Hi Boya - thanks for your suggestions. They don't tend to come up in probabilistic sensitivity analysis, but they are certainly essential for survival analysis and modelling, so I will be sure to do videos on them when I get back to making videos.
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Dear friend, what a nice explanation! Thanks again for sharing. With your example, I finally managed to run a VBA function CHOLESKI in Excel. Just one question: I understand form where the output of the mean values of 6.36 and -0.348 came from. But, what about the "-0.28"? Shouldn't be the value of 0.737 (scale parameter) instead according to you R output (6:30 of the video)?
Hi! The estimation of the survival model actually involves estimating the auxiliary parameter (for Weibull often k or gamma in textbooks but in the R survival package it is called "Scale") on the logarithmic scale. You will see at 8:17 that we specifically retrieve log(Scale) and it is -0.2797. When you come to use the parameter in the model you then need to exponentiate the sample so you can actually use it as intended.
Can you do videos on Weibull distribution and Exponential distribution.
Hi Boya - thanks for your suggestions. They don't tend to come up in probabilistic sensitivity analysis, but they are certainly essential for survival analysis and modelling, so I will be sure to do videos on them when I get back to making videos.
Dear friend, what a nice explanation! Thanks again for sharing. With your example, I finally managed to run a VBA function CHOLESKI in Excel. Just one question: I understand form where the output of the mean values of 6.36 and -0.348 came from. But, what about the "-0.28"? Shouldn't be the value of 0.737 (scale parameter) instead according to you R output (6:30 of the video)?
Hi! The estimation of the survival model actually involves estimating the auxiliary parameter (for Weibull often k or gamma in textbooks but in the R survival package it is called "Scale") on the logarithmic scale. You will see at 8:17 that we specifically retrieve log(Scale) and it is -0.2797. When you come to use the parameter in the model you then need to exponentiate the sample so you can actually use it as intended.
Thanks for sharing..
My pleasure