import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import saspy
import saspy.sascfg_personal
sas = saspy.SASsession()

Using SAS Config named: oda
Error trying to read authinfo file:/Users/crunning/.authinfo
[Errno 2] No such file or directory: '/Users/crunning/.authinfo'
Did not find key oda in authinfo file:/Users/crunning/.authinfo

SAS Connection established. Subprocess id is 89960

Updated 29 Aug 2022

#For Amylase analysis
puddlong = pd.read_csv('./PuddingByWeekLong_FINAL.csv')
#For sensory analysis
starchall = pd.read_csv('./LongDataAll_Final.csv')

Salivary amylase activity analysis

puddlong.head(2)

	Participant	PuddTime	PuddRemain	SubjectID	OrderAssigned	Week1	Week2	YOB	Race	Ethnicity	Gender	MedsYN	Height	Weight	BMI	DropoutYN	SalivaVolume	Visit	VisitType	Interventionwk
0	1101	Pudding40s	9.2	1	HighLow	High	Low	1994	Asian	NonHispanic	Female	Y	1.651	54.0	19.810691	N	NaN	1	Start	High
1	1102	Pudding40s	9.9	2	LowHigh	Low	High	1997	White	NonHispanic	Male	Y	1.753	68.0	22.128149	N	NaN	1	Start	Low

puddsas = sas.df2sd(puddlong, 'puddlong')

%%SAS sas

proc sort data = puddlong;
by puddtime orderassigned interventionwk visittype subjectID;
run;

title 'Pudding Remaining by timepoint';
ods output diffs = diff1 tests3=tests1 lsmeans =means1 
lsmESTIMATEs =Estim1 SolutionF= sol1;
Proc mixed data=puddlong;
where subjectid ne 15; *DropoutYN = "N" and ;
by puddtime;
class subjectID orderassigned gender visittype interventionwk medsyn;
model puddremain =  interventionwk salivavolume visittype medsyn 
interventionwk*visittype 
/ ddfm = kr solution; *residual ok; *removed orderassigned gender bmi; *added in BMI and Medsyn because of other analysis. BMI not signicant here in whole group.;
repeated / subject = subjectID type = cs; 
lsmeans visittype / pdiff ADJDFE=ROW;
lsmeans interventionwk/ pdiff ADJDFE=ROW;
lsmeans interventionwk*visittype / pdiff ADJDFE=ROW;
run;

Pudding Remaining by timepoint

The Mixed Procedure

PuddTime=Pudding240s

Model Information
Data Set	WORK.PUDDLONG
Dependent Variable	PuddRemain
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	34	1 2 4 5 6 7 9 10 11 12 13 14 16 17 18 20 22 23 24 26 28 29 31 32 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	2	High Low
MedsYN	2	N Y

Dimensions
Covariance Parameters	2
Columns in X	12
Columns in Z	0
Subjects	34
Max Obs per Subject	4

Number of Observations
Number of Observations Read	135
Number of Observations Used	130
Number of Observations Not Used	5

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	647.39004074
1	2	623.78263003	0.00000045
2	1	623.78254070	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	3.7812
Residual		5.4405

Fit Statistics
-2 Res Log Likelihood	623.8
AIC (Smaller is Better)	627.8
AICC (Smaller is Better)	627.9
BIC (Smaller is Better)	630.8

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	23.61	<.0001

Solution for Fixed Effects
Effect	VisitType	Interventionwk	MedsYN	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept				7.3503	0.9977	68.7	7.37	<.0001
Interventionwk		High		0.2898	0.5925	95	0.49	0.6259
Interventionwk		Low		0	.	.	.	.
SalivaVolume				-1.3485	0.4730	101	-2.85	0.0053
VisitType	End			0.6694	0.5852	94	1.14	0.2556
VisitType	Start			0	.	.	.	.
MedsYN			N	-1.1639	0.8301	32.7	-1.40	0.1703
MedsYN			Y	0	.	.	.	.
VisitType*Interventi	End	High		-0.1634	0.8265	94.1	-0.20	0.8437
VisitType*Interventi	End	Low		0	.	.	.	.
VisitType*Interventi	Start	High		0	.	.	.	.
VisitType*Interventi	Start	Low		0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Interventionwk	1	94.5	0.25	0.6164
SalivaVolume	1	101	8.13	0.0053
VisitType	1	93.3	2.04	0.1563
MedsYN	1	32.7	1.97	0.1703
VisitType*Interventi	1	94.1	0.04	0.8437

Least Squares Means
Effect	VisitType	Interventionwk	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End		5.7378	0.4597	48.6	12.48	<.0001
VisitType	Start		5.1502	0.4617	49.3	11.16	<.0001
Interventionwk		High	5.5480	0.4637	49.5	11.97	<.0001
Interventionwk		Low	5.3399	0.4589	48.5	11.64	<.0001
VisitType*Interventi	End	High	5.8010	0.5497	82.9	10.55	<.0001
VisitType*Interventi	End	Low	5.6746	0.5359	78.5	10.59	<.0001
VisitType*Interventi	Start	High	5.2951	0.5445	80.6	9.73	<.0001
VisitType*Interventi	Start	Low	5.0053	0.5526	84.1	9.06	<.0001

Differences of Least Squares Means
Effect	VisitType	Interventionwk	_VisitType	_Interventionwk	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End		Start		0.5876	0.4112	93.3	1.43	0.1563
Interventionwk		High		Low	0.2081	0.4140	94.5	0.50	0.6164
VisitType*Interventi	End	High	End	Low	0.1264	0.5773	93.6	0.22	0.8272
VisitType*Interventi	End	High	Start	High	0.5059	0.5807	93.4	0.87	0.3859
VisitType*Interventi	End	High	Start	Low	0.7957	0.5952	94.5	1.34	0.1845
VisitType*Interventi	End	Low	Start	High	0.3795	0.5715	93.3	0.66	0.5083
VisitType*Interventi	End	Low	Start	Low	0.6694	0.5852	94	1.14	0.2556
VisitType*Interventi	Start	High	Start	Low	0.2898	0.5925	95	0.49	0.6259

---

Pudding Remaining by timepoint

The Mixed Procedure

PuddTime=Pudding40s

Model Information
Data Set	WORK.PUDDLONG
Dependent Variable	PuddRemain
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	34	1 2 4 5 6 7 9 10 11 12 13 14 16 17 18 20 22 23 24 26 28 29 31 32 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	2	High Low
MedsYN	2	N Y

Dimensions
Covariance Parameters	2
Columns in X	12
Columns in Z	0
Subjects	34
Max Obs per Subject	4

Number of Observations
Number of Observations Read	135
Number of Observations Used	130
Number of Observations Not Used	5

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	586.62531669
1	2	556.45787100	0.00000003
2	1	556.45786536	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	2.7148
Residual		3.0075

Fit Statistics
-2 Res Log Likelihood	556.5
AIC (Smaller is Better)	560.5
AICC (Smaller is Better)	560.6
BIC (Smaller is Better)	563.5

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	30.17	<.0001

Solution for Fixed Effects
Effect	VisitType	Interventionwk	MedsYN	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept				8.7414	0.7903	66.8	11.06	<.0001
Interventionwk		High		0.4058	0.4410	93.6	0.92	0.3598
Interventionwk		Low		0	.	.	.	.
SalivaVolume				-0.9213	0.3674	109	-2.51	0.0136
VisitType	End			0.8480	0.4354	92.7	1.95	0.0545
VisitType	Start			0	.	.	.	.
MedsYN			N	-1.2321	0.6798	31.5	-1.81	0.0794
MedsYN			Y	0	.	.	.	.
VisitType*Interventi	End	High		-0.5387	0.6149	92.8	-0.88	0.3833
VisitType*Interventi	End	Low		0	.	.	.	.
VisitType*Interventi	Start	High		0	.	.	.	.
VisitType*Interventi	Start	Low		0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Interventionwk	1	93.1	0.20	0.6588
SalivaVolume	1	109	6.29	0.0136
VisitType	1	92	3.58	0.0616
MedsYN	1	31.5	3.29	0.0794
VisitType*Interventi	1	92.8	0.77	0.3833

Least Squares Means
Effect	VisitType	Interventionwk	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End		7.7023	0.3702	44.3	20.81	<.0001
VisitType	Start		7.1237	0.3715	44.8	19.17	<.0001
Interventionwk		High	7.4812	0.3730	45.1	20.06	<.0001
Interventionwk		Low	7.3448	0.3696	44.2	19.87	<.0001
VisitType*Interventi	End	High	7.6359	0.4328	73.6	17.64	<.0001
VisitType*Interventi	End	Low	7.7688	0.4230	69.4	18.37	<.0001
VisitType*Interventi	Start	High	7.3266	0.4292	71.5	17.07	<.0001
VisitType*Interventi	Start	Low	6.9208	0.4348	74.7	15.92	<.0001

Differences of Least Squares Means
Effect	VisitType	Interventionwk	_VisitType	_Interventionwk	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End		Start		0.5786	0.3058	92	1.89	0.0616
Interventionwk		High		Low	0.1364	0.3080	93.1	0.44	0.6588
VisitType*Interventi	End	High	End	Low	-0.1329	0.4294	92.3	-0.31	0.7576
VisitType*Interventi	End	High	Start	High	0.3093	0.4319	92.2	0.72	0.4757
VisitType*Interventi	End	High	Start	Low	0.7151	0.4429	93.1	1.61	0.1098
VisitType*Interventi	End	Low	Start	High	0.4422	0.4250	92	1.04	0.3009
VisitType*Interventi	End	Low	Start	Low	0.8480	0.4354	92.7	1.95	0.0545
VisitType*Interventi	Start	High	Start	Low	0.4058	0.4410	93.6	0.92	0.3598

PuddDiff = sas.sd2df('diff1')
PuddTests = sas.sd2df('tests1')
PuddMeans = sas.sd2df('means1')
PuddDiff.to_csv('./out/PuddDiff.csv')
PuddTests.to_csv('./out/PuddTests.csv')
PuddMeans.to_csv('./out/PuddMeans.csv')

Mouth behavior

starchsasrun = sas.df2sd(starchall, 'starchsas')

Code from below adapted to each, removing non-significant effects one by one (started with interactions, then main effects)

%%SAS sas

proc sort data = starchsas; by interventionwk Shortname orderassigned subjectID; run;

title 'General model start test MB'; copy this one; ods output diffs = diff2 tests3=tests2 lsmeans =means2 lsmESTIMATEs =Estim2 SolutionF= sol2; Proc mixed data=starchsas; by interventionwk; class subjectID orderassigned gender visittype interventionwk taste smell foodtype mbstory mbpicture; model Sweetness = Taste Smell Pudding40s Visittype FoodType MBStory pudding40svisittype foodtypevisittype MBStoryFoodtype
/ ddfm = kr solution residual; ; removed ; repeated / subject = subjectID type = cs; lsmeans visittype / pdiff ADJDFE=ROW;lsmeans Taste/ pdiff ADJDFE=ROW; lsmeans smell/ pdiff ADJDFE=ROW;lsmeans foodtype/ pdiff ADJDFE=ROW; run;

Sweetness

%%SAS sas

proc sort data = starchsas;
by interventionwk Shortname orderassigned subjectID;
run;

title 'Sweetness Final'; 
ods output diffs = diffsw tests3=testssw lsmeans =meanssw
lsmESTIMATEs =Estimsw SolutionF= solsw;
Proc mixed data=starchsas;
by interventionwk;
class subjectID orderassigned gender visittype interventionwk taste smell foodtype mbstory mbpicture;
model Sweetness = Taste FoodType 
/ ddfm = kr solution; *; *removed MBStory*Foodtype pudding40s*visittype foodtype*visittype Pudding40s Smell Visittype MBStory ; 
repeated / subject = subjectID type = cs; 
lsmeans Taste/ pdiff ADJDFE=ROW;
lsmeans foodtype/ pdiff ADJDFE=ROW;
run;

