Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes a...

Pregnancy is a condition of broad interest across many medical and health services research domains, but one not easily identified in healthcare claims data. Our objective was to establish an algorithm to identify pregnant women and their pregnancies in claims data. We identified pregnancy-related diagnosis, procedure, and diagnosis-related group codes, accounting for the transition to International Statistical Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis and procedure codes, in health encounter reporting on 10/1/2015. We selected women in Merative MarketScan commercial databases aged 15–49 years with pregnancy-related claims, and their infants, during 2008–2019. Pregnancies, pregnancy outcomes, and gestational ages were assigned using the constellation of service dates, code types, pregnancy outcomes, and linkage to infant records. We describe pregnancy outcomes and gestational ages, as well as maternal age, census region, and health plan type. In a sensitivity analysis, we compared our algorithm-assigned date of last menstrual period (LMP) to fertility procedure-based LMP (date of procedure + 14 days) among women with embryo transfer or insemination procedures. Among 5,812,699 identified pregnancies, most (77.9%) were livebirths, followed by spontaneous abortions (16.2%); 3,274,353 (72.2%) livebirths could be linked to infants. Most pregnancies were among women 25–34 years (59.1%), living in the South (39.1%) and Midwest (22.4%), with large employer-sponsored insurance (52.0%). Outcome distributions were similar across ICD-9 and ICD-10 eras, with some variation in gestational age distribution observed. Sensitivity analyses supported our algorithm’s framework; algorithm- and fertility procedure-derived LMP estimates were within a week of each other (mean difference: -4 days [IQR: -13 to 6 days]; n = 107,870). We have developed an algorithm to identify pregnancies, their gestational age, and outcomes, across ICD-9 and ICD-10 eras using administrative data. This algorithm may be useful to reproductive health researchers investigating a broad range of pregnancy and infant outcomes.

SAS PROC used to evaluate SSMT data

Mean ±SD HbA1c levels and the modeled 10th, 25th, 50th, 75th and 90th HbA1c percentiles as a function of age for all patients.

List of 56 characters used for cluster analysis and their significance levels from univariate test statistics using CANDISC procedure (SAS software).

SAS Code for Spatial Optimization of Supply Chain Network for Nitrogen Based Fertilizer in North America, by type, by mode of transportation, per county, for all major crops, using Proc OptModel. the code specifies set of random values to run the mixed integer stochastic spatial optimization model repeatedly and collect results for each simulation that are then compiled and exported to be projected in GIS (geographic information systems). Certain supply nodes (fertilizer plants) are specified to work at either 70 percent of their capacities or more. Capacities for nodes of supply (fertilizer plants), demand (county centroids), transhipment nodes (transfer points-mode may change), and actual distance travelled are specified over arcs.

Multienvironment trials (METs) enable the evaluation of the same genotypes under a v ariety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.

Procedures Services In Colombia Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

PurposeTo illustrate the distribution of Hemoglobin A1c (HbA1c) levels according to age and gender among children, adolescents and youth with type 1 diabetes (T1DM).MethodsConsecutive HbA1c measurements of 349 patients, aged 2 to 30 years with T1DM were obtained from 1995 through 2010. Measurement from patients diagnosed with celiac disease (n = 20), eating disorders (n = 41) and hemoglobinopathy (n = 1) were excluded. The study sample comprised 4815 measurements of HbA1c from 287 patients. Regression percentiles of HbA1c were calculated as a function of age and gender by the quantile regression method using the SAS procedure QUANTREG.ResultsCrude percentiles of HbA1c as a function of age and gender, and the modeled curves produced using quantile regression showed good concordance. The curves show a decline in HbA1c levels from age 2 to 4 years at each percentile. Thereafter, there is a gradual increase during the prepubertal years with a peak at ages 12 to 14 years. HbA1c levels subsequently decline to the lowest values in the third decade. Curves of females and males followed closely, with females having HbA1c levels about 0.1% (1.1 mmol/mol) higher in the 25th 50th and 75th percentiles.ConclusionWe constructed age-specific distribution curves for HbA1c levels for patients with T1DM. These percentiles may be used to demonstrate the individual patient's measurements longitudinally compared with age-matched patients.

Results from PROC MIXED (SAS) analysis of effects of inoculum origin on plant biomass production of mid-successional plant species relative to the sterilized control treatment.

