Rtime
Author: n | 2025-04-24
What does RTIME abbreviation stand for? Explore the list of 1 best RTIME meaning form based on popularity. Most common RTIME abbreviation full form updated in September 2025.
Squidco: Regev's, Reut RTime: It's Now: RTime Plays Doug
February 23 - 26 | san antonio, tx Visit Our Booth Explore our state-of-the-art products and services Learn more about industry trends and predictions Discuss your unique needs and challenges with our team of experts We look forward to connecting with you at NTCA RTIME 2025 at Booth #(pending). Your presence will make our first exhibition even MORE memorable!Click HERE for a detailed schedule of events! EVENT DETAILS sun, feb. 23 - wed, feb. 26 7:00 am - 12:00 pm CST NTCA RTIME 2025 is the must-attend event for rural broadband professionals, offering a unique opportunity to explore the latest innovations, regulatory updates, and strategies shaping the future of rural telecom. With dynamic keynote speakers, expert-led sessions, and valuable networking opportunities, this event brings together industry leaders to collaborate, learn, and discuss how to overcome the challenges of delivering high-quality broadband to rural communities. Join us at RTIME 2025 to stay ahead of industry trends and shape the future of rural connectivity. Our Staff In Attendance SENIOR STRATEGIC SALES MANAGER senior strategic SALES MANAGER JOIN POWER STORAGE SOLUTIONS TO DISCOVER HOW WE CAN OPTIMIZE YOUR telecom and broadband OPERATIONS Featured Products More Events & Tradeshows Join Power Storage Solutions at the Texas Communications Expo to discover our energy solutions for the telecom industry. From backup power systems to energy storage, we ensure your network runs smoothly. Visit our booth to learn more! Connect with Power Storage Solutions at Downstream USA to explore our energy solutions for the petrochemical, refining, and LNG industries. From backup power systems to energy storage, we ensure reliable operations. Visit our booth to learn more! Join Power Storage Solutions at WISPAPALOOZA 2025 to explore our energy solutions for telecom and broadband networks. From backup power systems to energy storage, we help optimize network reliability and performance. Visit our booth to learn more!
Exhibit at Expo at RTIME
Obs = 172No. of failures = 75Time at risk = 31,938.1 LR chi2(4) = 20.67Log likelihood = -213.35033 Prob > chi2 = 0.0004 _t Haz. ratio Std. err. z P>|z| [95% conf. interval] age 1.027406 .0150188 1.85 0.064 .9983874 1.057268 posttran 1.075476 .3354669 0.23 0.816 .583567 1.982034 surgery .2222415 .1218386 -2.74 0.006 .0758882 .6508429 year .5523966 .1132688 -2.89 0.004 .3695832 .825638 After our stratified model, we use option stratify with estat gofplot to produce a separate plot for each stratum of pgroup. . estat gofplot, stratify The model fits data well in all strata. The red line for pgroup = 2 deviates from the reference line toward the end. This is not uncommon to see in practice because fewer observations are available for estimation toward the end of the study. To aid visual inspection of the plot, we can also add option separate to produce separate graphs for each stratum. . estat gofplot, stratify separate GOF plots for interval-censored data We use the dataset of a study for early breast cancer patients (Finkelstein and Wolfe 1985) that compares the cosmetic effects of two cancer treatments (treat) on breast retraction. Because patients were observed at random follow-up times, the exact time of breast retraction was not observed and was known only to fall in the interval between visits (variables ltime and rtime). First, we fit an interval-censored Weibull model of time to breast retraction on treatment using stintreg: . stintreg i.treat, interval(ltime rtime) distribution(weibull) nologWeibull PH regression Number of obs = 94 Uncensored = 0 Left-censored = 5 Right-censored = 38 Interval-cens. = 51 LR chi2(1) = 10.93Log likelihood = -143.19228 Prob > chi2 = 0.0009 Haz. ratio Std. err. z P>|z| [95% conf. interval] treat Radio+Chemo 2.498526 .7069467 3.24 0.001 1.434961 4.350383 _cons .0018503 .0013452 -8.66 0.000 .000445 .007693 /ln_p .4785786 .1198972 3.99 0.000 .2435844 .7135729 p 1.613779 .1934876 1.275814 2.041271 l/p .6196635 .0742959 .4898907 .7838134Note: _cons estimates baseline hazard. We then use estat gofplot to produce the GOF plot. . estat gofplot With interval-censored data, Cox–Snell-like residuals are defined and used for plotting (Farrington 2000). If a model fits the data well, these residuals should approximate the censored standard exponential distribution. Also, the nonparametric Turnbull estimator (Turnbull 1976) is used to estimate the cumulative hazard. The jagged line stays close to the reference line in the above graph, which indicates that the Weibull model fits the data well. Suppose that we now want to fit an exponential model and check its model fit. We type . quietly stintreg i.treat, interval(ltime rtime) distribution(exponential). estat gofplot Comparing this GOF plot with the one above, we can see that the Weibull model fits our data better than the exponential model. GOF plotsSave the Date RTIME 2025
