Hi everyone, I'm fairly new to R, and I don't have a background in statistics, so please bear with me. ;-) I'm dealing with 2^k factorial designs, and I was just wondering if there's any way to analyze more than two factors of a gage R&R study in R. For example, Minitab has an "expanded gage R&R" function that lets you include up to eight additional factors besides the usual two that are present in gage studies (parts and operators). If I wanted to include n additional random factors, is there a package or built-in functionality that will allow me to do that? I've been experimenting with the SixSigma package, and that has a ss.rr method which works great---as long as your experiment only contains two factors. I've also been using lmer from lme4 to fit a linear model of my experiment, but the standard deviations generated by lmer don't match what I'm seeing in Minitab. Since all my factors are random, the formula I'm using looks like this: vals ~ 1 + (1|f1) + (1|f2) + (1|f3) + (1|f1:f2) + (1|f1:f3) + (1|f2:f3) What am I doing wrong, and how can I fix it? Thanks, Matt
Performing gage R&R study in R w/more than 2 factors
3 messages · Bert Gunter, Matt Jacob
I believe that you need to consult a local statistician, as there are likely way too many statistical issues here that you do not fully understand. Alternatively, try posting to a statistical list like stats.stackexchange.com, as I think most of your issues are primarily statistical, not R related. Cheers, Bert
On Mon, Nov 19, 2012 at 11:12 AM, Matt Jacob <matt at jacobmail.org> wrote:
Hi everyone, I'm fairly new to R, and I don't have a background in statistics, so please bear with me. ;-) I'm dealing with 2^k factorial designs, and I was just wondering if there's any way to analyze more than two factors of a gage R&R study in R. For example, Minitab has an "expanded gage R&R" function that lets you include up to eight additional factors besides the usual two that are present in gage studies (parts and operators). If I wanted to include n additional random factors, is there a package or built-in functionality that will allow me to do that? I've been experimenting with the SixSigma package, and that has a ss.rr method which works great---as long as your experiment only contains two factors. I've also been using lmer from lme4 to fit a linear model of my experiment, but the standard deviations generated by lmer don't match what I'm seeing in Minitab. Since all my factors are random, the formula I'm using looks like this: vals ~ 1 + (1|f1) + (1|f2) + (1|f3) + (1|f1:f2) + (1|f1:f3) + (1|f2:f3) What am I doing wrong, and how can I fix it? Thanks, Matt
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Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
On Mon, Nov 19, 2012, at 16:31, Bert Gunter wrote:
I believe that you need to consult a local statistician, as there are likely way too many statistical issues here that you do not fully understand. Alternatively, try posting to a statistical list like stats.stackexchange.com, as I think most of your issues are primarily statistical, not R related.
Yes, you are correct. I've actually been working with a statistician within my organization, but the dilemma is that he's a stats guy who knows Minitab, and I'm a software guy who's trying to deploy some tools that are dependent on R. I've basically been trying to match up the output of R with the output of Minitab to check my work. Matt