The VSNi Team
6 months agoWhen conducting an experiment, an important consideration is how to even out the variability among the experimental units to make comparisons between the treatments fair and precise. Ideally, we should try to minimize the variability by carefully controlling the conditions under which we conduct the experiment. However, there are many situations where the experimental units are nonuniform. For example:
When you know there are differences between the experimental units (and these differences may potentially affect your response), you can improve precision and avoid bias by blocking. Blocking involves grouping the experimental units into moreorless homogeneous groups, so that the experimental units within each block are as alike as possible. For example, in the field experiment described above, plots would be blocked (i.e., grouped) according to slope, and in medical trial, subjects would be blocked into groups of similar weight and age. Once the blocks are formed, the treatments are then randomized to the experimental units within each block.
Blocking is used to control nuisance variation by creating homogeneous groups of experimental units, known as blocks. 
Blocking can improve precision
Let’s look at an example[1] to see how blocking improves the precision of an experiment by reducing the unexplained variation. In this field trial, the yields (pounds per plot) of four strains of Gallipoli wheat were studied. During the design phase, the 20 experimental plots were grouped into five blocks (each containing 4 plots). Within each block, the four wheat strains (A, B, C and D) were randomly assigned to the plots. This is an example of a randomized complete block design (RCBD).
In randomized complete block design (RCBD)…

To demonstrate the advantage of blocking, we’ll analyse the data in Genstat[2]as both a completely randomized design (CRD, which ignores the blocking), and as a RCBD (which takes the blocking into account). One of the assumptions behind a CRD is that the set of experimental units to which the treatments are applied are effectively homogeneous.
CRD  RCBD 
CRD 
RCBD 
The ANOVA tables from the two analyses are given above. Notice that the ANOVA table for the RCBD has an additional line, “Blocks stratum”. This records the variation between blocks. Also note that the treatment effects (i.e., strains) are now estimated in the “Blocks.*Units* stratum”, which represents the variation within blocks. As a result:
A: the residual mean square (i.e., the unexplained variation) has decreased from 2.983 to 2.188
B: the standard error of the difference (s.e.d.) has decreased from 1.092 to 0.936.
That is, blocking has improved the precision of the experiment! This increase in precision means that we have a better chance of detecting differences between the wheat strains, making this experiment more efficient and with increased statistical power.
If you suspect that certain groups of experimental units may differ from each other, you can always use those groups as a blocking factor. If the differences do appear, your estimated treatment effects will be more precise than if you had not included blocking in the statistical model. 
Blocking can protect against bias
Let’s look at an example to see blocking how can guard against bias by evening out the variability among experimental units.
Imagine you want to test a new manufacturing process at your factory by measuring levels of daily productivity over four weeks. However, experience tells you that production levels tend to be lower on Thursdays and Fridays, compared to earlier on in the week as employees’ thoughts turn to going home for the weekend. Let’s consider what might happen should you simply randomly select 10 days to use the old manufacturing process and 10 days to use the new. The following table represents one possible randomization:
Notice that by not controlling for day of the week, the new manufacturing process is (randomly) overrepresented on days where production naturally tends to be higher, whereas the old manufacturing process is (randomly) overrepresented on Thursdays and Fridays, where production naturally tends to be lower, resulting in an unfair comparison.
Conversely, had you blocked by day of the week, then the inherent differences between days is evened out and the bias it can potentially cause is no longer an issue. For example, we can have a randomization like:
Note that every treatment (manufacturing process) occurs the same number of times every day. That is, we have a balanced experiment that controls for bias due to day difference. Hence, any resulting production increase or decrease can be more confidently attributed to the manufacturing process used.
As shown above, blocking and randomization are critical aspects of good experimental design, providing us with increased precision and protection against bias.
You can learn more about blocking and experimental design in Genstat by watching this short YouTube video: Experimental design in Genstat 
[1] Snedecor, G.W. (1946). Statistical methods. The Iowa State College Press, Ames, Iowa, USA.
[2] This data set can be accessed from within Genstat. From the menu select File  Open Example Data Sets then type “Wheatstrains.gsh” and click Open.
