About cpd47

cpd47 · ‎07-06-2017

Thank you! I will search around and see what I can find.

cpd47 · ‎07-05-2017

The data I attached is from an experiment where I am measuring the stress response of 3 types of fish (trt: 1, 2, 3) over a 1440 minute (24 hour) time period. Fish were divided into 9 tanks (12 fish per tank, 3 tanks per fish type) and were chased for 1 minute using dip nets to elicit a stress response. At each time point since the chasing (time: 0, 20, 60, 120, 360, 1440 minutes) blood was sampled from 2 fish in each tank. I measured many response variables, but "osmo" (osmolality) is the one I provided in my post. I hypothesize: a) trt: 2 fish will exhibit significantly lower levels of osmo than trt: 1 and trt: 3 fish b) trt: 1 fish will not differ from trt: 3 fish c) there will be no interaction between trt and time

cpd47 · ‎07-04-2017

Thank you! I meant to post it in that forum but somehow didn't. That is a really good question. I'm not sure which type of model I am trying to create. That's probably the question I should have asked. I guess I'm looking for some type of nonparametric equivalent to the model I attached, if such a thing exists. I believe Kruskall-Wallis is applicable for 1-way analyses, but I'm trying to see if I can analyze the factors "trt" and "time" as well as the interaction of "trt*time."

cpd47 · ‎07-04-2017

Hi, I'm not sure if it is possible to run a 2-way nonparametric analysis (a lot of sources on the internet say only 1-way is possible), but I have some response variables that fail the Shapiro-Wilk test for normality so I am trying to analyze them non parametrically. Below is a sample of my data and how I am analyzing variables that ARE normally distributed and have equal variances. However, this response variable (osmo) fails both Shapiro-Wilk and Levene's tests for normality and homogeneity of variance. I also attached a SAS file to this question (I am new here so am not sure which is preferred). If anyone has an idea of how to do this, I would greatly appreciate any help! data osmo; input trt time tank osmo; datalines; 1 0 1 297 1 0 1 293.5 1 0 2 301.5 1 0 2 298.5 1 0 3 299 1 0 3 314.5 1 20 1 297.5 1 20 1 . 1 20 2 291.5 1 20 2 298.5 1 20 3 290 1 20 3 299 1 60 1 305 1 60 1 297 1 60 2 . 1 60 2 295.5 1 60 3 291 1 60 3 298 1 120 1 295.5 1 120 1 298 1 120 2 296.5 1 120 2 293 1 120 3 299 1 120 3 305.5 1 360 1 329.5 1 360 1 287.5 1 360 2 291 1 360 2 303.5 1 360 3 297 1 360 3 290 1 1440 1 301 1 1440 1 294.5 1 1440 2 300.5 1 1440 2 291.5 1 1440 3 296 1 1440 3 303 2 0 1 299 2 0 1 307.25 2 0 2 299.5 2 0 2 326 2 0 3 295 2 0 3 310 2 20 1 . 2 20 1 291.5 2 20 2 . 2 20 2 291.5 2 20 3 292 2 20 3 . 2 60 1 313.5 2 60 1 305.5 2 60 2 310.5 2 60 2 . 2 60 3 306 2 60 3 300 2 120 1 316 2 120 1 . 2 120 2 287 2 120 2 286 2 120 3 282 2 120 3 300.5 2 360 1 304 2 360 1 298.5 2 360 2 306 2 360 2 . 2 360 3 299 2 360 3 312 2 1440 1 346.5 2 1440 1 286.5 2 1440 2 299.5 2 1440 2 337 2 1440 3 287 2 1440 3 309 3 0 1 305 3 0 1 302.5 3 0 2 308 3 0 2 322 3 0 3 316.5 3 0 3 335 3 20 1 298 3 20 1 296 3 20 2 299.5 3 20 2 306 3 20 3 291.5 3 20 3 302.5 3 60 1 304.5 3 60 1 312.5 3 60 2 303.5 3 60 2 292.5 3 60 3 301 3 60 3 294 3 120 1 306 3 120 1 303 3 120 2 . 3 120 2 . 3 120 3 304 3 120 3 324 3 360 1 304 3 360 1 312 3 360 2 306.5 3 360 2 308.5 3 360 3 306.5 3 360 3 318 3 1440 1 300.5 3 1440 1 293.5 3 1440 2 311 3 1440 2 266 3 1440 3 289 3 1440 3 297 ; proc print; run; /*testing normality and variance*/ proc glm data=osmo; class trt time; model osmo = trt*time; means trt*time/hovtest=levene (type=abs); output out=outosmo r=resid; run; quit; proc univariate data=outosmo normal; var resid; run; quit; proc univariate data=outosmo noprint; histogram resid; run; quit; /*This is how I am analyzing my variables that pass normality and variance. However, I have been unable to transform this and other variables to meet normality and variance assumptions. Looking for a nonparametric way to analyze*/ proc glm data=osmo; class trt time tank; model osmo=trt time trt*time tank(trt); test h=trt time trt*time e=tank(trt); lsmeans trt time trt*time/pdiff adjust=tukey e=tank(trt) lines; run; quit;

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Re: 2-way nonparametric factorial analysis with missing data

Re: 2-way nonparametric factorial analysis with missing data

Re: 2-way nonparametric factorial analysis with missing data

2-way nonparametric factorial analysis with missing data

Re: 2-way nonparametric factorial analysis with missing data

Re: 2-way nonparametric factorial analysis with missing data

Re: 2-way nonparametric factorial analysis with missing data

2-way nonparametric factorial analysis with missing data