Sweetness Final

The Mixed Procedure

Interventionwk=High

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Sweetness
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	41	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	High
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	5
Columns in Z	0
Subjects	41
Max Obs per Subject	24

Number of Observations
Number of Observations Read	947
Number of Observations Used	936
Number of Observations Not Used	11

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	8593.77159170
1	2	8548.35583740	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	57.3302
Residual		520.08

Fit Statistics
-2 Res Log Likelihood	8548.4
AIC (Smaller is Better)	8552.4
AICC (Smaller is Better)	8552.4
BIC (Smaller is Better)	8555.8

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	45.42	<.0001

Solution for Fixed Effects
Effect	Taste	FoodType	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept			55.4306	1.6518	63.8	33.56	<.0001
Taste	Fail		-7.9940	4.2308	64.5	-1.89	0.0633
Taste	Pass		0	.	.	.	.
FoodType		HighS	-6.9808	1.4908	894	-4.68	<.0001
FoodType		LowS	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Taste	1	64.5	3.57	0.0633
FoodType	1	894	21.93	<.0001

Least Squares Means
Effect	Taste	FoodType	Estimate	Standard Error	DF	t Value	Pr > |t|
Taste	Fail		43.9462	4.0312	61	10.90	<.0001
Taste	Pass		51.9402	1.4741	40.6	35.24	<.0001
FoodType		HighS	44.4528	2.3005	65.5	19.32	<.0001
FoodType		LowS	51.4336	2.3005	65.5	22.36	<.0001

Differences of Least Squares Means
Effect	Taste	FoodType	_Taste	_FoodType	Estimate	Standard Error	DF	t Value	Pr > |t|
Taste	Fail		Pass		-7.9940	4.2308	64.5	-1.89	0.0633
FoodType		HighS		LowS	-6.9808	1.4908	894	-4.68	<.0001

---

Sweetness Final

The Mixed Procedure

Interventionwk=Low

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Sweetness
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	38	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 26 28 29 30 31 32 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	Low
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	5
Columns in Z	0
Subjects	38
Max Obs per Subject	24

Number of Observations
Number of Observations Read	900
Number of Observations Used	900
Number of Observations Not Used	0

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	8294.66691757
1	2	8215.32792062	0.00000002
2	1	8215.32786582	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	87.2743
Residual		510.77

Fit Statistics
-2 Res Log Likelihood	8215.3
AIC (Smaller is Better)	8219.3
AICC (Smaller is Better)	8219.3
BIC (Smaller is Better)	8222.6

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	79.34	<.0001

Solution for Fixed Effects
Effect	Taste	FoodType	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept			56.7509	1.9097	57.8	29.72	<.0001
Taste	Fail		-0.8929	2.8908	470	-0.31	0.7576
Taste	Pass		0	.	.	.	.
FoodType		HighS	-8.9356	1.5067	861	-5.93	<.0001
FoodType		LowS	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Taste	1	470	0.10	0.7576
FoodType	1	861	35.17	<.0001

Least Squares Means
Effect	Taste	FoodType	Estimate	Standard Error	DF	t Value	Pr > |t|
Taste	Fail		51.3902	2.9642	150	17.34	<.0001
Taste	Pass		52.2831	1.7548	41.3	29.79	<.0001
FoodType		HighS	47.3689	2.1003	74.2	22.55	<.0001
FoodType		LowS	56.3044	2.1003	74.2	26.81	<.0001

Differences of Least Squares Means
Effect	Taste	FoodType	_Taste	_FoodType	Estimate	Standard Error	DF	t Value	Pr > |t|
Taste	Fail		Pass		-0.8929	2.8908	470	-0.31	0.7576
FoodType		HighS		LowS	-8.9356	1.5067	861	-5.93	<.0001

Hardness

%%SAS sas

proc sort data = starchsas;
by interventionwk Shortname orderassigned subjectID;
run;

title 'Hardness Final'; 
ods output diffs = diffha tests3=testsha lsmeans =meansha
lsmESTIMATEs =Estimha SolutionF= solha;
Proc mixed data=starchsas;
by interventionwk;
class subjectID orderassigned gender visittype interventionwk taste smell foodtype mbstory mbpicture;
model hardness = Visittype FoodType MBStory
 MBStory*Foodtype  
/ ddfm = kr solution; *solution residual; *removed pudding40s*visittype foodtype*visittype Smell Pudding40s Taste ; 
repeated / subject = subjectID type = cs; 
lsmeans visittype / pdiff ADJDFE=ROW;
lsmeans foodtype/ pdiff ADJDFE=ROW;
lsmeans mbstory/ pdiff ADJDFE=ROW;
lsmeans mbstory*foodtype/ pdiff ADJDFE=ROW;
run;

Hardness Final

The Mixed Procedure

Interventionwk=High

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Hardness
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	41	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	High
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	17
Columns in Z	0
Subjects	41
Max Obs per Subject	24

Number of Observations
Number of Observations Read	947
Number of Observations Used	936
Number of Observations Not Used	11

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	8374.97539012
1	2	8224.63538650	0.00000042
2	1	8224.63401294	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	106.58
Residual		367.41

Fit Statistics
-2 Res Log Likelihood	8224.6
AIC (Smaller is Better)	8228.6
AICC (Smaller is Better)	8228.6
BIC (Smaller is Better)	8232.1

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	150.34	<.0001

Solution for Fixed Effects
Effect	VisitType	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept				60.9341	4.7361	203	12.87	<.0001
VisitType	End			2.9152	1.2974	918	2.25	0.0249
VisitType	Start			0	.	.	.	.
FoodType		HighS		-19.1667	4.1828	889	-4.58	<.0001
FoodType		LowS		0	.	.	.	.
MBStory			Chewer	-4.2571	5.2775	350	-0.81	0.4204
MBStory			Cruncher	-3.0357	4.9736	285	-0.61	0.5421
MBStory			Smoosher	-5.7587	5.9044	178	-0.98	0.3307
MBStory			Sucker	0	.	.	.	.
FoodType*MBStory		HighS	Chewer	13.2353	4.9699	889	2.66	0.0079
FoodType*MBStory		HighS	Cruncher	11.1096	4.5518	889	2.44	0.0149
FoodType*MBStory		HighS	Smoosher	3.2708	5.0150	889	0.65	0.5144
FoodType*MBStory		HighS	Sucker	0	.	.	.	.
FoodType*MBStory		LowS	Chewer	0	.	.	.	.
FoodType*MBStory		LowS	Cruncher	0	.	.	.	.
FoodType*MBStory		LowS	Smoosher	0	.	.	.	.
FoodType*MBStory		LowS	Sucker	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
VisitType	1	918	5.05	0.0249
FoodType	1	889	67.63	<.0001
MBStory	3	155	1.17	0.3223
FoodType*MBStory	3	889	4.26	0.0053

Least Squares Means
Effect	VisitType	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End			54.4551	2.0097	60.4	27.10	<.0001
VisitType	Start			51.5399	1.9990	57.7	25.78	<.0001
FoodType		HighS		46.8661	2.0377	63.4	23.00	<.0001
FoodType		LowS		59.1288	2.0377	63.4	29.02	<.0001
MBStory			Chewer	55.1689	2.9416	90.8	18.75	<.0001
MBStory			Cruncher	55.3275	2.1550	60.7	25.67	<.0001
MBStory			Smoosher	48.6851	3.3756	72.4	14.42	<.0001
MBStory			Sucker	52.8084	4.2229	132	12.51	<.0001
FoodType*MBStory		HighS	Chewer	52.2032	3.2333	131	16.15	<.0001
FoodType*MBStory		HighS	Cruncher	51.2990	2.3345	83.2	21.97	<.0001
FoodType*MBStory		HighS	Smoosher	40.7372	3.6480	98.3	11.17	<.0001
FoodType*MBStory		HighS	Sucker	43.2250	4.7124	198	9.17	<.0001
FoodType*MBStory		LowS	Chewer	58.1346	3.2333	131	17.98	<.0001
FoodType*MBStory		LowS	Cruncher	59.3560	2.3345	83.2	25.43	<.0001
FoodType*MBStory		LowS	Smoosher	56.6330	3.6480	98.3	15.52	<.0001
FoodType*MBStory		LowS	Sucker	62.3917	4.7124	198	13.24	<.0001

Differences of Least Squares Means
Effect	VisitType	FoodType	MBStory	_VisitType	_FoodType	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End			Start			2.9152	1.2974	918	2.25	0.0249
FoodType		HighS			LowS		-12.2627	1.4912	889	-8.22	<.0001
MBStory			Chewer			Cruncher	-0.1586	3.1604	200	-0.05	0.9600
MBStory			Chewer			Smoosher	6.4838	4.3876	102	1.48	0.1425
MBStory			Chewer			Sucker	2.3605	4.6559	234	0.51	0.6126
MBStory			Cruncher			Smoosher	6.6424	3.6752	115	1.81	0.0733
MBStory			Cruncher			Sucker	2.5192	4.4223	191	0.57	0.5696
MBStory			Smoosher			Sucker	-4.1233	5.3455	123	-0.77	0.4420
FoodType*MBStory		HighS	Chewer		HighS	Cruncher	0.9042	3.5489	296	0.25	0.7991
FoodType*MBStory		HighS	Chewer		HighS	Smoosher	11.4660	4.7923	144	2.39	0.0180
FoodType*MBStory		HighS	Chewer		HighS	Sucker	8.9782	5.2775	350	1.70	0.0898
FoodType*MBStory		HighS	Chewer		LowS	Chewer	-5.9314	2.6841	889	-2.21	0.0274
FoodType*MBStory		HighS	Chewer		LowS	Cruncher	-7.1528	3.5489	296	-2.02	0.0448
FoodType*MBStory		HighS	Chewer		LowS	Smoosher	-4.4298	4.7923	144	-0.92	0.3568
FoodType*MBStory		HighS	Chewer		LowS	Sucker	-10.1885	5.2775	350	-1.93	0.0543
FoodType*MBStory		HighS	Cruncher		HighS	Smoosher	10.5618	4.0282	163	2.62	0.0096
FoodType*MBStory		HighS	Cruncher		HighS	Sucker	8.0740	4.9736	285	1.62	0.1056
FoodType*MBStory		HighS	Cruncher		LowS	Chewer	-6.8356	3.5489	296	-1.93	0.0551
FoodType*MBStory		HighS	Cruncher		LowS	Cruncher	-8.0570	1.7953	889	-4.49	<.0001
FoodType*MBStory		HighS	Cruncher		LowS	Smoosher	-5.3340	4.0282	163	-1.32	0.1873
FoodType*MBStory		HighS	Cruncher		LowS	Sucker	-11.0927	4.9736	285	-2.23	0.0265
FoodType*MBStory		HighS	Smoosher		HighS	Sucker	-2.4878	5.9044	178	-0.42	0.6740
FoodType*MBStory		HighS	Smoosher		LowS	Chewer	-17.3974	4.7923	144	-3.63	0.0004
FoodType*MBStory		HighS	Smoosher		LowS	Cruncher	-18.6188	4.0282	163	-4.62	<.0001
FoodType*MBStory		HighS	Smoosher		LowS	Smoosher	-15.8958	2.7667	889	-5.75	<.0001
FoodType*MBStory		HighS	Smoosher		LowS	Sucker	-21.6545	5.9044	178	-3.67	0.0003
FoodType*MBStory		HighS	Sucker		LowS	Chewer	-14.9096	5.2775	350	-2.83	0.0050
FoodType*MBStory		HighS	Sucker		LowS	Cruncher	-16.1310	4.9736	285	-3.24	0.0013
FoodType*MBStory		HighS	Sucker		LowS	Smoosher	-13.4080	5.9044	178	-2.27	0.0244
FoodType*MBStory		HighS	Sucker		LowS	Sucker	-19.1667	4.1828	889	-4.58	<.0001
FoodType*MBStory		LowS	Chewer		LowS	Cruncher	-1.2214	3.5489	296	-0.34	0.7310
FoodType*MBStory		LowS	Chewer		LowS	Smoosher	1.5016	4.7923	144	0.31	0.7545
FoodType*MBStory		LowS	Chewer		LowS	Sucker	-4.2571	5.2775	350	-0.81	0.4204
FoodType*MBStory		LowS	Cruncher		LowS	Smoosher	2.7230	4.0282	163	0.68	0.5000
FoodType*MBStory		LowS	Cruncher		LowS	Sucker	-3.0357	4.9736	285	-0.61	0.5421
FoodType*MBStory		LowS	Smoosher		LowS	Sucker	-5.7587	5.9044	178	-0.98	0.3307

