text: The complete range will appear in the future. as an RDS file). Documentation reproduced from package glmnet, version 3. They show CD14+ monocytes play a central role, identify CD169 as a potential biomarker of acute ZIKV. Results: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. (It also fits the lasso and ridge regression, since they are special cases of elastic net. Several models are investigated using various annotation levels of expression profiles (genes, transcripts. Estimation of the Structural Topic Model using semi-collapsed variational EM. Lasso and Elastic-Net Regularized Generalized Linear Models. In the United States, an estimated 51 million adults aged 18 years or older (approximately 1 in 5 of US adults) have experienced one or more M/SUDs in the past 12 months surveyed []. The aim of the study was to identify a broad range of CC correlates of PA, SB, and adiposity in a large sample of preschoolers. my x data is a matrix n_row by n_col and y is a vector of size n_row corresponding to the vector data. 0-1, License: GPL-2 Community examples. We did all the statistical computations using the R statistical programming environment (version 3. The matlab version of glmnet is maintained by Junyang Qian. In a series of posts, I hope to shed some light on what these options do. glmnet: This function predicts fitted values, logits, coefficients, and more from a fitted glmnet object. Markets are highly efficient, so we suspect with a high degree of certainty that we will not be able to beat the market with such a strategy. glmnet solves the following problem:. glmnet) on the training set. 1 i haven’t find the “gplots” package so i tried other packages like geneR and this time it’s work but i don’t find gplots package in the version 2. R glm and glmnet use different algorithms. r-cran-glmnet: 2. glmnet: This function predicts fitted values, logits, coefficients, and more from a fitted glmnet object. Introduction. GSVA Gene Set Variation Analysis for microarray and RNA-seq data. Oracle R Technologies blog shares best practices, tips, and tricks for applying Oracle R Distribution, ROracle, Oracle R Enterprise and Oracle R Advanced Analytics for Hadoop in database and big data environments. Least-Squares learners can also be weak or strong. BDD tests read like sentences, so they should make it easier to understand the specification of a function. Clinical variables are presented as mean± sem or mean (range). Older versions had some minor problems, but thanks to users trying it out and reporting problems directly back to the author an improved version is available in about 6 months. 74), and tumor size (HR = 1. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis. Confidence intervals for c-statistics were. I was a scrawny 17 year old white kid. 0) it should be compatible with R 3. In the case of OLS or GLM models, inference is meaningful; i. Using news reports and press releases from provinces, regions, and countries outside Wuhan, Hubei province, China, this analysis estimates the length of the incubation period of COVID-19 and its pu. Description. These two blocks of code represent the dataset in a graph. rubber: Abrasion loss for various hardness and tensile strength: Iowa: Iowa School Test: print. Train a glmnet model on the overfit data such that y is the response variable and all other variables are explanatory variables. In the context of. The non-exudative late-stage manifestation GA is hallmarked by atrophy of the retinal pigment epithelium and concurrent atrophy of the outer neuroretina. In my last post I discussed using coefplot on glmnet models and in particular discussed a brand new function, coefpath, that uses dygraphs to make an interactive visualization of the coefficient path. Both HER2+ and hormone-receptor posi tive subtypes demonstrated significant. For notebook support, I installed rpy2 and r-irkernel. list: a character vector. Title: Multiple Pairwise Comparison. On Unix-alikes: The ‘factory-fresh’ default is text help except from the macOS GUI, which uses HTML help displayed in its own browser window. Welcome to this new post of Machine Learning Explained. I started playing DnD in 1979 at a summer engineering program for high school students at USC. 3-45; mutoss, version 0. 1-10-1 12 r-cran-glmnet 2. 1-cp36-cp36m-macosx_10_7_x86_64. It's buggy as hell (both versions 2 and 3) but managed to get GLMNET to give me something reasonable (though I have trouble completely trusting it given the flagrant bugs in simpler munging functions). I've tested it out various ways and I think the problem occurs because in one of the folds there are no codes for at least one of the responses. Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. Actuarial Outpost > Exams - Please Limit Discussion to Exam-Related Topics > SoA/CAS Preliminary Exams > Exam PA: Predictive Analytics: Does anyone use SOA eLearning Module? and question on glmnet package. In a series of posts, I hope to shed some light on what these options do. the problem i am facing is that to install R 3. All programming was performed in R version 3. Release Date: 2020-03-30. This study conducted an in-depth comparison of prediction. Next, install the new R version. 99% of the time it isn't a problem, unless your R version is very old (you should be using 3. Note that, for all 12 datasets together, the average AUC rank for rf is only slightly better than for glmnet (2. 1999; Risnes et al. In Spark version 2. I'm writing a series of posts on various function options of the glmnet function (from the package of the same name), hoping to give more detail and insight beyond R's documentation. glmnet with an older version of glmnetUtils (pre-CRAN I think). I'm writing a series of posts on various function options of the glmnet function (from the package of the same name), hoping to give more detail and insight beyond R's documentation. The glmnetUtils package provides a collection of tools to streamline the process of fitting elastic net models with glmnet. Package developers can make use of new functions or arguments by selectively importing specific backports to support older installations. Perhaps frolicsome bunny has an opinion on this matter. In ctmle: Collaborative Targeted Maximum Likelihood Estimation. R/plot_glmnet. mexw32 is the 32 bit Windows version for new versions of Matlab, and. 8, according to their website), and the DLL library loading mechanism has changed since then. If you use an older version of glmnet (< 3. Re: GLMNET warning msg It means that 10/10 = 1 < 3. BDD tests read like sentences, so they should make it easier to understand the specification of a function. perform comprehensive immunoprofiling of pediatric cohort samples via RNA-seq, CyTOF, and Luminex cytokine/chemokine array, resulting in distinct temporal patterns of gene expression, cell profiles, and cytokines/chemokines. literal character strings or names. 4 years old) and older (73. Plotmo now works with both the old and new versions of gam. 1-10; and pROC. Security Insights version 3. protein-protein interactions), by including network-based regularizers. On Windows: The default for the type of help is selected when R is installed - the 'factory-fresh' default is HTML help. The LASSO penalization is thus very small. However, in a high dimensional data setting, empirical selection of hundreds of potential. 476 preschool children (mean age 3. You can still load the background file from the. Introduction. This improved the overall AUC to 0. 0), Matrix (>= 1. bestglm: Print method for bestglm object: glmnetGridTable: Multipanel Display and Table Glmnet CV Output. dta format you can use. However, I cannot make sense of the output glmnet gives me. Then you can install the notebook with: conda install-c conda-forge notebook pip. In this same population, the prevalence of HIV-associated Neurocognitive Disorders (HAND) in the US may be up to 50%, despite access to antiretroviral therapy (ART) 2. Again, when multiple properties are predicted, this is a list. R code tends to be very memory intensive as R processes primarily in memory. Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Fecal microbiota transplantation (FMT) is an alternative approach that induced remission in small series of patients with active UC. In the modern days, the desire to know the future is still of interest to many of us, even if my. Just like stringsAsFactors=FALSE happened on a transition from 3. l e t t e r s nature medicine advance online publication Alzheimer’s disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 9k 23 23 gold badges 244 244 silver badges 292 292 bronze badges. It's buggy as hell (both versions 2 and 3) but managed to get GLMNET to give me something reasonable (though I have trouble completely trusting it given the flagrant bugs in simpler munging functions). A cluster based method for missing value estimation is included for comparison. To do that, you should first load the. 2 with the two key packages dplyr version 0. It also includes tools for working. 50-1 We believe that the bug you reported is fixed in the latest version of r-cran-randomfields, which is due to be installed in the Debian FTP archive. 6, i need libc6 > 2. They show CD14+ monocytes play a central role, identify CD169 as a potential biomarker of acute ZIKV. Introduction. A summary of the changes between this version and the previous one is attached. packages(lib. glmnet but sometimes get: Evaluation error: arguments imply differing number of rows: 0, 16613. 4 kB) File type Wheel Python version cp36 Upload date Mar 11, 2019 Hashes View. 5 of coefplot is the ability to show coefficient plots from xgboost models. Watch 7 Star 26 Fork 43 Code. The object created by glmnet has a particular class (or classes) depending on what type of model is actually fitted. One approach not discussed in Shulman & Bostrom is embryo editing, which has compelling advantages over selection: Embryo selection must be done collectively for any meaningful gains, so one must score all viable embryos; while editing can potentially be done singly, editing only the embryo being implanted, and no more if that embryo yields a birth. 4-12% of all MS patients. Kenny Shirley Statistician I’m a research scientist in the demand forecasting group at Amazon in New York City. It has been suggested that poor health has influenced vote for Brexit and the US presidential election. Posted 12/6/17 6:15 AM, 23 messages. (glmnet) # Package to fit ridge/lasso/elastic net models. However, I also want to include a strata() term in the model. We investigated attrition in both early and late phase of an online panel with special focus on preferences regarding mode of participation. The selected frame is used to constrain the coefficient vector to provide upper and lower bounds. We again remove the missing data, which was all in the response variable, Salary. Alzheimer's disease (AD) is the most common type of dementia and is marked by progressive memory loss and cognitive impairment. 600397 Item_TypeFrozen Foods Item_TypeFruits and. 3 May 4, 2017. And I am getting an error,package 'devtools' is not available (for R version 3. What's trippy is that, at the time, I didn't realize how surreal that would seem to the much older me. R fortunes are fun and full of knowledge. The glmnet version for Matlab was tested only on Matlab 2013b (64-bit), but I wanted to know if someone was able to use it on other versions of matlab and how to do that. This is a list of functions and expressions that get used in the iteratively reweighted least squares algorithm for fitting the GLM. For lists of publications from each centre member, please go to our member profiles. R/plot_glmnet. Clinical variables are presented as mean± sem or mean (range). Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. control: This function views and/or changes the factory default parameters in glmnet. --file=file1 --file=file2). Further reading suggested "LASSO" could be a good choice and can be implemented using glmnet package in R. If you use an older version of glmnet (< 3. Fitting these models involves the selection of a tuning parameter (λ) that determines the number of selected peaks. rlambda top. Description. 2? Erik Iverson [R] How to speed up R with version 2. (glmnet) # Package to fit ridge/lasso/elastic net models. Created by DataCamp. 1 Like alistaire December 4, 2017, 2:14pm #3. Methods: CBF data from the HIV Over 60 cohort and the Alzheimer’s Disease Neuroimaging Initiative were retrospectively evaluated to identify 19 HIV + older adults, all with plasma viral suppression (including five with HIV-Associated Neurocognitive Disorder, or HAND), 13 healthy, aged matched controls (HC), and 19 participants with early Mild Cognitive Impairment (MCI). Nevertheless, it's fun to try!. Multivariate analysis showed that only older age at surgery (≥ 65 years; hazard ratio [HR] = 1. Williamson * a, Deborah Rush Walker b, Eric Chuu c, Susan Broadway d, Blain Mamiya e, Cynthia B. packages() function. Chapter 8 Statistical learning and predictive analytics. The availability of high-density panels of SNP markers has opened new perspectives for marker-assisted selection strategies, such that genotypes for these markers are used to predict the genetic merit of selection candidates. I am trying to install glmnet package (Version:2. If the sample size N were sufficiently large such that repeats were guaranteed and densely arranged, it would seem that these solutions might all tend to the limiting conditional expectation. This is unsurprisingly also by Max Kuhn. plot caret dplyr gridExtra plyr tidyverse were created using version 3. The problem mentioned in this question is fixed in version 1. It isn’t true. Another new capability for version 1. If the criterion is not met, go to. Hi! Yes I just released a new version with upgraded platformio to fix the problem. 96) for the assay alone and 0. The objective of this research was to. - The older package version needed may not be compatible with the version of R you have installed. shrinkage parameter, λ (lambda) as your "Perform 10-fold cross validation" section. Dabney c and Diana Mason d a Department of Chemistry, Texas A&M University, 77843-3255, USA. All PCA methods. The glmnet package gas been around since at least June 2008 (at least, that’s the date of the oldest version I can find in the CRAN archives). dta file with metadata. 0, and introduces a new build system based on msys2, which makes easier to build and maintain R itself as well as the system libraries needed by R packages on Windows. py install. In this context, improving the selection of patients for immediate. I've renamed my function to cva. are needed for older adults with poor balance and low balance confidence to assist them to initiate and maintain engagement in an exercise program. I tried with the > example in the manual. The matlab version of glmnet is maintained by Junyang Qian. But recently they added the support for both. Anyone using the "exam environment" described in the modules (R version 3. Moving up to a major version (1. Watch 7 Star 26 Fork 43 Code. Coronary heart disease (CHD) remains a major public health challenge, and is responsible for approximately one-third of deaths in middle-aged and older adults in the United States. cran / glmnet. 0) it should be compatible with R 3. The caret package in R has been called "R's competitive advantage". Package jessie-backports stretch Changed by Signed by; 0ad: 0. In order to develop an index that is more specific to geriatric patients, the aforementioned candidate predictors were used in a Least Angle Shrinkage and Selection Operator regression analysis in the NSQIP 2013 derivation data set on the geriatric subset 20 implemented in the R package glmnet. 7 million people 65 years and older are injured from falls annually in the United States. In Spark 3. For lists of publications from each centre member, please go to our member profiles. Hence, the model will be less likely to fit the noise of the training data […]. 1186/s13073-020-0716-9. reldist — 1. ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. Introduction Understanding the biological mechanism of subjective cognitive decline (SCD) in preclinical Alzheimer’s disease (AD) and identifying those who will soon convert to mild cognitive impairment (MCI) are critical for developing appropriate strategies for early diagnosis and intervention of AD. However, I cannot make sense of the output glmnet gives me. glmnet (x = X_train_3, y = y) Again, printing this object will give a list of all the models tried, ## R version 3. There is a growing need to address the variability in detecting cognitive deficits with standard tests in cocaine dependence (CD). It is a shame because glmnet works much faster than lassoglm. rxFastLinear is a trainer based on the Stochastic Dual Coordinate Ascent (SDCA) method, a state-of-the-art optimization technique for convex objective functions. Although obtaining marker data involves costs and pedigree information does not, pedigrees can sometimes be difficult to obtain, especially for older or proprietary germplasm. The non-exudative late-stage manifestation GA is hallmarked by atrophy of the retinal pigment epithelium and concurrent atrophy of the outer neuroretina. gz, read what's new in the latest version. glmnet(), the lambda parameter should be a sequence of values for lambda. Rdocumentation. pdf Lasso and glmnet, with applications in GWAS-scale prediction problems Wald_II. 0, and introduces a new build system based on msys2, which makes easier to build and maintain R itself as well as the system libraries needed by R packages on Windows. 2? FMH [R] How to speed up R with version 2. インストール済みの R パッケージのバージョンをチェックする関数として、packageVersion 関数があります。 使い方は以下です。. Suppose is the input matrix and the response vector. Watch 7 Star 26 Fork 43 Code. The alpha parameter need to be supplied based on the application need (for selecting a reduced set of variables, alpha=1 is preferred). 154291 -760. This is despite the fact that data scientists frequently work with complex statements at the command line, have to pay particular attention to system setup to ensure reproducibility of their experiments, use version control, and commonly deal with data from a wide range of sources, many of which will require API tokens or access credentials. We performed a cross-sectional analysis using a comprehensive set of socioeconomic and health indicators, to be compared with voting outcome at the first round of the 2017 French presidential election. Installing such a package can trigger errors similar to 'PyThreadState' {'struct _ts'} has no member named 'exc_type' (see GitHub issue 1978 for details). The underlying model is described in detail in Dondelinger and Mukherjee (2017). 2 and when i try other version like version 2. Ubuntu Desktop, Ubuntu Server, Kubuntu, Edubuntu, etc. Post a new example: Submit your. Other builds. I started playing DnD in 1979 at a summer engineering program for high school students at USC. Glance never returns information from the original call to the modelling function. After dealing with overfitting, today we will study a way to correct overfitting with regularization. Hello,Sir I am tring to install devtools on R version 3. 3-45; mutoss, version 0. I was using cvAlpha. Further reading suggested "LASSO" could be a good choice and can be implemented using glmnet package in R. Specifically, single-cell profiles. They show CD14+ monocytes play a central role, identify CD169 as a potential biomarker of acute ZIKV. Alzheimer's disease (AD) is the most common type of dementia and is marked by progressive memory loss and cognitive impairment. table, glmnet, xgboost with caret Rmarkdown script using data from House Prices: Advanced Regression Techniques · 10,315 views · 5mo ago · feature engineering , data cleaning , xgboost , +2 more regression analysis , ensembling. 9k 23 23 gold badges 244 244 silver badges 292 292 bronze badges. Graphics are often interpretable through intuitive inspection alone. " You'll want to use the warm start principle and begin the sequence with some larger values of lambda before decreasing to the range you're interested in. Running glmnet() which will fit models for various values of the regularisation parameter, λ. To install a CRAN package in R, use the install. pcreg: Predict. 4 kB) File type Wheel Python version cp36 Upload date Mar 11, 2019 Hashes View. This vignette describes the usage of glmnet in R. io Find an R package R language docs Run R in your browser R Notebooks. Many of the guys I played with were on the USC football team -- huge black guys and few big white guys too. Our imaging biomarker could be useful in estimating CD8 cell count and predicting clinical outcomes of patients treated. 3 May 4, 2017. This was done using the statistical package "Glmnet" (version 2. The median incubation period was estimated to be 5. Older versions had some minor problems, but thanks to users trying it out and reporting problems directly back to the author an improved version is available in about 6 months. However, I also want to include a strata() term in the model. rxFastLinear is a trainer based on the Stochastic Dual Coordinate Ascent (SDCA) method, a state-of-the-art optimization technique for convex objective functions. For help only, typeset as PDF – see the section on ‘Offline help’. Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Another new capability for version 1. Lasso and Elastic-Net Regularized Generalized Linear Models. It also means that what you're trying to do (fitting 10 cases to 12000 variables) is ridiculous (assuming I understand your message correctly). The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon, and the R package is maintained by Trevor Hastie. Accurate prognosis has remained a challenge for many patients. 3) and it worked perfectly!. 2? Gabor Grothendieck [R] How to speed up R with version 2. packages returns a matrix of details corresponding to packages currently available at one or more repositories. removal of non-informative peaks). Glmnet fits the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the cox model. as performing a multivariate version of soft thresholding The multivariate version of hard thresholding is ' 0 penalization, in which we minimize the objective function 1 2n ky X k2 + k k 0; where k k 0 = P j I( j6= 0) For the orthonormal case, the solution is given by b j= H( bOLS j; p 2 ) Estimating in this manner is equivalent to subset. We investigated attrition in both early and late phase of an online panel with special focus on preferences regarding mode of participation. By default, all packages in the search path are used, then the ‘ data ’ subdirectory (if present) of the current working directory. Anyone using the "exam environment" described in the modules (R version 3. This imaging predictor provided a promising way to predict the immune phenotype of tumours and to infer clinical outcomes for patients with cancer who had been treated with anti-PD-1 and PD-L1. 5 of coefplot is the ability to show coefficient plots from xgboost models. The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon. , they represent unbiased estimates of the underlying uncertainty, given the model. In patients with resectable tumors, surgery followed by adjuvant chemotherapy is the only curative treatment. The RBAS and the PRAS were more accurate when compared with the Spetzler. Here, we derive and validate a predictive model for falls based on a retrospective cohort of those 65 years and older. As a major update, it has many new features, user-visible changes and bug fixes. tidyr contains tools for changing the shape (pivoting) and hierarchy (nesting and unnesting) of a dataset, turning deeply nested lists into rectangular data frames (rectangling), and extracting values out of string columns. function CVerr = cvglmnet(x,y,family,options,type,nfolds,foldid,parallel,keep,grouped. gz, read what's new in the latest version. 0), Matrix (>= 1. However, epidemiological evidence on the association between PFAS and preeclampsia is inconsistent. By default, all packages in the search path are used, then the ‘ data ’ subdirectory (if present) of the current working directory. Collaborative Targeted Maximum Likelihood Estimation (C-TMLE) is an extention of Targeted Maximum Likelihood Estimation (TMLE). As @glmnet said, we've had some situations where minor version changes in dependencies caused issues multiple times now. I'm having some problems running glmnet with family=multinomial, and was wondering has encountered something similar or might be able to tell me what I'm doing wrong. Introduction. glmnet: This function predicts fitted values, logits, coefficients, and more from a fitted glmnet object. Implement ordinary least square (OLS) regression in Python and learn how to interpret the results. Ten-fold cross-validation was performed to determine the optimal value for the lasso penalty (i. Source: r-cran-randomfields Source-Version: 3. This I think I understand. 4 kB) File type Wheel Python version cp36 Upload date Mar 11, 2019 Hashes View. Rule based Learning Ensembles RuleFit3 with R. The glmnet package gas been around since at least June 2008 (at least, that's the date of the oldest version I can find in the CRAN archives). 1-10-1 12 r-cran-glmnet 2. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. My research interests (past and present) include hierarchical Bayesian modeling, MCMC methods, data and model visualization, text mining, and other topics related to applied statistics. i aloso tried other packages , i have the same problem i use R version 3. I also tried the above methods,but it can't solve my problem. Impact of basic arithmetic skills on success in first-semester general chemistry†. This is despite the fact that data scientists frequently work with complex statements at the command line, have to pay particular attention to system setup to ensure reproducibility of their experiments, use version control, and commonly deal with data from a wide range of sources, many of which will require API tokens or access credentials. The matlab version of glmnet is maintained by Junyang Qian. Log Book — Practical guide to Linear & Polynomial Regression in R This is a practical guide to linear and polynomial regression in R. This I think I understand. This was done using the statistical package "Glmnet" (version 2. observing that glmnet giving slightly better results than h2o for many small datasets. Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Released 5 months ago. A comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R-Forge. Security Insights Code. It is also marked by the inclusion of new example, including applications to real-world datasets. Hoeka 1 ·. As it’s a bit older it also does things a bit differently to how you would do them in the latest version of caret. Ridge regression Ridge regression uses L2 regularisation to weight/penalise residuals when the parameters of a regression model are being learned. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. 0) it should be compatible with R 3. metagenomeSeq is designed to address the effects of both normalization and. The following release notes provide information about Databricks Runtime 5. Re: GLMNET warning msg It means that 10/10 = 1 < 3. stats::filter. Based on recent popularisation of regularised regression models the goal of this study was to establish workflows for pharmacogenomic predictors of response to standard multidrug regimens using baseline gene expression data and origin specific. packages("devtools")". 5 of coefplot is the ability to show coefficient plots from xgboost models. 999375-16 lattice_0. 2? Gabor Grothendieck [R] How to speed up R with version 2. Beyond fitting boosted trees and boosted forests, xgboost can also fit a. A summary of the changes between this version and the previous one is attached. line annotate. Affected Spark. In Spark version 2. [R] How to speed up R with version 2. I also tried the above methods,but it can't solve my problem. A comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R-Forge. In the United States, an estimated 51 million adults aged 18 years or older (approximately 1 in 5 of US adults) have experienced one or more M/SUDs in the past 12 months surveyed []. Added support for glmnetUtils objects. glmnet: This function predicts fitted values, logits, coefficients, and more from a fitted glmnet object. If you use an older version of glmnet (< 3. If alpha = 0 then a ridge regression model is fit, and if alpha = 1 then a lasso model is fit. For the Gaussian family, glmnet solves the penalized residual sum of squares,. Machine Learning Mastery, 2016. It is a shame because glmnet works much faster than lassoglm. Many of the guys I played with were on the USC football team -- huge black guys and few big white guys too. Glmnet fits the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the cox model. Actuarial Outpost > Exams - Please Limit Discussion to Exam-Related Topics > SoA/CAS Preliminary Exams > Exam PA: Predictive Analytics: Does anyone use SOA eLearning Module? and question on glmnet package. Posted 12/6/17 6:15 AM, 23 messages. For reference, here is the full signature of the glmnet function (v3. package: a character vector giving the package(s) to look in for data sets, or NULL. 6 years old) but no middle‐aged individuals; GSE36166 and GSE40798 had no variability in age, as all individuals were 24-25 years old. On Unix-alikes: The 'factory-fresh' default is text help except from the macOS GUI, which uses HTML help displayed in its own browser window. Background Payers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. Hello,Sir I am tring to install devtools on R version 3. New replies are no longer allowed. By default, all packages in the search path are used, then the ‘ data ’ subdirectory (if present) of the current working directory. This topic was automatically closed 21 days after the last reply. 1 The Prometric computers will have the 2016 versions of Microsoft Word and Excel, version 3. glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. Japan is currently the world's foremost super-aging society, with elderly people aged 65 years and older accounting for 26. Post a new example: Submit your example. Balakumar (although both are a few versions behind). As it’s a bit older it also does things a bit differently to how you would do them in the latest version of caret. Running glmnet() which will fit models for various values of the regularisation parameter, λ. They may work, but we do not guarantee full compatibility with these. I am unable to locate anything in the documentation that explains HOW this choice of lambda series is made. There are additional vignettes that should be useful: one that describes in detail the new relaxed features in glmnet, along with some new capabilities. Some old versions of Python packages may not be forward compatible with Python 3. are needed for older adults with poor balance and low balance confidence to assist them to initiate and maintain engagement in an exercise program. Recommended for glmnet is that alpha is always 1, so that feature selection is possible and in case of multiclass classification both thresh and maxit should be adjusted to reduce training time. Candidates using a later version of R can force it to use the older generator by executing the following command at the start of. Once in a while I get told that most clinical data are normally, or log-normally, distributed, or I see it implicitly assumed in an analysis of some clinical data. R code tends to be very memory intensive as R processes primarily in memory. Provides computational methods for Immune Cell-type Subset. glmnet plot. It also means that what you're trying to do (fitting 10 cases to 12000 variables) is ridiculous (assuming I understand your message correctly). Release Date: 2020-03-30. Rule based Learning Ensembles RuleFit3 with R. 0-5-1 13 r. 102 when predicting articles relevant to the AAP and LBD efficacy/effectiveness analyses respectively (using a threshold of p ≥0. , they represent unbiased estimates of the underlying uncertainty, given the model. Halloran, 1,8 and the MMDx-Kidney study group 9. This study conducted an in-depth comparison of prediction. We have already seen that predictive models usually involve several optimization problems, including variable selection, transformation selection, and fitting. As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students. metagenomeSeq is designed to address the effects of both normalization and. glmSparseNet is an R-package that generalizes sparse regression models when the features (e. Pairwise comparison for accuracy showed no difference between the GLMnet and the RBAS (p = 0. “Crucially, plasma treatment of the old rats [109 weeks] reduced the epigenetic ages of blood, liver and heart by a very large and significant margin, to levels that are comparable with the young rats [30 weeks]…. The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon, and the R package is maintained by Trevor Hastie. 