Related Reads
Kanchana Punyawaew
a year agoThis blog illustrates how to analyze data from a field experiment with a balanced lattice square design using linear mixed models. We’ll consider two models: the balanced lattice square model and a spatial model.
The example data are from a field experiment conducted at Slate Hall Farm, UK, in 1976 (Gilmour et al., 1995). The experiment was set up to compare the performance of 25 varieties of barley and was designed as a balanced lattice square with six replicates laid out in a 10 x 15 rectangular grid. Each replicate contained exactly one plot for every variety. The variety grown in each plot, and the coding of the replicates and lattice blocks, is shown in the field layout below:
There are seven columns in the data frame: five blocking factors (Rep, RowRep, ColRep, Row, Column), one treatment factor, Variety, and the response variate, yield.
The six replicates are numbered from 1 to 6 (Rep). The lattice block numbering is coded within replicates. That is, within each replicates the lattice rows (RowRep) and lattice columns (ColRep) are both numbered from 1 to 5. The Row and Column factors define the row and column positions within the field (rather than within each replicate).
To analyze the response variable, yield, we need to identify the two basic components of the experiment: the treatment structure and the blocking (or design) structure. The treatment structure consists of the set of treatments, or treatment combinations, selected to study or to compare. In our example, there is one treatment factor with 25 levels, Variety (i.e. the 25 different varieties of barley). The blocking structure of replicates (Rep), lattice rows within replicates (Rep:RowRep), and lattice columns within replicates (Rep:ColRep) reflects the balanced lattice square design. In a mixed model analysis, the treatment factors are (usually) fitted as fixed effects and the blocking factors as random.
The balanced lattice square model is fitted in ASRemlR4 using the following code:
> lattice.asr < asreml(fixed = yield ~ Variety,
random = ~ Rep + Rep:RowRep + Rep:ColRep,
data=data1)
The REML loglikelihood is 707.786.
The model’s BIC is:
The estimated variance components are:
The table above contains the estimated variance components for all terms in the random model. The variance component measures the inherent variability of the term, over and above the variability of the subunits of which it is composed. The variance components for Rep, Rep:RowRep and Rep:ColRep are estimated as 4263, 15596, and 14813, respectively. As is typical, the largest unit (replicate) is more variable than its subunits (lattice rows and columns within replicates). The "units!R" component is the residual variance.
By default, fixed effects in ASRemlR4 are tested using sequential Wald tests:
In this example, there are two terms in the summary table: the overall mean, (Intercept), and Variety. As the tests are sequential, the effect of the Variety is assessed by calculating the change in sums of squares between the two models (Intercept)+Variety and (Intercept). The pvalue (Pr(Chisq)) of < 2.2 x 1016 indicates that Variety is a highly significant.
The predicted means for the Variety can be obtained using the predict() function. The standard error of the difference between any pair of variety means is 62. Note: all variety means have the same standard error as the design is balanced.
Note: the same analysis is obtained when the random model is redefined as replicates (Rep), rows within replicates (Rep:Row) and columns within replicates (Rep:Column).
As the plots are laid out in a grid, the data can also be analyzed using a spatial model. We’ll illustrate spatial analysis by fitting a model with a separable first order autoregressive process in the field row (Row) and field column (Column) directions. This is often a useful model to start the spatial modeling process.
The separable first order autoregressive spatial model is fitted in ASRemlR4 using the following code:
> spatial.asr < asreml(fixed = yield ~ Variety,
residual = ~ar1(Row):ar1(Column),
data = data1)
The BIC for this spatial model is:
The estimated variance components and sequential Wald tests are:
The residual variance is 38713, the estimated row correlation is 0.458, and the estimated column correlation is 0.684. As for the balanced lattice square model, there is strong evidence of a Variety effect (pvalue < 2.2 x 1016).