. What does RTIME abbreviation stand for? Explore the list of 1 best RTIME meaning form based on popularity. Most common RTIME abbreviation full form updated in September 2025.Rtime - Apps on Google Play
For interval-censored multiple-event data We can also use estat gofplot to visually assess the overall model fit for interval-censored multiple-event data. We use a simulated dataset based on the ARIC study described in Xu, Zeng, and Lin (2023). The participants were followed over time and assessed for two diseases (diabetes and hypertension) during several follow-up examinations. The investigators are interested in the factors that influence the times to onset for those two diseases. The factors of interest include three demographic variables—race, male, community—and five baseline risk factors: age, bmi, glucose, sysbp, and diabp. We first fit a marginal Cox proportional hazards model using stmgintcox: . webuse aric(Simulated ARIC data). stmgintcox age i.male i.community i.race bmi glucose sysbp diabp, id(id) event (event) interval(ltime rtime) nolog favorspeednote: using fixed step size with a multiplier of 5 to compute derivatives.note: using EM and VCE tolerances of 0.0001.note: option noemhsgtolerance assumed.Marginal interval-censored Cox regression Number of events = 2Baseline hazard: Reduced intervals Number of subjects = 200 Number of obs = 400ID variable: id Uncensored = 0Event variable: event Left-censored = 47Event-time interval: Right-censored = 240 Lower endpoint: ltime Interval-cens. = 113 Upper endpoint: rtime Wald chi2(20) = 84.36Log pseudolikelihood = -270.83984 Prob > chi2 = 0.0000 Robust Haz. ratio std. err. z P>|z| [95% conf. interval] Diabetes age .9552606 .0295589 -1.48 0.139 .8990481 1.014988 male Yes .8084224 .2400335 -0.72 0.474 .451755 1.446684 community Jackson 1.597828 .6069935 1.23 0.217 .7588748 3.364265Minneapolis 1.028054 .342976 0.08 0.934 .5346148 1.976929 Washington 1.407869 .5192024 0.93 0.354 .6833627 2.900504 race White .4289702 .1273669 -2.85 0.004 .2397145 .7676444 bmi 1.116579 .034187 3.60 0.000 1.051545 1.185636 glucose 1.139753 .0303702 4.91 0.000 1.081756 1.200859 sysbp 1.020295 .0122308 1.68 0.094 .9966021 1.04455 diabp .9928634 .0127512 -0.56 0.577 .9681835 1.018172 Hypertension age .9950085 .0225503 -0.22 0.825 .9517779 1.040203 male Yes .6671401 .1599892 -1.69 0.091 .4169533 1.067448 community Jackson .6085406 .1953944 -1.55 0.122 .3243246 1.141824Minneapolis .9040647 .2719638 -0.34 0.737 .5013468 1.630275 Washington .674088 .2085739 -1.27 0.202 .3675707 1.23621 race White 1.261355 .425064 0.69 0.491 .6516152 2.441652 bmi 1.012196 .0195117 0.63 0.529 .9746672 1.05117 glucose .989899 .0101396 -0.99 0.322 .9702238 1.009973 sysbp 1.075011 .0162901 4.77 0.000 1.043553 1.107418 diabp 1.025533 .0134835 1.92 0.055 .9994433 1.052303Note: Standard error estimates may be more variable for small datasets and datasets with low proportions of interval-censored observations. Now, let's produce the goodness-of-fit plots for all events. By default, estat gofplot creates a single graph with subgraphs for each event. . estat gofplot You can add the sepevents option to request that the plot for each event be placed on a separate graph. . estat gofplot, sepevents If we want to examine the goodness-of-fit plots for diabetes across different communities, we can use the by(community) option along with events("Diabetes"). The estatRTIME 2025: Postalytics Sponsorship - Postalytics
