list giving results of a least squares fitting procedure. An assignment such as. See the help facility for more details, and also for the follow-up function ls.

Note that a grand mean term is automatically included and need not be included explicitly as a column of X. Further note that you almost always will prefer using lm.

Reference: Jacquez, J. Meanwhile, Bayesian estimation can also be applied to clinical pharmacokinetic services with only a single data point Cp at steady-state.

Users can define their own model with population PK parameters using a single-dose, integral equation for the multiple-dosed or a steady-state integral equation. More drugs such as carbamazepine, theophylline IR, SR and IV infusion , lithium, immunosuppressants tacrolimus and everolimus and some anti-HIV drugs indinavir, ritonavir and enfuvirtide have been added. Also featuring a number of statistical tests on bioequivalence e.

Kinetica Thermo Scientific's pharmacokinetic analysis tool, offers fast high-throughput data analysis for clinical, preclinical, discovery, drug metabolism and drug delivery settings. This tool standardizes analyses across the organization and minimizes variability between PK analysts and analyses. Designed as a template format, Kinetica allows user to preset computation options before bringing data for analysis. With a direct link to Watson analytical LIMS, bioanalytical data are transferred electronically from Watson to Kinetica so that the overall data analysis experience improves while reducing the need to transfer data manually.

From non-compartment thru to population pharmacokinetics to population model validation, Kinetica reduces the need for multiple software packages.

Patient and drug characteristics are used to simulate drug concentrations. It can be used for teaching but also to simply explore a new compound for which only in-vitro data is available. Maxsim2 Maxsim2 provides a gallery of common PK and PD models by which one interacts using sliders, check boxes, and number fields, which in real time is mirrored in changes of concentration-time or response-time simulation profiles.

This interactivity and direct visual feedback of what-if scenarios give a very good understanding of both the qualitative and quantitative impact of different parameters such as volumes of distribution, clearance, partition coefficients, potency, and dosage.

MedRoc-A stand-alone Windows program to statistically analyze results from studies into the efficacy of drugs, new therapies, diagnostic procedures, etc. It performs ROC analysis signal detection analysis of research data. Holford is available from Biosoft, P. Box , Ferguson, MO The program, for MS DOS systems, performs nonlinear least squares regression with extended least squares. Models can be represented by integrated or differential equations.

MLXPlore MLXPlore is a graphical and interactive software for the exploration and visualization of complex models, including pharmacometric models. MLXPlore also includes the ability to study the statistical variability of the models.

It was built with Sawchuk-Zaske method aminoglycosides and vancomycin , Chiou method theophylline iv inf. With Bayesian estimation, one can use only one blood sample at steady-state to estimate individual pharmacokinetic parameters at steady-state.

ModKine is a kinetics modeling program with custom features for pharmacokinetics and pharmacodynamics Windows from Biosoft. Monolix is an easy, fast and powerful tool for parameter estimation in non-linear mixed effect models, model diagnosis and assessment, and advanced graphical representation. Yamaoka et al. Different version include fitting to integrated or differential equations, and Bayesian analysis,. Reference: Yamaoka, K. Tanigawara, Y.

Nakagawa, T. Tanaka, H. Yasuhara, M. Okumura, K. Published by Grabitech it runs on Windows systems. Clin Pharmacokinet. MwPharm was originally developed by Prof. The program is developed further in close collaboration with this group in order to implement the latest developments in the field of pharmacokinetics.

Also in Germany is it qualified as "best product for TDM-purposes". The system consists of a patient database and a drug database with over drugs. Several methods for calculating the renal function are supported.

Pharmacokinetic models include up to 3 compartment systems combined with injection, infusion, oral and intramuscular inputs. Dosage regimen selection is implemented in a unique interactive fashion.

The simulated plasma levels of alternate dosage regimens can be superimposed for easy evaluation. NCOMP is a program for noncompartmental analysis of pharmacokinetic data, is available free. Request it from its author, Paul B. Laub, at astatine at freeshell dot org. The macro is designed for hierarchical nonlinear mixed effects models. Example with population model for phenobarbital data is provided.

TXT file and follow instructions. A Postscript file with a manuscript [Ref] is also available. Lazic, S. The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC Neurosci. Maxwell, S. Nimon, K. Statistical assumptions of substantive analyses across the general linear model: a mini-review.

Nuzzo, R. Scientific method: statistical errors. Nature 13, — Oberfeld, D. Evaluating the robustness of repeated measures analyses: the case of small sample sizes and nonnormal data.

Overall, J. Pinheiro, J. New York, NY: Springer. On multi-level modeling of data from repeated measures designs: a tutorial. Speech Commun. R Core Team. Rosenthal, R. The interpretation of levels of significance by psychological researchers. Rosnow, R. Statistical procedures and the justification of knowledge in psychological science. Simmons, J. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Tukey, J. We need both exploratory and confirmatory.

Wagenmakers, E. An agenda for purely confirmatory research. Data needs to prepared in long format, with the first row being the variable names and each subsequent row representing a separate measurement.

Variable names should begin with a letter, and comprise standard alphanumeric characters a—z and 0—9 —no spaces or special characters. Implicitly coded nested factors need to be explicitly recoded.

If the listeners from town A are labeled L1, L2… etc. BL1, BL2, etc. Next, the data should be saved in text file using the comma , or semi-colon ; as delimiter. The file name should also begin with a letter and comprise standard alphanumeric characters. Users need to have already installed R, which is available at www. At present, LMMgui is only available on the Windows platform. If needed, LMMgui will automatically download the R package lme4.

To visualize the diagnostic plots, a pdf reader is required. Once analysis is complete, a number of text files will be written to the directory of the prepared data. Keywords: linear mixed-effects models, experimental psychology, within-participant design, graphical user interface, R.

Citation: Magezi DA Linear mixed-effects models for within-participant psychology experiments: an introductory tutorial and free, graphical user interface LMMgui. The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.