---

Hardness Final

The Mixed Procedure

Interventionwk=Low

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Hardness
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	38	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 26 28 29 30 31 32 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	Low
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	17
Columns in Z	0
Subjects	38
Max Obs per Subject	24

Number of Observations
Number of Observations Read	900
Number of Observations Used	900
Number of Observations Not Used	0

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	8151.21959506
1	2	8057.66731752	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	90.9946
Residual		441.18

Fit Statistics
-2 Res Log Likelihood	8057.7
AIC (Smaller is Better)	8061.7
AICC (Smaller is Better)	8061.7
BIC (Smaller is Better)	8064.9

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	93.55	<.0001

Solution for Fixed Effects
Effect	VisitType	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept				57.7419	4.7732	240	12.10	<.0001
VisitType	End			-1.0071	1.4242	880	-0.71	0.4797
VisitType	Start			0	.	.	.	.
FoodType		HighS		-7.9524	4.5835	856	-1.74	0.0831
FoodType		LowS		0	.	.	.	.
MBStory			Chewer	-2.7817	6.0179	183	-0.46	0.6445
MBStory			Cruncher	1.8186	4.9161	363	0.37	0.7117
MBStory			Smoosher	-2.6646	5.4795	275	-0.49	0.6272
MBStory			Sucker	0	.	.	.	.
FoodType*MBStory		HighS	Chewer	-0.07143	5.6136	856	-0.01	0.9899
FoodType*MBStory		HighS	Cruncher	-1.4597	5.0093	856	-0.29	0.7708
FoodType*MBStory		HighS	Smoosher	-3.6310	5.4017	856	-0.67	0.5016
FoodType*MBStory		HighS	Sucker	0	.	.	.	.
FoodType*MBStory		LowS	Chewer	0	.	.	.	.
FoodType*MBStory		LowS	Cruncher	0	.	.	.	.
FoodType*MBStory		LowS	Smoosher	0	.	.	.	.
FoodType*MBStory		LowS	Sucker	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
VisitType	1	880	0.50	0.4797
FoodType	1	856	31.23	<.0001
MBStory	3	105	0.94	0.4231
FoodType*MBStory	3	856	0.28	0.8365

Least Squares Means
Effect	VisitType	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End			51.2064	1.9951	56.4	25.67	<.0001
VisitType	Start			52.2135	1.9908	56.6	26.23	<.0001
FoodType		HighS		47.0885	2.0368	62	23.12	<.0001
FoodType		LowS		56.3314	2.0368	62	27.66	<.0001
MBStory			Chewer	50.4447	3.5061	62.3	14.39	<.0001
MBStory			Cruncher	54.3508	2.2027	51.8	24.67	<.0001
MBStory			Smoosher	48.7820	3.1593	55.3	15.44	<.0001
MBStory			Sucker	53.2621	4.0694	143	13.09	<.0001
FoodType*MBStory		HighS	Chewer	46.4328	3.8625	91.4	12.02	<.0001
FoodType*MBStory		HighS	Cruncher	49.6448	2.4235	75.6	20.49	<.0001
FoodType*MBStory		HighS	Smoosher	42.9904	3.4676	80.1	12.40	<.0001
FoodType*MBStory		HighS	Sucker	49.2859	4.6703	234	10.55	<.0001
FoodType*MBStory		LowS	Chewer	54.4566	3.8625	91.4	14.10	<.0001
FoodType*MBStory		LowS	Cruncher	59.0569	2.4235	75.6	24.37	<.0001
FoodType*MBStory		LowS	Smoosher	54.5737	3.4676	80.1	15.74	<.0001
FoodType*MBStory		LowS	Sucker	57.2383	4.6703	234	12.26	<.0001

Differences of Least Squares Means
Effect	VisitType	FoodType	MBStory	_VisitType	_FoodType	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End			Start			-1.0071	1.4242	880	-0.71	0.4797
FoodType		HighS			LowS		-9.2429	1.6539	856	-5.59	<.0001
MBStory			Chewer			Cruncher	-3.9061	3.8459	93.3	-1.02	0.3124
MBStory			Chewer			Smoosher	1.6627	4.6935	64.4	0.35	0.7243
MBStory			Chewer			Sucker	-2.8174	5.3232	116	-0.53	0.5976
MBStory			Cruncher			Smoosher	5.5688	3.6777	74.9	1.51	0.1342
MBStory			Cruncher			Sucker	1.0887	4.2302	226	0.26	0.7971
MBStory			Smoosher			Sucker	-4.4800	4.7677	171	-0.94	0.3487
FoodType*MBStory		HighS	Chewer		HighS	Cruncher	-3.2120	4.2939	142	-0.75	0.4557
FoodType*MBStory		HighS	Chewer		HighS	Smoosher	3.4424	5.1669	94.2	0.67	0.5069
FoodType*MBStory		HighS	Chewer		HighS	Sucker	-2.8531	6.0179	183	-0.47	0.6360
FoodType*MBStory		HighS	Chewer		LowS	Chewer	-8.0238	3.2410	856	-2.48	0.0135
FoodType*MBStory		HighS	Chewer		LowS	Cruncher	-12.6240	4.2939	142	-2.94	0.0038
FoodType*MBStory		HighS	Chewer		LowS	Smoosher	-8.1409	5.1669	94.2	-1.58	0.1185
FoodType*MBStory		HighS	Chewer		LowS	Sucker	-10.8055	6.0179	183	-1.80	0.0742
FoodType*MBStory		HighS	Cruncher		HighS	Smoosher	6.6544	4.0730	112	1.63	0.1051
FoodType*MBStory		HighS	Cruncher		HighS	Sucker	0.3589	4.9161	363	0.07	0.9418
FoodType*MBStory		HighS	Cruncher		LowS	Chewer	-4.8118	4.2939	142	-1.12	0.2643
FoodType*MBStory		HighS	Cruncher		LowS	Cruncher	-9.4120	2.0211	856	-4.66	<.0001
FoodType*MBStory		HighS	Cruncher		LowS	Smoosher	-4.9289	4.0730	112	-1.21	0.2288
FoodType*MBStory		HighS	Cruncher		LowS	Sucker	-7.5935	4.9161	363	-1.54	0.1233
FoodType*MBStory		HighS	Smoosher		HighS	Sucker	-6.2955	5.4795	275	-1.15	0.2516
FoodType*MBStory		HighS	Smoosher		LowS	Chewer	-11.4662	5.1669	94.2	-2.22	0.0289
FoodType*MBStory		HighS	Smoosher		LowS	Cruncher	-16.0665	4.0730	112	-3.94	0.0001
FoodType*MBStory		HighS	Smoosher		LowS	Smoosher	-11.5833	2.8583	856	-4.05	<.0001
FoodType*MBStory		HighS	Smoosher		LowS	Sucker	-14.2479	5.4795	275	-2.60	0.0098
FoodType*MBStory		HighS	Sucker		LowS	Chewer	-5.1707	6.0179	183	-0.86	0.3913
FoodType*MBStory		HighS	Sucker		LowS	Cruncher	-9.7710	4.9161	363	-1.99	0.0476
FoodType*MBStory		HighS	Sucker		LowS	Smoosher	-5.2878	5.4795	275	-0.97	0.3354
FoodType*MBStory		HighS	Sucker		LowS	Sucker	-7.9524	4.5835	856	-1.74	0.0831
FoodType*MBStory		LowS	Chewer		LowS	Cruncher	-4.6002	4.2939	142	-1.07	0.2858
FoodType*MBStory		LowS	Chewer		LowS	Smoosher	-0.1171	5.1669	94.2	-0.02	0.9820
FoodType*MBStory		LowS	Chewer		LowS	Sucker	-2.7817	6.0179	183	-0.46	0.6445
FoodType*MBStory		LowS	Cruncher		LowS	Smoosher	4.4831	4.0730	112	1.10	0.2734
FoodType*MBStory		LowS	Cruncher		LowS	Sucker	1.8186	4.9161	363	0.37	0.7117
FoodType*MBStory		LowS	Smoosher		LowS	Sucker	-2.6646	5.4795	275	-0.49	0.6272

Chewiness

%%SAS sas

proc sort data = starchsas;
by interventionwk Shortname orderassigned subjectID;
run;

title 'Chewiness final'; 
ods output diffs = diffch tests3=testsch lsmeans =meansch
lsmESTIMATEs =Estimch SolutionF= solch;
Proc mixed data=starchsas;
by interventionwk;
class subjectID orderassigned gender visittype interventionwk taste smell foodtype mbstory mbpicture;
model Chewiness = Pudding40s MBStory
/ ddfm = kr ; *solution residual; *removed pudding40s*visittype MBStory*Foodtype foodtype*visittype Visittype FoodType Smell Taste  ; 
repeated / subject = subjectID type = cs; 
lsmeans MBStory / pdiff ADJDFE=ROW;
run;

Chewiness final

The Mixed Procedure

Interventionwk=High

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Chewiness
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	41	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	High
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	6
Columns in Z	0
Subjects	41
Max Obs per Subject	24

Number of Observations
Number of Observations Read	947
Number of Observations Used	936
Number of Observations Not Used	11

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	8400.57566016
1	2	8318.00417520	0.00000122
2	1	8318.00007061	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	79.2551
Residual		402.22

Fit Statistics
-2 Res Log Likelihood	8318.0
AIC (Smaller is Better)	8322.0
AICC (Smaller is Better)	8322.0
BIC (Smaller is Better)	8325.4

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	82.58	<.0001

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Pudding40s	1	145	1.95	0.1649
MBStory	3	120	2.12	0.1009

Least Squares Means
Effect	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
MBStory	Chewer	51.2401	2.7870	84.1	18.39	<.0001
MBStory	Cruncher	53.9994	1.9853	60	27.20	<.0001
MBStory	Smoosher	49.9138	3.1139	63.2	16.03	<.0001
MBStory	Sucker	61.5506	4.0724	103	15.11	<.0001

Differences of Least Squares Means
Effect	MBStory	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
MBStory	Chewer	Cruncher	-2.7592	3.1274	171	-0.88	0.3789
MBStory	Chewer	Smoosher	1.3263	4.1087	79.7	0.32	0.7477
MBStory	Chewer	Sucker	-10.3105	4.6429	163	-2.22	0.0277
MBStory	Cruncher	Smoosher	4.0855	3.4750	100	1.18	0.2425
MBStory	Cruncher	Sucker	-7.5512	4.2968	131	-1.76	0.0812
MBStory	Smoosher	Sucker	-11.6368	5.1208	96.2	-2.27	0.0253