541684 Item_Fat_ContentRegular Item_Visibility Item_TypeBreads -150. The R package is maintained by Trevor Hastie. As far as I know, the glmnet only works for older versions of matlab(< 2016). Is MATLAB compatible with macOS Catalina?. 3 and glmnet version 2. The backports are conditionally exported in order to let R resolve the function name to either the implemented backport, or the respective base version, if available. The lars package has one version LASSO fitting algorithm. 82e-3 and 2. For the ranger implementation it's recommended in. More specifically, I use the glmnet package (Friedman, Hastie, and Tibshirani 2008. Interfaces for glmnet package for data science pipelines. Bielaa 2 · Piee J. The glmnet package gas been around since at least June 2008 (at least, that’s the date of the oldest version I can find in the CRAN archives). Exploratory analysis and machine learning model for predicting housing prices with R (data. All the experiments were run in an Intel(R) Xeon(R) server with 16 E5 − 2680 2. relevent — 1. Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit ). Variable selection analyses were conducted using the GLMNET and Foreach packages for the R statistical package, version 3. Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. 41-3), glmnet (version 2. Objectives This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial. Every time that I try to use a function (like 'cvglmnet') matlab crashes. OBJECTIVE Acylcarnitines were suggested as early biomarkers even prior to insulin resistance in animal studies, but their roles in predicting type 2 diabetes were unknown. We investigated its safety and efficacy in a placebo-controlled randomized trial. She then used these extended groups to categorise all stakeholders of the accommodation of universities. Introduction. Several models are investigated using various annotation levels of expression profiles (genes, transcripts. In this context, improving the selection of patients for immediate. Objectives We aimed to develop and internally validate a measure of multimorbidity burden using data from the Canadian Longitudinal Study on Aging (CLSA). Affected Spark. 2018 CDC Social Vulnerability Index: Co-morbidities (10%) Premature death: Static: Years of potential life lost before age 75 per 100,000 population (age-adjusted) based on 2016-2018 National Center for Health Statistics. Is MATLAB compatible with macOS Catalina?. Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. As @glmnet said, we've had some situations where minor version changes in dependencies caused issues multiple times now. 600397 Item_TypeFrozen Foods Item_TypeFruits and. A Stochastic Dual Coordinate Ascent (SDCA) optimization trainer for linear binary classification and regression. A copy of the Ubuntu Server installation disc ISO can be obtained from the Ubuntu download pages:. This was done using the statistical package "Glmnet" (version 2. In the 21 century, David Aaker stated that “Brand equity is a set of brand assets and liabilities linked to a brand, its name, and its symbol that add to (or subtract from) the value provided by a product or service to a firm and/or that firm’s customers. Impact of basic arithmetic skills on success in first-semester general chemistry†. It is a longer and more detailed slide deck than the first link on this page, and it includes a different example which shows a few additional features. CAD is caused by deposits in the arterial walls of the coronary arteries. In Spark 3. 3 of the R package glmnet. The result of which is a path of coefficients, evaluated as a function of the norm of the estimated coefficients GLMNET¶ The LASSO can scale up to very large problems because of efficient solvers that take advantage of the fact that for large values the solution is sparse so it can. Introduction. (It also fits the lasso and ridge regression, since they are special cases of elastic net. 6 days) of infection. H2O: I've been using this through R. 1-17, Volume 12, Issue 1, DOI: 10. list: a character vector. Introduction Understanding the biological mechanism of subjective cognitive decline (SCD) in preclinical Alzheimer’s disease (AD) and identifying those who will soon convert to mild cognitive impairment (MCI) are critical for developing appropriate strategies for early diagnosis and intervention of AD. I divided my data into 60-40 (TRAIN-TEST) proportions, and using the 60% to find those variables and then use them on 40% to find there success as prognostic markers. A strong ridge-penalty decreases the learner's flexibility, complexity, and degrees-of-freedom: it is more constrained and must discriminate among its potential covariates. 4 years old) and older (73. In the United States, an estimated 51 million adults aged 18 years or older (approximately 1 in 5 of US adults) have experienced one or more M/SUDs in the past 12 months surveyed []. cran / glmnet. I was using cvAlpha. Running glmnet() which will fit models for various values of the regularisation parameter, λ. glmnet) on the training set. com Outline Conventions in R Data Splitting and Estimating Performance Data Pre-Processing Over-Fitting and Resampling Training and Tuning Tree Models Training and Tuning A Support Vector Machine Comparing Models Parallel. A principal improvement is the use of glmnet (Friedman, Hastie, and Tibshirani 2008) to perform rule fitting. Postmenopausal osteoporosis is the most common disease in women above 50 years of age, leading to fragile bone and fractures even after mild skeletal trauma. I think the bigger point here is the person who wrote the 'glmnet' package, Trevor Hastie, is a close colleague and co-author of many papers and books (ISLR, ESLR) with the person who invented the Lasso, Rob Tibshirani. We consider GLMNet and SVM iterations as equivalent: even though. Some old versions of Python packages may not be forward compatible with Python 3. Fitting these models involves the selection of a tuning parameter (λ) that determines the number of selected peaks. It makes the process of training, tuning and evaluating machine learning models in R consistent, easy and even fun. 94 (95% CI: 0. julia> path = glmnet(X, y); The result is a set of 76 different models. observing that glmnet giving slightly better results than h2o for many small datasets. removal of non-informative peaks). These two blocks of code represent the dataset in a graph. A major update to the open-source R language, R 3. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers. 4 years old) and older (73. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Older note: This tutorial was based on an older version of the abalone data that had a binary old varibale rather than a numeric age variable. As a major update, it has many new features, user-visible changes and bug fixes. 2; Table 1). lambda is the hyper-parameter that indicates how much shrinkage there is. In 2016, the FDA approved an updated version of the OVA1 test which retained CA125 but replaced 2 of the markers with HE4 and FSH (Coleman et al. 1%) and 22 (81. The two sides of Table 5 use similar fitting criteria—some version of least squares for quantitative responses—but they do so with different paradigms in mind. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095002 (13 March 2019); doi: 10. In my last post I discussed using coefplot on glmnet models and in particular discussed a brand new function, coefpath, that uses dygraphs to make an interactive visualization of the coefficient path. 4 kB) File type Wheel Python version cp36 Upload date Mar 11, 2019 Hashes View. On Unix-alikes: The 'factory-fresh' default is text help except from the macOS GUI, which uses HTML help displayed in its own browser window. New replies are no longer allowed. Pulse Dismiss Be notified of new releases. mixed, and parameters. This vignette describes the usage of glmnet in Python. I tried with the > example in the manual. Both HER2+ and hormone-receptor posi tive subtypes demonstrated significant. 2? Gabor Grothendieck [R] How to speed up R with version 2. In summary, in an appropriate environment, with competent supervision and support, even prefrail older people can use PRBT to improve their strength, balance, quality of life, and balance confidence. The prodromal stage of dementia, mild cognitive impairment (MCI), provides a critical opportunity for potential intervention to prevent the onset of dementia. Additionally, if available, the model summary indices are also extracted from the following packages: broom, broom. Osteoarthritis (OA) is an extremely common musculoskeletal disease. % % OLDER UPDATES: % 26 Jan 2010: Fixed a bug in the description of y, pointed out by % Peter Rijnbeek from Erasmus University. The library provides a cross-validation test to automatically figure out what is the better lambda value. packages() is a character string. The Python package is maintained by B. I'm writing a series of posts on various function options of the glmnet function (from the package of the same name), hoping to give more detail and insight beyond R's documentation. Often, glmnet does not function too good, so, it is important to not use it as the sole method of identifying variables. By default the glmnet() function performs ridge regression for an automatically selected range of $\lambda$ values. glmnet) on the training set. Introduction. 0) it should be compatible with R 3. Older ages have been associated with more severe outcomes from COVID-19 infection. In this study, we developed a prognosis model fused clinical characteristics with drug resistance pharmacogenomic signature to. 6-4 Relative Distribution Methods. Dear All, I want to generate survival curve with cox model but I want to estimate the coefficients using glmnet. A major update to the open-source R language, R 3. It isn’t true. 74), and tumor size (HR = 1. Title: Multiple Pairwise Comparison. This vignette describes the usage of glmnet in Python. As @glmnet said, we've had some situations where minor version changes in dependencies caused issues multiple times now. Older versions had some minor problems, but thanks to users trying it out and reporting problems directly back to the author an improved version is available in about 6 months. As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students. The biggest problem with S&G’s article is that they present a straw-man version of MFT based on strong modularity (in which there are 5 innate and distinct Fodorean modules doing the processing), and then pit the “strong” version of MFT they inaccurately impute to us against a shape-shifting version of Dyadic Morality. Beyond fitting boosted trees and boosted forests, xgboost can also fit a. age was on average 7years older than respective chronological age ( p =1. To send content items to your Kindle, first ensure [email protected] Note that, for all 12 datasets together, the average AUC rank for rf is only slightly better than for glmnet (2. Package ‘glmnet’ June 16, 2020 Type Package Title Lasso and Elastic-Net Regularized Generalized Linear Models Version 4. To correct. glmnet) to do. The current list of packages is downloaded over the internet (or copied from a local mirror). Results: GLMnet- and GBM-based models performed similarly, with GLMnet (results shown below) performing slightly better. Balakumar (although both are a few versions behind). Elastic net regularized regression, which fits a logistic regression model with L1 and L2 penalties (the elastic net penalty), was performed with the glmnet (Friedman et al , 2010) R package (version 2. “Crucially, plasma treatment of the old rats [109 weeks] reduced the epigenetic ages of blood, liver and heart by a very large and significant margin, to levels that are comparable with the young rats [30 weeks]…. metagenomeSeq is designed to address the effects of both normalization and. 18) was used to estimate the. 29 but my libc6 is 2. It also includes tools for working. pdf Matrix completion and softImpute, with applications in functional data and collaborative filtering Wald_III. But recently they added the support for both. Following problem: I want to predict a categorical response variable with one (or more) categorical variables using glmnet(). bz2: 1 year and 7 months ago. Each newer model tries to successful predict what older models struggled with. Title: Multiple Pairwise Comparison. ) The glmnet function is very powerful and has several function options that users may not know about. Alzheimer's disease (AD) is the most common type of dementia and is marked by progressive memory loss and cognitive impairment. / Packages / sid / r-cran-glmnet / hppa / Download Download Page for r-cran-glmnet_4. gz Nov 15, 2019. The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon, and the R package is maintained by Trevor Hastie. Added support for package gam version 1. CONTRIBUTED RESEARCH ARTICLES 474 ggfortify: Uniﬁed Interface to Visualize Statistical Results of Popular R Packages by Yuan Tang, Masaaki Horikoshi, and Wenxuan Li Abstract The ggfortify package provides a uniﬁed interface that enables users to use one line of code to visualize statistical results of many R packages using ggplot2 idioms. For the Gaussian family, glmnet solves the penalized residual sum of squares,. Older versions had some minor problems, but thanks to users trying it out and reporting problems directly back to the author an improved version is available in about 6 months. Plasma phospholipids identify antecedent memory impairment in older adults 1. scikit-learn 0. 82e-3 and 2. 1186/s13073-020-0716-9. The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon, and the R package is maintained by Trevor Hastie. Due to the increased susceptibility of elderly people to pneumonia, it is crucial for policymakers in Japan and other aging. This includes the name of the modelling function or any arguments passed to the. The ensemble method is powerful as it combines the predictions from multiple machine learning algorithms. 0–2) and pROC (version 1. In the United States, an estimated 51 million adults aged 18 years or older (approximately 1 in 5 of US adults) have experienced one or more M/SUDs in the past 12 months surveyed []. GLM: glmnet objective function better than h2o. Interfaces for glmnet package for data science pipelines. This vignette describes the usage of glmnet in R. By 2013, more than 25% of the HIV-infected (HIV +) population in the US was over 55 years old, a number projected to double by 2045 1. 6, i need libc6 > 2. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. 4 and below, the current_timestamp function returns a timestamp with millisecond resolution only. R code tends to be very memory intensive as R processes primarily in memory. Re: GLMNET warning msg It means that 10/10 = 1 < 3. 41-3), glmnet (version 2. --file=file1 --file=file2). 92) remained as poor predictors of UTUC-specific survival. dll is for older versions of Matlab. 2? FMH [R] How to speed up R with version 2. They would be expected to fulfill the roles of an adult -- take care of themselves, begin to accumulate some money for a dowry (in the case of a woman) or begin to accumulate the means to support a wife (in the case of a man), etc. Japan is currently the world's foremost super-aging society, with elderly people aged 65 years and older accounting for 26. One of the most widely known examples of this kind of activity in the past is the Oracle of Delphi, who dispensed previews of the future to her petitioners in the form of divine inspired prophecies 1. It also means that what you're trying to do (fitting 10 cases to 12000 variables) is ridiculous (assuming I understand your message correctly). The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon. The function takes sparse representation of a document-term matrix, an integer number of topics, and covariates and returns fitted model parameters. 541684 Item_Fat_ContentRegular Item_Visibility Item_TypeBreads -150. PFAS were measured by liquid chromatography system. She then used these extended groups to categorise all stakeholders of the accommodation of universities.


