rss.livelink.threads-in-node

Visual Analytics - Scatter Plot blank with large datasets

sc5 — Mon, 29 Apr 2024 00:21:56 GMT

Hi,

I'm trying to get scatter plots of some numeric columns in my dataset with ~1million records, but it shows up empty. I've tried multiple combinations of columns and it's always just blank, like this:

I'm guessing it's an issue with the no. of data points, because when I charted a test dataset i ~ = j with 1000 records it works.

But when I expand the same dataset to a million records it's blank.

Does anyone know how I can fix this and plot scatter plots with large datasets?

Thank you!

logistic regression adjusting for covariate

Manije72 — Sun, 28 Apr 2024 19:09:09 GMT

I want to explore the relationship between fruit consumption and diabetes. I've categorized fruit intake into tertiles, while diabetes status is binary (yes/no). I want to adjust the findings to age (continuous), gender (categorical), race (categorical), and physical activity (categorical) in the analysis. Is the logistic regression code that I want to use correct?

proc logistic data=SS plots(only)=(effect oddsratio);

class Gender(ref='1') race(ref='1') physical_activity(ref='1')/param=ref ;

model diabetes(event='1')= fruitcode age Gender race physical_activity/Clodds=wald;

title " Association of fruit with diabetes (adjust for age, gender, race, physical activity) ";

run;

Fruitcode: I used fruit tertile in this code.

For finding the P-trend, I used this code:

proc logistic data=SS plots(only)=(effect oddsratio);

model diab(event='1')= fruit age Gender race physical_activity/Clodds=wald;;

run;

Fruit is continuous

Create new date variable based on specific condition

tan-wongv — Sun, 28 Apr 2024 18:16:01 GMT

I have a data set contains DMRN, last visit date, the presence of UI, and recorded date of UI.

DMRN	last_visit	UI	recorded_time
31	26AUG2021	0	06APR2018
31	26AUG2021	0	16JAN2020
31	26AUG2021	1	4MAY2021
33	24MAY2022	0	02MAR2020
33	24MAY2022	0	24MAY2022
35	01DEC2014	0	25MAR2013

I would like to create new_date for each DMRN based on these conditions:
if UI = 1 then new_date = recorded_time
else if UI = 0 then new_date = last_visit
The outcome would be like as follow:

DMRN	last_visit	UI	recorded_time	new_date
31	26AUG2021	0	06APR2018	4MAY2021
31	26AUG2021	0	16JAN2020	4MAY2021
31	26AUG2021	1	4MAY2021	4MAY2021
33	24MAY2022	0	02MAR2020	24MAY2022
33	24MAY2022	0	24MAY2022	24MAY2022
35	01DEC2014	0	25MAR2013	01DEC2014

Could you please help me with the code? Thank you very much

Keep only value and date that occurred before or at last visit date

tan-wongv — Sun, 28 Apr 2024 18:01:15 GMT

I have a dataset contains DMRN, last visit date, the presence of UI, and recorded date of UI

DMRN	last_visit	UI	recorded_time
31	26AUG2021	0	06APR2018
31	26AUG2021	0	16JAN2020
31	26AUG2021	1	4MAY2021
31	26AUG2021	1	26MAY2022
33	24MAY2022	0	02MAR2020
33	24MAY2022	0	24MAY2022
35	01DEC2014	0	25MAR2013
35	01DEC2014	1	05JAN2015

I would like to keep only UI value and recorded_time that occurred before or equal to last_visit date for each DMRN. Could you please help me with the code?The outcome would look like this:

DMRN	last_visit	UI	recorded_time
31	26AUG2021	0	06APR2018
31	26AUG2021	0	16JAN2020
31	26AUG2021	1	4MAY2021
33	24MAY2022	0	02MAR2020
33	24MAY2022	0	24MAY2022
35	01DEC2014	0	25MAR2013

Proc logistic effect plot using GTL

rs72 — Sun, 28 Apr 2024 17:52:08 GMT

hello ,
I am trying to use graph template use by Proc Logistics / effectplot , so that I can modify the graph template for some changes
like Y axis label . But when I use this below code

proc template;
source Stat.Logistic.Graphics.EffectCont/ file="&path/reg.txt";
run;

I get log message as-

"link Stat.Logistic.Graphics.EffectCont to Common.Zreg.Graphics.EffectCont;"

can someone tell me what does this above log message means ?