A loglikelihood ratio test cannot be used to compare the balanced lattice square model with the spatial models, as the variance models are not nested. However, the two models can be compared using BIC. As the spatial model has a smaller BIC (1415) than the balanced lattice square model (1435), of the two models explored in this blog, it is chosen as the preferred model. However, selecting the optimal spatial model can be difficult. The current spatial model can be extended by including measurement error (or nugget effect) or revised by selecting a different variance model for the spatial effects.
Butler, D.G., Cullis, B.R., Gilmour, A. R., Gogel, B.G. and Thompson, R. (2017). ASRemlR Reference Manual Version 4. VSN International Ltd, Hemel Hempstead, HP2 4TP UK.
Gilmour, A. R., Anderson, R. D. and Rae, A. L. (1995). The analysis of binomial data by a generalised linear mixed model, Biometrika 72: 593599..
Kanchana Punyawaew and Dr. Vanessa Cave
a year agoThe term "repeated measures" refers to experimental designs or observational studies in which each experimental unit (or subject) is measured repeatedly over time or space. "Longitudinal data" is a special case of repeated measures in which variables are measured over time (often for a comparatively long period of time) and duration itself is typically a variable of interest.
In terms of data analysis, it doesn’t really matter what type of data you have, as you can analyze both using mixed models. Remember, the key feature of both types of data is that the response variable is measured more than once on each experimental unit, and these repeated measurements are likely to be correlated.
To illustrate the use of mixed model approaches for analyzing repeated measures, we’ll examine a data set from Landau and Everitt’s 2004 book, “A Handbook of Statistical Analyses using SPSS”. Here, a doubleblind, placebocontrolled clinical trial was conducted to determine whether an estrogen treatment reduces postnatal depression. Sixty three subjects were randomly assigned to one of two treatment groups: placebo (27 subjects) and estrogen treatment (36 subjects). Depression scores were measured on each subject at baseline, i.e. before randomization (predep) and at six twomonthly visits after randomization (postdep at visits 16). However, not all the women in the trial had their depression score recorded on all scheduled visits.
In this example, the data were measured at fixed, equally spaced, time points. (Visit is time as a factor and nVisit is time as a continuous variable.) There is one betweensubject factor (Group, i.e. the treatment group, either placebo or estrogen treatment), one withinsubject factor (Visit or nVisit) and a covariate (predep).
Using the following plots, we can explore the data. In the first plot below, the depression scores for each subject are plotted against time, including the baseline, separately for each treatment group.
In the second plot, the mean depression score for each treatment group is plotted over time. From these plots, we can see variation among subjects within each treatment group that depression scores for subjects generally decrease with time, and on average the depression score at each visit is lower with the estrogen treatment than the placebo.
The simplest approach for analyzing repeated measures data is to use a random effects model with subject fitted as random. It assumes a constant correlation between all observations on the same subject. The analysis objectives can either be to measure the average treatment effect over time or to assess treatment effects at each time point and to test whether treatment interacts with time.
In this example, the treatment (Group), time (Visit), treatment by time interaction (Group:Visit) and baseline (predep) effects can all be fitted as fixed. The subject effects are fitted as random, allowing for constant correlation between depression scores taken on the same subject over time.
The code and output from fitting this model in ASRemlR 4 follows;
The output from summary() shows that the estimate of subject and residual variance from the model are 15.10 and 11.53, respectively, giving a total variance of 15.10 + 11.53 = 26.63. The Wald test (from the wald.asreml() table) for predep, Group and Visit are significant (probability level (Pr) ≤ 0.01). There appears to be no relationship between treatment group and time (Group:Visit) i.e. the probability level is greater than 0.05 (Pr = 0.8636).
In practice, often the correlation between observations on the same subject is not constant. It is common to expect that the covariances of measurements made closer together in time are more similar than those at more distant times. Mixed models can accommodate many different covariance patterns. The ideal usage is to select the pattern that best reflects the true covariance structure of the data. A typical strategy is to start with a simple pattern, such as compound symmetry or firstorder autoregressive, and test if a more complex pattern leads to a significant improvement in the likelihood.
Note: using a covariance model with a simple correlation structure (i.e. uniform) will provide the same results as fitting a random effects model with random subject.