SAS code to reproduce the simulation study and the analysis of the urine osmolarity example. (ZIP)

This platform gathers the variables, R scripts and SAS procedure mentionned and used in a paper entitled "Feed efficiency of lactating Holstein cows is less reproducible when changing dietary starch and fibre concentrations than within diet over subsequent lactation stages" available on BioRXiv. These data are based on data collected during the project ANR-15-CE20-0014 and are the ones got after outlier removal and calculation as specified in the paper. The files available are: - R script to create the dataset and do the analysis indicated in the paper (Rscript_datasetcreation_analysis.R ) - dataset to be downloaded at the beginning of the R script (data_origin.tab) - description of the previous dataset (description_variables_depositpaper.tab) - SAS procedure to estimate RFI (SASscript.txt ) - R script to analyse the NIRs spectra (Script_PCA_refusals.R ) - dataset with the NIRs spectra (data_nirs_refusals_v2.tab) - dataset with RFI estimation (RFI.tab )

Abstract

The main objective of the Seasonal Agricultural Survey is to provide timely, accurate, reliable and comprehensive agricultural statistics that describe the structure of agriculture in Rwanda mainly in terms of land use, crop area, yield and crop production to monitor current agricultural and food supply conditions and to facilitate evidence-based decision making for the development of the agricultural sector.

In this regard, the National Institute of Statistics of Rwanda conducted the Seasonal Agriculture Survey (SAS) from September 2018 to august 2019 to gather up-to-date information for monitoring progress on agriculture programs and policies. This 2019 SAS covered Main agricultural seasons are Season A (which starts from September to February of the following year) and Season B (which starts from March to June). Season C is the small agricultural season mainly for vegetables and sweet potato grown in swamps and Irish potato grown in volcanic agro-ecological zone and provides data on farm characteristics (area, yield and production), agricultural practices, agricultural inputs and use of crop production

Geographic coverage

National coverage allowing district-level estimation of key indicators

Analysis unit

This seasonal agriculture survey focused on the following units of analysis: Small scale agricultural farms and large scale farms

Universe

The SAS 2019 targeted potential agricultural land and large scale farmers

Kind of data

Sample survey data [ssd]

Sampling procedure

Out of 10 strata, only 4 are considered to represent the country land potential for agriculture, and they cover the total area of 1,787,571.2 hectares (ha). Those strata are: 1.0 (tea plantations), 1.1 (intensive agriculture land on hillsides), 2.0 (intensive agriculture land in marshlands) and 3.0 (rangelands). The remainder of land use strata represents all the non-agricultural land in Rwanda. Stratum 1.0, which represents tea plantations, is assumed to be well monitored through administrative records by the National Agriculture Export Board (NAEB), an institution whose main mission is to promote the agriculture export commodities. Thus, SAS is conducted on 3 strata (1.1; 2.0 & 3.0) to cover other major crops. Within district, the agriculture strata (1.1, 2.0 & 3.0) were divided into larger sampling units called first-step or primary sampling units (PSUs) (as shown in Figure 2). Strata 1.1 and 2.0 were divided into PSUs of around 100 ha while stratum 3.0 was divided into PSUs of around 500 ha. After sample size determination, a sample of PSUs was done by systematic sampling method with probability proportional to size, then a given number of PSUs to be selected for each stratum, was assigned in every district. In 2019, the 2018 SAS sample of 780 segments has been kept the same for SAS 2019 in Season A and B.

At first stage, 780 PSUs sampled countrywide were proportionally allocated in different levels of stratification (Hill side, marshland and rangeland strata) for 30 districts of Rwanda, to allow publication of results at district level. Sampled PSUs in each stratum were systematically selected from the frame with probability of selection proportional to the size of the PSU.

At the second stage 780 sampled PSUs were divided into secondary sampling units (SSUs) also called segments. Each segment is estimated to be around 10 ha for strata 1.1 and 2.0 and 50 ha for stratum 3.0 (as shown in Figure 3). For each PSU, only one SSU is selected by random sampling method without replacement. This is why for 2019 5 SAS season A and B, the same number of 780 SSUs was selected. In addition to this, a list frame of large-scale farmers (LSF), with at least 10 hectares of agricultural holdings, was done to complement the area frame just to cover crops mostly grown by large scale farmers and that cannot be easily covered in area frame