---

Chewiness final

The Mixed Procedure

Interventionwk=Low

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Chewiness
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	38	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 26 28 29 30 31 32 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	Low
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	6
Columns in Z	0
Subjects	38
Max Obs per Subject	24

Number of Observations
Number of Observations Read	900
Number of Observations Used	900
Number of Observations Not Used	0

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	8085.27430477
1	2	8009.92159763	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	72.3099
Residual		409.80

Fit Statistics
-2 Res Log Likelihood	8009.9
AIC (Smaller is Better)	8013.9
AICC (Smaller is Better)	8013.9
BIC (Smaller is Better)	8017.2

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	75.35	<.0001

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Pudding40s	1	197	2.78	0.0968
MBStory	3	96.5	1.77	0.1583

Least Squares Means
Effect	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
MBStory	Chewer	47.7483	3.1950	60.6	14.94	<.0001
MBStory	Cruncher	53.1726	2.0160	50.7	26.38	<.0001
MBStory	Smoosher	52.3759	2.8967	51.3	18.08	<.0001
MBStory	Sucker	58.9150	3.7923	133	15.54	<.0001

Differences of Least Squares Means
Effect	MBStory	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
MBStory	Chewer	Cruncher	-5.4244	3.5376	91.1	-1.53	0.1287
MBStory	Chewer	Smoosher	-4.6276	4.2871	59.4	-1.08	0.2848
MBStory	Chewer	Sucker	-11.1667	4.9077	110	-2.28	0.0248
MBStory	Cruncher	Smoosher	0.7967	3.4007	67.8	0.23	0.8155
MBStory	Cruncher	Sucker	-5.7424	3.9724	200	-1.45	0.1499
MBStory	Smoosher	Sucker	-6.5391	4.4628	147	-1.47	0.1450

Texture Liking

%%SAS sas

proc sort data = starchsas;
by interventionwk Shortname orderassigned subjectID;
run;

title 'Texture Liking Final';
ods output diffs = diffte tests3=testste lsmeans =meanste
lsmESTIMATEs =Estimte SolutionF= solte;
Proc mixed data=starchsas;
by interventionwk;
class subjectID orderassigned gender visittype interventionwk taste smell foodtype mbstory mbpicture;
model TextureL = FoodType MBStory MBStory*Foodtype  
/ ddfm = kr solution; * residual; *removed foodtype*visittype pudding40s*visittype Visittype Smell Taste Pudding40s ; 
repeated / subject = subjectID type = cs; 
lsmeans foodtype / pdiff ADJDFE=ROW;
lsmeans mbstory/ pdiff ADJDFE=ROW;
lsmeans MBStory*Foodtype/ pdiff ADJDFE=ROW;
run;

Texture Liking Final

The Mixed Procedure

Interventionwk=High

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	TextureL
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	41	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	High
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	15
Columns in Z	0
Subjects	41
Max Obs per Subject	24

Number of Observations
Number of Observations Read	947
Number of Observations Used	936
Number of Observations Not Used	11

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	9510.99596613
1	2	9449.82536208	0.00000008
2	1	9449.82504756	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	195.98
Residual		1404.96

Fit Statistics
-2 Res Log Likelihood	9449.8
AIC (Smaller is Better)	9453.8
AICC (Smaller is Better)	9453.8
BIC (Smaller is Better)	9457.3

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	61.17	<.0001

Solution for Fixed Effects
Effect	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept			20.2272	8.0603	173	2.51	0.0130
FoodType	HighS		1.7381	8.1794	891	0.21	0.8318
FoodType	LowS		0	.	.	.	.
MBStory		Chewer	3.2795	9.3157	241	0.35	0.7251
MBStory		Cruncher	1.6270	8.6690	206	0.19	0.8513
MBStory		Smoosher	-16.2304	9.9411	146	-1.63	0.1047
MBStory		Sucker	0	.	.	.	.
FoodType*MBStory	HighS	Chewer	1.8109	9.7186	891	0.19	0.8522
FoodType*MBStory	HighS	Cruncher	-11.0407	8.9009	891	-1.24	0.2152
FoodType*MBStory	HighS	Smoosher	20.5119	9.8068	891	2.09	0.0368
FoodType*MBStory	HighS	Sucker	0	.	.	.	.
FoodType*MBStory	LowS	Chewer	0	.	.	.	.
FoodType*MBStory	LowS	Cruncher	0	.	.	.	.
FoodType*MBStory	LowS	Smoosher	0	.	.	.	.
FoodType*MBStory	LowS	Sucker	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
FoodType	1	891	2.44	0.1183
MBStory	3	98.7	0.96	0.4171
FoodType*MBStory	3	891	8.09	<.0001

Least Squares Means
Effect	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
FoodType	HighS		21.9548	3.1849	75.3	6.89	<.0001
FoodType	LowS		17.3962	3.1849	75.3	5.46	<.0001
MBStory		Chewer	25.2812	4.6957	76.2	5.38	<.0001
MBStory		Cruncher	17.2029	3.2984	57.4	5.22	<.0001
MBStory		Smoosher	15.1218	5.2017	58.5	2.91	0.0051
MBStory		Sucker	21.0962	6.9457	98.4	3.04	0.0031
FoodType*MBStory	HighS	Chewer	27.0557	5.3793	129	5.03	<.0001
FoodType*MBStory	HighS	Cruncher	12.5516	3.7364	93.8	3.36	0.0011
FoodType*MBStory	HighS	Smoosher	26.2468	5.8630	93.8	4.48	<.0001
FoodType*MBStory	HighS	Sucker	21.9653	8.0603	173	2.73	0.0071
FoodType*MBStory	LowS	Chewer	23.5067	5.3793	129	4.37	<.0001
FoodType*MBStory	LowS	Cruncher	21.8542	3.7364	93.8	5.85	<.0001
FoodType*MBStory	LowS	Smoosher	3.9968	5.8630	93.8	0.68	0.4971
FoodType*MBStory	LowS	Sucker	20.2272	8.0603	173	2.51	0.0130

Differences of Least Squares Means
Effect	FoodType	MBStory	_FoodType	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
FoodType	HighS		LowS		4.5586	2.9160	891	1.56	0.1183
MBStory		Chewer		Cruncher	8.0783	5.3315	119	1.52	0.1324
MBStory		Chewer		Smoosher	10.1593	6.9628	72.2	1.46	0.1489
MBStory		Chewer		Sucker	4.1849	7.9479	136	0.53	0.5994
MBStory		Cruncher		Smoosher	2.0811	5.8677	79.7	0.35	0.7238
MBStory		Cruncher		Sucker	-3.8934	7.4394	117	-0.52	0.6017
MBStory		Smoosher		Sucker	-5.9744	8.6476	85.5	-0.69	0.4915
FoodType*MBStory	HighS	Chewer	HighS	Cruncher	14.5041	6.1962	207	2.34	0.0202
FoodType*MBStory	HighS	Chewer	HighS	Smoosher	0.8089	7.9174	119	0.10	0.9188
FoodType*MBStory	HighS	Chewer	HighS	Sucker	5.0904	9.3157	241	0.55	0.5853
FoodType*MBStory	HighS	Chewer	LowS	Chewer	3.5490	5.2486	891	0.68	0.4991
FoodType*MBStory	HighS	Chewer	LowS	Cruncher	5.2015	6.1962	207	0.84	0.4022
FoodType*MBStory	HighS	Chewer	LowS	Smoosher	23.0589	7.9174	119	2.91	0.0043
FoodType*MBStory	HighS	Chewer	LowS	Sucker	6.8285	9.3157	241	0.73	0.4643
FoodType*MBStory	HighS	Cruncher	HighS	Smoosher	-13.6953	6.6954	133	-2.05	0.0428
FoodType*MBStory	HighS	Cruncher	HighS	Sucker	-9.4137	8.6690	206	-1.09	0.2788
FoodType*MBStory	HighS	Cruncher	LowS	Chewer	-10.9551	6.1962	207	-1.77	0.0785
FoodType*MBStory	HighS	Cruncher	LowS	Cruncher	-9.3026	3.5106	891	-2.65	0.0082
FoodType*MBStory	HighS	Cruncher	LowS	Smoosher	8.5547	6.6954	133	1.28	0.2036
FoodType*MBStory	HighS	Cruncher	LowS	Sucker	-7.6756	8.6690	206	-0.89	0.3770
FoodType*MBStory	HighS	Smoosher	HighS	Sucker	4.2815	9.9411	146	0.43	0.6673
FoodType*MBStory	HighS	Smoosher	LowS	Chewer	2.7402	7.9174	119	0.35	0.7299
FoodType*MBStory	HighS	Smoosher	LowS	Cruncher	4.3926	6.6954	133	0.66	0.5129
FoodType*MBStory	HighS	Smoosher	LowS	Smoosher	22.2500	5.4102	891	4.11	<.0001
FoodType*MBStory	HighS	Smoosher	LowS	Sucker	6.0196	9.9411	146	0.61	0.5458
FoodType*MBStory	HighS	Sucker	LowS	Chewer	-1.5414	9.3157	241	-0.17	0.8687
FoodType*MBStory	HighS	Sucker	LowS	Cruncher	0.1111	8.6690	206	0.01	0.9898
FoodType*MBStory	HighS	Sucker	LowS	Smoosher	17.9685	9.9411	146	1.81	0.0727
FoodType*MBStory	HighS	Sucker	LowS	Sucker	1.7381	8.1794	891	0.21	0.8318
FoodType*MBStory	LowS	Chewer	LowS	Cruncher	1.6525	6.1962	207	0.27	0.7900
FoodType*MBStory	LowS	Chewer	LowS	Smoosher	19.5098	7.9174	119	2.46	0.0152
FoodType*MBStory	LowS	Chewer	LowS	Sucker	3.2795	9.3157	241	0.35	0.7251
FoodType*MBStory	LowS	Cruncher	LowS	Smoosher	17.8574	6.6954	133	2.67	0.0086
FoodType*MBStory	LowS	Cruncher	LowS	Sucker	1.6270	8.6690	206	0.19	0.8513
FoodType*MBStory	LowS	Smoosher	LowS	Sucker	-16.2304	9.9411	146	-1.63	0.1047

---

Texture Liking Final

The Mixed Procedure

Interventionwk=Low

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	TextureL
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	38	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 26 28 29 30 31 32 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	Low
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	15
Columns in Z	0
Subjects	38
Max Obs per Subject	24

Number of Observations
Number of Observations Read	900
Number of Observations Used	900
Number of Observations Not Used	0

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	9174.33334994
1	2	9115.24072220	0.00000006
2	1	9115.24048705	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	203.29
Residual		1455.94

Fit Statistics
-2 Res Log Likelihood	9115.2
AIC (Smaller is Better)	9119.2
AICC (Smaller is Better)	9119.3
BIC (Smaller is Better)	9122.5

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	59.09	<.0001

Solution for Fixed Effects
Effect	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept			34.7671	7.9900	222	4.35	<.0001
FoodType	HighS		-16.5714	8.3265	858	-1.99	0.0469
FoodType	LowS		0	.	.	.	.
MBStory		Chewer	-8.5947	10.1405	173	-0.85	0.3979
MBStory		Cruncher	-7.1984	8.5202	304	-0.84	0.3988
MBStory		Smoosher	-31.1606	9.4140	239	-3.31	0.0011
MBStory		Sucker	0	.	.	.	.
FoodType*MBStory	HighS	Chewer	26.4405	10.1978	858	2.59	0.0097
FoodType*MBStory	HighS	Cruncher	4.3353	9.1001	858	0.48	0.6339
FoodType*MBStory	HighS	Smoosher	34.2844	9.8129	858	3.49	0.0005
FoodType*MBStory	HighS	Sucker	0	.	.	.	.
FoodType*MBStory	LowS	Chewer	0	.	.	.	.
FoodType*MBStory	LowS	Cruncher	0	.	.	.	.
FoodType*MBStory	LowS	Smoosher	0	.	.	.	.
FoodType*MBStory	LowS	Sucker	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
FoodType	1	858	0.01	0.9188
MBStory	3	88.3	2.35	0.0782
FoodType*MBStory	3	858	9.75	<.0001