Making header border lines invisible/white in selective columns with PROC REPORT

smackerz1988 — Sun, 28 Apr 2024 15:17:46 GMT

Hello,

I'm creating an output that requires this header layout

I'm using this code

proc report data=final out=view missing nowindows split="*" contents=" ";
   column pag ("MedDRA System Organ Class /* Preferred Term" (" " aesoc))    
    ("trt (N=&trt_count.)" ("arm1*(N=&arm1_count.)" ('Subjects with TEAE*n (%)' arm1))
    ("arm2*(N=&arm2_count.)" ('Subjects with TEAE*n (%)' arm2)))
    ord;
   define pag / order  order = internal noprint ;
   define aesoc  / display " " left flow style(header)=[just=l] STYLE(column)=[&col_style asis=on cellwidth=12.00cm];
   define ord / order order =  internal noprint;   
   define arm2 / " " display   center flow STYLE(column)=[&col_style cellwidth=7.00cm];
   define arm1 / " " display center   flow STYLE(column)=[&col_style cellwidth=7.00cm];
   

    compute after pag / style ={&line_small};
        line "";
    endcomp;

    break after pag / page;

compute before _page_ / left style={&tit_style};
   line "Table 1.4.3 Severe treatment-emergent adverse events by System Organ Class and Preferred Term with incidence of >=5% in at least one arm - Separately by TPC regimen - Safety Analysis Set";   
endcomp;

	compute before ord / style ={&line_small};
		line "";		
	endcomp;

 compute after _page_ / left style={&foot_style};
   line "Notes: NE: Not evaluable; SOC: System Organ Class; PT: Preferred Term; TEAE: Treatment-emergent adverse event. Adverse events were coded using the MedDRA dictionary, Version 26.1 and graded according to NCI CTCAE v5.0.";   
endcomp;
run;

However this is my ouput

How am I able to make this border line for this column either not visibly or removed?

find the count of variable by group

CathyVI — Sun, 28 Apr 2024 14:54:45 GMT

Hello all,

I have initially posted this inappropriately, am sorry. I am creating a better representation of the issue so I can get help on this.

I have a dataset with category (teachers, students) and a list of forms (form2, event46, case32) which they are expected to fill within 60days after the school resume. Event46 has a group variable (grp=A, B, C). That is someone can be in any of the group. Among the teachers and students, some did not fill the form (0), some filled the form within 30 days (1), and some filled the form within 60 days (2).

The 0,1,2 represents the days that the form was filled below:

No form filled=0

Form filled within 0-30days=1

Form filled within 31-60days=2

This is what I want

Table 1:

1. If a teacher or student only filled form2 within 30days and did not fill event46 or case32 they should be in the form2 only column. Same apply for other forms.

If they filled the form after 30 days, they will be in the 31-60 days count by category.

2. If anyone filled more than one(1) forms either within 30 days or within 60 days or both, they should be in the multiple column.

Table 2:

3. Finally, I want a total of those who did not fill any form in both category(teacher or student).

I have created a sample output and a demo dataset. Thanks so much for your help.

data have;
input category $ form2 case32 event46 grp $;
datalines;
Teacher 1 0 0 .
Student 1 0 0 .
Teacher 0 1 0 .
Teacher 1 0 1 A
Teacher 0 0 1 C 
Teacher 0 0 1 B
Teacher 1 0 0 .
Student 0 1 1 C
Student 0 0 1 C
Student 0 0 1 C
Student 1 0 0 .
Student 0 1 0 .
Student 0 2 0 .
Teacher 1 1 1 B
Teacher 0 0 2 A 
Teacher 0 0 2 B
Teacher 2 0 0 .
Student 0 1 1 C
Teacher 0 0 0 .
Student 0 0 0 .
Student 0 0 1 B 
Teacher 0 0 1 B 
Teacher 0 2 0 .
Teacher 2 2 0 .
Student 0 1 0 .
Teacher 2 0 1 A
Student 0 0 1 A 
Teacher 0 0 0 .
Student 2 0 0 .
Teacher 0 0 2 C
Student 0 0 2 C
Student 0 0 2 B
Student 0 0 2 A
;
run;

The expected output should look like this:

	Form2 only	Case32 only	Event46 A only	Event46 B only	Event46 C Only	Multiple	Total
Teacher fill form within 0-30 days
Teacher fill form within 31-60 days
Student fill form within 0-30days
Student fill form within 31-60days
Total

	Total
Teacher who did not complete any form within 0-60 days
Student who did not complete any form within 0-60 days
Total

Need Advice on Handling High-Dimensional Data in Data Science Project

tress — Sun, 28 Apr 2024 11:57:46 GMT

Hey everyone,

I’m relatively new to data science and currently working on a project that involves a dataset with over 60 columns. Many of these columns are categorical, with more than 100 unique values each.