text:
# How to separate and analyse a sample of cations of alkaline and earth alkaline cations and ammonium?

I am teaching a section of qualitative analysis and I am trying to get a better grasp on the assignments the students are doing. These are the cations they may be presented with and must identify:

• $\ce{Na+}$
• $\ce{K+ }$
• $\ce{NH4+}$
• $\ce{Ca^2+ }$
• $\ce{Al^3+ }$
• $\ce{Mg^2+}$

Now, this lab hasn't changed for the last 20 years, and there is only one way the students perform the separations, which happens to be the same method found in an old lab report that is easily found with Google. The method is:

1. Add excess 3 M ammonia to the sample.

2. If a gel-like precipitate forms, then $\ce{Mg^2+ }$ and/or $\ce{Al^3+ }$ ion is present. They have been converted into their respective hydrated hydroxides. Separate the precipitate from the supernatant liquid.

3. Since hydrated $\ce{Mg^2+ }$ hydroxide is not readily amphoteric, while hydrated $\ce{Al^3+ }$ hydroxide is, addition of excess $\ce{NaOH}$ should cause the an $\ce{Al^3+ }$ precipitate to dissolve. If the precipitate from the previous step does dissolve with excess $\ce{NaOH}$, then $\ce{Al^3+ }$ ion is present in the original sample. If the precipitate does not dissolve, then $\ce{Mg^2+ }$ ion is present.

4. Time for the supernatant analysis (if there is a supernatant). If there is no ppt, then we can immediately rule out the presence of $\ce{Mg^2+}$ and $\ce{Al^3+ }$. Either way, the solution, non-precipitate portion can only contain $\ce{Na+}$, $\ce{K+ }$, $\ce{NH4+}$, and/or $\ce{Ca^2+ }$ since none of these form insoluble hydroxides at the concentration of ammonia used. Yes, calcium hydroxide is insoluble, but it doesn't form with 3 M $\ce{H3N}$; I've done the $Q_\mathrm{sp}$ vs. $K_\mathrm{sp}$ calculation.

5. Add sodium carbonate to the liquid portion. If a solid forms, the solid must be calcium carbonate, and therefore $\ce{Ca^2+ }$ was present in the original sample.

6. If no solid forms, or even if a solid forms, separate the solid from the liquid and perform further tests on the liquid to determine the presence of $\ce{Na+}$, $\ce{K+ }$, and $\ce{H4N+}$. A yellow flame tests tells us $\ce{Na+}$ is present and $\ce{K+}$ may or may not be present. A purple flame test tells us $\ce{Na+}$ is absent.