In ASRemlR 4 we use the corv() function on time (i.e. Visit) to specify uniform correlation between depression scores taken on the same subject over time.
Here, the estimate of the correlation among times (Visit) is 0.57 and the estimate of the residual variance is 26.63 (identical to the total variance of the random effects model, asr1).
Specifying a heterogeneous firstorder autoregressive covariance structure is easily done in ASRemlR 4 by changing the variancecovariance function in the residual term from corv() to ar1h().
When the relationship of a measurement with time is of interest, a random coefficients model is often appropriate. In a random coefficients model, time is considered a continuous variable, and the subject and subject by time interaction (Subject:nVisit) are fitted as random effects. This allows the slopes and intercepts to vary randomly between subjects, resulting in a separate regression line to be fitted for each subject. However, importantly, the slopes and intercepts are correlated.
The str() function of asreml() call is used for fitting a random coefficient model;
The summary table contains the variance parameter for Subject (the set of intercepts, 23.24) and Subject:nVisit (the set of slopes, 0.89), the estimate of correlation between the slopes and intercepts (0.57) and the estimate of residual variance (8.38).
Brady T. West, Kathleen B. Welch and Andrzej T. Galecki (2007). Linear Mixed Models: A Practical Guide Using Statistical Software. Chapman & Hall/CRC, Taylor & Francis Group, LLC.
Brown, H. and R. Prescott (2015). Applied Mixed Models in Medicine. Third Edition. John Wiley & Sons Ltd, England.
Sabine Landau and Brian S. Everitt (2004). A Handbook of Statistical Analyses using SPSS. Chapman & Hall/CRC Press LLC.
The VSNi Team
9 months agoIt is widely acknowledged that the most fundamental developments in statistics in the past 60+ years are driven by information technology (IT). We should not underestimate the importance of pen and paper as a form of IT but it is since people start using computers to do statistical analysis that we really changed the role statistics plays in our research as well as normal life.
In this blog we will give a brief historical overview, presenting some of the main general statistics software packages developed from 1957 onwards. Statistical software developed for special purposes will be ignored. We also ignore the most widely used ‘software for statistics’ as Brian Ripley (2002) stated in his famous quote: “Let’s not kid ourselves: the most widely used piece of software for statistics is Excel.” Our focus is some of the packages developed by statisticians for statisticians, which are still evolving to incorporate the latest development of statistics.
Pioneer statisticians like Ronald Fisher started out doing their statistics on pieces of paper and later upgraded to using calculating machines. Fisher bought the first Millionaire calculating machine when he was heading Rothamsted Research’s statistics department in the early 1920s. It cost about £200 at that time, which is equivalent in purchasing power to about £9,141 in 2020. This mechanical calculator could only calculate direct product, but it was very helpful for the statisticians at that time as Fisher mentioned: "Most of my statistics has been learned on the machine." The calculator was heavily used by Fisher’s successor Frank Yates (Head of Department 19331968) and contributed to much of Yates’ research, such as designs with confounding between treatment interactions and blocks, or split plots, or quasifactorials.
Frank Yates
Rothamsted Annual Report for 1952: "The analytical work has again involved a very considerable computing effort."
From the early 1950s we entered the computer age. The computer at this time looked little like its modern counterpart, whether it was an Elliott 401 from the UK or an IBM 700/7000 series in the US. Although the first documented statistical package, BMDP, was developed starting in 1957 for IBM mainframes at the UCLA Health Computing Facility, on the other side of the Atlantic Ocean statisticians at Rothamsted Research began their endeavours to program on an Elliot 401 in 1954.
When we teach statistics in schools or universities, students very often complain about the difficulties of programming. Looking back at programming in the 1950s will give modern students an appreciation of how easy programming today actually is!
An Elliott 401 served one user at a time and requested all input on paper tape (forget your keyboard and intelligent IDE editor). It provided the output to an electric typewriter. All programming had to be in machine code with the instructions and data on a rotating disk with 32bit word length, 5 "words" of fastaccess store, 7 intermediate access tracks of 128 words, 16 further tracks selectable one at a time (= 2949 words – 128 for system).