Least Squares Means
Effect	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
FoodType	HighS		22.7223	3.3015	70	6.88	<.0001
FoodType	LowS		23.0286	3.3015	70	6.98	<.0001
MBStory		Chewer	31.1069	5.6063	58	5.55	<.0001
MBStory		Cruncher	21.4506	3.5147	50.9	6.10	<.0001
MBStory		Smoosher	12.4630	5.0490	51.5	2.47	0.0169
MBStory		Sucker	26.4814	6.8197	125	3.88	0.0002
FoodType*MBStory	HighS	Chewer	36.0414	6.3323	93.8	5.69	<.0001
FoodType*MBStory	HighS	Cruncher	15.3325	3.9652	82	3.87	0.0002
FoodType*MBStory	HighS	Smoosher	21.3195	5.6774	82	3.76	0.0003
FoodType*MBStory	HighS	Sucker	18.1956	7.9900	222	2.28	0.0237
FoodType*MBStory	LowS	Chewer	26.1724	6.3323	93.8	4.13	<.0001
FoodType*MBStory	LowS	Cruncher	27.5686	3.9652	82	6.95	<.0001
FoodType*MBStory	LowS	Smoosher	3.6065	5.6774	82	0.64	0.5270
FoodType*MBStory	LowS	Sucker	34.7671	7.9900	222	4.35	<.0001

Differences of Least Squares Means
Effect	FoodType	MBStory	_FoodType	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
FoodType	HighS		LowS		-0.3064	3.0046	858	-0.10	0.9188
MBStory		Chewer		Cruncher	9.6564	6.2730	79.9	1.54	0.1277
MBStory		Chewer		Smoosher	18.6439	7.5235	57.8	2.48	0.0162
MBStory		Chewer		Sucker	4.6256	8.7653	100	0.53	0.5989
MBStory		Cruncher		Smoosher	8.9876	5.9682	64.5	1.51	0.1370
MBStory		Cruncher		Sucker	-5.0308	7.2035	172	-0.70	0.4859
MBStory		Smoosher		Sucker	-14.0184	8.0343	135	-1.74	0.0833
FoodType*MBStory	HighS	Chewer	HighS	Cruncher	20.7089	7.1685	134	2.89	0.0045
FoodType*MBStory	HighS	Chewer	HighS	Smoosher	14.7220	8.4859	93	1.73	0.0861
FoodType*MBStory	HighS	Chewer	HighS	Sucker	17.8458	10.1405	173	1.76	0.0802
FoodType*MBStory	HighS	Chewer	LowS	Chewer	9.8690	5.8877	858	1.68	0.0941
FoodType*MBStory	HighS	Chewer	LowS	Cruncher	8.4728	7.1685	134	1.18	0.2393
FoodType*MBStory	HighS	Chewer	LowS	Smoosher	32.4349	8.4859	93	3.82	0.0002
FoodType*MBStory	HighS	Chewer	LowS	Sucker	1.2744	10.1405	173	0.13	0.9001
FoodType*MBStory	HighS	Cruncher	HighS	Smoosher	-5.9870	6.7624	105	-0.89	0.3780
FoodType*MBStory	HighS	Cruncher	HighS	Sucker	-2.8631	8.5202	304	-0.34	0.7371
FoodType*MBStory	HighS	Cruncher	LowS	Chewer	-10.8399	7.1685	134	-1.51	0.1328
FoodType*MBStory	HighS	Cruncher	LowS	Cruncher	-12.2361	3.6716	858	-3.33	0.0009
FoodType*MBStory	HighS	Cruncher	LowS	Smoosher	11.7260	6.7624	105	1.73	0.0858
FoodType*MBStory	HighS	Cruncher	LowS	Sucker	-19.4346	8.5202	304	-2.28	0.0232
FoodType*MBStory	HighS	Smoosher	HighS	Sucker	3.1238	9.4140	239	0.33	0.7403
FoodType*MBStory	HighS	Smoosher	LowS	Chewer	-4.8529	8.4859	93	-0.57	0.5688
FoodType*MBStory	HighS	Smoosher	LowS	Cruncher	-6.2492	6.7624	105	-0.92	0.3575
FoodType*MBStory	HighS	Smoosher	LowS	Smoosher	17.7130	5.1925	858	3.41	0.0007
FoodType*MBStory	HighS	Smoosher	LowS	Sucker	-13.4476	9.4140	239	-1.43	0.1545
FoodType*MBStory	HighS	Sucker	LowS	Chewer	-7.9768	10.1405	173	-0.79	0.4326
FoodType*MBStory	HighS	Sucker	LowS	Cruncher	-9.3730	8.5202	304	-1.10	0.2722
FoodType*MBStory	HighS	Sucker	LowS	Smoosher	14.5891	9.4140	239	1.55	0.1225
FoodType*MBStory	HighS	Sucker	LowS	Sucker	-16.5714	8.3265	858	-1.99	0.0469
FoodType*MBStory	LowS	Chewer	LowS	Cruncher	-1.3962	7.1685	134	-0.19	0.8459
FoodType*MBStory	LowS	Chewer	LowS	Smoosher	22.5659	8.4859	93	2.66	0.0092
FoodType*MBStory	LowS	Chewer	LowS	Sucker	-8.5947	10.1405	173	-0.85	0.3979
FoodType*MBStory	LowS	Cruncher	LowS	Smoosher	23.9621	6.7624	105	3.54	0.0006
FoodType*MBStory	LowS	Cruncher	LowS	Sucker	-7.1984	8.5202	304	-0.84	0.3988
FoodType*MBStory	LowS	Smoosher	LowS	Sucker	-31.1606	9.4140	239	-3.31	0.0011

Overall Liking

%%SAS sas

proc sort data = starchsas;
by interventionwk Shortname orderassigned subjectID;
run;

title 'Liking Final'; 
ods output diffs = diffli tests3=testsli lsmeans =meansli
lsmESTIMATEs =Estimli SolutionF= solli;
Proc mixed data=starchsas;
by interventionwk;
class subjectID orderassigned gender visittype interventionwk taste smell foodtype mbstory mbpicture;
model Liking = FoodType MBStory MBStory*Foodtype  
/ ddfm = kr solution; * residual; *removed foodtype*visittype 
pudding40s*visittype Smell Taste Visittype Pudding40s ; 
repeated / subject = subjectID type = cs; 
lsmeans MBStory / pdiff ADJDFE=ROW;
lsmeans foodtype/ pdiff ADJDFE=ROW;
lsmeans MBStory*Foodtype/ pdiff ADJDFE=ROW;
run;

Liking Final

The Mixed Procedure

Interventionwk=High

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Liking
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	41	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	High
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	15
Columns in Z	0
Subjects	41
Max Obs per Subject	24

Number of Observations
Number of Observations Read	947
Number of Observations Used	936
Number of Observations Not Used	11

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	9709.55935521
1	2	9654.05831005	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	233.99
Residual		1753.21

Fit Statistics
-2 Res Log Likelihood	9654.1
AIC (Smaller is Better)	9658.1
AICC (Smaller is Better)	9658.1
BIC (Smaller is Better)	9661.5

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	55.50	<.0001

Solution for Fixed Effects
Effect	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept			20.2138	8.9363	168	2.26	0.0250
FoodType	HighS		-3.0476	9.1371	890	-0.33	0.7388
FoodType	LowS		0	.	.	.	.
MBStory		Chewer	-4.4634	10.3422	233	-0.43	0.6665
MBStory		Cruncher	4.2609	9.6181	200	0.44	0.6582
MBStory		Smoosher	-13.8846	11.0127	142	-1.26	0.2095
MBStory		Sucker	0	.	.	.	.
FoodType*MBStory	HighS	Chewer	14.9496	10.8565	890	1.38	0.1689
FoodType*MBStory	HighS	Cruncher	-5.5357	9.9431	890	-0.56	0.5778
FoodType*MBStory	HighS	Smoosher	24.8601	10.9550	890	2.27	0.0235
FoodType*MBStory	HighS	Sucker	0	.	.	.	.
FoodType*MBStory	LowS	Chewer	0	.	.	.	.
FoodType*MBStory	LowS	Cruncher	0	.	.	.	.
FoodType*MBStory	LowS	Smoosher	0	.	.	.	.
FoodType*MBStory	LowS	Sucker	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
FoodType	1	890	2.87	0.0904
MBStory	3	94.4	0.13	0.9433
FoodType*MBStory	3	890	7.05	0.0001

Least Squares Means
Effect	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
MBStory		Chewer	21.7014	5.1846	73.7	4.19	<.0001
MBStory		Cruncher	20.1830	3.6350	55.8	5.55	<.0001
MBStory		Smoosher	17.2355	5.7327	56.4	3.01	0.0039
MBStory		Sucker	18.6900	7.6802	94.7	2.43	0.0168
FoodType	HighS		22.2129	3.5159	74.4	6.32	<.0001
FoodType	LowS		16.6920	3.5159	74.4	4.75	<.0001
FoodType*MBStory	HighS	Chewer	27.6523	5.9560	126	4.64	<.0001
FoodType*MBStory	HighS	Cruncher	15.8913	4.1301	92.3	3.85	0.0002
FoodType*MBStory	HighS	Smoosher	28.1417	6.4803	91.7	4.34	<.0001
FoodType*MBStory	HighS	Sucker	17.1662	8.9363	168	1.92	0.0564
FoodType*MBStory	LowS	Chewer	15.7504	5.9560	126	2.64	0.0092
FoodType*MBStory	LowS	Cruncher	24.4747	4.1301	92.3	5.93	<.0001
FoodType*MBStory	LowS	Smoosher	6.3292	6.4803	91.7	0.98	0.3313
FoodType*MBStory	LowS	Sucker	20.2138	8.9363	168	2.26	0.0250