7. Heat the remaining solid sample (not any precipitate, but the original sample itself) to ascertain the presence of $\ce{NH4+}$. Heating a sample that contains $\ce{NH4+}$ will result in the release of white smoke. After heating the solid sample until smoking ceases or until one is certain the sample will not start generating white smoke, perform a sodium coboltinitrite test if necessary to determine whether $\ce{K+ }$ is present.

What other valid separations are there? Is this the easiest method, or are there easier ones?

What about initially adding $\ce{NaOH}$?

1. Add $\ce{NaOH}$. If a white precipitate forms, the sample contains $\ce{Ca^2+}$ (the white ppt is calcium hydroxide). If a gel-like ppt forms, then the sample contains either $\ce{Al^3+}$ or $\ce{Mg^2+}$. Perform the necessary tests described above to determine the nature of the gel-like ppt.

2. The liquid part of the solution must now only contain $\ce{Na+ }$ or $\ce{K+ }$. Ammonium ion has been converted to ammonia thanks to the hydroxide ion. $\ce{Na+}$ or $\ce{K+ }$ can easily be tested for using a flame test. The sodium coboltinitrite test can also be employed to detect $\ce{K+ }$ if the color of $\ce{K+ }$ has been masked by the color of Na+ in the flame test.

This method seems a bit faster. But are there any pitfalls?

• Dude, you gotta get with the program in re: typesetting those formulae... May 24 '16 at 2:32
• May 24 '16 at 19:35
• You want more than Mart's answer? o.O May 27 '16 at 0:06
• @Mithoron well I wanted to reward his answer and to see if anyone else wanted to chime in :). May 27 '16 at 0:08

In a qualitative analysis, the fast way is always the wrong way. There are already so many issues with the "long" version that I am surprised that this is remotely successful.

The cations of groups 1-3 are always the most difficult to separate and during my time as a teaching assistant were by far the biggest source of error.

The proper course of analysis is

1. dissolve sample (completely)
2. precipitate ion
3. dissolve precipitate
4. specific reaction
5. repeat

The reason for separating the cations in the first place is, that they often act in similar ways and the reactions are not very specific. Certain cations should be tested multiple times, too.

All the reactions you are performing are equilibria and very dependent on the overall concentration. None of them form a precipitate quantitatively. Sometimes you might have problems recognising precipitates in the first place.

You need to perform double blind tests for all used chemicals, since already small contaminations may lead to false positives.

The main issue with your proposed strategy is that you will not precipitate aluminium with this strategy, since this is extremely pH dependent. $$\ce{Al^3+ + 4OH- <=> Al(OH)3 + OH- <=> [Al(OH)4]- }$$ Without a buffered solution at around pH 9 and ageing the solution, there should not form a precipitate. I learned this by adding ammonium sulfide first and raising the pH slowly with ammonia. (This will co-precipitate cobalt, nickel, manganese, zinc, and iron [as sulfides], and chromium [as hydroxide]. This might be useful if you have to analyse for more ions later on.)
If a precipitate forms you can dissolve it with sodium hydroxide and use cobalt(II) nitrate to form Cobalt blue.
You can also test the presence of aluminium with morin as it forms a fluorescent complex with it. Note that $\ce{Mg(OH)2}$ is slightly fluorescent, too.

I cannot remember precipitating magnesium with that strategy in any sufficient amount. I would prefer precipitating it as an ammonium phosphate, which you can try to (re-)crystallise as struvite and look at them with a microscope. $$\ce{Mg^2+ + NH4+ + PO4^3- <=> MgNH4PO4 v ->C[H2O] MgNH4PO4.6H2O}$$

If you want to precipitate out aluminium and magnesium (and almost every other heavier element) you can use oxine, $\ce{C9H7NO}$ (abbr. $\ce{Hox}$), solution. $$\ce{ M^{x+} + x\,Hox <=> [M(ox)_{x}] + x\,H+ }$$

Then precipitating calcium is straight forward with carbonate solution. Other earth alkaline metals will precipitate, too. You can dissolve this precipitate with acids or you can use the solid to perform flame tests. Since there can only be calcium, strontium, or barium, this should be conclusive. (Of course you could precipitate barium sulfate.)

The main pitfall with your proposed strategy is, that you will not quantitatively precipitate calcium hydroxide and it is as mentioned above quite concentration dependent. The solubility constant is not too small, i.e. $K_\mathrm{sp}=5.5\times10^{-6}$.

If you have precipitated everything so far, then the last thing left over are ammonium, potassium, sodium(, and lithium). The biggest problem is, you cannot mask them properly and you cannot precipitate them. You can use flame tests to determine their presence, but as long as you have sodium in it you might not see anything else. You should use a prism, or a spectrometer to determine the characteristic spectral lines. Apparently phones can do it now, too. Also see publiclab.org... makes you think why separate at all.

You will need to analyse ammonium from the sample. You can add sodium hydroxide, which releases ammonia. You can detect the presence of ammonia in the sample by holding a wetted universal indicator paper over the sample. Additionally, you should be able to smell it.

In other words ...
... be patient with your analysis as there are more pitfalls, false negatives, false positives, contaminations. Don't use short cuts, except atomic emission spectroscopy.

In Qualitative analysis, cations are arranged in groups. For each group, there are a specific set of reactions that determines the cation. If result are negative, we proceed to next group.

\begin{array}{c|c} \mathbf{Group} & \mathbf{Cation} \\\hline \ce{Zero} & \mathrm{\ce{NH4^+}}\\ \ce{I} & \mathrm{\ce{Pb^2+}} \\ \ce{IIA} & \mathrm{\ce{Pb^2+{,Cu^2+}}}\\ \ce{IIB} & \mathrm{\ce{As^3+}}\\ \ce{III} & \mathrm{\ce{Fe^3+ {,Al^3+}}}\\ \ce{IV} & \mathrm{\ce{Co^2+ {,Ni^2+} {,Mn^2+} {,Zn^2+}}}\\ \ce{V} & \mathrm{\ce{Ba^2+ {,Sr^2+}, {,Ca^2+}}}\\ \ce{VI} & \mathrm{\ce{Na+ {,K+} {,Mg^2+}}}\\ \end{array}

Let's start with $\ce{NH4+}$. A simple test is Nessler's reagent test ($\ce{K2[HgI4]}$) which gives brown ppt.

$$\ce{2K2[HgI4] + NH4OH -> NH2.HgO.HgI↓+ 7I- + 2H2O}$$

Then we proceed to $\ce{Al^3+}$. A test called Lake test is performed which gives blue ppt. on a colorless solution.

$$\ce{Al(OH)3 + 3HCl -> AlCl3 + 3H2O}$$ $$\ce{AlCl3 + 3NH4OH -> 3NH4Cl + Al(OH)3↓}$$

Otherwise perform the cobalt nitrate test as described in Martin's answer.

Then proceed to $\ce{Ca^2+}$. For this, add ammonium oxalate. Explanation for this test is given in aventurin's answer.

For the alkali metal ion, there are indeed specific chemical test but the best test for determining them is the Flame test as described in Jan's answer.

• Magnesium

Ammonium phosphate test (described in Martin's answer).

• Sodium

Potassium pyroantimonate test (Milky ppt.)

$$\ce{2NaCl + K2H2Sb2O7 -> 2KCl + Na2H2Sb2O7↓}$$

• Potassium

Sodium cobaltinitrate test (yellow ppt.)

$$\ce{3KCl + Na3[Co(NO2)6] -> 3NaCl + K3[Co(NO2)6]↓}$$

Or Picric acid test (yellow ppt.)

$$\ce{KCl + C6H6(NO2)3OH -> HCl + C6H2(NO2)3OK↓}$$

# Flame test, flame test, flame test!

I assume that the lab has access to a simple optical spectroscope that will actually help you discern the lines different cation give in the flame test. If it doesn’t have one … Seriously how, because the technique is over a hundred years old.

• Sodium, of course, gives a very recogniseable yellow double line.

• Lithium (which is not present — why?) would give red lines where nothing else would.

• Potassium gives a faint red line at the very edge of the spectrum. It can also be detected by looking at the flame through a blue-coloured glass (it was called Kobaltglas, cobalt glass, in our lab) and noting a different flame colour.

• Calcium gives ‘traffic lights’, a red, yellow and green line approximately at equal distances of each other.

# Ammonium

This is easily detected by just adding 6 M $\ce{NaOH}$ (or whatever the highest non-concentrated solution your lab may have) to the sample, putting a watchglas above with a bit of wet indicator paper stuck to the watchglass’ bottom side. If ammonium is present, the indicator paper will quickly get coloured blue (or whatever the colour of ‘alkaline’ is for your indicator). Make sure that no students ‘accidentally’ added ammonia to the sodium hydroxide (i.e. perform a blind experiment without sample).

# The remaining

Which leaves magnesium and aluminium. I personally didn’t have magnesium in my ion lottery class, so I don’t really know how to cope with it. However, your method seems reasonable.

A secondary test for working with aluminium may be either the morin fluorescence (which is disturbed by sodium) or alizarin S precipitation (red). The latter worked like a charm in my ion lottery.

I’ll add a link to this beautiful graph from Professor Klüfers of the LMU Munich which shows the prevalent aluminium species at different pH values. The notation is as follows: $\ce{Al}$ is $\ce{[Al(H2O)6]^3+}$, and an added $\ce{H_{$-n$}}$ shows that this number of protons is missing. Hence, $\ce{AlH_{-3}}$ is equal to $\ce{Al(OH)3}$. Since I do not own the graph and am unsure about its copyright status, I prefer to link rather than upload here.

One additional suggestion for the identification of calcium is the precipitation as calcium oxalate (weddellite).

$$\ce{Ca^{2+} + C2O4^{2-} + 2 H2O-> CaC2O4 \cdot 2 H2O}$$

Adding a solution of ammonium oxalate to the weakly ammoniacal or weakly acidic (buffered acetic adid) solution will form a white crystalline precipitate. The reaction is very sensitive and specific if only $\ce{NH4+}$, $\ce{Na+}$, $\ce{K+}$, $\ce{Mg^{2+}}$, $\ce{Ca^{2+}}$, and $\ce{Al^{3+}}$ can be present.

format: plain


text:
Options :

1. 100

2. 10

3. 1

4. \frac{1}{10}
5. \frac{1}{100}

Explanation:

Given that,

Reciprocal of the sum

x and y is equal to 10

i.e.,
\frac{1}{x+y}=10

\frac{1}{10}=x+y

Square of the sum of x and y = \left(x+y\right)^2=\frac{1}{100}


text:
# How to represent this partial sum? [duplicate]

I'm trying to find a way to represent $\sum_{n=1}^\infty n^2$ as a partial sum. I know that every term in this series can be represented, for example when $n=5$, as $5^2+4^2+3^2+2^2+1^2$. I know that $1+2+3+4...$ can be represented as $\frac{n(n+1)}{2}$ but I can't figure out how to add the squared terms to each number.