At the last sampling stage, in strata 1.1 and 2.0 each segment of an average size of 10 ha (100,000 Square meters) has been divided into around 1,000 grids squares of 100 Sq. meters each, while for stratum 3.0 around 5,000 grids squares of 100 Sq. meters each have been divided. A point was placed at the center of every grid square and named a grid point (A grid point is a geographical location at the center of every grid square). A random sample of 5% of the total grid points were selected in each segment of strata 1.1 and 2.0 whereas a random sample of 2% of total grid points was selected in each segment of stratum 3.0. Grids points are reporting units within a segment, where enumerators go to every grid point, locate and delineate the plots in which the grid falls, and collect records of land use and related information. The recorded information represents the characteristics of the whole segment which are extrapolated to the stratum level and hence the combination of strata within each district provides district area related statistics.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were two types of questionnaires used for this survey namely screening questionnaire and plot questionnaire. A Screening questionnaire was used to collect information that enabled identification of a plot and its land use using the plot questionnaire. For point-sampling, the plot questionnaire is concerned with the collection of data on characteristics of crop identification, crop production and use of production, inputs (seeds, fertilizers and pesticides), agricultural practices and land tenure. All the surveys questionnaires used were published in English

Cleaning operations

The CAPI method of data collection allows the enumerators in the field to collect and enter data with their tablets and then synchronize to the server at headquarters where data are received by NISR staff, checked for consistency at NISR and thereafter transmitted to analysts for tabulation using STATA software, and reporting using office Excel and word as well.

Response rate

Data collection was done in 780 segments and 222 large scale farmers holdings for Season A, whereas in Season C data was collected in 232 segments, response rate was 100% of the sample

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. A Black supplement of 263 respondents, who were asked the same questions that were administered to the national cross-section sample, is included with the national cross-section of 1,571 respondents. In addition to the usual content, the study contains data on opinions about the Supreme Court, political knowledge, and further information concerning racial issues. Voter validation data have been included as an integral part of the election study, providing objective information from registration and voting records or from respondents' past voting behavior. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. United States citizens of voting age living in private households in the continental United States. A representative cross-section sample, consisting of 1,571 respondents, plus a Black supplement sample of 263 respondents. 2015-11-10 The study metadata was updated.1999-12-14 The data for this study are now available in SAS transport and SPSS export formats, in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. In addition, SAS and SPSS data definition statements have been created for this collection, and the data collection instruments are now available as a PDF file. face-to-face interview, telephone interviewThe SAS transport file was created using the SAS CPORT procedure.

We tested whether the probability of a visit was a function oftreatment (dietary N content as a continuous variable) using logistic regression in SAS (PROC GLIMMIX with a binomial distribution and logit link function, SAS 9.4). Day (fixed effect), site (random effect) and feeding station nested within site (random effect) were also included in the model. We then analysed the effect of treatment (dietary N content as a continuous variable) on visit length (min), each behaviour (% of total time) and GUD (count) separately using the generalized linear mixed model (GLMM) procedure in SAS (PROC GLIMMIX with lognormal distribution and identity link function, SAS 9.4). Day (1-4) was included in the models as a fixed effect, and site and feeding station (nested within site) were random effects.To analyse our VOCs data we looked at the odours of the diets using a canonical analysis of principal coordinates(CAP) analysis in the PERMANOVA+ add-on of PRIMER v6to determine whether the multivariate VOC data could differentiate the diets along a continuous (dietary nitrogencontent) gradient, similar to analyses of VOCs from other plant/food material. We applied a dispersion weighting followed by square root transformation to the VOC peak area values, then performed CAP analysis on the Bray-Curtis resemblance matrix of the transformed data. To tease apart the contributing VOCs we then applied the CAPanalysis using diet as a class variable. We also isolated the specific volatile signature of the highest quality diet usingthe Random Forests (RF). We analysed the data with RF, using a one treatment-versus-the rest approach with the VSURF package (version 1.0.3) in R (version 3.1.2; R Core Team, 2015). Before analysis, TQPA data were transformed using the centred log ratio method using CoDaPack v. 2.01.15.

This data collection contains Supplemental Nutrition Assistance Program (SNAP) SAS proc contents (metadata only) files for Arizona (AZ), Hawaii (HI), Illinois (IL), Kentucky (KY), New Jersey (NJ), New York (NY), Oregon (OR), Tennessee (TN), and Virginia (VA).

