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...

SAS PROC used to evaluate SSMT data

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.

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.

Parameter estimates for the generalized H2 model (SAS output).

Mortality rates were calculated as defined in the text.Summary statistics Black cervical cancer mortality by year in thirteen U.S. states.

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

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.

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).

Mortality rates were calculated as defined in the text.Summary statistics for White cervical cancer mortality rates in 13 U.S. states from 1975 to 2010.

These data were gathered to provide information on Kahn and Antonucci's life-span developmental model, "convoys of social support," which explores interpersonal relationships over time. Older adults (aged 50+) were interviewed on their health status, labor force status, and other demographic characteristics, and on the composition and degree of closeness of members of their current support network (e.g., spouses, children, friends). Three concentric circles of closeness were defined, varying in terms of transcendence of the relationship beyond role requirements, stability over the life span, and exchange of many different types of support (confiding, reassurance, respect, care when ill, discussion when upset, and talk about health). The principal respondents named a total of 6,341 network members, ranging in age from 18 to 96 years. Detailed structural and functional characteristics were collected from the principal respondents on the first ten named members of each support network. Similar interviews were then conducted with one to three network members of those 259 principal respondents who were 70+ years old. Two data files are provided: Part 1 contains merged data from the interviews of both the principal respondents aged 70+ and their network members, and Part 2 contains data from the principal respondents aged 50+. Datasets: DS0: Study-Level Files DS1: Principals, Aged 70+/Network Data DS2: Principals, Aged 50+ Data DS3: SAS Proc Format Statements for Principals, Aged 70+/Network Data DS4: SAS Input Statements for Principals, Aged 70+/Network Data DS5: SAS Format Statements for Principals, Aged 70+/Network Data DS6: SAS Label Statements for Principals, Aged 70+/Network Data DS7: SAS Missing Value Statements for Principals, Aged 70+/Network Data DS8: SPSS Data List Statements for Principals, Aged 70+/Network Data DS9: SPSS Variable Label Statements for Principals, Aged 70+/Network Data DS10: SPSS Value Label Statements for Principals, Aged 70+/Network Data DS11: SPSS Missing Value Statements for Principals, Aged 70+/Network Data DS12: SAS Proc Format Statements for Principals, Aged 50+ Data DS13: SAS Input Statements for Principals, Aged 50+ Data DS14: SAS Format Statements for Principals, Aged 50+ Data DS15: SAS Label Statements for Principals, Aged 50+ Data DS16: SAS Missing Value Statements for Principals, Aged 50+ Data DS17: SPSS Data List Statements for Principals, Aged 50+ Data DS18: SPSS Variable Label Statements for Principals, Aged 50+ Data DS19: SPSS Value Label Statements for Principals, Aged 50+ Data DS20: SPSS Missing Value Statements for Principals, Aged 50 Data Multistage national probability sample of households with at least one member aged 50 years or older and an oversampling of all household members aged 70 years or older. Additionally, up to three network members were interviewed for each of the respondents aged 70+ (as well as one child and one grandchild if not already named), for a total of 497 network members. There was some overlap between principal respondents and network members: 102 network members were also principal respondents, and 40 were named by more than one principal respondent. The age distribution of the 718 principal respondents was 50-64 years (N = 333), 65-74 years (N = 227), and 75-95 years (N = 158). Persons 50 years and older in households of the United States.

The data are at the block group level and include coordinates for population centroids. Population centroids were situated in police geographies using SAS Proc GINSIDE. The data facilitate demographic approximations of the residential population within Philadelphia police districts and police service areas (PSAs).

The data set is a crosswalk file for working with 2020 Census block group boundaries and Philadelphia Police Department district and police service areas (PSAs). Census blockgroup population centroids were situated in police geographies using SAS Proc GINSIDE. The data facilitate demographic approximations of the residential population within Philadelphia police districts and police service areas (PSAs).

The period of early ontogeny constitutes a time when the physical immaturity of an organism is highly susceptible to external stimuli. Thus, early development plays a major role in shaping later adult behavior. The aim of the study was to check whether stimulating puppies at this early stage in life with sound would improve their responsiveness towards unfamiliar noises during the selection process of the police behavioral test for puppies. The cohort comprised 37 puppies from the litters of three mothers. At the commencement of the experiment the dogs were aged 16 days, rising to the age of 32 days at its close. The mothers and litters of the treatment group were either exposed to radio broadcasts, (see below; three litters totaling 19 puppies), while the control group was not exposed to any radio programs (eight litters totaling 18 puppies). All three mothers had previously experienced both auditory circumstances, as described herein. Ordinary radio broadcasts were played to the puppies in the treatment group three times a day for 20 minute periods, always during feeding time. The cohort was subjected to the so-called Puppy Test, i.e. analysis of the potential of each animal, once the dogs had reached the age of 7 weeks. Such tests included exposure to a sudden noise caused by a shovel (100 dB), noise when alone in a room, and response to loud distracting stimuli (the latter two at 70 dB). Said tasks were rated by the same analyst on a scale of 0–5 points; the better the response of the dog, the higher the score given. The differences between the treatment and control groups were analyzed via Mixed Models (PROC MIXED) in SAS. The animals comprising the treatment group responded with a higher score to the sudden noise caused by the shovel than the control dogs (P

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.

The files submitted here contains data collected for the thesis titled "Relative preference for pecking blocks and its association with keel status and eggshell quality in laying hens housed in enriched cages." The purpose of this research was to determine pecking block preferences of White and Brown feathered laying hens strains, and if there is a time of day effect on pecking block use. We then investigated the association between pecking block preference, pecking block use, keel status, and eggshell quality. We also investigated if laying hens are consistent in their pecking block preference over time. Data on weekly pecking block disappearance, number of hens using pecking blocks across the day, eggshell quality and keel status in focal birds were also assessed. Data was analyzed using SAS Proc GLIMMIX, and consistency data was analyzed using SAS Proc Freq.

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.

A water impoundment facility was used to control the duration of soil flooding (0, 45, or 90 days) and shade houses were used to control light availability (high = 72 %, intermediate = 33 %, or low = 2 % of ambient light) received by L. melissifolia established on native soil of the MAV. A completely randomized, split-plot design was used to evaluate the effects of soil flooding and light availability on L. melissifolia reproductive intensity and mode. Analyses were conducted on plot means using PROC GLIMMIX with an adjustment in the error term for the whole-plot factor (SAS 9.4, SAS Institute, Inc., Cary, North Carolina, USA). PROC UNIVARIATE was used to test data normality for each response variable, and residual errors were normalized with Box-Cox, natural log, or square root transformations where appropriate prior to the PROC GLIMMIX analyses. Significance was accepted at ∠= 0.05, and we used the least significant difference (LSD) test to separate significant treatment effect means...

The dataset includes SAS codes and associated Excel files (.csv and .xlsx) containing data from Nigerian catfish farmers. The .xslx file includes the main variables and the formula for the other derived variables. The SAS code utilizes PROC GLM to produce Type III Sum of Squares, effect size measures (Partial Eta Squared, Semi-Partial Eta Squared, Partial Omega Squared, and Semi-Partial Omega Squared), and the linear regression estimates.

Use of SAS proc Mixed.