• are you searching for $$\sum_{i=1}^ni^2$$? Nov 26 '17 at 6:30
• Yeah I forgot to add the sigma notation Nov 26 '17 at 6:32
• What does the $\infty$ do in your sum then?
– user499203
Nov 26 '17 at 6:34
• Nov 26 '17 at 6:37
• $f(N) =\sum_{n=1}^N n^2$ is a polynomial of degree $3$, and it is not hard to find its coefficients, the largest first : If $f(N) = \sum_{d=0}^3 c_d N^d$ then $f(N+1)-f(N) = \sum_{d=0}^3 c_d ((N+1)^d-N^d)$ $= c_1 +c_2 (N^2+2N+1-N^2) + c_3 (N^3+3N+3N^2+1-N^3))$. Can you solve for $c_1,c_2,c_3$ to obtain $f(N+1)-f(N) = N^2$ ? Nov 26 '17 at 6:43

What you're looking for is $$\sum_{k=1}^nk^2=\frac{n(n+1)(2n+1)}{6}$$

It is $$\frac{n(n+1)(2n+1)}{6}$$

Using finite calculus we can write $n^2=n^\underline 2+n$ where $n^\underline k$ represent a falling factorial. Thus

$$\sum_{n=k}^m n^2=\sum_{n=k}^m (n^\underline 2+ n)=\sum\nolimits_k^{m+1}(n^\underline 2+n)\,\delta n=\left[\frac{n^\underline 3}3+\frac{n^\underline 2}2\right]_{n=k}^{n=m+1}\\=\frac16(2(m+1)^\underline 3+3(m+1)^\underline 2-2k^\underline 3-3k^\underline 2)$$

For $k=1$ it reduces to $\frac16(m+1)^\underline 2(2m+1)$.

It is $\sum_{i=0}^n i^2=\frac{n(n+1)(2n+1)}{6}$.

There are some ways do figure that out. You might calculate the first cases $n=1,2,3,4,5$ and try to to find the formula. This is arguably a little bit tough.

That this formula holds can be proven by induction and is easy.

• "There are some ways do figure that out." - There is actually a way to turn this problem into a system of linear equations. For any number $k\in\mathbb N$, finding $\sum_{i=1}^ni^k$ is equivalent to solving a linear system of $k+3$ equations.
– user499203
Nov 26 '17 at 6:55


text:
## Contents

1. Overview
1.1 Thesaurus
1.2 Keyword extraction
2. Running BibClassify

## 1. Overview

BibClassify automatically extracts keywords from fulltext documents. The automatic assignment of keywords to textual documents has clear benefits in the digital library environment as it aids catalogization, classification and retrieval of documents.

### 1.1 Thesaurus

BibClassify performs an extraction of keywords based on the recurrence of specific terms, taken from a controlled vocabulary. A controlled vocabulary is a thesaurus of all the terms that are relevant in a specific context. When a context is defined by a discipline or branch of knowledge then the vocabulary is said to be a subject thesaurus. Various existing subject thesauri can be found here.

A subject thesaurus can be expressed in several different formats. Different institutes/disciplines have developed different ways of representing their vocabulary systems. The taxonomy used by bibclassify is expressed in RDF/SKOS. It allows not only to list keywords but to specify relations between the keywords and alternative ways to represent the same keyword.

<Concept rdf:about="http://cern.ch/thesauri/HEP.rdf#scalar">
<composite rdf:resource="http://cern.ch/thesauri/HEP.rdf#Composite.fieldtheoryscalar"/>
<prefLabel xml:lang="en">scalar</prefLabel>
<note xml:lang="en">nostandalone</note>
</Concept>

<composite rdf:resource="http://cern.ch/thesauri/HEP.rdf#Composite.fieldtheoryscalar"/>
<prefLabel xml:lang="en">field theory</prefLabel>
<altLabel xml:lang="en">QFT</altLabel>
<hiddenLabel xml:lang="en">/field theor\w*/</hiddenLabel>
<note xml:lang="en">nostandalone</note>
</Concept>

<compositeOf rdf:resource="http://cern.ch/thesauri/HEP.rdf#scalar"/>
<compositeOf rdf:resource="http://cern.ch/thesauri/HEP.rdf#fieldtheory"/>
<prefLabel xml:lang="en">field theory: scalar</prefLabel>
<altLabel xml:lang="en">scalar field</altLabel>
</Concept>

In RDF/SKOS, every keyword is wrapped around a concept which encapsulates the full semantics and hierarchical status of a term - including synonyms, alternative forms, broader concepts, notes and so on - rather than just a plain keyword.

The specification of the SKOS language and various manuals that aid the building of a semantic thesaurus can be found at the SKOS W3C website. Furthermore, BibClassify can function on top of an extended version of SKOS, which includes special elements such as key chains, composite keywords and special annotations. The extension of the SKOS language is documented in the hacking guide.

### 1.2 Keyword extraction

BibClassify computes the keywords of a fulltext document based on the frequency of thesaurus terms in it. In other words, it calculates how many times a thesaurus keyword (and its alternative and hidden labels, defined in the taxonomy) appears in a text and it ranks the results. Unlike other similar systems, BibClassify does not use any machine learning or AI methodologies - a just plain phrase matching using regular expressions: it exploits the conformation and richness of the thesaurus to produce accurate results. It is then clear that BibClassify performs best on top of rich, well-structured, subject thesauri expressed in the RDF/SKOS language.

A detailed account of the phrase matching mechanisms used by BibClassify is included in the hacking guide.

## 2. Running BibClassify

Dependencies. BibClassify requires Python RDFLib in order to process the RDF/SKOS taxonomy.

In order to extract relevant keywords from a document fulltext.pdf based on a controlled vocabulary thesaurus.rdf, you would run BibClassify as follows:

$bibclassify.py -k thesaurus.rdf fulltext.pdf  Launching bibclassify --help shows the options available for BibClassify:  Usage: bibclassify [OPTION]... [FILE/URL]... bibclassify [OPTION]... [DIRECTORY]... Searches keywords in FILEs and/or files in DIRECTORY(ies). If a directory is specified, BibClassify will generate keywords for all PDF documents contained in the directory. Can also run in a daemon mode, in which case the files to be run are looked for from the database (=records modified since the last run). General options: -h, --help display this help and exit -V, --version output version information and exit -v, --verbose=LEVEL sets the verbose to LEVEL (=0) -k, --taxonomy=NAME sets the taxonomy NAME. It can be a simple controlled vocabulary or a descriptive RDF/SKOS and can be located in a local file or URL. Standalone file mode options: -o, --output-mode=TYPE changes the output format to TYPE (text, marcxml or html) (=text) -s, --spires outputs keywords in the SPIRES format -n, --keywords-number=INT sets the number of keywords displayed (=20), use 0 to set no limit -m, --matching-mode=TYPE changes the search mode to TYPE (full or partial) (=full) --detect-author-keywords detect keywords that are explicitely written in the document Daemon mode options: -i, --recid=RECID extract keywords for a record and store into DB (=all necessary ones for pre-defined taxonomies) -c, --collection=COLL extract keywords for a collection and store into DB (=all necessary ones for pre-defined taxonomies) Taxonomy management options: --check-taxonomy checks the taxonomy and reports warnings and errors --rebuild-cache ignores the existing cache and regenerates it --no-cache don't cache the taxonomy Backward compatibility options (discouraged): -q equivalent to -s -f FILE URL sets the file to read the keywords from Examples (standalone file mode):$ bibclassify -k HEP.rdf http://arxiv.org/pdf/0808.1825
$bibclassify -k HEP.rdf article.pdf$ bibclassify -k HEP.rdf directory/

Examples (daemon mode):
$bibclassify -u admin -s 24h -L 23:00-05:00$ bibclassify -u admin -i 1234
\$ bibclassify -u admin -c Preprints

NB. BibClassify can run as a CDS Invenio module or as a standalone program. If you already run a server with a Invenio installation, you can simply run /opt/invenio/bin/bibclassify [options]. Otherwise, you can run from BibClassify sources bibclassify [options].

As an example, running BibClassify on document nucl-th/0204033 using the high-energy physics RDF/SKOS taxonomy (HEP.rdf) would yield the following results (based on the HEP taxonomy from October 10th 2008):

Input file: 0204033.pdf

Author keywords:
Dense matter
Saturation
Unstable nuclei

Composite keywords:
10  nucleus: stability [36, 14]
6  saturation: density [25, 31]
6  energy: symmetry [35, 11]
4  nucleon: density [13, 31]
3  energy: Coulomb [35, 3]
2  energy: density [35, 31]
2  nuclear matter: asymmetry [21, 2]
1  n: matter [54, 36]
1  n: density [54, 31]
1  n: mass [54, 16]

Single keywords:
61  K0
23  equation of state
12  slope
4  mass number
4  nuclide
3  nuclear model
3  mass formula
2  charge distribution
2  elastic scattering
2  binding energy

or, the following keyword-cloud HTML visualization:


text:
# Comparing Mixed-Effect Models in R and SPSS

Occasionally we are asked to help students or faculty implement a mixed-effect model in SPSS. Our training and expertise is primarily in R, so it can be challenging to transfer and apply our knowledge to SPSS. In this article we document for posterity how to fit some basic mixed-effect models in R using the lme4 and nlme packages, and how to replicate the results in SPSS.

In this article we work with R 4.2.0, lme4 version 1.1-29, nlme version 3.1-157, and SPSS version 28.0.1.1.

To begin we fit a model in R using the sleepstudy dataset that comes with lme4. This dataset contains average reaction time per day (in milliseconds) to a psychomotor vigilance task (PVT) for subjects in a sleep deprivation study. Each subject was observed for 10 days. Of interest is how sleep-deprived subjects’ reaction times change over time.

After we load the lme4 package, we load the sleepstudy dataset into our global environment using the data() function. Once loaded we export the data as a CSV file to our working directory using write.csv(), so we can import it into SPSS.

# install.packages("lme4")
library(lme4)
data("sleepstudy")
write.csv(sleepstudy, "sleepstudy.csv", row.names = FALSE)

Here’s the exported CSV file if you would like to download it: sleepstudy.csv

Visualizing the data with ggplot2 reveals that a linear model would seem to be suitable, but one that takes into account the random variation between subjects. It appears the subjects exert a random effect on both the intercept and slope of the fitted lines. In other words it looks like we should fit a mixed-effect model.

library(ggplot2)
ggplot(sleepstudy) +
aes(x = Days, y = Reaction) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
facet_wrap(~Subject)

To model Reaction as a function Days with the intercept and slope coefficients conditional on Subject, we fit the following model using lmer().

m1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)

The (Days | Subject) syntax says we want to model a random effect for the intercept, a random effect for the Days coefficient, and the correlation between the random effects. Random effects are variances, so we can symbolize this with the following covariance matrix, where $$\sigma^2_1$$ is the intercept random effect, $$\sigma^2_2$$ is the Days random effects, and $$\rho_{12}$$ is the correlation between the two variances:

$\begin{bmatrix} \sigma^2_1 & \rho_{12} \\ \rho_{12} & \sigma^2_2 \end{bmatrix}$

Usually a covariance matrix contains covariance on the off-diagonals, not correlation. But we present it this way to conform to how lme4 summarizes random effects. Plus, correlation is simply a unitless version of covariance.