The focus of this report is to describe the statistical inference procedures used to produce design-based estimates as presented in the 2013 detailed tables, the 2013 mental health detailed tables, the 2013 national findings report, and the 2013 mental health findings report. Thestatistical procedures and information found in this report can also be generally applied to analyses based on the public use file as well as the restricted-use file available through the data portal. This report is organized as follows: Section 2 provides background informationconcerning the 2013 NSDUH; Section 3 discusses the prevalence rates and how they were calculated, including specifics on topics such as mental illness, major depressive episode, and serious psychological distress; Section 4 briefly discusses how missing item responses of variables that are not imputed may lead to biased estimates; Section 5 discusses sampling errors and how they were calculated; Section 6 describes the degrees of freedom that were used when comparing estimates; and Section 7 discusses how the statistical significance of differences between estimates was determined. Section 8 discusses confidence interval estimation, and Section 9 describes how past year incidence of drug use was computed. Finally, Section 10 discusses the conditions under which estimates with low precision were suppressed. Appendix A contains examples that demonstrate how to conduct various statistical procedures documented within this report using SAS® and SUDAAN® Software for Statistical Analysis of Correlated Data (RTI International, 2012) along with separate examples using Stata® software.

Abstract (en): The purpose of this data collection is to provide an official public record of the business of the federal courts. The data originate from 94 district and 12 appellate court offices throughout the United States. Information was obtained at two points in the life of a case: filing and termination. The termination data contain information on both filing and terminations, while the pending data contain only filing information. For the appellate and civil data, the unit of analysis is a single case. The unit of analysis for the criminal data is a single defendant. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. All federal court cases, 1970-2000. 2012-05-22 All parts are being moved to restricted access and will be available only using the restricted access procedures.2005-04-29 The codebook files in Parts 57, 94, and 95 have undergone minor edits and been incorporated with their respective datasets. The SAS files in Parts 90, 91, 227, and 229-231 have undergone minor edits and been incorporated with their respective datasets. The SPSS files in Parts 92, 93, 226, and 228 have undergone minor edits and been incorporated with their respective datasets. Parts 15-28, 34-56, 61-66, 70-75, 82-89, 96-105, 107, 108, and 115-121 have had identifying information removed from the public use file and restricted data files that still include that information have been created. These parts have had their SPSS, SAS, and PDF codebook files updated to reflect the change. The data, SPSS, and SAS files for Parts 34-37 have been updated from OSIRIS to LRECL format. The codebook files for Parts 109-113 have been updated. The case counts for Parts 61-66 and 71-75 have been corrected in the study description. The LRECL for Parts 82, 100-102, and 105 have been corrected in the study description.2003-04-03 A codebook was created for Part 105, Civil Pending, 1997. Parts 232-233, SAS and SPSS setup files for Civil Data, 1996-1997, were removed from the collection since the civil data files for those years have corresponding SAS and SPSS setup files.2002-04-25 Criminal data files for Parts 109-113 have all been replaced with updated files. The updated files contain Criminal Terminations and Criminal Pending data in one file for the years 1996-2000. Part 114, originally Criminal Pending 2000, has been removed from the study and the 2000 pending data are now included in Part 113.2001-08-13 The following data files were revised to include plaintiff and defendant information: Appellate Terminations, 2000 (Part 107), Appellate Pending, 2000 (Part 108), Civil Terminations, 1996-2000 (Parts 103, 104, 115-117), and Civil Pending, 2000 (Part 118). The corresponding SAS and SPSS setup files and PDF codebooks have also been edited.2001-04-12 Criminal Terminations (Parts 109-113) data for 1996-2000 and Criminal Pending (Part 114) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.2001-03-26 Appellate Terminations (Part 107) and Appellate Pending (Part 108) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.1997-07-16 The data for 18 of the Criminal Data files were matched to the wrong part numbers and names, and now have been corrected. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. (1) Several, but not all, of these record counts include a final blank record. Researchers may want to detect this occurrence and eliminate this record before analysis. (2) In July 1984, a major change in the recording and disposition of an appeal occurred, and several data fields dealing with disposition were restructured or replaced. The new structure more clearly delineates mutually exclusive dispositions. Researchers must exercise care in using these fields for comparisons. (3) In 1992, the Administrative Office of the United States Courts changed the reporting period for statistical data. Up to 1992, the reporting period...

The current study examined how racial/ethnic self-identification combines with gender to shape self-reports of everyday discrimination among youth in the U.S. as they transition to adulthood. Data came from seven waves of the Panel Study of Income Dynamics Transition into Adulthood Supplement (TAS). The sample included individuals with two or more observations who identified as White, Black, or Hispanic (n=2,532). Data includes average everyday discrimination scale scores over 9 time periods (i.e., ages 18 to 27) as well as pattern variables for race/ethnicity and sex groups and family SES proxied by highest level of education in household at baseline. Developmental trajectories of everyday discrimination across ages 18 to 27 were estimated using multilevel longitudinal models with the SAS Proc Mixed procedure.