My issue arises when I try to apply one-hot encoding to these categorical columns. It seems like I’m running into the curse of dimensionality problem, and I’m not quite sure how to proceed from here.

I’d really appreciate some advice or guidance on how to effectively handle high-dimensional data in this context. Are there alternative encoding techniques I should consider? Or perhaps there are preprocessing steps I’m overlooking?

Any insights or tips would be immensely helpful.

Thanks in advance!

Error message

Arjayita — Sun, 28 Apr 2024 10:35:14 GMT

/*Model 1*/

Proc logistic data= dormant_pca_cc;
class BU_419_DQ E1_B_07;
model FCS_bad_9m_Jun19 =
BU_419_DQ 
BU_26_AB_NUM 
BU_345_HN_NUM 
RR_CHR_105 
BU_162_GG_NUM 
RR_CHR_169 
BU_799_TEB_NUM 
RR_CHR_111
BU_301_PL_NUM 
BU_1552_SHC_NUM
BU_1577_RIC_NUM
RR_CHR_122
E1_B_07
bu_670_uz_num;
ods output GlobalTests = Globaltests_full;
run; 

data _null_;
set globaltests_full;
if test = "Likelihood Ratio" then do;
call symput ("ChiSq_full", ChiSq);
call symput ("DF_full", DF);
END;
RUN;


/*Model 2*/

Proc logistic data= dormant_pca_cc2;
class BU_419_DQ E1_B_07;
model FCS_bad_9m_Jun19 =
BU_419_DQ 
BU_26_AB_NUM 
BU_345_HN_NUM 
RR_CHR_105 
BU_162_GG_NUM 
RR_CHR_169 
BU_799_TEB_NUM 
RR_CHR_111
BU_301_PL_NUM 
BU_1552_SHC_NUM
BU_1577_RIC_NUM
RR_CHR_122
E1_B_07;
ods output GlobalTests = Globaltests_reduced;
run; 

data _null_;
set globaltests_reduced;
if test = "Likelihood Ratio" then do;
call symput ("ChiSq_reduced", ChiSq);
call symput ("DF_reduced", DF);
END;
RUN;

data = LRT_result;
LR =(&ChiSq_full - &ChiSq_reduced);
DF = (&DF_full - &DF_reduced);
p=1 - probchi(chiSq,DF);
RUN;

This is a code to check the change in likelihood ratio. I am getting the below error:

1                                                          The SAS System                               10:46 Sunday, April 28, 2024

1          ;*';*";*/;quit;run;
2          OPTIONS PAGENO=MIN;
3          %LET _CLIENTTASKLABEL='Test2';
4          %LET _CLIENTPROCESSFLOWNAME='Model Macro';
5          %LET _CLIENTPROJECTPATH='C:\Users\8522019\OneDrive - Lloyds Banking Group\Desktop\Likelihood test1.egp';
6          %LET _CLIENTPROJECTPATHHOST='MMD014713504257';
7          %LET _CLIENTPROJECTNAME='Likelihood test1.egp';
8          %LET _SASPROGRAMFILE='';
9          %LET _SASPROGRAMFILEHOST='';
10         
11         ODS _ALL_ CLOSE;
12         OPTIONS DEV=SVG;
13         GOPTIONS XPIXELS=0 YPIXELS=0;
14         %macro HTML5AccessibleGraphSupported;
15             %if %_SAS_VERCOMP_FV(9,4,4, 0,0,0) >= 0 %then ACCESSIBLE_GRAPH;
16         %mend;
17         FILENAME EGHTML TEMP;
18         ODS HTML5(ID=EGHTML) FILE=EGHTML
19             OPTIONS(BITMAP_MODE='INLINE')
20             %HTML5AccessibleGraphSupported
21             ENCODING='utf-8'
22             STYLE=HtmlBlue
23             NOGTITLE
24             NOGFOOTNOTE
25             GPATH=&sasworklocation
26         ;
NOTE: Writing HTML5(EGHTML) Body file: EGHTML
27         
28         data = LRT_result;
           ____
           180

ERROR 180-322: Statement is not valid or it is used out of proper order.