The summary output of the lme4 model lists the estimated variances/standard deviations and correlation under “Random effects”. (The corr = FALSE argument in the summary() function says to not show the correlation of fixed effects, which is not relevant to the current article.)

summary(m1, corr = FALSE)
## Linear mixed model fit by REML ['lmerMod']
## Formula: Reaction ~ Days + (Days | Subject)
##    Data: sleepstudy
##
## REML criterion at convergence: 1743.6
##
## Scaled residuals:
##     Min      1Q  Median      3Q     Max
## -3.9536 -0.4634  0.0231  0.4634  5.1793
##
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr
##  Subject  (Intercept) 612.10   24.741
##           Days         35.07    5.922   0.07
##  Residual             654.94   25.592
## Number of obs: 180, groups:  Subject, 18
##
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)  251.405      6.825  36.838
## Days          10.467      1.546   6.771

The estimated covariance matrix for the random effects (with correlation on the off-diagonal) is:

$\begin{bmatrix} 612.10 & 0.07 \\ 0.07 & 35.07 \end{bmatrix}$

Now let’s replicate the same result in SPSS.

First we need to load the data. Go to File…Import Data…CSV Data, navigate to the location of the “sleepstudy.csv” file and click Open. In the Read CSV File dialog, make sure the “First line contains variables names” is checked and click OK.

Now we fit the model by going to Analyze…Mixed Models…Linear… This can be performed using SPSS syntax but we’ll show how to use the dialogs, as this seems to be how most people prefer to use SPSS (at least in our experience).

In the “Specify Subjects and Repeated” dialog, add Subject to the “Subjects:” field and click Continue. (This variable does not have to be named “Subject”; this is only a coincidence. Common names for grouping structures include “id”, “plot”, “subject_id”, and so on.)

In the next dialog, “Linear Mixed Models”, add “Reaction” to the “Dependent Variable:” field and “Days” to the “Covariate(s):” field. (If we had any categorical predictors we would put them in “Factor(s)” field.)

Next click the “Fixed…” button to specify our model. In this case we only have one predictor and an intercept, so we select Days under “Factors and Covariates:” and click the “Add” button. Make sure the “Include intercept” option is checked and click “Continue”. Everything else can stay the same.

Next click the “Random…” button to specify our random effects. The first thing we need to do in this dialog is select “Unstructured: Correlation Metric” from the “Covariance Type:” pulldown. This says to use the covariance matrix we specified above. “Unstructured” means estimate all three parameters: the two variances and the covariance/correlation. We are not imposing any structure on our covariance matrix. “Correlation Metric” means return the correlation between the random effects instead of the covariance.

Then we specify which factors and covariates will have a random effect. Select Days and click “Add”. Make sure “Include intercept” is checked. Finally, under “Subject Groupings” we specify which grouping variables indicate random effects. In this case it’s Subject, so we select it, click the add arrow, and click “Continue”. Everything else can stay the same.

Next click the “Statistics…” button to select which statistics we want computed for our linear mixed-effect model. To keep output minimal, we select two choices:

• Parameter estimates for fixed effects
• Covariances of random effects

When done click “Continue”.

Finally we click OK to run the model. A partial listing of the output shows results equivalent to the lme4 output (within rounding error). SPSS also provides additional statistics that lme4 does not provide such as approximate p-values for the coefficient tests and standard errors for the random effects.

Notice in the “Estimates of Covariance Parameters” output the variances and correlation are labeled with numbers to indicate their position in the covariance matrix:

$\begin{bmatrix} \text{Var(1)} & \text{Corr(2,1)} \\ \text{Corr(2,1)} & \text{Var(2)} \end{bmatrix}$

This is identical to the previous covariance matrix we presented above, but with names instead of Greek symbols. SPSS also prints the covariance matrix in the next section “Random Effect Covariance Structure”, but with the esimated covariance instead of the correlation.

The estimated correlation between the random effects is quite small, about 0.07. Perhaps we could assume the correlation is 0 and fit a simpler model. This would mean imposing some structure on the covariance matrix for the random effects, as follows:

$\begin{bmatrix} \text{Var(1)} & 0 \\ 0 & \text{Var(2)} \end{bmatrix}$

To specify this using lmer(), we use two pipes instead of one when specifying the random effects:

# Notice the || in the random effects syntax
m2 <- lmer(Reaction ~ Days + (Days || Subject), sleepstudy)
summary(m2, corr = FALSE)
## Linear mixed model fit by REML ['lmerMod']
## Formula: Reaction ~ Days + ((1 | Subject) + (0 + Days | Subject))
##    Data: sleepstudy
##
## REML criterion at convergence: 1743.7
##
## Scaled residuals:
##     Min      1Q  Median      3Q     Max
## -3.9626 -0.4625  0.0204  0.4653  5.1860
##
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  Subject   (Intercept) 627.57   25.051
##  Subject.1 Days         35.86    5.988
##  Residual              653.58   25.565
## Number of obs: 180, groups:  Subject, 18
##
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)  251.405      6.885  36.513
## Days          10.467      1.560   6.712

Notice there is no estimated correlation in the “Random effects” section. That’s because we constrained it to be 0.

To replicate this model in SPSS, we follow all the same steps above except we select “Diagonal” in the “Covariance Type:” pulldown in the Random Effects dialog.

The SPSS output for the random effects is basically the same as the lme4 output with some mild discrepancies in the decimals. This is likely due to minor differences in the default settings of the optimization routines. Notice the Random Effect Covariance Structure shows a 0 on the off-diagonal.

A further constraint on the random effect covariance matrix would be to assume both variances are equal.

$\begin{bmatrix} \text{Var} & 0 \\ 0 & \text{Var} \end{bmatrix}$

In this case there is only one variance parameter to estimate. Based on the estimated random effects we’ve seen so far, this seems like a far-fetched assumption. There is far more variability between subject reaction times on day 0 (the intercept) than there is between their estimated slopes. However for the sake of completeness we’ll demonstrate how to fit this covariance structure.

Unfortunately, the lmer() function in the lme4 package does not provide facilities for this type of structure. To entertain this model we turn to the nlme package and the function lme(). To specify random effects in the lme() function we use the random argument. To specify that we want to estimate one common variance parameter for both random effects, we use the pdIdent() function. The syntax is a little awkward if you’re accustomed to lme4. We need to set Subject = pdIdent(~ Days) and nest in a list. The summary output shows one estimate of standard deviation (the square root of variance) for both Intercept and Days random effects: 8.89.

# no need to install, comes with R
library(nlme)
m3 <- lme(Reaction ~ Days, random = list(Subject = pdIdent(~ Days)),
data = sleepstudy)
summary(m3)
## Linear mixed-effects model fit by REML
##   Data: sleepstudy
##       AIC      BIC    logLik
##   1767.15 1779.877 -879.5749
##
## Random effects:
##  Formula: ~Days | Subject
##  Structure: Multiple of an Identity
##         (Intercept)     Days Residual
## StdDev:    8.898743 8.898743 27.37447
##
## Fixed effects:  Reaction ~ Days
##                 Value Std.Error  DF  t-value p-value
## (Intercept) 251.40510  4.333705 161 58.01158       0
## Days         10.46729  2.214483 161  4.72674       0
##  Correlation:
##      (Intr)
## Days -0.237
##
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max
## -3.77675971 -0.54676522  0.03842765  0.57370461  4.86391328
##
## Number of Observations: 180
## Number of Groups: 18

To replicate this model in SPSS, we follow all the same steps above except we select “Scaled Identity” in the “Covariance Type:” pulldown in the Random Effects dialog.

The SPSS output for the random effects shows one variance, 79.189, instead of one standard deviation. You can square the nlme output or take the square root of the SPSS output to see they’re the same.

Which model is better? One way to assess this is to compare information criteria such as AIC or BIC. We leave it to the interested reader to verify that m2 is probably the preferable model.

There is much more to mixed-effect modeling in lme4, nlme and SPSS. In this article we simply discussed modeling the covariance structure of random effects for a basic mixed-effect model, and showed how to implement the same models in R and SPSS. We hope anyone obligated to move from R to SPSS, or vice versa, will find this helpful.

References

• Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01.
• Gregory Belenky, Nancy J. Wesensten, David R. Thorne, Maria L. Thomas, Helen C. Sing, Daniel P. Redmond, Michael B. Russo and Thomas J. Balkin (2003) Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study. Journal of Sleep Research 12, 1–12.
• IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 28.0.0.1. Armonk, NY: IBM Corp
• Pinheiro J, Bates D, R Core Team (2022). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-157, https://CRAN.R-project.org/package=nlme.
• R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
• H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

For questions or clarifications regarding this article, contact the UVA Library StatLab: statlab@virginia.edu

View the entire collection of UVA Library StatLab articles.

Clay Ford
Statistical Research Consultant
University of Virginia Library
May 03, 2022


text:
## LEO PowerPoint

The LEO PowerPoint will explain the Low Earth Environment (the area from 99 - 1200 miles or 150 - 2,000 km above Earth) as well as geosynchronous satellites.


text:
# Boolean Logic: How to Explain + as OR?

In a previous question, I asked about the appropriate notation when teaching Boolean logic to students ages about 11–14. I selected the notation of engineering and computer science in part because I believe (without any real research) that's what the students are most likely to encounter later.

I'm still not sure how to explain + as OR other than by saying that the same symbol is used for different, but similar, things. Is there a better way to explain that?

Aside: I am going to use center-dot for AND because most of these students have not had an algebra course and implicit multiplication confuses them. They won't encounter the inner product concept until college.

• Because you use the same algebra rules, as if it is a $+$, because it is a $+$. Except $1=2$. See my answer on previous question. and $1\cdot1 = 1$ etc all as expected. But for $+$ it is as expected if $1=2$ – ctrl-alt-delor Feb 18 '18 at 18:36

Since your students are getting the beginning of Boolean from you, you have the advantage of being able to lay the proper groundwork to work within. To begin with, hopefully, you will be stressing the point that Boolean logic is two-state. You can use the word binary if you choose, as long as they are not predisposed to think binary = computer, since while Boolean logic is handy for computers to deal with, it is not just for computers.