Manchester Grand Hyatt - San Diego, CA, United States Come see us atNokia Booth #100-102Join us at RTIME 2023, where community-based broadband providers come together from small towns and rural areas across America for one of the largest gatherings focused exclusively on the business, the people, and the technology of rural broadband.With Nokia as your partner, you can effectively navigate the rural broadband opportunity and build a deployment plan that will serve you well into the future.Our broadband fiber solution lets you bring multi-gigabit access services to rural communities in under-served areas, allowing them to extend voice, data, IPTV and smart-meter communications services to every home and business.Our fixed-wireless access (FWA) solution complements fiber infrastructure by enabling you to cost-effectively reach widely dispersed homes in rural areas and realize aggressive rollout schedules.Meet us at our Exhibitor Booth or join one of our Speaking Sessions.Nokia Executive Track Session: Topic: 5G - Coming to a Town Near YouTier 1 operators continuing to invest and deploy 5G across the country, does this mean 5G will be coming to my community? What will it look like for small and medium sized communities, and how will 5G impact rural carriers, good or bad? Date: Tuesday, February 21st, 2023 Nokia Speakers: Fayyaz Patwa, Director Sales Engineering, Nokia Mike Howe, Account Manager, Nokia Nokia Exhibitor Booth 100-102:Visit us at our booth to learn more about:Why Nokia is a trusted partner for critical networksHow Nokia can help you meet your connectivity commitments and seize broadband opportunities sustainably andRTIME (@ntcartime) Instagram photos and videos
. What does RTIME abbreviation stand for? Explore the list of 1 best RTIME meaning form based on popularity. Most common RTIME abbreviation full form updated in September 2025.Comments
February 23 - 26 | san antonio, tx Visit Our Booth Explore our state-of-the-art products and services Learn more about industry trends and predictions Discuss your unique needs and challenges with our team of experts We look forward to connecting with you at NTCA RTIME 2025 at Booth #(pending). Your presence will make our first exhibition even MORE memorable!Click HERE for a detailed schedule of events! EVENT DETAILS sun, feb. 23 - wed, feb. 26 7:00 am - 12:00 pm CST NTCA RTIME 2025 is the must-attend event for rural broadband professionals, offering a unique opportunity to explore the latest innovations, regulatory updates, and strategies shaping the future of rural telecom. With dynamic keynote speakers, expert-led sessions, and valuable networking opportunities, this event brings together industry leaders to collaborate, learn, and discuss how to overcome the challenges of delivering high-quality broadband to rural communities. Join us at RTIME 2025 to stay ahead of industry trends and shape the future of rural connectivity. Our Staff In Attendance SENIOR STRATEGIC SALES MANAGER senior strategic SALES MANAGER JOIN POWER STORAGE SOLUTIONS TO DISCOVER HOW WE CAN OPTIMIZE YOUR telecom and broadband OPERATIONS Featured Products More Events & Tradeshows Join Power Storage Solutions at the Texas Communications Expo to discover our energy solutions for the telecom industry. From backup power systems to energy storage, we ensure your network runs smoothly. Visit our booth to learn more! Connect with Power Storage Solutions at Downstream USA to explore our energy solutions for the petrochemical, refining, and LNG industries. From backup power systems to energy storage, we ensure reliable operations. Visit our booth to learn more! Join Power Storage Solutions at WISPAPALOOZA 2025 to explore our energy solutions for telecom and broadband networks. From backup power systems to energy storage, we help optimize network reliability and performance. Visit our booth to learn more!
2025-04-14Obs = 172No. of failures = 75Time at risk = 31,938.1 LR chi2(4) = 20.67Log likelihood = -213.35033 Prob > chi2 = 0.0004 _t Haz. ratio Std. err. z P>|z| [95% conf. interval] age 1.027406 .0150188 1.85 0.064 .9983874 1.057268 posttran 1.075476 .3354669 0.23 0.816 .583567 1.982034 surgery .2222415 .1218386 -2.74 0.006 .0758882 .6508429 year .5523966 .1132688 -2.89 0.004 .3695832 .825638 After our stratified model, we use option stratify with estat gofplot to produce a separate plot for each stratum of pgroup. . estat gofplot, stratify The model fits data well in all strata. The red line for pgroup = 2 deviates from the reference line toward the end. This is not uncommon to see in practice because fewer observations are available for estimation toward the end of the study. To aid visual inspection of the plot, we can also add option separate to produce separate graphs for each stratum. . estat gofplot, stratify separate GOF plots for interval-censored data We use the dataset of a study for early breast cancer patients (Finkelstein and Wolfe 1985) that compares the cosmetic effects of two cancer treatments (treat) on breast retraction. Because patients were observed at random follow-up times, the exact time of breast retraction was not observed and was known only to fall in the interval between visits (variables ltime and rtime). First, we fit an interval-censored Weibull model of time to breast retraction on treatment using