Computer paper tape
fitting constants to main effects and interactions in multiway tables (1957), regression and multiple regression (1956), fitting many standard curves as well as multivariate analysis for latent roots and vectors (1955).
Although it sounds very promising with the emerging of statistical programs for research, routine statistical analyses were also performed and these still represented a big challenge, at least computationally. For example, in 1963, which was the last year with the Elliott 401 and Elliott 402 computers, Rothamsted Research statisticians analysed 14,357 data variables, and this took them 4,731 hours to complete the job. It is hard to imagine the energy consumption as well as the amount of paper tape used for programming. Probably the paper tape (all glued together) would be long enough to circle the equator.
The above collection of programs was mainly used for agricultural research at Rothamsted and was not given an umbrella name until John Nelder became Head of the Statistics Department in 1968. The development of Genstat (General Statistics) started from that year and the programming was done in FORTRAN, initially on an IBM machine. In that same year, at North Carolina State University, SAS (Statistical Analysis Software) was almost simultaneously developed by computational statisticians, also for analysing agricultural data to improve crop yields. At around the same time, social scientists at the University of Chicago started to develop SPSS (Statistical Package for the Social Sciences). Although the three packages (Genstat, SAS and SPSS) were developed for different purposes and their functions diverged somewhat later, the basic functions covered similar statistical methodologies.
The first version of SPSS was released in 1968. In 1970, the first version of Genstat was released with the functions of ANOVA, regression, principal components and principal coordinate analysis, singlelinkage cluster analysis and general calculations on vectors, matrices and tables. The first version of SAS, SAS 71, was released and named after the year of its release. The early versions of all three software packages were written in FORTRAN and designed for mainframe computers.
Since the 1980s, with the breakthrough of personal computers, a second generation of statistical software began to emerge. There was an MSDOS version of Genstat (Genstat 4.03) released with an interactive command line interface in 1980.
Genstat 4.03 for MSDOS
Around 1985, SAS and SPSS also released a version for personal computers. In the 1980s more players entered this market: STATA was developed from 1985 and JMP was developed from 1989. JMP was, from the very beginning, for Macintosh computers. As a consequence, JMP had a strong focus on visualization as well as graphics from its inception.
The development of the third generation of statistical computing systems had started before the emergence of software like Genstat 4.03e or SAS 6.01. This development was led by John Chambers and his group in Bell Laboratories since the 1970s. The outcome of their work is the S language. It had been developed into a general purpose language with implementations for classical as well as modern statistical inferences. S language was freely available, and its audience was mainly sophisticated academic users. After the acquisition of S language by the Insightful Corporation and rebranding as SPLUS, this leading third generation statistical software package was widely used in both theoretical and practical statistics in the 1990s, especially before the release of a stable beta version of the free and opensource software R in the year 2000. R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently widely used by statisticians in academia and industry, together with statistical software developers, data miners and data analysts.
Software like Genstat, SAS, SPSS and many other packages had to deal with the challenge from R. Each of these longstanding software packages developed an R interface R or even R interpreters to anticipate the change of user behaviour and everincreasing adoption of the R computing environment. For example, SAS and SPSS have some R plugins to talk to each other. VSNi’s ASRemlR software was developed for ASReml users who want to run mixed model analysis within the R environment, and at the present time there are more ASRemlR users than ASReml standalone users. Users who need reliable and robust mixed effects model fitting adopted ASRemlR as an alternative to other mixed model R packages due to its superior performance and simplified syntax. For Genstat users, msanova was also developed as an R package to provide traditional ANOVA users an R interface to run their analysis.
We have no clear idea about what will represent the fourth generation of statistical software. R, as an opensource software and a platform for prototyping and teaching has the potential to help this change in statistical innovation. An example is the R Shiny package, where web applications can be easily developed to provide statistical computing as online services. But all opensource and commercial software has to face the same challenges of providing fast, reliable and robust statistical analyses that allow for reproducibility of research and, most importantly, use sound and correct statistical inference and theory, something that Ronald Fisher will have expected from his computing machine!