29         LR =(&ChiSq_full - &ChiSq_reduced);
           __
           180

ERROR 180-322: Statement is not valid or it is used out of proper order.

30         DF = (&DF_full - &DF_reduced);
           __
           180

ERROR 180-322: Statement is not valid or it is used out of proper order.

31         p=1 - probchi(chiSq,DF);
           _
           180

ERROR 180-322: Statement is not valid or it is used out of proper order.

32         RUN;
33         
34         %LET _CLIENTTASKLABEL=;
35         %LET _CLIENTPROCESSFLOWNAME=;
36         %LET _CLIENTPROJECTPATH=;
37         %LET _CLIENTPROJECTPATHHOST=;
38         %LET _CLIENTPROJECTNAME=;
39         %LET _SASPROGRAMFILE=;
40         %LET _SASPROGRAMFILEHOST=;
41         
42         ;*';*";*/;quit;run;
43         ODS _ALL_ CLOSE;
44         
45         
46         QUIT; RUN;
47

Can anyone help me correcting the code? That would be great!

How to test the interaction between strata and treatment groups in a mmrm model?

Robin_moon — Sun, 28 Apr 2024 09:22:42 GMT

I constructed a mmrm model as below:

 proc mixed data = have;
 class id treatment week strata;
 model chg = base treatment week treatment*week treatment*strata/ ddfm = KR;
 repeated week/ subject = id type = UN;
 lsmeans treatment*week/ cl alpha = 0.05 diff
 ods output Tests3=tests3 lsmeans=lsmeans diffs=diffs;
 run;

(1) id: represents the patient id;

(2) treatment: contains 2 group, "drug" and "placebo";

(3) chg: change from baseline of hba1c;

(4) base: the value of hba1c at baseline;

(5) week: contains 3 levels: "week8", "week16" and "week24"

(6 )strata: stratification based on the median of baseline hba1c, ie

group1=patients of those who have baseline hba1c <= median of baseline hba1c;

group2=patients of those who have baseline hba1c > median of baseline hba1c.

In general, I'd like to know how to the test the interaction between strata and treatment groups. I am not sure if I constructed the model in a right way because I looked at the results (as below), the values of Diffs in LS means were pretty close between strata, but p for interaction was significant. (The Diffs in LS means were got from the output file diffs, and the p for interaction were got from the output file test3.)

I also checked the Diffs in LS means of week8 and week16, the diffs in LS means were all pretty close between 2 strata, but the p for interaction were all significant (p-value < 0.0001). So I am not sure if I interpret the result in the right way. What data should look quite different if the p for interaction is statistically significant? Thanks.

Specify non-inferiority margin using mixed model for repeated measurement

DingTao — Sun, 28 Apr 2024 06:15:05 GMT

I have a dataset structured as repeated measurement and Mixed model for repeated measurement is used for analysis. The standard code to get average change from baseline (note that here we are comparing baseline with the mean of the last 4 visits) and the associated p-value has been posted below. I wonder based on this, how can I test the non-inferiority using a margin of -1, e.g., change from baseline is greater than -1. The null hypothesis is that this change will be smaller or equal to -1. Can anyone suggest how to write SAS for this test?

proc mixed data=mydata; class id visit sex(ref='F'); model change = age sex baseline visit*baseline/ ddfm=kr fullx; repeated visit / subject = id type = un; lsmeans visit ; estimate 'Average' Intercept 1 age 1 sex 0.5 0.5 visit 0 0 0 0 0 0.25 0.25 0.25 0.25 base & basemean visit*base 0 0 0 0 0 &basemean1 &basemean1 &basemean1 &basemean1 / cl; run;

Find Standard deviation from 2, or more, columns/variables with aim of SQL

jonatan_velarde — Sun, 28 Apr 2024 00:11:40 GMT

Good night guys:

Last post i tried to explain an example, but for some logistic reasons i couldn't finish the explanation there so here i go again:

i have this data set:

data have;
input id South North east west ;
cards;
1 2 4 12 9
2 3 . 11 10
3 2 4 12 10
4 6 4 13 12
;

proc sql;
create table want as
select *,
sum(south + north)/(count(south + north)) as mean_south_north,
std(south and north) as sd_south_north
from have;
quit;

proc print data = want;
run;, but unfortunately i got this:

Obs	id	south	north	west	east	sum_south_north	mean_south_north	sd_south_north	sum_west_east	mean_west_east
1	1	10	.	15	9	116	25.2	8.48528	74	12.3333
2	2	40	12	10	14	116	25.2	8.48528	74	12.3333
3	3	50	14	13	13	116	25.2	8.48528	74	12.3333

The RED BOLD text in the table above represents the standard deviation wrongly found, where the correct value must be 18.4715998224301, as the real value of the standard deviation of 10, 40, 50, 12 and 14. Must be noted that it is a missing value in the data set, so it is very important to consider also.

Other important issue, it would be grate make the program to find the standard deviation from other columns and put them as other variable, for example the standard deviation of west and east observations , as std_west_east, considering GREEN DATA in the table above

i appreciate any help to complete this task correcting the code i provided to you above.

thanks in advance

Column mapping in load table step in SAS Viya 4.0

ParitoshACN — Sat, 27 Apr 2024 09:51:06 GMT

We are migrating SAS 9.4 DI jobs to SAS Viya 4. In a DI jobs, when we migrated table loader transformation to load table step, column mappings are not migrated properly. Columns with not matching names are not mapped. In sas 9.4 DI table loader transformation, we can map any column to anyone irrespective of its name as per data but in SAS Viya, this is a big concern. Can anyone help me with any solution because client has lots of DI jobs to migrate to SAS Viya.

fname middlename lastname

pavank — Sat, 27 Apr 2024 08:55:14 GMT

Data ds
infile datalines;
input name $40.;
datalines;
Virat Kohli
Surya Kumar Yadav
Rohit Sharma
Dhoni
Chetandra Pratap Singh Chauhan
;
run;

find firstname middlename lastname

Write a code on SAS on likelihood ratio

Arjayita — Sat, 27 Apr 2024 01:13:14 GMT

I have one dataset DORMANT_PCA_CC which has fourteen variables. Bu_419_DQ, Bu_26_ab_num, Bu_345_hn_num, rr_chr_105, bu_162_gg_num, rr_chr_169, bu_799_teb_num, rr_chr_111, bu_301_pl_num, bu_1552_shc_num, bu_1577_ric_num, rr_chr_122, e1_b_07, bu_670_uz_num.
i want to drop one variable which is bu_670_uz_num.

If I want to test whether the drop out variable bu_670_uz_num is significant or not, I shall perform a likelihood ratio test of two models. How to write a code on SAS to check the change in likelihood?

thanks in advance

Gompertz curve modelling using NLIN PROC

samkelomotsa — Fri, 26 Apr 2024 23:47:04 GMT

I want to use the Gompertz curve model to develop a growth curve and also get the analysis components. the model that i want to use is :

BWt = BW0 * exp*(L/K(1 - exp(-Kt))).

The equation includes parameters such as BWt (recorded body weight at age t), BW0 (estimated weight at hatching), L (initial specific growth rate), and K (maturation rate or exponential factor of decay of the specific growth rate).

how can i go about developing the code.

line in the data stands from the animal breed in other terms. this is a sample of the data set. otherwise, the data set is huge.

LINE	BW0	WEEK	BWT
F01	39.0	2	153.6
F01	39.0	2	128.4
F01	37.0	2	143.2
F01	31.8	2	107.0
F01	32.6	2	144.6
F01	32.4	2	117.2
F01	35.6	2	154.2
F01	37.4	2	.
F01	33.4	2	66.6
F01	32.0	2	92.6
F01	37.6	2	154.0
F01	34.8	2	125.6
F01	34.4	2	113.8
F01	37.2	2	146.8

Obtain the mean from two or more columns using SQL and put it as a new column

jonatan_velarde — Fri, 26 Apr 2024 23:42:15 GMT

i have this data corresponding to many participants, and were collected data from them in different locations.

id	South	North	east	west
1	2	4	12	9
2	3	.	11	10
3	2	4	12	10
4	6	4	13	12

now i need to obtain the mean ( or average) of all of them and put it into columns, as well as standard deviation.