To connect the symbols, meanings and math together, make it explicit, and demonstrate. Start with the concept that FALSE is the same as nothing and TRUE is the same as something. "I have an apple in my hand" is FALSE if you have no apples in your hand, even if you have an orange, or a pencil. The same statement is TRUE if you do have an apple in your hand. It is not, however, more TRUE if you have two apples, or twenty apples, in your hand, however, it is still just TRUE.

Zero, as a number, represents nothing as well - zero apples is no apples. Therefore, it makes sense (dare I say it is logical?) to use the number 0 to represent FALSE. Using the previous demonstrative phrase, it only takes one apple to make the statement TRUE, and at least one is something. Five is also something, but that would leave out everything from one to four as maybe, and Boolean logic has no MAYBE, so that creates a problem. If some starts at one then there is not need for a MAYBE. Using 1 for something, and therefore TRUE, gives two symbols to use instead of the words TRUE and FALSE, which can get quite long to write in a very short while.

The next step is to introduce the symbols for OR and AND. Here I will, marginally, disagree with how and why your are using center dot • for AND. I am not opposed to that usage, only the reasoning behind the choice. The students have not had algebra yet, so it seems a safer choice than ✖. You can tell the students you have chosen • for AND because it is what they are most likely to encounter later. They should also be told that it can also be written as ✖, and that both can also be used for multiplication ins algebra. The same way that 0 means FALSE for this class, and will mean zero in mathematics (and origin in rectangular coordinates). Likewise using ✚ for OR in your class, while it will mean addition in mathematics. Rather than shelter them now, only to create confusion later, deal with the reality that we only have so many different symbols we can use, and many times they will have different meanings in different places, and knowing when, and when not, to use ✚ as AND is part of learning about Boolean logic.

We need to toss in one more symbol here as well. Replacing the word is with the symbol = (or symbol of your choice) also comes in real handy.

Since the students have not had algebra the next step might be difficult. Connecting the Boolean operators with the mathematical functions. Connecting the addition to OR first, because it is easier to apprehend, you can give the samples FALSE OR FALSE is FALSE, TRUE OR FALSE is TRUE, and TRUE OR TRUE is TRUE. Rewrite them in symbols as 0 ✚ 0 = 0, 1 ✚ 0 = 1 and 1 ✚ 1 = 1. The first two will be obvious, make sense, and get no questions. The third will start them thinking and should get some questions about "why not 1 ✚ 1 = 2?" Return to the apples: zero apples made the statement false while any apples, one, two, or a dozen, made the statement true. So, any number other than zero is true. You can call it part of being a two-state system - either it is zero, or it is other than zero. Therefore, the last of the three samples can be expanded to:

1 ✚ 1 = 2
2 ≝ some (it isn't "none", our only other choice in Boolean algebra)
1 ✚ 1 = some
some ≝ TRUE (the apple demonstration)
1 ✚ 1 = TRUE
TRUE ≝ 1 (our definition of the symbols to use in Boolean algebra)
1 ✚ 1 = 1 (Final answer)

Doing the same thing with multiplication for AND is easier, if they understand that anything multiplied by zero is zero. If they do not have that concept mastered, then it can become more difficult. Finding a demonstration for that can be dicey, but it is possible. One option could be to use unit pricing. Say it costs 8 dollars (or your local currency) for a pizza. if you get one pizza, that's 8 ✖ 1 = 8. If you get four pizzas, that's 8 ✖ 4 = 32. Now, if you get no pizzas, that's the same as zero pizzas, so 8 ✖ 0 = ? The answer, of course, is nothing. What if the price of pizza goes up to 12 dollars, how much for no pizzas? 12 ✖ 0 = 0; still. No matter how much pizza costs, the total price for zero pizzas is zero dollars. Anything else could be used instead, such as speed and time for distance, or wage rate and hours for pay checks, etc.

With the multiplication by zero concept under control, it's time to connect AND with multiplication. Using the same three setups from above gets three samples of the conversion. FALSE AND FALSE is FALSE, TRUE AND FALSE is FALSE. Converting to symbols gives us 0 ✖ 0 = 0, 1 ✖ 0 = 0, and 1 ✖ 1 = 1.

You can expand that into the distributive property, if you wish. [Again, the rule of only two values comes into play with the OR operator.]

TRUE  AND (FALSE OR FALSE) ⇒ (TRUE  AND FALSE) OR (TRUE  AND FALSE) ⇒ FALSE OR FALSE  = FALSE
1     ✖  ( 0    ✚    0  ) ⇒ (  1    ✖   0   ) ✚  (  1    ✖   0   ) ⇒   0    ✚   0    =  0
TRUE  AND (TRUE  OR FALSE) ⇒ (TRUE  AND TRUE ) OR (TRUE  AND FALSE) ⇒ TRUE  OR FALSE  = TRUE
1     ✖  ( 1    ✚    0  ) ⇒ (  1    ✖   1   ) ✚  (  1    ✖   0   ) ⇒   1    ✚   0    =  1
TRUE  AND (TRUE  OR TRUE ) ⇒ (TRUE  AND TRUE ) OR (TRUE  AND TRUE ) ⇒ TRUE  OR TRUE   = TRUE
1     ✖  ( 1    ✚    1  ) ⇒ (  1    ✖   1   ) ✚  (  1    ✖   1   ) ⇒   1    ✚   1    =  2 ≝ 1
FALSE AND (FALSE OR FALSE) ⇒ (FALSE AND FALSE) OR (FALSE AND FALSE) ⇒ FALSE OR FALSE  = FALSE
0     ✖  ( 0    ✚    0  ) ⇒ (  0    ✖   0   ) ✚  (  0    ✖   0   ) ⇒   0    ✚   0    =  0
FALSE AND (TRUE  OR FALSE) ⇒ (FALSE AND TRUE ) OR (FALSE AND FALSE) ⇒ TRUE  OR FALSE  = TRUE
0     ✖  ( 1    ✚    0  ) ⇒ (  0    ✖   1   ) ✚  (  0    ✖   0   ) ⇒   1    ✚   0    =  1
FALSE AND (TRUE  OR TRUE ) ⇒ (FALSE AND TRUE ) OR (FALSE AND TRUE ) ⇒ TRUE  OR TRUE   = TRUE
0     ✖  ( 1    ✚    1  ) ⇒ (  0    ✖   1   ) ✚  (  0    ✖   1   ) ⇒   1    ✚   1    =  2 ≝ 1

TRUE  OR (FALSE AND FALSE) ⇒ (TRUE  OR FALSE) AND (TRUE  OR FALSE ) ⇒ FALSE AND TRUE  = FALSE
1    ✚  (  0    ✖    0  ) ⇒ (  1   ✚    0  )  ✖  (  1   ✚    0   ) ⇒   0    ✖   1    =  0
TRUE  OR (TRUE  AND FALSE) ⇒ (TRUE  OR TRUE ) AND (TRUE  OR FALSE ) ⇒ TRUE  AND TRUE  = TRUE
1    ✚  (  1    ✖    0  ) ⇒ (  1   ✚    1  )  ✖  (  1   ✚    0   ) ⇒   1    ✖   1    =  1
TRUE  OR (TRUE  AND TRUE ) ⇒ (TRUE  OR TRUE ) AND (TRUE  OR TRUE  ) ⇒ TRUE  AND TRUE  = TRUE
1    ✚  (  1    ✖    1  ) ⇒ (  1   ✚    1  )  ✖  (  1   ✚    1   ) ⇒   1    ✖   1    =  1
FALSE OR (FALSE AND FALSE) ⇒ (FALSE OR FALSE) AND (FALSE OR FALSE ) ⇒ FALSE AND FALSE = FALSE
0    ✚  (  0    ✖    0  ) ⇒ (  0   ✚    0  )  ✖  (  0   ✚    0   ) ⇒   0    ✖   0    =  0
FALSE OR (TRUE  AND FALSE) ⇒ (FALSE OR TRUE ) AND (FALSE OR FALSE ) ⇒ TRUE  AND FALSE = FALSE
0    ✚  (  1    ✖    0  ) ⇒ (  0   ✚    1  )  ✖  (  0   ✚    0   ) ⇒   1    ✖   0    =  0
FALSE OR (TRUE  AND TRUE ) ⇒ (FALSE OR TRUE ) AND (FALSE OR TRUE  ) ⇒ TRUE  AND TRUE  = TRUE
0    ✚  (  1    ✖    1  ) ⇒ (  0   ✚    1  )  ✖  (  1   ✚    1   ) ⇒   1    ✖   1    =  1

If you teach center dot as and, then + for or should be pretty natural. Tell them that or is logical addition and and is logical multiplication. Likewise, + can be used for set addition (everything in A "plus" everything in B). Likewise "or" means that something is in A or it is in B (maybe both). So, +, or, and Union are all alike. Naive Set Theory and Simple Logic. So it isn't just that the same symbol is used, the ideas are actually related.

If you teach them the distributive laws for these it will seem even more natural. What will be less natural is that or and and distribute over each other, while in arithmetic you only get one distributive law.

P or (Q and R) has the same truth table as (P or Q) and (P or R). Etc. Similarly for the other law and for set union and intersection. "Arithmetic is the anomaly, not logic." And if you put it like that, they may be more likely to remember.

Actually it is and that is a bit harder to finesse with language. "Everything in A and everything in B" (Union, or)is easy to confuse initially with "It is in A and it is in B." (Intersection, and) so watch out for that. The first sentence is, of course, the same as "It is in A or it is in B." Focus on whether the statement talks about an individual thing, "It..." or the set/concept as a whole "Everything..."

Algebraic data types use a notion of counting inhabitants in a correspondence between addition with OR, multiplication with AND (and exponentiation with implication).

The idea is that if something is an x OR y, then it has x + y possibilities — all the possibilities of x added to all the possibilities of y. If there are 3 possible x's and 4 possible y's then there are 3+4=7 possible inhabitants of x OR y.

And that if something is an x AND y then it has x * y possibilities — the Cartesian product of all possible x's with all possible y's. So here if there are 3 possible x's and 4 possible y's then there are 3*4=12 possible inhabitants of the Cartesian cross product representing the notion of x AND y.

• "...12 possible inhabitants of the Cartesian cross product..." This is what you would explain to an 11 year old? – Ben I. Feb 19 '18 at 1:55
• Yes, you could simply list them. – Erik Eidt Feb 19 '18 at 2:09
• All well and good, except the concept being taught is Boolean logic not set theory. There are only ever two possible answers using any operations. In addition, AND has 4 possible combinations of inputs, and OR also has 4 possible combinations of inputs. Not a good time to explain the difference between Cartesian cross products and set union. – Gypsy Spellweaver Feb 19 '18 at 18:23