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08-18-2015 09:28 PM

Hi,

I'm elementary user of SAS. I am working on agricultural research and my experiment was laid out in split plot design.

However, I'm facing a problem, in which several plants had died, even of plants in one of the treatment combination.

So, what should I do ?

Ok, I'll explain my data;

A1 B1 **1** died

A1 B1 **2** **20**

A1 B1 **3** died

A1 B1 **4** died

A1 B1 **5** died

A1 B2 **1** died

A1 B2 **2** **13**

A1 B2 **3** died

A1 B2 **4** **12**

A1 B2 **5** **10**

A1 B3 **1** died

A1 B3 **2** died

A1 B3 **3** died

A1 B3 **4** **15**

A1 B3 **5** **17**

A2 B1 **1** died

A2 B1 **2** died

A2 B1 **3** died

A2 B1 **4** died

A2 B1 **5** died

A2 B2 **1** died

A2 B2 **2** **28**

A2 B2 **3** died

A2 B2 **4** died

A2 B2 **5** **30**

A2 B3 **1** **17**

A2 B3 **2** **15**

A2 B3 **3** **16**

A2 B3 **4** died

A2 B3 **5** **18**

A3 B1 **1** **5**

A3 B1 **2** died

A3 B1 **3** **10**

A3 B1 **4** died

A3 B1 **5** died

A3 B2 **1** died

A3 B2 **2** **10**

A3 B2 **3** **8**

A3 B2 **4** **9**

A3 B2 **5** died

A3 B3 **1** died

A3 B3 **2** **5**

A3 B3 **3** died

A3 B3 **4** **10**

A3 B3 **5** **6**

**It would be appreciated if you could help me**

**A very big thank you for your help**

Accepted Solutions

Solution

08-20-2015
11:13 AM

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08-20-2015 11:13 AM

It all depends if the deaths are due to the treatment. If they are, then your dead plants cannot be considered missing at random, and you have problems. You can treat plant death as a binary variable (0: alive, 1: dead, one value for each record) and analyze the data as a generalized linear mixed model (check out the SAS User's Guide for GLIMMIX).

model z = A|B / dist=bin link=logit;

if z is the binary variable. See the rest of the code in your other posts for this split plot.

This idea is ignoring the quantitative response variable where the plants did not die. If plant death is related to treatment, then you can definitely NOT analyze the continuous variables for the alive plants.** If** plant death is unrelated to the treatments (unlikely), you could analyze the rest of the data (you don't have to do anything special). You have a lot of dead plants, so you probably need better experimental methods -- statistics cannot rescue an experiment that should not be analyzed.

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08-20-2015 05:51 AM

After I read several statistic book I knew that is missing value, but how to create the codes using SAS ? It would be appriciated if you could help me

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08-20-2015 06:22 AM

Errrm, water them more? Sorry, sarcasm aside, you have not specified any problem. Try rephrasing it with test data (just a small amount) in the form of a datastep. Provide example required output, and clarify any logical steps.

Solution

08-20-2015
11:13 AM

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08-20-2015 11:13 AM

It all depends if the deaths are due to the treatment. If they are, then your dead plants cannot be considered missing at random, and you have problems. You can treat plant death as a binary variable (0: alive, 1: dead, one value for each record) and analyze the data as a generalized linear mixed model (check out the SAS User's Guide for GLIMMIX).

model z = A|B / dist=bin link=logit;

if z is the binary variable. See the rest of the code in your other posts for this split plot.

This idea is ignoring the quantitative response variable where the plants did not die. If plant death is related to treatment, then you can definitely NOT analyze the continuous variables for the alive plants.** If** plant death is unrelated to the treatments (unlikely), you could analyze the rest of the data (you don't have to do anything special). You have a lot of dead plants, so you probably need better experimental methods -- statistics cannot rescue an experiment that should not be analyzed.

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08-20-2015 02:03 PM

@lvm's response reminds me of a famous quote by R. Fisher: "To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of."

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08-30-2015 10:02 AM

A very big thank you @lvm, @RW9, and @Rick Wicklin

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09-12-2015 03:47 AM

**My experiment arranged in Split Plot design. How to make a Charts/ Boxplots for Proportions of a Binary Variable** ?

data Survival;

input A $ B $ Block Y; if Y=1 then 1='Survive'; else 0='Died';

datalines;

A1 B1 **1** 0

A1 B1 **2** **1**

A1 B1 **3** 0

A1 B1 **4** 0

A1 B1 **5** 0

A1 B2 **1** **1**

A1 B2 **2** **1**

A1 B2 **3** **1**

A1 B2 **4** **1**

A1 B2 **5** **0**

A1 B3 **1** 1

A1 B3 **2** 0

A1 B3 **3** 0

A1 B3 **4** **1**

A1 B3 **5** **1**

A2 B1 **1** 0

A2 B1 **2** 0

A2 B1 **3** 0

A2 B1 **4** 0

A2 B1 **5** 0

A2 B2 **1** 1

A2 B2 **2** ** 0**

A2 B2 **3** 0

A2 B2 **4** 1

A2 B2 **5** **0**

A2 B3 **1** **1**

A2 B3 **2** **1**

A2 B3 **3** **1**

A2 B3 **4** 0

A2 B3 **5** **1**

A3 B1 **1** **1**

A3 B1 **2** 0

A3 B1 **3** **1**

A3 B1 **4** 0

A3 B1 **5** 0

A3 B2 **1** 0

A3 B2 **2** **1**

A3 B2 **3** **1**

A3 B2 **4** **1**

A3 B2 **5** 0

A3 B3 **1** 0

A3 B3 **2** **1**

A3 B3 **3** 0

A3 B3 **4** **1**

A3 B3 **5** **1**

**;**

**Is it true when it displayed in as shown below ? how to write the codes**

**Please help me it is so important for me... **