Repair that both mean and standard deviation are considering all observations, in sql i don't want to divide the sum by the number of the observations, is not fashion LOL.

i would like to obtain this:

id	South	North	mean_south_north	SD_south_north	east	west	mean_east_west	SD_east_west
1	2	4	3,57142857142857	1,39727626201154	12	9	11,125	1,35620268186054
2	3	.	3,57142857142857	1,39727626201154	11	10	11,125	1,35620268186054
3	2	4	3,57142857142857	1,39727626201154	12	10	11,125	1,35620268186054
4	6	4	3,57142857142857	1,39727626201154	13	12	11,125	1,35620268186054

the first mean comes from the observations obtained in south and north, and the second, on east and west. as well their respective standard deviations.

Thanks in advance

Proc sql-creating new table not working

billi_billi — Fri, 26 Apr 2024 22:42:17 GMT

Hello,

I am trying to use proc sql to create a new table (code below) but getting an error ( log screenshot attached). Could anyone please help me with this issue? Thank you.

proc sql;
create table ae as
select distinct usubjid, aedecod,
catx('@', smq01nam, smq02nam, smq03nam, smq04nam, smq05nam, smq06nam, smq07nam, smq08nam,
smq09nam, smq10nam) as aesmq,
aebodsys, trt01an
from snpm
where 'Broad' in (smq01sc, smq02sc, smq03sc, smq04sc, smq05sc, smq06sc, smq07sc, smq08sc,
smq09sc, smq10sc)
and not missing(catx('@', smq01nam, smq02nam, smq03nam, smq04nam, smq05nam, smq06nam, smq07nam, smq08nam,
smq09nam, smq10nam));
quit;

Dica da Semana: SAS Enterprise Guide – Conexão com Viya 4

ericlesvictor — Fri, 26 Apr 2024 20:07:36 GMT

A nova versão do SAS Enterprise Guide 8.4 pode se conectar com Viya 4. Veja neste artigo como fazer!

PROC COUNTREG - Scoring with Zero Inflate Conway Maxwell Poisson (ZICMP)

Psueri — Fri, 26 Apr 2024 16:13:50 GMT

Hello Sas Community,

I am currently working on a project where I need to utilize PROC COUNTREG in SAS, particularly focusing on utilizing ZICMP distribution with spatialeffects.

I'm trying to manually score based on the parameters, and from the documentation, I've reconstructed the following steps.

Let:

n be the number of observations
m be the number of model regressors
s be the number of spatial effects regressors
z be the number of zero model regressors
d be the number of dispersion model regressors
X1 (n x m) be the matrix containing the values of m model regressors
X2 (n x s) be the matrix containing the values of s spatial effects regressors
Z1 (n x z) be the matrix containing the values of z zero model regressors
D1 (n x d) be the matrix containing the values of d dispersion model regressors
Wmat (n x n) be the spatial weights matrix
Beta1 (m x 1) be the vector containing the coefficients of m model regressors
Beta2 (s x 1) be the vector containing the coefficients of s spatial effects regressors
Gamma (z x 1) be the vector containing the coefficients of z zero model regressors
Delta (d x 1) be the vector containing the coefficients of d dispersion model regressors
Intercept be the value of the intercept of the model
Inf_Intercept be the value of the intercept of the zero model
Disp_Intercept be the value of the intercept of the dispersion model

, or

But the values of lambda vector obtained in the output of the countreg differs from the ones computed as exp(xbeta), that is instead the value mu in the countreg output.

(in documentation there is a typo in the ZICMP, but it's correct in CMP and the results are the same of countreg output)

Now there isn't a finite form for this Normalization Factor, following the paper:

Approximating the Conway–Maxwell–Poisson distribution normalization constant

Steven B. Gillispie ∗ and Christopher G. Green

University of Washington, Seattle, USA — Department of Statistics

Technical Report no. 615

June 6, 2013

I found this approximation formula

And then

Now, the predicted results obtained do not match those obtained from the output of the countreg. The differing values are: Lambda (as mentioned above), P(y=0), P(y!=0) and Predicted (those three latter are probably due to the Lambda error)

Any examples, tips, or resources you could share would be greatly appreciated. Thank you in advance for your assistance!

Best regards,

Piero