Differences of Least Squares Means
Effect	FoodType	MBStory	_FoodType	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
MBStory		Chewer		Cruncher	1.5184	5.9012	113	0.26	0.7974
MBStory		Chewer		Smoosher	4.4659	7.6843	69.4	0.58	0.5630
MBStory		Chewer		Sucker	3.0114	8.8031	130	0.34	0.7328
MBStory		Cruncher		Smoosher	2.9475	6.4799	76.4	0.45	0.6505
MBStory		Cruncher		Sucker	1.4930	8.2335	112	0.18	0.8564
MBStory		Smoosher		Sucker	-1.4545	9.5538	82.1	-0.15	0.8794
FoodType	HighS		LowS		5.5209	3.2574	890	1.69	0.0904
FoodType*MBStory	HighS	Chewer	HighS	Cruncher	11.7610	6.8748	200	1.71	0.0887
FoodType*MBStory	HighS	Chewer	HighS	Smoosher	-0.4894	8.7621	116	-0.06	0.9556
FoodType*MBStory	HighS	Chewer	HighS	Sucker	10.4862	10.3422	233	1.01	0.3117
FoodType*MBStory	HighS	Chewer	LowS	Chewer	11.9020	5.8632	890	2.03	0.0427
FoodType*MBStory	HighS	Chewer	LowS	Cruncher	3.1777	6.8748	200	0.46	0.6444
FoodType*MBStory	HighS	Chewer	LowS	Smoosher	21.3231	8.7621	116	2.43	0.0165
FoodType*MBStory	HighS	Chewer	LowS	Sucker	7.4386	10.3422	233	0.72	0.4727
FoodType*MBStory	HighS	Cruncher	HighS	Smoosher	-12.2504	7.4138	129	-1.65	0.1009
FoodType*MBStory	HighS	Cruncher	HighS	Sucker	-1.2748	9.6181	200	-0.13	0.8947
FoodType*MBStory	HighS	Cruncher	LowS	Chewer	0.1409	6.8748	200	0.02	0.9837
FoodType*MBStory	HighS	Cruncher	LowS	Cruncher	-8.5833	3.9216	890	-2.19	0.0289
FoodType*MBStory	HighS	Cruncher	LowS	Smoosher	9.5621	7.4138	129	1.29	0.1994
FoodType*MBStory	HighS	Cruncher	LowS	Sucker	-4.3225	9.6181	200	-0.45	0.6536
FoodType*MBStory	HighS	Smoosher	HighS	Sucker	10.9755	11.0127	142	1.00	0.3206
FoodType*MBStory	HighS	Smoosher	LowS	Chewer	12.3913	8.7621	116	1.41	0.1600
FoodType*MBStory	HighS	Smoosher	LowS	Cruncher	3.6671	7.4138	129	0.49	0.6217
FoodType*MBStory	HighS	Smoosher	LowS	Smoosher	21.8125	6.0436	890	3.61	0.0003
FoodType*MBStory	HighS	Smoosher	LowS	Sucker	7.9279	11.0127	142	0.72	0.4728
FoodType*MBStory	HighS	Sucker	LowS	Chewer	1.4158	10.3422	233	0.14	0.8912
FoodType*MBStory	HighS	Sucker	LowS	Cruncher	-7.3085	9.6181	200	-0.76	0.4482
FoodType*MBStory	HighS	Sucker	LowS	Smoosher	10.8370	11.0127	142	0.98	0.3268
FoodType*MBStory	HighS	Sucker	LowS	Sucker	-3.0476	9.1371	890	-0.33	0.7388
FoodType*MBStory	LowS	Chewer	LowS	Cruncher	-8.7243	6.8748	200	-1.27	0.2059
FoodType*MBStory	LowS	Chewer	LowS	Smoosher	9.4212	8.7621	116	1.08	0.2845
FoodType*MBStory	LowS	Chewer	LowS	Sucker	-4.4634	10.3422	233	-0.43	0.6665
FoodType*MBStory	LowS	Cruncher	LowS	Smoosher	18.1454	7.4138	129	2.45	0.0157
FoodType*MBStory	LowS	Cruncher	LowS	Sucker	4.2609	9.6181	200	0.44	0.6582
FoodType*MBStory	LowS	Smoosher	LowS	Sucker	-13.8846	11.0127	142	-1.26	0.2095

---

Liking Final

The Mixed Procedure

Interventionwk=Low

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Liking
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	38	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 26 28 29 30 31 32 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	Low
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	15
Columns in Z	0
Subjects	38
Max Obs per Subject	24

Number of Observations
Number of Observations Read	900
Number of Observations Used	900
Number of Observations Not Used	0

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	9322.42789593
1	2	9270.20744742	0.00000006
2	1	9270.20721029	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	220.65
Residual		1737.07

Fit Statistics
-2 Res Log Likelihood	9270.2
AIC (Smaller is Better)	9274.2
AICC (Smaller is Better)	9274.2
BIC (Smaller is Better)	9277.5

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	52.22	<.0001

Solution for Fixed Effects
Effect	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept			27.1223	8.6087	218	3.15	0.0019
FoodType	HighS		-6.5952	9.0949	858	-0.73	0.4686
FoodType	LowS		0	.	.	.	.
MBStory		Chewer	-0.8315	10.9000	169	-0.08	0.9393
MBStory		Cruncher	-1.8779	9.2013	291	-0.20	0.8384
MBStory		Smoosher	-16.6534	10.1472	231	-1.64	0.1021
MBStory		Sucker	0	.	.	.	.
FoodType*MBStory	HighS	Chewer	13.0119	11.1390	858	1.17	0.2431
FoodType*MBStory	HighS	Cruncher	-1.8631	9.9399	858	-0.19	0.8514
FoodType*MBStory	HighS	Smoosher	21.7434	10.7185	858	2.03	0.0428
FoodType*MBStory	HighS	Sucker	0	.	.	.	.
FoodType*MBStory	LowS	Chewer	0	.	.	.	.
FoodType*MBStory	LowS	Cruncher	0	.	.	.	.
FoodType*MBStory	LowS	Smoosher	0	.	.	.	.
FoodType*MBStory	LowS	Sucker	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
FoodType	1	858	0.25	0.6200
MBStory	3	84.4	0.80	0.4984
FoodType*MBStory	3	858	4.37	0.0046

Least Squares Means
Effect	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
MBStory		Chewer	29.4991	5.9515	56.6	4.96	<.0001
MBStory		Cruncher	21.0152	3.7251	50.4	5.64	<.0001
MBStory		Smoosher	18.0429	5.3507	50.6	3.37	0.0014
MBStory		Sucker	23.8247	7.3096	120	3.26	0.0015
FoodType	HighS		23.9094	3.5160	71.9	6.80	<.0001
FoodType	LowS		22.2815	3.5160	71.9	6.34	<.0001
FoodType*MBStory	HighS	Chewer	32.7074	6.7646	93.8	4.84	<.0001
FoodType*MBStory	HighS	Cruncher	16.7860	4.2305	83.4	3.97	0.0002
FoodType*MBStory	HighS	Smoosher	25.6170	6.0557	82.6	4.23	<.0001
FoodType*MBStory	HighS	Sucker	20.5270	8.6087	218	2.38	0.0180
FoodType*MBStory	LowS	Chewer	26.2907	6.7646	93.8	3.89	0.0002
FoodType*MBStory	LowS	Cruncher	25.2443	4.2305	83.4	5.97	<.0001
FoodType*MBStory	LowS	Smoosher	10.4688	6.0557	82.6	1.73	0.0876
FoodType*MBStory	LowS	Sucker	27.1223	8.6087	218	3.15	0.0019

Differences of Least Squares Means
Effect	FoodType	MBStory	_FoodType	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
MBStory		Chewer		Cruncher	8.4839	6.6866	76.5	1.27	0.2084
MBStory		Chewer		Smoosher	11.4562	7.9848	56.1	1.43	0.1569
MBStory		Chewer		Sucker	5.6744	9.3696	95.7	0.61	0.5462
MBStory		Cruncher		Smoosher	2.9723	6.3439	62.4	0.47	0.6410
MBStory		Cruncher		Sucker	-2.8095	7.7436	161	-0.36	0.7172
MBStory		Smoosher		Sucker	-5.7818	8.6165	128	-0.67	0.5034
FoodType	HighS		LowS		1.6278	3.2818	858	0.50	0.6200
FoodType*MBStory	HighS	Chewer	HighS	Cruncher	15.9214	7.6858	131	2.07	0.0403
FoodType*MBStory	HighS	Chewer	HighS	Smoosher	7.0904	9.0630	92.6	0.78	0.4360
FoodType*MBStory	HighS	Chewer	HighS	Sucker	12.1804	10.9000	169	1.12	0.2654
FoodType*MBStory	HighS	Chewer	LowS	Chewer	6.4167	6.4311	858	1.00	0.3187
FoodType*MBStory	HighS	Chewer	LowS	Cruncher	7.4631	7.6858	131	0.97	0.3333
FoodType*MBStory	HighS	Chewer	LowS	Smoosher	22.2386	9.0630	92.6	2.45	0.0160
FoodType*MBStory	HighS	Chewer	LowS	Sucker	5.5851	10.9000	169	0.51	0.6090
FoodType*MBStory	HighS	Cruncher	HighS	Smoosher	-8.8310	7.2324	104	-1.22	0.2248
FoodType*MBStory	HighS	Cruncher	HighS	Sucker	-3.7410	9.2013	291	-0.41	0.6846
FoodType*MBStory	HighS	Cruncher	LowS	Chewer	-9.5047	7.6858	131	-1.24	0.2184
FoodType*MBStory	HighS	Cruncher	LowS	Cruncher	-8.4583	4.0105	858	-2.11	0.0352
FoodType*MBStory	HighS	Cruncher	LowS	Smoosher	6.3172	7.2324	104	0.87	0.3844
FoodType*MBStory	HighS	Cruncher	LowS	Sucker	-10.3363	9.2013	291	-1.12	0.2622
FoodType*MBStory	HighS	Smoosher	HighS	Sucker	5.0899	10.1472	231	0.50	0.6164
FoodType*MBStory	HighS	Smoosher	LowS	Chewer	-0.6738	9.0630	92.6	-0.07	0.9409
FoodType*MBStory	HighS	Smoosher	LowS	Cruncher	0.3726	7.2324	104	0.05	0.9590
FoodType*MBStory	HighS	Smoosher	LowS	Smoosher	15.1481	5.6717	858	2.67	0.0077
FoodType*MBStory	HighS	Smoosher	LowS	Sucker	-1.5053	10.1472	231	-0.15	0.8822
FoodType*MBStory	HighS	Sucker	LowS	Chewer	-5.7637	10.9000	169	-0.53	0.5976
FoodType*MBStory	HighS	Sucker	LowS	Cruncher	-4.7173	9.2013	291	-0.51	0.6086
FoodType*MBStory	HighS	Sucker	LowS	Smoosher	10.0582	10.1472	231	0.99	0.3226
FoodType*MBStory	HighS	Sucker	LowS	Sucker	-6.5952	9.0949	858	-0.73	0.4686
FoodType*MBStory	LowS	Chewer	LowS	Cruncher	1.0464	7.6858	131	0.14	0.8919
FoodType*MBStory	LowS	Chewer	LowS	Smoosher	15.8219	9.0630	92.6	1.75	0.0842
FoodType*MBStory	LowS	Chewer	LowS	Sucker	-0.8315	10.9000	169	-0.08	0.9393
FoodType*MBStory	LowS	Cruncher	LowS	Smoosher	14.7755	7.2324	104	2.04	0.0436
FoodType*MBStory	LowS	Cruncher	LowS	Sucker	-1.8779	9.2013	291	-0.20	0.8384
FoodType*MBStory	LowS	Smoosher	LowS	Sucker	-16.6534	10.1472	231	-1.64	0.1021

Desire to eat

%%SAS sas

proc sort data = starchsas;
by interventionwk Shortname orderassigned subjectID;
run;

title 'Desire to eat test'; 
ods output diffs = diffde tests3=testsde lsmeans =meansde
lsmESTIMATEs =Estimde SolutionF= solde;
Proc mixed data=starchsas;
by interventionwk;
class subjectID orderassigned gender visittype interventionwk taste smell foodtype mbstory mbpicture;
model 'Desire to Eat'n = Pudding40s Visittype FoodType MBStory
pudding40s*visittype MBStory*Foodtype  
/ ddfm = kr solution ; *residual; *removed foodtype*visittype Smell Taste ; 
repeated / subject = subjectID type = cs; 
lsmeans visittype / pdiff ADJDFE=ROW;
lsmeans foodtype/ pdiff ADJDFE=ROW;
lsmeans MBStory/ pdiff ADJDFE=ROW;
lsmeans MBStory*Foodtype/ pdiff ADJDFE=ROW;
run;