stintreg: . stintreg i.treat, interval(ltime rtime) distribution(weibull) nologWeibull PH regression Number of obs = 94 Uncensored = 0 Left-censored = 5 Right-censored = 38 Interval-cens. = 51 LR chi2(1) = 10.93Log likelihood = -143.19228 Prob > chi2 = 0.0009 Haz. ratio Std. err. z P>|z| [95% conf. interval] treat Radio+Chemo 2.498526 .7069467 3.24 0.001 1.434961 4.350383 _cons .0018503 .0013452 -8.66 0.000 .000445 .007693 /ln_p .4785786 .1198972 3.99 0.000 .2435844 .7135729 p 1.613779 .1934876 1.275814 2.041271 l/p .6196635 .0742959 .4898907 .7838134Note: _cons estimates baseline hazard. We then use estat gofplot to produce the GOF plot. . estat gofplot With interval-censored data, Cox–Snell-like residuals are defined and used for plotting (Farrington 2000). If a model fits the data well, these residuals should approximate the censored standard exponential distribution. Also, the nonparametric Turnbull estimator (Turnbull 1976) is used to estimate the cumulative hazard. The jagged line stays close to the reference line in the above graph, which indicates that the Weibull model fits the data well. Suppose that we now want to fit an exponential model and check its model fit. We type . quietly stintreg i.treat, interval(ltime rtime) distribution(exponential). estat gofplot Comparing this GOF plot with the one above, we can see that the Weibull model fits our data better than the exponential model. GOF plots
2025-04-06For interval-censored multiple-event data We can also use estat gofplot to visually assess the overall model fit for interval-censored multiple-event data. We use a simulated dataset based on the ARIC study described in Xu, Zeng, and Lin (2023). The participants were followed over time and assessed for two diseases (diabetes and hypertension) during several follow-up examinations. The investigators are interested in the factors that influence the times to onset for those two diseases. The factors of interest include three demographic variables—race, male, community—and five baseline risk factors: age, bmi, glucose, sysbp, and diabp. We first fit a marginal Cox proportional hazards model using stmgintcox: . webuse aric(Simulated ARIC data). stmgintcox age i.male i.community i.race bmi glucose sysbp diabp, id(id) event (event) interval(ltime rtime) nolog favorspeednote: using fixed step size with a multiplier of 5 to compute derivatives.note: using EM and VCE tolerances of 0.0001.note: option noemhsgtolerance assumed.Marginal interval-censored Cox regression Number of events = 2Baseline hazard: Reduced intervals Number of subjects = 200 Number of obs = 400ID variable: id Uncensored = 0Event variable: event Left-censored = 47Event-time interval: Right-censored = 240 Lower endpoint: ltime Interval-cens. = 113 Upper endpoint: rtime Wald chi2(20) = 84.36Log pseudolikelihood = -270.83984 Prob > chi2 = 0.0000 Robust Haz. ratio std. err. z P>|z| [95% conf. interval] Diabetes age .9552606 .0295589 -1.48 0.139 .8990481 1.014988 male Yes .8084224 .2400335 -0.72 0.474 .451755 1.446684 community Jackson 1.597828 .6069935 1.23 0.217 .7588748 3.364265Minneapolis 1.028054 .342976 0.08 0.934 .5346148 1.976929 Washington 1.407869 .5192024 0.93 0.354 .6833627 2.900504 race White .4289702 .1273669 -2.85 0.004 .2397145 .7676444 bmi 1.116579 .034187 3.60 0.000 1.051545 1.185636 glucose 1.139753 .0303702 4.91 0.000 1.081756 1.200859 sysbp 1.020295 .0122308 1.68 0.094 .9966021 1.04455 diabp .9928634 .0127512 -0.56 0.577 .9681835 1.018172 Hypertension age .9950085 .0225503 -0.22 0.825 .9517779 1.040203 male Yes .6671401 .1599892 -1.69 0.091 .4169533 1.067448 community Jackson .6085406 .1953944 -1.55 0.122 .3243246 1.141824Minneapolis .9040647 .2719638 -0.34 0.737 .5013468 1.630275 Washington .674088 .2085739 -1.27 0.202 .3675707 1.23621 race White 1.261355 .425064 0.69 0.491 .6516152 2.441652 bmi 1.012196 .0195117 0.63 0.529 .9746672 1.05117 glucose .989899 .0101396 -0.99 0.322 .9702238 1.009973 sysbp 1.075011 .0162901 4.77 0.000 1.043553 1.107418 diabp 1.025533 .0134835 1.92 0.055 .9994433 1.052303Note: Standard error estimates may be more variable for small datasets and datasets with low proportions of interval-censored observations. Now, let's produce the goodness-of-fit plots for all events. By default, estat gofplot creates a single graph with subgraphs for each event. . estat gofplot You can add the sepevents option to request that the plot for each event be placed on a separate graph. . estat gofplot, sepevents If we want to examine the goodness-of-fit plots for diabetes across different communities, we can use the by(community) option along with events("Diabetes"). The estat
2025-04-13