Desire to eat test

The Mixed Procedure

Interventionwk=High

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Desire to Eat
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	41	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	High
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	20
Columns in Z	0
Subjects	41
Max Obs per Subject	24

Number of Observations
Number of Observations Read	947
Number of Observations Used	936
Number of Observations Not Used	11

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	9920.10660098
1	2	9785.09052228	0.00000001

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	550.80
Residual		1994.89

Fit Statistics
-2 Res Log Likelihood	9785.1
AIC (Smaller is Better)	9789.1
AICC (Smaller is Better)	9789.1
BIC (Smaller is Better)	9792.5

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	135.02	<.0001

Solution for Fixed Effects
Effect	VisitType	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept				-0.3174	12.6625	235	-0.03	0.9800
Pudding40s				0.5235	1.2213	197	0.43	0.6687
VisitType	End			13.0759	9.0143	919	1.45	0.1472
VisitType	Start			0	.	.	.	.
FoodType		HighS		-2.1667	9.7465	887	-0.22	0.8241
FoodType		LowS		0	.	.	.	.
MBStory			Chewer	-2.7819	12.4293	322	-0.22	0.8230
MBStory			Cruncher	3.1355	11.6263	244	0.27	0.7876
MBStory			Smoosher	-12.1784	13.7323	173	-0.89	0.3764
MBStory			Sucker	0	.	.	.	.
Pudding40s*VisitType	End			-2.7760	1.2310	906	-2.26	0.0244
Pudding40s*VisitType	Start			0	.	.	.	.
FoodType*MBStory		HighS	Chewer	7.0784	11.5806	887	0.61	0.5412
FoodType*MBStory		HighS	Cruncher	-3.1184	10.6063	887	-0.29	0.7688
FoodType*MBStory		HighS	Smoosher	21.4792	11.6857	887	1.84	0.0664
FoodType*MBStory		HighS	Sucker	0	.	.	.	.
FoodType*MBStory		LowS	Chewer	0	.	.	.	.
FoodType*MBStory		LowS	Cruncher	0	.	.	.	.
FoodType*MBStory		LowS	Smoosher	0	.	.	.	.
FoodType*MBStory		LowS	Sucker	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Pudding40s	1	180	0.61	0.4339
VisitType	1	919	2.10	0.1472
FoodType	1	887	1.46	0.2278
MBStory	3	148	0.04	0.9873
Pudding40s*VisitType	1	906	5.09	0.0244
FoodType*MBStory	3	887	3.55	0.0142

Least Squares Means
Effect	VisitType	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End			-3.4185	4.6430	60.6	-0.74	0.4644
VisitType	Start			2.3825	4.6220	55.9	0.52	0.6083
FoodType		HighS		1.5786	4.7040	62.5	0.34	0.7383
FoodType		LowS		-2.6145	4.7040	62.5	-0.56	0.5803
MBStory			Chewer	0.01561	6.8252	93.4	0.00	0.9982
MBStory			Cruncher	0.8346	4.9554	61.3	0.17	0.8668
MBStory			Smoosher	-2.1805	7.7966	70.2	-0.28	0.7806
MBStory			Sucker	-0.7417	9.8874	121	-0.08	0.9403
FoodType*MBStory		HighS	Chewer	2.4715	7.5075	135	0.33	0.7425
FoodType*MBStory		HighS	Cruncher	-1.8079	5.3788	84.6	-0.34	0.7376
FoodType*MBStory		HighS	Smoosher	7.4758	8.4367	95.8	0.89	0.3778
FoodType*MBStory		HighS	Sucker	-1.8250	11.0231	181	-0.17	0.8687
FoodType*MBStory		LowS	Chewer	-2.4403	7.5075	135	-0.33	0.7457
FoodType*MBStory		LowS	Cruncher	3.4772	5.3788	84.6	0.65	0.5197
FoodType*MBStory		LowS	Smoosher	-11.8367	8.4367	95.8	-1.40	0.1638
FoodType*MBStory		LowS	Sucker	0.3417	11.0231	181	0.03	0.9753

Differences of Least Squares Means
Effect	VisitType	FoodType	MBStory	_VisitType	_FoodType	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End			Start			-5.8009	3.0666	924	-1.89	0.0588
FoodType		HighS			LowS		4.1931	3.4746	887	1.21	0.2278
MBStory			Chewer			Cruncher	-0.8190	7.4257	219	-0.11	0.9123
MBStory			Chewer			Smoosher	2.1961	10.1110	96.1	0.22	0.8285
MBStory			Chewer			Sucker	0.7573	10.9981	215	0.07	0.9452
MBStory			Cruncher			Smoosher	3.0151	8.5551	113	0.35	0.7252
MBStory			Cruncher			Sucker	1.5763	10.3464	161	0.15	0.8791
MBStory			Smoosher			Sucker	-1.4388	12.4272	119	-0.12	0.9080
FoodType*MBStory		HighS	Chewer		HighS	Cruncher	4.2794	8.3244	319	0.51	0.6076
FoodType*MBStory		HighS	Chewer		HighS	Smoosher	-5.0043	11.0635	136	-0.45	0.6518
FoodType*MBStory		HighS	Chewer		HighS	Sucker	4.2965	12.4293	322	0.35	0.7298
FoodType*MBStory		HighS	Chewer		LowS	Chewer	4.9118	6.2542	887	0.79	0.4325
FoodType*MBStory		HighS	Chewer		LowS	Cruncher	-1.0057	8.3244	319	-0.12	0.9039
FoodType*MBStory		HighS	Chewer		LowS	Smoosher	14.3082	11.0635	136	1.29	0.1981
FoodType*MBStory		HighS	Chewer		LowS	Sucker	2.1298	12.4293	322	0.17	0.8641
FoodType*MBStory		HighS	Cruncher		HighS	Smoosher	-9.2837	9.3784	160	-0.99	0.3237
FoodType*MBStory		HighS	Cruncher		HighS	Sucker	0.01710	11.6263	244	0.00	0.9988
FoodType*MBStory		HighS	Cruncher		LowS	Chewer	0.6324	8.3244	319	0.08	0.9395
FoodType*MBStory		HighS	Cruncher		LowS	Cruncher	-5.2851	4.1832	887	-1.26	0.2068
FoodType*MBStory		HighS	Cruncher		LowS	Smoosher	10.0288	9.3784	160	1.07	0.2865
FoodType*MBStory		HighS	Cruncher		LowS	Sucker	-2.1496	11.6263	244	-0.18	0.8535
FoodType*MBStory		HighS	Smoosher		HighS	Sucker	9.3008	13.7323	173	0.68	0.4991
FoodType*MBStory		HighS	Smoosher		LowS	Chewer	9.9160	11.0635	136	0.90	0.3717
FoodType*MBStory		HighS	Smoosher		LowS	Cruncher	3.9986	9.3784	160	0.43	0.6704
FoodType*MBStory		HighS	Smoosher		LowS	Smoosher	19.3125	6.4467	887	3.00	0.0028
FoodType*MBStory		HighS	Smoosher		LowS	Sucker	7.1341	13.7323	173	0.52	0.6041
FoodType*MBStory		HighS	Sucker		LowS	Chewer	0.6153	12.4293	322	0.05	0.9606
FoodType*MBStory		HighS	Sucker		LowS	Cruncher	-5.3022	11.6263	244	-0.46	0.6488
FoodType*MBStory		HighS	Sucker		LowS	Smoosher	10.0117	13.7323	173	0.73	0.4669
FoodType*MBStory		HighS	Sucker		LowS	Sucker	-2.1667	9.7465	887	-0.22	0.8241
FoodType*MBStory		LowS	Chewer		LowS	Cruncher	-5.9175	8.3244	319	-0.71	0.4777
FoodType*MBStory		LowS	Chewer		LowS	Smoosher	9.3965	11.0635	136	0.85	0.3972
FoodType*MBStory		LowS	Chewer		LowS	Sucker	-2.7819	12.4293	322	-0.22	0.8230
FoodType*MBStory		LowS	Cruncher		LowS	Smoosher	15.3139	9.3784	160	1.63	0.1045
FoodType*MBStory		LowS	Cruncher		LowS	Sucker	3.1355	11.6263	244	0.27	0.7876
FoodType*MBStory		LowS	Smoosher		LowS	Sucker	-12.1784	13.7323	173	-0.89	0.3764

---

Desire to eat test

The Mixed Procedure

Interventionwk=Low

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Desire to Eat
Covariance Structure	Compound Symmetry
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	38	1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 26 28 29 30 31 32 34 35 36 37 38 39 40 42 43 44
OrderAssigned	2	HighLow LowHigh
Gender	2	Female Male
VisitType	2	End Start
Interventionwk	1	Low
Taste	2	Fail Pass
Smell	2	Fail Pass
FoodType	2	HighS LowS
MBStory	4	Chewer Cruncher Smoosher Sucker
MBPicture	4	Chewer Cruncher Smoosher Sucker

Dimensions
Covariance Parameters	2
Columns in X	20
Columns in Z	0
Subjects	38
Max Obs per Subject	24

Number of Observations
Number of Observations Read	900
Number of Observations Used	900
Number of Observations Not Used	0

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	9629.93412671
1	2	9520.34304467	0.00000010
2	1	9520.34263570	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
CS	SubjectID	537.99
Residual		2283.47

Fit Statistics
-2 Res Log Likelihood	9520.3
AIC (Smaller is Better)	9524.3
AICC (Smaller is Better)	9524.4
BIC (Smaller is Better)	9527.6

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
1	109.59	<.0001

Solution for Fixed Effects
Effect	VisitType	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept				-9.3994	13.1734	306	-0.71	0.4761
Pudding40s				2.1013	1.0801	357	1.95	0.0525
VisitType	End			0.3054	9.5275	887	0.03	0.9744
VisitType	Start			0	.	.	.	.
FoodType		HighS		-0.9048	10.4277	854	-0.09	0.9309
FoodType		LowS		0	.	.	.	.
MBStory			Chewer	-13.4485	14.0283	192	-0.96	0.3389
MBStory			Cruncher	0.8233	11.3605	384	0.07	0.9423
MBStory			Smoosher	-22.0703	12.7271	279	-1.73	0.0840
MBStory			Sucker	0	.	.	.	.
Pudding40s*VisitType	End			-0.6961	1.2545	888	-0.55	0.5791
Pudding40s*VisitType	Start			0	.	.	.	.
FoodType*MBStory		HighS	Chewer	12.2024	12.7713	854	0.96	0.3396
FoodType*MBStory		HighS	Cruncher	-5.1786	11.3965	854	-0.45	0.6497
FoodType*MBStory		HighS	Smoosher	20.7844	12.2892	854	1.69	0.0911
FoodType*MBStory		HighS	Sucker	0	.	.	.	.
FoodType*MBStory		LowS	Chewer	0	.	.	.	.
FoodType*MBStory		LowS	Cruncher	0	.	.	.	.
FoodType*MBStory		LowS	Smoosher	0	.	.	.	.
FoodType*MBStory		LowS	Sucker	0	.	.	.	.

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Pudding40s	1	226	3.05	0.0820
VisitType	1	887	0.00	0.9744
FoodType	1	854	2.58	0.1084
MBStory	3	113	0.58	0.6304
Pudding40s*VisitType	1	888	0.31	0.5791
FoodType*MBStory	3	854	3.99	0.0078

Least Squares Means
Effect	VisitType	FoodType	MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End			-4.8400	4.7594	55.6	-1.02	0.3136
VisitType	Start			-0.2390	4.7413	54.4	-0.05	0.9600
FoodType		HighS		0.4841	4.8343	59.4	0.10	0.9206
FoodType		LowS		-5.5632	4.8343	59.4	-1.15	0.2544
MBStory			Chewer	-4.6890	8.3125	65.3	-0.56	0.5746
MBStory			Cruncher	0.8923	5.2575	52.5	0.17	0.8659
MBStory			Smoosher	-9.0197	7.5332	55.9	-1.20	0.2362
MBStory			Sucker	2.6583	9.5121	148	0.28	0.7803
FoodType*MBStory		HighS	Chewer	0.9598	9.0934	93.1	0.11	0.9162
FoodType*MBStory		HighS	Cruncher	-2.1493	5.7382	74.3	-0.37	0.7090
FoodType*MBStory		HighS	Smoosher	0.9201	8.2049	78.5	0.11	0.9110
FoodType*MBStory		HighS	Sucker	2.2060	10.8473	235	0.20	0.8390
FoodType*MBStory		LowS	Chewer	-10.3378	9.0934	93.1	-1.14	0.2585
FoodType*MBStory		LowS	Cruncher	3.9340	5.7382	74.3	0.69	0.4951
FoodType*MBStory		LowS	Smoosher	-18.9595	8.2049	78.5	-2.31	0.0235
FoodType*MBStory		LowS	Sucker	3.1107	10.8473	235	0.29	0.7745

Differences of Least Squares Means
Effect	VisitType	FoodType	MBStory	_VisitType	_FoodType	_MBStory	Estimate	Standard Error	DF	t Value	Pr > |t|
VisitType	End			Start			-4.6010	3.3075	886	-1.39	0.1646
FoodType		HighS			LowS		6.0473	3.7628	854	1.61	0.1084
MBStory			Chewer			Cruncher	-5.5813	9.0528	105	-0.62	0.5389
MBStory			Chewer			Smoosher	4.3307	11.1221	66.4	0.39	0.6982
MBStory			Chewer			Sucker	-7.3473	12.4907	125	-0.59	0.5574
MBStory			Cruncher			Smoosher	9.9121	8.7229	78.7	1.14	0.2593
MBStory			Cruncher			Sucker	-1.7660	9.8281	247	-0.18	0.8575
MBStory			Smoosher			Sucker	-11.6781	11.1455	178	-1.05	0.2962
FoodType*MBStory		HighS	Chewer		HighS	Cruncher	3.1091	10.0415	155	0.31	0.7573
FoodType*MBStory		HighS	Chewer		HighS	Smoosher	0.03972	12.1600	94.4	0.00	0.9974
FoodType*MBStory		HighS	Chewer		HighS	Sucker	-1.2461	14.0283	192	-0.09	0.9293
FoodType*MBStory		HighS	Chewer		LowS	Chewer	11.2976	7.3735	854	1.53	0.1258
FoodType*MBStory		HighS	Chewer		LowS	Cruncher	-2.9742	10.0415	155	-0.30	0.7675
FoodType*MBStory		HighS	Chewer		LowS	Smoosher	19.9193	12.1600	94.4	1.64	0.1047
FoodType*MBStory		HighS	Chewer		LowS	Sucker	-2.1509	14.0283	192	-0.15	0.8783
FoodType*MBStory		HighS	Cruncher		HighS	Smoosher	-3.0694	9.5888	114	-0.32	0.7495
FoodType*MBStory		HighS	Cruncher		HighS	Sucker	-4.3553	11.3605	384	-0.38	0.7017
FoodType*MBStory		HighS	Cruncher		LowS	Chewer	8.1885	10.0415	155	0.82	0.4161
FoodType*MBStory		HighS	Cruncher		LowS	Cruncher	-6.0833	4.5982	854	-1.32	0.1862
FoodType*MBStory		HighS	Cruncher		LowS	Smoosher	16.8102	9.5888	114	1.75	0.0823
FoodType*MBStory		HighS	Cruncher		LowS	Sucker	-5.2601	11.3605	384	-0.46	0.6436
FoodType*MBStory		HighS	Smoosher		HighS	Sucker	-1.2859	12.7271	279	-0.10	0.9196
FoodType*MBStory		HighS	Smoosher		LowS	Chewer	11.2579	12.1600	94.4	0.93	0.3569
FoodType*MBStory		HighS	Smoosher		LowS	Cruncher	-3.0139	9.5888	114	-0.31	0.7539
FoodType*MBStory		HighS	Smoosher		LowS	Smoosher	19.8796	6.5028	854	3.06	0.0023
FoodType*MBStory		HighS	Smoosher		LowS	Sucker	-2.1906	12.7271	279	-0.17	0.8635
FoodType*MBStory		HighS	Sucker		LowS	Chewer	12.5438	14.0283	192	0.89	0.3723
FoodType*MBStory		HighS	Sucker		LowS	Cruncher	-1.7280	11.3605	384	-0.15	0.8792
FoodType*MBStory		HighS	Sucker		LowS	Smoosher	21.1655	12.7271	279	1.66	0.0974
FoodType*MBStory		HighS	Sucker		LowS	Sucker	-0.9048	10.4277	854	-0.09	0.9309
FoodType*MBStory		LowS	Chewer		LowS	Cruncher	-14.2718	10.0415	155	-1.42	0.1572
FoodType*MBStory		LowS	Chewer		LowS	Smoosher	8.6217	12.1600	94.4	0.71	0.4801
FoodType*MBStory		LowS	Chewer		LowS	Sucker	-13.4485	14.0283	192	-0.96	0.3389
FoodType*MBStory		LowS	Cruncher		LowS	Smoosher	22.8935	9.5888	114	2.39	0.0186
FoodType*MBStory		LowS	Cruncher		LowS	Sucker	0.8233	11.3605	384	0.07	0.9423
FoodType*MBStory		LowS	Smoosher		LowS	Sucker	-22.0703	12.7271	279	-1.73	0.0840

SwDiff = sas.sd2df('diffsw')
HaDiff = sas.sd2df('diffha')
ChDiff = sas.sd2df('diffch')
TeDiff = sas.sd2df('diffte')
DeDiff = sas.sd2df('diffde')


# PuddTests = sas.sd2df('tests1')



# PuddMeans = sas.sd2df('means1')


# PuddDiff.to_csv('./out/PuddDiff.csv')
# PuddTests.to_csv('./out/PuddTests.csv')
# PuddMeans.to_csv('./out/PuddMeans.csv')

Diet and baseline

%%SAS sas


proc sort data = starchsas;
by uopreporter visit shortname;
run;

title 'Pudding Baseline';
ods output diffs = diff1 tests3=tests1 lsmeans =means1 
lsmESTIMATEs =Estim1 SolutionF= sol1;
Proc mixed data=starchsas;
by UOPreporter;
where uopreporter ne "O" and visittype = "Start" and shortname='Oreos' and subjectid ne 38; 
*Oreos drops it to just one line of subject data, removes all the duplicates;
*removing subject 38 removes and outlier for BMI (their bmi was 37, everyone else is under 30), 
this make BMI a trend instead of a significant effect;
class subjectID medsyn visit;
model pudding40s =  medsyn BMI visit salivavolume 
/ ddfm = kr solution; *residual?; *removed sugr iron zinc calc V_STARCHY_TOTAL G_TOTAL  ; 
repeated / subject = subjectID type = vc; 
lsmeans medsyn / pdiff ADJDFE=ROW;
run;

Pudding Baseline

The Mixed Procedure

UOPreporter=P

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Pudding40s
Covariance Structure	Variance Components
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Parameter
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	24	1 2 4 6 7 9 11 15 17 18 19 21 26 27 29 30 32 33 34 35 36 37 39 43
MedsYN	2	N Y
Visit	2	1 3

Dimensions
Covariance Parameters	1
Columns in X	7
Columns in Z	0
Subjects	24
Max Obs per Subject	2

Number of Observations
Number of Observations Read	45
Number of Observations Used	43
Number of Observations Not Used	2

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	201.21868862
1	1	201.21868862	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
Residual	SubjectID	7.1280

Fit Statistics
-2 Res Log Likelihood	201.2
AIC (Smaller is Better)	203.2
AICC (Smaller is Better)	203.3
BIC (Smaller is Better)	204.4

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
0	0.00	1.0000

Solution for Fixed Effects
Effect	MedsYN	Visit	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept			0.3244	3.5501	38	0.09	0.9277
MedsYN	N		-2.3405	0.8425	38	-2.78	0.0084
MedsYN	Y		0	.	.	.	.
BMI			0.3771	0.1453	38	2.60	0.0133
Visit		1	-1.0437	0.8300	38	-1.26	0.2163
Visit		3	0	.	.	.	.
SalivaVolume			-0.6406	0.2870	38	-2.23	0.0316

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
MedsYN	1	38	7.72	0.0084
BMI	1	38	6.74	0.0133
Visit	1	38	1.58	0.2163
SalivaVolume	1	38	4.98	0.0316

Least Squares Means
Effect	MedsYN	Estimate	Standard Error	DF	t Value	Pr > |t|
MedsYN	N	5.3822	0.5266	38	10.22	<.0001
MedsYN	Y	7.7227	0.6515	38	11.85	<.0001

Differences of Least Squares Means
Effect	MedsYN	_MedsYN	Estimate	Standard Error	DF	t Value	Pr > |t|
MedsYN	N	Y	-2.3405	0.8425	38	-2.78	0.0084

---

Pudding Baseline

The Mixed Procedure

UOPreporter=U

Model Information
Data Set	WORK.STARCHSAS
Dependent Variable	Pudding40s
Covariance Structure	Variance Components
Subject Effect	SubjectID
Estimation Method	REML
Residual Variance Method	Parameter
Fixed Effects SE Method	Kenward-Roger
Degrees of Freedom Method	Kenward-Roger

Class Level Information
Class	Levels	Values
SubjectID	13	5 8 12 13 14 16 20 22 23 31 40 42 44
MedsYN	2	N Y
Visit	2	1 3

Dimensions
Covariance Parameters	1
Columns in X	7
Columns in Z	0
Subjects	13
Max Obs per Subject	2

Number of Observations
Number of Observations Read	26
Number of Observations Used	24
Number of Observations Not Used	2

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	105.01426238
1	1	105.01426238	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
Residual	SubjectID	6.2629

Fit Statistics
-2 Res Log Likelihood	105.0
AIC (Smaller is Better)	107.0
AICC (Smaller is Better)	107.2
BIC (Smaller is Better)	107.6

Null Model Likelihood Ratio Test
DF	Chi-Square	Pr > ChiSq
0	0.00	1.0000

Solution for Fixed Effects
Effect	MedsYN	Visit	Estimate	Standard Error	DF	t Value	Pr > |t|
Intercept			12.6358	3.2026	19	3.95	0.0009
MedsYN	N		-0.6955	1.1458	19	-0.61	0.5511
MedsYN	Y		0	.	.	.	.
BMI			-0.1338	0.08266	19	-1.62	0.1219
Visit		1	0.2227	1.0650	19	0.21	0.8366
Visit		3	0	.	.	.	.
SalivaVolume			-1.5019	0.7314	19	-2.05	0.0540

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
MedsYN	1	19	0.37	0.5511
BMI	1	19	2.62	0.1219
Visit	1	19	0.04	0.8366
SalivaVolume	1	19	4.22	0.0540

Least Squares Means
Effect	MedsYN	Estimate	Standard Error	DF	t Value	Pr > |t|
MedsYN	N	6.5528	0.7057	19	9.29	<.0001
MedsYN	Y	7.2483	0.8365	19	8.67	<.0001

Differences of Least Squares Means
Effect	MedsYN	_MedsYN	Estimate	Standard Error	DF	t Value	Pr > |t|
MedsYN	N	Y	-0.6955	1.1458	19	-0.61	0.5511